






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 	__lowerCamelCase      (		snake_case__	,snake_case__		)							->  List[str]:






						"""simple docstring"""





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

						# load metric
						_SCREAMING_SNAKE_CASE     				=  load_metric("""wer"""		)
						_SCREAMING_SNAKE_CASE     				=  load_metric("""cer"""		)

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

						# print & log results
						_SCREAMING_SNAKE_CASE     				=  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:
												_SCREAMING_SNAKE_CASE     				=  F'log_{dataset_id}_predictions.txt'
												_SCREAMING_SNAKE_CASE     				=  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(snake_case__	,snake_case__		):
																								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 	__lowerCamelCase      (		snake_case__		)							->  str:






						"""simple docstring"""





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

						_SCREAMING_SNAKE_CASE     				=  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!
						_SCREAMING_SNAKE_CASE     				=  ['\n\n', '\n', '   ', '  ']

						for t in token_sequences_to_ignore:
												_SCREAMING_SNAKE_CASE     				=  ' '.join(text.split(_UpperCAmelCase		)		)

						return text

def 	__lowerCamelCase      (		snake_case__		)							->  Union[str, Any]:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  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
						_SCREAMING_SNAKE_CASE     				=  AutoFeatureExtractor.from_pretrained(args.model_id		)
						_SCREAMING_SNAKE_CASE     				=  feature_extractor.sampling_rate

						# resample audio
						_SCREAMING_SNAKE_CASE     				=  dataset.cast_column("""audio"""	,Audio(sampling_rate=_UpperCAmelCase		)		)

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

						# map function to decode audio
						def map_to_pred(snake_case__		):
												_SCREAMING_SNAKE_CASE     				=  asr(
												    batch["""audio"""]["""array"""]	,chunk_length_s=args.chunk_length_s	,stride_length_s=args.stride_length_s		)

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

						# run inference on all examples
						_SCREAMING_SNAKE_CASE     				=  dataset.map(_UpperCAmelCase	,remove_columns=dataset.column_names		)

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


if __name__ == "__main__":
		UpperCamelCase			    =     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.''',
		)
		UpperCamelCase			    =     parser.parse_args()

		main(args)









from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
		import numpy
		import tensorflow as tf

		from transformers import (
		    TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    BertConfig,
		    DPRConfig,
		    TFDPRContextEncoder,
		    TFDPRQuestionEncoder,
		    TFDPRReader,
		)



class 				__UpperCAmelCase		:







			def __init__(     self:  Optional[int]				,     UpperCAmelCase_:  List[str]				,     UpperCAmelCase_:  int=13				,     UpperCAmelCase_:  List[str]=7				,     UpperCAmelCase_:  Union[str, Any]=True				,     UpperCAmelCase_:  List[str]=True				,     UpperCAmelCase_:  Dict=True				,     UpperCAmelCase_:  str=True				,     UpperCAmelCase_:  List[str]=99				,     UpperCAmelCase_:  int=32				,     UpperCAmelCase_:  List[Any]=2				,     UpperCAmelCase_:  Union[str, Any]=4				,     UpperCAmelCase_:  Dict=37				,     UpperCAmelCase_:  Tuple="gelu"				,     UpperCAmelCase_:  Any=0.1				,     UpperCAmelCase_:  List[str]=0.1				,     UpperCAmelCase_:  List[Any]=512				,     UpperCAmelCase_:  Optional[int]=16				,     UpperCAmelCase_:  Optional[Any]=2				,     UpperCAmelCase_:  Optional[int]=0.02				,     UpperCAmelCase_:  Union[str, Any]=3				,     UpperCAmelCase_:  Optional[int]=4				,     UpperCAmelCase_:  Tuple=None				,     UpperCAmelCase_:  Any=0				,     ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  parent
									_SCREAMING_SNAKE_CASE     				=  batch_size
									_SCREAMING_SNAKE_CASE     				=  seq_length
									_SCREAMING_SNAKE_CASE     				=  is_training
									_SCREAMING_SNAKE_CASE     				=  use_input_mask
									_SCREAMING_SNAKE_CASE     				=  use_token_type_ids
									_SCREAMING_SNAKE_CASE     				=  use_labels
									_SCREAMING_SNAKE_CASE     				=  vocab_size
									_SCREAMING_SNAKE_CASE     				=  hidden_size
									_SCREAMING_SNAKE_CASE     				=  num_hidden_layers
									_SCREAMING_SNAKE_CASE     				=  num_attention_heads
									_SCREAMING_SNAKE_CASE     				=  intermediate_size
									_SCREAMING_SNAKE_CASE     				=  hidden_act
									_SCREAMING_SNAKE_CASE     				=  hidden_dropout_prob
									_SCREAMING_SNAKE_CASE     				=  attention_probs_dropout_prob
									_SCREAMING_SNAKE_CASE     				=  max_position_embeddings
									_SCREAMING_SNAKE_CASE     				=  type_vocab_size
									_SCREAMING_SNAKE_CASE     				=  type_sequence_label_size
									_SCREAMING_SNAKE_CASE     				=  initializer_range
									_SCREAMING_SNAKE_CASE     				=  num_labels
									_SCREAMING_SNAKE_CASE     				=  num_choices
									_SCREAMING_SNAKE_CASE     				=  scope
									_SCREAMING_SNAKE_CASE     				=  projection_dim







			def 							UpperCamelCase				(     self:  Any					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  ids_tensor([self.batch_size, self.seq_length]				,     self.vocab_size					)

									_SCREAMING_SNAKE_CASE     				=  None
									if self.use_input_mask:
															# follow test_modeling_tf_ctrl.py
															_SCREAMING_SNAKE_CASE     				=  random_attention_mask([self.batch_size, self.seq_length]					)

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

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

									_SCREAMING_SNAKE_CASE     				=  BertConfig(
									    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				,     is_decoder=UpperCAmelCase_				,     initializer_range=self.initializer_range				,     )
									_SCREAMING_SNAKE_CASE     				=  DPRConfig(projection_dim=self.projection_dim				,     **config.to_dict()					)

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







			def 							UpperCamelCase				(     self:  Optional[int]				,     UpperCAmelCase_:  Optional[Any]				,     UpperCAmelCase_:  Any				,     UpperCAmelCase_:  int				,     UpperCAmelCase_:  List[str]				,     UpperCAmelCase_:  Dict				,     UpperCAmelCase_:  Dict				,     UpperCAmelCase_:  Any					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  TFDPRContextEncoder(config=UpperCAmelCase_					)
									_SCREAMING_SNAKE_CASE     				=  model(UpperCAmelCase_				,     attention_mask=UpperCAmelCase_				,     token_type_ids=UpperCAmelCase_					)
									_SCREAMING_SNAKE_CASE     				=  model(UpperCAmelCase_				,     token_type_ids=UpperCAmelCase_					)
									_SCREAMING_SNAKE_CASE     				=  model(UpperCAmelCase_					)
									self.parent.assertEqual(result.pooler_output.shape				,     (self.batch_size, self.projection_dim or self.hidden_size)					)







			def 							UpperCamelCase				(     self:  List[Any]				,     UpperCAmelCase_:  Union[str, Any]				,     UpperCAmelCase_:  Any				,     UpperCAmelCase_:  Dict				,     UpperCAmelCase_:  Union[str, Any]				,     UpperCAmelCase_:  Union[str, Any]				,     UpperCAmelCase_:  List[Any]				,     UpperCAmelCase_:  Any					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  TFDPRQuestionEncoder(config=UpperCAmelCase_					)
									_SCREAMING_SNAKE_CASE     				=  model(UpperCAmelCase_				,     attention_mask=UpperCAmelCase_				,     token_type_ids=UpperCAmelCase_					)
									_SCREAMING_SNAKE_CASE     				=  model(UpperCAmelCase_				,     token_type_ids=UpperCAmelCase_					)
									_SCREAMING_SNAKE_CASE     				=  model(UpperCAmelCase_					)
									self.parent.assertEqual(result.pooler_output.shape				,     (self.batch_size, self.projection_dim or self.hidden_size)					)







			def 							UpperCamelCase				(     self:  Optional[Any]				,     UpperCAmelCase_:  Dict				,     UpperCAmelCase_:  Optional[int]				,     UpperCAmelCase_:  List[Any]				,     UpperCAmelCase_:  str				,     UpperCAmelCase_:  Optional[Any]				,     UpperCAmelCase_:  Union[str, Any]				,     UpperCAmelCase_:  str					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  TFDPRReader(config=UpperCAmelCase_					)
									_SCREAMING_SNAKE_CASE     				=  model(UpperCAmelCase_				,     attention_mask=UpperCAmelCase_					)

									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)					)
									self.parent.assertEqual(result.relevance_logits.shape				,     (self.batch_size,)					)







			def 							UpperCamelCase				(     self:  Optional[int]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  self.prepare_config_and_inputs()
									(
									    (
									    _SCREAMING_SNAKE_CASE
									)				,					(
									    _SCREAMING_SNAKE_CASE
									)				,					(
									    _SCREAMING_SNAKE_CASE
									)				,					(
									    _SCREAMING_SNAKE_CASE
									)				,					(
									    _SCREAMING_SNAKE_CASE
									)				,					(
									    _SCREAMING_SNAKE_CASE
									)				,					(
									    _SCREAMING_SNAKE_CASE
									)				,					
									)     				=  config_and_inputs
									_SCREAMING_SNAKE_CASE     				=  {"""input_ids""": input_ids}
									return config, inputs_dict



@require_tf
class 				__UpperCAmelCase		(_UpperCAmelCase	,_UpperCAmelCase	,unittest.TestCase   ):
			__snake_case    :				Dict									=							(
			    (
			        TFDPRContextEncoder,
			        TFDPRQuestionEncoder,
			        TFDPRReader,
			    )
			    if is_tf_available()
			    else ()
			)
			__snake_case    :				Optional[Any]									=							{"feature-extraction": TFDPRQuestionEncoder} if is_tf_available() else {}

			__snake_case    :				str									=							False
			__snake_case    :				List[Any]									=							False
			__snake_case    :				Any									=							False
			__snake_case    :				List[Any]									=							False
			__snake_case    :				Tuple									=							False







			def 							UpperCamelCase				(     self:  int					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  TFDPRModelTester(self					)
									_SCREAMING_SNAKE_CASE     				=  ConfigTester(self				,     config_class=UpperCAmelCase_				,     hidden_size=37					)







			def 							UpperCamelCase				(     self:  Any					):







									'''simple docstring'''




									self.config_tester.run_common_tests()







			def 							UpperCamelCase				(     self:  List[Any]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_dpr_context_encoder(*UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  Optional[Any]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_dpr_question_encoder(*UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  int					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_dpr_reader(*UpperCAmelCase_					)







			@slow
			def 							UpperCamelCase				(     self:  List[Any]					):







									'''simple docstring'''




									for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															_SCREAMING_SNAKE_CASE     				=  TFDPRContextEncoder.from_pretrained(UpperCAmelCase_					)
															self.assertIsNotNone(UpperCAmelCase_					)

									for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															_SCREAMING_SNAKE_CASE     				=  TFDPRContextEncoder.from_pretrained(UpperCAmelCase_					)
															self.assertIsNotNone(UpperCAmelCase_					)

									for model_name in TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															_SCREAMING_SNAKE_CASE     				=  TFDPRQuestionEncoder.from_pretrained(UpperCAmelCase_					)
															self.assertIsNotNone(UpperCAmelCase_					)

									for model_name in TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															_SCREAMING_SNAKE_CASE     				=  TFDPRReader.from_pretrained(UpperCAmelCase_					)
															self.assertIsNotNone(UpperCAmelCase_					)



@require_tf
class 				__UpperCAmelCase		(unittest.TestCase   ):







			@slow
			def 							UpperCamelCase				(     self:  Union[str, Any]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  TFDPRQuestionEncoder.from_pretrained("""facebook/dpr-question_encoder-single-nq-base"""					)

									_SCREAMING_SNAKE_CASE     				=  tf.constant(
									    [[101, 7_592, 1_010, 2_003, 2_026, 3_899, 10_140, 1_029, 102]]					)  # [CLS] hello, is my dog cute? [SEP]
									_SCREAMING_SNAKE_CASE     				=  model(UpperCAmelCase_					)[0]  # embedding shape = (1, 768)
									# compare the actual values for a slice.
									_SCREAMING_SNAKE_CASE     				=  tf.constant(
									    [
									        [
									            0.03_23_62_53,
									            0.12_75_33_35,
									            0.16_81_85_09,
									            0.00_27_97_86,
									            0.3_89_69_33,
									            0.24_26_49_45,
									            0.2_17_89_71,
									            -0.02_33_52_27,
									            -0.08_48_19_59,
									            -0.14_32_41_17,
									        ]
									    ]					)
									self.assertTrue(numpy.allclose(output[:, :10].numpy()				,     expected_slice.numpy()				,     atol=1E-4					)					)

