
"""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 				lowerCAmelCase_				(      datasets.Metric    ):



						'''simple docstring'''


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

"""simple docstring"""


import unittest

from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import (
		    XLMForMultipleChoice,
		    XLMForQuestionAnswering,
		    XLMForQuestionAnsweringSimple,
		    XLMForSequenceClassification,
		    XLMForTokenClassification,
		    XLMModel,
		    XLMWithLMHeadModel,
		)
		from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST







class 				lowerCAmelCase_				:



						'''simple docstring'''


						def __init__( self     :      Any  ,A_     :      Optional[Any]  ,A_     :      Tuple=13  ,A_     :      Optional[Any]=7  ,A_     :      Dict=True  ,A_     :      Optional[Any]=True  ,A_     :      str=True  ,A_     :      Union[str, Any]=True  ,A_     :      Optional[Any]=True  ,A_     :      Tuple=False  ,A_     :      Optional[int]=False  ,A_     :      str=False  ,A_     :      int=2  ,A_     :      Union[str, Any]=99  ,A_     :      int=0  ,A_     :      Dict=32  ,A_     :      List[str]=5  ,A_     :      Any=4  ,A_     :      str=0.1  ,A_     :      Any=0.1  ,A_     :      int=512  ,A_     :      List[Any]=2  ,A_     :      Union[str, Any]=0.02  ,A_     :      Optional[Any]=2  ,A_     :      List[str]=4  ,A_     :      Optional[int]="last"  ,A_     :      str=True  ,A_     :      List[str]=None  ,A_     :      List[Any]=0  ,)     ->   int:
							A						   =   parent
							A						   =   batch_size
							A						   =   seq_length
							A						   =   is_training
							A						   =   use_input_lengths
							A						   =   use_token_type_ids
							A						   =   use_labels
							A						   =   gelu_activation
							A						   =   sinusoidal_embeddings
							A						   =   causal
							A						   =   asm
							A						   =   n_langs
							A						   =   vocab_size
							A						   =   n_special
							A						   =   hidden_size
							A						   =   num_hidden_layers
							A						   =   num_attention_heads
							A						   =   hidden_dropout_prob
							A						   =   attention_probs_dropout_prob
							A						   =   max_position_embeddings
							A						   =   type_sequence_label_size
							A						   =   initializer_range
							A						   =   num_labels
							A						   =   num_choices
							A						   =   summary_type
							A						   =   use_proj
							A						   =   scope
							A						   =   bos_token_id


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   Dict:
							A						   =   ids_tensor([self.batch_size, self.seq_length]  ,self.vocab_size      )
							A						   =   random_attention_mask([self.batch_size, self.seq_length]      )

							A						   =   None
							if self.use_input_lengths:
								A						   =   (
								    ids_tensor([self.batch_size]  ,vocab_size=2      ) + self.seq_length - 2
								)  # small variation of seq_length

							A						   =   None
							if self.use_token_type_ids:
								A						   =   ids_tensor([self.batch_size, self.seq_length]  ,self.n_langs      )

							A						   =   None
							A						   =   None
							A						   =   None
							if self.use_labels:
								A						   =   ids_tensor([self.batch_size]  ,self.type_sequence_label_size      )
								A						   =   ids_tensor([self.batch_size, self.seq_length]  ,self.num_labels      )
								A						   =   ids_tensor([self.batch_size]  ,2      ).float()
								A						   =   ids_tensor([self.batch_size]  ,self.num_choices      )

							A						   =   self.get_config()

							return (
							    config,
							    input_ids,
							    token_type_ids,
							    input_lengths,
							    sequence_labels,
							    token_labels,
							    is_impossible_labels,
							    choice_labels,
							    input_mask,
							)


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]      )     ->   Any:
							return XLMConfig(
							    vocab_size=self.vocab_size  ,n_special=self.n_special  ,emb_dim=self.hidden_size  ,n_layers=self.num_hidden_layers  ,n_heads=self.num_attention_heads  ,dropout=self.hidden_dropout_prob  ,attention_dropout=self.attention_probs_dropout_prob  ,gelu_activation=self.gelu_activation  ,sinusoidal_embeddings=self.sinusoidal_embeddings  ,asm=self.asm  ,causal=self.causal  ,n_langs=self.n_langs  ,max_position_embeddings=self.max_position_embeddings  ,initializer_range=self.initializer_range  ,summary_type=self.summary_type  ,use_proj=self.use_proj  ,num_labels=self.num_labels  ,bos_token_id=self.bos_token_id  ,)


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]  ,A_     :      Optional[Any]  ,A_     :      List[str]  ,A_     :      int  ,A_     :      Dict  ,A_     :      Optional[Any]  ,A_     :      Optional[Any]  ,A_     :      Any  ,A_     :      List[str]  ,A_     :      Optional[int]  ,)     ->   Tuple:
							A						   =   XLMModel(config=A_      )
							model.to(A_      )
							model.eval()
							A						   =   model(A_  ,lengths=A_  ,langs=A_      )
							A						   =   model(A_  ,langs=A_      )
							A						   =   model(A_      )
							self.parent.assertEqual(result.last_hidden_state.shape  ,(self.batch_size, self.seq_length, self.hidden_size)      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      int  ,A_     :      str  ,A_     :      Union[str, Any]  ,A_     :      Any  ,A_     :      Any  ,A_     :      Any  ,A_     :      Union[str, Any]  ,A_     :      List[str]  ,A_     :      List[str]  ,A_     :      List[str]  ,)     ->   Union[str, Any]:
							A						   =   XLMWithLMHeadModel(A_      )
							model.to(A_      )
							model.eval()

							A						   =   model(A_  ,token_type_ids=A_  ,labels=A_      )
							self.parent.assertEqual(result.loss.shape  ,()      )
							self.parent.assertEqual(result.logits.shape  ,(self.batch_size, self.seq_length, self.vocab_size)      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]  ,A_     :      Tuple  ,A_     :      Tuple  ,A_     :      str  ,A_     :      int  ,A_     :      str  ,A_     :      Optional[Any]  ,A_     :      Any  ,A_     :      Any  ,A_     :      Dict  ,)     ->   List[str]:
							A						   =   XLMForQuestionAnsweringSimple(A_      )
							model.to(A_      )
							model.eval()

							A						   =   model(A_      )

							A						   =   model(A_  ,start_positions=A_  ,end_positions=A_      )
							A						   =   outputs
							self.parent.assertEqual(result.start_logits.shape  ,(self.batch_size, self.seq_length)      )
							self.parent.assertEqual(result.end_logits.shape  ,(self.batch_size, self.seq_length)      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any  ,A_     :      Optional[int]  ,A_     :      Dict  ,A_     :      Optional[Any]  ,A_     :      List[Any]  ,A_     :      List[str]  ,A_     :      List[Any]  ,A_     :      Optional[Any]  ,A_     :      str  ,A_     :      Any  ,)     ->   Optional[int]:
							A						   =   XLMForQuestionAnswering(A_      )
							model.to(A_      )
							model.eval()

							A						   =   model(A_      )

							A						   =   model(
							    A_  ,start_positions=A_  ,end_positions=A_  ,cls_index=A_  ,is_impossible=A_  ,p_mask=A_  ,)

							A						   =   model(
							    A_  ,start_positions=A_  ,end_positions=A_  ,cls_index=A_  ,is_impossible=A_  ,)

							((A)   ,      )						   =   result_with_labels.to_tuple()

							A						   =   model(A_  ,start_positions=A_  ,end_positions=A_      )

							((A)   ,      )						   =   result_with_labels.to_tuple()

							self.parent.assertEqual(result_with_labels.loss.shape  ,()      )
							self.parent.assertEqual(result.start_top_log_probs.shape  ,(self.batch_size, model.config.start_n_top)      )
							self.parent.assertEqual(result.start_top_index.shape  ,(self.batch_size, model.config.start_n_top)      )
							self.parent.assertEqual(
							    result.end_top_log_probs.shape  ,(self.batch_size, model.config.start_n_top * model.config.end_n_top)      )
							self.parent.assertEqual(
							    result.end_top_index.shape  ,(self.batch_size, model.config.start_n_top * model.config.end_n_top)      )
							self.parent.assertEqual(result.cls_logits.shape  ,(self.batch_size,)      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      int  ,A_     :      Union[str, Any]  ,A_     :      Tuple  ,A_     :      int  ,A_     :      Union[str, Any]  ,A_     :      List[Any]  ,A_     :      Optional[Any]  ,A_     :      Tuple  ,A_     :      Union[str, Any]  ,A_     :      str  ,)     ->   List[Any]:
							A						   =   XLMForSequenceClassification(A_      )
							model.to(A_      )
							model.eval()

							A						   =   model(A_      )
							A						   =   model(A_  ,labels=A_      )
							self.parent.assertEqual(result.loss.shape  ,()      )
							self.parent.assertEqual(result.logits.shape  ,(self.batch_size, self.type_sequence_label_size)      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]  ,A_     :      Optional[Any]  ,A_     :      str  ,A_     :      Tuple  ,A_     :      List[str]  ,A_     :      Dict  ,A_     :      Dict  ,A_     :      Union[str, Any]  ,A_     :      Dict  ,A_     :      Any  ,)     ->   Any:
							A						   =   self.num_labels
							A						   =   XLMForTokenClassification(A_      )
							model.to(A_      )
							model.eval()

							A						   =   model(A_  ,attention_mask=A_  ,labels=A_      )
							self.parent.assertEqual(result.logits.shape  ,(self.batch_size, self.seq_length, self.num_labels)      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]  ,A_     :      int  ,A_     :      str  ,A_     :      int  ,A_     :      Tuple  ,A_     :      List[Any]  ,A_     :      List[str]  ,A_     :      List[str]  ,A_     :      Optional[Any]  ,A_     :      int  ,)     ->   Tuple:
							A						   =   self.num_choices
							A						   =   XLMForMultipleChoice(config=A_      )
							model.to(A_      )
							model.eval()
							A						   =   input_ids.unsqueeze(1      ).expand(-1  ,self.num_choices  ,-1      ).contiguous()
							A						   =   token_type_ids.unsqueeze(1      ).expand(-1  ,self.num_choices  ,-1      ).contiguous()
							A						   =   input_mask.unsqueeze(1      ).expand(-1  ,self.num_choices  ,-1      ).contiguous()
							A						   =   model(
							    A_  ,attention_mask=A_  ,token_type_ids=A_  ,labels=A_  ,)
							self.parent.assertEqual(result.logits.shape  ,(self.batch_size, self.num_choices)      )




						def 						_SCREAMING_SNAKE_CASE      ( self     :      str      )     ->   List[Any]:
							A						   =   self.prepare_config_and_inputs()
							(
							    (
							    A
							)   ,      (
							    A
							)   ,      (
							    A
							)   ,      (
							    A
							)   ,      (
							    A
							)   ,      (
							    A
							)   ,      (
							    A
							)   ,      (
							    A
							)   ,      (
							    A
							)   ,      
							)						   =   config_and_inputs
							A						   =   {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'lengths': input_lengths}
							return config, inputs_dict







@require_torch
class 				lowerCAmelCase_				(      _lowercase		,					_lowercase		,					_lowercase		,					unittest.TestCase    ):



						'''simple docstring'''


						_lowerCamelCase:    Dict      =    (
						    (
						        XLMModel,
						        XLMWithLMHeadModel,
						        XLMForQuestionAnswering,
						        XLMForSequenceClassification,
						        XLMForQuestionAnsweringSimple,
						        XLMForTokenClassification,
						        XLMForMultipleChoice,
						    )
						    if is_torch_available()
						    else ()
						)
						_lowerCamelCase:    Dict      =    (
						    (XLMWithLMHeadModel,) if is_torch_available() else ()
						)  # TODO (PVP): Check other models whether language generation is also applicable
						_lowerCamelCase:    Optional[Any]      =    (
						    {
						        '''feature-extraction''': XLMModel,
						        '''fill-mask''': XLMWithLMHeadModel,
						        '''question-answering''': XLMForQuestionAnsweringSimple,
						        '''text-classification''': XLMForSequenceClassification,
						        '''text-generation''': XLMWithLMHeadModel,
						        '''token-classification''': XLMForTokenClassification,
						        '''zero-shot''': XLMForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]  ,A_     :      Optional[int]  ,A_     :      Optional[int]  ,A_     :      Tuple  ,A_     :      Any  ,A_     :      str      )     ->   Tuple:
							if (
							    pipeline_test_casse_name == "QAPipelineTests"
							    and tokenizer_name is not None
							    and not tokenizer_name.endswith('Fast'      )
							):
								# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
								# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
								# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
								return True

							return False


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]  ,A_     :      Optional[int]  ,A_     :      Tuple  ,A_     :      str=False      )     ->   Dict:
							A						   =   super()._prepare_for_class(A_  ,A_  ,return_labels=A_      )

							if return_labels:
								if model_class.__name__ == "XLMForQuestionAnswering":
									A						   =   torch.zeros(
									    self.model_tester.batch_size  ,dtype=torch.long  ,device=A_      )
									A						   =   torch.zeros(
									    self.model_tester.batch_size  ,dtype=torch.long  ,device=A_      )

							return inputs_dict


						def 						_SCREAMING_SNAKE_CASE      ( self     :      int      )     ->   List[str]:
							A						   =   XLMModelTester(self      )
							A						   =   ConfigTester(self  ,config_class=A_  ,emb_dim=37      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]      )     ->   str:
							self.config_tester.run_common_tests()


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   Optional[int]:
							A						   =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_xlm_model(*A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any      )     ->   Dict:
							A						   =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_xlm_lm_head(*A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]      )     ->   Dict:
							A						   =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_xlm_simple_qa(*A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]      )     ->   Tuple:
							A						   =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_xlm_qa(*A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      str      )     ->   List[Any]:
							A						   =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_xlm_sequence_classif(*A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]      )     ->   Tuple:
							A						   =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_xlm_token_classif(*A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      int      )     ->   Dict:
							A						   =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_xlm_for_multiple_choice(*A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any  ,A_     :      Dict  ,A_     :      List[Any]  ,A_     :      List[Any]  ,A_     :      Dict  ,A_     :      Union[str, Any]  ,A_     :      List[Any]=False  ,A_     :      Tuple=1      )     ->   List[Any]:
							self.assertIsInstance(A_  ,A_      )
							self.assertListEqual(
							    [isinstance(A_  ,A_      ) for iter_attentions in attentions]  ,[True] * len(A_      )      )
							self.assertEqual(len(A_      )  ,(max_length - min_length) * num_beam_groups      )

							for idx, iter_attentions in enumerate(A_      ):
								# adds PAD dummy token
								A						   =   min_length + idx + 1
								A						   =   min_length + idx + 1

								A						   =   (
								    batch_size * num_beam_groups,
								    config.num_attention_heads,
								    tgt_len,
								    src_len,
								)
								# check attn size
								self.assertListEqual(
								    [layer_attention.shape for layer_attention in iter_attentions]  ,[expected_shape] * len(A_      )      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]  ,A_     :      Optional[Any]  ,A_     :      List[str]  ,A_     :      Dict  ,A_     :      Optional[Any]  ,A_     :      Dict  ,A_     :      Tuple=False  ,A_     :      Optional[Any]=1      )     ->   List[str]:
							self.assertIsInstance(A_  ,A_      )
							self.assertListEqual(
							    [isinstance(A_  ,A_      ) for iter_hidden_states in hidden_states]  ,[True] * len(A_      )  ,)
							self.assertEqual(len(A_      )  ,(max_length - min_length) * num_beam_groups      )

							for idx, iter_hidden_states in enumerate(A_      ):
								# adds PAD dummy token
								A						   =   min_length + idx + 1
								A						   =   (batch_size * num_beam_groups, seq_len, config.hidden_size)
								# check hidden size
								self.assertListEqual(
								    [layer_hidden_states.shape for layer_hidden_states in iter_hidden_states]  ,[expected_shape] * len(A_      )  ,)
							pass




						@slow
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any      )     ->   List[str]:
							for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								A						   =   XLMModel.from_pretrained(A_      )
								self.assertIsNotNone(A_      )







@require_torch
class 				lowerCAmelCase_				(      unittest.TestCase    ):



						'''simple docstring'''


						@slow
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]      )     ->   Dict:
							A						   =   XLMWithLMHeadModel.from_pretrained('xlm-mlm-en-2048'      )
							model.to(A_      )
							A						   =   torch.tensor([[14, 447]]  ,dtype=torch.long  ,device=A_      )  # the president
							A						   =   [
							    14,
							    447,
							    14,
							    447,
							    14,
							    447,
							    14,
							    447,
							    14,
							    447,
							    14,
							    447,
							    14,
							    447,
							    14,
							    447,
							    14,
							    447,
							    14,
							    447,
							]  # the president the president the president the president the president the president the president the president the president the president
							# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
							A						   =   model.generate(A_  ,do_sample=A_      )
							self.assertListEqual(output_ids[0].cpu().numpy().tolist()  ,A_      )