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 DetaImageProcessor



class      _A (     unittest.TestCase				):






  def __init__(self ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_=7 ,					SCREAMING_SNAKE_CASE_=3 ,					SCREAMING_SNAKE_CASE_=30 ,					SCREAMING_SNAKE_CASE_=400 ,					SCREAMING_SNAKE_CASE_=True ,					SCREAMING_SNAKE_CASE_=None ,					SCREAMING_SNAKE_CASE_=True ,					SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5] ,					SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5] ,					SCREAMING_SNAKE_CASE_=True ,					SCREAMING_SNAKE_CASE_=1 / 255 ,					SCREAMING_SNAKE_CASE_=True ,					)		->   Tuple:





       '''simple docstring'''





       UpperCamelCase__					    =   size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1333}
       UpperCamelCase__					    =   parent
       UpperCamelCase__					    =   batch_size
       UpperCamelCase__					    =   num_channels
       UpperCamelCase__					    =   min_resolution
       UpperCamelCase__					    =   max_resolution
       UpperCamelCase__					    =   do_resize
       UpperCamelCase__					    =   size
       UpperCamelCase__					    =   do_normalize
       UpperCamelCase__					    =   image_mean
       UpperCamelCase__					    =   image_std
       UpperCamelCase__					    =   do_rescale
       UpperCamelCase__					    =   rescale_factor
       UpperCamelCase__					    =   do_pad






  def 		_a       (self						)		->   Dict:





       '''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 		_a       (self ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_=False						)		->   str:





       '''simple docstring'''





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

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

       return expected_height, expected_width



@require_torch
@require_vision
class      _A (     __UpperCamelCase						,			unittest.TestCase				):
  SCREAMING_SNAKE_CASE_						:						Any    =DetaImageProcessor if is_vision_available() else None






  def 		_a       (self						)		->   List[str]:





       '''simple docstring'''





       UpperCamelCase__					    =   DetaImageProcessingTester(self						)






  @property
  def 		_a       (self						)		->   Optional[int]:





       '''simple docstring'''





       return self.image_processor_tester.prepare_image_processor_dict()






  def 		_a       (self						)		->   List[str]:





       '''simple docstring'''





       UpperCamelCase__					    =   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 		_a       (self						)		->   Any:





       '''simple docstring'''





       UpperCamelCase__					    =   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 ,					_lowercase						)






  def 		_a       (self						)		->   Any:





       '''simple docstring'''





       pass






  def 		_a       (self						)		->   int:





       '''simple docstring'''





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

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

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

       UpperCamelCase__					    =   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 		_a       (self						)		->   Optional[int]:





       '''simple docstring'''





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

       UpperCamelCase__    ,							UpperCamelCase__					    =   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
       UpperCamelCase__					    =   image_processing(_lowercase ,					return_tensors='''pt'''						).pixel_values

       UpperCamelCase__    ,							UpperCamelCase__					    =   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 		_a       (self						)		->   List[str]:





       '''simple docstring'''





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

       UpperCamelCase__    ,							UpperCamelCase__					    =   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
       UpperCamelCase__					    =   image_processing(_lowercase ,					return_tensors='''pt'''						).pixel_values

       UpperCamelCase__    ,							UpperCamelCase__					    =   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 		_a       (self						)		->   Union[str, Any]:





       '''simple docstring'''





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

       UpperCamelCase__					    =   {'''image_id''': 3_9769, '''annotations''': target}

       # encode them
       UpperCamelCase__					    =   DetaImageProcessor()
       UpperCamelCase__					    =   image_processing(images=_lowercase ,					annotations=_lowercase ,					return_tensors='''pt'''						)

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

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

       # verify area
       UpperCamelCase__					    =   torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]						)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] ,					_lowercase						)						)
       # verify boxes
       UpperCamelCase__					    =   torch.Size([6, 4]						)
       self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape ,					_lowercase						)
       UpperCamelCase__					    =   torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]						)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] ,					_lowercase ,					atol=1E-3						)						)
       # verify image_id
       UpperCamelCase__					    =   torch.tensor([3_9769]						)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] ,					_lowercase						)						)
       # verify is_crowd
       UpperCamelCase__					    =   torch.tensor([0, 0, 0, 0, 0, 0]						)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] ,					_lowercase						)						)
       # verify class_labels
       UpperCamelCase__					    =   torch.tensor([75, 75, 63, 65, 17, 17]						)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] ,					_lowercase						)						)
       # verify orig_size
       UpperCamelCase__					    =   torch.tensor([480, 640]						)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] ,					_lowercase						)						)
       # verify size
       UpperCamelCase__					    =   torch.tensor([800, 1066]						)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] ,					_lowercase						)						)






  @slow
  def 		_a       (self						)		->   Dict:





       '''simple docstring'''





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

       UpperCamelCase__					    =   {'''file_name''': '''000000039769.png''', '''image_id''': 3_9769, '''segments_info''': target}

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

       # encode them
       UpperCamelCase__					    =   DetaImageProcessor(format='''coco_panoptic'''						)
       UpperCamelCase__					    =   image_processing(images=_lowercase ,					annotations=_lowercase ,					masks_path=_lowercase ,					return_tensors='''pt'''						)

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

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

       # verify area
       UpperCamelCase__					    =   torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147]						)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] ,					_lowercase						)						)
       # verify boxes
       UpperCamelCase__					    =   torch.Size([6, 4]						)
       self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape ,					_lowercase						)
       UpperCamelCase__					    =   torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]						)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] ,					_lowercase ,					atol=1E-3						)						)
       # verify image_id
       UpperCamelCase__					    =   torch.tensor([3_9769]						)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] ,					_lowercase						)						)
       # verify is_crowd
       UpperCamelCase__					    =   torch.tensor([0, 0, 0, 0, 0, 0]						)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] ,					_lowercase						)						)
       # verify class_labels
       UpperCamelCase__					    =   torch.tensor([17, 17, 63, 75, 75, 93]						)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] ,					_lowercase						)						)
       # verify masks
       UpperCamelCase__					    =   82_2873
       self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item() ,					_lowercase						)
       # verify orig_size
       UpperCamelCase__					    =   torch.tensor([480, 640]						)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] ,					_lowercase						)						)
       # verify size
       UpperCamelCase__					    =   torch.tensor([800, 1066]						)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] ,					_lowercase						)						)









'''simple docstring'''
import math
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class   __UpperCAmelCase				(     lowerCAmelCase			,lowerCAmelCase				):



					'''simple docstring'''





					_UpperCamelCase							=		1



					@register_to_config
					def __init__(	self     :						str      ,       _lowercase     :						int = 1_000      ,       _lowercase     :						Optional[Union[np.ndarray, List[float]]] = None)    ->							List[str]:
								# set `betas`, `alphas`, `timesteps`
								self.set_timesteps(_lowercase)

								# standard deviation of the initial noise distribution
								A_									= 1.0

								# For now we only support F-PNDM, i.e. the runge-kutta method
								# For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
								# mainly at formula (9), (12), (13) and the Algorithm 2.
								A_									= 4

								# running values
								A_									= []



					def     __snake_case						(	self     :						int      ,       _lowercase     :						int      ,       _lowercase     :						Union[str, torch.device] = None)    ->							Any:
								A_									= num_inference_steps
								A_									= torch.linspace(1      ,       0      ,       num_inference_steps + 1)[:-1]
								A_									= torch.cat([steps, torch.tensor([0.0])])

								if self.config.trained_betas is not None:
											A_									= torch.tensor(self.config.trained_betas      ,       dtype=torch.floataa)
								else:
											A_									= torch.sin(steps * math.pi / 2) ** 2

								A_									= (1.0 - self.betas**2) ** 0.5

								A_									= (torch.atana(self.betas      ,       self.alphas) / math.pi * 2)[:-1]
								A_									= timesteps.to(_lowercase)

								A_									= []



					def     __snake_case						(	self     :						Dict      ,       _lowercase     :						torch.FloatTensor      ,       _lowercase     :						int      ,       _lowercase     :						torch.FloatTensor      ,       _lowercase     :						bool = True      ,       )    ->							Union[SchedulerOutput, Tuple]:
								if self.num_inference_steps is None:
											raise ValueError(
											    'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler')

								A_									= (self.timesteps == timestep).nonzero().item()
								A_									= timestep_index + 1

								A_									= sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
								self.ets.append(_lowercase)

								if len(self.ets) == 1:
											A_									= self.ets[-1]
								elif len(self.ets) == 2:
											A_									= (3 * self.ets[-1] - self.ets[-2]) / 2
								elif len(self.ets) == 3:
											A_									= (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
								else:
											A_									= (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])

								A_									= self._get_prev_sample(_lowercase      ,       _lowercase      ,       _lowercase      ,       _lowercase)

								if not return_dict:
											return (prev_sample,)

								return SchedulerOutput(prev_sample=_lowercase)



					def     __snake_case						(	self     :						Dict      ,       _lowercase     :						torch.FloatTensor      ,       *_lowercase     :						Optional[Any]      ,       **_lowercase     :						int)    ->							torch.FloatTensor:
								return sample



					def     __snake_case						(	self     :						Optional[int]      ,       _lowercase     :						Optional[Any]      ,       _lowercase     :						Dict      ,       _lowercase     :						Union[str, Any]      ,       _lowercase     :						Union[str, Any])    ->							Union[str, Any]:
								A_									= self.alphas[timestep_index]
								A_									= self.betas[timestep_index]

								A_									= self.alphas[prev_timestep_index]
								A_									= self.betas[prev_timestep_index]

								A_									= (sample - sigma * ets) / max(_lowercase      ,       1E-8)
								A_									= next_alpha * pred + ets * next_sigma

								return prev_sample



					def __len__(	self     :						List[str])    ->							Union[str, Any]:
								return self.config.num_train_timesteps




