

"""simple docstring"""
import unittest

import torch

from diffusers import DDIMScheduler, TextToVideoZeroPipeline
from diffusers.utils import load_pt, require_torch_gpu, slow

from ..test_pipelines_common import assert_mean_pixel_difference

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


  """simple docstring"""





  def 							_UpperCAmelCase      ( self					: Optional[Any]      )	->			List[Any]:


         '''simple docstring'''





         __magic_name__			:		Dict        =     '''runwayml/stable-diffusion-v1-5'''
         __magic_name__			:		Union[str, Any]        =     TextToVideoZeroPipeline.from_pretrained(snake_case       ,       torch_dtype=torch.floataa      ).to('''cuda'''      )
         __magic_name__			:		List[str]        =     DDIMScheduler.from_config(pipe.scheduler.config      )
         __magic_name__			:		int        =     torch.Generator(device='''cuda'''      ).manual_seed(0      )

         __magic_name__			:		Optional[int]        =     '''A bear is playing a guitar on Times Square'''
         __magic_name__			:		Tuple        =     pipe(prompt=snake_case       ,       generator=snake_case      ).images

         __magic_name__			:		Optional[int]        =     load_pt(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text-to-video/A bear is playing a guitar on Times Square.pt'''      )

         assert_mean_pixel_difference(snake_case       ,       snake_case      )



"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


A									=						{
    """configuration_imagegpt""": ["""IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ImageGPTConfig""", """ImageGPTOnnxConfig"""]
}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 A									=						["""ImageGPTFeatureExtractor"""]
 A									=						["""ImageGPTImageProcessor"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 A									=						[
     """IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """ImageGPTForCausalImageModeling""",
     """ImageGPTForImageClassification""",
     """ImageGPTModel""",
     """ImageGPTPreTrainedModel""",
     """load_tf_weights_in_imagegpt""",
 ]


if TYPE_CHECKING:
 from .configuration_imagegpt import IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, ImageGPTConfig, ImageGPTOnnxConfig

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_imagegpt import ImageGPTFeatureExtractor
  from .image_processing_imagegpt import ImageGPTImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_imagegpt import (
      IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
      ImageGPTForCausalImageModeling,
      ImageGPTForImageClassification,
      ImageGPTModel,
      ImageGPTPreTrainedModel,
      load_tf_weights_in_imagegpt,
  )

else:
 import sys

 A									=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
