



import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer






class     __a			(  SCREAMING_SNAKE_CASE					):
	SCREAMING_SNAKE_CASE  	=							"Wav2Vec2FeatureExtractor"
	SCREAMING_SNAKE_CASE  	=							"AutoTokenizer"



	def __init__(    self				:     Tuple					,						snake_case_				:     Any					,						snake_case_				:     Optional[Any])->				Dict:
						super().__init__(snake_case_					,						snake_case_)
						__lowerCAmelCase						 =self.feature_extractor
						__lowerCAmelCase						 =False



	@classmethod
	def 		UpperCamelCase				(    cls				:     List[Any]					,						snake_case_				:     str					,						**snake_case_				:     List[str])->				str:
						try:
											return super().from_pretrained(snake_case_					,						**snake_case_)
						except OSError:
											warnings.warn(
											    F"""Loading a tokenizer inside {cls.__name__} from a config that does not"""
											    """ include a `tokenizer_class` attribute is deprecated and will be """
											    """removed in v5. Please add `'tokenizer_class': 'Wav2Vec2CTCTokenizer'`"""
											    """ attribute to either your `config.json` or `tokenizer_config.json` """
											    """file to suppress this warning: """					,						snake_case_					,						)

											__lowerCAmelCase						 =WavaVecaFeatureExtractor.from_pretrained(snake_case_					,						**snake_case_)
											__lowerCAmelCase						 =WavaVecaCTCTokenizer.from_pretrained(snake_case_					,						**snake_case_)

											return cls(feature_extractor=snake_case_					,						tokenizer=snake_case_)



	def __call__(    self				:     Any					,						*snake_case_				:     Tuple					,						**snake_case_				:     Union[str, Any])->				Optional[int]:
						# For backward compatibility
						if self._in_target_context_manager:
											return self.current_processor(*snake_case_					,						**snake_case_)

						if "raw_speech" in kwargs:
											warnings.warn("""Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.""")
											__lowerCAmelCase						 =kwargs.pop("""raw_speech""")
						else:
											__lowerCAmelCase						 =kwargs.pop("""audio"""					,						snake_case_)
						__lowerCAmelCase						 =kwargs.pop("""sampling_rate"""					,						snake_case_)
						__lowerCAmelCase						 =kwargs.pop("""text"""					,						snake_case_)
						if len(snake_case_) > 0:
											__lowerCAmelCase						 =args[0]
											__lowerCAmelCase						 =args[1:]

						if audio is None and text is None:
											raise ValueError("""You need to specify either an `audio` or `text` input to process.""")

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

						if text is None:
											return inputs
						elif audio is None:
											return encodings
						else:
											__lowerCAmelCase						 =encodings["""input_ids"""]
											return inputs



	def 		UpperCamelCase				(    self				:     int					,						*snake_case_				:     Any					,						**snake_case_				:     List[Any])->				Tuple:
						# For backward compatibility
						if self._in_target_context_manager:
											return self.current_processor.pad(*snake_case_					,						**snake_case_)

						__lowerCAmelCase						 =kwargs.pop("""input_features"""					,						snake_case_)
						__lowerCAmelCase						 =kwargs.pop("""labels"""					,						snake_case_)
						if len(snake_case_) > 0:
											__lowerCAmelCase						 =args[0]
											__lowerCAmelCase						 =args[1:]

						if input_features is not None:
											__lowerCAmelCase						 =self.feature_extractor.pad(snake_case_					,						*snake_case_					,						**snake_case_)
						if labels is not None:
											__lowerCAmelCase						 =self.tokenizer.pad(snake_case_					,						**snake_case_)

						if labels is None:
											return input_features
						elif input_features is None:
											return labels
						else:
											__lowerCAmelCase						 =labels["""input_ids"""]
											return input_features



	def 		UpperCamelCase				(    self				:     Optional[int]					,						*snake_case_				:     Union[str, Any]					,						**snake_case_				:     str)->				Any:
						return self.tokenizer.batch_decode(*snake_case_					,						**snake_case_)



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



	@contextmanager
	def 		UpperCamelCase				(    self				:     str)->				Union[str, Any]:
						warnings.warn(
						    """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
						    """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
						    """your audio inputs, or in a separate call.""")
						__lowerCAmelCase						 =True
						__lowerCAmelCase						 =self.tokenizer
						yield
						__lowerCAmelCase						 =self.feature_extractor
						__lowerCAmelCase						 =False









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()




