



"""simple docstring"""







import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMParallelScheduler,
    DDPMParallelScheduler,
    StableDiffusionParadigmsPipeline,
    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  UpperCamelCase__  (      lowercase_,   lowercase_,   unittest.TestCase    ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE    							=	StableDiffusionParadigmsPipeline
      _SCREAMING_SNAKE_CASE    							=	TEXT_TO_IMAGE_PARAMS
      _SCREAMING_SNAKE_CASE    							=	TEXT_TO_IMAGE_BATCH_PARAMS
      _SCREAMING_SNAKE_CASE    							=	TEXT_TO_IMAGE_IMAGE_PARAMS
      _SCREAMING_SNAKE_CASE    							=	TEXT_TO_IMAGE_IMAGE_PARAMS



      def 	SCREAMING_SNAKE_CASE__					( self							: Optional[Any]			):
        torch.manual_seed(0			)
        lowerCAmelCase_  :							List[Any]								=					UNetaDConditionModel(
            block_out_channels=(3_2, 6_4)       ,     layers_per_block=2       ,     sample_size=3_2       ,     in_channels=4       ,     out_channels=4       ,     down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')       ,     up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')       ,     cross_attention_dim=3_2       ,     attention_head_dim=(2, 4)       ,     use_linear_projection=SCREAMING_SNAKE_CASE_       ,     )
        lowerCAmelCase_  :							Optional[int]								=					DDIMParallelScheduler(
            beta_start=0.0_00_85       ,     beta_end=0.0_12       ,     beta_schedule='scaled_linear'       ,     clip_sample=SCREAMING_SNAKE_CASE_       ,     set_alpha_to_one=SCREAMING_SNAKE_CASE_       ,     )
        torch.manual_seed(0			)
        lowerCAmelCase_  :							Any								=					AutoencoderKL(
            block_out_channels=[3_2, 6_4]       ,     in_channels=3       ,     out_channels=3       ,     down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']       ,     up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']       ,     latent_channels=4       ,     sample_size=1_2_8       ,     )
        torch.manual_seed(0			)
        lowerCAmelCase_  :							int								=					CLIPTextConfig(
            bos_token_id=0       ,     eos_token_id=2       ,     hidden_size=3_2       ,     intermediate_size=3_7       ,     layer_norm_eps=1E-05       ,     num_attention_heads=4       ,     num_hidden_layers=5       ,     pad_token_id=1       ,     vocab_size=1_0_0_0       ,     hidden_act='gelu'       ,     projection_dim=5_1_2       ,     )
        lowerCAmelCase_  :							str								=					CLIPTextModel(SCREAMING_SNAKE_CASE_			)
        lowerCAmelCase_  :							Tuple								=					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'			)

        lowerCAmelCase_  :							List[Any]								=					{
            'unet': unet,
            'scheduler': scheduler,
            'vae': vae,
            'text_encoder': text_encoder,
            'tokenizer': tokenizer,
            'safety_checker': None,
            'feature_extractor': None,
        }
        return components



      def 	SCREAMING_SNAKE_CASE__					( self							: int       ,     SCREAMING_SNAKE_CASE_							: Dict       ,     SCREAMING_SNAKE_CASE_							: List[str]=0			):
        if str(SCREAMING_SNAKE_CASE_			).startswith('mps'			):
          lowerCAmelCase_  :							List[Any]								=					torch.manual_seed(SCREAMING_SNAKE_CASE_			)
        else:
          lowerCAmelCase_  :							Optional[int]								=					torch.Generator(device=SCREAMING_SNAKE_CASE_			).manual_seed(SCREAMING_SNAKE_CASE_			)
        lowerCAmelCase_  :							int								=					{
            'prompt': 'a photograph of an astronaut riding a horse',
            'generator': generator,
            'num_inference_steps': 1_0,
            'guidance_scale': 6.0,
            'output_type': 'numpy',
            'parallel': 3,
            'debug': True,
        }
        return inputs



      def 	SCREAMING_SNAKE_CASE__					( self							: Optional[int]			):
        lowerCAmelCase_  :							Any								=					'cpu'  # ensure determinism for the device-dependent torch.Generator
        lowerCAmelCase_  :							Optional[Any]								=					self.get_dummy_components()
        lowerCAmelCase_  :							Optional[Any]								=					StableDiffusionParadigmsPipeline(**SCREAMING_SNAKE_CASE_			)
        lowerCAmelCase_  :							Optional[Any]								=					sd_pipe.to(SCREAMING_SNAKE_CASE_			)
        sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_			)

        lowerCAmelCase_  :							List[str]								=					self.get_dummy_inputs(SCREAMING_SNAKE_CASE_			)
        lowerCAmelCase_  :							Optional[Any]								=					sd_pipe(**SCREAMING_SNAKE_CASE_			).images
        lowerCAmelCase_  :							Any								=					image[0, -3:, -3:, -1]
        assert image.shape == (1, 6_4, 6_4, 3)

        lowerCAmelCase_  :							str								=					np.array([0.47_73, 0.54_17, 0.47_23, 0.49_25, 0.56_31, 0.47_52, 0.52_40, 0.49_35, 0.50_23]			)

        assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2



      def 	SCREAMING_SNAKE_CASE__					( self							: Union[str, Any]			):
        lowerCAmelCase_  :							Dict								=					'cpu'  # ensure determinism for the device-dependent torch.Generator
        lowerCAmelCase_  :							Optional[Any]								=					self.get_dummy_components()
        torch.manual_seed(0			)
        lowerCAmelCase_  :							Any								=					DDPMParallelScheduler()
        torch.manual_seed(0			)
        lowerCAmelCase_  :							Any								=					StableDiffusionParadigmsPipeline(**SCREAMING_SNAKE_CASE_			)
        lowerCAmelCase_  :							Optional[Any]								=					sd_pipe.to(SCREAMING_SNAKE_CASE_			)
        sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_			)

        lowerCAmelCase_  :							int								=					self.get_dummy_inputs(SCREAMING_SNAKE_CASE_			)
        lowerCAmelCase_  :							str								=					sd_pipe(**SCREAMING_SNAKE_CASE_			).images
        lowerCAmelCase_  :							Union[str, Any]								=					image[0, -3:, -3:, -1]
        assert image.shape == (1, 6_4, 6_4, 3)

        lowerCAmelCase_  :							Optional[Any]								=					np.array([0.35_73, 0.44_20, 0.49_60, 0.47_99, 0.37_96, 0.38_79, 0.48_19, 0.43_65, 0.44_68]			)

        assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2



      def 	SCREAMING_SNAKE_CASE__					( self							: Any			):
        super().test_inference_batch_consistent(batch_sizes=[1, 2]			)



      def 	SCREAMING_SNAKE_CASE__					( self							: int			):
        super().test_inference_batch_single_identical(batch_size=2       ,     expected_max_diff=3E-3			)





      def 	SCREAMING_SNAKE_CASE__					( self							: List[str]			):
        lowerCAmelCase_  :							List[Any]								=					'cpu'  # ensure determinism for the device-dependent torch.Generator
        lowerCAmelCase_  :							str								=					self.get_dummy_components()
        lowerCAmelCase_  :							str								=					StableDiffusionParadigmsPipeline(**SCREAMING_SNAKE_CASE_			)
        lowerCAmelCase_  :							Dict								=					sd_pipe.to(SCREAMING_SNAKE_CASE_			)
        sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_			)

        lowerCAmelCase_  :							Union[str, Any]								=					self.get_dummy_inputs(SCREAMING_SNAKE_CASE_			)
        lowerCAmelCase_  :							Any								=					'french fries'
        lowerCAmelCase_  :							Optional[int]								=					sd_pipe(**SCREAMING_SNAKE_CASE_       ,     negative_prompt=SCREAMING_SNAKE_CASE_			)
        lowerCAmelCase_  :							Dict								=					output.images
        lowerCAmelCase_  :							Dict								=					image[0, -3:, -3:, -1]

        assert image.shape == (1, 6_4, 6_4, 3)

        lowerCAmelCase_  :							str								=					np.array([0.47_71, 0.54_20, 0.46_83, 0.49_18, 0.56_36, 0.47_25, 0.52_30, 0.49_23, 0.50_15]			)

        assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2







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




      """simple docstring"""




      def 	SCREAMING_SNAKE_CASE__					( self							: List[Any]			):
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()



      def 	SCREAMING_SNAKE_CASE__					( self							: str       ,     SCREAMING_SNAKE_CASE_							: int=0			):
        lowerCAmelCase_  :							Any								=					torch.Generator(device=SCREAMING_SNAKE_CASE_			).manual_seed(SCREAMING_SNAKE_CASE_			)
        lowerCAmelCase_  :							str								=					{
            'prompt': 'a photograph of an astronaut riding a horse',
            'generator': generator,
            'num_inference_steps': 1_0,
            'guidance_scale': 7.5,
            'output_type': 'numpy',
            'parallel': 3,
            'debug': True,
        }
        return inputs





      def 	SCREAMING_SNAKE_CASE__					( self							: Dict			):
        lowerCAmelCase_  :							List[str]								=					'stabilityai/stable-diffusion-2-base'
        lowerCAmelCase_  :							Optional[Any]								=					DDIMParallelScheduler.from_pretrained(SCREAMING_SNAKE_CASE_       ,     subfolder='scheduler'			)
        lowerCAmelCase_  :							str								=					StableDiffusionParadigmsPipeline.from_pretrained(SCREAMING_SNAKE_CASE_       ,     scheduler=SCREAMING_SNAKE_CASE_       ,     safety_checker=SCREAMING_SNAKE_CASE_			)
        pipe.to(SCREAMING_SNAKE_CASE_			)
        pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_			)
        pipe.enable_attention_slicing()

        lowerCAmelCase_  :							List[Any]								=					self.get_inputs()
        lowerCAmelCase_  :							str								=					pipe(**SCREAMING_SNAKE_CASE_			).images
        lowerCAmelCase_  :							Optional[int]								=					image[0, -3:, -3:, -1].flatten()

        assert image.shape == (1, 5_1_2, 5_1_2, 3)

        lowerCAmelCase_  :							Dict								=					np.array([0.96_22, 0.96_02, 0.97_48, 0.95_91, 0.96_30, 0.96_91, 0.96_61, 0.96_31, 0.97_41]			)

        assert np.abs(expected_slice - image_slice			).max() < 1E-2










"""simple docstring"""







import pytest

from datasets.utils.version import Version

@pytest.mark.parametrize(
    'other, expected_equality'       ,  [
        (Version('1.0.0'		), True),
        ('1.0.0', True),
        (Version('2.0.0'		), False),
        ('2.0.0', False),
        ('1', False),
        ('a', False),
        (1, False),
        (None, False),
    ]       ,  )
def 							UpperCamelCase_       (       lowerCAmelCase__ :							List[Any]       ,  lowerCAmelCase__ :							List[Any]		)		->  Tuple:

  """simple docstring"""




  lowerCAmelCase_  :							List[str]								=					Version('1.0.0'		)
  assert (version == other) is expected_equality
  assert (version != other) is not expected_equality
  assert (hash(lowerCAmelCase__		) == hash(lowerCAmelCase__		)) is expected_equality





