


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 SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		torch.Tensor  ,			__lowerCamelCase:		torch.Tensor  ,			__lowerCamelCase:		bool  ):







	'''simple docstring'''



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

	return ma




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		torch.Tensor  ,			__lowerCamelCase:		List[int]  ):







	'''simple docstring'''



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




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		torch.Tensor  ,			__lowerCamelCase:		int  ):







	'''simple docstring'''



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




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		torch.Tensor  ,			__lowerCamelCase:		torch.Tensor  ,			__lowerCamelCase:		int  ,			__lowerCamelCase:		float = 1E-4  ):







	'''simple docstring'''



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




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		torch.Tensor  ,			__lowerCamelCase:		torch.types.Number = 2.3125  ,			__lowerCamelCase:		torch.types.Number = 21.6875  ,			__lowerCamelCase:		int = 64  ):







	'''simple docstring'''



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




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		Callable[[list], Any]  ,			__lowerCamelCase:		List[dict]  ):







	'''simple docstring'''



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

	return new_dict




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		torch.Tensor  ,			__lowerCamelCase:		torch.Tensor  ):







	'''simple docstring'''



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




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		torch.Tensor  ,			__lowerCamelCase:		torch.Tensor  ,			__lowerCamelCase:		int = 0  ,			__lowerCamelCase:		int = 0  ):







	'''simple docstring'''



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

	lowercase_			 = [slice(__lowerCamelCase  ) for _ in range(len(data.shape  ) - no_batch_dims  )]
	lowercase_			 = inds
	ranges.extend(__lowerCamelCase  )
	# 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(__lowerCamelCase  )]


SCREAMING_SNAKE_CASE__       			=				TypeVar("""T""")




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







	'''simple docstring'''



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

	return new_dict




@overload
def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		Callable[[T], Any]  ,			__lowerCamelCase:		T  ,			__lowerCamelCase:		Type[T]  ):







	'''simple docstring'''



	...




@overload
def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		Callable[[T], Any]  ,			__lowerCamelCase:		dict  ,			__lowerCamelCase:		Type[T]  ):







	'''simple docstring'''



	...




@overload
def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		Callable[[T], Any]  ,			__lowerCamelCase:		list  ,			__lowerCamelCase:		Type[T]  ):







	'''simple docstring'''



	...




@overload
def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		Callable[[T], Any]  ,			__lowerCamelCase:		tuple  ,			__lowerCamelCase:		Type[T]  ):







	'''simple docstring'''



	...




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		List[str]  ,			__lowerCamelCase:		List[Any]  ,			__lowerCamelCase:		int  ):







	'''simple docstring'''



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


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





from typing import Callable, Optional, Union

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


SCREAMING_SNAKE_CASE__       			=				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__       			=				{
    """microsoft/prophetnet-large-uncased""": (
        """https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json"""
    ),
}







class     __lowerCamelCase     (  snake_case_     ):


							"""simple docstring"""

							lowerCAmelCase__          =				"prophetnet"
							lowerCAmelCase__          =				["past_key_values"]
							lowerCAmelCase__          =				{
							    "num_attention_heads": "num_encoder_attention_heads",
							}




							def __init__(  self						,       UpperCAmelCase = 0.1						,       UpperCAmelCase = "gelu"						,       UpperCAmelCase = 30522						,       UpperCAmelCase = 1024						,       UpperCAmelCase = 4096						,       UpperCAmelCase = 12						,       UpperCAmelCase = 16						,       UpperCAmelCase = 4096						,       UpperCAmelCase = 12						,       UpperCAmelCase = 16						,       UpperCAmelCase = 0.1						,       UpperCAmelCase = 0.1						,       UpperCAmelCase = 512						,       UpperCAmelCase = 0.02						,       UpperCAmelCase = True						,       UpperCAmelCase = True						,       UpperCAmelCase = 0						,       UpperCAmelCase = 2						,       UpperCAmelCase = 32						,       UpperCAmelCase = 128						,       UpperCAmelCase = False						,       UpperCAmelCase = 0.0						,       UpperCAmelCase = True						,       UpperCAmelCase = 0						,       UpperCAmelCase = 1						,       UpperCAmelCase = 2						,       **UpperCAmelCase						,       )     ->       Dict:






								'''simple docstring'''






								lowercase_			 = vocab_size
								lowercase_			 = hidden_size
								lowercase_			 = encoder_ffn_dim
								lowercase_			 = num_encoder_layers
								lowercase_			 = num_encoder_attention_heads
								lowercase_			 = decoder_ffn_dim
								lowercase_			 = num_decoder_layers
								lowercase_			 = num_decoder_attention_heads
								lowercase_			 = max_position_embeddings
								lowercase_			 = init_std  # Normal(0, this parameter)
								lowercase_			 = activation_function

								# parameters for prophetnet
								lowercase_			 = ngram
								lowercase_			 = num_buckets
								lowercase_			 = relative_max_distance
								lowercase_			 = disable_ngram_loss
								lowercase_			 = eps

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

								lowercase_			 = use_cache

								super().__init__(
								    pad_token_id=UpperCAmelCase						,       bos_token_id=UpperCAmelCase						,       eos_token_id=UpperCAmelCase						,       is_encoder_decoder=UpperCAmelCase						,       add_cross_attention=UpperCAmelCase						,       decoder_start_token_id=UpperCAmelCase						,       **UpperCAmelCase						,       )




							@property
							def 				A__      (  self  )     ->       int:






								'''simple docstring'''






								return self.num_encoder_layers + self.num_decoder_layers




							@num_hidden_layers.setter
							def 				A__      (  self						,       UpperCAmelCase  )     ->       Any:






								'''simple docstring'''






								raise NotImplementedError(
								    "This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and"
								    " `num_decoder_layers`."  )

