




'''simple docstring'''







from typing import List

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


if is_torch_available():
						import torch





class      lowerCamelCase_							(       UpperCAmelCase_							):




					'''simple docstring'''






					lowercase__    = ["""image_processor""", """tokenizer"""]
					lowercase__    = """OneFormerImageProcessor"""
					lowercase__    = ("""CLIPTokenizer""", """CLIPTokenizerFast""")






					def __init__(    self,  __UpperCamelCase=None,  __UpperCamelCase=None,  __UpperCamelCase = 77,  __UpperCamelCase = 77,  **__UpperCamelCase ):
							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`." )

							_snake_case	       =       max_seq_length
							_snake_case	       =       task_seq_length

							super().__init__(__UpperCamelCase,  __UpperCamelCase )






					def 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase=None,  __UpperCamelCase=77 ):
							if text_list is None:
									raise ValueError("tokens cannot be None." )

							_snake_case	       =       self.tokenizer(__UpperCamelCase,  padding="max_length",  max_length=__UpperCamelCase,  truncation=__UpperCamelCase )

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

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

							_snake_case	       =       torch.cat(__UpperCamelCase,  dim=0 )
							return token_inputs






					def __call__(    self,  __UpperCamelCase=None,  __UpperCamelCase=None,  __UpperCamelCase=None,  **__UpperCamelCase ):

							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." )

							_snake_case	       =       self.image_processor(__UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase,  **__UpperCamelCase )

							if isinstance(__UpperCamelCase,  __UpperCamelCase ):
									_snake_case	       =       [task_inputs]

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

							if hasattr(__UpperCamelCase,  "text_inputs" ):
									_snake_case	       =       encoded_inputs.text_inputs

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

									_snake_case	       =       torch.cat(__UpperCamelCase,  dim=0 )

							return encoded_inputs






					def 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase=None,  __UpperCamelCase=None,  __UpperCamelCase=None,  **__UpperCamelCase ):

							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." )

							_snake_case	       =       self.image_processor.encode_inputs(__UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase,  **__UpperCamelCase )

							if isinstance(__UpperCamelCase,  __UpperCamelCase ):
									_snake_case	       =       [task_inputs]

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

							if hasattr(__UpperCamelCase,  "text_inputs" ):
									_snake_case	       =       encoded_inputs.text_inputs

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

									_snake_case	       =       torch.cat(__UpperCamelCase,  dim=0 )

							return encoded_inputs






					def 				__SCREAMING_SNAKE_CASE	(    self,  *__UpperCamelCase,  **__UpperCamelCase ):
							return self.image_processor.post_process_semantic_segmentation(*__UpperCamelCase,  **__UpperCamelCase )






					def 				__SCREAMING_SNAKE_CASE	(    self,  *__UpperCamelCase,  **__UpperCamelCase ):
							return self.image_processor.post_process_instance_segmentation(*__UpperCamelCase,  **__UpperCamelCase )






					def 				__SCREAMING_SNAKE_CASE	(    self,  *__UpperCamelCase,  **__UpperCamelCase ):
							return self.image_processor.post_process_panoptic_segmentation(*__UpperCamelCase,  **__UpperCamelCase )











'''simple docstring'''







from __future__ import annotations

import math
from itertools import permutations






def 		_lowercase		(	lowercase_  ):



		'''simple docstring'''







		if 1 < number < 4:
				# 2 and 3 are primes
				return True
		elif number < 2 or number % 2 == 0 or number % 3 == 0:
				# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
				return False

		# All primes number are in format of 6k +/- 1
		for i in range(5						,							int(math.sqrt(lowercase_  ) + 1  )						,							6  ):
				if number % i == 0 or number % (i + 2) == 0:
						return False
		return True






def 		_lowercase		(	lowercase_ = 7  ):



		'''simple docstring'''







		_snake_case	       =       "".join(str(lowercase_  ) for i in range(1						,							n + 1  )  )
		_snake_case	       =       [int("".join(lowercase_  )  ) for i in permutations(lowercase_						,							lowercase_  )]
		_snake_case	       =       [num for num in perm_list if is_prime(lowercase_  )]
		return max(lowercase_  ) if pandigitals else 0


if __name__ == "__main__":
						print(F'''{solution() = }''')





