
from typing import List

from ...processing_utils import ProcessorMixin
from ...utils import is_torch_available


if is_torch_available():
	import torch





class 							UpperCAmelCase   (						SCREAMING_SNAKE_CASE__						):




	'''simple docstring'''





	snake_case_      					=  ['image_processor', 'tokenizer']
	snake_case_      					=  'OneFormerImageProcessor'
	snake_case_      					=  ('CLIPTokenizer', 'CLIPTokenizerFast')






	def __init__( self      :				Any							,A      :				Dict=None							,A      :				int=None							,A      :				int = 77							,A      :				int = 77							,**A      :				int				):



					if image_processor is None:
									raise ValueError("You need to specify an `image_processor`."				)
					if tokenizer is None:
									raise ValueError("You need to specify a `tokenizer`."				)

					__A         =				max_seq_length
					__A         =				task_seq_length

					super().__init__(a_							,a_				)






	def        UpperCamelCase_						( self      :				Any							,A      :				Optional[Any]=None							,A      :				Optional[int]=77				):



					if text_list is None:
									raise ValueError("tokens cannot be None."				)

					__A         =				self.tokenizer(a_							,padding="max_length"							,max_length=a_							,truncation=a_				)

					__A    ,							__A         =				tokens["attention_mask"], tokens["input_ids"]

					__A         =				[]
					for attn_mask, input_id in zip(a_							,a_				):
									__A         =				torch.tensor(a_				) * torch.tensor(a_				)
									token_inputs.append(token.unsqueeze(0				)				)

					__A         =				torch.cat(a_							,dim=0				)
					return token_inputs






	def __call__( self      :				str							,A      :				Dict=None							,A      :				str=None							,A      :				Tuple=None							,**A      :				Any				):



					if task_inputs is None:
									raise ValueError("You have to specify the task_input. Found None."				)
					elif images is None:
									raise ValueError("You have to specify the image. Found None."				)

					if not all(task in ["semantic", "instance", "panoptic"] for task in task_inputs				):
									raise ValueError("task_inputs must be semantic, instance, or panoptic."				)

					__A         =				self.image_processor(a_							,a_							,a_							,**a_				)

					if isinstance(a_							,a_				):
									__A         =				[task_inputs]

					if isinstance(a_							,a_				) and all(isinstance(a_							,a_				) for task_input in task_inputs				):
									__A         =				[]
									for task in task_inputs:
													__A         =				f'''the task is {task}'''
													task_token_inputs.append(a_				)
									__A         =				self._preprocess_text(a_							,max_length=self.task_seq_length				)
					else:
									raise TypeError("Task Inputs should be a string or a list of strings."				)

					if hasattr(a_							,"text_inputs"				):
									__A         =				encoded_inputs.text_inputs

									__A         =				[]
									for texts in texts_list:
													__A         =				self._preprocess_text(a_							,max_length=self.max_seq_length				)
													text_inputs.append(text_input_list.unsqueeze(0				)				)

									__A         =				torch.cat(a_							,dim=0				)

					return encoded_inputs






	def        UpperCamelCase_						( self      :				Union[str, Any]							,A      :				Tuple=None							,A      :				List[str]=None							,A      :				Optional[Any]=None							,**A      :				Tuple				):



					if task_inputs is None:
									raise ValueError("You have to specify the task_input. Found None."				)
					elif images is None:
									raise ValueError("You have to specify the image. Found None."				)

					if not all(task in ["semantic", "instance", "panoptic"] for task in task_inputs				):
									raise ValueError("task_inputs must be semantic, instance, or panoptic."				)

					__A         =				self.image_processor.encode_inputs(a_							,a_							,a_							,**a_				)

					if isinstance(a_							,a_				):
									__A         =				[task_inputs]

					if isinstance(a_							,a_				) and all(isinstance(a_							,a_				) for task_input in task_inputs				):
									__A         =				[]
									for task in task_inputs:
													__A         =				f'''the task is {task}'''
													task_token_inputs.append(a_				)
									__A         =				self._preprocess_text(a_							,max_length=self.task_seq_length				)
					else:
									raise TypeError("Task Inputs should be a string or a list of strings."				)

					if hasattr(a_							,"text_inputs"				):
									__A         =				encoded_inputs.text_inputs

									__A         =				[]
									for texts in texts_list:
													__A         =				self._preprocess_text(a_							,max_length=self.max_seq_length				)
													text_inputs.append(text_input_list.unsqueeze(0				)				)

									__A         =				torch.cat(a_							,dim=0				)

					return encoded_inputs






	def        UpperCamelCase_						( self      :				Any							,*A      :				int							,**A      :				Optional[int]				):
					return self.image_processor.post_process_semantic_segmentation(*a_							,**a_				)






	def        UpperCamelCase_						( self      :				str							,*A      :				Tuple							,**A      :				Optional[int]				):
					return self.image_processor.post_process_instance_segmentation(*a_							,**a_				)







	def        UpperCamelCase_						( self      :				Dict							,*A      :				str							,**A      :				int				):
					return self.image_processor.post_process_panoptic_segmentation(*a_							,**a_				)


from datetime import date

import requests

SCREAMING_SNAKE_CASE							:str											=       'https://api.carbonintensity.org.uk/intensity'


def 		UpperCAmelCase    (			)	->			str:







				"""simple docstring"""



				__A         =				requests.get(a_		).json()["data"][0]
				return last_half_hour["intensity"]["actual"]


def 		UpperCAmelCase    (			a_	,     a_		)	->			list:







				"""simple docstring"""



				return requests.get(F'''{BASE_URL}/{start}/{end}'''		).json()["data"]


if __name__ == "__main__":
					for entry in fetch_from_to(start=date(2020, 10, 1), end=date(2020, 10, 3)):
										print('from {from} to {to}: {intensity[actual]}'.format(**entry))
					print(f'''{fetch_last_half_hour() = }''')
