





'''simple docstring'''



import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
			import torch

if is_vision_available():
			from PIL import Image

			from transformers import EfficientNetImageProcessor

class     A_ (  unittest.TestCase  ):

			'''simple docstring'''





			def __init__(					self			,     A_			,     A_=13			,     A_=3			,     A_=18			,     A_=30			,     A_=4_00			,     A_=True			,     A_=None			,     A_=True			,     A_=[0.5, 0.5, 0.5]			,     A_=[0.5, 0.5, 0.5]			,     ):
								_UpperCamelCase											=   size if size is not None else {"height": 18, "width": 18}
								_UpperCamelCase											=   parent
								_UpperCamelCase											=   batch_size
								_UpperCamelCase											=   num_channels
								_UpperCamelCase											=   image_size
								_UpperCamelCase											=   min_resolution
								_UpperCamelCase											=   max_resolution
								_UpperCamelCase											=   do_resize
								_UpperCamelCase											=   size
								_UpperCamelCase											=   do_normalize
								_UpperCamelCase											=   image_mean
								_UpperCamelCase											=   image_std




			def       a	(					self   ):
								return {
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								    "do_normalize": self.do_normalize,
								    "do_resize": self.do_resize,
								    "size": self.size,
								}




@require_torch
@require_vision
class     A_ (  lowerCAmelCase_    ,			unittest.TestCase  ):

			'''simple docstring'''





			_lowerCAmelCase						=    EfficientNetImageProcessor if is_vision_available() else None




			def       a	(					self   ):
								_UpperCamelCase											=   EfficientNetImageProcessorTester(self   )




			@property
			def       a	(					self   ):
								return self.image_processor_tester.prepare_image_processor_dict()




			def       a	(					self   ):
								_UpperCamelCase											=   self.image_processing_class(**self.image_processor_dict   )
								self.assertTrue(hasattr(A_			,     "image_mean"   )   )
								self.assertTrue(hasattr(A_			,     "image_std"   )   )
								self.assertTrue(hasattr(A_			,     "do_normalize"   )   )
								self.assertTrue(hasattr(A_			,     "do_resize"   )   )
								self.assertTrue(hasattr(A_			,     "size"   )   )




			def       a	(					self   ):
								_UpperCamelCase											=   self.image_processing_class.from_dict(self.image_processor_dict   )
								self.assertEqual(image_processor.size			,     {"height": 18, "width": 18}   )

								_UpperCamelCase											=   self.image_processing_class.from_dict(self.image_processor_dict			,     size=42   )
								self.assertEqual(image_processor.size			,     {"height": 42, "width": 42}   )




			def       a	(					self   ):
								# Initialize image_processing
								_UpperCamelCase											=   self.image_processing_class(**self.image_processor_dict   )
								# create random PIL images
								_UpperCamelCase											=   prepare_image_inputs(self.image_processor_tester			,     equal_resolution=A_   )
								for image in image_inputs:
													self.assertIsInstance(A_			,     Image.Image   )

								# Test not batched input
								_UpperCamelCase											=   image_processing(image_inputs[0]			,     return_tensors="pt"   ).pixel_values
								self.assertEqual(
								    encoded_images.shape			,     (
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size["height"],
								        self.image_processor_tester.size["width"],
								    )			,     )

								# Test batched
								_UpperCamelCase											=   image_processing(A_			,     return_tensors="pt"   ).pixel_values
								self.assertEqual(
								    encoded_images.shape			,     (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size["height"],
								        self.image_processor_tester.size["width"],
								    )			,     )




			def       a	(					self   ):
								# Initialize image_processing
								_UpperCamelCase											=   self.image_processing_class(**self.image_processor_dict   )
								# create random numpy tensors
								_UpperCamelCase											=   prepare_image_inputs(self.image_processor_tester			,     equal_resolution=A_			,     numpify=A_   )
								for image in image_inputs:
													self.assertIsInstance(A_			,     np.ndarray   )

								# Test not batched input
								_UpperCamelCase											=   image_processing(image_inputs[0]			,     return_tensors="pt"   ).pixel_values
								self.assertEqual(
								    encoded_images.shape			,     (
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size["height"],
								        self.image_processor_tester.size["width"],
								    )			,     )

								# Test batched
								_UpperCamelCase											=   image_processing(A_			,     return_tensors="pt"   ).pixel_values
								self.assertEqual(
								    encoded_images.shape			,     (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size["height"],
								        self.image_processor_tester.size["width"],
								    )			,     )




			def       a	(					self   ):
								# Initialize image_processing
								_UpperCamelCase											=   self.image_processing_class(**self.image_processor_dict   )
								# create random PyTorch tensors
								_UpperCamelCase											=   prepare_image_inputs(self.image_processor_tester			,     equal_resolution=A_			,     torchify=A_   )
								for image in image_inputs:
													self.assertIsInstance(A_			,     torch.Tensor   )

								# Test not batched input
								_UpperCamelCase											=   image_processing(image_inputs[0]			,     return_tensors="pt"   ).pixel_values
								self.assertEqual(
								    encoded_images.shape			,     (
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size["height"],
								        self.image_processor_tester.size["width"],
								    )			,     )

								# Test batched
								_UpperCamelCase											=   image_processing(A_			,     return_tensors="pt"   ).pixel_values
								self.assertEqual(
								    encoded_images.shape			,     (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size["height"],
								        self.image_processor_tester.size["width"],
								    )			,     )










'''simple docstring'''



import math

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

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer

class     A_ (  nn.Linear  ):

			'''simple docstring'''





			def __init__(					self			,     A_			,     A_			,     A_ = True			,     A_ = "constant"			,     A_ = 0.0			,     A_ = "topK"			,     ):
								super(A_			,     self   ).__init__(in_features=A_			,     out_features=A_			,     bias=A_   )
								assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
								_UpperCamelCase											=   pruning_method

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




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



