


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 			lowerCamelCase_					(    __lowerCamelCase    ):
							_lowerCamelCase				: Union[str, Any]       =					'plbart'
							_lowerCamelCase				: Dict       =					['past_key_values']
							_lowerCamelCase				: int       =					{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}





							def __init__(							self			,       _SCREAMING_SNAKE_CASE=5_0005			,       _SCREAMING_SNAKE_CASE=1024			,       _SCREAMING_SNAKE_CASE=6			,       _SCREAMING_SNAKE_CASE=3072			,       _SCREAMING_SNAKE_CASE=12			,       _SCREAMING_SNAKE_CASE=6			,       _SCREAMING_SNAKE_CASE=3072			,       _SCREAMING_SNAKE_CASE=12			,       _SCREAMING_SNAKE_CASE=0.0			,       _SCREAMING_SNAKE_CASE=0.0			,       _SCREAMING_SNAKE_CASE=True			,       _SCREAMING_SNAKE_CASE=True			,       _SCREAMING_SNAKE_CASE="gelu"			,       _SCREAMING_SNAKE_CASE=768			,       _SCREAMING_SNAKE_CASE=0.1			,       _SCREAMING_SNAKE_CASE=0.1			,       _SCREAMING_SNAKE_CASE=0.0			,       _SCREAMING_SNAKE_CASE=0.0_2			,       _SCREAMING_SNAKE_CASE=0.0			,       _SCREAMING_SNAKE_CASE=True			,       _SCREAMING_SNAKE_CASE=1			,       _SCREAMING_SNAKE_CASE=0			,       _SCREAMING_SNAKE_CASE=2			,       _SCREAMING_SNAKE_CASE=2			,       **_SCREAMING_SNAKE_CASE			,       ):
								a_     			=   vocab_size
								a_     			=   max_position_embeddings
								a_     			=   d_model
								a_     			=   encoder_ffn_dim
								a_     			=   encoder_layers
								a_     			=   encoder_attention_heads
								a_     			=   decoder_ffn_dim
								a_     			=   decoder_layers
								a_     			=   decoder_attention_heads
								a_     			=   dropout
								a_     			=   attention_dropout
								a_     			=   activation_dropout
								a_     			=   activation_function
								a_     			=   init_std
								a_     			=   encoder_layerdrop
								a_     			=   decoder_layerdrop
								a_     			=   classifier_dropout
								a_     			=   use_cache
								a_     			=   encoder_layers
								a_     			=   scale_embedding  # scale factor will be sqrt(d_model) if True
								super().__init__(
								    pad_token_id=UpperCamelCase_			,       bos_token_id=UpperCamelCase_			,       eos_token_id=UpperCamelCase_			,       is_encoder_decoder=UpperCamelCase_			,       forced_eos_token_id=UpperCamelCase_			,       **UpperCamelCase_			,       )







class 			lowerCamelCase_					(    __lowerCamelCase    ):





							@property
							def   __magic_name__      (							self    ):
								return OrderedDict(
								    [
								        ("""input_ids""", {0: """batch""", 1: """sequence"""}),
								        ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
								    ]    )





							@property
							def   __magic_name__      (							self    ):
								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"""}),
									    ]    )





from functools import partial
from typing import Any, Callable, Dict, List, Type, TypeVar, Union, overload

import torch
import torch.nn as nn
import torch.types




def    lowerCamelCase__			(		A__			:   torch.Tensor    ,     A__			:   torch.Tensor    ,     A__			:   bool      ):





       '''simple docstring'''



       if not inplace:
              __lowerCamelCase  =		ma + ma
       else:
              ma += ma

       return ma




def    lowerCamelCase__			(		A__			:   torch.Tensor    ,     A__			:   List[int]      ):





       '''simple docstring'''



       __lowerCamelCase  =		-1 * len(A__      )
       __lowerCamelCase  =		list(range(len(tensor.shape[:zero_index]      )      )      )
       return tensor.permute(first_inds + [zero_index + i for i in inds]      )




def    lowerCamelCase__			(		A__			:   torch.Tensor    ,     A__			:   int      ):





       '''simple docstring'''



       return t.reshape(t.shape[:-no_dims] + (-1,)      )




def    lowerCamelCase__			(		A__			:   torch.Tensor    ,     A__			:   torch.Tensor    ,     A__			:   int    ,     A__			:   float = 1E-4      ):





       '''simple docstring'''



       __lowerCamelCase  =		mask.expand(*value.shape      )
       return torch.sum(mask * value    ,     dim=A__      ) / (eps + torch.sum(A__    ,     dim=A__      ))




def    lowerCamelCase__			(		A__			:   torch.Tensor    ,     A__			:   torch.types.Number = 2.3_125    ,     A__			:   torch.types.Number = 21.6_875    ,     A__			:   int = 64      ):





       '''simple docstring'''



       __lowerCamelCase  =		torch.linspace(A__    ,     A__    ,     no_bins - 1    ,     device=pts.device      )
       __lowerCamelCase  =		torch.sqrt(torch.sum((pts.unsqueeze(-2      ) - pts.unsqueeze(-3      )) ** 2    ,     dim=-1      )      )
       return torch.bucketize(A__    ,     A__      )




def    lowerCamelCase__			(		A__			:   Callable[[list], Any]    ,     A__			:   List[dict]      ):





       '''simple docstring'''



       __lowerCamelCase  =		dicts[0]
       __lowerCamelCase  =		{}
       for k, v in first.items():
              __lowerCamelCase  =		[d[k] for d in dicts]
              if isinstance(A__    ,     A__      ):
                     __lowerCamelCase  =		dict_multimap(A__    ,     A__      )
              else:
                     __lowerCamelCase  =		fn(A__      )

       return new_dict




def    lowerCamelCase__			(		A__			:   torch.Tensor    ,     A__			:   torch.Tensor      ):





       '''simple docstring'''



       __lowerCamelCase  =		v_bins.view(((1,) * len(x.shape      )) + (len(A__      ),)      )
       __lowerCamelCase  =		x[..., None] - reshaped_bins
       __lowerCamelCase  =		torch.argmin(torch.abs(A__      )    ,     dim=-1      )
       return nn.functional.one_hot(A__    ,     num_classes=len(A__      )      ).float()




def    lowerCamelCase__			(		A__			:   torch.Tensor    ,     A__			:   torch.Tensor    ,     A__			:   int = 0    ,     A__			:   int = 0      ):





       '''simple docstring'''



       __lowerCamelCase  =		[]
       for i, s in enumerate(data.shape[:no_batch_dims]      ):
              __lowerCamelCase  =		torch.arange(A__      )
              __lowerCamelCase  =		r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape      ) - i - 1)))      )
              ranges.append(A__      )

       __lowerCamelCase  =		[slice(A__      ) for _ in range(len(data.shape      ) - no_batch_dims      )]
       __lowerCamelCase  =		inds
       ranges.extend(A__      )
       # Matt note: Editing this to get around the behaviour of using a list as an array index changing
       # in recent Numpy versions
       return data[tuple(A__      )]


UpperCAmelCase_   							=      TypeVar('T')




def    lowerCamelCase__			(		A__			:   Callable[[T], Any]    ,     A__			:   Dict[Any, Union[dict, list, tuple, T]]    ,     A__			:   Type[T]      ):





       '''simple docstring'''



       __lowerCamelCase  =		{}
       for k, v in dic.items():
              if isinstance(A__    ,     A__      ):
                     __lowerCamelCase  =		dict_map(A__    ,     A__    ,     A__      )
              else:
                     __lowerCamelCase  =		tree_map(A__    ,     A__    ,     A__      )

       return new_dict




@overload
def    lowerCamelCase__			(		A__			:   Callable[[T], Any]    ,     A__			:   T    ,     A__			:   Type[T]      ):





       '''simple docstring'''



       ...




@overload
def    lowerCamelCase__			(		A__			:   Callable[[T], Any]    ,     A__			:   dict    ,     A__			:   Type[T]      ):





       '''simple docstring'''



       ...




@overload
def    lowerCamelCase__			(		A__			:   Callable[[T], Any]    ,     A__			:   list    ,     A__			:   Type[T]      ):





       '''simple docstring'''



       ...




@overload
def    lowerCamelCase__			(		A__			:   Callable[[T], Any]    ,     A__			:   tuple    ,     A__			:   Type[T]      ):





       '''simple docstring'''



       ...




def    lowerCamelCase__			(		A__			:   Dict    ,     A__			:   List[str]    ,     A__			:   List[str]      ):





       '''simple docstring'''



       if isinstance(A__    ,     A__      ):
              return dict_map(A__    ,     A__    ,     A__      )
       elif isinstance(A__    ,     A__      ):
              return [tree_map(A__    ,     A__    ,     A__      ) for x in tree]
       elif isinstance(A__    ,     A__      ):
              return tuple(tree_map(A__    ,     A__    ,     A__      ) for x in tree      )
       elif isinstance(A__    ,     A__      ):
              return fn(A__      )
       else:
              print(type(A__      )      )
              raise ValueError("""Not supported"""      )




UpperCAmelCase_   							=      partial(tree_map, leaf_type=torch.Tensor)

