



"""simple docstring"""
import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional

import torch

from ..utils import add_start_docstrings, logging


__a      =				logging.get_logger(__name__)


__a      =				r"\n    Args:\n        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):\n            Indices of input sequence tokens in the vocabulary.\n\n            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and\n            [`PreTrainedTokenizer.__call__`] for details.\n\n            [What are input IDs?](../glossary#input-ids)\n        scores (`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`):\n            Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax\n            or scores for each vocabulary token after SoftMax.\n        kwargs (`Dict[str, Any]`, *optional*):\n            Additional stopping criteria specific kwargs.\n\n    Return:\n        `bool`. `False` indicates we should continue, `True` indicates we should stop.\n\n"
class 				lowerCamelCase  (			_lowerCAmelCase				):






					'''simple docstring'''





					@add_start_docstrings(snake_case						)
					def __call__(       self:							int						,     snake_case:							torch.LongTensor						,     snake_case:							torch.FloatTensor						,     **snake_case:							Optional[Any]						)   ->	bool:
											raise NotImplementedError("""StoppingCriteria needs to be subclassed"""						)

class 				lowerCamelCase  (			_lowerCAmelCase				):






					'''simple docstring'''





					def __init__(       self:							Any						,     snake_case:							int						,     snake_case:							Optional[int] = None						)   ->	List[str]:
											snake_case_ :Dict						=			max_length
											snake_case_ :Union[str, Any]						=			max_position_embeddings






					@add_start_docstrings(snake_case						)
					def __call__(       self:							Dict						,     snake_case:							torch.LongTensor						,     snake_case:							torch.FloatTensor						,     **snake_case:							str						)   ->	bool:
											snake_case_ :Dict						=			input_ids.shape[-1]
											snake_case_ :Union[str, Any]						=			cur_len >= self.max_length
											if self.max_position_embeddings is not None and not is_done and cur_len >= self.max_position_embeddings:
																	logger.warning_once(
																	    """This is a friendly reminder - the current text generation call will exceed the model's predefined """
																	    f"""maximum length ({self.max_position_embeddings}). Depending on the model, you may observe """
																	    """exceptions, performance degradation, or nothing at all."""						)
											return is_done

class 				lowerCamelCase  (			_lowerCAmelCase				):






					'''simple docstring'''





					def __init__(       self:							Union[str, Any]						,     snake_case:							int						,     snake_case:							int						)   ->	Dict:
											warnings.warn(
											    """The class `MaxNewTokensCriteria` is deprecated. """
											    f"""Please use `MaxLengthCriteria(max_length={start_length + max_new_tokens})` """
											    """with `max_length = start_length + max_new_tokens` instead."""						,     snake_case						,     )
											snake_case_ :Union[str, Any]						=			start_length
											snake_case_ :int						=			max_new_tokens
											snake_case_ :int						=			start_length + max_new_tokens






					@add_start_docstrings(snake_case						)
					def __call__(       self:							Optional[int]						,     snake_case:							torch.LongTensor						,     snake_case:							torch.FloatTensor						,     **snake_case:							str						)   ->	bool:
											return input_ids.shape[-1] >= self.max_length

class 				lowerCamelCase  (			_lowerCAmelCase				):






					'''simple docstring'''





					def __init__(       self:							Optional[int]						,     snake_case:							float						,     snake_case:							Optional[float] = None						)   ->	Union[str, Any]:
											snake_case_ :Tuple						=			max_time
											snake_case_ :List[Any]						=			time.time() if initial_timestamp is None else initial_timestamp






					@add_start_docstrings(snake_case						)
					def __call__(       self:							Tuple						,     snake_case:							torch.LongTensor						,     snake_case:							torch.FloatTensor						,     **snake_case:							Union[str, Any]						)   ->	bool:
											return time.time() - self.initial_timestamp > self.max_time

class 				lowerCamelCase  (			_lowerCAmelCase				):






					'''simple docstring'''





					@add_start_docstrings(snake_case						)
					def __call__(       self:							str						,     snake_case:							torch.LongTensor						,     snake_case:							torch.FloatTensor						,     **snake_case:							int						)   ->	bool:
											return any(criteria(snake_case						,     snake_case						) for criteria in self						)






					@property
					def    lowerCAmelCase_				(       self:							Tuple						)   ->	Optional[int]:
											for stopping_criterium in self:
																	if isinstance(snake_case						,     snake_case						):
																							return stopping_criterium.max_length
																	elif isinstance(snake_case						,     snake_case						):
																							return stopping_criterium.max_length
											return None

def    A_  (			_lowercase,					_lowercase	):







						'''simple docstring'''


						snake_case_ :List[str]						=			stopping_criteria.max_length
						snake_case_ :List[str]						=			deepcopy(_lowercase	)
						if stopping_max_length is not None and stopping_max_length != max_length:
												warnings.warn("""You set different `max_length` for stopping criteria and `max_length` parameter""",					_lowercase	)
						elif stopping_max_length is None:
												new_stopping_criteria.append(MaxLengthCriteria(max_length=_lowercase	)	)
						return new_stopping_criteria


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__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )				->		List[str]:
			lowerCAmelCase__      :      Optional[Any]         =		args.log_outputs
			lowerCAmelCase__      :      Union[str, Any]         =		'_'.join(args.dataset.split('/'     ) + [args.config, args.split]     )

			# load metric
			lowerCAmelCase__      :      Dict         =		load_metric('wer'     )
			lowerCAmelCase__      :      Tuple         =		load_metric('cer'     )

			# compute metrics
			lowerCAmelCase__      :      Dict         =		wer.compute(references=result['target']       ,							predictions=result['prediction']     )
			lowerCAmelCase__      :      int         =		cer.compute(references=result['target']       ,							predictions=result['prediction']     )

			# print & log results
			lowerCAmelCase__      :      Optional[int]         =		F'''WER: {wer_result}\nCER: {cer_result}'''
			print(SCREAMING_SNAKE_CASE_     )

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

			# log all results in text file. Possibly interesting for analysis
			if log_outputs is not None:
						lowerCAmelCase__      :      List[str]         =		F'''log_{dataset_id}_predictions.txt'''
						lowerCAmelCase__      :      Union[str, Any]         =		F'''log_{dataset_id}_targets.txt'''

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

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

			lowerCAmelCase__      :      Union[str, Any]         =		re.sub(SCREAMING_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!
			lowerCAmelCase__      :      List[Any]         =		['\n\n', '\n', '   ', '  ']

			for t in token_sequences_to_ignore:
						lowerCAmelCase__      :      Optional[int]         =		' '.join(text.split(SCREAMING_SNAKE_CASE_     )     )

			return text
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_     )				->		List[Any]:
			# load dataset
			lowerCAmelCase__      :      Tuple         =		load_dataset(args.dataset       ,							args.config       ,							split=args.split       ,							use_auth_token=SCREAMING_SNAKE_CASE_     )

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

			# load processor
			lowerCAmelCase__      :      List[Any]         =		AutoFeatureExtractor.from_pretrained(args.model_id     )
			lowerCAmelCase__      :      Union[str, Any]         =		feature_extractor.sampling_rate

			# resample audio
			lowerCAmelCase__      :      Union[str, Any]         =		dataset.cast_column('audio'       ,							Audio(sampling_rate=SCREAMING_SNAKE_CASE_     )     )

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

			# map function to decode audio
			def map_to_pred(SCREAMING_SNAKE_CASE_     ):
						lowerCAmelCase__      :      Optional[int]         =		asr(
						    batch['audio']['array']       ,							chunk_length_s=args.chunk_length_s       ,							stride_length_s=args.stride_length_s     )

						lowerCAmelCase__      :      int         =		prediction['text']
						lowerCAmelCase__      :      Optional[int]         =		normalize_text(batch['sentence']     )
						return batch

			# run inference on all examples
			lowerCAmelCase__      :      Dict         =		dataset.map(SCREAMING_SNAKE_CASE_       ,							remove_columns=dataset.column_names     )

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


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)