import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest

class       snake_case					(      snake_case_						):
  lowercase_			=     (PNDMScheduler,)
  lowercase_			=     (("""num_inference_steps""", 50),)




  def 	__lowercase(      self     :	Optional[Any]     , **a_     :	List[str]				)->       List[str]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		{
           'num_train_timesteps': 1000,
           'beta_start': 0.0001,
           'beta_end': 0.02,
           'beta_schedule': 'linear',
       }

       config.update(**a_				)
       return config




  def 	__lowercase(      self     :	Optional[Any]     , a_     :	List[Any]=0     , **a_     :	Tuple				)->       str:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		dict(self.forward_default_kwargs				)
       SCREAMING_SNAKE_CASE__				:				Tuple			=		kwargs.pop('num_inference_steps'     , a_				)
       SCREAMING_SNAKE_CASE__				:				Dict			=		self.dummy_sample
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		0.1 * sample
       SCREAMING_SNAKE_CASE__				:				List[str]			=		[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

       for scheduler_class in self.scheduler_classes:
            SCREAMING_SNAKE_CASE__				:				List[Any]			=		self.get_scheduler_config(**a_				)
            SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		scheduler_class(**a_				)
            scheduler.set_timesteps(a_				)
            # copy over dummy past residuals
            SCREAMING_SNAKE_CASE__				:				Any			=		dummy_past_residuals[:]

            with tempfile.TemporaryDirectory() as tmpdirname:
                 scheduler.save_config(a_				)
                 SCREAMING_SNAKE_CASE__				:				Optional[int]			=		scheduler_class.from_pretrained(a_				)
                 new_scheduler.set_timesteps(a_				)
                 # copy over dummy past residuals
                 SCREAMING_SNAKE_CASE__				:				str			=		dummy_past_residuals[:]

            SCREAMING_SNAKE_CASE__				:				Optional[int]			=		scheduler.step_prk(a_     , a_     , a_     , **a_				).prev_sample
            SCREAMING_SNAKE_CASE__				:				Dict			=		new_scheduler.step_prk(a_     , a_     , a_     , **a_				).prev_sample

            assert torch.sum(torch.abs(output - new_output				)				) < 1e-5, "Scheduler outputs are not identical"

            SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		scheduler.step_plms(a_     , a_     , a_     , **a_				).prev_sample
            SCREAMING_SNAKE_CASE__				:				List[str]			=		new_scheduler.step_plms(a_     , a_     , a_     , **a_				).prev_sample

            assert torch.sum(torch.abs(output - new_output				)				) < 1e-5, "Scheduler outputs are not identical"




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




       """simple docstring"""
       pass




  def 	__lowercase(      self     :	List[Any]     , a_     :	str=0     , **a_     :	int				)->       Any:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		dict(self.forward_default_kwargs				)
       SCREAMING_SNAKE_CASE__				:				List[str]			=		kwargs.pop('num_inference_steps'     , a_				)
       SCREAMING_SNAKE_CASE__				:				str			=		self.dummy_sample
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		0.1 * sample
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

       for scheduler_class in self.scheduler_classes:
            SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		self.get_scheduler_config()
            SCREAMING_SNAKE_CASE__				:				Dict			=		scheduler_class(**a_				)
            scheduler.set_timesteps(a_				)

            # copy over dummy past residuals (must be after setting timesteps)
            SCREAMING_SNAKE_CASE__				:				Any			=		dummy_past_residuals[:]

            with tempfile.TemporaryDirectory() as tmpdirname:
                 scheduler.save_config(a_				)
                 SCREAMING_SNAKE_CASE__				:				Optional[int]			=		scheduler_class.from_pretrained(a_				)
                 # copy over dummy past residuals
                 new_scheduler.set_timesteps(a_				)

                 # copy over dummy past residual (must be after setting timesteps)
                 SCREAMING_SNAKE_CASE__				:				List[str]			=		dummy_past_residuals[:]

            SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		scheduler.step_prk(a_     , a_     , a_     , **a_				).prev_sample
            SCREAMING_SNAKE_CASE__				:				Dict			=		new_scheduler.step_prk(a_     , a_     , a_     , **a_				).prev_sample

            assert torch.sum(torch.abs(output - new_output				)				) < 1e-5, "Scheduler outputs are not identical"

            SCREAMING_SNAKE_CASE__				:				Tuple			=		scheduler.step_plms(a_     , a_     , a_     , **a_				).prev_sample
            SCREAMING_SNAKE_CASE__				:				List[str]			=		new_scheduler.step_plms(a_     , a_     , a_     , **a_				).prev_sample

            assert torch.sum(torch.abs(output - new_output				)				) < 1e-5, "Scheduler outputs are not identical"




  def 	__lowercase(      self     :	List[str]     , **a_     :	Dict				)->       Optional[int]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				str			=		self.scheduler_classes[0]
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		self.get_scheduler_config(**a_				)
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		scheduler_class(**a_				)

       SCREAMING_SNAKE_CASE__				:				List[str]			=		10
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		self.dummy_model()
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		self.dummy_sample_deter
       scheduler.set_timesteps(a_				)

       for i, t in enumerate(scheduler.prk_timesteps				):
            SCREAMING_SNAKE_CASE__				:				Dict			=		model(a_     , a_				)
            SCREAMING_SNAKE_CASE__				:				str			=		scheduler.step_prk(a_     , a_     , a_				).prev_sample

       for i, t in enumerate(scheduler.plms_timesteps				):
            SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		model(a_     , a_				)
            SCREAMING_SNAKE_CASE__				:				int			=		scheduler.step_plms(a_     , a_     , a_				).prev_sample

       return sample




  def 	__lowercase(      self     :	Tuple				)->       int:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		dict(self.forward_default_kwargs				)

       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		kwargs.pop('num_inference_steps'     , a_				)

       for scheduler_class in self.scheduler_classes:
            SCREAMING_SNAKE_CASE__				:				List[Any]			=		self.get_scheduler_config()
            SCREAMING_SNAKE_CASE__				:				Dict			=		scheduler_class(**a_				)

            SCREAMING_SNAKE_CASE__				:				str			=		self.dummy_sample
            SCREAMING_SNAKE_CASE__				:				List[Any]			=		0.1 * sample

            if num_inference_steps is not None and hasattr(a_     , 'set_timesteps'				):
                 scheduler.set_timesteps(a_				)
            elif num_inference_steps is not None and not hasattr(a_     , 'set_timesteps'				):
                 SCREAMING_SNAKE_CASE__				:				Tuple			=		num_inference_steps

            # copy over dummy past residuals (must be done after set_timesteps)
            SCREAMING_SNAKE_CASE__				:				Any			=		[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
            SCREAMING_SNAKE_CASE__				:				str			=		dummy_past_residuals[:]

            SCREAMING_SNAKE_CASE__				:				List[str]			=		scheduler.step_prk(a_     , 0     , a_     , **a_				).prev_sample
            SCREAMING_SNAKE_CASE__				:				List[Any]			=		scheduler.step_prk(a_     , 1     , a_     , **a_				).prev_sample

            self.assertEqual(output_a.shape     , sample.shape				)
            self.assertEqual(output_a.shape     , output_a.shape				)

            SCREAMING_SNAKE_CASE__				:				List[str]			=		scheduler.step_plms(a_     , 0     , a_     , **a_				).prev_sample
            SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		scheduler.step_plms(a_     , 1     , a_     , **a_				).prev_sample

            self.assertEqual(output_a.shape     , sample.shape				)
            self.assertEqual(output_a.shape     , output_a.shape				)




  def 	__lowercase(      self     :	Optional[int]				)->       List[Any]:




       """simple docstring"""
       for timesteps in [100, 1000]:
            self.check_over_configs(num_train_timesteps=a_				)




  def 	__lowercase(      self     :	str				)->       Any:




       """simple docstring"""
       for steps_offset in [0, 1]:
            self.check_over_configs(steps_offset=a_				)

       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		self.scheduler_classes[0]
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		self.get_scheduler_config(steps_offset=1				)
       SCREAMING_SNAKE_CASE__				:				str			=		scheduler_class(**a_				)
       scheduler.set_timesteps(10				)
       assert torch.equal(
           scheduler.timesteps     , torch.LongTensor(
               [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]				)     , )




  def 	__lowercase(      self     :	List[Any]				)->       Optional[int]:




       """simple docstring"""
       for beta_start, beta_end in zip([0.0001, 0.001]     , [0.002, 0.02]				):
            self.check_over_configs(beta_start=a_     , beta_end=a_				)




  def 	__lowercase(      self     :	Optional[int]				)->       Union[str, Any]:




       """simple docstring"""
       for schedule in ["linear", "squaredcos_cap_v2"]:
            self.check_over_configs(beta_schedule=a_				)




  def 	__lowercase(      self     :	Optional[Any]				)->       Dict:




       """simple docstring"""
       for prediction_type in ["epsilon", "v_prediction"]:
            self.check_over_configs(prediction_type=a_				)




  def 	__lowercase(      self     :	List[Any]				)->       Tuple:




       """simple docstring"""
       for t in [1, 5, 10]:
            self.check_over_forward(time_step=a_				)




  def 	__lowercase(      self     :	int				)->       Optional[int]:




       """simple docstring"""
       for t, num_inference_steps in zip([1, 5, 10]     , [10, 50, 100]				):
            self.check_over_forward(num_inference_steps=a_				)




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




       """simple docstring"""
       # earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
       SCREAMING_SNAKE_CASE__				:				Tuple			=		27

       for scheduler_class in self.scheduler_classes:
            SCREAMING_SNAKE_CASE__				:				Dict			=		self.dummy_sample
            SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		0.1 * sample

            SCREAMING_SNAKE_CASE__				:				List[str]			=		self.get_scheduler_config()
            SCREAMING_SNAKE_CASE__				:				Tuple			=		scheduler_class(**a_				)

            scheduler.set_timesteps(a_				)

            # before power of 3 fix, would error on first step, so we only need to do two
            for i, t in enumerate(scheduler.prk_timesteps[:2]				):
                 SCREAMING_SNAKE_CASE__				:				List[str]			=		scheduler.step_prk(a_     , a_     , a_				).prev_sample




  def 	__lowercase(      self     :	int				)->       Dict:




       """simple docstring"""
       with self.assertRaises(a_				):
            SCREAMING_SNAKE_CASE__				:				str			=		self.scheduler_classes[0]
            SCREAMING_SNAKE_CASE__				:				List[Any]			=		self.get_scheduler_config()
            SCREAMING_SNAKE_CASE__				:				Optional[int]			=		scheduler_class(**a_				)

            scheduler.step_plms(self.dummy_sample     , 1     , self.dummy_sample				).prev_sample




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		self.full_loop()
       SCREAMING_SNAKE_CASE__				:				List[str]			=		torch.sum(torch.abs(a_				)				)
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		torch.mean(torch.abs(a_				)				)

       assert abs(result_sum.item() - 198.1318				) < 1e-2
       assert abs(result_mean.item() - 0.2580				) < 1e-3




  def 	__lowercase(      self     :	Tuple				)->       List[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[str]			=		self.full_loop(prediction_type='v_prediction'				)
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		torch.sum(torch.abs(a_				)				)
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		torch.mean(torch.abs(a_				)				)

       assert abs(result_sum.item() - 67.3986				) < 1e-2
       assert abs(result_mean.item() - 0.0878				) < 1e-3




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




       """simple docstring"""
       # We specify different beta, so that the first alpha is 0.99
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		self.full_loop(set_alpha_to_one=a_     , beta_start=0.01				)
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		torch.sum(torch.abs(a_				)				)
       SCREAMING_SNAKE_CASE__				:				Any			=		torch.mean(torch.abs(a_				)				)

       assert abs(result_sum.item() - 230.0399				) < 1e-2
       assert abs(result_mean.item() - 0.2995				) < 1e-3




  def 	__lowercase(      self     :	Dict				)->       str:




       """simple docstring"""
       # We specify different beta, so that the first alpha is 0.99
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		self.full_loop(set_alpha_to_one=a_     , beta_start=0.01				)
       SCREAMING_SNAKE_CASE__				:				str			=		torch.sum(torch.abs(a_				)				)
       SCREAMING_SNAKE_CASE__				:				List[str]			=		torch.mean(torch.abs(a_				)				)

       assert abs(result_sum.item() - 186.9482				) < 1e-2
       assert abs(result_mean.item() - 0.2434				) < 1e-3




import unittest

import numpy as np
import requests

from transformers.testing_utils import require_torch, require_vision
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

      from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
      SCREAMING_SNAKE_CASE__		:					Any						=					False

if is_vision_available():
      from PIL import Image

      from transformers import PixaStructImageProcessor

class       snake_case					(      unittest.TestCase						):




  def __init__(      self     :	List[Any]     , a_     :	Optional[int]     , a_     :	Dict=7     , a_     :	Any=3     , a_     :	Any=18     , a_     :	int=30     , a_     :	int=400     , a_     :	List[Any]=None     , a_     :	int=True     , a_     :	int=True     , a_     :	Dict=None     , )->       Optional[int]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Dict			=		size if size is not None else {'height': 20, 'width': 20}
       SCREAMING_SNAKE_CASE__				:				str			=		parent
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		batch_size
       SCREAMING_SNAKE_CASE__				:				Any			=		num_channels
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		image_size
       SCREAMING_SNAKE_CASE__				:				List[str]			=		min_resolution
       SCREAMING_SNAKE_CASE__				:				Dict			=		max_resolution
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		size
       SCREAMING_SNAKE_CASE__				:				Tuple			=		do_normalize
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		do_convert_rgb
       SCREAMING_SNAKE_CASE__				:				List[str]			=		[512, 1024, 2048, 4096]
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		patch_size if patch_size is not None else {'height': 16, 'width': 16}




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




       """simple docstring"""
       return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}




  def 	__lowercase(      self     :	Dict				)->       Dict:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'
       SCREAMING_SNAKE_CASE__				:				str			=		Image.open(requests.get(a_     , stream=a_				).raw				).convert('RGB'				)
       return raw_image




@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11   ,						reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.'   ,						)
@require_torch
@require_vision
class       snake_case					(      UpperCamelCase_   ,						unittest.TestCase						):
  lowercase_			=     PixaStructImageProcessor if is_vision_available() else None




  def 	__lowercase(      self     :	List[str]				)->       Optional[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				int			=		PixaStructImageProcessingTester(self				)




  @property
  def 	__lowercase(      self     :	Dict				)->       Tuple:




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




  def 	__lowercase(      self     :	Any				)->       Tuple:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		self.image_processing_class(**self.image_processor_dict				)
       self.assertTrue(hasattr(a_     , 'do_normalize'				)				)
       self.assertTrue(hasattr(a_     , 'do_convert_rgb'				)				)




  def 	__lowercase(      self     :	List[Any]				)->       Tuple:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Dict			=		self.image_processor_tester.prepare_dummy_image()

       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		self.image_processing_class(**self.image_processor_dict				)
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		2048

       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		image_processor(a_     , return_tensors='pt'     , max_patches=a_				)
       self.assertTrue(torch.allclose(inputs.flattened_patches.mean()     , torch.tensor(0.0606				)     , atol=1e-3     , rtol=1e-3				)				)




  def 	__lowercase(      self     :	Any				)->       Tuple:




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

       # Test not batched input
       SCREAMING_SNAKE_CASE__				:				str			=		(
           (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
           * self.image_processor_tester.num_channels
       ) + 2

       for max_patch in self.image_processor_tester.max_patches:
            # Test not batched input
            SCREAMING_SNAKE_CASE__				:				List[str]			=		image_processor(
                image_inputs[0]     , return_tensors='pt'     , max_patches=a_				).flattened_patches
            self.assertEqual(
                encoded_images.shape     , (1, max_patch, expected_hidden_dim)     , )

            # Test batched
            SCREAMING_SNAKE_CASE__				:				Tuple			=		image_processor(
                a_     , return_tensors='pt'     , max_patches=a_				).flattened_patches
            self.assertEqual(
                encoded_images.shape     , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)     , )




  def 	__lowercase(      self     :	Any				)->       Any:




       """simple docstring"""
       # Initialize image_processor
       SCREAMING_SNAKE_CASE__				:				Tuple			=		self.image_processing_class(**self.image_processor_dict				)
       # create random PIL images
       SCREAMING_SNAKE_CASE__				:				Dict			=		prepare_image_inputs(self.image_processor_tester     , equal_resolution=a_				)
       for image in image_inputs:
            self.assertIsInstance(a_     , Image.Image				)

       # Test not batched input
       SCREAMING_SNAKE_CASE__				:				str			=		(
           (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
           * self.image_processor_tester.num_channels
       ) + 2

       SCREAMING_SNAKE_CASE__				:				int			=		True

       for max_patch in self.image_processor_tester.max_patches:
            # Test not batched input
            with self.assertRaises(a_				):
                 SCREAMING_SNAKE_CASE__				:				Dict			=		image_processor(
                     image_inputs[0]     , return_tensors='pt'     , max_patches=a_				).flattened_patches

            SCREAMING_SNAKE_CASE__				:				List[Any]			=		'Hello'

            SCREAMING_SNAKE_CASE__				:				List[Any]			=		image_processor(
                image_inputs[0]     , return_tensors='pt'     , max_patches=a_     , header_text=a_				).flattened_patches
            self.assertEqual(
                encoded_images.shape     , (1, max_patch, expected_hidden_dim)     , )

            # Test batched
            SCREAMING_SNAKE_CASE__				:				Any			=		image_processor(
                a_     , return_tensors='pt'     , max_patches=a_     , header_text=a_				).flattened_patches
            self.assertEqual(
                encoded_images.shape     , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)     , )




  def 	__lowercase(      self     :	List[Any]				)->       Dict:




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

       SCREAMING_SNAKE_CASE__				:				str			=		(
           (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
           * self.image_processor_tester.num_channels
       ) + 2

       for max_patch in self.image_processor_tester.max_patches:
            # Test not batched input
            SCREAMING_SNAKE_CASE__				:				str			=		image_processor(
                image_inputs[0]     , return_tensors='pt'     , max_patches=a_				).flattened_patches
            self.assertEqual(
                encoded_images.shape     , (1, max_patch, expected_hidden_dim)     , )

            # Test batched
            SCREAMING_SNAKE_CASE__				:				int			=		image_processor(
                a_     , return_tensors='pt'     , max_patches=a_				).flattened_patches
            self.assertEqual(
                encoded_images.shape     , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)     , )




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




       """simple docstring"""
       # Initialize image_processor
       SCREAMING_SNAKE_CASE__				:				Any			=		self.image_processing_class(**self.image_processor_dict				)
       # create random PyTorch tensors
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		prepare_image_inputs(self.image_processor_tester     , equal_resolution=a_     , torchify=a_				)
       for image in image_inputs:
            self.assertIsInstance(a_     , torch.Tensor				)

       # Test not batched input
       SCREAMING_SNAKE_CASE__				:				Any			=		(
           (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
           * self.image_processor_tester.num_channels
       ) + 2

       for max_patch in self.image_processor_tester.max_patches:
            # Test not batched input
            SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		image_processor(
                image_inputs[0]     , return_tensors='pt'     , max_patches=a_				).flattened_patches
            self.assertEqual(
                encoded_images.shape     , (1, max_patch, expected_hidden_dim)     , )

            # Test batched
            SCREAMING_SNAKE_CASE__				:				List[Any]			=		image_processor(
                a_     , return_tensors='pt'     , max_patches=a_				).flattened_patches
            self.assertEqual(
                encoded_images.shape     , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)     , )




@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11   ,						reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.'   ,						)
@require_torch
@require_vision
class       snake_case					(      UpperCamelCase_   ,						unittest.TestCase						):
  lowercase_			=     PixaStructImageProcessor if is_vision_available() else None




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				str			=		PixaStructImageProcessingTester(self     , num_channels=4				)
       SCREAMING_SNAKE_CASE__				:				Dict			=		3




  @property
  def 	__lowercase(      self     :	Any				)->       Any:




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




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				int			=		self.image_processing_class(**self.image_processor_dict				)
       self.assertTrue(hasattr(a_     , 'do_normalize'				)				)
       self.assertTrue(hasattr(a_     , 'do_convert_rgb'				)				)




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




       """simple docstring"""
       # Initialize image_processor
       SCREAMING_SNAKE_CASE__				:				str			=		self.image_processing_class(**self.image_processor_dict				)
       # create random PIL images
       SCREAMING_SNAKE_CASE__				:				Tuple			=		prepare_image_inputs(self.image_processor_tester     , equal_resolution=a_				)
       for image in image_inputs:
            self.assertIsInstance(a_     , Image.Image				)

       # Test not batched input
       SCREAMING_SNAKE_CASE__				:				Dict			=		(
           (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
           * (self.image_processor_tester.num_channels - 1)
       ) + 2

       for max_patch in self.image_processor_tester.max_patches:
            # Test not batched input
            SCREAMING_SNAKE_CASE__				:				Optional[int]			=		image_processor(
                image_inputs[0]     , return_tensors='pt'     , max_patches=a_				).flattened_patches
            self.assertEqual(
                encoded_images.shape     , (1, max_patch, expected_hidden_dim)     , )

            # Test batched
            SCREAMING_SNAKE_CASE__				:				Tuple			=		image_processor(
                a_     , return_tensors='pt'     , max_patches=a_				).flattened_patches
            self.assertEqual(
                encoded_images.shape     , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)     , )



