spaCy/spacy/errors.py

# coding: utf8
from __future__ import unicode_literals

import os
import warnings
import inspect


def add_codes(err_cls):
    """Add error codes to string messages via class attribute names."""

    class ErrorsWithCodes(object):
        def __getattribute__(self, code):
            msg = getattr(err_cls, code)
            return "[{code}] {msg}".format(code=code, msg=msg)

    return ErrorsWithCodes()


# fmt: off

@add_codes
class Warnings(object):
    W001 = ("As of spaCy v2.0, the keyword argument `path=` is deprecated. "
            "You can now call spacy.load with the path as its first argument, "
            "and the model's meta.json will be used to determine the language "
            "to load. For example:\nnlp = spacy.load('{path}')")
    W002 = ("Tokenizer.from_list is now deprecated. Create a new Doc object "
            "instead and pass in the strings as the `words` keyword argument, "
            "for example:\nfrom spacy.tokens import Doc\n"
            "doc = Doc(nlp.vocab, words=[...])")
    W003 = ("Positional arguments to Doc.merge are deprecated. Instead, use "
            "the keyword arguments, for example tag=, lemma= or ent_type=.")
    W004 = ("No text fixing enabled. Run `pip install ftfy` to enable fixing "
            "using ftfy.fix_text if necessary.")
    W005 = ("Doc object not parsed. This means displaCy won't be able to "
            "generate a dependency visualization for it. Make sure the Doc "
            "was processed with a model that supports dependency parsing, and "
            "not just a language class like `English()`. For more info, see "
            "the docs:\nhttps://spacy.io/usage/models")
    W006 = ("No entities to visualize found in Doc object. If this is "
            "surprising to you, make sure the Doc was processed using a model "
            "that supports named entity recognition, and check the `doc.ents` "
            "property manually if necessary.")
    W007 = ("The model you're using has no word vectors loaded, so the result "
            "of the {obj}.similarity method will be based on the tagger, "
            "parser and NER, which may not give useful similarity judgements. "
            "This may happen if you're using one of the small models, e.g. "
            "`en_core_web_sm`, which don't ship with word vectors and only "
            "use context-sensitive tensors. You can always add your own word "
            "vectors, or use one of the larger models instead if available.")
    W008 = ("Evaluating {obj}.similarity based on empty vectors.")
    W009 = ("Custom factory '{name}' provided by entry points of another "
            "package overwrites built-in factory.")
    W010 = ("As of v2.1.0, the PhraseMatcher doesn't have a phrase length "
            "limit anymore, so the max_length argument is now deprecated.")
    W011 = ("It looks like you're calling displacy.serve from within a "
            "Jupyter notebook or a similar environment. This likely means "
            "you're already running a local web server, so there's no need to "
            "make displaCy start another one. Instead, you should be able to "
            "replace displacy.serve with displacy.render to show the "
            "visualization.")
    W012 = ("A Doc object you're adding to the PhraseMatcher for pattern "
            "'{key}' is parsed and/or tagged, but to match on '{attr}', you "
            "don't actually need this information. This means that creating "
            "the patterns is potentially much slower, because all pipeline "
            "components are applied. To only create tokenized Doc objects, "
            "try using `nlp.make_doc(text)` or process all texts as a stream "
            "using `list(nlp.tokenizer.pipe(all_texts))`.")
    W013 = ("As of v2.1.0, {obj}.merge is deprecated. Please use the more "
            "efficient and less error-prone Doc.retokenize context manager "
            "instead.")
    W014 = ("As of v2.1.0, the `disable` keyword argument on the serialization "
            "methods is and should be replaced with `exclude`. This makes it "
            "consistent with the other objects serializable.")
    W015 = ("As of v2.1.0, the use of keyword arguments to exclude fields from "
            "being serialized or deserialized is deprecated. Please use the "
            "`exclude` argument instead. For example: exclude=['{arg}'].")
    W016 = ("The keyword argument `n_threads` on the is now deprecated, as "
            "the v2.x models cannot release the global interpreter lock. "
            "Future versions may introduce a `n_process` argument for "
            "parallel inference via multiprocessing.")
    W017 = ("Alias '{alias}' already exists in the Knowledge Base.")
    W018 = ("Entity '{entity}' already exists in the Knowledge Base.")
    W019 = ("Changing vectors name from {old} to {new}, to avoid clash with "
            "previously loaded vectors. See Issue #3853.")
    W020 = ("Unnamed vectors. This won't allow multiple vectors models to be "
            "loaded. (Shape: {shape})")
    W021 = ("Unexpected hash collision in PhraseMatcher. Matches may be "
            "incorrect. Modify PhraseMatcher._terminal_hash to fix.")
    W022 = ("Training a new part-of-speech tagger using a model with no "
            "lemmatization rules or data. This means that the trained model "
            "may not be able to lemmatize correctly. If this is intentional "
            "or the language you're using doesn't have lemmatization data, "
            "you can ignore this warning by setting SPACY_WARNING_IGNORE=W022. "
            "If this is surprising, make sure you have the spacy-lookups-data "
            "package installed.")
    W023 = ("Multiprocessing of Language.pipe is not supported in Python 2. "
            "'n_process' will be set to 1.")
    W024 = ("Entity '{entity}' - Alias '{alias}' combination already exists in "
            "the Knowledge Base.")
    W025 = ("'{name}' requires '{attr}' to be assigned, but none of the "
            "previous components in the pipeline declare that they assign it.")


@add_codes
class Errors(object):
    E001 = ("No component '{name}' found in pipeline. Available names: {opts}")
    E002 = ("Can't find factory for '{name}'. This usually happens when spaCy "
            "calls `nlp.create_pipe` with a component name that's not built "
            "in - for example, when constructing the pipeline from a model's "
            "meta.json. If you're using a custom component, you can write to "
            "`Language.factories['{name}']` or remove it from the model meta "
            "and add it via `nlp.add_pipe` instead.")
    E003 = ("Not a valid pipeline component. Expected callable, but "
            "got {component} (name: '{name}').")
    E004 = ("If you meant to add a built-in component, use `create_pipe`: "
            "`nlp.add_pipe(nlp.create_pipe('{component}'))`")
    E005 = ("Pipeline component '{name}' returned None. If you're using a "
            "custom component, maybe you forgot to return the processed Doc?")
    E006 = ("Invalid constraints. You can only set one of the following: "
            "before, after, first, last.")
    E007 = ("'{name}' already exists in pipeline. Existing names: {opts}")
    E008 = ("Some current components would be lost when restoring previous "
            "pipeline state. If you added components after calling "
            "`nlp.disable_pipes()`, you should remove them explicitly with "
            "`nlp.remove_pipe()` before the pipeline is restored. Names of "
            "the new components: {names}")
    E009 = ("The `update` method expects same number of docs and golds, but "
            "got: {n_docs} docs, {n_golds} golds.")
    E010 = ("Word vectors set to length 0. This may be because you don't have "
            "a model installed or loaded, or because your model doesn't "
            "include word vectors. For more info, see the docs:\n"
            "https://spacy.io/usage/models")
    E011 = ("Unknown operator: '{op}'. Options: {opts}")
    E012 = ("Cannot add pattern for zero tokens to matcher.\nKey: {key}")
    E013 = ("Error selecting action in matcher")
    E014 = ("Unknown tag ID: {tag}")
    E015 = ("Conflicting morphology exception for ({tag}, {orth}). Use "
            "`force=True` to overwrite.")
    E016 = ("MultitaskObjective target should be function or one of: dep, "
            "tag, ent, dep_tag_offset, ent_tag.")
    E017 = ("Can only add unicode or bytes. Got type: {value_type}")
    E018 = ("Can't retrieve string for hash '{hash_value}'. This usually "
            "refers to an issue with the `Vocab` or `StringStore`.")
    E019 = ("Can't create transition with unknown action ID: {action}. Action "
            "IDs are enumerated in spacy/syntax/{src}.pyx.")
    E020 = ("Could not find a gold-standard action to supervise the "
            "dependency parser. The tree is non-projective (i.e. it has "
            "crossing arcs - see spacy/syntax/nonproj.pyx for definitions). "
            "The ArcEager transition system only supports projective trees. "
            "To learn non-projective representations, transform the data "
            "before training and after parsing. Either pass "
            "`make_projective=True` to the GoldParse class, or use "
            "spacy.syntax.nonproj.preprocess_training_data.")
    E021 = ("Could not find a gold-standard action to supervise the "
            "dependency parser. The GoldParse was projective. The transition "
            "system has {n_actions} actions. State at failure: {state}")
    E022 = ("Could not find a transition with the name '{name}' in the NER "
            "model.")
    E023 = ("Error cleaning up beam: The same state occurred twice at "
            "memory address {addr} and position {i}.")
    E024 = ("Could not find an optimal move to supervise the parser. Usually, "
            "this means that the model can't be updated in a way that's valid "
            "and satisfies the correct annotations specified in the GoldParse. "
            "For example, are all labels added to the model? If you're "
            "training a named entity recognizer, also make sure that none of "
            "your annotated entity spans have leading or trailing whitespace. "
            "You can also use the experimental `debug-data` command to "
            "validate your JSON-formatted training data. For details, run:\n"
            "python -m spacy debug-data --help")
    E025 = ("String is too long: {length} characters. Max is 2**30.")
    E026 = ("Error accessing token at position {i}: out of bounds in Doc of "
            "length {length}.")
    E027 = ("Arguments 'words' and 'spaces' should be sequences of the same "
            "length, or 'spaces' should be left default at None. spaces "
            "should be a sequence of booleans, with True meaning that the "
            "word owns a ' ' character following it.")
    E028 = ("orths_and_spaces expects either a list of unicode string or a "
            "list of (unicode, bool) tuples. Got bytes instance: {value}")
    E029 = ("noun_chunks requires the dependency parse, which requires a "
            "statistical model to be installed and loaded. For more info, see "
            "the documentation:\nhttps://spacy.io/usage/models")
    E030 = ("Sentence boundaries unset. You can add the 'sentencizer' "
            "component to the pipeline with: "
            "nlp.add_pipe(nlp.create_pipe('sentencizer')) "
            "Alternatively, add the dependency parser, or set sentence "
            "boundaries by setting doc[i].is_sent_start.")
    E031 = ("Invalid token: empty string ('') at position {i}.")
    E032 = ("Conflicting attributes specified in doc.from_array(): "
            "(HEAD, SENT_START). The HEAD attribute currently sets sentence "
            "boundaries implicitly, based on the tree structure. This means "
            "the HEAD attribute would potentially override the sentence "
            "boundaries set by SENT_START.")
    E033 = ("Cannot load into non-empty Doc of length {length}.")
    E034 = ("Doc.merge received {n_args} non-keyword arguments. Expected "
            "either 3 arguments (deprecated), or 0 (use keyword arguments).\n"
            "Arguments supplied:\n{args}\nKeyword arguments:{kwargs}")
    E035 = ("Error creating span with start {start} and end {end} for Doc of "
            "length {length}.")
    E036 = ("Error calculating span: Can't find a token starting at character "
            "offset {start}.")
    E037 = ("Error calculating span: Can't find a token ending at character "
            "offset {end}.")
    E038 = ("Error finding sentence for span. Infinite loop detected.")
    E039 = ("Array bounds exceeded while searching for root word. This likely "
            "means the parse tree is in an invalid state. Please report this "
            "issue here: http://github.com/explosion/spaCy/issues")
    E040 = ("Attempt to access token at {i}, max length {max_length}.")
    E041 = ("Invalid comparison operator: {op}. Likely a Cython bug?")
    E042 = ("Error accessing doc[{i}].nbor({j}), for doc of length {length}.")
    E043 = ("Refusing to write to token.sent_start if its document is parsed, "
            "because this may cause inconsistent state.")
    E044 = ("Invalid value for token.sent_start: {value}. Must be one of: "
            "None, True, False")
    E045 = ("Possibly infinite loop encountered while looking for {attr}.")
    E046 = ("Can't retrieve unregistered extension attribute '{name}'. Did "
            "you forget to call the `set_extension` method?")
    E047 = ("Can't assign a value to unregistered extension attribute "
            "'{name}'. Did you forget to call the `set_extension` method?")
    E048 = ("Can't import language {lang} from spacy.lang: {err}")
    E049 = ("Can't find spaCy data directory: '{path}'. Check your "
            "installation and permissions, or use spacy.util.set_data_path "
            "to customise the location if necessary.")
    E050 = ("Can't find model '{name}'. It doesn't seem to be a shortcut "
            "link, a Python package or a valid path to a data directory.")
    E051 = ("Cant' load '{name}'. If you're using a shortcut link, make sure "
            "it points to a valid package (not just a data directory).")
    E052 = ("Can't find model directory: {path}")
    E053 = ("Could not read meta.json from {path}")
    E054 = ("No valid '{setting}' setting found in model meta.json.")
    E055 = ("Invalid ORTH value in exception:\nKey: {key}\nOrths: {orths}")
    E056 = ("Invalid tokenizer exception: ORTH values combined don't match "
            "original string.\nKey: {key}\nOrths: {orths}")
    E057 = ("Stepped slices not supported in Span objects. Try: "
            "list(tokens)[start:stop:step] instead.")
    E058 = ("Could not retrieve vector for key {key}.")
    E059 = ("One (and only one) keyword arg must be set. Got: {kwargs}")
    E060 = ("Cannot add new key to vectors: the table is full. Current shape: "
            "({rows}, {cols}).")
    E061 = ("Bad file name: {filename}. Example of a valid file name: "
            "'vectors.128.f.bin'")
    E062 = ("Cannot find empty bit for new lexical flag. All bits between 0 "
            "and 63 are occupied. You can replace one by specifying the "
            "`flag_id` explicitly, e.g. "
            "`nlp.vocab.add_flag(your_func, flag_id=IS_ALPHA`.")
    E063 = ("Invalid value for flag_id: {value}. Flag IDs must be between 1 "
            "and 63 (inclusive).")
    E064 = ("Error fetching a Lexeme from the Vocab. When looking up a "
            "string, the lexeme returned had an orth ID that did not match "
            "the query string. This means that the cached lexeme structs are "
            "mismatched to the string encoding table. The mismatched:\n"
            "Query string: {string}\nOrth cached: {orth}\nOrth ID: {orth_id}")
    E065 = ("Only one of the vector table's width and shape can be specified. "
            "Got width {width} and shape {shape}.")
    E066 = ("Error creating model helper for extracting columns. Can only "
            "extract columns by positive integer. Got: {value}.")
    E067 = ("Invalid BILUO tag sequence: Got a tag starting with 'I' (inside "
            "an entity) without a preceding 'B' (beginning of an entity). "
            "Tag sequence:\n{tags}")
    E068 = ("Invalid BILUO tag: '{tag}'.")
    E069 = ("Invalid gold-standard parse tree. Found cycle between word "
            "IDs: {cycle} (tokens: {cycle_tokens}) in the document starting "
            "with tokens: {doc_tokens}.")
    E070 = ("Invalid gold-standard data. Number of documents ({n_docs}) "
            "does not align with number of annotations ({n_annots}).")
    E071 = ("Error creating lexeme: specified orth ID ({orth}) does not "
            "match the one in the vocab ({vocab_orth}).")
    E072 = ("Error serializing lexeme: expected data length {length}, "
            "got {bad_length}.")
    E073 = ("Cannot assign vector of length {new_length}. Existing vectors "
            "are of length {length}. You can use `vocab.reset_vectors` to "
            "clear the existing vectors and resize the table.")
    E074 = ("Error interpreting compiled match pattern: patterns are expected "
            "to end with the attribute {attr}. Got: {bad_attr}.")
    E075 = ("Error accepting match: length ({length}) > maximum length "
            "({max_len}).")
    E076 = ("Error setting tensor on Doc: tensor has {rows} rows, while Doc "
            "has {words} words.")
    E077 = ("Error computing {value}: number of Docs ({n_docs}) does not "
            "equal number of GoldParse objects ({n_golds}) in batch.")
    E078 = ("Error computing score: number of words in Doc ({words_doc}) does "
            "not equal number of words in GoldParse ({words_gold}).")
    E079 = ("Error computing states in beam: number of predicted beams "
            "({pbeams}) does not equal number of gold beams ({gbeams}).")
    E080 = ("Duplicate state found in beam: {key}.")
    E081 = ("Error getting gradient in beam: number of histories ({n_hist}) "
            "does not equal number of losses ({losses}).")
    E082 = ("Error deprojectivizing parse: number of heads ({n_heads}), "
            "projective heads ({n_proj_heads}) and labels ({n_labels}) do not "
            "match.")
    E083 = ("Error setting extension: only one of `default`, `method`, or "
            "`getter` (plus optional `setter`) is allowed. Got: {nr_defined}")
    E084 = ("Error assigning label ID {label} to span: not in StringStore.")
    E085 = ("Can't create lexeme for string '{string}'.")
    E086 = ("Error deserializing lexeme '{string}': orth ID {orth_id} does "
            "not match hash {hash_id} in StringStore.")
    E087 = ("Unknown displaCy style: {style}.")
    E088 = ("Text of length {length} exceeds maximum of {max_length}. The "
            "v2.x parser and NER models require roughly 1GB of temporary "
            "memory per 100,000 characters in the input. This means long "
            "texts may cause memory allocation errors. If you're not using "
            "the parser or NER, it's probably safe to increase the "
            "`nlp.max_length` limit. The limit is in number of characters, so "
            "you can check whether your inputs are too long by checking "
            "`len(text)`.")
    E089 = ("Extensions can't have a setter argument without a getter "
            "argument. Check the keyword arguments on `set_extension`.")
    E090 = ("Extension '{name}' already exists on {obj}. To overwrite the "
            "existing extension, set `force=True` on `{obj}.set_extension`.")
    E091 = ("Invalid extension attribute {name}: expected callable or None, "
            "but got: {value}")
    E092 = ("Could not find or assign name for word vectors. Ususally, the "
            "name is read from the model's meta.json in vector.name. "
            "Alternatively, it is built from the 'lang' and 'name' keys in "
            "the meta.json. Vector names are required to avoid issue #1660.")
    E093 = ("token.ent_iob values make invalid sequence: I without B\n{seq}")
    E094 = ("Error reading line {line_num} in vectors file {loc}.")
    E095 = ("Can't write to frozen dictionary. This is likely an internal "
            "error. Are you writing to a default function argument?")
    E096 = ("Invalid object passed to displaCy: Can only visualize Doc or "
            "Span objects, or dicts if set to manual=True.")
    E097 = ("Invalid pattern: expected token pattern (list of dicts) or "
            "phrase pattern (string) but got:\n{pattern}")
    E098 = ("Invalid pattern specified: expected both SPEC and PATTERN.")
    E099 = ("First node of pattern should be a root node. The root should "
            "only contain NODE_NAME.")
    E100 = ("Nodes apart from the root should contain NODE_NAME, NBOR_NAME and "
            "NBOR_RELOP.")
    E101 = ("NODE_NAME should be a new node and NBOR_NAME should already have "
            "have been declared in previous edges.")
    E102 = ("Can't merge non-disjoint spans. '{token}' is already part of "
            "tokens to merge. If you want to find the longest non-overlapping "
            "spans, you can use the util.filter_spans helper:\n"
            "https://spacy.io/api/top-level#util.filter_spans")
    E103 = ("Trying to set conflicting doc.ents: '{span1}' and '{span2}'. A "
            "token can only be part of one entity, so make sure the entities "
            "you're setting don't overlap.")
    E104 = ("Can't find JSON schema for '{name}'.")
    E105 = ("The Doc.print_tree() method is now deprecated. Please use "
            "Doc.to_json() instead or write your own function.")
    E106 = ("Can't find doc._.{attr} attribute specified in the underscore "
            "settings: {opts}")
    E107 = ("Value of doc._.{attr} is not JSON-serializable: {value}")
    E108 = ("As of spaCy v2.1, the pipe name `sbd` has been deprecated "
            "in favor of the pipe name `sentencizer`, which does the same "
            "thing. For example, use `nlp.create_pipeline('sentencizer')`")
    E109 = ("Model for component '{name}' not initialized. Did you forget to "
            "load a model, or forget to call begin_training()?")
    E110 = ("Invalid displaCy render wrapper. Expected callable, got: {obj}")
    E111 = ("Pickling a token is not supported, because tokens are only views "
            "of the parent Doc and can't exist on their own. A pickled token "
            "would always have to include its Doc and Vocab, which has "
            "practically no advantage over pickling the parent Doc directly. "
            "So instead of pickling the token, pickle the Doc it belongs to.")
    E112 = ("Pickling a span is not supported, because spans are only views "
            "of the parent Doc and can't exist on their own. A pickled span "
            "would always have to include its Doc and Vocab, which has "
            "practically no advantage over pickling the parent Doc directly. "
            "So instead of pickling the span, pickle the Doc it belongs to or "
            "use Span.as_doc to convert the span to a standalone Doc object.")
    E113 = ("The newly split token can only have one root (head = 0).")
    E114 = ("The newly split token needs to have a root (head = 0).")
    E115 = ("All subtokens must have associated heads.")
    E116 = ("Cannot currently add labels to pretrained text classifier. Add "
            "labels before training begins. This functionality was available "
            "in previous versions, but had significant bugs that led to poor "
            "performance.")
    E117 = ("The newly split tokens must match the text of the original token. "
            "New orths: {new}. Old text: {old}.")
    E118 = ("The custom extension attribute '{attr}' is not registered on the "
            "Token object so it can't be set during retokenization. To "
            "register an attribute, use the Token.set_extension classmethod.")
    E119 = ("Can't set custom extension attribute '{attr}' during "
            "retokenization because it's not writable. This usually means it "
            "was registered with a getter function (and no setter) or as a "
            "method extension, so the value is computed dynamically. To "
            "overwrite a custom attribute manually, it should be registered "
            "with a default value or with a getter AND setter.")
    E120 = ("Can't set custom extension attributes during retokenization. "
            "Expected dict mapping attribute names to values, but got: {value}")
    E121 = ("Can't bulk merge spans. Attribute length {attr_len} should be "
            "equal to span length ({span_len}).")
    E122 = ("Cannot find token to be split. Did it get merged?")
    E123 = ("Cannot find head of token to be split. Did it get merged?")
    E124 = ("Cannot read from file: {path}. Supported formats: {formats}")
    E125 = ("Unexpected value: {value}")
    E126 = ("Unexpected matcher predicate: '{bad}'. Expected one of: {good}. "
            "This is likely a bug in spaCy, so feel free to open an issue.")
    E127 = ("Cannot create phrase pattern representation for length 0. This "
            "is likely a bug in spaCy.")
    E128 = ("Unsupported serialization argument: '{arg}'. The use of keyword "
            "arguments to exclude fields from being serialized or deserialized "
            "is now deprecated. Please use the `exclude` argument instead. "
            "For example: exclude=['{arg}'].")
    E129 = ("Cannot write the label of an existing Span object because a Span "
            "is a read-only view of the underlying Token objects stored in the "
            "Doc. Instead, create a new Span object and specify the `label` "
            "keyword argument, for example:\nfrom spacy.tokens import Span\n"
            "span = Span(doc, start={start}, end={end}, label='{label}')")
    E130 = ("You are running a narrow unicode build, which is incompatible "
            "with spacy >= 2.1.0. To fix this, reinstall Python and use a wide "
            "unicode build instead. You can also rebuild Python and set the "
            "--enable-unicode=ucs4 flag.")
    E131 = ("Cannot write the kb_id of an existing Span object because a Span "
            "is a read-only view of the underlying Token objects stored in "
            "the Doc. Instead, create a new Span object and specify the "
            "`kb_id` keyword argument, for example:\nfrom spacy.tokens "
            "import Span\nspan = Span(doc, start={start}, end={end}, "
            "label='{label}', kb_id='{kb_id}')")
    E132 = ("The vectors for entities and probabilities for alias '{alias}' "
            "should have equal length, but found {entities_length} and "
            "{probabilities_length} respectively.")
    E133 = ("The sum of prior probabilities for alias '{alias}' should not "
            "exceed 1, but found {sum}.")
    E134 = ("Entity '{entity}' is not defined in the Knowledge Base.")
    E135 = ("If you meant to replace a built-in component, use `create_pipe`: "
            "`nlp.replace_pipe('{name}', nlp.create_pipe('{name}'))`")
    E136 = ("This additional feature requires the jsonschema library to be "
            "installed:\npip install jsonschema")
    E137 = ("Expected 'dict' type, but got '{type}' from '{line}'. Make sure "
            "to provide a valid JSON object as input with either the `text` "
            "or `tokens` key. For more info, see the docs:\n"
            "https://spacy.io/api/cli#pretrain-jsonl")
    E138 = ("Invalid JSONL format for raw text '{text}'. Make sure the input "
            "includes either the `text` or `tokens` key. For more info, see "
            "the docs:\nhttps://spacy.io/api/cli#pretrain-jsonl")
    E139 = ("Knowledge Base for component '{name}' not initialized. Did you "
            "forget to call set_kb()?")
    E140 = ("The list of entities, prior probabilities and entity vectors "
            "should be of equal length.")
    E141 = ("Entity vectors should be of length {required} instead of the "
            "provided {found}.")
    E142 = ("Unsupported loss_function '{loss_func}'. Use either 'L2' or "
            "'cosine'.")
    E143 = ("Labels for component '{name}' not initialized. Did you forget to "
            "call add_label()?")
    E144 = ("Could not find parameter `{param}` when building the entity "
            "linker model.")
    E145 = ("Error reading `{param}` from input file.")
    E146 = ("Could not access `{path}`.")
    E147 = ("Unexpected error in the {method} functionality of the "
            "EntityLinker: {msg}. This is likely a bug in spaCy, so feel free "
            "to open an issue.")
    E148 = ("Expected {ents} KB identifiers but got {ids}. Make sure that "
            "each entity in `doc.ents` is assigned to a KB identifier.")
    E149 = ("Error deserializing model. Check that the config used to create "
            "the component matches the model being loaded.")
    E150 = ("The language of the `nlp` object and the `vocab` should be the "
            "same, but found '{nlp}' and '{vocab}' respectively.")
    E151 = ("Trying to call nlp.update without required annotation types. "
            "Expected top-level keys: {exp}. Got: {unexp}.")
    E152 = ("The attribute {attr} is not supported for token patterns. "
            "Please use the option validate=True with Matcher, PhraseMatcher, "
            "or EntityRuler for more details.")
    E153 = ("The value type {vtype} is not supported for token patterns. "
            "Please use the option validate=True with Matcher, PhraseMatcher, "
            "or EntityRuler for more details.")
    E154 = ("One of the attributes or values is not supported for token "
            "patterns. Please use the option validate=True with Matcher, "
            "PhraseMatcher, or EntityRuler for more details.")
    E155 = ("The pipeline needs to include a tagger in order to use "
            "Matcher or PhraseMatcher with the attributes POS, TAG, or LEMMA. "
            "Try using nlp() instead of nlp.make_doc() or list(nlp.pipe()) "
            "instead of list(nlp.tokenizer.pipe()).")
    E156 = ("The pipeline needs to include a parser in order to use "
            "Matcher or PhraseMatcher with the attribute DEP. Try using "
            "nlp() instead of nlp.make_doc() or list(nlp.pipe()) instead of "
            "list(nlp.tokenizer.pipe()).")
    E157 = ("Can't render negative values for dependency arc start or end. "
            "Make sure that you're passing in absolute token indices, not "
            "relative token offsets.\nstart: {start}, end: {end}, label: "
            "{label}, direction: {dir}")
    E158 = ("Can't add table '{name}' to lookups because it already exists.")
    E159 = ("Can't find table '{name}' in lookups. Available tables: {tables}")
    E160 = ("Can't find language data file: {path}")
    E161 = ("Found an internal inconsistency when predicting entity links. "
            "This is likely a bug in spaCy, so feel free to open an issue.")
    E162 = ("Cannot evaluate textcat model on data with different labels.\n"
            "Labels in model: {model_labels}\nLabels in evaluation "
            "data: {eval_labels}")
    E163 = ("cumsum was found to be unstable: its last element does not "
            "correspond to sum")
    E164 = ("x is neither increasing nor decreasing: {}.")
    E165 = ("Only one class present in y_true. ROC AUC score is not defined in "
            "that case.")
    E166 = ("Can only merge DocBins with the same pre-defined attributes.\n"
            "Current DocBin: {current}\nOther DocBin: {other}")
    E167 = ("Unknown morphological feature: '{feat}' ({feat_id}). This can "
            "happen if the tagger was trained with a different set of "
            "morphological features. If you're using a pretrained model, make "
            "sure that your models are up to date:\npython -m spacy validate")
    E168 = ("Unknown field: {field}")
    E169 = ("Can't find module: {module}")
    E170 = ("Cannot apply transition {name}: invalid for the current state.")
    E171 = ("Matcher.add received invalid on_match callback argument: expected "
            "callable or None, but got: {arg_type}")
    E172 = ("The Lemmatizer.load classmethod is deprecated. To create a "
            "Lemmatizer, initialize the class directly. See the docs for "
            "details: https://spacy.io/api/lemmatizer")
    E173 = ("As of v2.2, the Lemmatizer is initialized with an instance of "
            "Lookups containing the lemmatization tables. See the docs for "
            "details: https://spacy.io/api/lemmatizer#init")
    E174 = ("Architecture '{name}' not found in registry. Available "
            "names: {names}")
    E175 = ("Can't remove rule for unknown match pattern ID: {key}")
    E176 = ("Alias '{alias}' is not defined in the Knowledge Base.")
    E177 = ("Ill-formed IOB input detected: {tag}")
    E178 = ("Invalid pattern. Expected list of dicts but got: {pat}. Maybe you "
            "accidentally passed a single pattern to Matcher.add instead of a "
            "list of patterns? If you only want to add one pattern, make sure "
            "to wrap it in a list. For example: matcher.add('{key}', [pattern])")
    E179 = ("Invalid pattern. Expected a list of Doc objects but got a single "
            "Doc. If you only want to add one pattern, make sure to wrap it "
            "in a list. For example: matcher.add('{key}', [doc])")
    E180 = ("Span attributes can't be declared as required or assigned by "
            "components, since spans are only views of the Doc. Use Doc and "
            "Token attributes only and remove the following: {attrs}")
    E181 = ("Received invalid attributes for unkown object {obj}: {attrs}. "
            "Only Doc and Token attributes are supported.")
    E182 = ("Received invalid attribute declaration: {attr}\nDid you forget "
            "to define the attribute? For example: {attr}.???")
    E183 = ("Received invalid attribute declaration: {attr}\nOnly top-level "
            "attributes are supported, for example: {solution}")
    E184 = ("Only attributes without underscores are supported in component "
            "attribute declarations (because underscore and non-underscore "
            "attributes are connected anyways): {attr} -> {solution}")
    E185 = ("Received invalid attribute in component attribute declaration: "
            "{obj}.{attr}\nAttribute '{attr}' does not exist on {obj}.")
    E186 = ("'{tok_a}' and '{tok_b}' are different texts.")


@add_codes
class TempErrors(object):
    T003 = ("Resizing pretrained Tagger models is not currently supported.")
    T004 = ("Currently parser depth is hard-coded to 1. Received: {value}.")
    T007 = ("Can't yet set {attr} from Span. Vote for this feature on the "
            "issue tracker: http://github.com/explosion/spaCy/issues")
    T008 = ("Bad configuration of Tagger. This is probably a bug within "
            "spaCy. We changed the name of an internal attribute for loading "
            "pretrained vectors, and the class has been passed the old name "
            "(pretrained_dims) but not the new name (pretrained_vectors).")


# fmt: on


class MatchPatternError(ValueError):
    def __init__(self, key, errors):
        """Custom error for validating match patterns.

        key (unicode): The name of the matcher rule.
        errors (dict): Validation errors (sequence of strings) mapped to pattern
            ID, i.e. the index of the added pattern.
        """
        msg = "Invalid token patterns for matcher rule '{}'\n".format(key)
        for pattern_idx, error_msgs in errors.items():
            pattern_errors = "\n".join(["- {}".format(e) for e in error_msgs])
            msg += "\nPattern {}:\n{}\n".format(pattern_idx, pattern_errors)
        ValueError.__init__(self, msg)


class AlignmentError(ValueError):
    pass


class ModelsWarning(UserWarning):
    pass


WARNINGS = {
    "user": UserWarning,
    "deprecation": DeprecationWarning,
    "models": ModelsWarning,
}


def _get_warn_types(arg):
    if arg == "":  # don't show any warnings
        return []
    if not arg or arg == "all":  # show all available warnings
        return WARNINGS.keys()
    return [w_type.strip() for w_type in arg.split(",") if w_type.strip() in WARNINGS]


def _get_warn_excl(arg):
    if not arg:
        return []
    return [w_id.strip() for w_id in arg.split(",")]


SPACY_WARNING_FILTER = os.environ.get("SPACY_WARNING_FILTER")
SPACY_WARNING_TYPES = _get_warn_types(os.environ.get("SPACY_WARNING_TYPES"))
SPACY_WARNING_IGNORE = _get_warn_excl(os.environ.get("SPACY_WARNING_IGNORE"))


def user_warning(message):
    _warn(message, "user")


def deprecation_warning(message):
    _warn(message, "deprecation")


def models_warning(message):
    _warn(message, "models")


def _warn(message, warn_type="user"):
    """
    message (unicode): The message to display.
    category (Warning): The Warning to show.
    """
    if message.startswith("["):
        w_id = message.split("[", 1)[1].split("]", 1)[0]  # get ID from string
    else:
        w_id = None
    ignore_warning = w_id and w_id in SPACY_WARNING_IGNORE
    if warn_type in SPACY_WARNING_TYPES and not ignore_warning:
        category = WARNINGS[warn_type]
        stack = inspect.stack()[-1]
        with warnings.catch_warnings():
            if SPACY_WARNING_FILTER:
                warnings.simplefilter(SPACY_WARNING_FILTER, category)
            warnings.warn_explicit(message, category, stack[1], stack[2])