"""simple docstring"""




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 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__  ):





		"""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(lowerCamelCase__  )

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

		# 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(lowerCamelCase__      ,	"""w"""  ) as p, open(lowerCamelCase__      ,	"""w"""  ) as t:
						# mapping function to write output
						def write_to_file(lowerCamelCase__      ,	lowerCamelCase__  ):
								p.write(f"""{i}""" + """\n"""  )
								p.write(batch["""prediction"""] + """\n"""  )
								t.write(f"""{i}""" + """\n"""  )
								t.write(batch["""target"""] + """\n"""  )

						result.map(lowerCamelCase__      ,	with_indices=lowerCamelCase__  )







def 				_UpperCAmelCase		(							lowerCamelCase__  ):





		"""simple docstring"""




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

		lowerCAmelCase__   = re.sub(lowerCamelCase__      ,	""""""      ,	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(lowerCamelCase__  )  )

		return text







def 				_UpperCAmelCase		(							lowerCamelCase__  ):





		"""simple docstring"""




		lowerCAmelCase__   = load_dataset(args.dataset      ,	args.config      ,	split=args.split      ,	use_auth_token=lowerCamelCase__  )

		# 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=lowerCamelCase__  )  )

		# 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(lowerCamelCase__  ):
				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(lowerCamelCase__      ,	remove_columns=dataset.column_names  )

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


if __name__ == "__main__":
			__lowerCAmelCase     :				Dict    				=    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     :				str    				=    parser.parse_args()

			main(args)





"""simple docstring"""




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    a_   :




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




					def     _SCREAMING_SNAKE_CASE    (						self    :  Optional[int]  ):
							lowerCAmelCase__   = ids_tensor([self.batch_size, self.seq_length]	,    self.vocab_size  )

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

							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__   = 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=snake_case__	,    initializer_range=self.initializer_range	,    )
							lowerCAmelCase__   = 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     _SCREAMING_SNAKE_CASE    (						self    :  Optional[Any]	,    snake_case__    :  int	,    snake_case__    :  Optional[int]	,    snake_case__    :  Optional[Any]	,    snake_case__    :  str	,    snake_case__    :  Tuple	,    snake_case__    :  Dict	,    snake_case__    :  List[Any]  ):
							lowerCAmelCase__   = TFDPRContextEncoder(config=snake_case__  )
							lowerCAmelCase__   = model(snake_case__	,    attention_mask=snake_case__	,    token_type_ids=snake_case__  )
							lowerCAmelCase__   = model(snake_case__	,    token_type_ids=snake_case__  )
							lowerCAmelCase__   = model(snake_case__  )
							self.parent.assertEqual(result.pooler_output.shape	,    (self.batch_size, self.projection_dim or self.hidden_size)  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Any	,    snake_case__    :  str	,    snake_case__    :  Dict	,    snake_case__    :  str	,    snake_case__    :  Any	,    snake_case__    :  Union[str, Any]	,    snake_case__    :  List[str]	,    snake_case__    :  Dict  ):
							lowerCAmelCase__   = TFDPRQuestionEncoder(config=snake_case__  )
							lowerCAmelCase__   = model(snake_case__	,    attention_mask=snake_case__	,    token_type_ids=snake_case__  )
							lowerCAmelCase__   = model(snake_case__	,    token_type_ids=snake_case__  )
							lowerCAmelCase__   = model(snake_case__  )
							self.parent.assertEqual(result.pooler_output.shape	,    (self.batch_size, self.projection_dim or self.hidden_size)  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Tuple	,    snake_case__    :  Tuple	,    snake_case__    :  Any	,    snake_case__    :  str	,    snake_case__    :  Tuple	,    snake_case__    :  List[str]	,    snake_case__    :  List[str]	,    snake_case__    :  Tuple  ):
							lowerCAmelCase__   = TFDPRReader(config=snake_case__  )
							lowerCAmelCase__   = model(snake_case__	,    attention_mask=snake_case__  )

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

@require_tf
class    a_   (  __UpperCamelCase			,		__UpperCamelCase			,		unittest.TestCase      ):
					UpperCamelCase_			:						Optional[Any]      =     (
					    (
					        TFDPRContextEncoder,
					        TFDPRQuestionEncoder,
					        TFDPRReader,
					    )
					    if is_tf_available()
					    else ()
					)
					UpperCamelCase_			:						Any      =     {"feature-extraction": TFDPRQuestionEncoder} if is_tf_available() else {}

					UpperCamelCase_			:						Optional[Any]      =     False
					UpperCamelCase_			:						Tuple      =     False
					UpperCamelCase_			:						Tuple      =     False
					UpperCamelCase_			:						Optional[int]      =     False
					UpperCamelCase_			:						Optional[Any]      =     False




					def     _SCREAMING_SNAKE_CASE    (						self    :  Any  ):
							lowerCAmelCase__   = TFDPRModelTester(self  )
							lowerCAmelCase__   = ConfigTester(self	,    config_class=snake_case__	,    hidden_size=37  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Optional[int]  ):
							self.config_tester.run_common_tests()




					def     _SCREAMING_SNAKE_CASE    (						self    :  Dict  ):
							lowerCAmelCase__   = self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_dpr_context_encoder(*snake_case__  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Tuple  ):
							lowerCAmelCase__   = self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_dpr_question_encoder(*snake_case__  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  int  ):
							lowerCAmelCase__   = self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_dpr_reader(*snake_case__  )




					@slow
					def     _SCREAMING_SNAKE_CASE    (						self    :  List[Any]  ):
							for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									lowerCAmelCase__   = TFDPRContextEncoder.from_pretrained(snake_case__  )
									self.assertIsNotNone(snake_case__  )

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

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

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

@require_tf
class    a_   (  unittest.TestCase      ):




					@slow
					def     _SCREAMING_SNAKE_CASE    (						self    :  List[str]  ):
							lowerCAmelCase__   = TFDPRQuestionEncoder.from_pretrained("""facebook/dpr-question_encoder-single-nq-base"""  )

							lowerCAmelCase__   = tf.constant(
							    [[101, 7592, 1010, 2003, 2026, 3899, 10140, 1029, 102]]  )  # [CLS] hello, is my dog cute? [SEP]
							lowerCAmelCase__   = model(snake_case__  )[0]  # embedding shape = (1, 768)
							# compare the actual values for a slice.
							lowerCAmelCase__   = tf.constant(
							    [
							        [
							            0.0323_6253,
							            0.1275_3335,
							            0.1681_8509,
							            0.0027_9786,
							            0.389_6933,
							            0.2426_4945,
							            0.217_8971,
							            -0.0233_5227,
							            -0.0848_1959,
							            -0.1432_4117,
							        ]
							    ]  )
							self.assertTrue(numpy.allclose(output[:, :10].numpy()	,    expected_slice.numpy()	,    atol=1E-4  )  )




