code: 
  
  
  from collections import OrderedDict
  from typing import Mapping
  
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfigWithPast
  from ...utils import logging
  
  
  A_														=      logging.get_logger(__name__)
  
  A_														=      {
      "uclanlp/plbart-base": "https://huggingface.co/uclanlp/plbart-base/resolve/main/config.json",
      # See all PLBART models at https://huggingface.co/models?filter=plbart
  }
  
  class 						snake_case			(       lowerCAmelCase__	):
  			'''simple docstring'''
  
  			UpperCAmelCase       :       List[Any]       = """plbart"""
  			UpperCAmelCase       :       Optional[int]       = ["""past_key_values"""]
  			UpperCAmelCase       :       Any       = {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
  
  			def __init__(	self     :     Tuple							,							lowerCAmelCase_     :     Union[str, Any]=50_005							,							lowerCAmelCase_     :     str=1_024							,							lowerCAmelCase_     :     Optional[int]=6							,							lowerCAmelCase_     :     Any=3_072							,							lowerCAmelCase_     :     List[str]=12							,							lowerCAmelCase_     :     Union[str, Any]=6							,							lowerCAmelCase_     :     Any=3_072							,							lowerCAmelCase_     :     Optional[Any]=12							,							lowerCAmelCase_     :     Optional[Any]=0.0							,							lowerCAmelCase_     :     List[str]=0.0							,							lowerCAmelCase_     :     Optional[Any]=True							,							lowerCAmelCase_     :     List[Any]=True							,							lowerCAmelCase_     :     Any="gelu"							,							lowerCAmelCase_     :     Any=768							,							lowerCAmelCase_     :     Optional[int]=0.1							,							lowerCAmelCase_     :     Optional[Any]=0.1							,							lowerCAmelCase_     :     Union[str, Any]=0.0							,							lowerCAmelCase_     :     Union[str, Any]=0.02							,							lowerCAmelCase_     :     str=0.0							,							lowerCAmelCase_     :     Optional[Any]=True							,							lowerCAmelCase_     :     List[str]=1							,							lowerCAmelCase_     :     Any=0							,							lowerCAmelCase_     :     Union[str, Any]=2							,							lowerCAmelCase_     :     Union[str, Any]=2							,							**lowerCAmelCase_     :     Tuple							,							)					->					Tuple:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       vocab_size
  								SCREAMING_SNAKE_CASE_	      =       max_position_embeddings
  								SCREAMING_SNAKE_CASE_	      =       d_model
  								SCREAMING_SNAKE_CASE_	      =       encoder_ffn_dim
  								SCREAMING_SNAKE_CASE_	      =       encoder_layers
  								SCREAMING_SNAKE_CASE_	      =       encoder_attention_heads
  								SCREAMING_SNAKE_CASE_	      =       decoder_ffn_dim
  								SCREAMING_SNAKE_CASE_	      =       decoder_layers
  								SCREAMING_SNAKE_CASE_	      =       decoder_attention_heads
  								SCREAMING_SNAKE_CASE_	      =       dropout
  								SCREAMING_SNAKE_CASE_	      =       attention_dropout
  								SCREAMING_SNAKE_CASE_	      =       activation_dropout
  								SCREAMING_SNAKE_CASE_	      =       activation_function
  								SCREAMING_SNAKE_CASE_	      =       init_std
  								SCREAMING_SNAKE_CASE_	      =       encoder_layerdrop
  								SCREAMING_SNAKE_CASE_	      =       decoder_layerdrop
  								SCREAMING_SNAKE_CASE_	      =       classifier_dropout
  								SCREAMING_SNAKE_CASE_	      =       use_cache
  								SCREAMING_SNAKE_CASE_	      =       encoder_layers
  								SCREAMING_SNAKE_CASE_	      =       scale_embedding  # scale factor will be sqrt(d_model) if True
  								super().__init__(
  								    pad_token_id=lowerCAmelCase_							,							bos_token_id=lowerCAmelCase_							,							eos_token_id=lowerCAmelCase_							,							is_encoder_decoder=lowerCAmelCase_							,							forced_eos_token_id=lowerCAmelCase_							,							**lowerCAmelCase_							,							)
  
  class 						snake_case			(       lowerCAmelCase__	):
  			'''simple docstring'''
  
  			@property
  			def _lowercase (	self     :     Dict					)					->					Mapping[str, Mapping[int, str]]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								return OrderedDict(
  								    [
  								        ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
  								        ('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
  								    ]					)
  
  
  
  
  			@property
  			def _lowercase (	self     :     Union[str, Any]					)					->					Mapping[str, Mapping[int, str]]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								if self.use_past:
  													return OrderedDict(
  													    [
  													        ('''last_hidden_state''', {0: '''batch''', 1: '''sequence'''}),
  													        ('''past_keys''', {0: '''batch''', 2: '''sequence'''}),
  													        ('''encoder_last_hidden_state''', {0: '''batch''', 1: '''sequence'''}),
  													    ]					)
  								else:
  													return OrderedDict(
  													    [
  													        ('''last_hidden_state''', {0: '''batch''', 1: '''sequence'''}),
  													        ('''encoder_last_hidden_state''', {0: '''batch''', 1: '''sequence'''}),
  													    ]					)
  
  
  
style_context: 
  
  
  import json
  import os
  from collections.abc import Mapping
  from typing import Dict, List, Optional, Union
  
  import numpy as np
  import torch
  
  from .imports import is_safetensors_available
  
  
  
  
  
  
  
  def 							UpperCAmelCase				(     UpperCAmelCase				,UpperCAmelCase				,UpperCAmelCase				,UpperCAmelCase=None      )->		Dict:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					SCREAMING_SNAKE_CASE_	      =       None
  					# Check the string instead of the dtype to be compatible with versions of PyTorch that don't have bfloat16.
  					if str(weight.dtype      ) == "torch.bfloat16":
  										# Need to reinterpret the underlined data as int16 since NumPy does not handle bfloat16s.
  										SCREAMING_SNAKE_CASE_	      =       weight.view(torch.intaa      )
  										SCREAMING_SNAKE_CASE_	      =       '''bfloat16'''
  					SCREAMING_SNAKE_CASE_	      =       weight.cpu().numpy()
  					SCREAMING_SNAKE_CASE_	      =       os.path.join(UpperCAmelCase				,f'''{weight_name}.dat'''      )
  					if index is not None:
  										if dtype is None:
  															SCREAMING_SNAKE_CASE_	      =       str(array.dtype      )
  										SCREAMING_SNAKE_CASE_	      =       {'''dtype''': dtype, '''shape''': list(array.shape      )}
  					if array.ndim == 0:
  										SCREAMING_SNAKE_CASE_	      =       array[None]
  					SCREAMING_SNAKE_CASE_	      =       np.memmap(UpperCAmelCase				,dtype=array.dtype				,mode='''w+'''				,shape=array.shape      )
  					SCREAMING_SNAKE_CASE_	      =       array[:]
  					file_array.flush()
  					return index
  
  
  
  
  
  
  
  def 							UpperCAmelCase				(     UpperCAmelCase				,UpperCAmelCase      )->		List[str]:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					SCREAMING_SNAKE_CASE_	      =       tuple(weight_info['''shape''']      )
  					if shape == ():
  										# NumPy memory-mapped arrays can't have 0 dims so it was saved as 1d tensor
  										SCREAMING_SNAKE_CASE_	      =       (1,)
  
  					SCREAMING_SNAKE_CASE_	      =       weight_info['''dtype''']
  					if dtype == "bfloat16":
  										# NumPy does not support bfloat16 so this was saved as a int16
  										SCREAMING_SNAKE_CASE_	      =       '''int16'''
  
  					SCREAMING_SNAKE_CASE_	      =       np.memmap(UpperCAmelCase				,dtype=UpperCAmelCase				,shape=UpperCAmelCase				,mode='''r'''      )
  
  					if len(weight_info['''shape''']      ) == 0:
  										SCREAMING_SNAKE_CASE_	      =       weight[0]
  					SCREAMING_SNAKE_CASE_	      =       torch.tensor(UpperCAmelCase      )
  					if weight_info["dtype"] == "bfloat16":
  										SCREAMING_SNAKE_CASE_	      =       weight.view(torch.bfloataa      )
  
  					return weight
  
  
  
  
  
  
  
  def 							UpperCAmelCase				(     UpperCAmelCase				,UpperCAmelCase      )->		str:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					if index is None or len(UpperCAmelCase      ) == 0:
  										# Nothing to save
  										return
  
  					SCREAMING_SNAKE_CASE_	      =       os.path.join(UpperCAmelCase				,'''index.json'''      )
  					if os.path.isfile(UpperCAmelCase      ):
  										with open(UpperCAmelCase				,'''r'''				,encoding='''utf-8'''      ) as f:
  															SCREAMING_SNAKE_CASE_	      =       json.load(UpperCAmelCase      )
  					else:
  										SCREAMING_SNAKE_CASE_	      =       {}
  					current_index.update(UpperCAmelCase      )
  
  					with open(UpperCAmelCase				,'''w'''				,encoding='''utf-8'''      ) as f:
  										json.dump(UpperCAmelCase				,UpperCAmelCase				,indent=2      )
  
  
  
  
  
  
  
  def 							UpperCAmelCase				(     UpperCAmelCase				,UpperCAmelCase      )->		Optional[Any]:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					os.makedirs(UpperCAmelCase				,exist_ok=UpperCAmelCase      )
  					SCREAMING_SNAKE_CASE_	      =       {}
  					for name, parameter in state_dict.items():
  										SCREAMING_SNAKE_CASE_	      =       offload_weight(UpperCAmelCase				,UpperCAmelCase				,UpperCAmelCase				,index=UpperCAmelCase      )
  
  					# Update index
  					save_offload_index(UpperCAmelCase				,UpperCAmelCase      )
  
  class 						snake_case			(       lowerCAmelCase__	):
  			'''simple docstring'''
  
  			def __init__(	self     :     int							,							lowerCAmelCase_     :     Mapping							,							lowerCAmelCase_     :     str					)					->					Dict:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       dataset
  								SCREAMING_SNAKE_CASE_	      =       prefix
  
  			def __getitem__(	self     :     Optional[int]							,							lowerCAmelCase_     :     Optional[int]					)					->					Union[str, Any]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								return self.dataset[F'''{self.prefix}{key}''']
  
  			def __iter__(	self     :     Tuple					)					->					str:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								return iter([key for key in self.dataset if key.startswith(self.prefix					)]					)
  
  
  
  
  			def __len__(	self     :     Dict					)					->					Optional[int]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								return len(self.dataset					)
  
  class 						snake_case			(       lowerCAmelCase__	):
  			'''simple docstring'''
  
  			def __init__(	self     :     int							,							lowerCAmelCase_     :     Dict[str, torch.Tensor] = None							,							lowerCAmelCase_     :     Optional[Union[str, os.PathLike]] = None							,							lowerCAmelCase_     :     Mapping = None							,							lowerCAmelCase_     :     int=None							,							)					->					Tuple:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								if state_dict is None and save_folder is None:
  													raise ValueError('''Need either a `state_dict` or a `save_folder` containing offloaded weights.'''					)
  
  								SCREAMING_SNAKE_CASE_	      =       {} if state_dict is None else state_dict
  								SCREAMING_SNAKE_CASE_	      =       save_folder
  								if index is None and save_folder is not None:
  													with open(os.path.join(lowerCAmelCase_							,							'''index.json'''					)					) as f:
  																		SCREAMING_SNAKE_CASE_	      =       json.load(lowerCAmelCase_					)
  								SCREAMING_SNAKE_CASE_	      =       {} if index is None else index
  								SCREAMING_SNAKE_CASE_	      =       list(self.state_dict.keys()					)
  								self.all_keys.extend([key for key in self.index if key not in self.all_keys]					)
  								SCREAMING_SNAKE_CASE_	      =       device
  
  			def __getitem__(	self     :     str							,							lowerCAmelCase_     :     str					)					->					Union[str, Any]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								if key in self.state_dict:
  													return self.state_dict[key]
  								SCREAMING_SNAKE_CASE_	      =       self.index[key]
  								if weight_info.get('''safetensors_file'''					) is not None:
  													if not is_safetensors_available():
  																		raise ImportError('''These offloaded weights require the use of safetensors: `pip install safetensors`.'''					)
  
  													from safetensors import safe_open
  
  													SCREAMING_SNAKE_CASE_	      =       '''cpu''' if self.device is None else self.device
  													with safe_open(weight_info['''safetensors_file''']							,							framework='''pt'''							,							device=lowerCAmelCase_					) as f:
  																		SCREAMING_SNAKE_CASE_	      =       f.get_tensor(weight_info.get('''weight_name'''							,							lowerCAmelCase_					)					)
  
  													if "dtype" in weight_info:
  																		return tensor.to(getattr(lowerCAmelCase_							,							weight_info['''dtype''']					)					)
  													else:
  																		return tensor
  
  								SCREAMING_SNAKE_CASE_	      =       os.path.join(self.save_folder							,							F'''{key}.dat'''					)
  								return load_offloaded_weight(lowerCAmelCase_							,							lowerCAmelCase_					)
  
  			def __iter__(	self     :     str					)					->					Optional[int]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								return iter(self.all_keys					)
  
  
  
  
  			def __len__(	self     :     Optional[Any]					)					->					Optional[Any]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								return len(self.all_keys					)
  
  
  
  
  
  
  
  def 							UpperCAmelCase				(     UpperCAmelCase				,UpperCAmelCase      )->		List[str]:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					SCREAMING_SNAKE_CASE_	      =       {}
  					for module_name in submodule_names:
  										# We want to catch module_name parameter (module_name.xxx) or potentially module_name, but not any of the
  										# submodules that could being like module_name (transformers.h.1 and transformers.h.10 for instance)
  										result.update(
  										    {
  										        key: param
  										        for key, param in state_dict.items()
  										        if key == module_name or key.startswith(module_name + '''.'''      )
  										    }      )
  					return result
  
  
  