





"""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


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import CLIPImageProcessor




class  _a						(      unittest.TestCase				):






				def __init__( self		:    Dict, lowerCAmelCase__		:    Dict, lowerCAmelCase__		:    Any=7, lowerCAmelCase__		:    str=3, lowerCAmelCase__		:    Tuple=1_8, lowerCAmelCase__		:    Optional[int]=3_0, lowerCAmelCase__		:    Dict=4_0_0, lowerCAmelCase__		:    str=True, lowerCAmelCase__		:    Dict=None, lowerCAmelCase__		:    Dict=True, lowerCAmelCase__		:    Tuple=None, lowerCAmelCase__		:    int=True, lowerCAmelCase__		:    Tuple=[0.48_145_466, 0.4_578_275, 0.40_821_073], lowerCAmelCase__		:    Union[str, Any]=[0.26_862_954, 0.26_130_258, 0.27_577_711], lowerCAmelCase__		:    List[str]=True, )	->      List[str]:




										'''simple docstring'''



										_UpperCamelCase			:					int     =    size if size is not None else {'''shortest_edge''': 2_0}
										_UpperCamelCase			:					Optional[int]     =    crop_size if crop_size is not None else {'''height''': 1_8, '''width''': 1_8}
										_UpperCamelCase			:					Tuple     =    parent
										_UpperCamelCase			:					Tuple     =    batch_size
										_UpperCamelCase			:					Any     =    num_channels
										_UpperCamelCase			:					Optional[Any]     =    image_size
										_UpperCamelCase			:					Tuple     =    min_resolution
										_UpperCamelCase			:					str     =    max_resolution
										_UpperCamelCase			:					Optional[int]     =    do_resize
										_UpperCamelCase			:					str     =    size
										_UpperCamelCase			:					Any     =    do_center_crop
										_UpperCamelCase			:					Optional[Any]     =    crop_size
										_UpperCamelCase			:					Optional[int]     =    do_normalize
										_UpperCamelCase			:					Optional[Any]     =    image_mean
										_UpperCamelCase			:					Optional[int]     =    image_std
										_UpperCamelCase			:					Dict     =    do_convert_rgb






				def 				snake_case	( self		:    List[Any]							)	->      Union[str, Any]:




										'''simple docstring'''



										return {
										    "do_resize": self.do_resize,
										    "size": self.size,
										    "do_center_crop": self.do_center_crop,
										    "crop_size": self.crop_size,
										    "do_normalize": self.do_normalize,
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										    "do_convert_rgb": self.do_convert_rgb,
										}






				def 				snake_case	( self		:    str, lowerCAmelCase__		:    Any=False, lowerCAmelCase__		:    Optional[Any]=False, lowerCAmelCase__		:    List[Any]=False							)	->      Tuple:




										'''simple docstring'''



										assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

										if equal_resolution:
																_UpperCamelCase			:					Optional[Any]     =    []
																for i in range(self.batch_size							):
																						image_inputs.append(
																						    np.random.randint(
																						        2_5_5, size=(self.num_channels, self.max_resolution, self.max_resolution), dtype=np.uinta							)							)
										else:
																_UpperCamelCase			:					List[str]     =    []
																for i in range(self.batch_size							):
																						_UpperCamelCase   ,							_UpperCamelCase			:					Optional[Any]     =    np.random.choice(np.arange(self.min_resolution, self.max_resolution							), 2							)
																						image_inputs.append(np.random.randint(2_5_5, size=(self.num_channels, width, height), dtype=np.uinta							)							)

										if not numpify and not torchify:
																# PIL expects the channel dimension as last dimension
																_UpperCamelCase			:					List[Any]     =    [Image.fromarray(np.moveaxis(lowerCAmelCase__, 0, -1							)							) for x in image_inputs]

										if torchify:
																_UpperCamelCase			:					Any     =    [torch.from_numpy(lowerCAmelCase__							) for x in image_inputs]

										return image_inputs




@require_torch
@require_vision
class  _a						(      _lowerCAmelCase	,				unittest.TestCase				):
				UpperCamelCase								=    CLIPImageProcessor if is_vision_available() else None






				def 				snake_case	( self		:    Any							)	->      Optional[int]:




										'''simple docstring'''



										_UpperCamelCase			:					Tuple     =    CLIPImageProcessingTester(self							)






				@property
				def 				snake_case	( self		:    Tuple							)	->      List[Any]:




										'''simple docstring'''



										return self.image_processor_tester.prepare_image_processor_dict()






				def 				snake_case	( self		:    Tuple							)	->      Tuple:




										'''simple docstring'''



										_UpperCamelCase			:					Union[str, Any]     =    self.image_processing_class(**self.image_processor_dict							)
										self.assertTrue(hasattr(lowerCAmelCase__, '''do_resize'''							)							)
										self.assertTrue(hasattr(lowerCAmelCase__, '''size'''							)							)
										self.assertTrue(hasattr(lowerCAmelCase__, '''do_center_crop'''							)							)
										self.assertTrue(hasattr(lowerCAmelCase__, '''center_crop'''							)							)
										self.assertTrue(hasattr(lowerCAmelCase__, '''do_normalize'''							)							)
										self.assertTrue(hasattr(lowerCAmelCase__, '''image_mean'''							)							)
										self.assertTrue(hasattr(lowerCAmelCase__, '''image_std'''							)							)
										self.assertTrue(hasattr(lowerCAmelCase__, '''do_convert_rgb'''							)							)






				def 				snake_case	( self		:    Union[str, Any]							)	->      Optional[int]:




										'''simple docstring'''



										_UpperCamelCase			:					List[Any]     =    self.image_processing_class.from_dict(self.image_processor_dict							)
										self.assertEqual(image_processor.size, {'''shortest_edge''': 2_0}							)
										self.assertEqual(image_processor.crop_size, {'''height''': 1_8, '''width''': 1_8}							)

										_UpperCamelCase			:					List[Any]     =    self.image_processing_class.from_dict(self.image_processor_dict, size=4_2, crop_size=8_4							)
										self.assertEqual(image_processor.size, {'''shortest_edge''': 4_2}							)
										self.assertEqual(image_processor.crop_size, {'''height''': 8_4, '''width''': 8_4}							)






				def 				snake_case	( self		:    str							)	->      Dict:




										'''simple docstring'''



										pass






				def 				snake_case	( self		:    List[Any]							)	->      Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase			:					List[Any]     =    self.image_processing_class(**self.image_processor_dict							)
										# create random PIL images
										_UpperCamelCase			:					Optional[Any]     =    self.image_processor_tester.prepare_inputs(equal_resolution=lowerCAmelCase__							)
										for image in image_inputs:
																self.assertIsInstance(lowerCAmelCase__, Image.Image							)

										# Test not batched input
										_UpperCamelCase			:					Tuple     =    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.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    ), )

										# Test batched
										_UpperCamelCase			:					Dict     =    image_processing(lowerCAmelCase__, 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.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    ), )






				def 				snake_case	( self		:    Dict							)	->      str:




										'''simple docstring'''



										_UpperCamelCase			:					str     =    self.image_processing_class(**self.image_processor_dict							)
										# create random numpy tensors
										_UpperCamelCase			:					Dict     =    self.image_processor_tester.prepare_inputs(equal_resolution=lowerCAmelCase__, numpify=lowerCAmelCase__							)
										for image in image_inputs:
																self.assertIsInstance(lowerCAmelCase__, np.ndarray							)

										# Test not batched input
										_UpperCamelCase			:					Optional[int]     =    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.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    ), )

										# Test batched
										_UpperCamelCase			:					Tuple     =    image_processing(lowerCAmelCase__, 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.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    ), )






				def 				snake_case	( self		:    Any							)	->      Tuple:




										'''simple docstring'''



										_UpperCamelCase			:					List[str]     =    self.image_processing_class(**self.image_processor_dict							)
										# create random PyTorch tensors
										_UpperCamelCase			:					Optional[Any]     =    self.image_processor_tester.prepare_inputs(equal_resolution=lowerCAmelCase__, torchify=lowerCAmelCase__							)
										for image in image_inputs:
																self.assertIsInstance(lowerCAmelCase__, torch.Tensor							)

										# Test not batched input
										_UpperCamelCase			:					str     =    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.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    ), )

										# Test batched
										_UpperCamelCase			:					List[str]     =    image_processing(lowerCAmelCase__, 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.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    ), )




@require_torch
@require_vision
class  _a						(      _lowerCAmelCase	,				unittest.TestCase				):
				UpperCamelCase								=    CLIPImageProcessor if is_vision_available() else None






				def 				snake_case	( self		:    Union[str, Any]							)	->      List[str]:




										'''simple docstring'''



										_UpperCamelCase			:					Tuple     =    CLIPImageProcessingTester(self, num_channels=4							)
										_UpperCamelCase			:					Optional[Any]     =    3






				@property
				def 				snake_case	( self		:    int							)	->      List[str]:




										'''simple docstring'''



										return self.image_processor_tester.prepare_image_processor_dict()






				def 				snake_case	( self		:    Tuple							)	->      List[str]:




										'''simple docstring'''



										_UpperCamelCase			:					List[Any]     =    self.image_processing_class(**self.image_processor_dict							)
										self.assertTrue(hasattr(lowerCAmelCase__, '''do_resize'''							)							)
										self.assertTrue(hasattr(lowerCAmelCase__, '''size'''							)							)
										self.assertTrue(hasattr(lowerCAmelCase__, '''do_center_crop'''							)							)
										self.assertTrue(hasattr(lowerCAmelCase__, '''center_crop'''							)							)
										self.assertTrue(hasattr(lowerCAmelCase__, '''do_normalize'''							)							)
										self.assertTrue(hasattr(lowerCAmelCase__, '''image_mean'''							)							)
										self.assertTrue(hasattr(lowerCAmelCase__, '''image_std'''							)							)
										self.assertTrue(hasattr(lowerCAmelCase__, '''do_convert_rgb'''							)							)






				def 				snake_case	( self		:    Tuple							)	->      Any:




										'''simple docstring'''



										pass






				def 				snake_case	( self		:    Optional[Any]							)	->      Optional[int]:




										'''simple docstring'''



										_UpperCamelCase			:					int     =    self.image_processing_class(**self.image_processor_dict							)
										# create random PIL images
										_UpperCamelCase			:					Optional[Any]     =    self.image_processor_tester.prepare_inputs(equal_resolution=lowerCAmelCase__							)
										for image in image_inputs:
																self.assertIsInstance(lowerCAmelCase__, Image.Image							)

										# Test not batched input
										_UpperCamelCase			:					int     =    image_processing(image_inputs[0], return_tensors='''pt'''							).pixel_values
										self.assertEqual(
										    encoded_images.shape, (
										        1,
										        self.expected_encoded_image_num_channels,
										        self.image_processor_tester.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    ), )

										# Test batched
										_UpperCamelCase			:					Optional[int]     =    image_processing(lowerCAmelCase__, return_tensors='''pt'''							).pixel_values
										self.assertEqual(
										    encoded_images.shape, (
										        self.image_processor_tester.batch_size,
										        self.expected_encoded_image_num_channels,
										        self.image_processor_tester.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    ), )




import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer
def 					snake_case						(__lowercase						)       ->       Optional[Any]:

						'''simple docstring'''



						_snake_case						:     List[str]				=    args.serialization_dir
						_snake_case						:     Union[str, Any]				=    args.pruning_method
						_snake_case						:     Any				=    args.threshold

						_snake_case						:     Union[str, Any]				=    torch.load(os.path.join(__lowercase					,			"pytorch_model.bin"						)					,			map_location="cpu"						)

						_snake_case						:     Dict				=    0  # Number of remaining (not pruned) params in the encoder
						_snake_case						:     int				=    0  # Number of params in the encoder

						print("name".ljust(60					,			" "						)					,			"Remaining Weights %"					,			"Remaining Weight"						)
						for name, param in st.items():
												if "encoder" not in name:
																		continue

												if "mask_scores" in name:
																		if pruning_method == "topK":
																								_snake_case						:     Union[str, Any]				=    TopKBinarizer.apply(__lowercase					,			__lowercase						).sum().item()
																		elif pruning_method == "sigmoied_threshold":
																								_snake_case						:     str				=    ThresholdBinarizer.apply(__lowercase					,			__lowercase					,			__lowercase						).sum().item()
																		elif pruning_method == "l0":
																								_snake_case				,_snake_case						:     List[str]				=    -0.1, 1.1
																								_snake_case						:     Any				=    torch.sigmoid(__lowercase						)
																								_snake_case						:     str				=    s * (r - l) + l
																								_snake_case						:     Dict				=    s_bar.clamp(min=0.0					,			max=1.0						)
																								_snake_case						:     Tuple				=    (mask > 0.0).sum().item()
																		else:
																								raise ValueError("Unknown pruning method"						)
																		remaining_count += mask_ones
																		print(name.ljust(60					,			" "						)					,			str(round(100 * mask_ones / param.numel()					,			3						)						).ljust(20					,			" "						)					,			str(__lowercase						)						)
												else:
																		encoder_count += param.numel()
																		if "bias" in name or "LayerNorm" in name:
																								remaining_count += param.numel()

						print(""						)
						print("Remaining Weights (global) %: "					,			100 * remaining_count / encoder_count						)


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE						:    str     = argparse.ArgumentParser()

				parser.add_argument(
				    '--pruning_method',
				    choices=['l0', 'topK', 'sigmoied_threshold'],
				    type=str,
				    required=True,
				    help=(
				        'Pruning Method (l0 = L0 regularization, topK = Movement pruning, sigmoied_threshold = Soft movement'
				        ' pruning)'
				    ),
				)
				parser.add_argument(
				    '--threshold',
				    type=float,
				    required=False,
				    help=(
				        'For `topK`, it is the level of remaining weights (in %) in the fine-pruned model.'
				        'For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared.'
				        'Not needed for `l0`'
				    ),
				)
				parser.add_argument(
				    '--serialization_dir',
				    type=str,
				    required=True,
				    help='Folder containing the model that was previously fine-pruned',
				)

				__SCREAMING_SNAKE_CASE						:    Any     = parser.parse_args()

				main(args)