
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


lowerCamelCase__          =		logging.get_logger(__name__)

lowerCamelCase__          =		{
    """google/vit-base-patch16-224""": """https://huggingface.co/vit-base-patch16-224/resolve/main/config.json""",
    # See all ViT models at https://huggingface.co/models?filter=vit
}







class      A__							(     __magic_name__	):
				lowercase			    =  'vit'






				def __init__(						self					:      List[Any]    ,     a					:      List[str]=768    ,     a					:      str=12    ,     a					:      List[Any]=12    ,     a					:      List[str]=3_072    ,     a					:      str="gelu"    ,     a					:      List[Any]=0.0    ,     a					:      int=0.0    ,     a					:      List[str]=0.0_2    ,     a					:      Union[str, Any]=1E-12    ,     a					:      str=224    ,     a					:      Dict=16    ,     a					:      Optional[int]=3    ,     a					:      Dict=True    ,     a					:      Any=16    ,     **a					:      List[str]    ,     ):






							'''simple docstring'''







							super().__init__(**a   )

							lowerCAmelCase__      :      Union[str, Any]         =		hidden_size
							lowerCAmelCase__      :      str         =		num_hidden_layers
							lowerCAmelCase__      :      Union[str, Any]         =		num_attention_heads
							lowerCAmelCase__      :      List[str]         =		intermediate_size
							lowerCAmelCase__      :      Optional[int]         =		hidden_act
							lowerCAmelCase__      :      Any         =		hidden_dropout_prob
							lowerCAmelCase__      :      List[Any]         =		attention_probs_dropout_prob
							lowerCAmelCase__      :      Dict         =		initializer_range
							lowerCAmelCase__      :      str         =		layer_norm_eps
							lowerCAmelCase__      :      List[Any]         =		image_size
							lowerCAmelCase__      :      Tuple         =		patch_size
							lowerCAmelCase__      :      List[Any]         =		num_channels
							lowerCAmelCase__      :      List[Any]         =		qkv_bias
							lowerCAmelCase__      :      Union[str, Any]         =		encoder_stride







class      A__							(     __magic_name__	):
				lowercase			    =  version.parse('1.11'	)






				@property
				def 							_lowerCamelCase		(						self					:      Dict   ):






							'''simple docstring'''







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






				@property
				def 							_lowerCamelCase		(						self					:      str   ):






							'''simple docstring'''







							return 1E-4

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)