


'''simple docstring'''
from typing import Dict, Optional, Tuple

import torch




def      __snake_case       (       lowerCamelCase_ :				torch.Tensor						):
				'''simple docstring'''




				__magic_name__	  = boundaries[1] - boundaries[0]
				__magic_name__	  = boundaries + step / 2
				__magic_name__	  = torch.cat([bin_centers, (bin_centers[-1] + step).unsqueeze(-1						)]	,  dim=0						)
				return bin_centers




def      __snake_case       (       lowerCamelCase_ :				torch.Tensor	,  lowerCamelCase_ :				torch.Tensor	,  ):
				'''simple docstring'''




				__magic_name__	  = _calculate_bin_centers(lowerCamelCase_						)
				return (
				    torch.sum(aligned_distance_error_probs * bin_centers	,  dim=-1						),
				    bin_centers[-1],
				)




def      __snake_case       (       lowerCamelCase_ :				torch.Tensor	,  lowerCamelCase_ :				int = 31	,  lowerCamelCase_ :				int = 64	,  **lowerCamelCase_ :				Union[str, Any]	,  ):
				'''simple docstring'''




				__magic_name__	  = torch.linspace(0	,  lowerCamelCase_	,  steps=(no_bins - 1)	,  device=logits.device						)

				__magic_name__	  = torch.nn.functional.softmax(lowerCamelCase_	,  dim=-1						)
				__magic_name__  ,      __magic_name__	  = _calculate_expected_aligned_error(
				    alignment_confidence_breaks=lowerCamelCase_	,  aligned_distance_error_probs=lowerCamelCase_	,  )

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




def      __snake_case       (       lowerCamelCase_ :				torch.Tensor	,  lowerCamelCase_ :				Optional[torch.Tensor] = None	,  lowerCamelCase_ :				int = 31	,  lowerCamelCase_ :				int = 64	,  lowerCamelCase_ :				float = 1e-8	,  **lowerCamelCase_ :				Dict	,  ):
				'''simple docstring'''




				if residue_weights is None:
								__magic_name__	  = logits.new_ones(logits.shape[-2]						)

				__magic_name__	  = torch.linspace(0	,  lowerCamelCase_	,  steps=(no_bins - 1)	,  device=logits.device						)

				__magic_name__	  = _calculate_bin_centers(lowerCamelCase_						)
				torch.sum(lowerCamelCase_						)
				__magic_name__	  = logits.shape[-2]
				__magic_name__	  = max(lowerCamelCase_	,  19						)

				__magic_name__	  = 1.24 * (clipped_n - 15) ** (1.0 / 3) - 1.8

				__magic_name__	  = torch.nn.functional.softmax(lowerCamelCase_	,  dim=-1						)

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

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

				__magic_name__	  = per_alignment * residue_weights

				__magic_name__	  = (weighted == torch.max(lowerCamelCase_						)).nonzero()[0]
				return per_alignment[tuple(lowerCamelCase_						)]







'''simple docstring'''
import numpy as np

__magic_name__   :   str  							=[
    ['a', 'b', 'c', 'd', 'e'],
    ['f', 'g', 'h', 'i', 'k'],
    ['l', 'm', 'n', 'o', 'p'],
    ['q', 'r', 's', 't', 'u'],
    ['v', 'w', 'x', 'y', 'z'],
]






class 		UpperCamelCase_		:





						"""simple docstring"""




						def __init__( self				:					Union[str, Any]  )							->					None:
										__magic_name__	  = np.array(_lowerCamelCase  )
						def 					__A					( self				:					List[str]	,				_lowerCamelCase				:					str  )							->					np.ndarray:
										__magic_name__  ,      __magic_name__	  = np.where(letter == self.SQUARE  )
										__magic_name__	  = np.concatenate([indexa + 1, indexa + 1]  )
										return indexes
						def 					__A					( self				:					Any	,				_lowerCamelCase				:					int	,				_lowerCamelCase				:					int  )							->					str:
										return self.SQUARE[indexa - 1, indexa - 1]
						def 					__A					( self				:					Union[str, Any]	,				_lowerCamelCase				:					str  )							->					str:
										__magic_name__	  = message.lower()
										__magic_name__	  = message.replace("j"	,				"i"  )

										__magic_name__	  = ""
										for letter_index in range(len(_lowerCamelCase  )  ):
														if message[letter_index] != " ":
																		__magic_name__	  = self.letter_to_numbers(message[letter_index]  )
																		__magic_name__	  = encoded_message + str(numbers[0]  ) + str(numbers[1]  )
														elif message[letter_index] == " ":
																		__magic_name__	  = encoded_message + " "

										return encoded_message

						def 					__A					( self				:					Optional[Any]	,				_lowerCamelCase				:					str  )							->					str:
										__magic_name__	  = message.replace(" "	,				"  "  )
										__magic_name__	  = ""
										for numbers_index in range(int(len(_lowerCamelCase  ) / 2  )  ):
														if message[numbers_index * 2] != " ":
																		__magic_name__	  = message[numbers_index * 2]
																		__magic_name__	  = message[numbers_index * 2 + 1]

																		__magic_name__	  = self.numbers_to_letter(int(_lowerCamelCase  )	,				int(_lowerCamelCase  )  )
																		__magic_name__	  = decoded_message + letter
														elif message[numbers_index * 2] == " ":
																		__magic_name__	  = decoded_message + " "

										return decoded_message



