code: 
  
  
  '''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    lowercase_       (							self			)       ->  Optional[int]:
           __lowerCamelCase     :			List[Any]              =	'runwayml/stable-diffusion-v1-5'
           __lowerCamelCase     :			Union[str, Any]              =	TextToVideoZeroPipeline.from_pretrained(SCREAMING_SNAKE_CASE_    ,  torch_dtype=torch.floataa			).to('cuda'			)
           __lowerCamelCase     :			Dict              =	DDIMScheduler.from_config(pipe.scheduler.config			)
           __lowerCamelCase     :			Any              =	torch.Generator(device='cuda'			).manual_seed(0			)
  
           __lowerCamelCase     :			Union[str, Any]              =	'A bear is playing a guitar on Times Square'
           __lowerCamelCase     :			List[Any]              =	pipe(prompt=SCREAMING_SNAKE_CASE_    ,  generator=SCREAMING_SNAKE_CASE_			).images
  
           __lowerCamelCase     :			List[str]              =	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(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)
  
  
  
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available
  
  
  A__	:      Optional[Any]				=						{
      """configuration_xlm_prophetnet""": ["""XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMProphetNetConfig"""],
  }
  
  try:
        if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
        pass
  else:
        A__	:      List[Any]				=						["""XLMProphetNetTokenizer"""]
  
  try:
        if not is_torch_available():
              raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
        pass
  else:
        A__	:      Optional[Any]				=						[
            """XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
            """XLMProphetNetDecoder""",
            """XLMProphetNetEncoder""",
            """XLMProphetNetForCausalLM""",
            """XLMProphetNetForConditionalGeneration""",
            """XLMProphetNetModel""",
            """XLMProphetNetPreTrainedModel""",
        ]
  
  
  if TYPE_CHECKING:
        from .configuration_xlm_prophetnet import XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMProphetNetConfig
  
        try:
              if not is_sentencepiece_available():
                    raise OptionalDependencyNotAvailable()
        except OptionalDependencyNotAvailable:
              pass
        else:
              from .tokenization_xlm_prophetnet import XLMProphetNetTokenizer
  
        try:
              if not is_torch_available():
                    raise OptionalDependencyNotAvailable()
        except OptionalDependencyNotAvailable:
              pass
        else:
              from .modeling_xlm_prophetnet import (
                  XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                  XLMProphetNetDecoder,
                  XLMProphetNetEncoder,
                  XLMProphetNetForCausalLM,
                  XLMProphetNetForConditionalGeneration,
                  XLMProphetNetModel,
                  XLMProphetNetPreTrainedModel,
              )
  
  else:
        import sys
  
        A__	:      List[str]				=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  
  
  
  
  
  
  