

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_							:     Dataset							,       UpperCAmelCase_							:     Dict[str, str]	)						->				List[str]:
	_UpperCamelCase   :		int			=						args.log_outputs
	_UpperCamelCase   :		str			=						"""_""".join(args.dataset.split('/'	) + [args.config, args.split]	)

	# load metric
	_UpperCamelCase   :		str			=						load_metric('wer'	)
	_UpperCamelCase   :		Optional[int]			=						load_metric('cer'	)

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

	# print & log results
	_UpperCamelCase   :		Union[str, Any]			=						F'''WER: {wer_result}\nCER: {cer_result}'''
	print(lowercase_	)

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

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

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

			result.map(lowercase_							,       with_indices=lowercase_	)






def 					lowerCamelCase_     (							UpperCAmelCase_							:     str	)						->				str:
	_UpperCamelCase   :		Optional[int]			=						"""[,?.!\-\;\:\"“%‘”�—’…–]"""  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

	_UpperCamelCase   :		int			=						re.sub(lowercase_							,       ''							,       text.lower()	)

	# In addition, we can normalize the target text, e.g. removing new lines characters etc...
	# note that order is important here!
	_UpperCamelCase   :		Optional[Any]			=						["""\n\n""", """\n""", """   """, """  """]

	for t in token_sequences_to_ignore:
		_UpperCamelCase   :		Optional[Any]			=						""" """.join(text.split(lowercase_	)	)

	return text






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Dict	)						->				Any:
	# load dataset
	_UpperCamelCase   :		Any			=						load_dataset(args.dataset							,       args.config							,       split=args.split							,       use_auth_token=lowercase_	)

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

	# load processor
	_UpperCamelCase   :		Tuple			=						AutoFeatureExtractor.from_pretrained(args.model_id	)
	_UpperCamelCase   :		List[Any]			=						feature_extractor.sampling_rate

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

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

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

		_UpperCamelCase   :		str			=						prediction["""text"""]
		_UpperCamelCase   :		Dict			=						normalize_text(batch['sentence']	)
		return batch

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

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


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)







from typing import TYPE_CHECKING

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


lowerCAmelCase__								=		{
    """configuration_blip""": [
        """BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """BlipConfig""",
        """BlipTextConfig""",
        """BlipVisionConfig""",
    ],
    """processing_blip""": ["""BlipProcessor"""],
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase__								=		["""BlipImageProcessor"""]


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase__								=		[
							    """BLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """BlipModel""",
							    """BlipPreTrainedModel""",
							    """BlipForConditionalGeneration""",
							    """BlipForQuestionAnswering""",
							    """BlipVisionModel""",
							    """BlipTextModel""",
							    """BlipForImageTextRetrieval""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase__								=		[
							    """TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFBlipModel""",
							    """TFBlipPreTrainedModel""",
							    """TFBlipForConditionalGeneration""",
							    """TFBlipForQuestionAnswering""",
							    """TFBlipVisionModel""",
							    """TFBlipTextModel""",
							    """TFBlipForImageTextRetrieval""",
							]

if TYPE_CHECKING:
							from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
							from .processing_blip import BlipProcessor

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .image_processing_blip import BlipImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_blip import (
														    BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
														    BlipForConditionalGeneration,
														    BlipForImageTextRetrieval,
														    BlipForQuestionAnswering,
														    BlipModel,
														    BlipPreTrainedModel,
														    BlipTextModel,
														    BlipVisionModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_blip import (
														    TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFBlipForConditionalGeneration,
														    TFBlipForImageTextRetrieval,
														    TFBlipForQuestionAnswering,
														    TFBlipModel,
														    TFBlipPreTrainedModel,
														    TFBlipTextModel,
														    TFBlipVisionModel,
														)

else:
							import sys

							lowerCAmelCase__								=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




