"""simple docstring"""






import os

from apache_beam.io.filesystems import FileSystems
from apache_beam.pipeline import Pipeline

from .logging import get_logger


UpperCamelCase     					=     2 << 20  # 2mb
UpperCamelCase     					=     get_logger(__name__)
class   UpperCAmelCase__				(    lowercase_			):


	"""simple docstring"""







	def       A    (			self )    ->	List[Any]:
							a_							:			Any     		=					self._options.get_all_options().get("runner" )
							return runner in [None, "DirectRunner", "PortableRunner"]




def  lowerCAmelCase_      (_SCREAMING_SNAKE_CASE	:str		,			_SCREAMING_SNAKE_CASE	:Union[str, Any]		,			_SCREAMING_SNAKE_CASE	:Any=False  )				-> Any:
						a_							:			int     		=					FileSystems
						if fs.exists(_SCREAMING_SNAKE_CASE  ):
												if force_upload:
																		logger.info(F'''Remote path already exist: {remote_file_path}. Overwriting it as force_upload=True.'''  )
												else:
																		logger.info(F'''Remote path already exist: {remote_file_path}. Skipping it as force_upload=False.'''  )
																		return
						with fs.create(_SCREAMING_SNAKE_CASE  ) as remote_file:
												with open(_SCREAMING_SNAKE_CASE		,			"rb"  ) as local_file:
																		a_							:			Any     		=					local_file.read(_SCREAMING_SNAKE_CASE  )
																		while chunk:
																								remote_file.write(_SCREAMING_SNAKE_CASE  )
																								a_							:			Union[str, Any]     		=					local_file.read(_SCREAMING_SNAKE_CASE  )



def  lowerCAmelCase_      (_SCREAMING_SNAKE_CASE	:str		,			_SCREAMING_SNAKE_CASE	:Optional[int]		,			_SCREAMING_SNAKE_CASE	:Tuple=False  )				-> Optional[int]:
						a_							:			Union[str, Any]     		=					FileSystems
						if os.path.exists(_SCREAMING_SNAKE_CASE  ):
												if force_download:
																		logger.info(F'''Local path already exist: {remote_file_path}. Overwriting it as force_upload=True.'''  )
												else:
																		logger.info(F'''Local path already exist: {remote_file_path}. Skipping it as force_upload=False.'''  )
																		return
						with fs.open(_SCREAMING_SNAKE_CASE  ) as remote_file:
												with open(_SCREAMING_SNAKE_CASE		,			"wb"  ) as local_file:
																		a_							:			List[str]     		=					remote_file.read(_SCREAMING_SNAKE_CASE  )
																		while chunk:
																								local_file.write(_SCREAMING_SNAKE_CASE  )
																								a_							:			Tuple     		=					remote_file.read(_SCREAMING_SNAKE_CASE  )





"""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       A    (			self )    ->	List[str]:
							a_							:			List[str]     		=					"runwayml/stable-diffusion-v1-5"
							a_							:			Optional[int]     		=					TextToVideoZeroPipeline.from_pretrained(_SCREAMING_SNAKE_CASE       ,     torch_dtype=torch.floataa ).to("cuda" )
							a_							:			Dict     		=					DDIMScheduler.from_config(pipe.scheduler.config )
							a_							:			Optional[Any]     		=					torch.Generator(device="cuda" ).manual_seed(0 )

							a_							:			List[Any]     		=					"A bear is playing a guitar on Times Square"
							a_							:			List[Any]     		=					pipe(prompt=_SCREAMING_SNAKE_CASE       ,     generator=_SCREAMING_SNAKE_CASE ).images

							a_							:			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(_SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE )




