



import copy
import json
import os
import tempfile

from transformers import is_torch_available

from .test_configuration_utils import config_common_kwargs


class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					def __init__(     self					:       List[str]				,					_snake_case					:       Optional[int]				,					_snake_case					:       Optional[Any]=None				,					_snake_case					:       Dict=True				,					_snake_case					:       Union[str, Any]=None				,					**_snake_case					:       int					):






								"""simple docstring"""


								A__       =    parent
								A__       =    config_class
								A__       =    has_text_modality
								A__       =    kwargs
								A__       =    common_properties



					def     _a							(     self					:       Tuple					):






								"""simple docstring"""


								A__       =    self.config_class(**self.inputs_dict					)
								A__       =    (
								    ['hidden_size', 'num_attention_heads', 'num_hidden_layers']
								    if self.common_properties is None
								    else self.common_properties
								)

								# Add common fields for text models
								if self.has_text_modality:
											common_properties.extend(['vocab_size']					)

								# Test that config has the common properties as getters
								for prop in common_properties:
											self.parent.assertTrue(hasattr(_snake_case				,					_snake_case					)				,					msg=F'''`{prop}` does not exist'''					)

								# Test that config has the common properties as setter
								for idx, name in enumerate(_snake_case					):
											try:
														setattr(_snake_case				,					_snake_case				,					_snake_case					)
														self.parent.assertEqual(
														    getattr(_snake_case				,					_snake_case					)				,					_snake_case				,					msg=F'''`{name} value {idx} expected, but was {getattr(_snake_case				,					_snake_case					)}'''					)
											except NotImplementedError:
														# Some models might not be able to implement setters for common_properties
														# In that case, a NotImplementedError is raised
														pass

        # Test if config class can be called with Config(prop_name=..)
								for idx, name in enumerate(_snake_case					):
											try:
														A__       =    self.config_class(**{name: idx}					)
														self.parent.assertEqual(
														    getattr(_snake_case				,					_snake_case					)				,					_snake_case				,					msg=F'''`{name} value {idx} expected, but was {getattr(_snake_case				,					_snake_case					)}'''					)
											except NotImplementedError:
														# Some models might not be able to implement setters for common_properties
														# In that case, a NotImplementedError is raised
														pass



					def     _a							(     self					:       Optional[int]					):






								"""simple docstring"""


								A__       =    self.config_class(**self.inputs_dict					)
								A__       =    json.loads(config.to_json_string()					)
								for key, value in self.inputs_dict.items():
											self.parent.assertEqual(obj[key]				,					_snake_case					)



					def     _a							(     self					:       Optional[int]					):






								"""simple docstring"""


								A__       =    self.config_class(**self.inputs_dict					)

								with tempfile.TemporaryDirectory() as tmpdirname:
											A__       =    os.path.join(_snake_case				,					'config.json'					)
											config_first.to_json_file(_snake_case					)
											A__       =    self.config_class.from_json_file(_snake_case					)

								self.parent.assertEqual(config_second.to_dict()				,					config_first.to_dict()					)



					def     _a							(     self					:       List[str]					):






								"""simple docstring"""


								A__       =    self.config_class(**self.inputs_dict					)

								with tempfile.TemporaryDirectory() as tmpdirname:
											config_first.save_pretrained(_snake_case					)
											A__       =    self.config_class.from_pretrained(_snake_case					)

								self.parent.assertEqual(config_second.to_dict()				,					config_first.to_dict()					)



					def     _a							(     self					:       Dict					):






								"""simple docstring"""


								A__       =    self.config_class(**self.inputs_dict					)

								A__       =    'test'
								with tempfile.TemporaryDirectory() as tmpdirname:
											A__       =    os.path.join(_snake_case				,					_snake_case					)
											config_first.save_pretrained(_snake_case					)
											A__       =    self.config_class.from_pretrained(_snake_case				,					subfolder=_snake_case					)

								self.parent.assertEqual(config_second.to_dict()				,					config_first.to_dict()					)



					def     _a							(     self					:       Dict					):






								"""simple docstring"""


								A__       =    self.config_class(**self.inputs_dict				,					num_labels=5					)
								self.parent.assertEqual(len(config.idalabel					)				,					5					)
								self.parent.assertEqual(len(config.labelaid					)				,					5					)

								A__       =    3
								self.parent.assertEqual(len(config.idalabel					)				,					3					)
								self.parent.assertEqual(len(config.labelaid					)				,					3					)



					def     _a							(     self					:       List[Any]					):






								"""simple docstring"""


								if self.config_class.is_composition:
											return
								A__       =    self.config_class()
								self.parent.assertIsNotNone(_snake_case					)



					def     _a							(     self					:       Optional[Any]					):






								"""simple docstring"""


								A__       =    copy.deepcopy(_snake_case					)
								A__       =    self.config_class(**_snake_case					)
								A__       =    []
								for key, value in config_common_kwargs.items():
											if key == "torch_dtype":
														if not is_torch_available():
																	continue
														else:
																	import torch

																	if config.torch_dtype != torch.floataa:
																				wrong_values.append(('torch_dtype', config.torch_dtype, torch.floataa)					)
											elif getattr(_snake_case				,					_snake_case					) != value:
														wrong_values.append((key, getattr(_snake_case				,					_snake_case					), value)					)

								if len(_snake_case					) > 0:
											A__       =    '\n'.join([F'''- {v[0]}: got {v[1]} instead of {v[2]}''' for v in wrong_values]					)
											raise ValueError(F'''The following keys were not properly set in the config:\n{errors}'''					)



					def     _a							(     self					:       Dict					):






								"""simple docstring"""


								self.create_and_test_config_common_properties()
								self.create_and_test_config_to_json_string()
								self.create_and_test_config_to_json_file()
								self.create_and_test_config_from_and_save_pretrained()
								self.create_and_test_config_from_and_save_pretrained_subfolder()
								self.create_and_test_config_with_num_labels()
								self.check_config_can_be_init_without_params()
								self.check_config_arguments_init()









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 A						(       __UpperCamelCase ,	__UpperCamelCase					)						->      Tuple:
			A__       =    args.log_outputs
			A__       =    '_'.join(args.dataset.split('/'					) + [args.config, args.split]					)

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

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

			# print & log results
			A__       =    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:
						A__       =    f'''log_{dataset_id}_predictions.txt'''
						A__       =    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 A						(       __UpperCamelCase					)						->      str:
			A__       =    '[,?.!\-\;\:"“%‘”�—’…–]'  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

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

			for t in token_sequences_to_ignore:
						A__       =    ' '.join(text.split(__UpperCamelCase					)					)

			return text


def A						(       __UpperCamelCase					)						->      Union[str, Any]:
			# load dataset
			A__       =    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
			A__       =    AutoFeatureExtractor.from_pretrained(args.model_id					)
			A__       =    feature_extractor.sampling_rate

			# resample audio
			A__       =    dataset.cast_column('audio' ,	Audio(sampling_rate=__UpperCamelCase					)					)

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

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

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

			# run inference on all examples
			A__       =    dataset.map(__UpperCamelCase ,	remove_columns=dataset.column_names					)

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


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

							main(args)




