
import math

import torch
from torch import nn
from torch.nn import init

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer




class      SCREAMING_SNAKE_CASE__  (			nn.Linear      ):





	'''simple docstring'''



	def __init__(						self			:					Tuple    ,						lowercase			:					int    ,						lowercase			:					int    ,						lowercase			:					bool = True    ,						lowercase			:					str = "constant"    ,						lowercase			:					float = 0.0    ,						lowercase			:					str = "topK"    ,						):


				'''simple docstring'''


				super(lowercase    ,						self			).__init__(in_features=lowercase    ,						out_features=lowercase    ,						bias=lowercase			)
				assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
				_snake_case						=    pruning_method

				if self.pruning_method in ["topK", "threshold", "sigmoied_threshold", "l0"]:
							_snake_case						=    mask_scale
							_snake_case						=    mask_init
							_snake_case						=    nn.Parameter(torch.empty(self.weight.size()			)			)
							self.init_mask()



	def 				A							(						self			:					Any			):


				'''simple docstring'''


				if self.mask_init == "constant":
							init.constant_(self.mask_scores    ,						val=self.mask_scale			)
				elif self.mask_init == "uniform":
							init.uniform_(self.mask_scores    ,						a=-self.mask_scale    ,						b=self.mask_scale			)
				elif self.mask_init == "kaiming":
							init.kaiming_uniform_(self.mask_scores    ,						a=math.sqrt(5			)			)





	def 				A							(						self			:					List[Any]    ,						lowercase			:					torch.tensor    ,						lowercase			:					float			):


				'''simple docstring'''


				if self.pruning_method == "topK":
							_snake_case						=    TopKBinarizer.apply(self.mask_scores    ,						lowercase			)
				elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
							_snake_case						=    'sigmoied' in self.pruning_method
							_snake_case						=    ThresholdBinarizer.apply(self.mask_scores    ,						lowercase    ,						lowercase			)
				elif self.pruning_method == "magnitude":
							_snake_case						=    MagnitudeBinarizer.apply(self.weight    ,						lowercase			)
				elif self.pruning_method == "l0":
							_snake_case			,      _snake_case			,      _snake_case						=    -0.1, 1.1, 2 / 3
							if self.training:
										_snake_case						=    torch.zeros_like(self.mask_scores			).uniform_().clamp(0.0001    ,						0.9999			)
										_snake_case						=    torch.sigmoid((u.log() - (1 - u).log() + self.mask_scores) / b			)
							else:
										_snake_case						=    torch.sigmoid(self.mask_scores			)
							_snake_case						=    s * (r - l) + l
							_snake_case						=    s_bar.clamp(min=0.0    ,						max=1.0			)
				# Mask weights with computed mask
				_snake_case						=    mask * self.weight
				# Compute output (linear layer) with masked weights
				return nn.functional.linear(lowercase    ,						lowercase    ,						self.bias			)

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding




class      SCREAMING_SNAKE_CASE__  (			UpperCAmelCase      ):





	'''simple docstring'''



	_UpperCAmelCase :	int					     =      ["image_processor", "tokenizer"]
	_UpperCAmelCase :	Tuple					     =      "EfficientNetImageProcessor"
	_UpperCAmelCase :	Optional[Any]					     =      ("BertTokenizer", "BertTokenizerFast")



	def __init__(						self			:					Tuple    ,						lowercase			:					List[Any]    ,						lowercase			:					Optional[int]			):


				'''simple docstring'''


				super().__init__(lowercase    ,						lowercase			)



	def __call__(						self			:					Union[str, Any]    ,						lowercase			:					Dict=None    ,						lowercase			:					Optional[int]=None    ,						lowercase			:					List[Any]="max_length"    ,						lowercase			:					Optional[int]=64    ,						lowercase			:					int=None    ,						**lowercase			:					Tuple			):


				'''simple docstring'''


				if text is None and images is None:
							raise ValueError('You have to specify either text or images. Both cannot be none.'			)

				if text is not None:
							_snake_case						=    self.tokenizer(
							    lowercase    ,						padding=lowercase    ,						max_length=lowercase    ,						return_tensors=lowercase    ,						**lowercase			)

				if images is not None:
							_snake_case						=    self.image_processor(lowercase    ,						return_tensors=lowercase    ,						**lowercase			)

				if text is not None and images is not None:
							_snake_case						=    image_features.pixel_values
							return encoding
				elif text is not None:
							return encoding
				else:
							return BatchEncoding(data=dict(**lowercase			)    ,						tensor_type=lowercase			)



	def 				A							(						self			:					Any    ,						*lowercase			:					List[str]    ,						**lowercase			:					Optional[Any]			):


				'''simple docstring'''


				return self.tokenizer.batch_decode(*lowercase    ,						**lowercase			)



	def 				A							(						self			:					List[Any]    ,						*lowercase			:					Union[str, Any]    ,						**lowercase			:					Tuple			):


				'''simple docstring'''


				return self.tokenizer.decode(*lowercase    ,						**lowercase			)





	@property
	def 				A							(						self			:					List[str]			):


				'''simple docstring'''


				_snake_case						=    self.tokenizer.model_input_names
				_snake_case						=    self.image_processor.model_input_names
				return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)