
'''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 				SCREAMING_SNAKE_CASE__	(   __A			,				__A	)       ->	List[str]:
	_snake_case   						=		args.log_outputs
	_snake_case   						=		'''_'''.join(args.dataset.split('/'	) + [args.config, args.split]	)

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

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

	# print & log results
	_snake_case   						=		F'WER: {wer_result}\nCER: {cer_result}'
	print(_snake_case	)

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

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

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

			result.map(_snake_case			,				with_indices=_snake_case	)







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

	_snake_case   						=		re.sub(_snake_case			,				''			,				text.lower()	)

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

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

	return text







def 				SCREAMING_SNAKE_CASE__	(   __A	)       ->	Any:
	_snake_case   						=		load_dataset(args.dataset			,				args.config			,				split=args.split			,				use_auth_token=_snake_case	)

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

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

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

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

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

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

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

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


if __name__ == "__main__":
					lowercase    :							str      =					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.",
					)
					lowercase    :							Union[str, Any]      =					parser.parse_args()

					main(args)





"""simple docstring"""
import argparse

import torch

from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()






def 		lowercase							(     _snake_case						:      List[Any]     ,   _snake_case						:      Tuple     ,   _snake_case						:      int     )     ->List[Any]:
	"""simple docstring"""





	if openai_config_file == "":
		__snake_case	:						Dict						=				OpenAIGPTConfig()
	else:
		__snake_case	:						int						=				OpenAIGPTConfig.from_json_file(_snake_case     )
	__snake_case	:						Tuple						=				OpenAIGPTModel(_snake_case     )

	# Load weights from numpy
	load_tf_weights_in_openai_gpt(_snake_case     ,   _snake_case     ,   _snake_case     )

	# Save pytorch-model
	__snake_case	:						str						=				pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
	__snake_case	:						Optional[Any]						=				pytorch_dump_folder_path + '''/''' + CONFIG_NAME
	print(f"""Save PyTorch model to {pytorch_weights_dump_path}"""     )
	torch.save(model.state_dict()     ,   _snake_case     )
	print(f"""Save configuration file to {pytorch_config_dump_path}"""     )
	with open(_snake_case     ,   '''w'''     ,   encoding='''utf-8'''     ) as f:
		f.write(config.to_json_string()     )


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE :    Any									=				argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    """--openai_checkpoint_folder_path""",
			    default=None,
			    type=str,
			    required=True,
			    help="""Path to the TensorFlow checkpoint path.""",
			)
			parser.add_argument(
			    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
			)
			parser.add_argument(
			    """--openai_config_file""",
			    default="""""",
			    type=str,
			    help=(
			        """An optional config json file corresponding to the pre-trained OpenAI model. \n"""
			        """This specifies the model architecture."""
			    ),
			)
			SCREAMING_SNAKE_CASE :    Optional[int]									=				parser.parse_args()
			convert_openai_checkpoint_to_pytorch(
			    args.openai_checkpoint_folder_path, args.openai_config_file, args.pytorch_dump_folder_path
			)





