
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      (	_lowerCamelCase				:    Optional[Any]		,						_lowerCamelCase				:    Optional[Any])      ->					int:






							'''simple docstring'''





							__UpperCamelCase			:     str         =      args.log_outputs
							__UpperCamelCase			:     Optional[Any]         =      "_".join(args.dataset.split("/") + [args.config, args.split])

							# load metric
							__UpperCamelCase			:     Dict         =      load_metric("wer")
							__UpperCamelCase			:     List[str]         =      load_metric("cer")

							# compute metrics
							__UpperCamelCase			:     int         =      wer.compute(references=result["target"]		,						predictions=result["prediction"])
							__UpperCamelCase			:     Optional[Any]         =      cer.compute(references=result["target"]		,						predictions=result["prediction"])

							# print & log results
							__UpperCamelCase			:     List[Any]         =      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:
														__UpperCamelCase			:     Optional[Any]         =      F'log_{dataset_id}_predictions.txt'
														__UpperCamelCase			:     int         =      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				:    Tuple		,						_lowerCamelCase				:    Optional[int]):
																												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 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    List[Any])      ->					str:






							'''simple docstring'''





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

							__UpperCamelCase			:     Union[str, Any]         =      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!
							__UpperCamelCase			:     List[str]         =      ["\n\n", "\n", "   ", "  "]

							for t in token_sequences_to_ignore:
														__UpperCamelCase			:     Tuple         =      " ".join(text.split(_lowerCamelCase))

							return text




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Any)      ->					Dict:






							'''simple docstring'''





							__UpperCamelCase			:     Dict         =      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
							__UpperCamelCase			:     Optional[Any]         =      AutoFeatureExtractor.from_pretrained(args.model_id)
							__UpperCamelCase			:     Tuple         =      feature_extractor.sampling_rate

							# resample audio
							__UpperCamelCase			:     Any         =      dataset.cast_column("audio"		,						Audio(sampling_rate=_lowerCamelCase))

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

							# map function to decode audio
							def map_to_pred(_lowerCamelCase				:    int):
														__UpperCamelCase			:     Tuple         =      asr(
														    batch["audio"]["array"]		,						chunk_length_s=args.chunk_length_s		,						stride_length_s=args.stride_length_s)

														__UpperCamelCase			:     Dict         =      prediction["text"]
														__UpperCamelCase			:     List[str]         =      normalize_text(batch["sentence"])
														return batch

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

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


if __name__ == "__main__":
	lowercase						:      Optional[int]									= 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						:      Any									= parser.parse_args()

	main(args)

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowercase						:      str									= {
    'configuration_vision_encoder_decoder': ['VisionEncoderDecoderConfig', 'VisionEncoderDecoderOnnxConfig']
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase						:      str									= ['VisionEncoderDecoderModel']

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase						:      List[Any]									= ['TFVisionEncoderDecoderModel']

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase						:      int									= ['FlaxVisionEncoderDecoderModel']

if TYPE_CHECKING:
		from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig, VisionEncoderDecoderOnnxConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_vision_encoder_decoder import VisionEncoderDecoderModel

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_vision_encoder_decoder import TFVisionEncoderDecoderModel

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_vision_encoder_decoder import FlaxVisionEncoderDecoderModel

else:
		import sys

		lowercase						:      Dict									= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)