

'''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 DeformableDetrImageProcessor






class     lowercase_			(unittest.TestCase   ):




  """simple docstring"""




  def __init__(   self     :  Any      ,lowercase__     :  Any      ,lowercase__     :  Dict=7      ,lowercase__     :  Optional[int]=3      ,lowercase__     :  Dict=3_0      ,lowercase__     :  List[str]=4_0_0      ,lowercase__     :  Union[str, Any]=True      ,lowercase__     :  List[Any]=None      ,lowercase__     :  int=True      ,lowercase__     :  Any=[0.5, 0.5, 0.5]      ,lowercase__     :  str=[0.5, 0.5, 0.5]      ,lowercase__     :  Dict=True      ,lowercase__     :  Optional[int]=1 / 2_5_5      ,lowercase__     :  str=True      ,):
   __lowercase													=				size if size is not None else {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}
   __lowercase													=				parent
   __lowercase													=				batch_size
   __lowercase													=				num_channels
   __lowercase													=				min_resolution
   __lowercase													=				max_resolution
   __lowercase													=				do_resize
   __lowercase													=				size
   __lowercase													=				do_normalize
   __lowercase													=				image_mean
   __lowercase													=				image_std
   __lowercase													=				do_rescale
   __lowercase													=				rescale_factor
   __lowercase													=				do_pad






  def       SCREAMING_SNAKE_CASE   (   self     :  str    ):
   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       SCREAMING_SNAKE_CASE   (   self     :  Union[str, Any]      ,lowercase__     :  int      ,lowercase__     :  Tuple=False    ):







   if not batched:
    __lowercase													=				image_inputs[0]
    if isinstance(lowercase__      ,Image.Image    ):
     __lowercase						,			__lowercase													=				image.size
    else:
     __lowercase						,			__lowercase													=				image.shape[1], image.shape[2]
    if w < h:
     __lowercase													=				int(self.size['''shortest_edge'''] * h / w    )
     __lowercase													=				self.size['''shortest_edge''']
    elif w > h:
     __lowercase													=				self.size['''shortest_edge''']
     __lowercase													=				int(self.size['''shortest_edge'''] * w / h    )
    else:
     __lowercase													=				self.size['''shortest_edge''']
     __lowercase													=				self.size['''shortest_edge''']

   else:
    __lowercase													=				[]
    for image in image_inputs:
     __lowercase						,			__lowercase													=				self.get_expected_values([image]    )
     expected_values.append((expected_height, expected_width)    )
    __lowercase													=				max(lowercase__      ,key=lambda lowercase__    : item[0]    )[0]
    __lowercase													=				max(lowercase__      ,key=lambda lowercase__    : item[1]    )[1]

   return expected_height, expected_width






@require_torch
@require_vision
class     lowercase_			(lowerCamelCase__       , unittest.TestCase   ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE					:						Optional[Any]           =    DeformableDetrImageProcessor if is_vision_available() else None






  def       SCREAMING_SNAKE_CASE   (   self     :  str    ):
   __lowercase													=				DeformableDetrImageProcessingTester(self    )






  @property
  def       SCREAMING_SNAKE_CASE   (   self     :  Any    ):
   return self.image_processor_tester.prepare_image_processor_dict()






  def       SCREAMING_SNAKE_CASE   (   self     :  Dict    ):
   __lowercase													=				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_resize'''    )    )
   self.assertTrue(hasattr(lowercase__      ,'''do_rescale'''    )    )
   self.assertTrue(hasattr(lowercase__      ,'''do_pad'''    )    )
   self.assertTrue(hasattr(lowercase__      ,'''size'''    )    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Tuple    ):
   __lowercase													=				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      ,lowercase__    )

   __lowercase													=				self.image_processing_class.from_dict(
       self.image_processor_dict      ,size=4_2      ,max_size=8_4      ,pad_and_return_pixel_mask=lowercase__    )
   self.assertEqual(image_processor.size      ,{'''shortest_edge''': 4_2, '''longest_edge''': 8_4}    )
   self.assertEqual(image_processor.do_pad      ,lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]    ):
   pass






  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]    ):
   __lowercase													=				self.image_processing_class(**self.image_processor_dict    )
   # create random PIL images
   __lowercase													=				prepare_image_inputs(self.image_processor_tester      ,equal_resolution=lowercase__    )
   for image in image_inputs:
    self.assertIsInstance(lowercase__      ,Image.Image    )

   # Test not batched input
   __lowercase													=				image_processing(image_inputs[0]      ,return_tensors='''pt'''    ).pixel_values

   __lowercase						,			__lowercase													=				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
   __lowercase						,			__lowercase													=				self.image_processor_tester.get_expected_values(lowercase__      ,batched=lowercase__    )

   __lowercase													=				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       SCREAMING_SNAKE_CASE   (   self     :  Any    ):
   __lowercase													=				self.image_processing_class(**self.image_processor_dict    )
   # create random numpy tensors
   __lowercase													=				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
   __lowercase													=				image_processing(image_inputs[0]      ,return_tensors='''pt'''    ).pixel_values

   __lowercase						,			__lowercase													=				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
   __lowercase													=				image_processing(lowercase__      ,return_tensors='''pt'''    ).pixel_values

   __lowercase						,			__lowercase													=				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       SCREAMING_SNAKE_CASE   (   self     :  Dict    ):
   __lowercase													=				self.image_processing_class(**self.image_processor_dict    )
   # create random PyTorch tensors
   __lowercase													=				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
   __lowercase													=				image_processing(image_inputs[0]      ,return_tensors='''pt'''    ).pixel_values

   __lowercase						,			__lowercase													=				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
   __lowercase													=				image_processing(lowercase__      ,return_tensors='''pt'''    ).pixel_values

   __lowercase						,			__lowercase													=				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       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]    ):
   __lowercase													=				Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''    )
   with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''      ,'''r'''    ) as f:
    __lowercase													=				json.loads(f.read()    )

   __lowercase													=				{'''image_id''': 3_9_7_6_9, '''annotations''': target}

   # encode them
   __lowercase													=				DeformableDetrImageProcessor()
   __lowercase													=				image_processing(images=lowercase__      ,annotations=lowercase__      ,return_tensors='''pt'''    )

   # verify pixel values
   __lowercase													=				torch.Size([1, 3, 8_0_0, 1_0_6_6]    )
   self.assertEqual(encoding['''pixel_values'''].shape      ,lowercase__    )

   __lowercase													=				torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]    )
   self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]      ,lowercase__      ,atol=1e-4    )    )

   # verify area
   __lowercase													=				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''']      ,lowercase__    )    )
   # verify boxes
   __lowercase													=				torch.Size([6, 4]    )
   self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape      ,lowercase__    )
   __lowercase													=				torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]    )
   self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]      ,lowercase__      ,atol=1e-3    )    )
   # verify image_id
   __lowercase													=				torch.tensor([3_9_7_6_9]    )
   self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']      ,lowercase__    )    )
   # verify is_crowd
   __lowercase													=				torch.tensor([0, 0, 0, 0, 0, 0]    )
   self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']      ,lowercase__    )    )
   # verify class_labels
   __lowercase													=				torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]    )
   self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']      ,lowercase__    )    )
   # verify orig_size
   __lowercase													=				torch.tensor([4_8_0, 6_4_0]    )
   self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']      ,lowercase__    )    )
   # verify size
   __lowercase													=				torch.tensor([8_0_0, 1_0_6_6]    )
   self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']      ,lowercase__    )    )






  @slow
  def       SCREAMING_SNAKE_CASE   (   self     :  Tuple    ):
   __lowercase													=				Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''    )
   with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''      ,'''r'''    ) as f:
    __lowercase													=				json.loads(f.read()    )

   __lowercase													=				{'''file_name''': '''000000039769.png''', '''image_id''': 3_9_7_6_9, '''segments_info''': target}

   __lowercase													=				pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''    )

   # encode them
   __lowercase													=				DeformableDetrImageProcessor(format='''coco_panoptic'''    )
   __lowercase													=				image_processing(images=lowercase__      ,annotations=lowercase__      ,masks_path=lowercase__      ,return_tensors='''pt'''    )

   # verify pixel values
   __lowercase													=				torch.Size([1, 3, 8_0_0, 1_0_6_6]    )
   self.assertEqual(encoding['''pixel_values'''].shape      ,lowercase__    )

   __lowercase													=				torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]    )
   self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]      ,lowercase__      ,atol=1e-4    )    )

   # verify area
   __lowercase													=				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''']      ,lowercase__    )    )
   # verify boxes
   __lowercase													=				torch.Size([6, 4]    )
   self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape      ,lowercase__    )
   __lowercase													=				torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]    )
   self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]      ,lowercase__      ,atol=1e-3    )    )
   # verify image_id
   __lowercase													=				torch.tensor([3_9_7_6_9]    )
   self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']      ,lowercase__    )    )
   # verify is_crowd
   __lowercase													=				torch.tensor([0, 0, 0, 0, 0, 0]    )
   self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']      ,lowercase__    )    )
   # verify class_labels
   __lowercase													=				torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]    )
   self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']      ,lowercase__    )    )
   # verify masks
   __lowercase													=				8_2_2_8_7_3
   self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()      ,lowercase__    )
   # verify orig_size
   __lowercase													=				torch.tensor([4_8_0, 6_4_0]    )
   self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']      ,lowercase__    )    )
   # verify size
   __lowercase													=				torch.tensor([8_0_0, 1_0_6_6]    )
   self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']      ,lowercase__    )    )



'''simple docstring'''






import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


lowerCAmelCase__      		=			False
lowerCAmelCase__      		=			True
lowerCAmelCase__      		=			False


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

 parser.add_argument(
     '''--repo_path''',
     default=None,
     type=str,
     required=True,
     help='''The config json file corresponding to the architecture.''',
 )

 parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

 lowerCAmelCase__      		=			parser.parse_args()

 lowerCAmelCase__      		=			{
     '''image_size''': '''sample_size''',
     '''num_res_blocks''': '''layers_per_block''',
     '''block_channels''': '''block_out_channels''',
     '''down_blocks''': '''down_block_types''',
     '''up_blocks''': '''up_block_types''',
     '''downscale_freq_shift''': '''freq_shift''',
     '''resnet_num_groups''': '''norm_num_groups''',
     '''resnet_act_fn''': '''act_fn''',
     '''resnet_eps''': '''norm_eps''',
     '''num_head_channels''': '''attention_head_dim''',
 }

 lowerCAmelCase__      		=			{
     '''time_steps''': '''time_proj''',
     '''mid''': '''mid_block''',
     '''downsample_blocks''': '''down_blocks''',
     '''upsample_blocks''': '''up_blocks''',
 }

 lowerCAmelCase__      		=			'''''' if has_file(args.repo_path, '''config.json''') else '''unet'''

 with open(os.path.join(args.repo_path, subfolder, '''config.json'''), '''r''', encoding='''utf-8''') as reader:
  lowerCAmelCase__      		=			reader.read()
  lowerCAmelCase__      		=			json.loads(text)

 if do_only_config:
  for key in config_parameters_to_change.keys():
   config.pop(key, None)

 if has_file(args.repo_path, '''config.json'''):
  lowerCAmelCase__      		=			UNetaDModel(**config)
 else:
  lowerCAmelCase__      		=			UNetaDConditionModel if '''ldm-text2im-large-256''' in args.repo_path else UNetaDModel
  lowerCAmelCase__      		=			class_name(**config)

 if do_only_config:
  model.save_config(os.path.join(args.repo_path, subfolder))

 lowerCAmelCase__      		=			dict(model.config)

 if do_only_renaming:
  for key, value in config_parameters_to_change.items():
   if key in config:
    lowerCAmelCase__      		=			config[key]
    del config[key]

  lowerCAmelCase__      		=			[k.replace('''UNetRes''', '''''') for k in config['''down_block_types''']]
  lowerCAmelCase__      		=			[k.replace('''UNetRes''', '''''') for k in config['''up_block_types''']]

 if do_only_weights:
  lowerCAmelCase__      		=			torch.load(os.path.join(args.repo_path, subfolder, '''diffusion_pytorch_model.bin'''))

  lowerCAmelCase__      		=			{}
  for param_key, param_value in state_dict.items():
   if param_key.endswith('''.op.bias''') or param_key.endswith('''.op.weight'''):
    continue
   lowerCAmelCase__      		=			False
   for key, new_key in key_parameters_to_change.items():
    if not has_changed and param_key.split('''.''')[0] == key:
     lowerCAmelCase__      		=			param_value
     lowerCAmelCase__      		=			True
   if not has_changed:
    lowerCAmelCase__      		=			param_value

  model.load_state_dict(new_state_dict)
  model.save_pretrained(os.path.join(args.repo_path, subfolder))

