



'''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 A__   (					UpperCAmelCase_  ,  UpperCAmelCase_							):
						_UpperCamelCase :		List[str]			    =       args.log_outputs
						_UpperCamelCase :		Any			    =       '_'.join(args.dataset.split('/'							) + [args.config, args.split]							)

						# load metric
						_UpperCamelCase :		Optional[Any]			    =       load_metric('wer'							)
						_UpperCamelCase :		List[Any]			    =       load_metric('cer'							)

						# compute metrics
						_UpperCamelCase :		int			    =       wer.compute(references=result['target']  ,  predictions=result['prediction']							)
						_UpperCamelCase :		str			    =       cer.compute(references=result['target']  ,  predictions=result['prediction']							)

						# print & log results
						_UpperCamelCase :		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:
												_UpperCamelCase :		Optional[int]			    =       f'log_{dataset_id}_predictions.txt'
												_UpperCamelCase :		Union[str, Any]			    =       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_  ,  UpperCAmelCase_							):
																								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 A__   (					UpperCAmelCase_							):
						_UpperCamelCase :		Tuple			    =       '[,?.!\-\;\:"“%‘”�—’…–]'  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

						_UpperCamelCase :		int			    =       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!
						_UpperCamelCase :		str			    =       ['\n\n', '\n', '   ', '  ']

						for t in token_sequences_to_ignore:
												_UpperCamelCase :		Tuple			    =       ' '.join(text.split(UpperCAmelCase_							)							)

						return text






def A__   (					UpperCAmelCase_							):
						# load dataset
						_UpperCamelCase :		Optional[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
						_UpperCamelCase :		Any			    =       AutoFeatureExtractor.from_pretrained(args.model_id							)
						_UpperCamelCase :		Optional[int]			    =       feature_extractor.sampling_rate

						# resample audio
						_UpperCamelCase :		List[str]			    =       dataset.cast_column('audio'  ,  Audio(sampling_rate=UpperCAmelCase_							)							)

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

						# map function to decode audio
						def map_to_pred(UpperCAmelCase_							):
												_UpperCamelCase :		Optional[Any]			    =       asr(
												    batch['audio']['array']  ,  chunk_length_s=args.chunk_length_s  ,  stride_length_s=args.stride_length_s							)

												_UpperCamelCase :		Optional[Any]			    =       prediction['text']
												_UpperCamelCase :		str			    =       normalize_text(batch['sentence']							)
												return batch

						# run inference on all examples
						_UpperCamelCase :		int			    =       dataset.map(UpperCAmelCase_  ,  remove_columns=dataset.column_names							)

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


if __name__ == "__main__":
					snake_case_			:     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.',
					)
					snake_case_			:     str  	=  parser.parse_args()

					main(args)







'''simple docstring'''
from typing import TYPE_CHECKING

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


snake_case_			:     Optional[Any]  	=  {
    'configuration_clip': [
        'CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'CLIPConfig',
        'CLIPOnnxConfig',
        'CLIPTextConfig',
        'CLIPVisionConfig',
    ],
    'processing_clip': ['CLIPProcessor'],
    'tokenization_clip': ['CLIPTokenizer'],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_			:     Optional[int]  	=  ['CLIPTokenizerFast']

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_			:     Optional[int]  	=  ['CLIPFeatureExtractor']
					snake_case_			:     Dict  	=  ['CLIPImageProcessor']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_			:     Tuple  	=  [
					    'CLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'CLIPModel',
					    'CLIPPreTrainedModel',
					    'CLIPTextModel',
					    'CLIPTextModelWithProjection',
					    'CLIPVisionModel',
					    'CLIPVisionModelWithProjection',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_			:     List[Any]  	=  [
					    'TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFCLIPModel',
					    'TFCLIPPreTrainedModel',
					    'TFCLIPTextModel',
					    'TFCLIPVisionModel',
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_			:     Optional[int]  	=  [
					    'FlaxCLIPModel',
					    'FlaxCLIPPreTrainedModel',
					    'FlaxCLIPTextModel',
					    'FlaxCLIPTextPreTrainedModel',
					    'FlaxCLIPVisionModel',
					    'FlaxCLIPVisionPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_clip import (
					    CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    CLIPConfig,
					    CLIPOnnxConfig,
					    CLIPTextConfig,
					    CLIPVisionConfig,
					)
					from .processing_clip import CLIPProcessor
					from .tokenization_clip import CLIPTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_clip_fast import CLIPTokenizerFast

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_clip import CLIPFeatureExtractor
										from .image_processing_clip import CLIPImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_clip import (
										    CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
										    CLIPModel,
										    CLIPPreTrainedModel,
										    CLIPTextModel,
										    CLIPTextModelWithProjection,
										    CLIPVisionModel,
										    CLIPVisionModelWithProjection,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_clip import (
										    TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFCLIPModel,
										    TFCLIPPreTrainedModel,
										    TFCLIPTextModel,
										    TFCLIPVisionModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_clip import (
										    FlaxCLIPModel,
										    FlaxCLIPPreTrainedModel,
										    FlaxCLIPTextModel,
										    FlaxCLIPTextPreTrainedModel,
										    FlaxCLIPVisionModel,
										    FlaxCLIPVisionPreTrainedModel,
										)


else:
					import sys

					snake_case_			:     str  	=  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


