
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, prepare_video_inputs


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import VivitImageProcessor


class 						__lowerCAmelCase	(	unittest.TestCase      ):



      """simple docstring"""







      def __init__(      self		:       List[Any]				,       _snake_case		:       int				,       _snake_case		:       Dict=7				,       _snake_case		:       Dict=3				,       _snake_case		:       List[str]=10				,       _snake_case		:       List[Any]=18				,       _snake_case		:       Union[str, Any]=30				,       _snake_case		:       Dict=400				,       _snake_case		:       Optional[Any]=True				,       _snake_case		:       List[str]=None				,       _snake_case		:       List[str]=True				,       _snake_case		:       Dict=[0.5, 0.5, 0.5]				,       _snake_case		:       str=[0.5, 0.5, 0.5]				,       _snake_case		:       Optional[Any]=None				,       ):
          __lowercase		:			int	    =       size if size is not None else {'''shortest_edge''': 18}
          __lowercase		:			Any	    =       crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}

          __lowercase		:			Dict	    =       parent
          __lowercase		:			Any	    =       batch_size
          __lowercase		:			str	    =       num_channels
          __lowercase		:			Optional[Any]	    =       num_frames
          __lowercase		:			str	    =       image_size
          __lowercase		:			Optional[int]	    =       min_resolution
          __lowercase		:			List[Any]	    =       max_resolution
          __lowercase		:			Any	    =       do_resize
          __lowercase		:			Dict	    =       size
          __lowercase		:			List[Any]	    =       do_normalize
          __lowercase		:			Dict	    =       image_mean
          __lowercase		:			str	    =       image_std
          __lowercase		:			int	    =       crop_size




      def 		snake_case_			(      self		:       Dict				):
          return {
              "image_mean": self.image_mean,
              "image_std": self.image_std,
              "do_normalize": self.do_normalize,
              "do_resize": self.do_resize,
              "size": self.size,
              "crop_size": self.crop_size,
          }






@require_torch
@require_vision
class 						__lowerCAmelCase	(	lowerCAmelCase_ , unittest.TestCase      ):



      """simple docstring"""







      A__	:			Any        =    VivitImageProcessor if is_vision_available() else None




      def 		snake_case_			(      self		:       List[Any]				):
          __lowercase		:			Union[str, Any]	    =       VivitImageProcessingTester(self				)




      @property
      def 		snake_case_			(      self		:       Union[str, Any]				):
          return self.image_processor_tester.prepare_image_processor_dict()




      def 		snake_case_			(      self		:       List[str]				):
          __lowercase		:			List[Any]	    =       self.image_processing_class(**self.image_processor_dict				)
          self.assertTrue(hasattr(_snake_case				,       '''image_mean'''				)				)
          self.assertTrue(hasattr(_snake_case				,       '''image_std'''				)				)
          self.assertTrue(hasattr(_snake_case				,       '''do_normalize'''				)				)
          self.assertTrue(hasattr(_snake_case				,       '''do_resize'''				)				)
          self.assertTrue(hasattr(_snake_case				,       '''do_center_crop'''				)				)
          self.assertTrue(hasattr(_snake_case				,       '''size'''				)				)




      def 		snake_case_			(      self		:       Any				):
          __lowercase		:			int	    =       self.image_processing_class.from_dict(self.image_processor_dict				)
          self.assertEqual(image_processor.size				,       {'''shortest_edge''': 18}				)
          self.assertEqual(image_processor.crop_size				,       {'''height''': 18, '''width''': 18}				)

          __lowercase		:			Optional[int]	    =       self.image_processing_class.from_dict(self.image_processor_dict				,       size=42				,       crop_size=84				)
          self.assertEqual(image_processor.size				,       {'''shortest_edge''': 42}				)
          self.assertEqual(image_processor.crop_size				,       {'''height''': 84, '''width''': 84}				)




      def 		snake_case_			(      self		:       List[Any]				):
          # Initialize image_processing
          __lowercase		:			int	    =       self.image_processing_class(**self.image_processor_dict				)
          # create random PIL videos
          __lowercase		:			Optional[int]	    =       prepare_video_inputs(self.image_processor_tester				,       equal_resolution=_snake_case				)
          for video in video_inputs:
              self.assertIsInstance(_snake_case				,       _snake_case				)
              self.assertIsInstance(video[0]				,       Image.Image				)

          # Test not batched input
          __lowercase		:			Optional[int]	    =       image_processing(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
          __lowercase		:			Tuple	    =       image_processing(_snake_case				,       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 		snake_case_			(      self		:       List[Any]				):
          # Initialize image_processing
          __lowercase		:			List[Any]	    =       self.image_processing_class(**self.image_processor_dict				)
          # create random numpy tensors
          __lowercase		:			List[str]	    =       prepare_video_inputs(self.image_processor_tester				,       equal_resolution=_snake_case				,       numpify=_snake_case				)
          for video in video_inputs:
              self.assertIsInstance(_snake_case				,       _snake_case				)
              self.assertIsInstance(video[0]				,       np.ndarray				)

          # Test not batched input
          __lowercase		:			Dict	    =       image_processing(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
          __lowercase		:			Optional[Any]	    =       image_processing(_snake_case				,       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 		snake_case_			(      self		:       Any				):
          # Initialize image_processing
          __lowercase		:			Optional[int]	    =       self.image_processing_class(**self.image_processor_dict				)
          # create random PyTorch tensors
          __lowercase		:			Union[str, Any]	    =       prepare_video_inputs(self.image_processor_tester				,       equal_resolution=_snake_case				,       torchify=_snake_case				)
          for video in video_inputs:
              self.assertIsInstance(_snake_case				,       _snake_case				)
              self.assertIsInstance(video[0]				,       torch.Tensor				)

          # Test not batched input
          __lowercase		:			Optional[int]	    =       image_processing(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
          __lowercase		:			str	    =       image_processing(_snake_case				,       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'''],
              )				,       )




import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionSAGPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


class 						__lowerCAmelCase	(	lowerCAmelCase_ , lowerCAmelCase_ , unittest.TestCase      ):



      """simple docstring"""







      A__	:			int        =    StableDiffusionSAGPipeline
      A__	:			Union[str, Any]        =    TEXT_TO_IMAGE_PARAMS
      A__	:			Dict        =    TEXT_TO_IMAGE_BATCH_PARAMS
      A__	:			List[str]        =    TEXT_TO_IMAGE_IMAGE_PARAMS
      A__	:			int        =    TEXT_TO_IMAGE_IMAGE_PARAMS
      A__	:			List[str]        =    False




      def 		snake_case_			(      self		:       Optional[Any]				):
          torch.manual_seed(0				)
          __lowercase		:			Optional[Any]	    =       UNetaDConditionModel(
              block_out_channels=(32, 64)				,       layers_per_block=2				,       sample_size=32				,       in_channels=4				,       out_channels=4				,       down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')				,       up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')				,       cross_attention_dim=32				,       )
          __lowercase		:			int	    =       DDIMScheduler(
              beta_start=0.0_00_85				,       beta_end=0.0_12				,       beta_schedule='''scaled_linear'''				,       clip_sample=_snake_case				,       set_alpha_to_one=_snake_case				,       )
          torch.manual_seed(0				)
          __lowercase		:			List[Any]	    =       AutoencoderKL(
              block_out_channels=[32, 64]				,       in_channels=3				,       out_channels=3				,       down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']				,       up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']				,       latent_channels=4				,       )
          torch.manual_seed(0				)
          __lowercase		:			Tuple	    =       CLIPTextConfig(
              bos_token_id=0				,       eos_token_id=2				,       hidden_size=32				,       intermediate_size=37				,       layer_norm_eps=1E-0_5				,       num_attention_heads=4				,       num_hidden_layers=5				,       pad_token_id=1				,       vocab_size=1000				,       )
          __lowercase		:			List[Any]	    =       CLIPTextModel(_snake_case				)
          __lowercase		:			List[Any]	    =       CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				)

          __lowercase		:			int	    =       {
              '''unet''': unet,
              '''scheduler''': scheduler,
              '''vae''': vae,
              '''text_encoder''': text_encoder,
              '''tokenizer''': tokenizer,
              '''safety_checker''': None,
              '''feature_extractor''': None,
          }
          return components




      def 		snake_case_			(      self		:       Tuple				,       _snake_case		:       List[str]				,       _snake_case		:       List[str]=0				):
          if str(_snake_case				).startswith('''mps'''				):
              __lowercase		:			Optional[int]	    =       torch.manual_seed(_snake_case				)
          else:
              __lowercase		:			Tuple	    =       torch.Generator(device=_snake_case				).manual_seed(_snake_case				)
          __lowercase		:			Tuple	    =       {
              '''prompt''': '''.''',
              '''generator''': generator,
              '''num_inference_steps''': 2,
              '''guidance_scale''': 1.0,
              '''sag_scale''': 1.0,
              '''output_type''': '''numpy''',
          }
          return inputs




      def 		snake_case_			(      self		:       Any				):
          super().test_inference_batch_single_identical(expected_max_diff=3E-3				)






@slow
@require_torch_gpu
class 						__lowerCAmelCase	(	unittest.TestCase      ):



      """simple docstring"""







      def 		snake_case_			(      self		:       int				):
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()




      def 		snake_case_			(      self		:       Dict				):
          __lowercase		:			Optional[int]	    =       StableDiffusionSAGPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4'''				)
          __lowercase		:			Optional[int]	    =       sag_pipe.to(_snake_case				)
          sag_pipe.set_progress_bar_config(disable=_snake_case				)

          __lowercase		:			List[str]	    =       '''.'''
          __lowercase		:			List[str]	    =       torch.manual_seed(0				)
          __lowercase		:			List[str]	    =       sag_pipe(
              [prompt]				,       generator=_snake_case				,       guidance_scale=7.5				,       sag_scale=1.0				,       num_inference_steps=20				,       output_type='''np'''				)

          __lowercase		:			Optional[Any]	    =       output.images

          __lowercase		:			Dict	    =       image[0, -3:, -3:, -1]

          assert image.shape == (1, 512, 512, 3)
          __lowercase		:			int	    =       np.array([0.15_68, 0.17_38, 0.16_95, 0.16_93, 0.15_07, 0.17_05, 0.15_47, 0.17_51, 0.19_49]				)

          assert np.abs(image_slice.flatten() - expected_slice				).max() < 5E-2




      def 		snake_case_			(      self		:       Optional[int]				):
          __lowercase		:			Dict	    =       StableDiffusionSAGPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''				)
          __lowercase		:			Any	    =       sag_pipe.to(_snake_case				)
          sag_pipe.set_progress_bar_config(disable=_snake_case				)

          __lowercase		:			List[str]	    =       '''.'''
          __lowercase		:			List[str]	    =       torch.manual_seed(0				)
          __lowercase		:			int	    =       sag_pipe(
              [prompt]				,       generator=_snake_case				,       guidance_scale=7.5				,       sag_scale=1.0				,       num_inference_steps=20				,       output_type='''np'''				)

          __lowercase		:			str	    =       output.images

          __lowercase		:			Tuple	    =       image[0, -3:, -3:, -1]

          assert image.shape == (1, 512, 512, 3)
          __lowercase		:			Any	    =       np.array([0.34_59, 0.28_76, 0.25_37, 0.30_02, 0.26_71, 0.21_60, 0.30_26, 0.22_62, 0.23_71]				)

          assert np.abs(image_slice.flatten() - expected_slice				).max() < 5E-2




      def 		snake_case_			(      self		:       int				):
          __lowercase		:			List[str]	    =       StableDiffusionSAGPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''				)
          __lowercase		:			Tuple	    =       sag_pipe.to(_snake_case				)
          sag_pipe.set_progress_bar_config(disable=_snake_case				)

          __lowercase		:			Union[str, Any]	    =       '''.'''
          __lowercase		:			Tuple	    =       torch.manual_seed(0				)
          __lowercase		:			Optional[int]	    =       sag_pipe(
              [prompt]				,       width=768				,       height=512				,       generator=_snake_case				,       guidance_scale=7.5				,       sag_scale=1.0				,       num_inference_steps=20				,       output_type='''np'''				,       )

          __lowercase		:			Dict	    =       output.images

          assert image.shape == (1, 512, 768, 3)


