






"""simple docstring"""


import unittest

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


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import TvltImageProcessor




def 			snake_case  (A_      :Optional[Any]	,			A_      :str=1_0	,			A_      :Dict=1_0	,			A_      :Union[str, Any]=False	,			A_      :str=False      ):







  '''simple docstring'''

  a     :			Tuple				=   []
  for i in range(image_processor_tester.num_frames      ):
    video.append(np.random.randint(2_5_5	,			size=(image_processor_tester.num_channels, width, height)	,			dtype=np.uinta      )      )

  if not numpify and not torchify:
    # PIL expects the channel dimension as last dimension
    a     :			Dict				=   [Image.fromarray(np.moveaxis(A_	,			0	,			-1      )      ) for frame in video]

  if torchify:
    a     :			Optional[Any]				=   [torch.from_numpy(A_      ) for frame in video]

  return video




def 			snake_case  (A_      :List[Any]	,			A_      :List[Any]=False	,			A_      :Optional[Any]=False	,			A_      :Any=False      ):







  '''simple docstring'''

  assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

  a     :			Union[str, Any]				=   []
  for i in range(image_processor_tester.batch_size      ):
    if equal_resolution:
      a     :			Optional[int]				=   image_processor_tester.max_resolution
    else:
      a,     a     :			Optional[int]				=   np.random.choice(
          np.arange(image_processor_tester.min_resolution	,			image_processor_tester.max_resolution      )	,			2      )
      a     :			int				=   prepare_video(
          image_processor_tester=A_	,			width=A_	,			height=A_	,			numpify=A_	,			torchify=A_	,			)
    video_inputs.append(A_      )

  return video_inputs






class 						snake_case  (  unittest.TestCase       ):





    def __init__(							self	:					int				,		A	:					Optional[int]				,		A	:					str=7				,		A	:					Optional[Any]=3				,		A	:					int=4				,		A	:					int=1_8				,		A	:					Dict=3_0				,		A	:					Any=4_0_0				,		A	:					Dict=True				,		A	:					Any=None				,		A	:					Union[str, Any]=True				,		A	:					Optional[int]=[0.5, 0.5, 0.5]				,		A	:					Dict=[0.5, 0.5, 0.5]				,		A	:					int=True				,		A	:					Optional[Any]=None				,		):


      '''simple docstring'''




      a     :			int				=   size if size is not None else {'shortest_edge': 1_8}
      a     :			Tuple				=   crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}

      a     :			Tuple				=   parent
      a     :			Any				=   batch_size
      a     :			Tuple				=   num_channels
      a     :			Tuple				=   num_frames
      a     :			Tuple				=   image_size
      a     :			Tuple				=   min_resolution
      a     :			List[Any]				=   max_resolution
      a     :			str				=   do_resize
      a     :			List[Any]				=   size
      a     :			List[str]				=   do_normalize
      a     :			str				=   image_mean
      a     :			Any				=   image_std
      a     :			Union[str, Any]				=   do_center_crop
      a     :			Optional[int]				=   crop_size





    def 				lowerCamelCase__      (							self	:					Optional[Any]			):


      '''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,
          "do_center_crop": self.do_center_crop,
          "crop_size": self.crop_size,
      }






@require_torch
@require_vision
class 						snake_case  (  UpperCAmelCase   , unittest.TestCase       ):
    __magic_name__         =     TvltImageProcessor if is_vision_available() else None





    def 				lowerCamelCase__      (							self	:					Optional[int]			):


      '''simple docstring'''




      a     :			Union[str, Any]				=   TvltImageProcessorTester(self			)





    @property
    def 				lowerCamelCase__      (							self	:					List[Any]			):


      '''simple docstring'''




      return self.image_processor_tester.prepare_image_processor_dict()





    def 				lowerCamelCase__      (							self	:					Any			):


      '''simple docstring'''




      a     :			Optional[int]				=   self.image_processing_class(**self.image_processor_dict			)
      self.assertTrue(hasattr(A				,		'image_mean'			)			)
      self.assertTrue(hasattr(A				,		'image_std'			)			)
      self.assertTrue(hasattr(A				,		'do_normalize'			)			)
      self.assertTrue(hasattr(A				,		'do_resize'			)			)
      self.assertTrue(hasattr(A				,		'do_center_crop'			)			)
      self.assertTrue(hasattr(A				,		'size'			)			)





    def 				lowerCamelCase__      (							self	:					Union[str, Any]			):


      '''simple docstring'''




      a     :			List[Any]				=   self.image_processing_class(**self.image_processor_dict			)
      # create random PIL videos
      a     :			Optional[int]				=   prepare_video_inputs(self.image_processor_tester				,		equal_resolution=A			)
      for video in video_inputs:
        self.assertIsInstance(A				,		A			)
        self.assertIsInstance(video[0]				,		Image.Image			)

      # Test not batched input
      a     :			Dict				=   image_processor(video_inputs[0]				,		return_tensors='pt'			).pixel_values
      self.assertEqual(
          encoded_videos.shape				,		(
              1,
              self.image_processor_tester.num_frames,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )				,		)

      # Test batched
      a     :			Tuple				=   image_processor(A				,		return_tensors='pt'			).pixel_values
      self.assertEqual(
          encoded_videos.shape				,		(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_frames,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )				,		)





    def 				lowerCamelCase__      (							self	:					int			):


      '''simple docstring'''




      a     :			int				=   self.image_processing_class(**self.image_processor_dict			)
      # create random numpy tensors
      a     :			Dict				=   prepare_video_inputs(self.image_processor_tester				,		equal_resolution=A				,		numpify=A			)
      for video in video_inputs:
        self.assertIsInstance(A				,		A			)
        self.assertIsInstance(video[0]				,		np.ndarray			)

      # Test not batched input
      a     :			Optional[int]				=   image_processor(video_inputs[0]				,		return_tensors='pt'			).pixel_values
      self.assertEqual(
          encoded_videos.shape				,		(
              1,
              self.image_processor_tester.num_frames,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )				,		)

      # Test batched
      a     :			Tuple				=   image_processor(A				,		return_tensors='pt'			).pixel_values
      self.assertEqual(
          encoded_videos.shape				,		(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_frames,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )				,		)





    def 				lowerCamelCase__      (							self	:					Optional[int]			):


      '''simple docstring'''




      a     :			Optional[int]				=   self.image_processing_class(**self.image_processor_dict			)
      # create random PyTorch tensors
      a     :			Union[str, Any]				=   prepare_video_inputs(self.image_processor_tester				,		equal_resolution=A				,		torchify=A			)
      for video in video_inputs:
        self.assertIsInstance(A				,		A			)
        self.assertIsInstance(video[0]				,		torch.Tensor			)

      # Test not batched input
      a     :			Union[str, Any]				=   image_processor(video_inputs[0]				,		return_tensors='pt'			).pixel_values
      self.assertEqual(
          encoded_videos.shape				,		(
              1,
              self.image_processor_tester.num_frames,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )				,		)

      # Test batched
      a     :			int				=   image_processor(A				,		return_tensors='pt'			).pixel_values
      self.assertEqual(
          encoded_videos.shape				,		(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_frames,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )				,		)










# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class  __lowerCamelCase       (    UpperCamelCase__    ):




    """simple docstring"""

    def __init__(       self     :     Union[str, Any]   ,	SCREAMING_SNAKE_CASE__     :     Union[str, Any]   ,	SCREAMING_SNAKE_CASE__     :     Any   )      ->       Union[str, Any]:
         super().__init__()
         self.register_modules(unet=SCREAMING_SNAKE_CASE__   ,	scheduler=SCREAMING_SNAKE_CASE__   )


    @torch.no_grad()
    def __call__(       self     :     Dict   ,	SCREAMING_SNAKE_CASE__     :     int = 1   ,	SCREAMING_SNAKE_CASE__     :     Optional[torch.Generator] = None   ,	SCREAMING_SNAKE_CASE__     :     int = 50   ,	SCREAMING_SNAKE_CASE__     :     Optional[str] = "pil"   ,	SCREAMING_SNAKE_CASE__     :     bool = True   ,	**SCREAMING_SNAKE_CASE__     :     Union[str, Any]   ,	)      ->       Union[ImagePipelineOutput, Tuple]:
         lowerCAmelCase__		= torch.randn(
             (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)   ,	generator=SCREAMING_SNAKE_CASE__   ,	)
         lowerCAmelCase__		= image.to(self.device   )

         # set step values
         self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE__   )

         for t in self.progress_bar(self.scheduler.timesteps   ):
              # 1. predict noise model_output
              lowerCAmelCase__		= self.unet(SCREAMING_SNAKE_CASE__   ,	SCREAMING_SNAKE_CASE__   ).sample

              # 2. predict previous mean of image x_t-1 and add variance depending on eta
              # eta corresponds to η in paper and should be between [0, 1]
              # do x_t -> x_t-1
              lowerCAmelCase__		= self.scheduler.step(SCREAMING_SNAKE_CASE__   ,	SCREAMING_SNAKE_CASE__   ,	SCREAMING_SNAKE_CASE__   ).prev_sample

         lowerCAmelCase__		= (image / 2 + 0.5).clamp(0   ,	1   )
         lowerCAmelCase__		= image.cpu().permute(0   ,	2   ,	3   ,	1   ).numpy()
         if output_type == "pil":
              lowerCAmelCase__		= self.numpy_to_pil(SCREAMING_SNAKE_CASE__   )

         if not return_dict:
              return (image,), "This is a local test"

         return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE__   ), "This is a local test"
