code: 
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_tf_available,
      is_torch_available,
  )
  
  
  _SCREAMING_SNAKE_CASE    :				int       =					{"configuration_gptj": ["GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTJConfig", "GPTJOnnxConfig"]}
  
  try:
   if not is_torch_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   _SCREAMING_SNAKE_CASE    :				str       =					[
       "GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST",
       "GPTJForCausalLM",
       "GPTJForQuestionAnswering",
       "GPTJForSequenceClassification",
       "GPTJModel",
       "GPTJPreTrainedModel",
   ]
  
  try:
   if not is_tf_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   _SCREAMING_SNAKE_CASE    :				str       =					[
       "TFGPTJForCausalLM",
       "TFGPTJForQuestionAnswering",
       "TFGPTJForSequenceClassification",
       "TFGPTJModel",
       "TFGPTJPreTrainedModel",
   ]
  
  try:
   if not is_flax_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   _SCREAMING_SNAKE_CASE    :				Optional[Any]       =					[
       "FlaxGPTJForCausalLM",
       "FlaxGPTJModel",
       "FlaxGPTJPreTrainedModel",
   ]
  
  
  if TYPE_CHECKING:
   from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig
  
   try:
    if not is_torch_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_gptj import (
        GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
        GPTJForCausalLM,
        GPTJForQuestionAnswering,
        GPTJForSequenceClassification,
        GPTJModel,
        GPTJPreTrainedModel,
    )
  
   try:
    if not is_tf_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_tf_gptj import (
        TFGPTJForCausalLM,
        TFGPTJForQuestionAnswering,
        TFGPTJForSequenceClassification,
        TFGPTJModel,
        TFGPTJPreTrainedModel,
    )
  
   try:
    if not is_flax_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel
  
  else:
   import sys
  
   _SCREAMING_SNAKE_CASE    :				Dict       =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  
  
  
  
style_context: 
  
  
  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 						A__      (					snake_case__ ,      snake_case__ ,      unittest.TestCase			):
  
  
  
  
  
  
  
      """simple docstring"""
  
      __magic_name__    						=	StableDiffusionParadigmsPipeline
      __magic_name__    						=	TEXT_TO_IMAGE_PARAMS
      __magic_name__    						=	TEXT_TO_IMAGE_BATCH_PARAMS
      __magic_name__    						=	TEXT_TO_IMAGE_IMAGE_PARAMS
      __magic_name__    						=	TEXT_TO_IMAGE_IMAGE_PARAMS
      def 	a_    ( self					):
           torch.manual_seed(0					)
           snake_case					=       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=__snake_case    ,      )
           snake_case					=       DDIMParallelScheduler(
               beta_start=0.0_0085    ,      beta_end=0.012    ,      beta_schedule='''scaled_linear'''    ,      clip_sample=__snake_case    ,      set_alpha_to_one=__snake_case    ,      )
           torch.manual_seed(0					)
           snake_case					=       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					)
           snake_case					=       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    ,      )
           snake_case					=       CLIPTextModel(__snake_case					)
           snake_case					=       CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''					)
  
           snake_case					=       {
               '''unet''': unet,
               '''scheduler''': scheduler,
               '''vae''': vae,
               '''text_encoder''': text_encoder,
               '''tokenizer''': tokenizer,
               '''safety_checker''': None,
               '''feature_extractor''': None,
           }
           return components
      def 	a_    ( self    ,      __snake_case    ,      __snake_case=0					):
           if str(__snake_case					).startswith('''mps'''					):
                snake_case					=       torch.manual_seed(__snake_case					)
           else:
                snake_case					=       torch.Generator(device=__snake_case					).manual_seed(__snake_case					)
           snake_case					=       {
               '''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 	a_    ( self					):
           snake_case					=       '''cpu'''  # ensure determinism for the device-dependent torch.Generator
           snake_case					=       self.get_dummy_components()
           snake_case					=       StableDiffusionParadigmsPipeline(**__snake_case					)
           snake_case					=       sd_pipe.to(__snake_case					)
           sd_pipe.set_progress_bar_config(disable=__snake_case					)
  
           snake_case					=       self.get_dummy_inputs(__snake_case					)
           snake_case					=       sd_pipe(**__snake_case					).images
           snake_case					=       image[0, -3:, -3:, -1]
           assert image.shape == (1, 6_4, 6_4, 3)
  
           snake_case					=       np.array([0.4773, 0.5417, 0.4723, 0.4925, 0.5631, 0.4752, 0.5240, 0.4935, 0.5023]					)
  
           assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-2
      def 	a_    ( self					):
           snake_case					=       '''cpu'''  # ensure determinism for the device-dependent torch.Generator
           snake_case					=       self.get_dummy_components()
           torch.manual_seed(0					)
           snake_case					=       DDPMParallelScheduler()
           torch.manual_seed(0					)
           snake_case					=       StableDiffusionParadigmsPipeline(**__snake_case					)
           snake_case					=       sd_pipe.to(__snake_case					)
           sd_pipe.set_progress_bar_config(disable=__snake_case					)
  
           snake_case					=       self.get_dummy_inputs(__snake_case					)
           snake_case					=       sd_pipe(**__snake_case					).images
           snake_case					=       image[0, -3:, -3:, -1]
           assert image.shape == (1, 6_4, 6_4, 3)
  
           snake_case					=       np.array([0.3573, 0.4420, 0.4960, 0.4799, 0.3796, 0.3879, 0.4819, 0.4365, 0.4468]					)
  
           assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-2
      def 	a_    ( self					):
           super().test_inference_batch_consistent(batch_sizes=[1, 2]					)
      def 	a_    ( self					):
           super().test_inference_batch_single_identical(batch_size=2    ,      expected_max_diff=3E-3					)
  
  
  
  
      def 	a_    ( self					):
           snake_case					=       '''cpu'''  # ensure determinism for the device-dependent torch.Generator
           snake_case					=       self.get_dummy_components()
           snake_case					=       StableDiffusionParadigmsPipeline(**__snake_case					)
           snake_case					=       sd_pipe.to(__snake_case					)
           sd_pipe.set_progress_bar_config(disable=__snake_case					)
  
           snake_case					=       self.get_dummy_inputs(__snake_case					)
           snake_case					=       '''french fries'''
           snake_case					=       sd_pipe(**__snake_case    ,      negative_prompt=__snake_case					)
           snake_case					=       output.images
           snake_case					=       image[0, -3:, -3:, -1]
  
           assert image.shape == (1, 6_4, 6_4, 3)
  
           snake_case					=       np.array([0.4771, 0.5420, 0.4683, 0.4918, 0.5636, 0.4725, 0.5230, 0.4923, 0.5015]					)
  
           assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-2
  
  
  
  
  
  
  @slow
  @require_torch_gpu
  class 						A__      (					unittest.TestCase			):
  
  
  
  
  
  
  
      """simple docstring"""
  
      def 	a_    ( self					):
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()
      def 	a_    ( self    ,      __snake_case=0					):
           snake_case					=       torch.Generator(device=__snake_case					).manual_seed(__snake_case					)
           snake_case					=       {
               '''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 	a_    ( self					):
           snake_case					=       '''stabilityai/stable-diffusion-2-base'''
           snake_case					=       DDIMParallelScheduler.from_pretrained(__snake_case    ,      subfolder='''scheduler'''					)
           snake_case					=       StableDiffusionParadigmsPipeline.from_pretrained(__snake_case    ,      scheduler=__snake_case    ,      safety_checker=__snake_case					)
           pipe.to(__snake_case					)
           pipe.set_progress_bar_config(disable=__snake_case					)
           pipe.enable_attention_slicing()
  
           snake_case					=       self.get_inputs()
           snake_case					=       pipe(**__snake_case					).images
           snake_case					=       image[0, -3:, -3:, -1].flatten()
  
           assert image.shape == (1, 5_1_2, 5_1_2, 3)
  
           snake_case					=       np.array([0.9622, 0.9602, 0.9748, 0.9591, 0.9630, 0.9691, 0.9661, 0.9631, 0.9741]					)
  
           assert np.abs(expected_slice - image_slice					).max() < 1E-2
  
  
  
  
  