



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 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase    ):
			A							:					Dict              =						args.log_outputs
			A							:					Tuple              =						"_".join(args.dataset.split("/"    ) + [args.config, args.split]    )

			# load metric
			A							:					int              =						load_metric("wer"    )
			A							:					List[Any]              =						load_metric("cer"    )

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

			# print & log results
			A							:					List[Any]              =						f"""WER: {wer_result}\nCER: {cer_result}"""
			print(_lowerCamelCase    )

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

			# log all results in text file. Possibly interesting for analysis
			if log_outputs is not None:
						A							:					Dict              =						f"""log_{dataset_id}_predictions.txt"""
						A							:					List[str]              =						f"""log_{dataset_id}_targets.txt"""

						with open(_lowerCamelCase ,    "w"    ) as p, open(_lowerCamelCase ,    "w"    ) as t:
									# mapping function to write output
									def write_to_file(_lowerCamelCase ,    _lowerCamelCase    ):
												p.write(f"""{i}""" + "\n"    )
												p.write(batch["prediction"] + "\n"    )
												t.write(f"""{i}""" + "\n"    )
												t.write(batch["target"] + "\n"    )

									result.map(_lowerCamelCase ,    with_indices=_lowerCamelCase    )




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

			A							:					Tuple              =						re.sub(_lowerCamelCase ,    "" ,    text.lower()    )

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

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

			return text




def 	UpperCAmelCase   (						_lowerCamelCase    ):
			# load dataset
			A							:					List[Any]              =						load_dataset(args.dataset ,    args.config ,    split=args.split ,    use_auth_token=_lowerCamelCase    )

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

			# load processor
			A							:					Union[str, Any]              =						AutoFeatureExtractor.from_pretrained(args.model_id    )
			A							:					List[str]              =						feature_extractor.sampling_rate

			# resample audio
			A							:					Tuple              =						dataset.cast_column("audio" ,    Audio(sampling_rate=_lowerCamelCase    )    )

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

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

						A							:					Union[str, Any]              =						prediction["text"]
						A							:					Dict              =						normalize_text(batch["sentence"]    )
						return batch

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

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


if __name__ == "__main__":
							__SCREAMING_SNAKE_CASE           =  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.""",
							)
							__SCREAMING_SNAKE_CASE           =  parser.parse_args()

							main(args)




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__SCREAMING_SNAKE_CASE           =  {
    """configuration_instructblip""": [
        """INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """InstructBlipConfig""",
        """InstructBlipQFormerConfig""",
        """InstructBlipVisionConfig""",
    ],
    """processing_instructblip""": ["""InstructBlipProcessor"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__SCREAMING_SNAKE_CASE           =  [
							    """INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """InstructBlipQFormerModel""",
							    """InstructBlipPreTrainedModel""",
							    """InstructBlipForConditionalGeneration""",
							    """InstructBlipVisionModel""",
							]

if TYPE_CHECKING:
							from .configuration_instructblip import (
							    INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    InstructBlipConfig,
							    InstructBlipQFormerConfig,
							    InstructBlipVisionConfig,
							)
							from .processing_instructblip import InstructBlipProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_instructblip import (
														    INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
														    InstructBlipForConditionalGeneration,
														    InstructBlipPreTrainedModel,
														    InstructBlipQFormerModel,
														    InstructBlipVisionModel,
														)

else:
							import sys

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