




import functools
import operator

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


__A						:							Any							=  logging.get_logger(__name__)

__A						:							Dict							=  {
    """facebook/wav2vec2-base-960h""": """https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json""",
    # See all Wav2Vec2 models at https://huggingface.co/models?filter=wav2vec2
}




class 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							a__						  =     '''wav2vec2'''





							def __init__(					self       :	Dict       ,     a       :	str=32       ,     a       :	Any=768       ,     a       :	Dict=12       ,     a       :	Dict=12       ,     a       :	Union[str, Any]=3_072       ,     a       :	Optional[int]="gelu"       ,     a       :	List[Any]=0.1       ,     a       :	Optional[int]=0.1       ,     a       :	Dict=0.1       ,     a       :	int=0.0       ,     a       :	Tuple=0.0       ,     a       :	Dict=0.1       ,     a       :	Dict=0.1       ,     a       :	Any=0.02       ,     a       :	Optional[int]=1E-5       ,     a       :	int="group"       ,     a       :	str="gelu"       ,     a       :	Dict=(512, 512, 512, 512, 512, 512, 512)       ,     a       :	int=(5, 2, 2, 2, 2, 2, 2)       ,     a       :	str=(10, 3, 3, 3, 3, 2, 2)       ,     a       :	int=False       ,     a       :	List[str]=128       ,     a       :	List[str]=16       ,     a       :	Any=False       ,     a       :	Union[str, Any]=True       ,     a       :	Optional[int]=0.05       ,     a       :	Optional[Any]=10       ,     a       :	str=2       ,     a       :	Union[str, Any]=0.0       ,     a       :	Dict=10       ,     a       :	List[str]=0       ,     a       :	List[str]=320       ,     a       :	int=2       ,     a       :	Dict=0.1       ,     a       :	Tuple=100       ,     a       :	Optional[Any]=256       ,     a       :	List[Any]=256       ,     a       :	Union[str, Any]=0.1       ,     a       :	Any="sum"       ,     a       :	str=False       ,     a       :	Any=False       ,     a       :	Dict=256       ,     a       :	Tuple=(512, 512, 512, 512, 1_500)       ,     a       :	List[str]=(5, 3, 3, 1, 1)       ,     a       :	List[Any]=(1, 2, 3, 1, 1)       ,     a       :	Any=512       ,     a       :	Any=0       ,     a       :	Optional[int]=1       ,     a       :	int=2       ,     a       :	List[Any]=False       ,     a       :	List[str]=3       ,     a       :	List[str]=2       ,     a       :	Tuple=3       ,     a       :	List[Any]=None       ,     a       :	List[str]=None       ,     **a       :	Tuple       ,     )			->					List[Any]:
										super().__init__(**a       ,     pad_token_id=a       ,     bos_token_id=a       ,     eos_token_id=a			)
										SCREAMING_SNAKE_CASE           =					hidden_size
										SCREAMING_SNAKE_CASE           =					feat_extract_norm
										SCREAMING_SNAKE_CASE           =					feat_extract_activation
										SCREAMING_SNAKE_CASE           =					list(a			)
										SCREAMING_SNAKE_CASE           =					list(a			)
										SCREAMING_SNAKE_CASE           =					list(a			)
										SCREAMING_SNAKE_CASE           =					conv_bias
										SCREAMING_SNAKE_CASE           =					num_conv_pos_embeddings
										SCREAMING_SNAKE_CASE           =					num_conv_pos_embedding_groups
										SCREAMING_SNAKE_CASE           =					len(self.conv_dim			)
										SCREAMING_SNAKE_CASE           =					num_hidden_layers
										SCREAMING_SNAKE_CASE           =					intermediate_size
										SCREAMING_SNAKE_CASE           =					hidden_act
										SCREAMING_SNAKE_CASE           =					num_attention_heads
										SCREAMING_SNAKE_CASE           =					hidden_dropout
										SCREAMING_SNAKE_CASE           =					attention_dropout
										SCREAMING_SNAKE_CASE           =					activation_dropout
										SCREAMING_SNAKE_CASE           =					feat_proj_dropout
										SCREAMING_SNAKE_CASE           =					final_dropout
										SCREAMING_SNAKE_CASE           =					layerdrop
										SCREAMING_SNAKE_CASE           =					layer_norm_eps
										SCREAMING_SNAKE_CASE           =					initializer_range
										SCREAMING_SNAKE_CASE           =					vocab_size
										SCREAMING_SNAKE_CASE           =					do_stable_layer_norm
										SCREAMING_SNAKE_CASE           =					use_weighted_layer_sum

										if (
										    (len(self.conv_stride			) != self.num_feat_extract_layers)
										    or (len(self.conv_kernel			) != self.num_feat_extract_layers)
										    or (len(self.conv_dim			) != self.num_feat_extract_layers)
										):
													raise ValueError(
													    """Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="""
													    """ `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="""
													    f""" {len(self.conv_dim			)}`, `len(config.conv_stride) = {len(self.conv_stride			)}`,"""
													    f""" `len(config.conv_kernel) = {len(self.conv_kernel			)}`."""			)

										# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
										SCREAMING_SNAKE_CASE           =					apply_spec_augment
										SCREAMING_SNAKE_CASE           =					mask_time_prob
										SCREAMING_SNAKE_CASE           =					mask_time_length
										SCREAMING_SNAKE_CASE           =					mask_time_min_masks
										SCREAMING_SNAKE_CASE           =					mask_feature_prob
										SCREAMING_SNAKE_CASE           =					mask_feature_length
										SCREAMING_SNAKE_CASE           =					mask_feature_min_masks

										# parameters for pretraining with codevector quantized representations
										SCREAMING_SNAKE_CASE           =					num_codevectors_per_group
										SCREAMING_SNAKE_CASE           =					num_codevector_groups
										SCREAMING_SNAKE_CASE           =					contrastive_logits_temperature
										SCREAMING_SNAKE_CASE           =					feat_quantizer_dropout
										SCREAMING_SNAKE_CASE           =					num_negatives
										SCREAMING_SNAKE_CASE           =					codevector_dim
										SCREAMING_SNAKE_CASE           =					proj_codevector_dim
										SCREAMING_SNAKE_CASE           =					diversity_loss_weight

										# ctc loss
										SCREAMING_SNAKE_CASE           =					ctc_loss_reduction
										SCREAMING_SNAKE_CASE           =					ctc_zero_infinity

										# adapter
										SCREAMING_SNAKE_CASE           =					add_adapter
										SCREAMING_SNAKE_CASE           =					adapter_kernel_size
										SCREAMING_SNAKE_CASE           =					adapter_stride
										SCREAMING_SNAKE_CASE           =					num_adapter_layers
										SCREAMING_SNAKE_CASE           =					output_hidden_size or hidden_size
										SCREAMING_SNAKE_CASE           =					adapter_attn_dim

										# SequenceClassification-specific parameter. Feel free to ignore for other classes.
										SCREAMING_SNAKE_CASE           =					classifier_proj_size

										# XVector-specific parameters. Feel free to ignore for other classes.
										SCREAMING_SNAKE_CASE           =					list(a			)
										SCREAMING_SNAKE_CASE           =					list(a			)
										SCREAMING_SNAKE_CASE           =					list(a			)
										SCREAMING_SNAKE_CASE           =					xvector_output_dim







							@property
							def    _UpperCAmelCase     (					self       :	Any			)			->					Any:
										return functools.reduce(operator.mul       ,     self.conv_stride       ,     1			)












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   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					args.log_outputs
			SCREAMING_SNAKE_CASE           =					"""_""".join(args.dataset.split("""/"""   ) + [args.config, args.split]   )

			# load metric
			SCREAMING_SNAKE_CASE           =					load_metric("""wer"""   )
			SCREAMING_SNAKE_CASE           =					load_metric("""cer"""   )

			# compute metrics
			SCREAMING_SNAKE_CASE           =					wer.compute(references=result["""target"""]					,	predictions=result["""prediction"""]   )
			SCREAMING_SNAKE_CASE           =					cer.compute(references=result["""target"""]					,	predictions=result["""prediction"""]   )

			# print & log results
			SCREAMING_SNAKE_CASE           =					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:
						SCREAMING_SNAKE_CASE           =					f"""log_{dataset_id}_predictions.txt"""
						SCREAMING_SNAKE_CASE           =					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   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					"""[,?.!\-\;\:\"“%‘”�—’…–]"""  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

			SCREAMING_SNAKE_CASE           =					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!
			SCREAMING_SNAKE_CASE           =					["""\n\n""", """\n""", """   """, """  """]

			for t in token_sequences_to_ignore:
						SCREAMING_SNAKE_CASE           =					""" """.join(text.split(SCREAMING_SNAKE_CASE   )   )

			return text
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			# load dataset
			SCREAMING_SNAKE_CASE           =					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
			SCREAMING_SNAKE_CASE           =					AutoFeatureExtractor.from_pretrained(args.model_id   )
			SCREAMING_SNAKE_CASE           =					feature_extractor.sampling_rate

			# resample audio
			SCREAMING_SNAKE_CASE           =					dataset.cast_column("""audio"""					,	Audio(sampling_rate=SCREAMING_SNAKE_CASE   )   )

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

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

						SCREAMING_SNAKE_CASE           =					prediction["""text"""]
						SCREAMING_SNAKE_CASE           =					normalize_text(batch["""sentence"""]   )
						return batch

			# run inference on all examples
			SCREAMING_SNAKE_CASE           =					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__":
		__A						:							List[Any]							=  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						:							Tuple							=  parser.parse_args()

		main(args)






