



import os
import unittest

from transformers.models.bertweet.tokenization_bertweet import VOCAB_FILES_NAMES, BertweetTokenizer

from ...test_tokenization_common import TokenizerTesterMixin





class 							lowerCAmelCase_		(		_snake_case							,		unittest.TestCase     ):
		SCREAMING_SNAKE_CASE_       :			Union[str, Any]					       =	BertweetTokenizer
		SCREAMING_SNAKE_CASE_       :			Dict					       =	False







		def 					a_  (    self  :   int					)       ->		Optional[Any]:


						'''simple docstring'''







						super().setUp()

						# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
						_UpperCAmelCase						:  Dict     					=	['''I''', '''m''', '''V@@''', '''R@@''', '''r''', '''e@@''']
						_UpperCAmelCase						:  str     					=	dict(zip(lowerCAmelCase__       ,		range(len(lowerCAmelCase__					)					)					)					)
						_UpperCAmelCase						:  str     					=	['''#version: 0.2''', '''a m</w>''']
						_UpperCAmelCase						:  Optional[int]     					=	{'''unk_token''': '''<unk>'''}

						_UpperCAmelCase						:  Tuple     					=	os.path.join(self.tmpdirname       ,		VOCAB_FILES_NAMES['''vocab_file''']					)
						_UpperCAmelCase						:  Any     					=	os.path.join(self.tmpdirname       ,		VOCAB_FILES_NAMES['''merges_file''']					)
						with open(self.vocab_file       ,		'''w'''       ,		encoding='''utf-8'''					) as fp:
										for token in vocab_tokens:
														fp.write(F'''{token} {vocab_tokens[token]}\n'''					)
						with open(self.merges_file       ,		'''w'''       ,		encoding='''utf-8'''					) as fp:
										fp.write('''\n'''.join(lowerCAmelCase__					)					)







		def 					a_  (    self  :   Optional[int]       ,		**UpperCAmelCase_  :   str					)       ->		Dict:


						'''simple docstring'''







						kwargs.update(self.special_tokens_map					)
						return BertweetTokenizer.from_pretrained(self.tmpdirname       ,		**lowerCAmelCase__					)







		def 					a_  (    self  :   Any       ,		UpperCAmelCase_  :   Tuple					)       ->		Dict:


						'''simple docstring'''







						_UpperCAmelCase						:  Union[str, Any]     					=	'''I am VinAI Research'''
						_UpperCAmelCase						:  Union[str, Any]     					=	'''I <unk> m V<unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'''
						return input_text, output_text







		def 					a_  (    self  :   List[str]					)       ->		List[Any]:


						'''simple docstring'''







						_UpperCAmelCase						:  Any     					=	BertweetTokenizer(self.vocab_file       ,		self.merges_file       ,		**self.special_tokens_map					)
						_UpperCAmelCase						:  Dict     					=	'''I am VinAI Research'''
						_UpperCAmelCase						:  List[str]     					=	'''I a@@ m V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'''.split()
						_UpperCAmelCase						:  List[Any]     					=	tokenizer.tokenize(lowerCAmelCase__					)
						self.assertListEqual(lowerCAmelCase__       ,		lowerCAmelCase__					)

						_UpperCAmelCase						:  Optional[Any]     					=	tokens + [tokenizer.unk_token]

						_UpperCAmelCase						:  Tuple     					=	[4, 3, 5, 6, 3, 3, 3, 4, 7, 9, 3, 9, 3, 3, 3, 3, 3]
						self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__					)       ,		lowerCAmelCase__					)



from typing import Dict, Optional, Tuple

import torch
def   _lowerCAmelCase(						__A      ):
  UpperCAmelCase          =	boundaries[1] - boundaries[0]
  UpperCAmelCase          =	boundaries + step / 2
  UpperCAmelCase          =	torch.cat([bin_centers, (bin_centers[-1] + step).unsqueeze(-1      )] ,							dim=0      )
  return bin_centers
def   _lowerCAmelCase(						__A ,							__A ,							):
  UpperCAmelCase          =	_calculate_bin_centers(__A      )
  return (
      torch.sum(aligned_distance_error_probs * bin_centers ,							dim=-1      ),
      bin_centers[-1],
  )
def   _lowerCAmelCase(						__A ,							__A = 31 ,							__A = 64 ,							**__A ,							):
  UpperCAmelCase          =	torch.linspace(0 ,							__A ,							steps=(no_bins - 1) ,							device=logits.device      )

  UpperCAmelCase          =	torch.nn.functional.softmax(__A ,							dim=-1      )
  UpperCAmelCase   ,						UpperCAmelCase          =	_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   _lowerCAmelCase(						__A ,							__A = None ,							__A = 31 ,							__A = 64 ,							__A = 1E-8 ,							**__A ,							):
  if residue_weights is None:
    UpperCAmelCase          =	logits.new_ones(logits.shape[-2]      )

  UpperCAmelCase          =	torch.linspace(0 ,							__A ,							steps=(no_bins - 1) ,							device=logits.device      )

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

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

  UpperCAmelCase          =	torch.nn.functional.softmax(__A ,							dim=-1      )

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

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

  UpperCAmelCase          =	per_alignment * residue_weights

  UpperCAmelCase          =	(weighted == torch.max(__A      )).nonzero()[0]
  return per_alignment[tuple(__A      )]



