
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     ):


  '''simple docstring'''







  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]:







    """simple docstring"""






    __magic_name__				     =       size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 1333}
    __magic_name__				     =       parent
    __magic_name__				     =       batch_size
    __magic_name__				     =       num_channels
    __magic_name__				     =       min_resolution
    __magic_name__				     =       max_resolution
    __magic_name__				     =       do_resize
    __magic_name__				     =       size
    __magic_name__				     =       do_rescale
    __magic_name__				     =       rescale_factor
    __magic_name__				     =       do_normalize
    __magic_name__				     =       image_mean
    __magic_name__				     =       image_std
    __magic_name__				     =       do_pad
  def 							_lowercase							( self			: Any    )			->	int:







    """simple docstring"""






    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:







    """simple docstring"""






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

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

    return expected_height, expected_width







@require_torch
@require_vision
class        UpperCAmelCase_			( _A  ,       unittest.TestCase     ):


  '''simple docstring'''







  a__           =		DetrImageProcessor if is_vision_available() else None
  def 							_lowercase							( self			: Dict    )			->	int:







    """simple docstring"""






    __magic_name__				     =       DetrImageProcessingTester(self    )
  @property
  def 							_lowercase							( self			: List[str]    )			->	List[Any]:







    """simple docstring"""






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







    """simple docstring"""






    __magic_name__				     =       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]:







    """simple docstring"""






    __magic_name__				     =       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__    )

    __magic_name__				     =       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:







    """simple docstring"""






    pass
  def 							_lowercase							( self			: Optional[Any]    )			->	str:







    """simple docstring"""






    __magic_name__				     =       self.image_processing_class(**self.image_processor_dict    )
    # create random PIL images
    __magic_name__				     =       prepare_image_inputs(self.image_processor_tester			,							equal_resolution=UpperCamelCase__    )
    for image in image_inputs:
      self.assertIsInstance(UpperCamelCase__			,							Image.Image    )

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

    __magic_name__     ,    __magic_name__				     =       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
    __magic_name__     ,    __magic_name__				     =       self.image_processor_tester.get_expected_values(UpperCamelCase__			,							batched=UpperCamelCase__    )

    __magic_name__				     =       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]:







    """simple docstring"""






    __magic_name__				     =       self.image_processing_class(**self.image_processor_dict    )
    # create random numpy tensors
    __magic_name__				     =       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
    __magic_name__				     =       image_processing(image_inputs[0]			,							return_tensors="""pt"""    ).pixel_values

    __magic_name__     ,    __magic_name__				     =       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
    __magic_name__				     =       image_processing(UpperCamelCase__			,							return_tensors="""pt"""    ).pixel_values

    __magic_name__     ,    __magic_name__				     =       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]:







    """simple docstring"""






    __magic_name__				     =       self.image_processing_class(**self.image_processor_dict    )
    # create random PyTorch tensors
    __magic_name__				     =       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
    __magic_name__				     =       image_processing(image_inputs[0]			,							return_tensors="""pt"""    ).pixel_values

    __magic_name__     ,    __magic_name__				     =       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
    __magic_name__				     =       image_processing(UpperCamelCase__			,							return_tensors="""pt"""    ).pixel_values

    __magic_name__     ,    __magic_name__				     =       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:







    """simple docstring"""






    __magic_name__				     =       Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""    )
    with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""			,							"""r"""    ) as f:
      __magic_name__				     =       json.loads(f.read()    )

    __magic_name__				     =       {"""image_id""": 3_9769, """annotations""": target}

    # encode them
    __magic_name__				     =       DetrImageProcessor.from_pretrained("""facebook/detr-resnet-50"""    )
    __magic_name__				     =       image_processing(images=UpperCamelCase__			,							annotations=UpperCamelCase__			,							return_tensors="""pt"""    )

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

    __magic_name__				     =       torch.tensor([0.2796, 0.3138, 0.3481]    )
    self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]			,							UpperCamelCase__			,							atol=1E-4    )    )

    # verify area
    __magic_name__				     =       torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]    )
    self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]			,							UpperCamelCase__    )    )
    # verify boxes
    __magic_name__				     =       torch.Size([6, 4]    )
    self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape			,							UpperCamelCase__    )
    __magic_name__				     =       torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]    )
    self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]			,							UpperCamelCase__			,							atol=1E-3    )    )
    # verify image_id
    __magic_name__				     =       torch.tensor([3_9769]    )
    self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]			,							UpperCamelCase__    )    )
    # verify is_crowd
    __magic_name__				     =       torch.tensor([0, 0, 0, 0, 0, 0]    )
    self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]			,							UpperCamelCase__    )    )
    # verify class_labels
    __magic_name__				     =       torch.tensor([75, 75, 63, 65, 17, 17]    )
    self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]			,							UpperCamelCase__    )    )
    # verify orig_size
    __magic_name__				     =       torch.tensor([480, 640]    )
    self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]			,							UpperCamelCase__    )    )
    # verify size
    __magic_name__				     =       torch.tensor([800, 1066]    )
    self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]			,							UpperCamelCase__    )    )





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







    """simple docstring"""






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

    __magic_name__				     =       {"""file_name""": """000000039769.png""", """image_id""": 3_9769, """segments_info""": target}

    __magic_name__				     =       pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""    )

    # encode them
    __magic_name__				     =       DetrImageProcessor.from_pretrained("""facebook/detr-resnet-50-panoptic"""    )
    __magic_name__				     =       image_processing(images=UpperCamelCase__			,							annotations=UpperCamelCase__			,							masks_path=UpperCamelCase__			,							return_tensors="""pt"""    )

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

    __magic_name__				     =       torch.tensor([0.2796, 0.3138, 0.3481]    )
    self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]			,							UpperCamelCase__			,							atol=1E-4    )    )

    # verify area
    __magic_name__				     =       torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]    )
    self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]			,							UpperCamelCase__    )    )
    # verify boxes
    __magic_name__				     =       torch.Size([6, 4]    )
    self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape			,							UpperCamelCase__    )
    __magic_name__				     =       torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]    )
    self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]			,							UpperCamelCase__			,							atol=1E-3    )    )
    # verify image_id
    __magic_name__				     =       torch.tensor([3_9769]    )
    self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]			,							UpperCamelCase__    )    )
    # verify is_crowd
    __magic_name__				     =       torch.tensor([0, 0, 0, 0, 0, 0]    )
    self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]			,							UpperCamelCase__    )    )
    # verify class_labels
    __magic_name__				     =       torch.tensor([17, 17, 63, 75, 75, 93]    )
    self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]			,							UpperCamelCase__    )    )
    # verify masks
    __magic_name__				     =       82_2873
    self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()			,							UpperCamelCase__    )
    # verify orig_size
    __magic_name__				     =       torch.tensor([480, 640]    )
    self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]			,							UpperCamelCase__    )    )
    # verify size
    __magic_name__				     =       torch.tensor([800, 1066]    )
    self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]			,							UpperCamelCase__    )    )





# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin







class       lowerCamelCase__   (       lowerCAmelCase   ,							lowerCAmelCase):
     SCREAMING_SNAKE_CASE__      			=			1





     @register_to_config
     def __init__(self		,     UpperCAmelCase=2_0_0_0		,     UpperCAmelCase=0.1		,     UpperCAmelCase=2_0		,     UpperCAmelCase=1e-3       )     ->   List[str]:
      _lowercase							      =None
      _lowercase							      =None
      _lowercase							      =None





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase = None       )     ->   str:
      _lowercase							      =torch.linspace(1		,     self.config.sampling_eps		,     UpperCAmelCase		,     device=UpperCAmelCase       )





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase=None       )     ->   Optional[int]:
      if self.timesteps is None:
       raise ValueError(
           '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''       )

      # TODO(Patrick) better comments + non-PyTorch
      # postprocess model score
      _lowercase							      =(
          -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
      )
      _lowercase							      =torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff       )       )
      _lowercase							      =std.flatten()
      while len(std.shape       ) < len(score.shape       ):
       _lowercase							      =std.unsqueeze(-1       )
      _lowercase							      =-score / std

      # compute
      _lowercase							      =-1.0 / len(self.timesteps       )

      _lowercase							      =self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
      _lowercase							      =beta_t.flatten()
      while len(beta_t.shape       ) < len(x.shape       ):
       _lowercase							      =beta_t.unsqueeze(-1       )
      _lowercase							      =-0.5 * beta_t * x

      _lowercase							      =torch.sqrt(UpperCAmelCase       )
      _lowercase							      =drift - diffusion**2 * score
      _lowercase							      =x + drift * dt

      # add noise
      _lowercase							      =randn_tensor(x.shape		,     layout=x.layout		,     generator=UpperCAmelCase		,     device=x.device		,     dtype=x.dtype       )
      _lowercase							      =x_mean + diffusion * math.sqrt(-dt       ) * noise

      return x, x_mean





     def __len__(self       )     ->   str:
      return self.config.num_train_timesteps

