

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


a_      =     logging.get_logger(__name__)

a_      =     {
    '''s-JoL/Open-Llama-V1''': '''https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json''',
}






class _UpperCamelCase				(				_lowercase ):
			'''simple docstring'''







			lowerCamelCase__							     ='''open-llama'''
			def __init__(      self				:			Union[str, Any]   ,   a				:			Dict=10_0000   ,   a				:			Optional[int]=4096   ,   a				:			str=1_1008   ,   a				:			str=32   ,   a				:			Union[str, Any]=32   ,   a				:			str="silu"   ,   a				:			Tuple=2048   ,   a				:			Optional[Any]=0.02   ,   a				:			List[Any]=1e-6   ,   a				:			Dict=True   ,   a				:			Optional[Any]=0   ,   a				:			str=1   ,   a				:			Any=2   ,   a				:			Optional[int]=False   ,   a				:			List[Any]=True   ,   a				:			Any=0.1   ,   a				:			int=0.1   ,   a				:			Optional[int]=True   ,   a				:			Optional[int]=True   ,   a				:			Optional[Any]=None   ,   **a				:			str   ,   )				->							Union[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[str]					  =							vocab_size
				SCREAMING_SNAKE_CASE :   Dict					  =							max_position_embeddings
				SCREAMING_SNAKE_CASE :   Dict					  =							hidden_size
				SCREAMING_SNAKE_CASE :   str					  =							intermediate_size
				SCREAMING_SNAKE_CASE :   Tuple					  =							num_hidden_layers
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							num_attention_heads
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							hidden_act
				SCREAMING_SNAKE_CASE :   List[Any]					  =							initializer_range
				SCREAMING_SNAKE_CASE :   Tuple					  =							rms_norm_eps
				SCREAMING_SNAKE_CASE :   str					  =							use_cache
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							kwargs.pop(
				    "use_memorry_efficient_attention"   ,   __A						)
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							hidden_dropout_prob
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							attention_dropout_prob
				SCREAMING_SNAKE_CASE :   str					  =							use_stable_embedding
				SCREAMING_SNAKE_CASE :   Dict					  =							shared_input_output_embedding
				SCREAMING_SNAKE_CASE :   List[str]					  =							rope_scaling
				self._rope_scaling_validation()

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






			def 				__UpperCamelCase			(      self				:			List[Any]						)				->							List[Any]:

				"""simple docstring"""

				if self.rope_scaling is None:
					return

				if not isinstance(self.rope_scaling   ,   __A						) 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}"						)
				SCREAMING_SNAKE_CASE :   Dict					  =							self.rope_scaling.get("type"   ,   __A						)
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							self.rope_scaling.get("factor"   ,   __A						)
				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(__A   ,   __A						) or rope_scaling_factor <= 1.0:
					raise ValueError(F"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}"						)


import functools
import operator

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


UpperCAmelCase_							:					List[str]		 =   logging.get_logger(__name__)

UpperCAmelCase_							:					str		 =   {
    '''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_      (	_lowercase	):
	_lowercase		:   Dict     					=				'''unispeech'''




	def __init__(     self						:     str					,      __A						:     Tuple=32					,      __A						:     List[str]=768					,      __A						:     Dict=12					,      __A						:     Union[str, Any]=12					,      __A						:     Tuple=3072					,      __A						:     Any="gelu"					,      __A						:     int=0.1					,      __A						:     Optional[int]=0.1					,      __A						:     List[Any]=0.1					,      __A						:     Any=0.0					,      __A						:     List[str]=0.0					,      __A						:     int=0.1					,      __A						:     List[Any]=0.1					,      __A						:     List[str]=0.0_2					,      __A						:     List[str]=1e-5					,      __A						:     List[Any]="group"					,      __A						:     int="gelu"					,      __A						:     Any=(512, 512, 512, 512, 512, 512, 512)					,      __A						:     Union[str, Any]=(5, 2, 2, 2, 2, 2, 2)					,      __A						:     Tuple=(10, 3, 3, 3, 3, 2, 2)					,      __A						:     Optional[int]=False					,      __A						:     Any=128					,      __A						:     Union[str, Any]=16					,      __A						:     Optional[Any]=False					,      __A						:     str=True					,      __A						:     Dict=0.0_5					,      __A						:     Optional[Any]=10					,      __A						:     Dict=2					,      __A						:     int=0.0					,      __A						:     List[str]=10					,      __A						:     str=0					,      __A						:     List[str]=320					,      __A						:     List[Any]=2					,      __A						:     Tuple=0.1					,      __A						:     Optional[int]=100					,      __A						:     Any=256					,      __A						:     Dict=256					,      __A						:     Tuple=0.1					,      __A						:     List[str]="mean"					,      __A						:     int=False					,      __A						:     List[str]=False					,      __A						:     List[Any]=256					,      __A						:     str=80					,      __A						:     Tuple=0					,      __A						:     Tuple=1					,      __A						:     int=2					,      __A						:     Dict=0.5					,      **__A						:     List[Any]					,      ):
		super().__init__(**__A					,      pad_token_id=__A					,      bos_token_id=__A					,      eos_token_id=__A							)
		__A :			Dict		    =			hidden_size
		__A :			Optional[Any]		    =			feat_extract_norm
		__A :			List[Any]		    =			feat_extract_activation
		__A :			str		    =			list(__A							)
		__A :			Optional[Any]		    =			list(__A							)
		__A :			Optional[int]		    =			list(__A							)
		__A :			List[Any]		    =			conv_bias
		__A :			Optional[int]		    =			num_conv_pos_embeddings
		__A :			List[Any]		    =			num_conv_pos_embedding_groups
		__A :			int		    =			len(self.conv_dim							)
		__A :			Optional[Any]		    =			num_hidden_layers
		__A :			List[str]		    =			intermediate_size
		__A :			Union[str, Any]		    =			hidden_act
		__A :			Optional[int]		    =			num_attention_heads
		__A :			Tuple		    =			hidden_dropout
		__A :			Optional[Any]		    =			attention_dropout
		__A :			Union[str, Any]		    =			activation_dropout
		__A :			Dict		    =			feat_proj_dropout
		__A :			Optional[int]		    =			final_dropout
		__A :			Dict		    =			layerdrop
		__A :			Optional[int]		    =			layer_norm_eps
		__A :			Optional[Any]		    =			initializer_range
		__A :			Optional[int]		    =			num_ctc_classes
		__A :			Dict		    =			vocab_size
		__A :			List[str]		    =			do_stable_layer_norm
		__A :			Tuple		    =			use_weighted_layer_sum
		__A :			Any		    =			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
		__A :			Tuple		    =			apply_spec_augment
		__A :			Union[str, Any]		    =			mask_time_prob
		__A :			Optional[Any]		    =			mask_time_length
		__A :			List[Any]		    =			mask_time_min_masks
		__A :			List[Any]		    =			mask_feature_prob
		__A :			Any		    =			mask_feature_length
		__A :			List[Any]		    =			mask_feature_min_masks

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

		# ctc loss
		__A :			List[str]		    =			ctc_loss_reduction
		__A :			Any		    =			ctc_zero_infinity

		# pretraining loss
		__A :			Union[str, Any]		    =			replace_prob




	@property
	def        lowerCAmelCase_       (     self						:     int							):
		return functools.reduce(operator.mul					,      self.conv_stride					,      1							)





