'''simple docstring'''

from jiwer import compute_measures

import datasets


lowercase_     		= '''\
@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.}
}
'''

lowercase_     		= '''\
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.
'''

lowercase_     		= '''
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 		UpperCamelCase__       (   self				) ->     List[str]:
								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 		UpperCamelCase__       (   self		,						__A=None		,						__A=None		,						__A=False				) ->     str:
								if concatenate_texts:
														return compute_measures(__A		,						__A				)["wer"]
								else:
														_lowerCAmelCase										=0
														_lowerCAmelCase										=0
														for prediction, reference in zip(__A		,						__A				):
																				_lowerCAmelCase										=compute_measures(__A		,						__A				)
																				incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
																				total += measures["substitutions"] + measures["deletions"] + measures["hits"]
														return incorrect / total






'''simple docstring'''

import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoConfig, BertConfig, GPTaConfig
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test


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

from test_module.custom_configuration import CustomConfig  # noqa E402


lowercase_     		= {
    '''return_dict''': False,
    '''output_hidden_states''': True,
    '''output_attentions''': True,
    '''torchscript''': True,
    '''torch_dtype''': '''float16''',
    '''use_bfloat16''': True,
    '''tf_legacy_loss''': True,
    '''pruned_heads''': {'''a''': 1},
    '''tie_word_embeddings''': False,
    '''is_decoder''': True,
    '''cross_attention_hidden_size''': 128,
    '''add_cross_attention''': True,
    '''tie_encoder_decoder''': True,
    '''max_length''': 50,
    '''min_length''': 3,
    '''do_sample''': True,
    '''early_stopping''': True,
    '''num_beams''': 3,
    '''num_beam_groups''': 3,
    '''diversity_penalty''': 0.5,
    '''temperature''': 2.0,
    '''top_k''': 10,
    '''top_p''': 0.7,
    '''typical_p''': 0.2,
    '''repetition_penalty''': 0.8,
    '''length_penalty''': 0.8,
    '''no_repeat_ngram_size''': 5,
    '''encoder_no_repeat_ngram_size''': 5,
    '''bad_words_ids''': [1, 2, 3],
    '''num_return_sequences''': 3,
    '''chunk_size_feed_forward''': 5,
    '''output_scores''': True,
    '''return_dict_in_generate''': True,
    '''forced_bos_token_id''': 2,
    '''forced_eos_token_id''': 3,
    '''remove_invalid_values''': True,
    '''architectures''': ['''BertModel'''],
    '''finetuning_task''': '''translation''',
    '''id2label''': {0: '''label'''},
    '''label2id''': {'''label''': '''0'''},
    '''tokenizer_class''': '''BertTokenizerFast''',
    '''prefix''': '''prefix''',
    '''bos_token_id''': 6,
    '''pad_token_id''': 7,
    '''eos_token_id''': 8,
    '''sep_token_id''': 9,
    '''decoder_start_token_id''': 10,
    '''exponential_decay_length_penalty''': (5, 1.01),
    '''suppress_tokens''': [0, 1],
    '''begin_suppress_tokens''': 2,
    '''task_specific_params''': {'''translation''': '''some_params'''},
    '''problem_type''': '''regression''',
}
@is_staging_test
class   SCREAMING_SNAKE_CASE			(						unittest.TestCase):







		"""simple docstring"""






		@classmethod
		def 		UpperCamelCase__       (   cls				) ->     Optional[Any]:
								_lowerCAmelCase										=TOKEN
								HfFolder.save_token(__A				)






		@classmethod
		def 		UpperCamelCase__       (   cls				) ->     List[str]:
								try:
														delete_repo(token=cls._token		,						repo_id='test-config'				)
								except HTTPError:
														pass

								try:
														delete_repo(token=cls._token		,						repo_id='valid_org/test-config-org'				)
								except HTTPError:
														pass

								try:
														delete_repo(token=cls._token		,						repo_id='test-dynamic-config'				)
								except HTTPError:
														pass






		def 		UpperCamelCase__       (   self				) ->     str:
								_lowerCAmelCase										=BertConfig(
								    vocab_size=99		,						hidden_size=32		,						num_hidden_layers=5		,						num_attention_heads=4		,						intermediate_size=37				)
								config.push_to_hub('test-config'		,						use_auth_token=self._token				)

								_lowerCAmelCase										=BertConfig.from_pretrained(F'''{USER}/test-config'''				)
								for k, v in config.to_dict().items():
														if k != "transformers_version":
																				self.assertEqual(__A		,						getattr(__A		,						__A				)				)

        # Reset repo
								delete_repo(token=self._token		,						repo_id='test-config'				)

								# Push to hub via save_pretrained
								with tempfile.TemporaryDirectory() as tmp_dir:
														config.save_pretrained(__A		,						repo_id='test-config'		,						push_to_hub=__A		,						use_auth_token=self._token				)

								_lowerCAmelCase										=BertConfig.from_pretrained(F'''{USER}/test-config'''				)
								for k, v in config.to_dict().items():
														if k != "transformers_version":
																				self.assertEqual(__A		,						getattr(__A		,						__A				)				)






		def 		UpperCamelCase__       (   self				) ->     Dict:
								_lowerCAmelCase										=BertConfig(
								    vocab_size=99		,						hidden_size=32		,						num_hidden_layers=5		,						num_attention_heads=4		,						intermediate_size=37				)
								config.push_to_hub('valid_org/test-config-org'		,						use_auth_token=self._token				)

								_lowerCAmelCase										=BertConfig.from_pretrained('valid_org/test-config-org'				)
								for k, v in config.to_dict().items():
														if k != "transformers_version":
																				self.assertEqual(__A		,						getattr(__A		,						__A				)				)

        # Reset repo
								delete_repo(token=self._token		,						repo_id='valid_org/test-config-org'				)

								# Push to hub via save_pretrained
								with tempfile.TemporaryDirectory() as tmp_dir:
														config.save_pretrained(
														    __A		,						repo_id='valid_org/test-config-org'		,						push_to_hub=__A		,						use_auth_token=self._token				)

								_lowerCAmelCase										=BertConfig.from_pretrained('valid_org/test-config-org'				)
								for k, v in config.to_dict().items():
														if k != "transformers_version":
																				self.assertEqual(__A		,						getattr(__A		,						__A				)				)






		def 		UpperCamelCase__       (   self				) ->     List[str]:
								CustomConfig.register_for_auto_class()
								_lowerCAmelCase										=CustomConfig(attribute=42				)

								config.push_to_hub('test-dynamic-config'		,						use_auth_token=self._token				)

								# This has added the proper auto_map field to the config
								self.assertDictEqual(config.auto_map		,						{'AutoConfig': 'custom_configuration.CustomConfig'}				)

								_lowerCAmelCase										=AutoConfig.from_pretrained(F'''{USER}/test-dynamic-config'''		,						trust_remote_code=__A				)
								# Can't make an isinstance check because the new_config is from the FakeConfig class of a dynamic module
								self.assertEqual(new_config.__class__.__name__		,						'CustomConfig'				)
								self.assertEqual(new_config.attribute		,						42				)

class   SCREAMING_SNAKE_CASE			(						unittest.TestCase):







		"""simple docstring"""






		def 		UpperCamelCase__       (   self				) ->     List[Any]:
								_lowerCAmelCase										=GPTaConfig()

								# attempt to modify each of int/float/bool/str config records and verify they were updated
								_lowerCAmelCase										=c.n_embd + 1  # int
								_lowerCAmelCase										=c.resid_pdrop + 1.0  # float
								_lowerCAmelCase										=not c.scale_attn_weights  # bool
								_lowerCAmelCase										=c.summary_type + 'foo'  # str
								c.update_from_string(
								    F'''n_embd={n_embd},resid_pdrop={resid_pdrop},scale_attn_weights={scale_attn_weights},summary_type={summary_type}'''				)
								self.assertEqual(__A		,						c.n_embd		,						'mismatch for key: n_embd'				)
								self.assertEqual(__A		,						c.resid_pdrop		,						'mismatch for key: resid_pdrop'				)
								self.assertEqual(__A		,						c.scale_attn_weights		,						'mismatch for key: scale_attn_weights'				)
								self.assertEqual(__A		,						c.summary_type		,						'mismatch for key: summary_type'				)






		def 		UpperCamelCase__       (   self				) ->     List[str]:
								_lowerCAmelCase										=PretrainedConfig()
								_lowerCAmelCase										=[key for key in base_config.__dict__ if key not in config_common_kwargs]
								# If this part of the test fails, you have arguments to addin config_common_kwargs above.
								self.assertListEqual(
								    __A		,						['is_encoder_decoder', '_name_or_path', '_commit_hash', 'transformers_version']				)
								_lowerCAmelCase										=[key for key, value in config_common_kwargs.items() if value == getattr(__A		,						__A				)]
								if len(__A				) > 0:
														raise ValueError(
														    'The following keys are set with the default values in'
														    ' `test_configuration_common.config_common_kwargs` pick another value for them:'
														    F''' {', '.join(__A				)}.'''				)






		def 		UpperCamelCase__       (   self				) ->     Optional[int]:
								with self.assertRaises(__A				):
														# config is in subfolder, the following should not work without specifying the subfolder
														_lowerCAmelCase										=BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert-subfolder'				)

								_lowerCAmelCase										=BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert-subfolder'		,						subfolder='bert'				)

								self.assertIsNotNone(__A				)






		def 		UpperCamelCase__       (   self				) ->     List[str]:
								# A mock response for an HTTP head request to emulate server down
								_lowerCAmelCase										=mock.Mock()
								_lowerCAmelCase										=500
								_lowerCAmelCase										={}
								_lowerCAmelCase										=HTTPError
								_lowerCAmelCase										={}

								# Download this model to make sure it's in the cache.
								_lowerCAmelCase										=BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert'				)

								# Under the mock environment we get a 500 error when trying to reach the model.
								with mock.patch('requests.Session.request'		,						return_value=__A				) as mock_head:
														_lowerCAmelCase										=BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert'				)
														# This check we did call the fake head request
														mock_head.assert_called()






		def 		UpperCamelCase__       (   self				) ->     Optional[int]:
								# This test is for deprecated behavior and can be removed in v5
								_lowerCAmelCase										=BertConfig.from_pretrained(
								    'https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/config.json'				)






		def 		UpperCamelCase__       (   self				) ->     Any:
								_lowerCAmelCase										=AutoConfig.from_pretrained('bert-base-cased'				)
								_lowerCAmelCase										=['config.4.0.0.json']

								with tempfile.TemporaryDirectory() as tmp_dir:
														configuration.save_pretrained(__A				)
														_lowerCAmelCase										=2
														json.dump(configuration.to_dict()		,						open(os.path.join(__A		,						'config.4.0.0.json'				)		,						'w'				)				)

														# This should pick the new configuration file as the version of Transformers is > 4.0.0
														_lowerCAmelCase										=AutoConfig.from_pretrained(__A				)
														self.assertEqual(new_configuration.hidden_size		,						2				)

														# Will need to be adjusted if we reach v42 and this test is still here.
														# Should pick the old configuration file as the version of Transformers is < 4.42.0
														_lowerCAmelCase										=['config.42.0.0.json']
														_lowerCAmelCase										=768
														configuration.save_pretrained(__A				)
														shutil.move(os.path.join(__A		,						'config.4.0.0.json'				)		,						os.path.join(__A		,						'config.42.0.0.json'				)				)
														_lowerCAmelCase										=AutoConfig.from_pretrained(__A				)
														self.assertEqual(new_configuration.hidden_size		,						768				)






		def 		UpperCamelCase__       (   self				) ->     Any:
								# This repo has two configuration files, one for v4.0.0 and above with a different hidden size.
								_lowerCAmelCase										='hf-internal-testing/test-two-configs'

								import transformers as new_transformers

								_lowerCAmelCase										='v4.0.0'
								_lowerCAmelCase						,				_lowerCAmelCase										=new_transformers.models.auto.AutoConfig.from_pretrained(
								    __A		,						return_unused_kwargs=__A				)
								self.assertEqual(new_configuration.hidden_size		,						2				)
								# This checks `_configuration_file` ia not kept in the kwargs by mistake.
								self.assertDictEqual(__A		,						{}				)

								# Testing an older version by monkey-patching the version in the module it's used.
								import transformers as old_transformers

								_lowerCAmelCase										='v3.0.0'
								_lowerCAmelCase										=old_transformers.models.auto.AutoConfig.from_pretrained(__A				)
								self.assertEqual(old_configuration.hidden_size		,						768				)





