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					(   a_				: Dataset						,			a_				: Dict[str, str]):
			lowerCamelCase						:str						  =     args.log_outputs
			lowerCamelCase						:int						  =     '''_'''.join(args.dataset.split('''/''') + [args.config, args.split])

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

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

			# print & log results
			lowerCamelCase						:List[str]						  =     F"WER: {wer_result}\nCER: {cer_result}"
			print(a_)

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

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

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

									result.map(a_						,			with_indices=a_)





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

			lowerCamelCase						:int						  =     re.sub(a_						,			''''''						,			text.lower())

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

			for t in token_sequences_to_ignore:
						lowerCamelCase						:List[Any]						  =     ''' '''.join(text.split(a_))

			return text





def  _lowerCamelCase					(   a_				: int):
			# load dataset
			lowerCamelCase						:Tuple						  =     load_dataset(args.dataset						,			args.config						,			split=args.split						,			use_auth_token=a_)

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

			# load processor
			lowerCamelCase						:str						  =     AutoFeatureExtractor.from_pretrained(args.model_id)
			lowerCamelCase						:Dict						  =     feature_extractor.sampling_rate

			# resample audio
			lowerCamelCase						:Tuple						  =     dataset.cast_column('''audio'''						,			Audio(sampling_rate=a_))

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

			# map function to decode audio
			def map_to_pred(a_				: List[str]):
						lowerCamelCase						:str						  =     asr(
						    batch['''audio''']['''array''']						,			chunk_length_s=args.chunk_length_s						,			stride_length_s=args.stride_length_s)

						lowerCamelCase						:Any						  =     prediction['''text''']
						lowerCamelCase						:Optional[int]						  =     normalize_text(batch['''sentence'''])
						return batch

			# run inference on all examples
			lowerCamelCase						:Union[str, Any]						  =     dataset.map(a_						,			remove_columns=dataset.column_names)

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


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

			main(args)




from typing import TYPE_CHECKING

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


A__           =							{
    """configuration_layoutlmv2""": ["""LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LayoutLMv2Config"""],
    """processing_layoutlmv2""": ["""LayoutLMv2Processor"""],
    """tokenization_layoutlmv2""": ["""LayoutLMv2Tokenizer"""],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A__           =							["""LayoutLMv2TokenizerFast"""]

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A__           =							["""LayoutLMv2FeatureExtractor"""]
			A__           =							["""LayoutLMv2ImageProcessor"""]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A__           =							[
			    """LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """LayoutLMv2ForQuestionAnswering""",
			    """LayoutLMv2ForSequenceClassification""",
			    """LayoutLMv2ForTokenClassification""",
			    """LayoutLMv2Layer""",
			    """LayoutLMv2Model""",
			    """LayoutLMv2PreTrainedModel""",
			]

if TYPE_CHECKING:
			from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
			from .processing_layoutlmva import LayoutLMvaProcessor
			from .tokenization_layoutlmva import LayoutLMvaTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_layoutlmva import (
						    LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    LayoutLMvaForQuestionAnswering,
						    LayoutLMvaForSequenceClassification,
						    LayoutLMvaForTokenClassification,
						    LayoutLMvaLayer,
						    LayoutLMvaModel,
						    LayoutLMvaPreTrainedModel,
						)
else:
			import sys

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



