





"""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 YolosImageProcessor
class lowerCamelCase__	(     unittest.TestCase					):



   def __init__(							self   , snake_case   , snake_case=7   , snake_case=3   , snake_case=3_0   , snake_case=4_0_0   , snake_case=True   , snake_case=None   , snake_case=True   , snake_case=[0.5, 0.5, 0.5]   , snake_case=[0.5, 0.5, 0.5]   , snake_case=True   , snake_case=1 / 2_5_5   , snake_case=True   , )			->     List[str]:







      """simple docstring"""
      lowercase     :      List[str]	=							size if size is not None else {"""shortest_edge""": 1_8, """longest_edge""": 1_3_3_3}
      lowercase     :      List[Any]	=							parent
      lowercase     :      Union[str, Any]	=							batch_size
      lowercase     :      int	=							num_channels
      lowercase     :      Any	=							min_resolution
      lowercase     :      List[Any]	=							max_resolution
      lowercase     :      Dict	=							do_resize
      lowercase     :      Tuple	=							size
      lowercase     :      Tuple	=							do_normalize
      lowercase     :      Union[str, Any]	=							image_mean
      lowercase     :      Tuple	=							image_std
      lowercase     :      Optional[int]	=							do_rescale
      lowercase     :      int	=							rescale_factor
      lowercase     :      Optional[int]	=							do_pad



   def  _UpperCAmelCase						(							self						)			->     str:







      """simple docstring"""
      return {
          "do_resize": self.do_resize,
          "size": self.size,
          "do_normalize": self.do_normalize,
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_rescale": self.do_rescale,
          "rescale_factor": self.rescale_factor,
          "do_pad": self.do_pad,
      }





   def  _UpperCAmelCase						(							self   , snake_case   , snake_case=False						)			->     Union[str, Any]:







      """simple docstring"""






      if not batched:
         lowercase     :      Optional[int]	=							image_inputs[0]
         if isinstance(snake_case__   , Image.Image						):
            lowercase	,       lowercase     :      Any	=							image.size
         else:
            lowercase	,       lowercase     :      Any	=							image.shape[1], image.shape[2]
         if w < h:
            lowercase     :      str	=							int(self.size["""shortest_edge"""] * h / w						)
            lowercase     :      Tuple	=							self.size["""shortest_edge"""]
         elif w > h:
            lowercase     :      Union[str, Any]	=							self.size["""shortest_edge"""]
            lowercase     :      Union[str, Any]	=							int(self.size["""shortest_edge"""] * w / h						)
         else:
            lowercase     :      Any	=							self.size["""shortest_edge"""]
            lowercase     :      int	=							self.size["""shortest_edge"""]

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

      return expected_height, expected_width




@require_torch
@require_vision
class lowerCamelCase__	(     __UpperCamelCase	,					unittest.TestCase					):
   __UpperCAmelCase										=       YolosImageProcessor if is_vision_available() else None



   def  _UpperCAmelCase						(							self						)			->     List[Any]:







      """simple docstring"""
      lowercase     :      str	=							YolosImageProcessingTester(self						)



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







      """simple docstring"""
      return self.image_processor_tester.prepare_image_processor_dict()



   def  _UpperCAmelCase						(							self						)			->     Tuple:







      """simple docstring"""
      lowercase     :      Any	=							self.image_processing_class(**self.image_processor_dict						)
      self.assertTrue(hasattr(snake_case__   , """image_mean"""						)						)
      self.assertTrue(hasattr(snake_case__   , """image_std"""						)						)
      self.assertTrue(hasattr(snake_case__   , """do_normalize"""						)						)
      self.assertTrue(hasattr(snake_case__   , """do_resize"""						)						)
      self.assertTrue(hasattr(snake_case__   , """size"""						)						)



   def  _UpperCAmelCase						(							self						)			->     Optional[Any]:







      """simple docstring"""
      lowercase     :      str	=							self.image_processing_class.from_dict(self.image_processor_dict						)
      self.assertEqual(image_processor.size   , {"""shortest_edge""": 1_8, """longest_edge""": 1_3_3_3}						)
      self.assertEqual(image_processor.do_pad   , snake_case__						)

      lowercase     :      int	=							self.image_processing_class.from_dict(
          self.image_processor_dict   , size=4_2   , max_size=8_4   , pad_and_return_pixel_mask=snake_case__						)
      self.assertEqual(image_processor.size   , {"""shortest_edge""": 4_2, """longest_edge""": 8_4}						)
      self.assertEqual(image_processor.do_pad   , snake_case__						)



   def  _UpperCAmelCase						(							self						)			->     List[Any]:







      """simple docstring"""
      pass



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







      """simple docstring"""
      lowercase     :      Union[str, Any]	=							self.image_processing_class(**self.image_processor_dict						)
      # create random PIL images
      lowercase     :      int	=							prepare_image_inputs(self.image_processor_tester   , equal_resolution=snake_case__						)
      for image in image_inputs:
         self.assertIsInstance(snake_case__   , Image.Image						)

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

      lowercase	,       lowercase     :      Optional[int]	=							self.image_processor_tester.get_expected_values(snake_case__						)

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

      # Test batched
      lowercase	,       lowercase     :      int	=							self.image_processor_tester.get_expected_values(snake_case__   , batched=snake_case__						)

      lowercase     :      Union[str, Any]	=							image_processing(snake_case__   , 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						)			->     List[str]:







      """simple docstring"""
      lowercase     :      int	=							self.image_processing_class(**self.image_processor_dict						)
      # create random numpy tensors
      lowercase     :      Optional[int]	=							prepare_image_inputs(self.image_processor_tester   , equal_resolution=snake_case__   , numpify=snake_case__						)
      for image in image_inputs:
         self.assertIsInstance(snake_case__   , np.ndarray						)

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

      lowercase	,       lowercase     :      Optional[Any]	=							self.image_processor_tester.get_expected_values(snake_case__						)

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

      # Test batched
      lowercase     :      List[str]	=							image_processing(snake_case__   , return_tensors="""pt"""						).pixel_values

      lowercase	,       lowercase     :      Any	=							self.image_processor_tester.get_expected_values(snake_case__   , batched=snake_case__						)

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



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







      """simple docstring"""
      lowercase     :      Union[str, Any]	=							self.image_processing_class(**self.image_processor_dict						)
      # create random PyTorch tensors
      lowercase     :      int	=							prepare_image_inputs(self.image_processor_tester   , equal_resolution=snake_case__   , torchify=snake_case__						)
      for image in image_inputs:
         self.assertIsInstance(snake_case__   , torch.Tensor						)

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

      lowercase	,       lowercase     :      Optional[Any]	=							self.image_processor_tester.get_expected_values(snake_case__						)

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

      # Test batched
      lowercase     :      Dict	=							image_processing(snake_case__   , return_tensors="""pt"""						).pixel_values

      lowercase	,       lowercase     :      Dict	=							self.image_processor_tester.get_expected_values(snake_case__   , batched=snake_case__						)

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



   def  _UpperCAmelCase						(							self						)			->     List[Any]:







      """simple docstring"""
      lowercase     :      Any	=							self.image_processing_class(**self.image_processor_dict						)
      lowercase     :      List[Any]	=							self.image_processing_class(do_resize=snake_case__   , do_normalize=snake_case__   , do_rescale=snake_case__						)
      # create random PyTorch tensors
      lowercase     :      Optional[int]	=							prepare_image_inputs(self.image_processor_tester   , equal_resolution=snake_case__   , torchify=snake_case__						)
      for image in image_inputs:
         self.assertIsInstance(snake_case__   , torch.Tensor						)

      # Test whether the method "pad" and calling the image processor return the same tensors
      lowercase     :      Tuple	=							image_processing_a.pad(snake_case__   , return_tensors="""pt"""						)
      lowercase     :      List[Any]	=							image_processing_a(snake_case__   , return_tensors="""pt"""						)

      self.assertTrue(
          torch.allclose(encoded_images_with_method["""pixel_values"""]   , encoded_images["""pixel_values"""]   , atol=1E-4						)						)



   @slow
   def  _UpperCAmelCase						(							self						)			->     List[str]:







      """simple docstring"""
      lowercase     :      Union[str, Any]	=							Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)
      with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""   , """r"""						) as f:
         lowercase     :      Dict	=							json.loads(f.read()						)

      lowercase     :      str	=							{"""image_id""": 3_9_7_6_9, """annotations""": target}

      # encode them
      lowercase     :      int	=							YolosImageProcessor.from_pretrained("""hustvl/yolos-small"""						)
      lowercase     :      Optional[int]	=							image_processing(images=snake_case__   , annotations=snake_case__   , return_tensors="""pt"""						)

      # verify pixel values
      lowercase     :      Optional[int]	=							torch.Size([1, 3, 8_0_0, 1_0_6_6]						)
      self.assertEqual(encoding["""pixel_values"""].shape   , snake_case__						)

      lowercase     :      Optional[int]	=							torch.tensor([0.27_96, 0.31_38, 0.34_81]						)
      self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]   , snake_case__   , atol=1E-4						)						)

      # verify area
      lowercase     :      Tuple	=							torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]						)
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]   , snake_case__						)						)
      # verify boxes
      lowercase     :      str	=							torch.Size([6, 4]						)
      self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape   , snake_case__						)
      lowercase     :      Dict	=							torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]						)
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]   , snake_case__   , atol=1E-3						)						)
      # verify image_id
      lowercase     :      List[Any]	=							torch.tensor([3_9_7_6_9]						)
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]   , snake_case__						)						)
      # verify is_crowd
      lowercase     :      List[Any]	=							torch.tensor([0, 0, 0, 0, 0, 0]						)
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]   , snake_case__						)						)
      # verify class_labels
      lowercase     :      Optional[int]	=							torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]						)
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]   , snake_case__						)						)
      # verify orig_size
      lowercase     :      Any	=							torch.tensor([4_8_0, 6_4_0]						)
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]   , snake_case__						)						)
      # verify size
      lowercase     :      Union[str, Any]	=							torch.tensor([8_0_0, 1_0_6_6]						)
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]   , snake_case__						)						)





   @slow
   def  _UpperCAmelCase						(							self						)			->     List[Any]:







      """simple docstring"""
      lowercase     :      Any	=							Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)
      with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"""   , """r"""						) as f:
         lowercase     :      str	=							json.loads(f.read()						)

      lowercase     :      str	=							{"""file_name""": """000000039769.png""", """image_id""": 3_9_7_6_9, """segments_info""": target}

      lowercase     :      Any	=							pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""						)

      # encode them
      lowercase     :      Union[str, Any]	=							YolosImageProcessor(format="""coco_panoptic"""						)
      lowercase     :      Union[str, Any]	=							image_processing(images=snake_case__   , annotations=snake_case__   , masks_path=snake_case__   , return_tensors="""pt"""						)

      # verify pixel values
      lowercase     :      Any	=							torch.Size([1, 3, 8_0_0, 1_0_6_6]						)
      self.assertEqual(encoding["""pixel_values"""].shape   , snake_case__						)

      lowercase     :      str	=							torch.tensor([0.27_96, 0.31_38, 0.34_81]						)
      self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]   , snake_case__   , atol=1E-4						)						)

      # verify area
      lowercase     :      int	=							torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]						)
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]   , snake_case__						)						)
      # verify boxes
      lowercase     :      int	=							torch.Size([6, 4]						)
      self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape   , snake_case__						)
      lowercase     :      int	=							torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]						)
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]   , snake_case__   , atol=1E-3						)						)
      # verify image_id
      lowercase     :      Any	=							torch.tensor([3_9_7_6_9]						)
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]   , snake_case__						)						)
      # verify is_crowd
      lowercase     :      List[Any]	=							torch.tensor([0, 0, 0, 0, 0, 0]						)
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]   , snake_case__						)						)
      # verify class_labels
      lowercase     :      Union[str, Any]	=							torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]						)
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]   , snake_case__						)						)
      # verify masks
      lowercase     :      List[Any]	=							8_2_2_8_7_3
      self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()   , snake_case__						)
      # verify orig_size
      lowercase     :      Tuple	=							torch.tensor([4_8_0, 6_4_0]						)
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]   , snake_case__						)						)
      # verify size
      lowercase     :      List[str]	=							torch.tensor([8_0_0, 1_0_6_6]						)
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]   , snake_case__						)						)





import argparse
import os
import shutil

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


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Optional[int]						)		->				int:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      args.pruning_method
 SCREAMING_SNAKE_CASE		    =      args.threshold

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

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

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

 if target_model_path is None:
  SCREAMING_SNAKE_CASE		    =      os.path.join(
      os.path.dirname(_UpperCamelCase						)		,				f"""bertarized_{os.path.basename(_UpperCamelCase						)}"""						)

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

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


if __name__ == "__main__":
      a_     :    Union[str, 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",
      )

      a_     :    Dict     		=   parser.parse_args()

      main(args)

