

import unittest

from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch

			from transformers import (
			    DebertaVaForMaskedLM,
			    DebertaVaForMultipleChoice,
			    DebertaVaForQuestionAnswering,
			    DebertaVaForSequenceClassification,
			    DebertaVaForTokenClassification,
			    DebertaVaModel,
			)
			from transformers.models.deberta_va.modeling_deberta_va import DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST





class     snake_case_			(				__UpperCAmelCase							):


							"""simple docstring"""





							def __init__( self					,						_A					,						_A=1_3					,						_A=7					,						_A=True					,						_A=True					,						_A=True					,						_A=True					,						_A=9_9					,						_A=3_2					,						_A=5					,						_A=4					,						_A=3_7					,						_A="gelu"					,						_A=0.1					,						_A=0.1					,						_A=5_1_2					,						_A=1_6					,						_A=2					,						_A=0.02					,						_A=False					,						_A=True					,						_A="None"					,						_A=3					,						_A=4					,						_A=None					,						):
												__lowerCAmelCase					=							parent
												__lowerCAmelCase					=							batch_size
												__lowerCAmelCase					=							seq_length
												__lowerCAmelCase					=							is_training
												__lowerCAmelCase					=							use_input_mask
												__lowerCAmelCase					=							use_token_type_ids
												__lowerCAmelCase					=							use_labels
												__lowerCAmelCase					=							vocab_size
												__lowerCAmelCase					=							hidden_size
												__lowerCAmelCase					=							num_hidden_layers
												__lowerCAmelCase					=							num_attention_heads
												__lowerCAmelCase					=							intermediate_size
												__lowerCAmelCase					=							hidden_act
												__lowerCAmelCase					=							hidden_dropout_prob
												__lowerCAmelCase					=							attention_probs_dropout_prob
												__lowerCAmelCase					=							max_position_embeddings
												__lowerCAmelCase					=							type_vocab_size
												__lowerCAmelCase					=							type_sequence_label_size
												__lowerCAmelCase					=							initializer_range
												__lowerCAmelCase					=							num_labels
												__lowerCAmelCase					=							num_choices
												__lowerCAmelCase					=							relative_attention
												__lowerCAmelCase					=							position_biased_input
												__lowerCAmelCase					=							pos_att_type
												__lowerCAmelCase					=							scope




							def        A__  ( self							):
												__lowerCAmelCase					=							ids_tensor([self.batch_size, self.seq_length]					,						self.vocab_size							)

												__lowerCAmelCase					=							None
												if self.use_input_mask:
																	__lowerCAmelCase					=							ids_tensor([self.batch_size, self.seq_length]					,						vocab_size=2							)

												__lowerCAmelCase					=							None
												if self.use_token_type_ids:
																	__lowerCAmelCase					=							ids_tensor([self.batch_size, self.seq_length]					,						self.type_vocab_size							)

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

												__lowerCAmelCase					=							self.get_config()

												return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels




							def        A__  ( self							):
												return DebertaVaConfig(
												    vocab_size=self.vocab_size					,						hidden_size=self.hidden_size					,						num_hidden_layers=self.num_hidden_layers					,						num_attention_heads=self.num_attention_heads					,						intermediate_size=self.intermediate_size					,						hidden_act=self.hidden_act					,						hidden_dropout_prob=self.hidden_dropout_prob					,						attention_probs_dropout_prob=self.attention_probs_dropout_prob					,						max_position_embeddings=self.max_position_embeddings					,						type_vocab_size=self.type_vocab_size					,						initializer_range=self.initializer_range					,						relative_attention=self.relative_attention					,						position_biased_input=self.position_biased_input					,						pos_att_type=self.pos_att_type					,						)




							def        A__  ( self					,						_A							):
												self.parent.assertListEqual(list(result.loss.size()							)					,						[]							)




							def        A__  ( self					,						_A					,						_A					,						_A					,						_A					,						_A					,						_A					,						_A							):
												__lowerCAmelCase					=							DebertaVaModel(config=lowerCAmelCase_							)
												model.to(lowerCAmelCase_							)
												model.eval()
												__lowerCAmelCase					=							model(lowerCAmelCase_					,						attention_mask=lowerCAmelCase_					,						token_type_ids=lowerCAmelCase_							)[0]
												__lowerCAmelCase					=							model(lowerCAmelCase_					,						token_type_ids=lowerCAmelCase_							)[0]
												__lowerCAmelCase					=							model(lowerCAmelCase_							)[0]

												self.parent.assertListEqual(list(sequence_output.size()							)					,						[self.batch_size, self.seq_length, self.hidden_size]							)




							def        A__  ( self					,						_A					,						_A					,						_A					,						_A					,						_A					,						_A					,						_A							):
												__lowerCAmelCase					=							DebertaVaForMaskedLM(config=lowerCAmelCase_							)
												model.to(lowerCAmelCase_							)
												model.eval()
												__lowerCAmelCase					=							model(lowerCAmelCase_					,						attention_mask=lowerCAmelCase_					,						token_type_ids=lowerCAmelCase_					,						labels=lowerCAmelCase_							)

												self.parent.assertEqual(result.logits.shape					,						(self.batch_size, self.seq_length, self.vocab_size)							)




							def        A__  ( self					,						_A					,						_A					,						_A					,						_A					,						_A					,						_A					,						_A							):
												__lowerCAmelCase					=							self.num_labels
												__lowerCAmelCase					=							DebertaVaForSequenceClassification(lowerCAmelCase_							)
												model.to(lowerCAmelCase_							)
												model.eval()
												__lowerCAmelCase					=							model(lowerCAmelCase_					,						attention_mask=lowerCAmelCase_					,						token_type_ids=lowerCAmelCase_					,						labels=lowerCAmelCase_							)
												self.parent.assertListEqual(list(result.logits.size()							)					,						[self.batch_size, self.num_labels]							)
												self.check_loss_output(lowerCAmelCase_							)




							def        A__  ( self					,						_A					,						_A					,						_A					,						_A					,						_A					,						_A					,						_A							):
												__lowerCAmelCase					=							self.num_labels
												__lowerCAmelCase					=							DebertaVaForTokenClassification(config=lowerCAmelCase_							)
												model.to(lowerCAmelCase_							)
												model.eval()
												__lowerCAmelCase					=							model(lowerCAmelCase_					,						attention_mask=lowerCAmelCase_					,						token_type_ids=lowerCAmelCase_					,						labels=lowerCAmelCase_							)
												self.parent.assertEqual(result.logits.shape					,						(self.batch_size, self.seq_length, self.num_labels)							)




							def        A__  ( self					,						_A					,						_A					,						_A					,						_A					,						_A					,						_A					,						_A							):
												__lowerCAmelCase					=							DebertaVaForQuestionAnswering(config=lowerCAmelCase_							)
												model.to(lowerCAmelCase_							)
												model.eval()
												__lowerCAmelCase					=							model(
												    lowerCAmelCase_					,						attention_mask=lowerCAmelCase_					,						token_type_ids=lowerCAmelCase_					,						start_positions=lowerCAmelCase_					,						end_positions=lowerCAmelCase_					,						)
												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        A__  ( self					,						_A					,						_A					,						_A					,						_A					,						_A					,						_A					,						_A							):
												__lowerCAmelCase					=							DebertaVaForMultipleChoice(config=lowerCAmelCase_							)
												model.to(lowerCAmelCase_							)
												model.eval()
												__lowerCAmelCase					=							input_ids.unsqueeze(1							).expand(-1					,						self.num_choices					,						-1							).contiguous()
												__lowerCAmelCase					=							token_type_ids.unsqueeze(1							).expand(-1					,						self.num_choices					,						-1							).contiguous()
												__lowerCAmelCase					=							input_mask.unsqueeze(1							).expand(-1					,						self.num_choices					,						-1							).contiguous()
												__lowerCAmelCase					=							model(
												    lowerCAmelCase_					,						attention_mask=lowerCAmelCase_					,						token_type_ids=lowerCAmelCase_					,						labels=lowerCAmelCase_					,						)
												self.parent.assertEqual(result.logits.shape					,						(self.batch_size, self.num_choices)							)




							def        A__  ( self							):
												__lowerCAmelCase					=							self.prepare_config_and_inputs()
												(
												    (
												    __lowerCAmelCase    
												),	(
												    __lowerCAmelCase    
												),	(
												    __lowerCAmelCase    
												),	(
												    __lowerCAmelCase    
												),	(
												    __lowerCAmelCase    
												),	(
												    __lowerCAmelCase    
												),	(
												    __lowerCAmelCase    
												),	    
												)					=							config_and_inputs
												__lowerCAmelCase					=							{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
												return config, inputs_dict





@require_torch
class     snake_case_			(				__UpperCAmelCase			, __UpperCAmelCase			, unittest.TestCase							):


							"""simple docstring"""





							__UpperCAmelCase       =(
							    (
							        DebertaVaModel,
							        DebertaVaForMaskedLM,
							        DebertaVaForSequenceClassification,
							        DebertaVaForTokenClassification,
							        DebertaVaForQuestionAnswering,
							        DebertaVaForMultipleChoice,
							    )
							    if is_torch_available()
							    else ()
							)
							__UpperCAmelCase       =(
							    {
							        """feature-extraction""": DebertaVaModel,
							        """fill-mask""": DebertaVaForMaskedLM,
							        """question-answering""": DebertaVaForQuestionAnswering,
							        """text-classification""": DebertaVaForSequenceClassification,
							        """token-classification""": DebertaVaForTokenClassification,
							        """zero-shot""": DebertaVaForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)

							__UpperCAmelCase       =True
							__UpperCAmelCase       =False
							__UpperCAmelCase       =False
							__UpperCAmelCase       =False
							__UpperCAmelCase       =False




							def        A__  ( self							):
												__lowerCAmelCase					=							DebertaVaModelTester(self							)
												__lowerCAmelCase					=							ConfigTester(self					,						config_class=lowerCAmelCase_					,						hidden_size=3_7							)




							def        A__  ( self							):
												self.config_tester.run_common_tests()




							def        A__  ( self							):
												__lowerCAmelCase					=							self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_deberta_model(*lowerCAmelCase_							)




							def        A__  ( self							):
												__lowerCAmelCase					=							self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_deberta_for_sequence_classification(*lowerCAmelCase_							)




							def        A__  ( self							):
												__lowerCAmelCase					=							self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_deberta_for_masked_lm(*lowerCAmelCase_							)




							def        A__  ( self							):
												__lowerCAmelCase					=							self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_deberta_for_question_answering(*lowerCAmelCase_							)




							def        A__  ( self							):
												__lowerCAmelCase					=							self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_deberta_for_token_classification(*lowerCAmelCase_							)




							def        A__  ( self							):
												__lowerCAmelCase					=							self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_deberta_for_multiple_choice(*lowerCAmelCase_							)




							@slow
							def        A__  ( self							):







												for model_name in DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	__lowerCAmelCase					=							DebertaVaModel.from_pretrained(lowerCAmelCase_							)
																	self.assertIsNotNone(lowerCAmelCase_							)





@require_torch
@require_sentencepiece
@require_tokenizers
class     snake_case_			(				unittest.TestCase							):


							"""simple docstring"""





							@unittest.skip(reason='Model not available yet'							)
							def        A__  ( self							):
												pass




							@slow
							def        A__  ( self							):
												__lowerCAmelCase					=							DebertaVaModel.from_pretrained('microsoft/deberta-v2-xlarge'							)

												__lowerCAmelCase					=							torch.tensor([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]							)
												__lowerCAmelCase					=							torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]							)
												with torch.no_grad():
																	__lowerCAmelCase					=							model(lowerCAmelCase_					,						attention_mask=lowerCAmelCase_							)[0]
												# compare the actual values for a slice.
												__lowerCAmelCase					=							torch.tensor(
												    [[[0.2356, 0.1948, 0.0369], [-0.1063, 0.3586, -0.5152], [-0.6399, -0.0259, -0.2525]]]							)
												self.assertTrue(torch.allclose(output[:, 1:4, 1:4]					,						lowerCAmelCase_					,						atol=1e-4							)					,						F"""{output[:, 1:4, 1:4]}"""							)









import argparse
import re
from typing import Dict

import torch
from datasets import Audio, Dataset, load_dataset, load_metric

from transformers import AutoFeatureExtractor, pipeline


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							args.log_outputs
					__lowerCAmelCase					=							'_'.join(args.dataset.split('/'      ) + [args.config, args.split]      )

					# load metric
					__lowerCAmelCase					=							load_metric('wer'      )
					__lowerCAmelCase					=							load_metric('cer'      )

					# compute metrics
					__lowerCAmelCase					=							wer.compute(references=result['target']		,					predictions=result['prediction']      )
					__lowerCAmelCase					=							cer.compute(references=result['target']		,					predictions=result['prediction']      )

					# print & log results
					__lowerCAmelCase					=							F"""WER: {wer_result}\nCER: {cer_result}"""
					print(UpperCAmelCase__      )

					with open(F"""{dataset_id}_eval_results.txt"""		,					'w'      ) as f:
										f.write(UpperCAmelCase__      )

					# log all results in text file. Possibly interesting for analysis
					if log_outputs is not None:
										__lowerCAmelCase					=							F"""log_{dataset_id}_predictions.txt"""
										__lowerCAmelCase					=							F"""log_{dataset_id}_targets.txt"""

										with open(UpperCAmelCase__		,					'w'      ) as p, open(UpperCAmelCase__		,					'w'      ) as t:
															# mapping function to write output
															def write_to_file(UpperCAmelCase__		,					UpperCAmelCase__      ):
																				p.write(F"""{i}""" + '\n'      )
																				p.write(batch['prediction'] + '\n'      )
																				t.write(F"""{i}""" + '\n'      )
																				t.write(batch['target'] + '\n'      )

															result.map(UpperCAmelCase__		,					with_indices=UpperCAmelCase__      )


def        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							'[,?.!\-\;\:"“%‘”�—’…–]'  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

					__lowerCAmelCase					=							re.sub(UpperCAmelCase__		,					''		,					text.lower()      )

					# In addition, we can normalize the target text, e.g. removing new lines characters etc...
					# note that order is important here!
					__lowerCAmelCase					=							['\n\n', '\n', '   ', '  ']

					for t in token_sequences_to_ignore:
										__lowerCAmelCase					=							' '.join(text.split(UpperCAmelCase__      )      )

					return text


def        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							load_dataset(args.dataset		,					args.config		,					split=args.split		,					use_auth_token=UpperCAmelCase__      )

					# for testing: only process the first two examples as a test
					# dataset = dataset.select(range(10))

					# load processor
					__lowerCAmelCase					=							AutoFeatureExtractor.from_pretrained(args.model_id      )
					__lowerCAmelCase					=							feature_extractor.sampling_rate

					# resample audio
					__lowerCAmelCase					=							dataset.cast_column('audio'		,					Audio(sampling_rate=UpperCAmelCase__      )      )

					# load eval pipeline
					if args.device is None:
										__lowerCAmelCase					=							0 if torch.cuda.is_available() else -1
					__lowerCAmelCase					=							pipeline('automatic-speech-recognition'		,					model=args.model_id		,					device=args.device      )

					# map function to decode audio
					def map_to_pred(UpperCAmelCase__      ):
										__lowerCAmelCase					=							asr(
										    batch['audio']['array']		,					chunk_length_s=args.chunk_length_s		,					stride_length_s=args.stride_length_s      )

										__lowerCAmelCase					=							prediction['text']
										__lowerCAmelCase					=							normalize_text(batch['sentence']      )
										return batch

					# run inference on all examples
					__lowerCAmelCase					=							dataset.map(UpperCAmelCase__		,					remove_columns=dataset.column_names      )

					# compute and log_results
					# do not change function below
					log_results(UpperCAmelCase__		,					UpperCAmelCase__      )


if __name__ == "__main__":
		lowerCamelCase      				=     argparse.ArgumentParser()

		parser.add_argument(
		    '''--model_id''', type=str, required=True, help='''Model identifier. Should be loadable with 🤗 Transformers'''
		)
		parser.add_argument(
		    '''--dataset''',
		    type=str,
		    required=True,
		    help='''Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets''',
		)
		parser.add_argument(
		    '''--config''', type=str, required=True, help='''Config of the dataset. *E.g.* `\'en\'`  for Common Voice'''
		)
		parser.add_argument('''--split''', type=str, required=True, help='''Split of the dataset. *E.g.* `\'test\'`''')
		parser.add_argument(
		    '''--chunk_length_s''', type=float, default=None, help='''Chunk length in seconds. Defaults to 5 seconds.'''
		)
		parser.add_argument(
		    '''--stride_length_s''', type=float, default=None, help='''Stride of the audio chunks. Defaults to 1 second.'''
		)
		parser.add_argument(
		    '''--log_outputs''', action='''store_true''', help='''If defined, write outputs to log file for analysis.'''
		)
		parser.add_argument(
		    '''--device''',
		    type=int,
		    default=None,
		    help='''The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.''',
		)
		lowerCamelCase      				=     parser.parse_args()

		main(args)






