

"""simple docstring"""




import functools
import operator

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


A_							:			List[Any]											= logging.get_logger(__name__)

A_							:			Dict											= {
    "microsoft/unispeech-large-1500h-cv": (
        "https://huggingface.co/microsoft/unispeech-large-1500h-cv/resolve/main/config.json"
    ),
    # See all UniSpeech models at https://huggingface.co/models?filter=unispeech
}





class    lowerCamelCase						(A__			):
			lowerCamelCase__   :		int							=							'unispeech'

			def __init__(					self				:						Union[str, Any]    ,				__UpperCAmelCase				:						List[Any]=3_2    ,				__UpperCAmelCase				:						Union[str, Any]=7_6_8    ,				__UpperCAmelCase				:						Tuple=1_2    ,				__UpperCAmelCase				:						Dict=1_2    ,				__UpperCAmelCase				:						Optional[Any]=3_0_7_2    ,				__UpperCAmelCase				:						Optional[Any]="gelu"    ,				__UpperCAmelCase				:						Union[str, Any]=0.1    ,				__UpperCAmelCase				:						List[Any]=0.1    ,				__UpperCAmelCase				:						List[str]=0.1    ,				__UpperCAmelCase				:						List[str]=0.0    ,				__UpperCAmelCase				:						Tuple=0.0    ,				__UpperCAmelCase				:						List[Any]=0.1    ,				__UpperCAmelCase				:						Union[str, Any]=0.1    ,				__UpperCAmelCase				:						Union[str, Any]=0.02    ,				__UpperCAmelCase				:						Union[str, Any]=1e-5    ,				__UpperCAmelCase				:						List[Any]="group"    ,				__UpperCAmelCase				:						Union[str, Any]="gelu"    ,				__UpperCAmelCase				:						Any=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2)    ,				__UpperCAmelCase				:						List[str]=(5, 2, 2, 2, 2, 2, 2)    ,				__UpperCAmelCase				:						int=(1_0, 3, 3, 3, 3, 2, 2)    ,				__UpperCAmelCase				:						str=False    ,				__UpperCAmelCase				:						Any=1_2_8    ,				__UpperCAmelCase				:						str=1_6    ,				__UpperCAmelCase				:						Optional[Any]=False    ,				__UpperCAmelCase				:						Optional[int]=True    ,				__UpperCAmelCase				:						Union[str, Any]=0.05    ,				__UpperCAmelCase				:						str=1_0    ,				__UpperCAmelCase				:						Optional[Any]=2    ,				__UpperCAmelCase				:						List[Any]=0.0    ,				__UpperCAmelCase				:						Tuple=1_0    ,				__UpperCAmelCase				:						Tuple=0    ,				__UpperCAmelCase				:						Tuple=3_2_0    ,				__UpperCAmelCase				:						Any=2    ,				__UpperCAmelCase				:						Optional[int]=0.1    ,				__UpperCAmelCase				:						Any=1_0_0    ,				__UpperCAmelCase				:						str=2_5_6    ,				__UpperCAmelCase				:						Dict=2_5_6    ,				__UpperCAmelCase				:						Dict=0.1    ,				__UpperCAmelCase				:						List[str]="mean"    ,				__UpperCAmelCase				:						Optional[Any]=False    ,				__UpperCAmelCase				:						List[Any]=False    ,				__UpperCAmelCase				:						str=2_5_6    ,				__UpperCAmelCase				:						Dict=8_0    ,				__UpperCAmelCase				:						List[Any]=0    ,				__UpperCAmelCase				:						int=1    ,				__UpperCAmelCase				:						Optional[int]=2    ,				__UpperCAmelCase				:						Any=0.5    ,				**__UpperCAmelCase				:						List[str]    ,				)   ->    Tuple:
						super().__init__(**__UpperCAmelCase    ,				pad_token_id=__UpperCAmelCase    ,				bos_token_id=__UpperCAmelCase    ,				eos_token_id=__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      hidden_size
						SCREAMING_SNAKE_CASE__								=      feat_extract_norm
						SCREAMING_SNAKE_CASE__								=      feat_extract_activation
						SCREAMING_SNAKE_CASE__								=      list(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      list(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      list(__UpperCAmelCase       )
						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__								=      num_ctc_classes
						SCREAMING_SNAKE_CASE__								=      vocab_size
						SCREAMING_SNAKE_CASE__								=      do_stable_layer_norm
						SCREAMING_SNAKE_CASE__								=      use_weighted_layer_sum
						SCREAMING_SNAKE_CASE__								=      classifier_proj_size

						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

						# pretraining loss
						SCREAMING_SNAKE_CASE__								=      replace_prob





			@property
			def 					SCREAMING_SNAKE_CASE				(					self				:						int       )   ->    Optional[Any]:
						return functools.reduce(operator.mul    ,				self.conv_stride    ,				1       )





"""simple docstring"""




import functools
import operator

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


A_							:			List[Any]											= logging.get_logger(__name__)

A_							:			Dict											= {
    "microsoft/unispeech-large-1500h-cv": (
        "https://huggingface.co/microsoft/unispeech-large-1500h-cv/resolve/main/config.json"
    ),
    # See all UniSpeech models at https://huggingface.co/models?filter=unispeech
}





class    lowerCamelCase						(A__			):
			lowerCamelCase__   :		int							=							'unispeech'

			def __init__(					self				:						Union[str, Any]    ,				__UpperCAmelCase				:						List[Any]=3_2    ,				__UpperCAmelCase				:						Union[str, Any]=7_6_8    ,				__UpperCAmelCase				:						Tuple=1_2    ,				__UpperCAmelCase				:						Dict=1_2    ,				__UpperCAmelCase				:						Optional[Any]=3_0_7_2    ,				__UpperCAmelCase				:						Optional[Any]="gelu"    ,				__UpperCAmelCase				:						Union[str, Any]=0.1    ,				__UpperCAmelCase				:						List[Any]=0.1    ,				__UpperCAmelCase				:						List[str]=0.1    ,				__UpperCAmelCase				:						List[str]=0.0    ,				__UpperCAmelCase				:						Tuple=0.0    ,				__UpperCAmelCase				:						List[Any]=0.1    ,				__UpperCAmelCase				:						Union[str, Any]=0.1    ,				__UpperCAmelCase				:						Union[str, Any]=0.02    ,				__UpperCAmelCase				:						Union[str, Any]=1e-5    ,				__UpperCAmelCase				:						List[Any]="group"    ,				__UpperCAmelCase				:						Union[str, Any]="gelu"    ,				__UpperCAmelCase				:						Any=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2)    ,				__UpperCAmelCase				:						List[str]=(5, 2, 2, 2, 2, 2, 2)    ,				__UpperCAmelCase				:						int=(1_0, 3, 3, 3, 3, 2, 2)    ,				__UpperCAmelCase				:						str=False    ,				__UpperCAmelCase				:						Any=1_2_8    ,				__UpperCAmelCase				:						str=1_6    ,				__UpperCAmelCase				:						Optional[Any]=False    ,				__UpperCAmelCase				:						Optional[int]=True    ,				__UpperCAmelCase				:						Union[str, Any]=0.05    ,				__UpperCAmelCase				:						str=1_0    ,				__UpperCAmelCase				:						Optional[Any]=2    ,				__UpperCAmelCase				:						List[Any]=0.0    ,				__UpperCAmelCase				:						Tuple=1_0    ,				__UpperCAmelCase				:						Tuple=0    ,				__UpperCAmelCase				:						Tuple=3_2_0    ,				__UpperCAmelCase				:						Any=2    ,				__UpperCAmelCase				:						Optional[int]=0.1    ,				__UpperCAmelCase				:						Any=1_0_0    ,				__UpperCAmelCase				:						str=2_5_6    ,				__UpperCAmelCase				:						Dict=2_5_6    ,				__UpperCAmelCase				:						Dict=0.1    ,				__UpperCAmelCase				:						List[str]="mean"    ,				__UpperCAmelCase				:						Optional[Any]=False    ,				__UpperCAmelCase				:						List[Any]=False    ,				__UpperCAmelCase				:						str=2_5_6    ,				__UpperCAmelCase				:						Dict=8_0    ,				__UpperCAmelCase				:						List[Any]=0    ,				__UpperCAmelCase				:						int=1    ,				__UpperCAmelCase				:						Optional[int]=2    ,				__UpperCAmelCase				:						Any=0.5    ,				**__UpperCAmelCase				:						List[str]    ,				)   ->    Tuple:
						super().__init__(**__UpperCAmelCase    ,				pad_token_id=__UpperCAmelCase    ,				bos_token_id=__UpperCAmelCase    ,				eos_token_id=__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      hidden_size
						SCREAMING_SNAKE_CASE__								=      feat_extract_norm
						SCREAMING_SNAKE_CASE__								=      feat_extract_activation
						SCREAMING_SNAKE_CASE__								=      list(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      list(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      list(__UpperCAmelCase       )
						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__								=      num_ctc_classes
						SCREAMING_SNAKE_CASE__								=      vocab_size
						SCREAMING_SNAKE_CASE__								=      do_stable_layer_norm
						SCREAMING_SNAKE_CASE__								=      use_weighted_layer_sum
						SCREAMING_SNAKE_CASE__								=      classifier_proj_size

						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

						# pretraining loss
						SCREAMING_SNAKE_CASE__								=      replace_prob





			@property
			def 					SCREAMING_SNAKE_CASE				(					self				:						int       )   ->    Optional[Any]:
						return functools.reduce(operator.mul    ,				self.conv_stride    ,				1       )


