
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_						(				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE				):
	lowercase	    =       args.log_outputs
	lowercase	    =       '_'.join(args.dataset.split('/'				) + [args.config, args.split]				)

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

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

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

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

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

	return text






def 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE				):
	# load dataset
	lowercase	    =       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
	lowercase	    =       AutoFeatureExtractor.from_pretrained(args.model_id				)
	lowercase	    =       feature_extractor.sampling_rate

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

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

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

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

	# run inference on all examples
	lowercase	    =       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__":
	UpperCAmelCase       =					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.''',
	)
	UpperCAmelCase       =					parser.parse_args()

	main(args)




'''simple docstring'''

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


UpperCAmelCase					 =	logging.get_logger(__name__)

UpperCAmelCase					 =	{
    '''shi-labs/dinat-mini-in1k-224''': '''https://huggingface.co/shi-labs/dinat-mini-in1k-224/resolve/main/config.json''',
    # See all Dinat models at https://huggingface.co/models?filter=dinat
}


class 	lowerCAmelCase  (   A ,      A ):
       lowerCAmelCase_												=			"dinat"

       lowerCAmelCase_												=			{
           "num_attention_heads": "num_heads",
           "num_hidden_layers": "num_layers",
       }







       def __init__(				self      :			int    ,   __lowercase      :			str=4    ,   __lowercase      :			str=3    ,   __lowercase      :			int=64    ,   __lowercase      :			int=[3, 4, 6, 5]    ,   __lowercase      :			Union[str, Any]=[2, 4, 8, 16]    ,   __lowercase      :			Optional[int]=7    ,   __lowercase      :			str=[[1, 8, 1], [1, 4, 1, 4], [1, 2, 1, 2, 1, 2], [1, 1, 1, 1, 1]]    ,   __lowercase      :			Any=3.0    ,   __lowercase      :			Optional[Any]=True    ,   __lowercase      :			List[Any]=0.0    ,   __lowercase      :			Optional[Any]=0.0    ,   __lowercase      :			int=0.1    ,   __lowercase      :			Union[str, Any]="gelu"    ,   __lowercase      :			Any=0.0_2    ,   __lowercase      :			int=1E-5    ,   __lowercase      :			Optional[int]=0.0    ,   __lowercase      :			Optional[int]=None    ,   __lowercase      :			str=None    ,   **__lowercase      :			Any    ,   ):







            """simple docstring"""







            super().__init__(**__lowercase   )

            __lowercase      		=patch_size
            __lowercase      		=num_channels
            __lowercase      		=embed_dim
            __lowercase      		=depths
            __lowercase      		=len(__lowercase   )
            __lowercase      		=num_heads
            __lowercase      		=kernel_size
            __lowercase      		=dilations
            __lowercase      		=mlp_ratio
            __lowercase      		=qkv_bias
            __lowercase      		=hidden_dropout_prob
            __lowercase      		=attention_probs_dropout_prob
            __lowercase      		=drop_path_rate
            __lowercase      		=hidden_act
            __lowercase      		=layer_norm_eps
            __lowercase      		=initializer_range
            # we set the hidden_size attribute in order to make Dinat work with VisionEncoderDecoderModel
            # this indicates the channel dimension after the last stage of the model
            __lowercase      		=int(embed_dim * 2 ** (len(__lowercase   ) - 1)   )
            __lowercase      		=layer_scale_init_value
            __lowercase      		=['stem'] + [f'''stage{idx}''' for idx in range(1    ,   len(__lowercase   ) + 1   )]
            __lowercase		,  __lowercase      		=get_aligned_output_features_output_indices(
                out_features=__lowercase    ,   out_indices=__lowercase    ,   stage_names=self.stage_names   )


