
'''simple docstring'''







from collections import OrderedDict
from typing import Mapping

from packaging import version

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


lowercase					:							List[Any]   =      logging.get_logger(__name__)

lowercase					:							Tuple   =      {
    "microsoft/swin-tiny-patch4-window7-224": (
        "https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json"
    ),
    # See all Swin models at https://huggingface.co/models?filter=swin
}


class 		__UpperCAmelCase   (      _lowerCamelCase		,     _lowerCamelCase		):
							__lowercase   	= """swin"""

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


							def __init__( self					,     lowerCAmelCase_=2_24					,     lowerCAmelCase_=4					,     lowerCAmelCase_=3					,     lowerCAmelCase_=96					,     lowerCAmelCase_=[2, 2, 6, 2]					,     lowerCAmelCase_=[3, 6, 12, 24]					,     lowerCAmelCase_=7					,     lowerCAmelCase_=4.0					,     lowerCAmelCase_=True					,     lowerCAmelCase_=0.0					,     lowerCAmelCase_=0.0					,     lowerCAmelCase_=0.1					,     lowerCAmelCase_="gelu"					,     lowerCAmelCase_=False					,     lowerCAmelCase_=0.02					,     lowerCAmelCase_=1E-5					,     lowerCAmelCase_=32					,     lowerCAmelCase_=None					,     lowerCAmelCase_=None					,     **lowerCAmelCase_					,     ):





								"""simple docstring"""






								super().__init__(**lowerCAmelCase_						)

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


class 		__UpperCAmelCase   (      _lowerCamelCase		):
							__lowercase   	= version.parse("""1.11"""		)


							@property
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								return OrderedDict(
								    [
								        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
								    ]						)




							@property
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								return 1E-4



'''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			) ->			int:
	_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(__A			)

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

	# 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(__A    ,       'w'			) as p, open(__A    ,       '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(__A    ,       with_indices=__A			)







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(__A    ,       ''    ,       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(__A			)			)

	return text







def        SCREAMING_SNAKE_CASE__			(     __A			) ->			Optional[int]:
	# load dataset
	_snake_case     		=	load_dataset(args.dataset    ,       args.config    ,       split=args.split    ,       use_auth_token=__A			)

	# 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=__A			)			)

	# 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(__A    ,       remove_columns=dataset.column_names			)

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


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					:							Optional[int]   =      parser.parse_args()

					main(args)

