




"""simple docstring"""





import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


A				=							logging.get_logger(__name__)

A				=							{
    """facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/config.json""",
    # See all BART models at https://huggingface.co/models?filter=bart
}



class   a__					(						__magic_name__ ):
			lowercase_		=						"bart"
			lowercase_		=						["past_key_values"]
			lowercase_		=						{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}


			def __init__(   self   :     List[Any]   ,	UpperCamelCase_   :     Optional[Any]=50265   ,	UpperCamelCase_   :     int=1024   ,	UpperCamelCase_   :     List[str]=12   ,	UpperCamelCase_   :     List[str]=4096   ,	UpperCamelCase_   :     int=16   ,	UpperCamelCase_   :     Optional[int]=12   ,	UpperCamelCase_   :     Any=4096   ,	UpperCamelCase_   :     Dict=16   ,	UpperCamelCase_   :     Optional[Any]=0.0   ,	UpperCamelCase_   :     int=0.0   ,	UpperCamelCase_   :     Dict="gelu"   ,	UpperCamelCase_   :     int=1024   ,	UpperCamelCase_   :     Optional[int]=0.1   ,	UpperCamelCase_   :     int=0.0   ,	UpperCamelCase_   :     Optional[Any]=0.0   ,	UpperCamelCase_   :     Any=0.02   ,	UpperCamelCase_   :     str=0.0   ,	UpperCamelCase_   :     Tuple=False   ,	UpperCamelCase_   :     Dict=True   ,	UpperCamelCase_   :     Union[str, Any]=3   ,	UpperCamelCase_   :     Optional[int]=1   ,	UpperCamelCase_   :     Any=0   ,	UpperCamelCase_   :     Tuple=2   ,	UpperCamelCase_   :     Optional[int]=True   ,	UpperCamelCase_   :     Dict=2   ,	UpperCamelCase_   :     Optional[int]=2   ,	**UpperCamelCase_   :     Any   ,	):


						"""simple docstring"""




						__UpperCAmelCase						:			str												=       vocab_size
						__UpperCAmelCase						:			Optional[int]												=       max_position_embeddings
						__UpperCAmelCase						:			List[str]												=       d_model
						__UpperCAmelCase						:			List[str]												=       encoder_ffn_dim
						__UpperCAmelCase						:			List[str]												=       encoder_layers
						__UpperCAmelCase						:			Optional[int]												=       encoder_attention_heads
						__UpperCAmelCase						:			Tuple												=       decoder_ffn_dim
						__UpperCAmelCase						:			List[Any]												=       decoder_layers
						__UpperCAmelCase						:			Dict												=       decoder_attention_heads
						__UpperCAmelCase						:			int												=       dropout
						__UpperCAmelCase						:			str												=       attention_dropout
						__UpperCAmelCase						:			Dict												=       activation_dropout
						__UpperCAmelCase						:			str												=       activation_function
						__UpperCAmelCase						:			Union[str, Any]												=       init_std
						__UpperCAmelCase						:			Optional[int]												=       encoder_layerdrop
						__UpperCAmelCase						:			List[str]												=       decoder_layerdrop
						__UpperCAmelCase						:			Dict												=       classifier_dropout
						__UpperCAmelCase						:			Any												=       use_cache
						__UpperCAmelCase						:			Optional[int]												=       encoder_layers
						__UpperCAmelCase						:			int												=       scale_embedding  # scale factor will be sqrt(d_model) if True

						super().__init__(
						    num_labels=UpperCamelCase_   ,	pad_token_id=UpperCamelCase_   ,	bos_token_id=UpperCamelCase_   ,	eos_token_id=UpperCamelCase_   ,	is_encoder_decoder=UpperCamelCase_   ,	decoder_start_token_id=UpperCamelCase_   ,	forced_eos_token_id=UpperCamelCase_   ,	**UpperCamelCase_   ,	)

						# ensure backward compatibility for BART CNN models
						if self.forced_bos_token_id is None and kwargs.get("force_bos_token_to_be_generated"   ,	UpperCamelCase_):
									__UpperCAmelCase						:			Dict												=       self.bos_token_id
									warnings.warn(
									    F"Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. "
									    "The config can simply be saved and uploaded again to be fixed.")






class   a__					(						__magic_name__ ):


			@property
			def    a_  (   self   :     int):


						"""simple docstring"""




						if self.task in ["default", "seq2seq-lm"]:
									__UpperCAmelCase						:			Optional[Any]												=       OrderedDict(
									    [
									        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
									        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
									    ])

									if self.use_past:
												__UpperCAmelCase						:			Dict												=       {0: "batch"}
												__UpperCAmelCase						:			str												=       {0: "batch", 1: "past_decoder_sequence + sequence"}
									else:
												__UpperCAmelCase						:			Optional[Any]												=       {0: "batch", 1: "decoder_sequence"}
												__UpperCAmelCase						:			Optional[int]												=       {0: "batch", 1: "decoder_sequence"}

									if self.use_past:
												self.fill_with_past_key_values_(UpperCamelCase_   ,	direction="inputs")
						elif self.task == "causal-lm":
									# TODO: figure this case out.
									__UpperCAmelCase						:			Dict												=       OrderedDict(
									    [
									        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
									        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
									    ])
									if self.use_past:
												__UpperCAmelCase						,  __UpperCAmelCase						:			int												=       self.num_layers
												for i in range(UpperCamelCase_):
															__UpperCAmelCase						:			int												=       {0: "batch", 2: "past_sequence + sequence"}
															__UpperCAmelCase						:			int												=       {0: "batch", 2: "past_sequence + sequence"}
						else:
									__UpperCAmelCase						:			Union[str, Any]												=       OrderedDict(
									    [
									        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
									        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
									        ("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
									        ("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
									    ])

						return common_inputs


			@property
			def    a_  (   self   :     Any):


						"""simple docstring"""




						if self.task in ["default", "seq2seq-lm"]:
									__UpperCAmelCase						:			Optional[Any]												=       super().outputs
						else:
									__UpperCAmelCase						:			Any												=       super(UpperCamelCase_   ,	self).outputs
									if self.use_past:
												__UpperCAmelCase						,  __UpperCAmelCase						:			Optional[Any]												=       self.num_layers
												for i in range(UpperCamelCase_):
															__UpperCAmelCase						:			Tuple												=       {0: "batch", 2: "past_sequence + sequence"}
															__UpperCAmelCase						:			List[str]												=       {0: "batch", 2: "past_sequence + sequence"}
						return common_outputs


			def    a_  (   self   :     List[str]   ,	UpperCamelCase_   :     PreTrainedTokenizer   ,	UpperCamelCase_   :     int = -1   ,	UpperCamelCase_   :     int = -1   ,	UpperCamelCase_   :     bool = False   ,	UpperCamelCase_   :     Optional[TensorType] = None   ,	):


						"""simple docstring"""




						__UpperCAmelCase						:			List[str]												=       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
						    UpperCamelCase_   ,	UpperCamelCase_   ,	UpperCamelCase_   ,	UpperCamelCase_   ,	UpperCamelCase_)

						# Generate decoder inputs
						__UpperCAmelCase						:			str												=       seq_length if not self.use_past else 1
						__UpperCAmelCase						:			Tuple												=       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
						    UpperCamelCase_   ,	UpperCamelCase_   ,	UpperCamelCase_   ,	UpperCamelCase_   ,	UpperCamelCase_)
						__UpperCAmelCase						:			Union[str, Any]												=       {F"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
						__UpperCAmelCase						:			Optional[Any]												=       dict(**UpperCamelCase_   ,	**UpperCamelCase_)

						if self.use_past:
									if not is_torch_available():
												raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed.")
									else:
												import torch
									__UpperCAmelCase						,  __UpperCAmelCase						:			Tuple												=       common_inputs["input_ids"].shape
									__UpperCAmelCase						:			str												=       common_inputs["decoder_input_ids"].shape[1]
									__UpperCAmelCase						,  __UpperCAmelCase						:			Dict												=       self.num_attention_heads
									__UpperCAmelCase						:			Dict												=       (
									    batch,
									    num_encoder_attention_heads,
									    encoder_seq_length,
									    self._config.hidden_size // num_encoder_attention_heads,
									)
									__UpperCAmelCase						:			List[str]												=       decoder_seq_length + 3
									__UpperCAmelCase						:			Union[str, Any]												=       (
									    batch,
									    num_decoder_attention_heads,
									    decoder_past_length,
									    self._config.hidden_size // num_decoder_attention_heads,
									)

									__UpperCAmelCase						:			str												=       torch.cat(
									    [common_inputs["decoder_attention_mask"], torch.ones(UpperCamelCase_   ,	UpperCamelCase_)]   ,	dim=1)

									__UpperCAmelCase						:			str												=       []
									# If the number of encoder and decoder layers are present in the model configuration, both are considered
									__UpperCAmelCase						,  __UpperCAmelCase						:			List[Any]												=       self.num_layers
									__UpperCAmelCase						:			Any												=       min(UpperCamelCase_   ,	UpperCamelCase_)
									__UpperCAmelCase						:			int												=       max(UpperCamelCase_   ,	UpperCamelCase_) - min_num_layers
									__UpperCAmelCase						:			int												=       "encoder" if num_encoder_layers > num_decoder_layers else "decoder"

									for _ in range(UpperCamelCase_):
												common_inputs["past_key_values"].append(
												    (
												        torch.zeros(UpperCamelCase_),
												        torch.zeros(UpperCamelCase_),
												        torch.zeros(UpperCamelCase_),
												        torch.zeros(UpperCamelCase_),
												    ))
									# TODO: test this.
									__UpperCAmelCase						:			Tuple												=       encoder_shape if remaining_side_name == "encoder" else decoder_shape
									for _ in range(UpperCamelCase_   ,	UpperCamelCase_):
												common_inputs["past_key_values"].append((torch.zeros(UpperCamelCase_), torch.zeros(UpperCamelCase_)))
						return common_inputs


			def    a_  (   self   :     str   ,	UpperCamelCase_   :     PreTrainedTokenizer   ,	UpperCamelCase_   :     int = -1   ,	UpperCamelCase_   :     int = -1   ,	UpperCamelCase_   :     bool = False   ,	UpperCamelCase_   :     Optional[TensorType] = None   ,	):


						"""simple docstring"""




						__UpperCAmelCase						:			Any												=       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
						    UpperCamelCase_   ,	UpperCamelCase_   ,	UpperCamelCase_   ,	UpperCamelCase_   ,	UpperCamelCase_)

						if self.use_past:
									if not is_torch_available():
												raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed.")
									else:
												import torch
									__UpperCAmelCase						,  __UpperCAmelCase						:			Tuple												=       common_inputs["input_ids"].shape
									# Not using the same length for past_key_values
									__UpperCAmelCase						:			Dict												=       seqlen + 2
									__UpperCAmelCase						,  __UpperCAmelCase						:			str												=       self.num_layers
									__UpperCAmelCase						,  __UpperCAmelCase						:			Tuple												=       self.num_attention_heads
									__UpperCAmelCase						:			List[Any]												=       (
									    batch,
									    num_encoder_attention_heads,
									    past_key_values_length,
									    self._config.hidden_size // num_encoder_attention_heads,
									)

									__UpperCAmelCase						:			List[Any]												=       common_inputs["attention_mask"].dtype
									__UpperCAmelCase						:			Optional[int]												=       torch.cat(
									    [common_inputs["attention_mask"], torch.ones(UpperCamelCase_   ,	UpperCamelCase_   ,	dtype=UpperCamelCase_)]   ,	dim=1)
									__UpperCAmelCase						:			Tuple												=       [
									    (torch.zeros(UpperCamelCase_), torch.zeros(UpperCamelCase_)) for _ in range(UpperCamelCase_)
									]
						return common_inputs


			def    a_  (   self   :     Tuple   ,	UpperCamelCase_   :     PreTrainedTokenizer   ,	UpperCamelCase_   :     int = -1   ,	UpperCamelCase_   :     int = -1   ,	UpperCamelCase_   :     bool = False   ,	UpperCamelCase_   :     Optional[TensorType] = None   ,	):


						"""simple docstring"""




						__UpperCAmelCase						:			Optional[int]												=       compute_effective_axis_dimension(
						    UpperCamelCase_   ,	fixed_dimension=OnnxConfig.default_fixed_batch   ,	num_token_to_add=0)

						# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
						__UpperCAmelCase						:			Dict												=       tokenizer.num_special_tokens_to_add(UpperCamelCase_)
						__UpperCAmelCase						:			Optional[Any]												=       compute_effective_axis_dimension(
						    UpperCamelCase_   ,	fixed_dimension=OnnxConfig.default_fixed_sequence   ,	num_token_to_add=UpperCamelCase_)

						# Generate dummy inputs according to compute batch and sequence
						__UpperCAmelCase						:			List[Any]												=       [" ".join([tokenizer.unk_token]) * seq_length] * batch_size
						__UpperCAmelCase						:			Optional[Any]												=       dict(tokenizer(UpperCamelCase_   ,	return_tensors=UpperCamelCase_))
						return common_inputs


			def    a_  (   self   :     Optional[Any]   ,	UpperCamelCase_   :     PreTrainedTokenizer   ,	UpperCamelCase_   :     int = -1   ,	UpperCamelCase_   :     int = -1   ,	UpperCamelCase_   :     bool = False   ,	UpperCamelCase_   :     Optional[TensorType] = None   ,	):


						"""simple docstring"""




						if self.task in ["default", "seq2seq-lm"]:
									__UpperCAmelCase						:			int												=       self._generate_dummy_inputs_for_default_and_seqaseq_lm(
									    UpperCamelCase_   ,	batch_size=UpperCamelCase_   ,	seq_length=UpperCamelCase_   ,	is_pair=UpperCamelCase_   ,	framework=UpperCamelCase_)

						elif self.task == "causal-lm":
									__UpperCAmelCase						:			Tuple												=       self._generate_dummy_inputs_for_causal_lm(
									    UpperCamelCase_   ,	batch_size=UpperCamelCase_   ,	seq_length=UpperCamelCase_   ,	is_pair=UpperCamelCase_   ,	framework=UpperCamelCase_)
						else:
									__UpperCAmelCase						:			Dict												=       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    UpperCamelCase_   ,	batch_size=UpperCamelCase_   ,	seq_length=UpperCamelCase_   ,	is_pair=UpperCamelCase_   ,	framework=UpperCamelCase_)

						return common_inputs


			def    a_  (   self   :     Any   ,	UpperCamelCase_   :     Optional[Any]   ,	UpperCamelCase_   :     Tuple   ,	UpperCamelCase_   :     Any   ,	UpperCamelCase_   :     Optional[int]):


						"""simple docstring"""




						if self.task in ["default", "seq2seq-lm"]:
									__UpperCAmelCase						:			Any												=       super()._flatten_past_key_values_(UpperCamelCase_   ,	UpperCamelCase_   ,	UpperCamelCase_   ,	UpperCamelCase_)
						else:
									__UpperCAmelCase						:			List[str]												=       super(UpperCamelCase_   ,	self)._flatten_past_key_values_(
									    UpperCamelCase_   ,	UpperCamelCase_   ,	UpperCamelCase_   ,	UpperCamelCase_)







'''simple docstring'''





from __future__ import annotations

import time

import numpy as np

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


class 					snake_case			:



       def __init__(					self  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_  ,)	->				None:
           lowercase__									=    claim_vector
           lowercase__									=    allocated_resources_table
           lowercase__									=    maximum_claim_table



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



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



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



       def 				_a   (					self  ,**UpperCAmelCase_   )	->				None:
           lowercase__									=    self.__need()
           lowercase__									=    self.__allocated_resources_table
           lowercase__									=    self.__available_resources()
           lowercase__									=    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:
               lowercase__									=    False
               for each_need in need_list:
                   lowercase__									=    True
                   for index, need in enumerate(UpperCAmelCase_   ):
                       if need > available_resources[index]:
                           lowercase__									=    False
                           break
                   if execution:
                       lowercase__									=    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:
                               lowercase__									=    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
                       lowercase__									=    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 				_a   (					self   )	->				List[Any]:
           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()




