



import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class     __a			(  SCREAMING_SNAKE_CASE					):
	SCREAMING_SNAKE_CASE  	=							["image_processor", "tokenizer"]
	SCREAMING_SNAKE_CASE  	=							"CLIPImageProcessor"
	SCREAMING_SNAKE_CASE  	=							("CLIPTokenizer", "CLIPTokenizerFast")



	def __init__(    self				:     Optional[int]					,						snake_case_				:     Any=None					,						snake_case_				:     Optional[Any]=None					,						**snake_case_				:     Optional[Any])->				Union[str, Any]:
						__lowerCAmelCase						 =None
						if "feature_extractor" in kwargs:
											warnings.warn(
											    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
											    """ instead."""					,						snake_case_					,						)
											__lowerCAmelCase						 =kwargs.pop("""feature_extractor""")

						__lowerCAmelCase						 =image_processor if image_processor is not None else feature_extractor
						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`.""")

						super().__init__(snake_case_					,						snake_case_)



	def __call__(    self				:     Dict					,						snake_case_				:     Dict=None					,						snake_case_				:     Union[str, Any]=None					,						snake_case_				:     str=None					,						**snake_case_				:     Any)->				int:

						if text is None and images is None:
											raise ValueError("""You have to specify either text or images. Both cannot be none.""")

						if text is not None:
											__lowerCAmelCase						 =self.tokenizer(snake_case_					,						return_tensors=snake_case_					,						**snake_case_)

						if images is not None:
											__lowerCAmelCase						 =self.image_processor(snake_case_					,						return_tensors=snake_case_					,						**snake_case_)

						if text is not None and images is not None:
											__lowerCAmelCase						 =image_features.pixel_values
											return encoding
						elif text is not None:
											return encoding
						else:
											return BatchEncoding(data=dict(**snake_case_)					,						tensor_type=snake_case_)



	def 		UpperCamelCase				(    self				:     str					,						*snake_case_				:     Dict					,						**snake_case_				:     Any)->				int:
						return self.tokenizer.batch_decode(*snake_case_					,						**snake_case_)



	def 		UpperCamelCase				(    self				:     str					,						*snake_case_				:     str					,						**snake_case_				:     Optional[int])->				Dict:
						return self.tokenizer.decode(*snake_case_					,						**snake_case_)



	@property
	def 		UpperCamelCase				(    self				:     List[Any])->				str:
						__lowerCAmelCase						 =self.tokenizer.model_input_names
						__lowerCAmelCase						 =self.image_processor.model_input_names
						return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))



	@property
	def 		UpperCamelCase				(    self				:     List[Any])->				int:
						warnings.warn(
						    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""					,						snake_case_					,						)
						return self.image_processor_class



	@property
	def 		UpperCamelCase				(    self				:     Optional[int])->				str:
						warnings.warn(
						    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""					,						snake_case_					,						)
						return self.image_processor









from __future__ import annotations

import time

import numpy as np

lowercase_													=      [8, 5, 9, 7]
lowercase_													=      [
    [2, 0, 1, 1],
    [0, 1, 2, 1],
    [4, 0, 0, 3],
    [0, 2, 1, 0],
    [1, 0, 3, 0],
]
lowercase_													=      [
    [3, 2, 1, 4],
    [0, 2, 5, 2],
    [5, 1, 0, 5],
    [1, 5, 3, 0],
    [3, 0, 3, 3],
]






class     __a			:



	def __init__(    self				:     Tuple					,						snake_case_				:     list[int]					,						snake_case_				:     list[list[int]]					,						snake_case_				:     list[list[int]]					,						)->				None:
						__lowerCAmelCase						 =claim_vector
						__lowerCAmelCase						 =allocated_resources_table
						__lowerCAmelCase						 =maximum_claim_table



	def 		UpperCamelCase				(    self				:     Optional[int])->				list[int]:
						return [
						    sum(p_item[i] for p_item in self.__allocated_resources_table)
						    for i in range(len(self.__allocated_resources_table[0]))
						]



	def 		UpperCamelCase				(    self				:     Dict)->				list[int]:
						return np.array(self.__claim_vector) - np.array(
						    self.__processes_resource_summation())



	def 		UpperCamelCase				(    self				:     Optional[Any])->				list[list[int]]:
						return [
						    list(np.array(self.__maximum_claim_table[i]) - np.array(snake_case_))
						    for i, allocated_resource in enumerate(self.__allocated_resources_table)
						]



	def 		UpperCamelCase				(    self				:     Dict)->				dict[int, list[int]]:
						return {self.__need().index(snake_case_): i for i in self.__need()}



	def 		UpperCamelCase				(    self				:     Optional[Any]					,						**snake_case_				:     Optional[Any])->				None:
						__lowerCAmelCase						 =self.__need()
						__lowerCAmelCase						 =self.__allocated_resources_table
						__lowerCAmelCase						 =self.__available_resources()
						__lowerCAmelCase						 =self.__need_index_manager()
						for kw, val in kwargs.items():
											if kw and val is True:
																self.__pretty_data()
						print("""_""" * 50 + """\n""")
						while need_list:
											__lowerCAmelCase						 =False
											for each_need in need_list:
																__lowerCAmelCase						 =True
																for index, need in enumerate(snake_case_):
																					if need > available_resources[index]:
																										__lowerCAmelCase						 =False
																										break
																if execution:
																					__lowerCAmelCase						 =True
																					# get the original index of the process from ind_ctrl db
																					for original_need_index, need_clone in need_index_manager.items():
																										if each_need == need_clone:
																															__lowerCAmelCase						 =original_need_index
																					print(F"""Process {process_number + 1} is executing.""")
																					# remove the process run from stack
																					need_list.remove(snake_case_)
																					# update available/freed resources stack
																					__lowerCAmelCase						 =np.array(snake_case_) + np.array(
																					    alloc_resources_table[process_number])
																					print(
																					    """Updated available resource stack for processes: """
																					    + """ """.join([str(snake_case_) for x in available_resources]))
																					break
											if safe:
																print("""The process is in a safe state.\n""")
											else:
																print("""System in unsafe state. Aborting...\n""")
																break



	def 		UpperCamelCase				(    self				:     List[str])->				Optional[Any]:
						print(""" """ * 9 + """Allocated Resource Table""")
						for item in self.__allocated_resources_table:
											print(
											    F"""P{self.__allocated_resources_table.index(snake_case_) + 1}"""
											    + """ """.join(F"""{it:>8}""" for it in item)
											    + """\n""")
						print(""" """ * 9 + """System Resource Table""")
						for item in self.__maximum_claim_table:
											print(
											    F"""P{self.__maximum_claim_table.index(snake_case_) + 1}"""
											    + """ """.join(F"""{it:>8}""" for it in item)
											    + """\n""")
						print(
						    """Current Usage by Active Processes: """
						    + """ """.join(str(snake_case_) for x in self.__claim_vector))
						print(
						    """Initial Available Resources:       """
						    + """ """.join(str(snake_case_) for x in self.__available_resources()))
						time.sleep(1)


if __name__ == "__main__":
				import doctest

				doctest.testmod()




