from typing import Dict, Optional, Tuple

import torch

def      SCREAMING_SNAKE_CASE_  (		snake_case__							)  ->			torch.Tensor:
							lowerCAmelCase				=	boundaries[1] - boundaries[0]
							lowerCAmelCase				=	boundaries + step / 2
							lowerCAmelCase				=	torch.cat([bin_centers, (bin_centers[-1] + step).unsqueeze(-1							)]     ,						dim=0							)
							return bin_centers

def      SCREAMING_SNAKE_CASE_  (		snake_case__     ,						snake_case__     ,						)  ->			Tuple[torch.Tensor, torch.Tensor]:
							lowerCAmelCase				=	_calculate_bin_centers(snake_case__							)
							return (
							    torch.sum(aligned_distance_error_probs * bin_centers     ,						dim=-1							),
							    bin_centers[-1],
							)

def      SCREAMING_SNAKE_CASE_  (		snake_case__     ,						snake_case__ = 3_1     ,						snake_case__ = 6_4     ,						**snake_case__     ,						)  ->			Dict[str, torch.Tensor]:
							lowerCAmelCase				=	torch.linspace(0     ,						snake_case__     ,						steps=(no_bins - 1)     ,						device=logits.device							)

							lowerCAmelCase				=	torch.nn.functional.softmax(snake_case__     ,						dim=-1							)
							lowerCAmelCase	,   lowerCAmelCase				=	_calculate_expected_aligned_error(
							    alignment_confidence_breaks=snake_case__     ,						aligned_distance_error_probs=snake_case__     ,						)

							return {
							    "aligned_confidence_probs": aligned_confidence_probs,
							    "predicted_aligned_error": predicted_aligned_error,
							    "max_predicted_aligned_error": max_predicted_aligned_error,
							}






def      SCREAMING_SNAKE_CASE_  (		snake_case__     ,						snake_case__ = None     ,						snake_case__ = 3_1     ,						snake_case__ = 6_4     ,						snake_case__ = 1E-8     ,						**snake_case__     ,						)  ->			torch.Tensor:
							if residue_weights is None:
														lowerCAmelCase				=	logits.new_ones(logits.shape[-2]							)

							lowerCAmelCase				=	torch.linspace(0     ,						snake_case__     ,						steps=(no_bins - 1)     ,						device=logits.device							)

							lowerCAmelCase				=	_calculate_bin_centers(snake_case__							)
							torch.sum(snake_case__							)
							lowerCAmelCase				=	logits.shape[-2]
							lowerCAmelCase				=	max(snake_case__     ,						1_9							)

							lowerCAmelCase				=	1.24 * (clipped_n - 1_5) ** (1.0 / 3) - 1.8

							lowerCAmelCase				=	torch.nn.functional.softmax(snake_case__     ,						dim=-1							)

							lowerCAmelCase				=	1.0 / (1 + (bin_centers**2) / (da**2))
							lowerCAmelCase				=	torch.sum(probs * tm_per_bin     ,						dim=-1							)

							lowerCAmelCase				=	residue_weights / (eps + residue_weights.sum())
							lowerCAmelCase				=	torch.sum(predicted_tm_term * normed_residue_mask     ,						dim=-1							)

							lowerCAmelCase				=	per_alignment * residue_weights

							lowerCAmelCase				=	(weighted == torch.max(snake_case__							)).nonzero()[0]
							return per_alignment[tuple(snake_case__							)]




from typing import Callable, Optional, Union

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


lowercase__    :       Optional[Any]						=			logging.get_logger(__name__)

lowercase__    :       Union[str, Any]						=			{
    '''microsoft/prophetnet-large-uncased''': (
        '''https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json'''
    ),
}




class      lowercase_						(						UpperCamelCase_		):



	"""simple docstring"""





	UpperCAmelCase_						:      Optional[int]     =       """prophetnet"""
	UpperCAmelCase_						:      Optional[int]     =       ["""past_key_values"""]
	UpperCAmelCase_						:      Any     =       {
	    """num_attention_heads""": """num_encoder_attention_heads""",
	}


	def __init__(							self	,    __SCREAMING_SNAKE_CASE = 0.1	,    __SCREAMING_SNAKE_CASE = "gelu"	,    __SCREAMING_SNAKE_CASE = 30522	,    __SCREAMING_SNAKE_CASE = 1024	,    __SCREAMING_SNAKE_CASE = 4096	,    __SCREAMING_SNAKE_CASE = 12	,    __SCREAMING_SNAKE_CASE = 16	,    __SCREAMING_SNAKE_CASE = 4096	,    __SCREAMING_SNAKE_CASE = 12	,    __SCREAMING_SNAKE_CASE = 16	,    __SCREAMING_SNAKE_CASE = 0.1	,    __SCREAMING_SNAKE_CASE = 0.1	,    __SCREAMING_SNAKE_CASE = 512	,    __SCREAMING_SNAKE_CASE = 0.0_2	,    __SCREAMING_SNAKE_CASE = True	,    __SCREAMING_SNAKE_CASE = True	,    __SCREAMING_SNAKE_CASE = 0	,    __SCREAMING_SNAKE_CASE = 2	,    __SCREAMING_SNAKE_CASE = 32	,    __SCREAMING_SNAKE_CASE = 128	,    __SCREAMING_SNAKE_CASE = False	,    __SCREAMING_SNAKE_CASE = 0.0	,    __SCREAMING_SNAKE_CASE = True	,    __SCREAMING_SNAKE_CASE = 0	,    __SCREAMING_SNAKE_CASE = 1	,    __SCREAMING_SNAKE_CASE = 2	,    **__SCREAMING_SNAKE_CASE	,    )		->int:
								lowerCAmelCase				=	vocab_size
								lowerCAmelCase				=	hidden_size
								lowerCAmelCase				=	encoder_ffn_dim
								lowerCAmelCase				=	num_encoder_layers
								lowerCAmelCase				=	num_encoder_attention_heads
								lowerCAmelCase				=	decoder_ffn_dim
								lowerCAmelCase				=	num_decoder_layers
								lowerCAmelCase				=	num_decoder_attention_heads
								lowerCAmelCase				=	max_position_embeddings
								lowerCAmelCase				=	init_std  # Normal(0, this parameter)
								lowerCAmelCase				=	activation_function

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

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

								lowerCAmelCase				=	use_cache

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


	@property
	def     SCREAMING_SNAKE_CASE_       (							self       )		->int:
								return self.num_encoder_layers + self.num_decoder_layers




	@num_hidden_layers.setter
	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE       )		->Union[str, Any]:
								raise NotImplementedError(
								    '''This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'''
								    ''' `num_decoder_layers`.'''       )



