
import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


lowerCamelCase_  :       Tuple					      =     logging.get_logger(__name__)

class 				_UpperCamelCase (    _A     ):






			'''simple docstring'''

			__UpperCamelCase					:  Optional[Any]    					=       """speech-encoder-decoder"""
			__UpperCamelCase					:  str    					=       True

			def __init__(				self       :				str  ,		**snake_case_       :				str					):
							super().__init__(**snake_case_					)
							if "encoder" not in kwargs or "decoder" not in kwargs:
											raise ValueError(
											    f'''A configuraton of type {self.model_type} cannot be instantiated because not both `encoder` and'''
											    f''' `decoder` sub-configurations are passed, but only {kwargs}'''					)

							UpperCamelCase_:							int		=  kwargs.pop("""encoder"""					)
							UpperCamelCase_:							List[Any]		=  encoder_config.pop("""model_type"""					)
							UpperCamelCase_:							Optional[Any]		=  kwargs.pop("""decoder"""					)
							UpperCamelCase_:							Tuple		=  decoder_config.pop("""model_type"""					)

							UpperCamelCase_:							Tuple		=  AutoConfig.for_model(snake_case_  ,		**snake_case_					)
							UpperCamelCase_:							Any		=  AutoConfig.for_model(snake_case_  ,		**snake_case_					)
							UpperCamelCase_:							Any		=  True

			@classmethod
			def 					lowerCAmelCase__		(				cls       :				Dict  ,		snake_case_       :				PretrainedConfig  ,		snake_case_       :				PretrainedConfig  ,		**snake_case_       :				Any					):
							logger.info("""Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config"""					)
							UpperCamelCase_:							Any		=  True
							UpperCamelCase_:							List[str]		=  True

							return cls(encoder=encoder_config.to_dict()  ,		decoder=decoder_config.to_dict()  ,		**snake_case_					)




			def 					lowerCAmelCase__		(				self       :				Tuple					):
							UpperCamelCase_:							Dict		=  copy.deepcopy(self.__dict__					)
							UpperCamelCase_:							Optional[Any]		=  self.encoder.to_dict()
							UpperCamelCase_:							List[str]		=  self.decoder.to_dict()
							UpperCamelCase_:							List[str]		=  self.__class__.model_type
							return output













"""simple docstring"""




import torch
from torch import autograd




class  _a						(      autograd.Function				):






				@staticmethod
				def 				snake_case	( lowerCAmelCase__		:    str, lowerCAmelCase__		:    torch.tensor, lowerCAmelCase__		:    float, lowerCAmelCase__		:    bool							)	->      List[Any]:




										'''simple docstring'''



										_UpperCamelCase			:					Optional[Any]     =    inputs.numel()
										_UpperCamelCase			:					int     =    int(0.005 * nb_elems							) + 1
										if sigmoid:
																_UpperCamelCase			:					Dict     =    (torch.sigmoid(lowerCAmelCase__							) > threshold).type(inputs.type()							)
										else:
																_UpperCamelCase			:					Optional[int]     =    (inputs > threshold).type(inputs.type()							)
										if mask.sum() < nb_min:
																# We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
																_UpperCamelCase			:					int     =    inputs.flatten().kthvalue(max(nb_elems - nb_min, 1							)							).values
																_UpperCamelCase			:					List[str]     =    (inputs > k_threshold).type(inputs.type()							)
										return mask






				@staticmethod
				def 				snake_case	( lowerCAmelCase__		:    str, lowerCAmelCase__		:    Optional[int]							)	->      Union[str, Any]:




										'''simple docstring'''



										return gradOutput, None, None




class  _a						(      autograd.Function				):






				@staticmethod
				def 				snake_case	( lowerCAmelCase__		:    Any, lowerCAmelCase__		:    torch.tensor, lowerCAmelCase__		:    float							)	->      Dict:




										'''simple docstring'''



										_UpperCamelCase			:					str     =    inputs.clone()
										_UpperCamelCase   ,							_UpperCamelCase			:					Union[str, Any]     =    inputs.flatten().sort(descending=lowerCAmelCase__							)
										_UpperCamelCase			:					Optional[Any]     =    int(threshold * inputs.numel()							)

										# flat_out and mask access the same memory.
										_UpperCamelCase			:					Tuple     =    mask.flatten()
										_UpperCamelCase			:					int     =    0
										_UpperCamelCase			:					Tuple     =    1
										return mask






				@staticmethod
				def 				snake_case	( lowerCAmelCase__		:    Any, lowerCAmelCase__		:    Union[str, Any]							)	->      int:




										'''simple docstring'''



										return gradOutput, None




class  _a						(      _lowerCAmelCase				):






				@staticmethod
				def 				snake_case	( lowerCAmelCase__		:    torch.tensor, lowerCAmelCase__		:    float							)	->      Dict:




										'''simple docstring'''



										_UpperCamelCase			:					List[Any]     =    inputs.clone()
										_UpperCamelCase   ,							_UpperCamelCase			:					Optional[int]     =    inputs.abs().flatten().sort(descending=lowerCAmelCase__							)
										_UpperCamelCase			:					List[Any]     =    int(threshold * inputs.numel()							)

										# flat_out and mask access the same memory.
										_UpperCamelCase			:					List[str]     =    mask.flatten()
										_UpperCamelCase			:					List[Any]     =    0
										_UpperCamelCase			:					str     =    1
										return mask



