






"""simple docstring"""


from typing import TYPE_CHECKING

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


__SCREAMING_SNAKE_CASE      =      {
    'configuration_donut_swin': ['DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DonutSwinConfig'],
    'processing_donut': ['DonutProcessor'],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE      =      [
		    'DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'DonutSwinModel',
		    'DonutSwinPreTrainedModel',
		]

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE      =      ['DonutFeatureExtractor']
		__SCREAMING_SNAKE_CASE      =      ['DonutImageProcessor']


if TYPE_CHECKING:
		from .configuration_donut_swin import DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, DonutSwinConfig
		from .processing_donut import DonutProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_donut_swin import (
				    DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DonutSwinModel,
				    DonutSwinPreTrainedModel,
				)

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_donut import DonutFeatureExtractor
				from .image_processing_donut import DonutImageProcessor

else:
		import sys

		__SCREAMING_SNAKE_CASE      =      _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 	__UpperCamelCase    (    unittest.TestCase	):
					def 					UpperCAmelCase__   (       self       :  Union[str, Any]      )  ->					Optional[Any]:
											lowerCAmelCase      :Union[str, Any]				  = 'runwayml/stable-diffusion-v1-5'
											lowerCAmelCase      :Tuple				  = TextToVideoZeroPipeline.from_pretrained(UpperCAmelCase    ,   torch_dtype=torch.floataa      ).to('cuda'      )
											lowerCAmelCase      :Union[str, Any]				  = DDIMScheduler.from_config(pipe.scheduler.config      )
											lowerCAmelCase      :str				  = torch.Generator(device='cuda'      ).manual_seed(0      )

											lowerCAmelCase      :Any				  = 'A bear is playing a guitar on Times Square'
											lowerCAmelCase      :Optional[Any]				  = pipe(prompt=UpperCAmelCase    ,   generator=UpperCAmelCase      ).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(UpperCAmelCase    ,   UpperCAmelCase      )