






import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
     import torch






class 		lowercase_					(	lowercase   ):






      '''simple docstring'''




      __snake_case       =  '''char'''
      __snake_case       =  '''bpe'''
      __snake_case       =  '''wp'''
UpperCAmelCase__				   =		(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)






class 		lowercase_					(	lowercase   ):






      '''simple docstring'''




      __snake_case       =  ['''image_processor''', '''char_tokenizer''']
      __snake_case       =  '''ViTImageProcessor'''
      __snake_case       =  '''MgpstrTokenizer'''







      def __init__(      self      :						Optional[int] ,					__UpperCAmelCase      :						Optional[int]=None ,					__UpperCAmelCase      :						Dict=None ,					**__UpperCAmelCase      :						Optional[Any]  )    ->Tuple:




       """simple docstring"""


       a		  =      None
       if "feature_extractor" in kwargs:
        warnings.warn(
            '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
            ''' instead.''' ,					__UpperCAmelCase ,					)
        a		  =      kwargs.pop('''feature_extractor'''  )

       a		  =      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`.'''  )

       a		  =      tokenizer
       a		  =      AutoTokenizer.from_pretrained('''gpt2'''  )
       a		  =      AutoTokenizer.from_pretrained('''bert-base-uncased'''  )

       super().__init__(__UpperCAmelCase ,					__UpperCAmelCase  )







      def __call__(      self      :						List[str] ,					__UpperCAmelCase      :						Optional[int]=None ,					__UpperCAmelCase      :						Dict=None ,					__UpperCAmelCase      :						Optional[Any]=None ,					**__UpperCAmelCase      :						List[str]  )    ->List[Any]:




       """simple docstring"""


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

       if images is not None:
        a		  =      self.image_processor(__UpperCAmelCase ,					return_tensors=__UpperCAmelCase ,					**__UpperCAmelCase  )
       if text is not None:
        a		  =      self.char_tokenizer(__UpperCAmelCase ,					return_tensors=__UpperCAmelCase ,					**__UpperCAmelCase  )

       if text is None:
        return inputs
       elif images is None:
        return encodings
       else:
        a		  =      encodings['''input_ids''']
        return inputs







      def       __lowerCAmelCase					(      self      :						List[Any] ,					__UpperCAmelCase      :						List[Any]  )    ->List[Any]:




       """simple docstring"""


       a  ,     a  ,     a		  =      sequences
       a		  =      char_preds.size(0  )

       a  ,     a		  =      self._decode_helper(__UpperCAmelCase ,					'''char'''  )
       a  ,     a		  =      self._decode_helper(__UpperCAmelCase ,					'''bpe'''  )
       a  ,     a		  =      self._decode_helper(__UpperCAmelCase ,					'''wp'''  )

       a		  =      []
       a		  =      []
       for i in range(__UpperCAmelCase  ):
        a		  =      [char_scores[i], bpe_scores[i], wp_scores[i]]
        a		  =      [char_strs[i], bpe_strs[i], wp_strs[i]]
        a		  =      scores.index(max(__UpperCAmelCase  )  )
        final_strs.append(strs[max_score_index]  )
        final_scores.append(scores[max_score_index]  )

       a		  =      {}
       a		  =      final_strs
       a		  =      final_scores
       a		  =      char_strs
       a		  =      bpe_strs
       a		  =      wp_strs
       return out







      def       __lowerCAmelCase					(      self      :						int ,					__UpperCAmelCase      :						List[Any] ,					__UpperCAmelCase      :						str  )    ->int:




       """simple docstring"""


       if format == DecodeType.CHARACTER:
        a		  =      self.char_decode
        a		  =      1
        a		  =      '''[s]'''
       elif format == DecodeType.BPE:
        a		  =      self.bpe_decode
        a		  =      2
        a		  =      '''#'''
       elif format == DecodeType.WORDPIECE:
        a		  =      self.wp_decode
        a		  =      102
        a		  =      '''[SEP]'''
       else:
        raise ValueError(F"""Format {format} is not supported."""  )

       a  ,     a		  =      [], []
       a		  =      pred_logits.size(0  )
       a		  =      pred_logits.size(1  )
       a  ,     a		  =      pred_logits.topk(1 ,					dim=-1 ,					largest=__UpperCAmelCase ,					sorted=__UpperCAmelCase  )
       a		  =      preds_index.view(-1 ,					__UpperCAmelCase  )[:, 1:]
       a		  =      decoder(__UpperCAmelCase  )
       a  ,     a		  =      torch.nn.functional.softmax(__UpperCAmelCase ,					dim=2  ).max(dim=2  )
       a		  =      preds_max_prob[:, 1:]

       for index in range(__UpperCAmelCase  ):
        a		  =      preds_str[index].find(__UpperCAmelCase  )
        a		  =      preds_str[index][:pred_eos]
        a		  =      preds_index[index].cpu().tolist()
        a		  =      pred_index.index(__UpperCAmelCase  ) if eos_token in pred_index else -1
        a		  =      preds_max_prob[index][: pred_eos_index + 1]
        a		  =      pred_max_prob.cumprod(dim=0  )[-1] if pred_max_prob.nelement() != 0 else 0.0
        dec_strs.append(__UpperCAmelCase  )
        conf_scores.append(__UpperCAmelCase  )

       return dec_strs, conf_scores







      def       __lowerCAmelCase					(      self      :						List[str] ,					__UpperCAmelCase      :						str  )    ->Union[str, Any]:




       """simple docstring"""


       a		  =      [seq.replace(''' ''' ,					''''''  ) for seq in self.char_tokenizer.batch_decode(__UpperCAmelCase  )]
       return decode_strs







      def       __lowerCAmelCase					(      self      :						Tuple ,					__UpperCAmelCase      :						List[Any]  )    ->List[Any]:




       """simple docstring"""


       return self.bpe_tokenizer.batch_decode(__UpperCAmelCase  )







      def       __lowerCAmelCase					(      self      :						Any ,					__UpperCAmelCase      :						List[Any]  )    ->List[Any]:




       """simple docstring"""


       a		  =      [seq.replace(''' ''' ,					''''''  ) for seq in self.wp_tokenizer.batch_decode(__UpperCAmelCase  )]
       return decode_strs










from __future__ import annotations

import time

import numpy as np

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






class 		lowercase_					:






      '''simple docstring'''




      def __init__(      self      :						Optional[int] ,					__UpperCAmelCase      :						list[int] ,					__UpperCAmelCase      :						list[list[int]] ,					__UpperCAmelCase      :						list[list[int]] ,					)    ->None:




       """simple docstring"""


       a		  =      claim_vector
       a		  =      allocated_resources_table
       a		  =      maximum_claim_table







      def       __lowerCAmelCase					(      self      :						Any  )    ->list[int]:




       """simple docstring"""


       return [
           sum(p_item[i] for p_item in self.__allocated_resources_table  )
           for i in range(len(self.__allocated_resources_table[0]  )  )
       ]







      def       __lowerCAmelCase					(      self      :						Optional[int]  )    ->list[int]:




       """simple docstring"""


       return np.array(self.__claim_vector  ) - np.array(
           self.__processes_resource_summation()  )







      def       __lowerCAmelCase					(      self      :						Union[str, Any]  )    ->list[list[int]]:




       """simple docstring"""


       return [
           list(np.array(self.__maximum_claim_table[i]  ) - np.array(__UpperCAmelCase  )  )
           for i, allocated_resource in enumerate(self.__allocated_resources_table  )
       ]







      def       __lowerCAmelCase					(      self      :						Tuple  )    ->dict[int, list[int]]:




       """simple docstring"""


       return {self.__need().index(__UpperCAmelCase  ): i for i in self.__need()}







      def       __lowerCAmelCase					(      self      :						Optional[Any] ,					**__UpperCAmelCase      :						Any  )    ->None:




       """simple docstring"""


       a		  =      self.__need()
       a		  =      self.__allocated_resources_table
       a		  =      self.__available_resources()
       a		  =      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:
        a		  =      False
        for each_need in need_list:
         a		  =      True
         for index, need in enumerate(__UpperCAmelCase  ):
          if need > available_resources[index]:
           a		  =      False
           break
         if execution:
          a		  =      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:
            a		  =      original_need_index
          print(F"""Process {process_number + 1} is executing."""  )
          # remove the process run from stack
          need_list.remove(__UpperCAmelCase  )
          # update available/freed resources stack
          a		  =      np.array(__UpperCAmelCase  ) + np.array(
              alloc_resources_table[process_number]  )
          print(
              '''Updated available resource stack for processes: '''
              + ''' '''.join([str(__UpperCAmelCase  ) 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       __lowerCAmelCase					(      self      :						List[Any]  )    ->Dict:




       """simple docstring"""


       print(''' ''' * 9 + '''Allocated Resource Table'''  )
       for item in self.__allocated_resources_table:
        print(
            F"""P{self.__allocated_resources_table.index(__UpperCAmelCase  ) + 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(__UpperCAmelCase  ) + 1}"""
            + ''' '''.join(F"""{it:>8}""" for it in item  )
            + '''\n'''  )
       print(
           '''Current Usage by Active Processes: '''
           + ''' '''.join(str(__UpperCAmelCase  ) for x in self.__claim_vector  )  )
       print(
           '''Initial Available Resources:       '''
           + ''' '''.join(str(__UpperCAmelCase  ) for x in self.__available_resources()  )  )
       time.sleep(1  )


if __name__ == "__main__":
     import doctest

     doctest.testmod()


