



"""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 	__lowerCAmelCase						(		__UpperCamelCase      :			List[str]					, __UpperCamelCase      :			str    ):


						'''simple docstring'''






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

						# load metric
						snake_case_   : Dict						=			load_metric("""wer"""    )
						snake_case_   : List[Any]						=			load_metric("""cer"""    )

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

						# print & log results
						snake_case_   : List[str]						=			F'WER: {wer_result}\nCER: {cer_result}'
						print(__UpperCamelCase    )

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

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

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

																		result.map(__UpperCamelCase					, with_indices=__UpperCamelCase    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Optional[int]    ):


						'''simple docstring'''






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

						snake_case_   : str						=			re.sub(__UpperCamelCase					, """"""					, 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_   : Optional[Any]						=			["""\n\n""", """\n""", """   """, """  """]

						for t in token_sequences_to_ignore:
												snake_case_   : int						=			""" """.join(text.split(__UpperCamelCase    )    )

						return text
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Optional[int]    ):


						'''simple docstring'''






						snake_case_   : Any						=			load_dataset(args.dataset					, args.config					, split=args.split					, use_auth_token=__UpperCamelCase    )

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

						# load processor
						snake_case_   : List[str]						=			AutoFeatureExtractor.from_pretrained(args.model_id    )
						snake_case_   : List[str]						=			feature_extractor.sampling_rate

						# resample audio
						snake_case_   : Optional[int]						=			dataset.cast_column("""audio"""					, Audio(sampling_rate=__UpperCamelCase    )    )

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

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

												snake_case_   : int						=			prediction["""text"""]
												snake_case_   : Dict						=			normalize_text(batch["""sentence"""]    )
												return batch

						# run inference on all examples
						snake_case_   : List[str]						=			dataset.map(__UpperCamelCase					, remove_columns=dataset.column_names    )

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


if __name__ == "__main__":
					__lowerCAmelCase       :					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.''',
					)
					__lowerCAmelCase       :					Tuple             =					parser.parse_args()

					main(args)






"""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


__lowerCAmelCase       :					Tuple      					=					logging.get_logger(__name__)

__lowerCAmelCase       :					Dict      					=					{
    '''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    _lowerCAmelCase    (				SCREAMING_SNAKE_CASE__  ,				SCREAMING_SNAKE_CASE__		):





		"""simple docstring"""






		_lowerCamelCase			  =       '''swin'''

		_lowerCamelCase			  =       {
		    '''num_attention_heads''': '''num_heads''',
		    '''num_hidden_layers''': '''num_layers''',
		}





		def __init__(					self							,    _lowercase=2_2_4							,    _lowercase=4							,    _lowercase=3							,    _lowercase=9_6							,    _lowercase=[2, 2, 6, 2]							,    _lowercase=[3, 6, 1_2, 2_4]							,    _lowercase=7							,    _lowercase=4.0							,    _lowercase=True							,    _lowercase=0.0							,    _lowercase=0.0							,    _lowercase=0.1							,    _lowercase="gelu"							,    _lowercase=False							,    _lowercase=0.02							,    _lowercase=1E-5							,    _lowercase=3_2							,    _lowercase=None							,    _lowercase=None							,    **_lowercase							,    ) ->						Union[str, Any]:







								'''simple docstring'''

								super().__init__(**_lowercase   )

								snake_case_   : str						=			image_size
								snake_case_   : int						=			patch_size
								snake_case_   : Optional[int]						=			num_channels
								snake_case_   : Union[str, Any]						=			embed_dim
								snake_case_   : Optional[int]						=			depths
								snake_case_   : Optional[int]						=			len(_lowercase   )
								snake_case_   : Optional[Any]						=			num_heads
								snake_case_   : Optional[Any]						=			window_size
								snake_case_   : Optional[Any]						=			mlp_ratio
								snake_case_   : Optional[Any]						=			qkv_bias
								snake_case_   : Optional[Any]						=			hidden_dropout_prob
								snake_case_   : Tuple						=			attention_probs_dropout_prob
								snake_case_   : Union[str, Any]						=			drop_path_rate
								snake_case_   : List[Any]						=			hidden_act
								snake_case_   : str						=			use_absolute_embeddings
								snake_case_   : str						=			layer_norm_eps
								snake_case_   : Optional[Any]						=			initializer_range
								snake_case_   : Any						=			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_   : Tuple						=			int(embed_dim * 2 ** (len(_lowercase   ) - 1)   )
								snake_case_   : Tuple						=			["""stem"""] + [f'stage{idx}' for idx in range(1							,    len(_lowercase   ) + 1   )]
								snake_case_			,		snake_case_   : Any						=			get_aligned_output_features_output_indices(
								    out_features=_lowercase							,    out_indices=_lowercase							,    stage_names=self.stage_names   )




class    _lowerCAmelCase    (				SCREAMING_SNAKE_CASE__		):





		"""simple docstring"""






		_lowerCamelCase			  =       version.parse('''1.11'''		)





		@property
		def    UpperCAmelCase__							(					self   ) ->						Mapping[str, Mapping[int, str]]:







								'''simple docstring'''

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





		@property
		def    UpperCAmelCase__							(					self   ) ->						float:







								'''simple docstring'''

								return 1E-4

