
import argparse
import importlib
from pathlib import Path


# Test all the extensions added in the setup
lowerCamelCase__          =		[
    """kernels/rwkv/wkv_cuda.cu""",
    """kernels/rwkv/wkv_op.cpp""",
    """kernels/deformable_detr/ms_deform_attn.h""",
    """kernels/deformable_detr/cuda/ms_deform_im2col_cuda.cuh""",
    """models/graphormer/algos_graphormer.pyx""",
]
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_     )				->		str:
			# Test all the extensions added in the setup
			for file in FILES_TO_FIND:
						if not (transformers_path / file).exists():
									return False
			return True


if __name__ == "__main__":
					lowerCamelCase__          =		argparse.ArgumentParser()
					parser.add_argument("""--check_lib""", action="""store_true""", help="""Whether to check the build or the actual package.""")
					lowerCamelCase__          =		parser.parse_args()
					if args.check_lib:
										lowerCamelCase__          =		importlib.import_module("""transformers""")
										lowerCamelCase__          =		Path(transformers_module.__file__).parent
					else:
										lowerCamelCase__          =		Path.cwd() / """build/lib/transformers"""
					if not test_custom_files_are_present(transformers_path):
										raise ValueError("""The built release does not contain the custom files. Fix this before going further!""")

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)