"""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 				__UpperCAmelCase       (			self     :							Tuple    ):

					"""simple docstring"""






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

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

					_lowercase			:     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(lowerCamelCase_      ,       lowerCamelCase_    )




"""simple docstring"""







from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE						=    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE						=    {
    'facebook/regnet-y-040': 'https://huggingface.co/facebook/regnet-y-040/blob/main/config.json',
}



class 			_lowerCamelCase      (__lowerCamelCase  ):
				_snake_case  =	"regnet"
				_snake_case  =	["x", "y"]







				def __init__(			self     :							Any      ,       lowerCamelCase_     :							Optional[Any]=3      ,       lowerCamelCase_     :							List[str]=3_2      ,       lowerCamelCase_     :							str=[1_2_8, 1_9_2, 5_1_2, 1_0_8_8]      ,       lowerCamelCase_     :							int=[2, 6, 1_2, 2]      ,       lowerCamelCase_     :							Union[str, Any]=6_4      ,       lowerCamelCase_     :							int="y"      ,       lowerCamelCase_     :							Optional[Any]="relu"      ,       **lowerCamelCase_     :							Optional[Any]      ,       ):

					"""simple docstring"""






					super().__init__(**lowerCamelCase_    )
					if layer_type not in self.layer_types:
						raise ValueError(F'''layer_type={layer_type} is not one of {','.join(self.layer_types    )}'''    )
					_lowercase			:     str      					=				num_channels
					_lowercase			:     Optional[Any]      					=				embedding_size
					_lowercase			:     Union[str, Any]      					=				hidden_sizes
					_lowercase			:     Optional[int]      					=				depths
					_lowercase			:     Optional[int]      					=				groups_width
					_lowercase			:     Tuple      					=				layer_type
					_lowercase			:     Union[str, Any]      					=				hidden_act
					# always downsample in the first stage
					_lowercase			:     Optional[int]      					=				True



