




import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision, slow
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 ImageGPTImageProcessor


class 							__UpperCamelCase			( unittest.TestCase  ):





				'''simple docstring'''



				def __init__( self       , UpperCAmelCase_       , UpperCAmelCase_=7       , UpperCAmelCase_=3       , UpperCAmelCase_=18       , UpperCAmelCase_=30       , UpperCAmelCase_=4_00       , UpperCAmelCase_=True       , UpperCAmelCase_=None       , UpperCAmelCase_=True       , ):
											lowerCAmelCase   =		size if size is not None else {'''height''': 18, '''width''': 18}
											lowerCAmelCase   =		parent
											lowerCAmelCase   =		batch_size
											lowerCAmelCase   =		num_channels
											lowerCAmelCase   =		image_size
											lowerCAmelCase   =		min_resolution
											lowerCAmelCase   =		max_resolution
											lowerCAmelCase   =		do_resize
											lowerCAmelCase   =		size
											lowerCAmelCase   =		do_normalize






				def       __snake_case		( self						):
											return {
											    # here we create 2 clusters for the sake of simplicity
											    "clusters": np.asarray(
											        [
											            [0.8866443634033203, 0.6618829369544983, 0.3891746401786804],
											            [-0.6042559146881104, -0.02295008860528469, 0.5423797369003296],
											        ]						),
											    "do_resize": self.do_resize,
											    "size": self.size,
											    "do_normalize": self.do_normalize,
											}




@require_torch
@require_vision
class 							__UpperCamelCase			( __UpperCAmelCase     ,					unittest.TestCase  ):





				'''simple docstring'''



				__a							: Optional[Any]										=ImageGPTImageProcessor if is_vision_available() else None



				def       __snake_case		( self						):
											lowerCAmelCase   =		ImageGPTImageProcessingTester(self						)



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



				def       __snake_case		( self						):
											lowerCAmelCase   =		self.image_processing_class(**self.image_processor_dict						)
											self.assertTrue(hasattr(UpperCAmelCase_       , '''clusters'''						)						)
											self.assertTrue(hasattr(UpperCAmelCase_       , '''do_resize'''						)						)
											self.assertTrue(hasattr(UpperCAmelCase_       , '''size'''						)						)
											self.assertTrue(hasattr(UpperCAmelCase_       , '''do_normalize'''						)						)



				def       __snake_case		( self						):
											lowerCAmelCase   =		self.image_processing_class.from_dict(self.image_processor_dict						)
											self.assertEqual(image_processor.size       , {'''height''': 18, '''width''': 18}						)

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



				def       __snake_case		( self						):
											lowerCAmelCase   =		self.image_processing_class(**self.image_processor_dict						)
											lowerCAmelCase   =		json.loads(image_processor.to_json_string()						)
											for key, value in self.image_processor_dict.items():
																		if key == "clusters":
																									self.assertTrue(np.array_equal(UpperCAmelCase_       , obj[key]						)						)
																		else:
																									self.assertEqual(obj[key]       , UpperCAmelCase_						)



				def       __snake_case		( self						):
											lowerCAmelCase   =		self.image_processing_class(**self.image_processor_dict						)

											with tempfile.TemporaryDirectory() as tmpdirname:
																		lowerCAmelCase   =		os.path.join(UpperCAmelCase_       , '''image_processor.json'''						)
																		image_processor_first.to_json_file(UpperCAmelCase_						)
																		lowerCAmelCase   =		self.image_processing_class.from_json_file(UpperCAmelCase_						).to_dict()

											lowerCAmelCase   =		image_processor_first.to_dict()
											for key, value in image_processor_first.items():
																		if key == "clusters":
																									self.assertTrue(np.array_equal(UpperCAmelCase_       , image_processor_second[key]						)						)
																		else:
																									self.assertEqual(image_processor_first[key]       , UpperCAmelCase_						)



				def       __snake_case		( self						):
											lowerCAmelCase   =		self.image_processing_class(**self.image_processor_dict						)

											with tempfile.TemporaryDirectory() as tmpdirname:
																		image_processor_first.save_pretrained(UpperCAmelCase_						)
																		lowerCAmelCase   =		self.image_processing_class.from_pretrained(UpperCAmelCase_						).to_dict()

											lowerCAmelCase   =		image_processor_first.to_dict()
											for key, value in image_processor_first.items():
																		if key == "clusters":
																									self.assertTrue(np.array_equal(UpperCAmelCase_       , image_processor_second[key]						)						)
																		else:
																									self.assertEqual(image_processor_first[key]       , UpperCAmelCase_						)






				@unittest.skip('''ImageGPT requires clusters at initialization'''						)
				def       __snake_case		( self						):
											pass




def       UpperCAmelCase       (     ):
							lowerCAmelCase   =		load_dataset('''hf-internal-testing/fixtures_image_utils'''						, split='''test'''							)

							lowerCAmelCase   =		Image.open(dataset[4]['''file''']							)
							lowerCAmelCase   =		Image.open(dataset[5]['''file''']							)

							lowerCAmelCase   =		[imagea, imagea]

							return images


@require_vision
@require_torch
class 							__UpperCamelCase			( unittest.TestCase  ):





				'''simple docstring'''



				@slow
				def       __snake_case		( self						):
											lowerCAmelCase   =		ImageGPTImageProcessor.from_pretrained('''openai/imagegpt-small'''						)

											lowerCAmelCase   =		prepare_images()

											# test non-batched
											lowerCAmelCase   =		image_processing(images[0]       , return_tensors='''pt'''						)

											self.assertIsInstance(encoding.input_ids       , torch.LongTensor						)
											self.assertEqual(encoding.input_ids.shape       , (1, 10_24)						)

											lowerCAmelCase   =		[3_06, 1_91, 1_91]
											self.assertEqual(encoding.input_ids[0, :3].tolist()       , UpperCAmelCase_						)

											# test batched
											lowerCAmelCase   =		image_processing(UpperCAmelCase_       , return_tensors='''pt'''						)

											self.assertIsInstance(encoding.input_ids       , torch.LongTensor						)
											self.assertEqual(encoding.input_ids.shape       , (2, 10_24)						)

											lowerCAmelCase   =		[3_03, 13, 13]
											self.assertEqual(encoding.input_ids[1, -3:].tolist()       , UpperCAmelCase_						)









import argparse
import os
import shutil

import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer



def       UpperCAmelCase       (     _snake_case							):
							lowerCAmelCase   =		args.pruning_method
							lowerCAmelCase   =		args.threshold

							lowerCAmelCase   =		args.model_name_or_path.rstrip('''/'''							)
							lowerCAmelCase   =		args.target_model_path

							print(F"""Load fine-pruned model from {model_name_or_path}"""							)
							lowerCAmelCase   =		torch.load(os.path.join(_snake_case						, '''pytorch_model.bin'''							)							)
							lowerCAmelCase   =		{}

							for name, tensor in model.items():
														if "embeddings" in name or "LayerNorm" in name or "pooler" in name:
																					lowerCAmelCase   =		tensor
																					print(F"""Copied layer {name}"""							)
														elif "classifier" in name or "qa_output" in name:
																					lowerCAmelCase   =		tensor
																					print(F"""Copied layer {name}"""							)
														elif "bias" in name:
																					lowerCAmelCase   =		tensor
																					print(F"""Copied layer {name}"""							)
														else:
																					if pruning_method == "magnitude":
																												lowerCAmelCase   =		MagnitudeBinarizer.apply(inputs=_snake_case						, threshold=_snake_case							)
																												lowerCAmelCase   =		tensor * mask
																												print(F"""Pruned layer {name}"""							)
																					elif pruning_method == "topK":
																												if "mask_scores" in name:
																																			continue
																												lowerCAmelCase   =		name[:-6]
																												lowerCAmelCase   =		model[F"""{prefix_}mask_scores"""]
																												lowerCAmelCase   =		TopKBinarizer.apply(_snake_case						, _snake_case							)
																												lowerCAmelCase   =		tensor * mask
																												print(F"""Pruned layer {name}"""							)
																					elif pruning_method == "sigmoied_threshold":
																												if "mask_scores" in name:
																																			continue
																												lowerCAmelCase   =		name[:-6]
																												lowerCAmelCase   =		model[F"""{prefix_}mask_scores"""]
																												lowerCAmelCase   =		ThresholdBinarizer.apply(_snake_case						, _snake_case						, _snake_case							)
																												lowerCAmelCase   =		tensor * mask
																												print(F"""Pruned layer {name}"""							)
																					elif pruning_method == "l0":
																												if "mask_scores" in name:
																																			continue
																												lowerCAmelCase   =		name[:-6]
																												lowerCAmelCase   =		model[F"""{prefix_}mask_scores"""]
																												lowerCAmelCase   ,     lowerCAmelCase   =		-0.1, 1.1
																												lowerCAmelCase   =		torch.sigmoid(_snake_case							)
																												lowerCAmelCase   =		s * (r - l) + l
																												lowerCAmelCase   =		s_bar.clamp(min=0.0						, max=1.0							)
																												lowerCAmelCase   =		tensor * mask
																												print(F"""Pruned layer {name}"""							)
																					else:
																												raise ValueError('''Unknown pruning method'''							)

							if target_model_path is None:
														lowerCAmelCase   =		os.path.join(
														    os.path.dirname(_snake_case							)						, F"""bertarized_{os.path.basename(_snake_case							)}"""							)

							if not os.path.isdir(_snake_case							):
														shutil.copytree(_snake_case						, _snake_case							)
														print(F"""\nCreated folder {target_model_path}"""							)

							torch.save(_snake_case						, os.path.join(_snake_case						, '''pytorch_model.bin'''							)							)
							print('''\nPruned model saved! See you later!'''							)


if __name__ == "__main__":
		UpperCAmelCase_						=argparse.ArgumentParser()

		parser.add_argument(
		    """--pruning_method""",
		    choices=["""l0""", """magnitude""", """topK""", """sigmoied_threshold"""],
		    type=str,
		    required=True,
		    help=(
		        """Pruning Method (l0 = L0 regularization, magnitude = Magnitude pruning, topK = Movement pruning,"""
		        """ sigmoied_threshold = Soft movement pruning)"""
		    ),
		)
		parser.add_argument(
		    """--threshold""",
		    type=float,
		    required=False,
		    help=(
		        """For `magnitude` and `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(
		    """--model_name_or_path""",
		    type=str,
		    required=True,
		    help="""Folder containing the model that was previously fine-pruned""",
		)
		parser.add_argument(
		    """--target_model_path""",
		    default=None,
		    type=str,
		    required=False,
		    help="""Folder containing the model that was previously fine-pruned""",
		)

		UpperCAmelCase_						=parser.parse_args()

		main(args)



