




from typing import Dict, Optional, Tuple

import torch






def  snake_case (							A      )							->							torch.Tensor:




	'''simple docstring'''


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






def  snake_case (							A						,       A						,       )							->							Tuple[torch.Tensor, torch.Tensor]:




	'''simple docstring'''


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






def  snake_case (							A						,       A = 31						,       A = 64						,       **A						,       )							->							Dict[str, torch.Tensor]:




	'''simple docstring'''


	lowerCamelCase_							:		Tuple							       =							torch.linspace(0						,       A						,       steps=(no_bins - 1)						,       device=logits.device      )

	lowerCamelCase_							:		str							       =							torch.nn.functional.softmax(A						,       dim=-1      )
	lowerCamelCase_       ,      lowerCamelCase_							:		int							       =							_calculate_expected_aligned_error(
	    alignment_confidence_breaks=A						,       aligned_distance_error_probs=A						,       )

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






def  snake_case (							A						,       A = None						,       A = 31						,       A = 64						,       A = 1e-8						,       **A						,       )							->							torch.Tensor:




	'''simple docstring'''


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

	lowerCamelCase_							:		List[str]							       =							torch.linspace(0						,       A						,       steps=(no_bins - 1)						,       device=logits.device      )

	lowerCamelCase_							:		Any							       =							_calculate_bin_centers(A      )
	torch.sum(A      )
	lowerCamelCase_							:		str							       =							logits.shape[-2]
	lowerCamelCase_							:		Union[str, Any]							       =							max(A						,       19      )

	lowerCamelCase_							:		str							       =							1.24 * (clipped_n - 15) ** (1.0 / 3) - 1.8

	lowerCamelCase_							:		Optional[Any]							       =							torch.nn.functional.softmax(A						,       dim=-1      )

	lowerCamelCase_							:		int							       =							1.0 / (1 + (bin_centers**2) / (da**2))
	lowerCamelCase_							:		Optional[Any]							       =							torch.sum(probs * tm_per_bin						,       dim=-1      )

	lowerCamelCase_							:		Any							       =							residue_weights / (eps + residue_weights.sum())
	lowerCamelCase_							:		Tuple							       =							torch.sum(predicted_tm_term * normed_residue_mask						,       dim=-1      )

	lowerCamelCase_							:		int							       =							per_alignment * residue_weights

	lowerCamelCase_							:		Tuple							       =							(weighted == torch.max(A      )).nonzero()[0]
	return per_alignment[tuple(A      )]






import sys

from transformers import AutoTokenizer


UpperCamelCase__		=						sys.argv[1]
UpperCamelCase__		=						sys.argv[2]
UpperCamelCase__		=						int(sys.argv[3])

UpperCamelCase__		=						0

UpperCamelCase__		=						AutoTokenizer.from_pretrained(model_name_or_path)
max_len -= tokenizer.num_special_tokens_to_add()

with open(dataset, 'rt') as f_p:
							for line in f_p:
														UpperCamelCase__		=						line.rstrip()

														if not line:
																					print(line)
																					UpperCamelCase__		=						0
																					continue

														UpperCamelCase__		=						line.split()[0]

														UpperCamelCase__		=						len(tokenizer.tokenize(token))

														# Token contains strange control characters like \x96 or \x95
														# Just filter out the complete line
														if current_subwords_len == 0:
																					continue

														if (subword_len_counter + current_subwords_len) > max_len:
																					print('')
																					print(line)
																					UpperCamelCase__		=						current_subwords_len
																					continue

														subword_len_counter += current_subwords_len

														print(line)
