






"""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        UpperCamelCase			(	unittest.TestCase      ):


 def __init__(     self		:						Optional[int]							,						UpperCAmelCase__		:						Optional[Any]							,						UpperCAmelCase__		:						List[str]=7							,						UpperCAmelCase__		:						Dict=3							,						UpperCAmelCase__		:						Any=30							,						UpperCAmelCase__		:						Optional[int]=400							,						UpperCAmelCase__		:						str=True							,						UpperCAmelCase__		:						List[Any]=None							,						UpperCAmelCase__		:						List[Any]=True							,						UpperCAmelCase__		:						int=1 / 255							,						UpperCAmelCase__		:						List[Any]=True							,						UpperCAmelCase__		:						int=[0.5, 0.5, 0.5]							,						UpperCAmelCase__		:						Union[str, Any]=[0.5, 0.5, 0.5]							,						UpperCAmelCase__		:						Tuple=True							,						)					->       List[str]:
   _a  :       int             =	size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 1333}
   _a  :       Dict             =	parent
   _a  :       Dict             =	batch_size
   _a  :       Optional[int]             =	num_channels
   _a  :       Optional[int]             =	min_resolution
   _a  :       Union[str, Any]             =	max_resolution
   _a  :       Dict             =	do_resize
   _a  :       Any             =	size
   _a  :       Union[str, Any]             =	do_rescale
   _a  :       Dict             =	rescale_factor
   _a  :       Union[str, Any]             =	do_normalize
   _a  :       List[str]             =	image_mean
   _a  :       Dict             =	image_std
   _a  :       Tuple             =	do_pad


 def _lowercase	(     self		:						Any							)					->       int:
   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 _lowercase	(     self		:						List[Any]							,						UpperCAmelCase__		:						Optional[int]							,						UpperCAmelCase__		:						Tuple=False							)					->       int:






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

   else:
     _a  :       Any             =	[]
     for image in image_inputs:
       _a      ,  _a  :       Optional[int]             =	self.get_expected_values([image]							)
       expected_values.append((expected_height, expected_width)							)
     _a  :       str             =	max(UpperCamelCase__							,						key=lambda UpperCAmelCase__							: item[0]							)[0]
     _a  :       List[Any]             =	max(UpperCamelCase__							,						key=lambda UpperCAmelCase__							: item[1]							)[1]

   return expected_height, expected_width







@require_torch
@require_vision
class        UpperCamelCase			(	_A		,     unittest.TestCase      ):
 UpperCamelCase					:       str			=					DetrImageProcessor if is_vision_available() else None


 def _lowercase	(     self		:						Dict							)					->       int:
   _a  :       Tuple             =	DetrImageProcessingTester(self							)


 @property
 def _lowercase	(     self		:						List[str]							)					->       List[Any]:
   return self.image_processor_tester.prepare_image_processor_dict()


 def _lowercase	(     self		:						Any							)					->       List[str]:
   _a  :       str             =	self.image_processing_class(**self.image_processor_dict							)
   self.assertTrue(hasattr(UpperCamelCase__							,						"""image_mean"""							)							)
   self.assertTrue(hasattr(UpperCamelCase__							,						"""image_std"""							)							)
   self.assertTrue(hasattr(UpperCamelCase__							,						"""do_normalize"""							)							)
   self.assertTrue(hasattr(UpperCamelCase__							,						"""do_rescale"""							)							)
   self.assertTrue(hasattr(UpperCamelCase__							,						"""rescale_factor"""							)							)
   self.assertTrue(hasattr(UpperCamelCase__							,						"""do_resize"""							)							)
   self.assertTrue(hasattr(UpperCamelCase__							,						"""size"""							)							)
   self.assertTrue(hasattr(UpperCamelCase__							,						"""do_pad"""							)							)


 def _lowercase	(     self		:						str							)					->       Union[str, Any]:
   _a  :       Any             =	self.image_processing_class.from_dict(self.image_processor_dict							)
   self.assertEqual(image_processor.size							,						{"""shortest_edge""": 18, """longest_edge""": 1333}							)
   self.assertEqual(image_processor.do_pad							,						UpperCamelCase__							)

   _a  :       Dict             =	self.image_processing_class.from_dict(
       self.image_processor_dict							,						size=42							,						max_size=84							,						pad_and_return_pixel_mask=UpperCamelCase__							)
   self.assertEqual(image_processor.size							,						{"""shortest_edge""": 42, """longest_edge""": 84}							)
   self.assertEqual(image_processor.do_pad							,						UpperCamelCase__							)


 def _lowercase	(     self		:						Optional[int]							)					->       Dict:
   pass


 def _lowercase	(     self		:						Optional[Any]							)					->       str:
   _a  :       Dict             =	self.image_processing_class(**self.image_processor_dict							)
   # create random PIL images
   _a  :       List[str]             =	prepare_image_inputs(self.image_processor_tester							,						equal_resolution=UpperCamelCase__							)
   for image in image_inputs:
     self.assertIsInstance(UpperCamelCase__							,						Image.Image							)

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

   _a      ,  _a  :       Tuple             =	self.image_processor_tester.get_expected_values(UpperCamelCase__							)

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

   # Test batched
   _a      ,  _a  :       List[Any]             =	self.image_processor_tester.get_expected_values(UpperCamelCase__							,						batched=UpperCamelCase__							)

   _a  :       Tuple             =	image_processing(UpperCamelCase__							,						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 _lowercase	(     self		:						Any							)					->       List[str]:
   _a  :       List[Any]             =	self.image_processing_class(**self.image_processor_dict							)
   # create random numpy tensors
   _a  :       Union[str, Any]             =	prepare_image_inputs(self.image_processor_tester							,						equal_resolution=UpperCamelCase__							,						numpify=UpperCamelCase__							)
   for image in image_inputs:
     self.assertIsInstance(UpperCamelCase__							,						np.ndarray							)

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

   _a      ,  _a  :       Union[str, Any]             =	self.image_processor_tester.get_expected_values(UpperCamelCase__							)

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

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

   _a      ,  _a  :       Dict             =	self.image_processor_tester.get_expected_values(UpperCamelCase__							,						batched=UpperCamelCase__							)

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


 def _lowercase	(     self		:						str							)					->       Optional[Any]:
   _a  :       List[Any]             =	self.image_processing_class(**self.image_processor_dict							)
   # create random PyTorch tensors
   _a  :       Optional[Any]             =	prepare_image_inputs(self.image_processor_tester							,						equal_resolution=UpperCamelCase__							,						torchify=UpperCamelCase__							)
   for image in image_inputs:
     self.assertIsInstance(UpperCamelCase__							,						torch.Tensor							)

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

   _a      ,  _a  :       Any             =	self.image_processor_tester.get_expected_values(UpperCamelCase__							)

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

   # Test batched
   _a  :       Dict             =	image_processing(UpperCamelCase__							,						return_tensors="""pt"""							).pixel_values

   _a      ,  _a  :       Union[str, Any]             =	self.image_processor_tester.get_expected_values(UpperCamelCase__							,						batched=UpperCamelCase__							)

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


 @slow
 def _lowercase	(     self		:						List[Any]							)					->       Tuple:
   _a  :       Optional[Any]             =	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""							)
   with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""							,						"""r"""							) as f:
     _a  :       List[Any]             =	json.loads(f.read()							)

   _a  :       Optional[int]             =	{"""image_id""": 39769, """annotations""": target}

   # encode them
   _a  :       str             =	DetrImageProcessor.from_pretrained("""facebook/detr-resnet-50"""							)
   _a  :       List[Any]             =	image_processing(images=UpperCamelCase__							,						annotations=UpperCamelCase__							,						return_tensors="""pt"""							)

   # verify pixel values
   _a  :       str             =	torch.Size([1, 3, 800, 1066]							)
   self.assertEqual(encoding["""pixel_values"""].shape							,						UpperCamelCase__							)

   _a  :       Union[str, Any]             =	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]							,						UpperCamelCase__							,						atol=1E-4							)							)

   # verify area
   _a  :       Dict             =	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"""]							,						UpperCamelCase__							)							)
   # verify boxes
   _a  :       int             =	torch.Size([6, 4]							)
   self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape							,						UpperCamelCase__							)
   _a  :       int             =	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]							,						UpperCamelCase__							,						atol=1E-3							)							)
   # verify image_id
   _a  :       Dict             =	torch.tensor([39769]							)
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]							,						UpperCamelCase__							)							)
   # verify is_crowd
   _a  :       Tuple             =	torch.tensor([0, 0, 0, 0, 0, 0]							)
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]							,						UpperCamelCase__							)							)
   # verify class_labels
   _a  :       Union[str, Any]             =	torch.tensor([75, 75, 63, 65, 17, 17]							)
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]							,						UpperCamelCase__							)							)
   # verify orig_size
   _a  :       Tuple             =	torch.tensor([480, 640]							)
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]							,						UpperCamelCase__							)							)
   # verify size
   _a  :       Optional[int]             =	torch.tensor([800, 1066]							)
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]							,						UpperCamelCase__							)							)







 @slow
 def _lowercase	(     self		:						Any							)					->       List[Any]:
   _a  :       Dict             =	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""							)
   with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"""							,						"""r"""							) as f:
     _a  :       Dict             =	json.loads(f.read()							)

   _a  :       int             =	{"""file_name""": """000000039769.png""", """image_id""": 39769, """segments_info""": target}

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

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

   # verify pixel values
   _a  :       List[Any]             =	torch.Size([1, 3, 800, 1066]							)
   self.assertEqual(encoding["""pixel_values"""].shape							,						UpperCamelCase__							)

   _a  :       List[str]             =	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]							,						UpperCamelCase__							,						atol=1E-4							)							)

   # verify area
   _a  :       Optional[Any]             =	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"""]							,						UpperCamelCase__							)							)
   # verify boxes
   _a  :       List[str]             =	torch.Size([6, 4]							)
   self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape							,						UpperCamelCase__							)
   _a  :       Optional[Any]             =	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]							,						UpperCamelCase__							,						atol=1E-3							)							)
   # verify image_id
   _a  :       str             =	torch.tensor([39769]							)
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]							,						UpperCamelCase__							)							)
   # verify is_crowd
   _a  :       Optional[Any]             =	torch.tensor([0, 0, 0, 0, 0, 0]							)
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]							,						UpperCamelCase__							)							)
   # verify class_labels
   _a  :       Optional[int]             =	torch.tensor([17, 17, 63, 75, 75, 93]							)
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]							,						UpperCamelCase__							)							)
   # verify masks
   _a  :       List[str]             =	822873
   self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()							,						UpperCamelCase__							)
   # verify orig_size
   _a  :       str             =	torch.tensor([480, 640]							)
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]							,						UpperCamelCase__							)							)
   # verify size
   _a  :       int             =	torch.tensor([800, 1066]							)
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]							,						UpperCamelCase__							)							)



from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCAmelCase			:     Union[str, Any]           =   logging.get_logger(__name__)

__lowerCAmelCase			:     Tuple           =   {
    'google/pegasus-large': 'https://huggingface.co/google/pegasus-large/resolve/main/config.json',
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}




class        UpperCAmelCase_			( _A     ):


  '''simple docstring'''







  a__           =		"""pegasus"""
  a__           =		["""past_key_values"""]
  a__           =		{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
  def __init__( self			: Optional[int]			,							UpperCamelCase__			: Optional[int]=5_0265			,							UpperCamelCase__			: Optional[int]=1024			,							UpperCamelCase__			: Any=12			,							UpperCamelCase__			: Union[str, Any]=4096			,							UpperCamelCase__			: Any=16			,							UpperCamelCase__			: Union[str, Any]=12			,							UpperCamelCase__			: List[str]=4096			,							UpperCamelCase__			: Tuple=16			,							UpperCamelCase__			: Optional[int]=0.0			,							UpperCamelCase__			: List[Any]=0.0			,							UpperCamelCase__			: List[str]=True			,							UpperCamelCase__			: List[Any]=True			,							UpperCamelCase__			: List[Any]="gelu"			,							UpperCamelCase__			: List[Any]=1024			,							UpperCamelCase__			: Optional[Any]=0.1			,							UpperCamelCase__			: str=0.0			,							UpperCamelCase__			: Any=0.0			,							UpperCamelCase__			: Union[str, Any]=0.02			,							UpperCamelCase__			: Any=0			,							UpperCamelCase__			: int=False			,							UpperCamelCase__			: Any=0			,							UpperCamelCase__			: List[str]=1			,							UpperCamelCase__			: Tuple=1			,							**UpperCamelCase__			: Union[str, Any]			,							)			->	str:







    """simple docstring"""






    __magic_name__				     =       vocab_size
    __magic_name__				     =       max_position_embeddings
    __magic_name__				     =       d_model
    __magic_name__				     =       encoder_ffn_dim
    __magic_name__				     =       encoder_layers
    __magic_name__				     =       encoder_attention_heads
    __magic_name__				     =       decoder_ffn_dim
    __magic_name__				     =       decoder_layers
    __magic_name__				     =       decoder_attention_heads
    __magic_name__				     =       dropout
    __magic_name__				     =       attention_dropout
    __magic_name__				     =       activation_dropout
    __magic_name__				     =       activation_function
    __magic_name__				     =       init_std
    __magic_name__				     =       encoder_layerdrop
    __magic_name__				     =       decoder_layerdrop
    __magic_name__				     =       use_cache
    __magic_name__				     =       encoder_layers
    __magic_name__				     =       scale_embedding  # scale factor will be sqrt(d_model) if True
    super().__init__(
        pad_token_id=UpperCamelCase__			,							eos_token_id=UpperCamelCase__			,							is_encoder_decoder=UpperCamelCase__			,							decoder_start_token_id=UpperCamelCase__			,							forced_eos_token_id=UpperCamelCase__			,							**UpperCamelCase__			,							)
  @property
  def 							_lowercase							( self			: List[Any]    )			->	int:







    """simple docstring"""






    return self.encoder_attention_heads





  @property
  def 							_lowercase							( self			: Dict    )			->	int:







    """simple docstring"""






    return self.d_model

