"""simple docstring"""




import json
import pathlib
import unittest

import numpy as np

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, prepare_image_inputs


if is_torch_available():
				import torch

if is_vision_available():
				from PIL import Image

				from transformers import DetrImageProcessor



class        lowercase_   (					unittest.TestCase		):



							def __init__(						self				,    lowercase_				,    lowercase_=7				,    lowercase_=3				,    lowercase_=30				,    lowercase_=400				,    lowercase_=True				,    lowercase_=None				,    lowercase_=True				,    lowercase_=1 / 255				,    lowercase_=True				,    lowercase_=[0.5, 0.5, 0.5]				,    lowercase_=[0.5, 0.5, 0.5]				,    lowercase_=True				,    ):
													# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
													_snake_case						:					Any    =   size if size is not None else {"shortest_edge": 18, "longest_edge": 1_333}
													_snake_case						:					Union[str, Any]    =   parent
													_snake_case						:					Optional[int]    =   batch_size
													_snake_case						:					Optional[Any]    =   num_channels
													_snake_case						:					str    =   min_resolution
													_snake_case						:					Dict    =   max_resolution
													_snake_case						:					Optional[Any]    =   do_resize
													_snake_case						:					List[str]    =   size
													_snake_case						:					Union[str, Any]    =   do_rescale
													_snake_case						:					Optional[Any]    =   rescale_factor
													_snake_case						:					str    =   do_normalize
													_snake_case						:					Optional[int]    =   image_mean
													_snake_case						:					Optional[int]    =   image_std
													_snake_case						:					Tuple    =   do_pad



							def 	UpperCamelCase	(						self    ):
													return {
													    "do_resize": self.do_resize,
													    "size": self.size,
													    "do_rescale": self.do_rescale,
													    "rescale_factor": self.rescale_factor,
													    "do_normalize": self.do_normalize,
													    "image_mean": self.image_mean,
													    "image_std": self.image_std,
													    "do_pad": self.do_pad,
													}



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_=False    ):
													if not batched:
																			_snake_case						:					Any    =   image_inputs[0]
																			if isinstance(lowercase_				,    Image.Image    ):
																									_snake_case						:					Optional[Any]    =   image.size
																			else:
																									_snake_case						:					List[str]    =   image.shape[1], image.shape[2]
																			if w < h:
																									_snake_case						:					List[Any]    =   int(self.size["shortest_edge"] * h / w    )
																									_snake_case						:					Optional[int]    =   self.size["shortest_edge"]
																			elif w > h:
																									_snake_case						:					str    =   self.size["shortest_edge"]
																									_snake_case						:					List[str]    =   int(self.size["shortest_edge"] * w / h    )
																			else:
																									_snake_case						:					Optional[int]    =   self.size["shortest_edge"]
																									_snake_case						:					int    =   self.size["shortest_edge"]

													else:
																			_snake_case						:					Optional[int]    =   []
																			for image in image_inputs:
																									_snake_case						:					Union[str, Any]    =   self.get_expected_values([image]    )
																									expected_values.append((expected_height, expected_width)    )
																			_snake_case						:					int    =   max(lowercase_				,    key=lambda lowercase_    : item[0]    )[0]
																			_snake_case						:					Tuple    =   max(lowercase_				,    key=lambda lowercase_    : item[1]    )[1]

													return expected_height, expected_width




@require_torch
@require_vision
class        lowercase_   (					__snake_case						,			unittest.TestCase		):
							_lowerCamelCase						=		DetrImageProcessor if is_vision_available() else None



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Optional[int]    =   DetrImageProcessingTester(self    )



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



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Dict    =   self.image_processing_class(**self.image_processor_dict    )
													self.assertTrue(hasattr(lowercase_				,    "image_mean"    )    )
													self.assertTrue(hasattr(lowercase_				,    "image_std"    )    )
													self.assertTrue(hasattr(lowercase_				,    "do_normalize"    )    )
													self.assertTrue(hasattr(lowercase_				,    "do_rescale"    )    )
													self.assertTrue(hasattr(lowercase_				,    "rescale_factor"    )    )
													self.assertTrue(hasattr(lowercase_				,    "do_resize"    )    )
													self.assertTrue(hasattr(lowercase_				,    "size"    )    )
													self.assertTrue(hasattr(lowercase_				,    "do_pad"    )    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					List[Any]    =   self.image_processing_class.from_dict(self.image_processor_dict    )
													self.assertEqual(image_processor.size				,    {"shortest_edge": 18, "longest_edge": 1_333}    )
													self.assertEqual(image_processor.do_pad				,    lowercase_    )

													_snake_case						:					List[Any]    =   self.image_processing_class.from_dict(
													    self.image_processor_dict				,    size=42				,    max_size=84				,    pad_and_return_pixel_mask=lowercase_    )
													self.assertEqual(image_processor.size				,    {"shortest_edge": 42, "longest_edge": 84}    )
													self.assertEqual(image_processor.do_pad				,    lowercase_    )



							def 	UpperCamelCase	(						self    ):
													pass



							def 	UpperCamelCase	(						self    ):
													# Initialize image_processing
													_snake_case						:					Any    =   self.image_processing_class(**self.image_processor_dict    )
													# create random PIL images
													_snake_case						:					int    =   prepare_image_inputs(self.image_processor_tester				,    equal_resolution=lowercase_    )
													for image in image_inputs:
																			self.assertIsInstance(lowercase_				,    Image.Image    )

													# Test not batched input
													_snake_case						:					List[Any]    =   image_processing(image_inputs[0]				,    return_tensors="pt"    ).pixel_values

													_snake_case						:					Optional[Any]    =   self.image_processor_tester.get_expected_values(lowercase_    )

													self.assertEqual(
													    encoded_images.shape				,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)				,    )

													# Test batched
													_snake_case						:					int    =   self.image_processor_tester.get_expected_values(lowercase_				,    batched=lowercase_    )

													_snake_case						:					Tuple    =   image_processing(lowercase_				,    return_tensors="pt"    ).pixel_values
													self.assertEqual(
													    encoded_images.shape				,    (
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        expected_height,
													        expected_width,
													    )				,    )



							def 	UpperCamelCase	(						self    ):
													# Initialize image_processing
													_snake_case						:					Union[str, Any]    =   self.image_processing_class(**self.image_processor_dict    )
													# create random numpy tensors
													_snake_case						:					int    =   prepare_image_inputs(self.image_processor_tester				,    equal_resolution=lowercase_				,    numpify=lowercase_    )
													for image in image_inputs:
																			self.assertIsInstance(lowercase_				,    np.ndarray    )

													# Test not batched input
													_snake_case						:					Optional[int]    =   image_processing(image_inputs[0]				,    return_tensors="pt"    ).pixel_values

													_snake_case						:					Union[str, Any]    =   self.image_processor_tester.get_expected_values(lowercase_    )

													self.assertEqual(
													    encoded_images.shape				,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)				,    )

													# Test batched
													_snake_case						:					Dict    =   image_processing(lowercase_				,    return_tensors="pt"    ).pixel_values

													_snake_case						:					Union[str, Any]    =   self.image_processor_tester.get_expected_values(lowercase_				,    batched=lowercase_    )

													self.assertEqual(
													    encoded_images.shape				,    (
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        expected_height,
													        expected_width,
													    )				,    )



							def 	UpperCamelCase	(						self    ):
													# Initialize image_processing
													_snake_case						:					Tuple    =   self.image_processing_class(**self.image_processor_dict    )
													# create random PyTorch tensors
													_snake_case						:					List[Any]    =   prepare_image_inputs(self.image_processor_tester				,    equal_resolution=lowercase_				,    torchify=lowercase_    )
													for image in image_inputs:
																			self.assertIsInstance(lowercase_				,    torch.Tensor    )

													# Test not batched input
													_snake_case						:					Tuple    =   image_processing(image_inputs[0]				,    return_tensors="pt"    ).pixel_values

													_snake_case						:					Optional[Any]    =   self.image_processor_tester.get_expected_values(lowercase_    )

													self.assertEqual(
													    encoded_images.shape				,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)				,    )

													# Test batched
													_snake_case						:					Union[str, Any]    =   image_processing(lowercase_				,    return_tensors="pt"    ).pixel_values

													_snake_case						:					List[str]    =   self.image_processor_tester.get_expected_values(lowercase_				,    batched=lowercase_    )

													self.assertEqual(
													    encoded_images.shape				,    (
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        expected_height,
													        expected_width,
													    )				,    )



							@slow
							def 	UpperCamelCase	(						self    ):
													# prepare image and target
													_snake_case						:					List[str]    =   Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
													with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"				,    "r"    ) as f:
																			_snake_case						:					str    =   json.loads(f.read()    )

													_snake_case						:					List[str]    =   {"image_id": 39_769, "annotations": target}

													# encode them
													_snake_case						:					Optional[int]    =   DetrImageProcessor.from_pretrained("facebook/detr-resnet-50"    )
													_snake_case						:					Dict    =   image_processing(images=lowercase_				,    annotations=lowercase_				,    return_tensors="pt"    )

													# verify pixel values
													_snake_case						:					Union[str, Any]    =   torch.Size([1, 3, 800, 1_066]    )
													self.assertEqual(encoding["pixel_values"].shape				,    lowercase_    )

													_snake_case						:					Dict    =   torch.tensor([0.2_796, 0.3_138, 0.3_481]    )
													self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]				,    lowercase_				,    atol=1e-4    )    )

													# verify area
													_snake_case						:					Optional[Any]    =   torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438]    )
													self.assertTrue(torch.allclose(encoding["labels"][0]["area"]				,    lowercase_    )    )
													# verify boxes
													_snake_case						:					List[Any]    =   torch.Size([6, 4]    )
													self.assertEqual(encoding["labels"][0]["boxes"].shape				,    lowercase_    )
													_snake_case						:					Dict    =   torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]    )
													self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]				,    lowercase_				,    atol=1e-3    )    )
													# verify image_id
													_snake_case						:					int    =   torch.tensor([39_769]    )
													self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]				,    lowercase_    )    )
													# verify is_crowd
													_snake_case						:					Tuple    =   torch.tensor([0, 0, 0, 0, 0, 0]    )
													self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]				,    lowercase_    )    )
													# verify class_labels
													_snake_case						:					Tuple    =   torch.tensor([75, 75, 63, 65, 17, 17]    )
													self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]				,    lowercase_    )    )
													# verify orig_size
													_snake_case						:					str    =   torch.tensor([480, 640]    )
													self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]				,    lowercase_    )    )
													# verify size
													_snake_case						:					List[str]    =   torch.tensor([800, 1_066]    )
													self.assertTrue(torch.allclose(encoding["labels"][0]["size"]				,    lowercase_    )    )



							@slow
							def 	UpperCamelCase	(						self    ):
													# prepare image, target and masks_path
													_snake_case						:					List[str]    =   Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
													with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"				,    "r"    ) as f:
																			_snake_case						:					List[str]    =   json.loads(f.read()    )

													_snake_case						:					Union[str, Any]    =   {"file_name": "000000039769.png", "image_id": 39_769, "segments_info": target}

													_snake_case						:					List[str]    =   pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"    )

													# encode them
													_snake_case						:					Any    =   DetrImageProcessor.from_pretrained("facebook/detr-resnet-50-panoptic"    )
													_snake_case						:					Optional[Any]    =   image_processing(images=lowercase_				,    annotations=lowercase_				,    masks_path=lowercase_				,    return_tensors="pt"    )

													# verify pixel values
													_snake_case						:					List[Any]    =   torch.Size([1, 3, 800, 1_066]    )
													self.assertEqual(encoding["pixel_values"].shape				,    lowercase_    )

													_snake_case						:					Dict    =   torch.tensor([0.2_796, 0.3_138, 0.3_481]    )
													self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]				,    lowercase_				,    atol=1e-4    )    )

													# verify area
													_snake_case						:					Dict    =   torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147]    )
													self.assertTrue(torch.allclose(encoding["labels"][0]["area"]				,    lowercase_    )    )
													# verify boxes
													_snake_case						:					Optional[Any]    =   torch.Size([6, 4]    )
													self.assertEqual(encoding["labels"][0]["boxes"].shape				,    lowercase_    )
													_snake_case						:					Optional[Any]    =   torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]    )
													self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]				,    lowercase_				,    atol=1e-3    )    )
													# verify image_id
													_snake_case						:					str    =   torch.tensor([39_769]    )
													self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]				,    lowercase_    )    )
													# verify is_crowd
													_snake_case						:					Any    =   torch.tensor([0, 0, 0, 0, 0, 0]    )
													self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]				,    lowercase_    )    )
													# verify class_labels
													_snake_case						:					Dict    =   torch.tensor([17, 17, 63, 75, 75, 93]    )
													self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]				,    lowercase_    )    )
													# verify masks
													_snake_case						:					int    =   822_873
													self.assertEqual(encoding["labels"][0]["masks"].sum().item()				,    lowercase_    )
													# verify orig_size
													_snake_case						:					Optional[int]    =   torch.tensor([480, 640]    )
													self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]				,    lowercase_    )    )
													# verify size
													_snake_case						:					Dict    =   torch.tensor([800, 1_066]    )
													self.assertTrue(torch.allclose(encoding["labels"][0]["size"]				,    lowercase_    )    )
import argparse
import os
import shutil

import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def  snake_case						(__lowercase						)	->	str:

						'''simple docstring'''



						_snake_case						:					int    =   args.pruning_method
						_snake_case						:					List[Any]    =   args.threshold

						_snake_case						:					Optional[Any]    =   args.model_name_or_path.rstrip("/"						)
						_snake_case						:					List[str]    =   args.target_model_path

						print(F"""Load fine-pruned model from {model_name_or_path}"""						)
						_snake_case						:					List[Any]    =   torch.load(os.path.join(__lowercase ,      "pytorch_model.bin"						)						)
						_snake_case						:					List[str]    =   {}

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

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

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

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


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE						:   List[Any]         =			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',
				)

				__SCREAMING_SNAKE_CASE						:   Union[str, Any]         =			parser.parse_args()

				main(args)