

"""simple docstring"""


import warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available

class      __a							(__UpperCAmelCase):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:Union[str, Any]       = ["""image_processor""", """tokenizer"""]
     _SCREAMING_SNAKE_CASE						:List[str]       = """OwlViTImageProcessor"""
     _SCREAMING_SNAKE_CASE						:List[Any]       = ("""CLIPTokenizer""", """CLIPTokenizerFast""")

     def __init__(						self	,    _a=None	,    _a=None	,    **_a  )  ->      List[str]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      int      		=	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_	,    )
                 SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	kwargs.pop("""feature_extractor"""  )

           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	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__(UpperCAmelCase_	,    UpperCAmelCase_  )

     def __call__(						self	,    _a=None	,    _a=None	,    _a=None	,    _a="max_length"	,    _a="np"	,    **_a  )  ->      Dict:


           """simple docstring"""


           if text is None and query_images is None and images is None:
                 raise ValueError(
                     """You have to specify at least one text or query image or image. All three cannot be none."""  )

           if text is not None:
                 if isinstance(UpperCAmelCase_	,    UpperCAmelCase_  ) or (isinstance(UpperCAmelCase_	,    UpperCAmelCase_  ) and not isinstance(text[0]	,    UpperCAmelCase_  )):
                       SCREAMING_SNAKE_CASE__   :      Tuple      		=	[self.tokenizer(UpperCAmelCase_	,    padding=UpperCAmelCase_	,    return_tensors=UpperCAmelCase_	,    **UpperCAmelCase_  )]

                 elif isinstance(UpperCAmelCase_	,    UpperCAmelCase_  ) and isinstance(text[0]	,    UpperCAmelCase_  ):
                       SCREAMING_SNAKE_CASE__   :      int      		=	[]

                       # Maximum number of queries across batch
                       SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	max([len(UpperCAmelCase_  ) for t in text]  )

                       # Pad all batch samples to max number of text queries
                       for t in text:
                             if len(UpperCAmelCase_  ) != max_num_queries:
                                   SCREAMING_SNAKE_CASE__   :      Tuple      		=	t + [""" """] * (max_num_queries - len(UpperCAmelCase_  ))

                             SCREAMING_SNAKE_CASE__   :      Dict      		=	self.tokenizer(UpperCAmelCase_	,    padding=UpperCAmelCase_	,    return_tensors=UpperCAmelCase_	,    **UpperCAmelCase_  )
                             encodings.append(UpperCAmelCase_  )
                 else:
                       raise TypeError("""Input text should be a string, a list of strings or a nested list of strings"""  )

                 if return_tensors == "np":
                       SCREAMING_SNAKE_CASE__   :      List[str]      		=	np.concatenate([encoding["""input_ids"""] for encoding in encodings]	,    axis=0  )
                       SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	np.concatenate([encoding["""attention_mask"""] for encoding in encodings]	,    axis=0  )

                 elif return_tensors == "jax" and is_flax_available():
                       import jax.numpy as jnp

                       SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	jnp.concatenate([encoding["""input_ids"""] for encoding in encodings]	,    axis=0  )
                       SCREAMING_SNAKE_CASE__   :      Any      		=	jnp.concatenate([encoding["""attention_mask"""] for encoding in encodings]	,    axis=0  )

                 elif return_tensors == "pt" and is_torch_available():
                       import torch

                       SCREAMING_SNAKE_CASE__   :      int      		=	torch.cat([encoding["""input_ids"""] for encoding in encodings]	,    dim=0  )
                       SCREAMING_SNAKE_CASE__   :      str      		=	torch.cat([encoding["""attention_mask"""] for encoding in encodings]	,    dim=0  )

                 elif return_tensors == "tf" and is_tf_available():
                       import tensorflow as tf

                       SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	tf.stack([encoding["""input_ids"""] for encoding in encodings]	,    axis=0  )
                       SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	tf.stack([encoding["""attention_mask"""] for encoding in encodings]	,    axis=0  )

                 else:
                       raise ValueError("""Target return tensor type could not be returned"""  )

                 SCREAMING_SNAKE_CASE__   :      Any      		=	BatchEncoding()
                 SCREAMING_SNAKE_CASE__   :      str      		=	input_ids
                 SCREAMING_SNAKE_CASE__   :      Dict      		=	attention_mask

           if query_images is not None:
                 SCREAMING_SNAKE_CASE__   :      Any      		=	BatchEncoding()
                 SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	self.image_processor(
                     UpperCAmelCase_	,    return_tensors=UpperCAmelCase_	,    **UpperCAmelCase_  ).pixel_values
                 SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	query_pixel_values

           if images is not None:
                 SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	self.image_processor(UpperCAmelCase_	,    return_tensors=UpperCAmelCase_	,    **UpperCAmelCase_  )

           if text is not None and images is not None:
                 SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	image_features.pixel_values
                 return encoding
           elif query_images is not None and images is not None:
                 SCREAMING_SNAKE_CASE__   :      int      		=	image_features.pixel_values
                 return encoding
           elif text is not None or query_images is not None:
                 return encoding
           else:
                 return BatchEncoding(data=dict(**UpperCAmelCase_  )	,    tensor_type=UpperCAmelCase_  )

     def 					_a			(						self	,    *_a	,    **_a  )  ->      Any:


           """simple docstring"""


           return self.image_processor.post_process(*UpperCAmelCase_	,    **UpperCAmelCase_  )

     def 					_a			(						self	,    *_a	,    **_a  )  ->      Union[str, Any]:


           """simple docstring"""


           return self.image_processor.post_process_object_detection(*UpperCAmelCase_	,    **UpperCAmelCase_  )

     def 					_a			(						self	,    *_a	,    **_a  )  ->      Union[str, Any]:


           """simple docstring"""


           return self.image_processor.post_process_image_guided_detection(*UpperCAmelCase_	,    **UpperCAmelCase_  )

     def 					_a			(						self	,    *_a	,    **_a  )  ->      Optional[int]:


           """simple docstring"""


           return self.tokenizer.batch_decode(*UpperCAmelCase_	,    **UpperCAmelCase_  )

     def 					_a			(						self	,    *_a	,    **_a  )  ->      List[Any]:


           """simple docstring"""


           return self.tokenizer.decode(*UpperCAmelCase_	,    **UpperCAmelCase_  )

     @property
     def 					_a			(						self  )  ->      List[str]:


           """simple docstring"""


           warnings.warn(
               """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""	,    UpperCAmelCase_	,    )
           return self.image_processor_class





     @property
     def 					_a			(						self  )  ->      str:


           """simple docstring"""


           warnings.warn(
               """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""	,    UpperCAmelCase_	,    )
           return self.image_processor







"""simple docstring"""


from __future__ import annotations

import time

import numpy as np

a					:Optional[Any]    = [8, 5, 9, 7]
a					:List[Any]    = [
    [2, 0, 1, 1],
    [0, 1, 2, 1],
    [4, 0, 0, 3],
    [0, 2, 1, 0],
    [1, 0, 3, 0],
]
a					:int    = [
    [3, 2, 1, 4],
    [0, 2, 5, 2],
    [5, 1, 0, 5],
    [1, 5, 3, 0],
    [3, 0, 3, 3],
]

class      __a							:






     '''simple docstring'''

     def __init__(						self	,    _a	,    _a	,    _a	,    )  ->      None:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      str      		=	claim_vector
           SCREAMING_SNAKE_CASE__   :      Any      		=	allocated_resources_table
           SCREAMING_SNAKE_CASE__   :      Any      		=	maximum_claim_table

     def 					_a			(						self  )  ->      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 					_a			(						self  )  ->      list[int]:


           """simple docstring"""


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

     def 					_a			(						self  )  ->      list[list[int]]:


           """simple docstring"""


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

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


           """simple docstring"""


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

     def 					_a			(						self	,    **_a  )  ->      None:


           """simple docstring"""


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


           """simple docstring"""


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


if __name__ == "__main__":
  import doctest

  doctest.testmod()




