



"""simple docstring"""





import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest



class  _lowerCAmelCase     (					SCREAMING_SNAKE_CASE__		):





		"""simple docstring"""






		_lowerCamelCase      	=							(DDIMParallelScheduler,)
		_lowerCamelCase      	=							(('''eta''', 0.0), ('''num_inference_steps''', 50))





		def  UpperCAmelCase__				(       self				,     **_lowercase )			->    int:







								'''simple docstring'''

								snake_case_ :			Optional[int]            =      {
								    """num_train_timesteps""": 1_0_0_0,
								    """beta_start""": 0.0001,
								    """beta_end""": 0.02,
								    """beta_schedule""": """linear""",
								    """clip_sample""": True,
								}

								config.update(**_lowercase )
								return config





		def  UpperCAmelCase__				(       self				,     **_lowercase )			->    int:







								'''simple docstring'''

								snake_case_ :			Union[str, Any]            =      self.scheduler_classes[0]
								snake_case_ :			Dict            =      self.get_scheduler_config(**_lowercase )
								snake_case_ :			Optional[Any]            =      scheduler_class(**_lowercase )

								snake_case_      ,		snake_case_ :			int            =      1_0, 0.0

								snake_case_ :			Any            =      self.dummy_model()
								snake_case_ :			List[str]            =      self.dummy_sample_deter

								scheduler.set_timesteps(_lowercase )

								for t in scheduler.timesteps:
														snake_case_ :			Dict            =      model(_lowercase				,     _lowercase )
														snake_case_ :			Any            =      scheduler.step(_lowercase				,     _lowercase				,     _lowercase				,     _lowercase ).prev_sample

								return sample





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







								'''simple docstring'''

								for timesteps in [1_0_0, 5_0_0, 1_0_0_0]:
														self.check_over_configs(num_train_timesteps=_lowercase )





		def  UpperCAmelCase__				(       self )			->    str:







								'''simple docstring'''

								for steps_offset in [0, 1]:
														self.check_over_configs(steps_offset=_lowercase )

								snake_case_ :			str            =      self.scheduler_classes[0]
								snake_case_ :			Optional[int]            =      self.get_scheduler_config(steps_offset=1 )
								snake_case_ :			Any            =      scheduler_class(**_lowercase )
								scheduler.set_timesteps(5 )
								assert torch.equal(scheduler.timesteps				,     torch.LongTensor([8_0_1, 6_0_1, 4_0_1, 2_0_1, 1] ) )





		def  UpperCAmelCase__				(       self )			->    List[Any]:







								'''simple docstring'''

								for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]				,     [0.002, 0.02, 0.2, 2] ):
														self.check_over_configs(beta_start=_lowercase				,     beta_end=_lowercase )





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







								'''simple docstring'''

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





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







								'''simple docstring'''

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





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







								'''simple docstring'''

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





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







								'''simple docstring'''

								for timestep_spacing in ["trailing", "leading"]:
														self.check_over_configs(timestep_spacing=_lowercase )





		def  UpperCAmelCase__				(       self )			->    str:







								'''simple docstring'''

								for rescale_betas_zero_snr in [True, False]:
														self.check_over_configs(rescale_betas_zero_snr=_lowercase )





		def  UpperCAmelCase__				(       self )			->    Any:







								'''simple docstring'''

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





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







								'''simple docstring'''

								for t in [1, 1_0, 4_9]:
														self.check_over_forward(time_step=_lowercase )





		def  UpperCAmelCase__				(       self )			->    Any:







								'''simple docstring'''

								for t, num_inference_steps in zip([1, 1_0, 5_0]				,     [1_0, 5_0, 5_0_0] ):
														self.check_over_forward(time_step=_lowercase				,     num_inference_steps=_lowercase )





		def  UpperCAmelCase__				(       self )			->    Dict:







								'''simple docstring'''

								for t, eta in zip([1, 1_0, 4_9]				,     [0.0, 0.5, 1.0] ):
														self.check_over_forward(time_step=_lowercase				,     eta=_lowercase )





		def  UpperCAmelCase__				(       self )			->    List[Any]:







								'''simple docstring'''

								snake_case_ :			List[Any]            =      self.scheduler_classes[0]
								snake_case_ :			List[str]            =      self.get_scheduler_config()
								snake_case_ :			str            =      scheduler_class(**_lowercase )

								assert torch.sum(torch.abs(scheduler._get_variance(0				,     0 ) - 0.0 ) ) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(4_2_0				,     4_0_0 ) - 0.1_4771 ) ) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(9_8_0				,     9_6_0 ) - 0.3_2460 ) ) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(0				,     0 ) - 0.0 ) ) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(4_8_7				,     4_8_6 ) - 0.0_0979 ) ) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(9_9_9				,     9_9_8 ) - 0.02 ) ) < 1E-5





		def  UpperCAmelCase__				(       self )			->    Tuple:







								'''simple docstring'''

								snake_case_ :			List[Any]            =      self.scheduler_classes[0]
								snake_case_ :			str            =      self.get_scheduler_config()
								snake_case_ :			Any            =      scheduler_class(**_lowercase )

								snake_case_      ,		snake_case_ :			List[str]            =      1_0, 0.0
								scheduler.set_timesteps(_lowercase )

								snake_case_ :			Tuple            =      self.dummy_model()
								snake_case_ :			List[str]            =      self.dummy_sample_deter
								snake_case_ :			List[str]            =      self.dummy_sample_deter + 0.1
								snake_case_ :			List[Any]            =      self.dummy_sample_deter - 0.1

								snake_case_ :			Tuple            =      samplea.shape[0]
								snake_case_ :			List[str]            =      torch.stack([samplea, samplea, samplea]				,     dim=0 )
								snake_case_ :			int            =      torch.arange(_lowercase )[0:3, None].repeat(1				,     _lowercase )

								snake_case_ :			Any            =      model(samples.flatten(0				,     1 )				,     timesteps.flatten(0				,     1 ) )
								snake_case_ :			Optional[Any]            =      scheduler.batch_step_no_noise(_lowercase				,     timesteps.flatten(0				,     1 )				,     samples.flatten(0				,     1 )				,     _lowercase )

								snake_case_ :			Dict            =      torch.sum(torch.abs(_lowercase ) )
								snake_case_ :			List[str]            =      torch.mean(torch.abs(_lowercase ) )

								assert abs(result_sum.item() - 1147.7904 ) < 1E-2
								assert abs(result_mean.item() - 0.4982 ) < 1E-3





		def  UpperCAmelCase__				(       self )			->    Any:







								'''simple docstring'''

								snake_case_ :			List[Any]            =      self.full_loop()

								snake_case_ :			Tuple            =      torch.sum(torch.abs(_lowercase ) )
								snake_case_ :			Optional[Any]            =      torch.mean(torch.abs(_lowercase ) )

								assert abs(result_sum.item() - 172.0067 ) < 1E-2
								assert abs(result_mean.item() - 0.22_3967 ) < 1E-3





		def  UpperCAmelCase__				(       self )			->    str:







								'''simple docstring'''

								snake_case_ :			List[Any]            =      self.full_loop(prediction_type="""v_prediction""" )

								snake_case_ :			Tuple            =      torch.sum(torch.abs(_lowercase ) )
								snake_case_ :			Dict            =      torch.mean(torch.abs(_lowercase ) )

								assert abs(result_sum.item() - 52.5302 ) < 1E-2
								assert abs(result_mean.item() - 0.0684 ) < 1E-3





		def  UpperCAmelCase__				(       self )			->    str:







								'''simple docstring'''

								snake_case_ :			Optional[Any]            =      self.full_loop(set_alpha_to_one=_lowercase				,     beta_start=0.01 )
								snake_case_ :			Any            =      torch.sum(torch.abs(_lowercase ) )
								snake_case_ :			List[str]            =      torch.mean(torch.abs(_lowercase ) )

								assert abs(result_sum.item() - 149.8295 ) < 1E-2
								assert abs(result_mean.item() - 0.1951 ) < 1E-3





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







								'''simple docstring'''

								snake_case_ :			Dict            =      self.full_loop(set_alpha_to_one=_lowercase				,     beta_start=0.01 )
								snake_case_ :			Optional[Any]            =      torch.sum(torch.abs(_lowercase ) )
								snake_case_ :			Dict            =      torch.mean(torch.abs(_lowercase ) )

								assert abs(result_sum.item() - 149.0784 ) < 1E-2
								assert abs(result_mean.item() - 0.1941 ) < 1E-3




import unittest
from typing import Dict, List, Optional, Union

import numpy as np

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

if is_vision_available():
      from PIL import Image

      from transformers import BridgeTowerImageProcessor

class    _lowercase				(					unittest.TestCase			):

   def __init__(				self	:    List[str]			,						lowerCamelCase__	:    List[str]			,						lowerCamelCase__	:    bool = True			,						lowerCamelCase__	:    Dict[str, int] = None			,						lowerCamelCase__	:    int = 3_2			,						lowerCamelCase__	:    bool = True			,						lowerCamelCase__	:    Union[int, float] = 1 / 2_5_5			,						lowerCamelCase__	:    bool = True			,						lowerCamelCase__	:    bool = True			,						lowerCamelCase__	:    Optional[Union[float, List[float]]] = [0.48145466, 0.4578275, 0.40821073]			,						lowerCamelCase__	:    Optional[Union[float, List[float]]] = [0.26862954, 0.26130258, 0.27577711]			,						lowerCamelCase__	:    bool = True			,						lowerCamelCase__	:    List[str]=7			,						lowerCamelCase__	:    int=3_0			,						lowerCamelCase__	:    List[Any]=4_0_0			,						lowerCamelCase__	:    int=3			,						) ->			Optional[Any]:
         """simple docstring"""





         A_							       =		parent
         A_							       =		do_resize
         A_							       =		size if size is not None else {'''shortest_edge''': 2_8_8}
         A_							       =		size_divisor
         A_							       =		do_rescale
         A_							       =		rescale_factor
         A_							       =		do_normalize
         A_							       =		do_center_crop
         A_							       =		image_mean
         A_							       =		image_std
         A_							       =		do_pad
         A_							       =		batch_size
         A_							       =		num_channels
         A_							       =		min_resolution
         A_							       =		max_resolution

   def     UpperCamelCase					(				self	:    str       ) ->			int:
         """simple docstring"""





         return {
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_normalize": self.do_normalize,
             "do_resize": self.do_resize,
             "size": self.size,
             "size_divisor": self.size_divisor,
         }





   def     UpperCamelCase					(				self	:    List[str]			,						lowerCamelCase__	:    Optional[Any]			,						lowerCamelCase__	:    int=False       ) ->			Dict:
         """simple docstring"""





         if not batched:
               A_							       =		self.size['''shortest_edge''']
               A_							       =		image_inputs[0]
               if isinstance(lowerCamelCase__			,						Image.Image       ):
                     A_       ,A_							       =		image.size
               else:
                     A_       ,A_							       =		image.shape[1], image.shape[2]
               A_							       =		size / min(lowerCamelCase__			,						lowerCamelCase__       )
               if h < w:
                     A_       ,A_							       =		size, scale * w
               else:
                     A_       ,A_							       =		scale * h, size

               A_							       =		int((1_3_3_3 / 8_0_0) * size       )
               if max(lowerCamelCase__			,						lowerCamelCase__       ) > max_size:
                     A_							       =		max_size / max(lowerCamelCase__			,						lowerCamelCase__       )
                     A_							       =		newh * scale
                     A_							       =		neww * scale

               A_       ,A_							       =		int(newh + 0.5       ), int(neww + 0.5       )
               A_       ,A_							       =		(
                   newh // self.size_divisor * self.size_divisor,
                   neww // self.size_divisor * self.size_divisor,
               )

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

         return expected_height, expected_width



@require_torch
@require_vision
class    _lowercase				(					__lowerCamelCase,unittest.TestCase			):
   _lowercase     :       List[str]  					=    BridgeTowerImageProcessor if is_vision_available() else None

   def     UpperCamelCase					(				self	:    int       ) ->			Tuple:
         """simple docstring"""





         A_							       =		BridgeTowerImageProcessingTester(self       )

   @property
   def     UpperCamelCase					(				self	:    str       ) ->			Optional[int]:
         """simple docstring"""





         return self.image_processor_tester.prepare_image_processor_dict()

   def     UpperCamelCase					(				self	:    Tuple       ) ->			List[Any]:
         """simple docstring"""





         A_							       =		self.image_processing_class(**self.image_processor_dict       )
         self.assertTrue(hasattr(lowerCamelCase__			,						'''image_mean'''       )       )
         self.assertTrue(hasattr(lowerCamelCase__			,						'''image_std'''       )       )
         self.assertTrue(hasattr(lowerCamelCase__			,						'''do_normalize'''       )       )
         self.assertTrue(hasattr(lowerCamelCase__			,						'''do_resize'''       )       )
         self.assertTrue(hasattr(lowerCamelCase__			,						'''size'''       )       )
         self.assertTrue(hasattr(lowerCamelCase__			,						'''size_divisor'''       )       )

   def     UpperCamelCase					(				self	:    Optional[int]       ) ->			str:
         """simple docstring"""





         pass

   def     UpperCamelCase					(				self	:    Optional[int]       ) ->			Union[str, Any]:
         """simple docstring"""





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

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

         A_       ,A_							       =		self.image_processor_tester.get_expected_values(lowerCamelCase__       )
         self.assertEqual(
             encoded_images.shape			,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)			,						)

         # Test batched
         A_							       =		image_processing(lowerCamelCase__			,						return_tensors='''pt'''       ).pixel_values

         A_       ,A_							       =		self.image_processor_tester.get_expected_values(lowerCamelCase__			,						batched=lowerCamelCase__       )
         self.assertEqual(
             encoded_images.shape			,						(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )			,						)

   def     UpperCamelCase					(				self	:    str       ) ->			Optional[int]:
         """simple docstring"""





         A_							       =		self.image_processing_class(**self.image_processor_dict       )
         # create random numpy tensors
         A_							       =		prepare_image_inputs(self.image_processor_tester			,						equal_resolution=lowerCamelCase__			,						numpify=lowerCamelCase__       )
         for image in image_inputs:
               self.assertIsInstance(lowerCamelCase__			,						np.ndarray       )

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

         A_       ,A_							       =		self.image_processor_tester.get_expected_values(lowerCamelCase__       )
         self.assertEqual(
             encoded_images.shape			,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)			,						)

         # Test batched
         A_							       =		image_processing(lowerCamelCase__			,						return_tensors='''pt'''       ).pixel_values

         A_       ,A_							       =		self.image_processor_tester.get_expected_values(lowerCamelCase__			,						batched=lowerCamelCase__       )
         self.assertEqual(
             encoded_images.shape			,						(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )			,						)





   def     UpperCamelCase					(				self	:    Union[str, Any]       ) ->			int:
         """simple docstring"""





         A_							       =		self.image_processing_class(**self.image_processor_dict       )
         # create random PyTorch tensors
         A_							       =		prepare_image_inputs(self.image_processor_tester			,						equal_resolution=lowerCamelCase__			,						torchify=lowerCamelCase__       )
         for image in image_inputs:
               self.assertIsInstance(lowerCamelCase__			,						torch.Tensor       )

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

         A_       ,A_							       =		self.image_processor_tester.get_expected_values(lowerCamelCase__       )
         self.assertEqual(
             encoded_images.shape			,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)			,						)

         # Test batched
         A_							       =		image_processing(lowerCamelCase__			,						return_tensors='''pt'''       ).pixel_values

         A_       ,A_							       =		self.image_processor_tester.get_expected_values(lowerCamelCase__			,						batched=lowerCamelCase__       )
         self.assertEqual(
             encoded_images.shape			,						(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )			,						)





