


'''simple docstring'''





import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest

class 						A__							(		UpperCamelCase__  ):
						UpperCAmelCase             =						(DDPMScheduler,)




						def 							__UpperCamelCase				(       self					:      Optional[int]				,    **_a					:      Optional[int]  )							->     List[str]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  ={
										    '''num_train_timesteps''': 1000,
										    '''beta_start''': 0.00_01,
										    '''beta_end''': 0.02,
										    '''beta_schedule''': '''linear''',
										    '''variance_type''': '''fixed_small''',
										    '''clip_sample''': True,
										}

										config.update(**_a  )
										return config




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


										"""simple docstring"""





										for timesteps in [1, 5, 100, 1000]:
														self.check_over_configs(num_train_timesteps=_a  )




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


										"""simple docstring"""





										for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]				,    [0.0_02, 0.02, 0.2, 2]  ):
														self.check_over_configs(beta_start=_a				,    beta_end=_a  )




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


										"""simple docstring"""





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




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


										"""simple docstring"""





										for variance in ["fixed_small", "fixed_large", "other"]:
														self.check_over_configs(variance_type=_a  )




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


										"""simple docstring"""





										for clip_sample in [True, False]:
														self.check_over_configs(clip_sample=_a  )




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


										"""simple docstring"""





										self.check_over_configs(thresholding=_a  )
										for threshold in [0.5, 1.0, 2.0]:
														for prediction_type in ["epsilon", "sample", "v_prediction"]:
																		self.check_over_configs(
																		    thresholding=_a				,    prediction_type=_a				,    sample_max_value=_a				,    )




						def 							__UpperCamelCase				(       self					:      str  )							->     Any:


										"""simple docstring"""





										for prediction_type in ["epsilon", "sample", "v_prediction"]:
														self.check_over_configs(prediction_type=_a  )




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


										"""simple docstring"""





										for t in [0, 500, 999]:
														self.check_over_forward(time_step=_a  )




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.scheduler_classes[0]
										_SCREAMING_SNAKE_CASE			  =self.get_scheduler_config()
										_SCREAMING_SNAKE_CASE			  =scheduler_class(**_a  )

										assert torch.sum(torch.abs(scheduler._get_variance(0  ) - 0.0  )  ) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(487  ) - 0.0_09_79  )  ) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(999  ) - 0.02  )  ) < 1E-5




						def 							__UpperCamelCase				(       self					:      str  )							->     int:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.scheduler_classes[0]
										_SCREAMING_SNAKE_CASE			  =self.get_scheduler_config()
										_SCREAMING_SNAKE_CASE			  =scheduler_class(**_a  )

										_SCREAMING_SNAKE_CASE			  =len(_a  )

										_SCREAMING_SNAKE_CASE			  =self.dummy_model()
										_SCREAMING_SNAKE_CASE			  =self.dummy_sample_deter
										_SCREAMING_SNAKE_CASE			  =torch.manual_seed(0  )

										for t in reversed(range(_a  )  ):
														# 1. predict noise residual
														_SCREAMING_SNAKE_CASE			  =model(_a				,    _a  )

														# 2. predict previous mean of sample x_t-1
														_SCREAMING_SNAKE_CASE			  =scheduler.step(_a				,    _a				,    _a				,    generator=_a  ).prev_sample

														# if t > 0:
														#     noise = self.dummy_sample_deter
														#     variance = scheduler.get_variance(t) ** (0.5) * noise
														#
														# sample = pred_prev_sample + variance
														_SCREAMING_SNAKE_CASE			  =pred_prev_sample

										_SCREAMING_SNAKE_CASE			  =torch.sum(torch.abs(_a  )  )
										_SCREAMING_SNAKE_CASE			  =torch.mean(torch.abs(_a  )  )

										assert abs(result_sum.item() - 258.9606  ) < 1E-2
										assert abs(result_mean.item() - 0.33_72  ) < 1E-3




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.scheduler_classes[0]
										_SCREAMING_SNAKE_CASE			  =self.get_scheduler_config(prediction_type='''v_prediction'''  )
										_SCREAMING_SNAKE_CASE			  =scheduler_class(**_a  )

										_SCREAMING_SNAKE_CASE			  =len(_a  )

										_SCREAMING_SNAKE_CASE			  =self.dummy_model()
										_SCREAMING_SNAKE_CASE			  =self.dummy_sample_deter
										_SCREAMING_SNAKE_CASE			  =torch.manual_seed(0  )

										for t in reversed(range(_a  )  ):
														# 1. predict noise residual
														_SCREAMING_SNAKE_CASE			  =model(_a				,    _a  )

														# 2. predict previous mean of sample x_t-1
														_SCREAMING_SNAKE_CASE			  =scheduler.step(_a				,    _a				,    _a				,    generator=_a  ).prev_sample

														# if t > 0:
														#     noise = self.dummy_sample_deter
														#     variance = scheduler.get_variance(t) ** (0.5) * noise
														#
														# sample = pred_prev_sample + variance
														_SCREAMING_SNAKE_CASE			  =pred_prev_sample

										_SCREAMING_SNAKE_CASE			  =torch.sum(torch.abs(_a  )  )
										_SCREAMING_SNAKE_CASE			  =torch.mean(torch.abs(_a  )  )

										assert abs(result_sum.item() - 202.0296  ) < 1E-2
										assert abs(result_mean.item() - 0.26_31  ) < 1E-3




						def 							__UpperCamelCase				(       self					:      Any  )							->     Optional[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.scheduler_classes[0]
										_SCREAMING_SNAKE_CASE			  =self.get_scheduler_config()
										_SCREAMING_SNAKE_CASE			  =scheduler_class(**_a  )

										_SCREAMING_SNAKE_CASE			  =[100, 87, 50, 1, 0]

										scheduler.set_timesteps(timesteps=_a  )

										_SCREAMING_SNAKE_CASE			  =scheduler.timesteps

										for i, timestep in enumerate(_a  ):
														if i == len(_a  ) - 1:
																		_SCREAMING_SNAKE_CASE			  =-1
														else:
																		_SCREAMING_SNAKE_CASE			  =timesteps[i + 1]

														_SCREAMING_SNAKE_CASE			  =scheduler.previous_timestep(_a  )
														_SCREAMING_SNAKE_CASE			  =prev_t.item()

														self.assertEqual(_a				,    _a  )




						def 							__UpperCamelCase				(       self					:      str  )							->     Tuple:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.scheduler_classes[0]
										_SCREAMING_SNAKE_CASE			  =self.get_scheduler_config()
										_SCREAMING_SNAKE_CASE			  =scheduler_class(**_a  )

										_SCREAMING_SNAKE_CASE			  =[100, 87, 50, 51, 0]

										with self.assertRaises(_a				,    msg='''`custom_timesteps` must be in descending order.'''  ):
														scheduler.set_timesteps(timesteps=_a  )




						def 							__UpperCamelCase				(       self					:      int  )							->     List[str]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.scheduler_classes[0]
										_SCREAMING_SNAKE_CASE			  =self.get_scheduler_config()
										_SCREAMING_SNAKE_CASE			  =scheduler_class(**_a  )

										_SCREAMING_SNAKE_CASE			  =[100, 87, 50, 1, 0]
										_SCREAMING_SNAKE_CASE			  =len(_a  )

										with self.assertRaises(_a				,    msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''  ):
														scheduler.set_timesteps(num_inference_steps=_a				,    timesteps=_a  )




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.scheduler_classes[0]
										_SCREAMING_SNAKE_CASE			  =self.get_scheduler_config()
										_SCREAMING_SNAKE_CASE			  =scheduler_class(**_a  )

										_SCREAMING_SNAKE_CASE			  =[scheduler.config.num_train_timesteps]

										with self.assertRaises(
										    _a				,    msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''				,    ):
														scheduler.set_timesteps(timesteps=_a  )



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:
							snake_case_						:			Any													=							False

if is_vision_available():
							from PIL import Image

							from transformers import PixaStructImageProcessor

class 						A__							(		unittest.TestCase  ):




						def __init__(       self					:      Tuple				,    _a					:      str				,    _a					:      List[str]=7				,    _a					:      Union[str, Any]=3				,    _a					:      List[str]=18				,    _a					:      List[Any]=30				,    _a					:      Optional[Any]=400				,    _a					:      Dict=None				,    _a					:      Union[str, Any]=True				,    _a					:      List[str]=True				,    _a					:      Optional[Any]=None				,    )							->     Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =size if size is not None else {'''height''': 20, '''width''': 20}
										_SCREAMING_SNAKE_CASE			  =parent
										_SCREAMING_SNAKE_CASE			  =batch_size
										_SCREAMING_SNAKE_CASE			  =num_channels
										_SCREAMING_SNAKE_CASE			  =image_size
										_SCREAMING_SNAKE_CASE			  =min_resolution
										_SCREAMING_SNAKE_CASE			  =max_resolution
										_SCREAMING_SNAKE_CASE			  =size
										_SCREAMING_SNAKE_CASE			  =do_normalize
										_SCREAMING_SNAKE_CASE			  =do_convert_rgb
										_SCREAMING_SNAKE_CASE			  =[512, 1024, 2048, 4096]
										_SCREAMING_SNAKE_CASE			  =patch_size if patch_size is not None else {'''height''': 16, '''width''': 16}




						def 							__UpperCamelCase				(       self					:      int  )							->     Any:


										"""simple docstring"""





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




						def 							__UpperCamelCase				(       self					:      str  )							->     Optional[int]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  ='''https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'''
										_SCREAMING_SNAKE_CASE			  =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 						A__							(		UpperCamelCase__   ,     unittest.TestCase  ):
						UpperCAmelCase             =						PixaStructImageProcessor if is_vision_available() else None




						def 							__UpperCamelCase				(       self					:      int  )							->     Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =PixaStructImageProcessingTester(self  )




						@property
						def 							__UpperCamelCase				(       self					:      int  )							->     Any:


										"""simple docstring"""





										return self.image_processor_tester.prepare_image_processor_dict()




						def 							__UpperCamelCase				(       self					:      str  )							->     Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.image_processing_class(**self.image_processor_dict  )
										self.assertTrue(hasattr(_a				,    '''do_normalize'''  )  )
										self.assertTrue(hasattr(_a				,    '''do_convert_rgb'''  )  )




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.image_processor_tester.prepare_dummy_image()

										_SCREAMING_SNAKE_CASE			  =self.image_processing_class(**self.image_processor_dict  )
										_SCREAMING_SNAKE_CASE			  =2048

										_SCREAMING_SNAKE_CASE			  =image_processor(_a				,    return_tensors='''pt'''				,    max_patches=_a  )
										self.assertTrue(torch.allclose(inputs.flattened_patches.mean()				,    torch.tensor(0.06_06  )				,    atol=1E-3				,    rtol=1E-3  )  )




						def 							__UpperCamelCase				(       self					:      Tuple  )							->     Optional[int]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.image_processing_class(**self.image_processor_dict  )
										# create random PIL images
										_SCREAMING_SNAKE_CASE			  =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			  =(
										    (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			  =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			  =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 							__UpperCamelCase				(       self					:      Dict  )							->     List[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.image_processing_class(**self.image_processor_dict  )
										# create random PIL images
										_SCREAMING_SNAKE_CASE			  =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			  =(
										    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
										    * self.image_processor_tester.num_channels
										) + 2

										_SCREAMING_SNAKE_CASE			  =True

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

														_SCREAMING_SNAKE_CASE			  ='''Hello'''

														_SCREAMING_SNAKE_CASE			  =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			  =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 							__UpperCamelCase				(       self					:      List[Any]  )							->     List[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.image_processing_class(**self.image_processor_dict  )
										# create random numpy tensors
										_SCREAMING_SNAKE_CASE			  =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			  =(
										    (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			  =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			  =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 							__UpperCamelCase				(       self					:      Union[str, Any]  )							->     List[str]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.image_processing_class(**self.image_processor_dict  )
										# create random PyTorch tensors
										_SCREAMING_SNAKE_CASE			  =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			  =(
										    (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			  =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			  =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 						A__							(		UpperCamelCase__   ,     unittest.TestCase  ):
						UpperCAmelCase             =						PixaStructImageProcessor if is_vision_available() else None




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =PixaStructImageProcessingTester(self				,    num_channels=4  )
										_SCREAMING_SNAKE_CASE			  =3




						@property
						def 							__UpperCamelCase				(       self					:      Optional[int]  )							->     List[str]:


										"""simple docstring"""





										return self.image_processor_tester.prepare_image_processor_dict()




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.image_processing_class(**self.image_processor_dict  )
										self.assertTrue(hasattr(_a				,    '''do_normalize'''  )  )
										self.assertTrue(hasattr(_a				,    '''do_convert_rgb'''  )  )




						def 							__UpperCamelCase				(       self					:      Dict  )							->     Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.image_processing_class(**self.image_processor_dict  )
										# create random PIL images
										_SCREAMING_SNAKE_CASE			  =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			  =(
										    (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			  =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			  =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)				,    )