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

			# load metric
			lowerCAmelCase__      :      int         =		load_metric('wer'     )
			lowerCAmelCase__      :      str         =		load_metric('cer'     )

			# compute metrics
			lowerCAmelCase__      :      List[Any]         =		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(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__      :      Optional[Any]         =		F'''log_{dataset_id}_predictions.txt'''
						lowerCAmelCase__      :      Tuple         =		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_     )				->		Dict:
			lowerCAmelCase__      :      List[str]         =		"[,?.!\-\;\:\"“%‘”�—’…–]"  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

			lowerCAmelCase__      :      List[str]         =		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__      :      Dict         =		["\n\n", "\n", "   ", "  "]

			for t in token_sequences_to_ignore:
						lowerCAmelCase__      :      List[str]         =		" ".join(text.split(SCREAMING_SNAKE_CASE_     )     )

			return text
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_     )				->		int:
			lowerCAmelCase__      :      Any         =		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__      :      Tuple         =		AutoFeatureExtractor.from_pretrained(args.model_id     )
			lowerCAmelCase__      :      Dict         =		feature_extractor.sampling_rate

			# resample audio
			lowerCAmelCase__      :      Any         =		dataset.cast_column('audio'       ,							Audio(sampling_rate=SCREAMING_SNAKE_CASE_     )     )

			# load eval pipeline
			if args.device is None:
						lowerCAmelCase__      :      int         =		0 if torch.cuda.is_available() else -1
			lowerCAmelCase__      :      Union[str, 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__      :      Union[str, Any]         =		asr(
						    batch['audio']['array']       ,							chunk_length_s=args.chunk_length_s       ,							stride_length_s=args.stride_length_s     )

						lowerCAmelCase__      :      List[Any]         =		prediction["text"]
						lowerCAmelCase__      :      int         =		normalize_text(batch['sentence']     )
						return batch

			# run inference on all examples
			lowerCAmelCase__      :      List[str]         =		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)



"""simple docstring"""







from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase__      				=   logging.get_logger(__name__)

lowerCAmelCase__      				=   {}


class       SCREAMING_SNAKE_CASE__				(		lowercase				):
       """simple docstring"""


       a  :				str   ="llama"
       a  :				List[str]   =["past_key_values"]

       def __init__(						self		,     snake_case__=32_000		,     snake_case__=4_096		,     snake_case__=11_008		,     snake_case__=32		,     snake_case__=32		,     snake_case__=None		,     snake_case__="silu"		,     snake_case__=2_048		,     snake_case__=0.02		,     snake_case__=1e-6		,     snake_case__=True		,     snake_case__=0		,     snake_case__=1		,     snake_case__=2		,     snake_case__=1		,     snake_case__=False		,     snake_case__=None		,     **snake_case__		,     ):






         """simple docstring"""





         lowerCAmelCase     :     Optional[Any]								=							vocab_size
         lowerCAmelCase     :     str								=							max_position_embeddings
         lowerCAmelCase     :     str								=							hidden_size
         lowerCAmelCase     :     Optional[int]								=							intermediate_size
         lowerCAmelCase     :     Any								=							num_hidden_layers
         lowerCAmelCase     :     List[str]								=							num_attention_heads

         # for backward compatibility
         if num_key_value_heads is None:
           lowerCAmelCase     :     Tuple								=							num_attention_heads

         lowerCAmelCase     :     Dict								=							num_key_value_heads
         lowerCAmelCase     :     Optional[Any]								=							hidden_act
         lowerCAmelCase     :     Optional[Any]								=							initializer_range
         lowerCAmelCase     :     Any								=							rms_norm_eps
         lowerCAmelCase     :     List[Any]								=							pretraining_tp
         lowerCAmelCase     :     int								=							use_cache
         lowerCAmelCase     :     List[str]								=							rope_scaling
         self._rope_scaling_validation()

         super().__init__(
             pad_token_id=snake_case__		,     bos_token_id=snake_case__		,     eos_token_id=snake_case__		,     tie_word_embeddings=snake_case__		,     **snake_case__		,     )







       def        lowercase__							(						self						):






         """simple docstring"""





         if self.rope_scaling is None:
           return

         if not isinstance(self.rope_scaling		,     snake_case__						) or len(self.rope_scaling						) != 2:
           raise ValueError(
               "`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, "
               f"""got {self.rope_scaling}"""						)
         lowerCAmelCase     :     Optional[Any]								=							self.rope_scaling.get("type"		,     snake_case__						)
         lowerCAmelCase     :     int								=							self.rope_scaling.get("factor"		,     snake_case__						)
         if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
           raise ValueError(
               f"""`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"""						)
         if rope_scaling_factor is None or not isinstance(snake_case__		,     snake_case__						) or rope_scaling_factor <= 1.0:
           raise ValueError(f"""`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}"""						)





