



import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path

import pytest

import transformers
from transformers import (
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AutoTokenizer,
    BertConfig,
    BertTokenizer,
    BertTokenizerFast,
    CTRLTokenizer,
    GPTaTokenizer,
    GPTaTokenizerFast,
    PreTrainedTokenizerFast,
    RobertaTokenizer,
    RobertaTokenizerFast,
    is_tokenizers_available,
)
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.auto.tokenization_auto import (
    TOKENIZER_MAPPING,
    get_tokenizer_config,
    tokenizer_class_from_name,
)
from transformers.models.roberta.configuration_roberta import RobertaConfig
from transformers.testing_utils import (
    DUMMY_DIFF_TOKENIZER_IDENTIFIER,
    DUMMY_UNKNOWN_IDENTIFIER,
    SMALL_MODEL_IDENTIFIER,
    RequestCounter,
    require_tokenizers,
    slow,
)


sys.path.append(str(Path(__file__).parent.parent.parent.parent / """utils"""))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_tokenization import CustomTokenizer  # noqa E402


if is_tokenizers_available():
     from test_module.custom_tokenization_fast import CustomTokenizerFast






class SCREAMING_SNAKE_CASE__					(		unittest.TestCase   ):




    '''simple docstring'''






    def       _lowerCAmelCase (	self		):
        A			:	Any										=       0






    @slow
    def       _lowerCAmelCase (	self		):
        for model_name in (x for x in BERT_PRETRAINED_CONFIG_ARCHIVE_MAP.keys() if "japanese" not in x):
            A			:	Any										=       AutoTokenizer.from_pretrained(lowerCamelCase__		)
            self.assertIsNotNone(lowerCamelCase__		)
            self.assertIsInstance(lowerCamelCase__,		(BertTokenizer, BertTokenizerFast)		)
            self.assertGreater(len(lowerCamelCase__		),		0		)

        for model_name in GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP.keys():
            A			:	Any										=       AutoTokenizer.from_pretrained(lowerCamelCase__		)
            self.assertIsNotNone(lowerCamelCase__		)
            self.assertIsInstance(lowerCamelCase__,		(GPTaTokenizer, GPTaTokenizerFast)		)
            self.assertGreater(len(lowerCamelCase__		),		0		)






    def       _lowerCAmelCase (	self		):
        A			:	Union[str, Any]										=       AutoTokenizer.from_pretrained(lowerCamelCase__		)
        self.assertIsInstance(lowerCamelCase__,		(BertTokenizer, BertTokenizerFast)		)
        self.assertEqual(tokenizer.vocab_size,		12		)






    def       _lowerCAmelCase (	self		):
        A			:	Union[str, Any]										=       AutoTokenizer.from_pretrained(lowerCamelCase__		)
        self.assertIsInstance(lowerCamelCase__,		(RobertaTokenizer, RobertaTokenizerFast)		)
        self.assertEqual(tokenizer.vocab_size,		20		)






    def       _lowerCAmelCase (	self		):
        A			:	List[Any]										=       AutoConfig.from_pretrained(lowerCamelCase__		)
        self.assertIsInstance(lowerCamelCase__,		lowerCamelCase__		)
        # Check that tokenizer_type ≠ model_type
        A			:	Union[str, Any]										=       AutoTokenizer.from_pretrained(lowerCamelCase__,		config=lowerCamelCase__		)
        self.assertIsInstance(lowerCamelCase__,		(BertTokenizer, BertTokenizerFast)		)
        self.assertEqual(tokenizer.vocab_size,		12		)






    def       _lowerCAmelCase (	self		):
        with tempfile.TemporaryDirectory() as tmp_dir:
            shutil.copy("""./tests/fixtures/vocab.txt""",		os.path.join(lowerCamelCase__,		"""vocab.txt"""		)		)

            A			:	List[Any]										=       AutoTokenizer.from_pretrained(lowerCamelCase__,		tokenizer_type="""bert""",		use_fast=lowerCamelCase__		)
            self.assertIsInstance(lowerCamelCase__,		lowerCamelCase__		)

        with tempfile.TemporaryDirectory() as tmp_dir:
            shutil.copy("""./tests/fixtures/vocab.json""",		os.path.join(lowerCamelCase__,		"""vocab.json"""		)		)
            shutil.copy("""./tests/fixtures/merges.txt""",		os.path.join(lowerCamelCase__,		"""merges.txt"""		)		)

            A			:	str										=       AutoTokenizer.from_pretrained(lowerCamelCase__,		tokenizer_type="""gpt2""",		use_fast=lowerCamelCase__		)
            self.assertIsInstance(lowerCamelCase__,		lowerCamelCase__		)






    @require_tokenizers
    def       _lowerCAmelCase (	self		):
        with tempfile.TemporaryDirectory() as tmp_dir:
            shutil.copy("""./tests/fixtures/vocab.txt""",		os.path.join(lowerCamelCase__,		"""vocab.txt"""		)		)

            A			:	List[Any]										=       AutoTokenizer.from_pretrained(lowerCamelCase__,		tokenizer_type="""bert"""		)
            self.assertIsInstance(lowerCamelCase__,		lowerCamelCase__		)

        with tempfile.TemporaryDirectory() as tmp_dir:
            shutil.copy("""./tests/fixtures/vocab.json""",		os.path.join(lowerCamelCase__,		"""vocab.json"""		)		)
            shutil.copy("""./tests/fixtures/merges.txt""",		os.path.join(lowerCamelCase__,		"""merges.txt"""		)		)

            A			:	int										=       AutoTokenizer.from_pretrained(lowerCamelCase__,		tokenizer_type="""gpt2"""		)
            self.assertIsInstance(lowerCamelCase__,		lowerCamelCase__		)






    def       _lowerCAmelCase (	self		):
        with pytest.raises(lowerCamelCase__		):
            AutoTokenizer.from_pretrained("""./""",		tokenizer_type="""xxx"""		)






    @require_tokenizers
    def       _lowerCAmelCase (	self		):
        for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]:
            A			:	Optional[int]										=       tokenizer_class.from_pretrained("""wietsedv/bert-base-dutch-cased"""		)
            self.assertIsInstance(lowerCamelCase__,		(BertTokenizer, BertTokenizerFast)		)

            if isinstance(lowerCamelCase__,		lowerCamelCase__		):
                self.assertEqual(tokenizer.basic_tokenizer.do_lower_case,		lowerCamelCase__		)
            else:
                self.assertEqual(tokenizer.do_lower_case,		lowerCamelCase__		)

            self.assertEqual(tokenizer.model_max_length,		512		)






    @require_tokenizers
    def       _lowerCAmelCase (	self		):
        for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]:
            with self.assertRaisesRegex(
                lowerCamelCase__,		"""julien-c/herlolip-not-exists is not a local folder and is not a valid model identifier""",		):
                A			:	int										=       tokenizer_class.from_pretrained("""julien-c/herlolip-not-exists"""		)






    def       _lowerCAmelCase (	self		):
        # tests: https://github.com/huggingface/transformers/pull/13251
        # 1. models with `-`, e.g. xlm-roberta -> xlm_roberta
        # 2. models that don't remap 1-1 from model-name to model file, e.g., openai-gpt -> openai
        A			:	List[Any]										=       TOKENIZER_MAPPING.values()
        A			:	str										=       []

        for slow_tok, fast_tok in tokenizers:
            if slow_tok is not None:
                tokenizer_names.append(slow_tok.__name__		)

            if fast_tok is not None:
                tokenizer_names.append(fast_tok.__name__		)

        for tokenizer_name in tokenizer_names:
            # must find the right class
            tokenizer_class_from_name(lowerCamelCase__		)






    @require_tokenizers
    def       _lowerCAmelCase (	self		):
        self.assertIsInstance(AutoTokenizer.from_pretrained("""bert-base-cased""",		use_fast=lowerCamelCase__		),		lowerCamelCase__		)
        self.assertIsInstance(AutoTokenizer.from_pretrained("""bert-base-cased"""		),		lowerCamelCase__		)






    @require_tokenizers
    def       _lowerCAmelCase (	self		):
        A			:	List[Any]										=       AutoTokenizer.from_pretrained("""distilbert-base-uncased""",		do_lower_case=lowerCamelCase__		)
        A			:	Dict										=       """Hello, world. How are you?"""
        A			:	Optional[int]										=       tokenizer.tokenize(lowerCamelCase__		)
        self.assertEqual("""[UNK]""",		tokens[0]		)

        A			:	List[str]										=       AutoTokenizer.from_pretrained("""microsoft/mpnet-base""",		do_lower_case=lowerCamelCase__		)
        A			:	Tuple										=       tokenizer.tokenize(lowerCamelCase__		)
        self.assertEqual("""[UNK]""",		tokens[0]		)






    @require_tokenizers
    def       _lowerCAmelCase (	self		):
        A			:	List[Any]										=       AutoTokenizer.from_pretrained("""robot-test/dummy-tokenizer-fast-with-model-config"""		)
        self.assertEqual(type(lowerCamelCase__		),		lowerCamelCase__		)
        self.assertEqual(tokenizer.model_max_length,		512		)
        self.assertEqual(tokenizer.vocab_size,		3_0000		)
        self.assertEqual(tokenizer.unk_token,		"""[UNK]"""		)
        self.assertEqual(tokenizer.padding_side,		"""right"""		)
        self.assertEqual(tokenizer.truncation_side,		"""right"""		)






    def       _lowerCAmelCase (	self		):
        A			:	List[Any]										=       AutoTokenizer.from_pretrained(lowerCamelCase__		)
        self.assertIsInstance(lowerCamelCase__,		(BertTokenizer, BertTokenizerFast)		)
        with tempfile.TemporaryDirectory() as tmp_dir:
            tokenizer.save_pretrained(lowerCamelCase__		)
            A			:	Any										=       AutoTokenizer.from_pretrained(lowerCamelCase__		)

        self.assertIsInstance(lowerCamelCase__,		tokenizer.__class__		)
        self.assertEqual(tokenizera.vocab_size,		12		)






    def       _lowerCAmelCase (	self		):
        A			:	int										=       AutoTokenizer.from_pretrained("""ctrl"""		)
        # There is no fast CTRL so this always gives us a slow tokenizer.
        self.assertIsInstance(lowerCamelCase__,		lowerCamelCase__		)






    def       _lowerCAmelCase (	self		):
        # Check we can load the tokenizer config of an online model.
        A			:	Optional[Any]										=       get_tokenizer_config("""bert-base-cased"""		)
        A			:	Optional[int]										=       config.pop("""_commit_hash""",		lowerCamelCase__		)
        # If we ever update bert-base-cased tokenizer config, this dict here will need to be updated.
        self.assertEqual(lowerCamelCase__,		{"""do_lower_case""": False}		)

        # This model does not have a tokenizer_config so we get back an empty dict.
        A			:	int										=       get_tokenizer_config(lowerCamelCase__		)
        self.assertDictEqual(lowerCamelCase__,		{}		)

        # A tokenizer saved with `save_pretrained` always creates a tokenizer config.
        A			:	Any										=       AutoTokenizer.from_pretrained(lowerCamelCase__		)
        with tempfile.TemporaryDirectory() as tmp_dir:
            tokenizer.save_pretrained(lowerCamelCase__		)
            A			:	Optional[int]										=       get_tokenizer_config(lowerCamelCase__		)

        # Check the class of the tokenizer was properly saved (note that it always saves the slow class).
        self.assertEqual(config["""tokenizer_class"""],		"""BertTokenizer"""		)






    def       _lowerCAmelCase (	self		):
        try:
            AutoConfig.register("""custom""",		lowerCamelCase__		)

            AutoTokenizer.register(lowerCamelCase__,		slow_tokenizer_class=lowerCamelCase__		)
            # Trying to register something existing in the Transformers library will raise an error
            with self.assertRaises(lowerCamelCase__		):
                AutoTokenizer.register(lowerCamelCase__,		slow_tokenizer_class=lowerCamelCase__		)

            A			:	int										=       CustomTokenizer.from_pretrained(lowerCamelCase__		)
            with tempfile.TemporaryDirectory() as tmp_dir:
                tokenizer.save_pretrained(lowerCamelCase__		)

                A			:	Tuple										=       AutoTokenizer.from_pretrained(lowerCamelCase__		)
                self.assertIsInstance(lowerCamelCase__,		lowerCamelCase__		)

        finally:
            if "custom" in CONFIG_MAPPING._extra_content:
                del CONFIG_MAPPING._extra_content["custom"]
            if CustomConfig in TOKENIZER_MAPPING._extra_content:
                del TOKENIZER_MAPPING._extra_content[CustomConfig]






    @require_tokenizers
    def       _lowerCAmelCase (	self		):
        try:
            AutoConfig.register("""custom""",		lowerCamelCase__		)

            # Can register in two steps
            AutoTokenizer.register(lowerCamelCase__,		slow_tokenizer_class=lowerCamelCase__		)
            self.assertEqual(TOKENIZER_MAPPING[CustomConfig],		(CustomTokenizer, None)		)
            AutoTokenizer.register(lowerCamelCase__,		fast_tokenizer_class=lowerCamelCase__		)
            self.assertEqual(TOKENIZER_MAPPING[CustomConfig],		(CustomTokenizer, CustomTokenizerFast)		)

            del TOKENIZER_MAPPING._extra_content[CustomConfig]
            # Can register in one step
            AutoTokenizer.register(
                lowerCamelCase__,		slow_tokenizer_class=lowerCamelCase__,		fast_tokenizer_class=lowerCamelCase__		)
            self.assertEqual(TOKENIZER_MAPPING[CustomConfig],		(CustomTokenizer, CustomTokenizerFast)		)

            # Trying to register something existing in the Transformers library will raise an error
            with self.assertRaises(lowerCamelCase__		):
                AutoTokenizer.register(lowerCamelCase__,		fast_tokenizer_class=lowerCamelCase__		)

            # We pass through a bert tokenizer fast cause there is no converter slow to fast for our new toknizer
            # and that model does not have a tokenizer.json
            with tempfile.TemporaryDirectory() as tmp_dir:
                A			:	Tuple										=       BertTokenizerFast.from_pretrained(lowerCamelCase__		)
                bert_tokenizer.save_pretrained(lowerCamelCase__		)
                A			:	Optional[int]										=       CustomTokenizerFast.from_pretrained(lowerCamelCase__		)

            with tempfile.TemporaryDirectory() as tmp_dir:
                tokenizer.save_pretrained(lowerCamelCase__		)

                A			:	List[Any]										=       AutoTokenizer.from_pretrained(lowerCamelCase__		)
                self.assertIsInstance(lowerCamelCase__,		lowerCamelCase__		)

                A			:	Dict										=       AutoTokenizer.from_pretrained(lowerCamelCase__,		use_fast=lowerCamelCase__		)
                self.assertIsInstance(lowerCamelCase__,		lowerCamelCase__		)

        finally:
            if "custom" in CONFIG_MAPPING._extra_content:
                del CONFIG_MAPPING._extra_content["custom"]
            if CustomConfig in TOKENIZER_MAPPING._extra_content:
                del TOKENIZER_MAPPING._extra_content[CustomConfig]






    def       _lowerCAmelCase (	self		):
        # If remote code is not set, we will time out when asking whether to load the model.
        with self.assertRaises(lowerCamelCase__		):
            A			:	List[Any]										=       AutoTokenizer.from_pretrained("""hf-internal-testing/test_dynamic_tokenizer"""		)
        # If remote code is disabled, we can't load this config.
        with self.assertRaises(lowerCamelCase__		):
            A			:	int										=       AutoTokenizer.from_pretrained(
                """hf-internal-testing/test_dynamic_tokenizer""",		trust_remote_code=lowerCamelCase__		)

        A			:	Tuple										=       AutoTokenizer.from_pretrained("""hf-internal-testing/test_dynamic_tokenizer""",		trust_remote_code=lowerCamelCase__		)
        self.assertTrue(tokenizer.special_attribute_present		)
        # Test tokenizer can be reloaded.
        with tempfile.TemporaryDirectory() as tmp_dir:
            tokenizer.save_pretrained(lowerCamelCase__		)
            A			:	int										=       AutoTokenizer.from_pretrained(lowerCamelCase__,		trust_remote_code=lowerCamelCase__		)
        self.assertTrue(reloaded_tokenizer.special_attribute_present		)

        if is_tokenizers_available():
            self.assertEqual(tokenizer.__class__.__name__,		"""NewTokenizerFast"""		)
            self.assertEqual(reloaded_tokenizer.__class__.__name__,		"""NewTokenizerFast"""		)

            # Test we can also load the slow version
            A			:	Tuple										=       AutoTokenizer.from_pretrained(
                """hf-internal-testing/test_dynamic_tokenizer""",		trust_remote_code=lowerCamelCase__,		use_fast=lowerCamelCase__		)
            self.assertTrue(tokenizer.special_attribute_present		)
            self.assertEqual(tokenizer.__class__.__name__,		"""NewTokenizer"""		)
            # Test tokenizer can be reloaded.
            with tempfile.TemporaryDirectory() as tmp_dir:
                tokenizer.save_pretrained(lowerCamelCase__		)
                A			:	Union[str, Any]										=       AutoTokenizer.from_pretrained(lowerCamelCase__,		trust_remote_code=lowerCamelCase__,		use_fast=lowerCamelCase__		)
            self.assertEqual(reloaded_tokenizer.__class__.__name__,		"""NewTokenizer"""		)
            self.assertTrue(reloaded_tokenizer.special_attribute_present		)
        else:
            self.assertEqual(tokenizer.__class__.__name__,		"""NewTokenizer"""		)
            self.assertEqual(reloaded_tokenizer.__class__.__name__,		"""NewTokenizer"""		)






    @require_tokenizers
    def       _lowerCAmelCase (	self		):






        class SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__   ):




            '''simple docstring'''






            __lowerCamelCase      :			str =				False






        class SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__   ):




            '''simple docstring'''






            __lowerCamelCase      :			List[Any] =				NewTokenizer
            __lowerCamelCase      :			List[str] =				False

        try:
            AutoConfig.register("""custom""",		lowerCamelCase__		)
            AutoTokenizer.register(lowerCamelCase__,		slow_tokenizer_class=lowerCamelCase__		)
            AutoTokenizer.register(lowerCamelCase__,		fast_tokenizer_class=lowerCamelCase__		)
            # If remote code is not set, the default is to use local
            A			:	Optional[int]										=       AutoTokenizer.from_pretrained("""hf-internal-testing/test_dynamic_tokenizer"""		)
            self.assertEqual(tokenizer.__class__.__name__,		"""NewTokenizerFast"""		)
            self.assertFalse(tokenizer.special_attribute_present		)
            A			:	List[str]										=       AutoTokenizer.from_pretrained("""hf-internal-testing/test_dynamic_tokenizer""",		use_fast=lowerCamelCase__		)
            self.assertEqual(tokenizer.__class__.__name__,		"""NewTokenizer"""		)
            self.assertFalse(tokenizer.special_attribute_present		)

            # If remote code is disabled, we load the local one.
            A			:	Union[str, Any]										=       AutoTokenizer.from_pretrained(
                """hf-internal-testing/test_dynamic_tokenizer""",		trust_remote_code=lowerCamelCase__		)
            self.assertEqual(tokenizer.__class__.__name__,		"""NewTokenizerFast"""		)
            self.assertFalse(tokenizer.special_attribute_present		)
            A			:	Any										=       AutoTokenizer.from_pretrained(
                """hf-internal-testing/test_dynamic_tokenizer""",		trust_remote_code=lowerCamelCase__,		use_fast=lowerCamelCase__		)
            self.assertEqual(tokenizer.__class__.__name__,		"""NewTokenizer"""		)
            self.assertFalse(tokenizer.special_attribute_present		)

            # If remote is enabled, we load from the Hub
            A			:	Tuple										=       AutoTokenizer.from_pretrained(
                """hf-internal-testing/test_dynamic_tokenizer""",		trust_remote_code=lowerCamelCase__		)
            self.assertEqual(tokenizer.__class__.__name__,		"""NewTokenizerFast"""		)
            self.assertTrue(tokenizer.special_attribute_present		)
            A			:	Optional[int]										=       AutoTokenizer.from_pretrained(
                """hf-internal-testing/test_dynamic_tokenizer""",		trust_remote_code=lowerCamelCase__,		use_fast=lowerCamelCase__		)
            self.assertEqual(tokenizer.__class__.__name__,		"""NewTokenizer"""		)
            self.assertTrue(tokenizer.special_attribute_present		)

        finally:
            if "custom" in CONFIG_MAPPING._extra_content:
                del CONFIG_MAPPING._extra_content["custom"]
            if CustomConfig in TOKENIZER_MAPPING._extra_content:
                del TOKENIZER_MAPPING._extra_content[CustomConfig]






    def       _lowerCAmelCase (	self		):
        A			:	Optional[Any]										=       AutoTokenizer.from_pretrained(
            """hf-internal-testing/test_dynamic_tokenizer_legacy""",		trust_remote_code=lowerCamelCase__		)
        self.assertTrue(tokenizer.special_attribute_present		)
        if is_tokenizers_available():
            self.assertEqual(tokenizer.__class__.__name__,		"""NewTokenizerFast"""		)

            # Test we can also load the slow version
            A			:	Tuple										=       AutoTokenizer.from_pretrained(
                """hf-internal-testing/test_dynamic_tokenizer_legacy""",		trust_remote_code=lowerCamelCase__,		use_fast=lowerCamelCase__		)
            self.assertTrue(tokenizer.special_attribute_present		)
            self.assertEqual(tokenizer.__class__.__name__,		"""NewTokenizer"""		)
        else:
            self.assertEqual(tokenizer.__class__.__name__,		"""NewTokenizer"""		)






    def       _lowerCAmelCase (	self		):
        with self.assertRaisesRegex(
            lowerCamelCase__,		"""bert-base is not a local folder and is not a valid model identifier"""		):
            A			:	Tuple										=       AutoTokenizer.from_pretrained("""bert-base"""		)






    def       _lowerCAmelCase (	self		):
        with self.assertRaisesRegex(
            lowerCamelCase__,		R"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"""		):
            A			:	Any										=       AutoTokenizer.from_pretrained(lowerCamelCase__,		revision="""aaaaaa"""		)






    def       _lowerCAmelCase (	self		):
        # Make sure we have cached the tokenizer.
        A			:	Optional[Any]										=       AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-bert"""		)
        with RequestCounter() as counter:
            A			:	Optional[int]										=       AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-bert"""		)
            self.assertEqual(counter.get_request_count,		0		)
            self.assertEqual(counter.head_request_count,		1		)
            self.assertEqual(counter.other_request_count,		0		)







from jiwer import compute_measures

import datasets


SCREAMING_SNAKE_CASE_:str					=						"""\
@inproceedings{inproceedings,
    author = {Morris, Andrew and Maier, Viktoria and Green, Phil},
    year = {2004},
    month = {01},
    pages = {},
    title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}
}
"""

SCREAMING_SNAKE_CASE_:Union[str, Any]					=						"""\
Word error rate (WER) is a common metric of the performance of an automatic speech recognition system.

The general difficulty of measuring performance lies in the fact that the recognized word sequence can have a different length from the reference word sequence (supposedly the correct one). The WER is derived from the Levenshtein distance, working at the word level instead of the phoneme level. The WER is a valuable tool for comparing different systems as well as for evaluating improvements within one system. This kind of measurement, however, provides no details on the nature of translation errors and further work is therefore required to identify the main source(s) of error and to focus any research effort.

This problem is solved by first aligning the recognized word sequence with the reference (spoken) word sequence using dynamic string alignment. Examination of this issue is seen through a theory called the power law that states the correlation between perplexity and word error rate.

Word error rate can then be computed as:

WER = (S + D + I) / N = (S + D + I) / (S + D + C)

where

S is the number of substitutions,
D is the number of deletions,
I is the number of insertions,
C is the number of correct words,
N is the number of words in the reference (N=S+D+C).

This value indicates the average number of errors per reference word. The lower the value, the better the
performance of the ASR system with a WER of 0 being a perfect score.
"""

SCREAMING_SNAKE_CASE_:List[Any]					=						"""
Compute WER score of transcribed segments against references.

Args:
    references: List of references for each speech input.
    predictions: List of transcriptions to score.
    concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.

Returns:
    (float): the word error rate

Examples:

    >>> predictions = [\"this is the prediction\", \"there is an other sample\"]
    >>> references = [\"this is the reference\", \"there is another one\"]
    >>> wer = datasets.load_metric(\"wer\")
    >>> wer_score = wer.compute(predictions=predictions, references=references)
    >>> print(wer_score)
    0.5
"""






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION			,						_KWARGS_DESCRIPTION   )
class SCREAMING_SNAKE_CASE__					(		datasets.Metric   ):




    '''simple docstring'''






    def       _lowerCAmelCase (	self		):
        return datasets.MetricInfo(
            description=_DESCRIPTION,		citation=_CITATION,		inputs_description=_KWARGS_DESCRIPTION,		features=datasets.Features(
                {
                    """predictions""": datasets.Value("""string""",		id="""sequence"""		),
                    """references""": datasets.Value("""string""",		id="""sequence"""		),
                }		),		codebase_urls=["""https://github.com/jitsi/jiwer/"""],		reference_urls=[
                """https://en.wikipedia.org/wiki/Word_error_rate""",
            ],		)






    def       _lowerCAmelCase (	self,		lowerCamelCase__=None,		lowerCamelCase__=None,		lowerCamelCase__=False		):
        if concatenate_texts:
            return compute_measures(lowerCamelCase__,		lowerCamelCase__		)["wer"]
        else:
            A			:	str										=       0
            A			:	int										=       0
            for prediction, reference in zip(lowerCamelCase__,		lowerCamelCase__		):
                A			:	Tuple										=       compute_measures(lowerCamelCase__,		lowerCamelCase__		)
                incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
                total += measures["substitutions"] + measures["deletions"] + measures["hits"]
            return incorrect / total


