
import functools
import operator

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


A     :	List[str]    = 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 			__A( a			):
				snake_case_						   =    '''wav2vec2'''




				def __init__(     self		,     _snake_case=32		,     _snake_case=768		,     _snake_case=12		,     _snake_case=12		,     _snake_case=3_072		,     _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="sum"		,     _snake_case=False		,     _snake_case=False		,     _snake_case=256		,     _snake_case=(512, 512, 512, 512, 1_500)		,     _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=False		,     _snake_case=3		,     _snake_case=2		,     _snake_case=3		,     _snake_case=None		,     _snake_case=None		,     **_snake_case		,     )    ->      Optional[Any]:


								'''simple docstring'''




								super().__init__(**_snake_case		,     pad_token_id=_snake_case		,     bos_token_id=_snake_case		,     eos_token_id=_snake_case				)
								__a										=    hidden_size
								__a										=    feat_extract_norm
								__a										=    feat_extract_activation
								__a										=    list(_snake_case				)
								__a										=    list(_snake_case				)
								__a										=    list(_snake_case				)
								__a										=    conv_bias
								__a										=    num_conv_pos_embeddings
								__a										=    num_conv_pos_embedding_groups
								__a										=    len(self.conv_dim				)
								__a										=    num_hidden_layers
								__a										=    intermediate_size
								__a										=    hidden_act
								__a										=    num_attention_heads
								__a										=    hidden_dropout
								__a										=    attention_dropout
								__a										=    activation_dropout
								__a										=    feat_proj_dropout
								__a										=    final_dropout
								__a										=    layerdrop
								__a										=    layer_norm_eps
								__a										=    initializer_range
								__a										=    vocab_size
								__a										=    do_stable_layer_norm
								__a										=    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
								__a										=    apply_spec_augment
								__a										=    mask_time_prob
								__a										=    mask_time_length
								__a										=    mask_time_min_masks
								__a										=    mask_feature_prob
								__a										=    mask_feature_length
								__a										=    mask_feature_min_masks

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

								# ctc loss
								__a										=    ctc_loss_reduction
								__a										=    ctc_zero_infinity

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

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

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




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Dict:


								'''simple docstring'''




								return functools.reduce(operator.mul		,     self.conv_stride		,     1				)



'''simple docstring'''
from pathlib import Path

import fire
from tqdm import tqdm


def 			a      (			__a="ro" ,					__a="en" ,					__a="wmt16" ,					__a=None   )  ->	None:



					'''simple docstring'''






					try:
										import datasets
					except (ModuleNotFoundError, ImportError):
										raise ImportError('''run pip install datasets'''   )
					UpperCamelCase__   :int												= f'''{src_lang}-{tgt_lang}'''
					print(f'''Converting {dataset}-{pair}'''   )
					UpperCamelCase__   :Tuple												= datasets.load_dataset(__a ,					__a   )
					if save_dir is None:
										UpperCamelCase__   :Any												= f'''{dataset}-{pair}'''
					UpperCamelCase__   :Dict												= 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
										UpperCamelCase__   :Dict												= '''val''' if split == '''validation''' else split
										UpperCamelCase__   :List[Any]												= save_dir.joinpath(f'''{fn}.source'''   )
										UpperCamelCase__   :int												= save_dir.joinpath(f'''{fn}.target'''   )
										UpperCamelCase__   :Union[str, Any]												= src_path.open('''w+'''   )
										UpperCamelCase__   :Tuple												= 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]   ):
															UpperCamelCase__   :Union[str, Any]												= 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)