






from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


lowerCamelCase__							=     logging.get_logger(__name__)

lowerCamelCase__							=     {
    """google/umt5-small""": """https://huggingface.co/google/umt5-small/resolve/main/config.json""",
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}




class 			snake_case__   (							__UpperCAmelCase):

       '''simple docstring'''
       lowerCamelCase							:				int 				= "umt5"
       lowerCamelCase							:				Dict 				= ["past_key_values"]



       def __init__(							self			,						a__=25_01_12			,						a__=5_12			,						a__=64			,						a__=10_24			,						a__=8			,						a__=None			,						a__=6			,						a__=32			,						a__=1_28			,						a__=0.1			,						a__=1e-6			,						a__=1.0			,						a__="gated-gelu"			,						a__=True			,						a__=True			,						a__="T5Tokenizer"			,						a__=True			,						a__=0			,						a__=1			,						a__=0			,						**a__			,						) ->      Tuple:






            '''simple docstring'''





            super().__init__(
                is_encoder_decoder=a__			,						tokenizer_class=a__			,						tie_word_embeddings=a__			,						pad_token_id=a__			,						eos_token_id=a__			,						decoder_start_token_id=a__			,						**a__			,						)
            __snake_case			:Dict          =						vocab_size
            __snake_case			:Dict          =						d_model
            __snake_case			:Union[str, Any]          =						d_kv
            __snake_case			:Dict          =						d_ff
            __snake_case			:List[Any]          =						num_layers
            __snake_case			:Any          =						(
                num_decoder_layers if num_decoder_layers is not None else self.num_layers
            )  # default = symmetry
            __snake_case			:Dict          =						num_heads
            __snake_case			:Optional[Any]          =						relative_attention_num_buckets
            __snake_case			:Union[str, Any]          =						relative_attention_max_distance
            __snake_case			:str          =						dropout_rate
            __snake_case			:Any          =						layer_norm_epsilon
            __snake_case			:List[str]          =						initializer_factor
            __snake_case			:Optional[int]          =						feed_forward_proj
            __snake_case			:Optional[int]          =						use_cache

            __snake_case			:int          =						self.feed_forward_proj.split("""-"""    )
            __snake_case			:Optional[Any]          =						act_info[-1]
            __snake_case			:List[Any]          =						act_info[0] == """gated"""

            if len(a__    ) > 1 and act_info[0] != "gated" or len(a__    ) > 2:
                 raise ValueError(
                     F'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
                     """Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. """
                     """\'gated-gelu\' or \'relu\'"""    )

            if feed_forward_proj == "gated-gelu":
                 __snake_case			:Tuple          =						"""gelu_new"""



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






            '''simple docstring'''





            return self.d_model



       @property
       def 					__lowercase						(							self    ) ->      Union[str, Any]:






            '''simple docstring'''





            return self.num_heads



       @property
       def 					__lowercase						(							self    ) ->      List[Any]:






            '''simple docstring'''





            return self.num_layers




class 			snake_case__   (							__UpperCAmelCase):

       '''simple docstring'''



       @property
       # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
       def 					__lowercase						(							self    ) ->      List[str]:






            '''simple docstring'''





            __snake_case			:List[str]          =						{
                """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
                """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
            }
            if self.use_past:
                 __snake_case			:Tuple          =						"""past_encoder_sequence + sequence"""
                 __snake_case			:Dict          =						{0: """batch"""}
                 __snake_case			:Dict          =						{0: """batch""", 1: """past_decoder_sequence + sequence"""}
            else:
                 __snake_case			:str          =						{0: """batch""", 1: """decoder_sequence"""}
                 __snake_case			:Optional[int]          =						{0: """batch""", 1: """decoder_sequence"""}

            if self.use_past:
                 self.fill_with_past_key_values_(a__			,						direction="""inputs"""    )

            return common_inputs



       @property
       # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
       def 					__lowercase						(							self    ) ->      Any:






            '''simple docstring'''





            return 13



       @property
       def 					__lowercase						(							self    ) ->      Dict:






            '''simple docstring'''





            return 5e-4










from __future__ import annotations

import time

import numpy as np

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




class 			snake_case__   :

       '''simple docstring'''



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






            '''simple docstring'''





            __snake_case			:Dict          =						claim_vector
            __snake_case			:Optional[int]          =						allocated_resources_table
            __snake_case			:Optional[int]          =						maximum_claim_table



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






            '''simple docstring'''





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



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






            '''simple docstring'''





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



       def 					__lowercase						(							self			,						**a__    ) ->      None:






            '''simple docstring'''





            __snake_case			:Optional[int]          =						self.__need()
            __snake_case			:List[Any]          =						self.__allocated_resources_table
            __snake_case			:str          =						self.__available_resources()
            __snake_case			:List[Any]          =						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:
                 __snake_case			:Dict          =						False
                 for each_need in need_list:
                      __snake_case			:Dict          =						True
                      for index, need in enumerate(a__    ):
                           if need > available_resources[index]:
                                __snake_case			:Dict          =						False
                                break
                      if execution:
                           __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:
                                     __snake_case			:List[str]          =						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
                           __snake_case			:Tuple          =						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 					__lowercase						(							self    ) ->      Dict:






            '''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()


