






"""simple docstring"""





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 							A(    snake_case_		,  snake_case_		,  snake_case_						):
       """simple docstring"""





       if not inplace:
              lowercase__:							Optional[int]         =     ma + ma
       else:
              ma += ma

       return ma






def 							A(    snake_case_		,  snake_case_						):
       """simple docstring"""





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






def 							A(    snake_case_		,  snake_case_						):
       """simple docstring"""





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






def 							A(    snake_case_		,  snake_case_		,  snake_case_		,  snake_case_ = 1E-4						):
       """simple docstring"""





       lowercase__:							List[Any]         =     mask.expand(*value.shape						)
       return torch.sum(mask * value		,  dim=snake_case_						) / (eps + torch.sum(snake_case_		,  dim=snake_case_						))






def 							A(    snake_case_		,  snake_case_ = 2.31_25		,  snake_case_ = 21.68_75		,  snake_case_ = 64						):
       """simple docstring"""





       lowercase__:							Tuple         =     torch.linspace(snake_case_		,  snake_case_		,  no_bins - 1		,  device=pts.device						)
       lowercase__:							Dict         =     torch.sqrt(torch.sum((pts.unsqueeze(-2						) - pts.unsqueeze(-3						)) ** 2		,  dim=-1						)						)
       return torch.bucketize(snake_case_		,  snake_case_						)






def 							A(    snake_case_		,  snake_case_						):
       """simple docstring"""





       lowercase__:							Optional[Any]         =     dicts[0]
       lowercase__:							Optional[int]         =     {}
       for k, v in first.items():
              lowercase__:							Optional[int]         =     [d[k] for d in dicts]
              if isinstance(snake_case_		,  snake_case_						):
                     lowercase__:							Optional[Any]         =     dict_multimap(snake_case_		,  snake_case_						)
              else:
                     lowercase__:							List[str]         =     fn(snake_case_						)

       return new_dict






def 							A(    snake_case_		,  snake_case_						):
       """simple docstring"""





       lowercase__:							Optional[Any]         =     v_bins.view(((1,) * len(x.shape						)) + (len(snake_case_						),)						)
       lowercase__:							List[str]         =     x[..., None] - reshaped_bins
       lowercase__:							Optional[int]         =     torch.argmin(torch.abs(snake_case_						)		,  dim=-1						)
       return nn.functional.one_hot(snake_case_		,  num_classes=len(snake_case_						)						).float()






def 							A(    snake_case_		,  snake_case_		,  snake_case_ = 0		,  snake_case_ = 0						):
       """simple docstring"""





       lowercase__:							List[Union[slice, torch.Tensor]]         =     []
       for i, s in enumerate(data.shape[:no_batch_dims]						):
              lowercase__:							Optional[Any]         =     torch.arange(snake_case_						)
              lowercase__:							Any         =     r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape						) - i - 1)))						)
              ranges.append(snake_case_						)

       lowercase__:							List[Union[slice, torch.Tensor]]         =     [slice(snake_case_						) for _ in range(len(data.shape						) - no_batch_dims						)]
       lowercase__:							Dict         =     inds
       ranges.extend(snake_case_						)
       # 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(snake_case_						)]


UpperCamelCase					=     TypeVar("""T""")






def 							A(    snake_case_		,  snake_case_		,  snake_case_						):
       """simple docstring"""





       lowercase__:							Dict[Any, Union[dict, list, tuple, Any]]         =     {}
       for k, v in dic.items():
              if isinstance(snake_case_		,  snake_case_						):
                     lowercase__:							Optional[Any]         =     dict_map(snake_case_		,  snake_case_		,  snake_case_						)
              else:
                     lowercase__:							Union[str, Any]         =     tree_map(snake_case_		,  snake_case_		,  snake_case_						)

       return new_dict






@overload
def 							A(    snake_case_		,  snake_case_		,  snake_case_						):
       """simple docstring"""





       ...






@overload
def 							A(    snake_case_		,  snake_case_		,  snake_case_						):
       """simple docstring"""





       ...






@overload
def 							A(    snake_case_		,  snake_case_		,  snake_case_						):
       """simple docstring"""





       ...






@overload
def 							A(    snake_case_		,  snake_case_		,  snake_case_						):
       """simple docstring"""





       ...






def 							A(    snake_case_		,  snake_case_		,  snake_case_						):
       """simple docstring"""





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







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












"""simple docstring"""

import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase__			:     List[Any]			       =				logging.get_logger(__name__)

UpperCamelCase__			:     List[str]			       =				{}
class      A__   (   lowercase_  ):
	A_				:					Dict      =   """fsmt_decoder"""




	def __init__( self	:						List[Any]   ,     lowercase	:						Any=0   ,     lowercase	:						Dict=0						)       ->			str:
						super().__init__()
						__snake_case        =						vocab_size
						__snake_case        =						bos_token_id

class      A__   (   lowercase_  ):
	A_				:					Union[str, Any]      =   """fsmt"""
	A_				:					int      =   {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}




	def __init__( self	:						Optional[Any]   ,     lowercase	:						Any=["en", "de"]   ,     lowercase	:						Optional[int]=42_024   ,     lowercase	:						Optional[int]=42_024   ,     lowercase	:						Tuple="relu"   ,     lowercase	:						Tuple=1_024   ,     lowercase	:						Union[str, Any]=200   ,     lowercase	:						List[str]=1_024   ,     lowercase	:						Tuple=4_096   ,     lowercase	:						int=12   ,     lowercase	:						Any=16   ,     lowercase	:						List[Any]=0.0   ,     lowercase	:						str=4_096   ,     lowercase	:						str=12   ,     lowercase	:						Optional[int]=16   ,     lowercase	:						Dict=0.0   ,     lowercase	:						Any=0.0   ,     lowercase	:						Union[str, Any]=0.1   ,     lowercase	:						Tuple=0.0   ,     lowercase	:						List[str]=0.02   ,     lowercase	:						int=2   ,     lowercase	:						Any=True   ,     lowercase	:						int=True   ,     lowercase	:						int=False   ,     lowercase	:						List[str]=5   ,     lowercase	:						List[Any]=1.0   ,     lowercase	:						Union[str, Any]=False   ,     lowercase	:						Optional[Any]=True   ,     lowercase	:						int=1   ,     lowercase	:						Any=0   ,     lowercase	:						Optional[Any]=2   ,     lowercase	:						Any=2   ,     **lowercase	:						List[Any]   ,     )       ->			Union[str, Any]:
						__snake_case        =						langs
						__snake_case        =						src_vocab_size
						__snake_case        =						tgt_vocab_size
						__snake_case        =						d_model  # encoder_embed_dim and decoder_embed_dim

						__snake_case        =						encoder_ffn_dim
						__snake_case        =						__snake_case        =						encoder_layers
						__snake_case        =						encoder_attention_heads
						__snake_case        =						encoder_layerdrop
						__snake_case        =						decoder_layerdrop
						__snake_case        =						decoder_ffn_dim
						__snake_case        =						decoder_layers
						__snake_case        =						decoder_attention_heads
						__snake_case        =						max_position_embeddings
						__snake_case        =						init_std  # Normal(0, this parameter)
						__snake_case        =						activation_function

						__snake_case        =						DecoderConfig(vocab_size=lowercase   ,     bos_token_id=lowercase						)
						if "decoder" in common_kwargs:
											del common_kwargs["decoder"]

						__snake_case        =						scale_embedding  # scale factor will be sqrt(d_model) if True

						# 3 Types of Dropout
						__snake_case        =						attention_dropout
						__snake_case        =						activation_dropout
						__snake_case        =						dropout

						__snake_case        =						use_cache
						super().__init__(
						    pad_token_id=lowercase   ,     bos_token_id=lowercase   ,     eos_token_id=lowercase   ,     decoder_start_token_id=lowercase   ,     is_encoder_decoder=lowercase   ,     tie_word_embeddings=lowercase   ,     forced_eos_token_id=lowercase   ,     max_length=lowercase   ,     num_beams=lowercase   ,     length_penalty=lowercase   ,     early_stopping=lowercase   ,     **lowercase   ,     )




	def 				a_		( self	:						Union[str, Any]						)       ->			Dict:
						__snake_case        =						copy.deepcopy(self.__dict__						)
						__snake_case        =						self.decoder.to_dict()
						__snake_case        =						self.__class__.model_type
						return output


