






"""simple docstring"""







import functools
import operator

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


a			:    Dict				 =		logging.get_logger(__name__)

a			:    List[Any]				 =		{
    """microsoft/unispeech-sat-base-100h-libri-ft""": (
        """https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft/resolve/main/config.json"""
    ),
    # See all UniSpeechSat models at https://huggingface.co/models?filter=unispeech_sat
}


class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						__lowerCamelCase    	= "unispeech-sat"




						def __init__(       self	,  snake_case__=32	,  snake_case__=768	,  snake_case__=12	,  snake_case__=12	,  snake_case__=3072	,  snake_case__="gelu"	,  snake_case__=0.1	,  snake_case__=0.1	,  snake_case__=0.1	,  snake_case__=0.0	,  snake_case__=0.0	,  snake_case__=0.1	,  snake_case__=0.1	,  snake_case__=0.02	,  snake_case__=1e-5	,  snake_case__="group"	,  snake_case__="gelu"	,  snake_case__=(512, 512, 512, 512, 512, 512, 512)	,  snake_case__=(5, 2, 2, 2, 2, 2, 2)	,  snake_case__=(10, 3, 3, 3, 3, 2, 2)	,  snake_case__=False	,  snake_case__=128	,  snake_case__=16	,  snake_case__=False	,  snake_case__=True	,  snake_case__=0.05	,  snake_case__=10	,  snake_case__=2	,  snake_case__=0.0	,  snake_case__=10	,  snake_case__=0	,  snake_case__=320	,  snake_case__=2	,  snake_case__=0.1	,  snake_case__=100	,  snake_case__=256	,  snake_case__=256	,  snake_case__=0.1	,  snake_case__="mean"	,  snake_case__=False	,  snake_case__=False	,  snake_case__=256	,  snake_case__=(512, 512, 512, 512, 1500)	,  snake_case__=(5, 3, 3, 1, 1)	,  snake_case__=(1, 2, 3, 1, 1)	,  snake_case__=512	,  snake_case__=0	,  snake_case__=1	,  snake_case__=2	,  snake_case__=504	,  **snake_case__	,  ):
												'''simple docstring'''
												super().__init__(**snake_case__	,  pad_token_id=snake_case__	,  bos_token_id=snake_case__	,  eos_token_id=snake_case__      )
												lowercase__    :				Union[str, Any]=	hidden_size
												lowercase__    :				int=	feat_extract_norm
												lowercase__    :				str=	feat_extract_activation
												lowercase__    :				Any=	list(snake_case__      )
												lowercase__    :				List[str]=	list(snake_case__      )
												lowercase__    :				int=	list(snake_case__      )
												lowercase__    :				Optional[int]=	conv_bias
												lowercase__    :				List[Any]=	num_conv_pos_embeddings
												lowercase__    :				str=	num_conv_pos_embedding_groups
												lowercase__    :				int=	len(self.conv_dim      )
												lowercase__    :				Dict=	num_hidden_layers
												lowercase__    :				Tuple=	intermediate_size
												lowercase__    :				Union[str, Any]=	hidden_act
												lowercase__    :				Dict=	num_attention_heads
												lowercase__    :				Union[str, Any]=	hidden_dropout
												lowercase__    :				Union[str, Any]=	attention_dropout
												lowercase__    :				int=	activation_dropout
												lowercase__    :				str=	feat_proj_dropout
												lowercase__    :				List[Any]=	final_dropout
												lowercase__    :				int=	layerdrop
												lowercase__    :				Union[str, Any]=	layer_norm_eps
												lowercase__    :				int=	initializer_range
												lowercase__    :				Tuple=	vocab_size
												lowercase__    :				Optional[int]=	num_clusters
												lowercase__    :				str=	do_stable_layer_norm
												lowercase__    :				str=	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
												lowercase__    :				List[Any]=	apply_spec_augment
												lowercase__    :				Optional[int]=	mask_time_prob
												lowercase__    :				List[Any]=	mask_time_length
												lowercase__    :				int=	mask_time_min_masks
												lowercase__    :				Union[str, Any]=	mask_feature_prob
												lowercase__    :				List[str]=	mask_feature_length
												lowercase__    :				Dict=	mask_feature_min_masks

												# parameters for pretraining with codevector quantized representations
												lowercase__    :				Any=	num_codevectors_per_group
												lowercase__    :				str=	num_codevector_groups
												lowercase__    :				Optional[Any]=	contrastive_logits_temperature
												lowercase__    :				int=	feat_quantizer_dropout
												lowercase__    :				Union[str, Any]=	num_negatives
												lowercase__    :				int=	codevector_dim
												lowercase__    :				Union[str, Any]=	proj_codevector_dim
												lowercase__    :				Union[str, Any]=	diversity_loss_weight

												# ctc loss
												lowercase__    :				str=	ctc_loss_reduction
												lowercase__    :				str=	ctc_zero_infinity

												# SequenceClassification-specific parameter. Feel free to ignore for other classes.
												lowercase__    :				Tuple=	classifier_proj_size

												# XVector-specific parameters. Feel free to ignore for other classes.
												lowercase__    :				Tuple=	list(snake_case__      )
												lowercase__    :				Optional[int]=	list(snake_case__      )
												lowercase__    :				Dict=	list(snake_case__      )
												lowercase__    :				int=	xvector_output_dim




						@property
						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												return functools.reduce(operator.mul	,  self.conv_stride	,  1      )














"""simple docstring"""







from pathlib import Path

import fire
from tqdm import tqdm





def 						lowercase__(A="ro" ,		A="en" ,		A="wmt16" ,		A=None						)    ->None:




						"""simple docstring"""



						try:
												import datasets
						except (ModuleNotFoundError, ImportError):
												raise ImportError("run pip install datasets"						)
						lowercase__    :				int=	f'''{src_lang}-{tgt_lang}'''
						print(f'''Converting {dataset}-{pair}'''						)
						lowercase__    :				List[Any]=	datasets.load_dataset(A ,		A						)
						if save_dir is None:
												lowercase__    :				Union[str, Any]=	f'''{dataset}-{pair}'''
						lowercase__    :				str=	Path(A						)
						save_dir.mkdir(exist_ok=A						)

						for split in ds.keys():
												print(f'''Splitting {split} with {ds[split].num_rows} records'''						)

												# to save to val.source, val.target like summary datasets
												lowercase__    :				Any=	"val" if split == "validation" else split
												lowercase__    :				List[Any]=	save_dir.joinpath(f'''{fn}.source'''						)
												lowercase__    :				Optional[Any]=	save_dir.joinpath(f'''{fn}.target'''						)
												lowercase__    :				Optional[int]=	src_path.open("w+"						)
												lowercase__    :				Any=	tgt_path.open("w+"						)

												# reader is the bottleneck so writing one record at a time doesn't slow things down
												for x in tqdm(ds[split]						):
																		lowercase__    :				int=	x["translation"]
																		src_fp.write(ex[src_lang] + "\n"						)
																		tgt_fp.write(ex[tgt_lang] + "\n"						)

						print(f'''Saved {dataset} dataset to {save_dir}'''						)


if __name__ == "__main__":
			fire.Fire(download_wmt_dataset)






