


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase_           =	{"""configuration_sew_d""": ["""SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SEWDConfig"""]}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase_           =	[
           """SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """SEWDForCTC""",
           """SEWDForSequenceClassification""",
           """SEWDModel""",
           """SEWDPreTrainedModel""",
       ]

if TYPE_CHECKING:
       from .configuration_sew_d import SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWDConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_sew_d import (
                  SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST,
                  SEWDForCTC,
                  SEWDForSequenceClassification,
                  SEWDModel,
                  SEWDPreTrainedModel,
              )


else:
       import sys

       lowercase_           =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)









"""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        lowerCAmelCase__       (  unittest.TestCase					):
   def 	__a				(						self	:     Optional[Any]				):


        '''simple docstring'''
        UpperCAmelCase__		:  int				=				"runwayml/stable-diffusion-v1-5"
        UpperCAmelCase__		:  Optional[Any]				=				TextToVideoZeroPipeline.from_pretrained(snake_case__				,       torch_dtype=torch.floataa				).to("cuda"				)
        UpperCAmelCase__		:  Any				=				DDIMScheduler.from_config(pipe.scheduler.config				)
        UpperCAmelCase__		:  Optional[Any]				=				torch.Generator(device="cuda"				).manual_seed(0				)

        UpperCAmelCase__		:  Any				=				"A bear is playing a guitar on Times Square"
        UpperCAmelCase__		:  Dict				=				pipe(prompt=snake_case__				,       generator=snake_case__				).images

        UpperCAmelCase__		:  List[Any]				=				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__				)


