





import json
import os
from typing import Optional, Tuple

import regex as re

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_snake_case  :   Dict						   =  logging.get_logger(__name__)

_snake_case  :   Any						   =  {
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
}

_snake_case  :   Optional[int]						   =  {
    'vocab_file': {'ctrl': 'https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-vocab.json'},
    'merges_file': {'ctrl': 'https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-merges.txt'},
}

_snake_case  :   Union[str, Any]						   =  {
    'ctrl': 256,
}

_snake_case  :   str						   =  {
    'Pregnancy': 168629,
    'Christianity': 7675,
    'Explain': 106423,
    'Fitness': 63440,
    'Saving': 63163,
    'Ask': 27171,
    'Ass': 95985,
    'Joke': 163509,
    'Questions': 45622,
    'Thoughts': 49605,
    'Retail': 52342,
    'Feminism': 164338,
    'Writing': 11992,
    'Atheism': 192263,
    'Netflix': 48616,
    'Computing': 39639,
    'Opinion': 43213,
    'Alone': 44967,
    'Funny': 58917,
    'Gaming': 40358,
    'Human': 4088,
    'India': 1331,
    'Joker': 77138,
    'Diet': 36206,
    'Legal': 11859,
    'Norman': 4939,
    'Tip': 72689,
    'Weight': 52343,
    'Movies': 46273,
    'Running': 23425,
    'Science': 2090,
    'Horror': 37793,
    'Confession': 60572,
    'Finance': 12250,
    'Politics': 16360,
    'Scary': 191985,
    'Support': 12654,
    'Technologies': 32516,
    'Teenage': 66160,
    'Event': 32769,
    'Learned': 67460,
    'Notion': 182770,
    'Wikipedia': 37583,
    'Books': 6665,
    'Extract': 76050,
    'Confessions': 102701,
    'Conspiracy': 75932,
    'Links': 63674,
    'Narcissus': 150425,
    'Relationship': 54766,
    'Relationships': 134796,
    'Reviews': 41671,
    'News': 4256,
    'Translation': 26820,
    'multilingual': 128406,
}

def   a_   (			lowerCAmelCase_				:      str     ):
			__lowerCAmelCase							     =     set()
			__lowerCAmelCase							     =     word[0]
			for char in word[1:]:
						pairs.add((prev_char, char)     )
						__lowerCAmelCase							     =     char

			__lowerCAmelCase							     =     set(__SCREAMING_SNAKE_CASE     )
			return pairs




class     _UpperCAmelCase   (   __UpperCamelCase   ):

		"""simple docstring"""


		a_      =			VOCAB_FILES_NAMES
		a_      =			PRETRAINED_VOCAB_FILES_MAP
		a_      =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		a_      =			CONTROL_CODES






		def __init__(    self			:		str ,  lowerCAmelCase_			:		str ,  lowerCAmelCase_			:		Tuple ,  lowerCAmelCase_			:		int="<unk>" ,  **lowerCAmelCase_			:		str					)							->						Optional[Any]:
					super().__init__(unk_token=_lowerCAmelCase ,  **_lowerCAmelCase					)

					with open(_lowerCAmelCase ,  encoding='utf-8'					) as vocab_handle:
								__lowerCAmelCase							     =     json.load(_lowerCAmelCase					)
					__lowerCAmelCase							     =     {v: k for k, v in self.encoder.items()}
					with open(_lowerCAmelCase ,  encoding='utf-8'					) as merges_handle:
								__lowerCAmelCase							     =     merges_handle.read().split('\n'					)[1:-1]
					__lowerCAmelCase							     =     [tuple(merge.split()					) for merge in merges]
					__lowerCAmelCase							     =     dict(zip(_lowerCAmelCase ,  range(len(_lowerCAmelCase					)					)					)					)
					__lowerCAmelCase							     =     {}






		@property
		def     lowercase		(    self			:		Dict					)							->						str:
					return len(self.encoder					)






		def     lowercase		(    self			:		Any					)							->						Union[str, Any]:
					return dict(self.encoder ,  **self.added_tokens_encoder					)






		def     lowercase		(    self			:		Optional[Any] ,  lowerCAmelCase_			:		Tuple					)							->						str:
					if token in self.cache:
								return self.cache[token]
					__lowerCAmelCase							     =     tuple(_lowerCAmelCase					)
					__lowerCAmelCase							     =     tuple(list(word[:-1]					) + [word[-1] + '</w>']					)
					__lowerCAmelCase							     =     get_pairs(_lowerCAmelCase					)

					if not pairs:
								return token

					while True:
								__lowerCAmelCase							     =     min(_lowerCAmelCase ,  key=lambda lowerCAmelCase_					: self.bpe_ranks.get(_lowerCAmelCase ,  float('inf'					)					)					)
								if bigram not in self.bpe_ranks:
											break
								__lowerCAmelCase							     =     bigram
								__lowerCAmelCase							     =     []
								__lowerCAmelCase							     =     0
								while i < len(_lowerCAmelCase					):
											try:
														__lowerCAmelCase							     =     word.index(_lowerCAmelCase ,  _lowerCAmelCase					)
											except ValueError:
														new_word.extend(word[i:]					)
														break
											else:
														new_word.extend(word[i:j]					)
														__lowerCAmelCase							     =     j

											if word[i] == first and i < len(_lowerCAmelCase					) - 1 and word[i + 1] == second:
														new_word.append(first + second					)
														i += 2
											else:
														new_word.append(word[i]					)
														i += 1
								__lowerCAmelCase							     =     tuple(_lowerCAmelCase					)
								__lowerCAmelCase							     =     new_word
								if len(_lowerCAmelCase					) == 1:
											break
								else:
											__lowerCAmelCase							     =     get_pairs(_lowerCAmelCase					)
					__lowerCAmelCase							     =     """@@ """.join(_lowerCAmelCase					)
					__lowerCAmelCase							     =     word[:-4]
					__lowerCAmelCase							     =     word
					return word






		def     lowercase		(    self			:		List[str] ,  lowerCAmelCase_			:		Optional[int]					)							->						Optional[Any]:
					__lowerCAmelCase							     =     []

					__lowerCAmelCase							     =     re.findall(R'\S+\n?' ,  _lowerCAmelCase					)

					for token in words:
								split_tokens.extend(list(self.bpe(_lowerCAmelCase					).split(' '					)					)					)
					return split_tokens






		def     lowercase		(    self			:		Optional[Any] ,  lowerCAmelCase_			:		Optional[Any]					)							->						List[Any]:
					return self.encoder.get(_lowerCAmelCase ,  self.encoder.get(self.unk_token					)					)






		def     lowercase		(    self			:		int ,  lowerCAmelCase_			:		List[str]					)							->						Union[str, Any]:
					return self.decoder.get(_lowerCAmelCase ,  self.unk_token					)






		def     lowercase		(    self			:		str ,  lowerCAmelCase_			:		Tuple					)							->						Optional[int]:
					__lowerCAmelCase							     =     """ """.join(_lowerCAmelCase					).replace('@@ ' ,  ''					).strip()
					return out_string






		def     lowercase		(    self			:		List[str] ,  lowerCAmelCase_			:		str ,  lowerCAmelCase_			:		Optional[str] = None					)							->						Optional[int]:
					if not os.path.isdir(_lowerCAmelCase					):
								logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""					)
								return
					__lowerCAmelCase							     =     os.path.join(
					    _lowerCAmelCase ,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']					)
					__lowerCAmelCase							     =     os.path.join(
					    _lowerCAmelCase ,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']					)

					with open(_lowerCAmelCase ,  'w' ,  encoding='utf-8'					) as f:
								f.write(json.dumps(self.encoder ,  indent=2 ,  sort_keys=_lowerCAmelCase ,  ensure_ascii=_lowerCAmelCase					) + '\n'					)

					__lowerCAmelCase							     =     0
					with open(_lowerCAmelCase ,  'w' ,  encoding='utf-8'					) as writer:
								writer.write('#version: 0.2\n'					)
								for bpe_tokens, token_index in sorted(self.bpe_ranks.items() ,  key=lambda lowerCAmelCase_					: kv[1]					):
											if index != token_index:
														logger.warning(
														    f"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
														    ' Please check that the tokenizer is not corrupted!'					)
														__lowerCAmelCase							     =     token_index
											writer.write(' '.join(_lowerCAmelCase					) + '\n'					)
											index += 1

					return vocab_file, merge_file

		# def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
		#     filtered_tokens = ' '.join(self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens))
		#     tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens)
		#     tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
		#     return ''.join(tokens_generated_so_far)








from typing import Dict

import numpy as np
import torch

from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map

def   a_   (			lowerCAmelCase_				:      Dict[str, torch.Tensor]     ):
   __lowerCAmelCase							     =     []
   __lowerCAmelCase							     =     []
   __lowerCAmelCase							     =     []

   for rt in rc.restypes:
      __lowerCAmelCase							     =     rc.restype_name_to_atomaa_names[rc.restype_atoa[rt]]
      restype_atomaa_to_atomaa_list.append([(rc.atom_order[name] if name else 0) for name in atom_names]     )
      __lowerCAmelCase							     =     {name: i for i, name in enumerate(lowerCAmelCase_     )}
      restype_atomaa_to_atomaa_list.append(
          [(atom_name_to_idxaa[name] if name in atom_name_to_idxaa else 0) for name in rc.atom_types]     )

      restype_atomaa_mask_list.append([(1.0 if name else 0.0) for name in atom_names]     )

   # Add dummy mapping for restype 'UNK'
   restype_atomaa_to_atomaa_list.append([0] * 14     )
   restype_atomaa_to_atomaa_list.append([0] * 37     )
   restype_atomaa_mask_list.append([0.0] * 14     )

   __lowerCAmelCase							     =     torch.tensor(
       lowerCAmelCase_,     dtype=torch.intaa,     device=protein['aatype'].device,     )
   __lowerCAmelCase							     =     torch.tensor(
       lowerCAmelCase_,     dtype=torch.intaa,     device=protein['aatype'].device,     )
   __lowerCAmelCase							     =     torch.tensor(
       lowerCAmelCase_,     dtype=torch.floataa,     device=protein['aatype'].device,     )
   __lowerCAmelCase							     =     protein['aatype'].to(torch.long     )

   # create the mapping for (residx, atom14) --> atom37, i.e. an array
   # with shape (num_res, 14) containing the atom37 indices for this protein
   __lowerCAmelCase							     =     restype_atomaa_to_atomaa[protein_aatype]
   __lowerCAmelCase							     =     restype_atomaa_mask[protein_aatype]

   __lowerCAmelCase							     =     residx_atomaa_mask
   __lowerCAmelCase							     =     residx_atomaa_to_atomaa.long()

   # create the gather indices for mapping back
   __lowerCAmelCase							     =     restype_atomaa_to_atomaa[protein_aatype]
   __lowerCAmelCase							     =     residx_atomaa_to_atomaa.long()

   # create the corresponding mask
   __lowerCAmelCase							     =     torch.zeros([21, 37],     dtype=torch.floataa,     device=protein['aatype'].device     )
   for restype, restype_letter in enumerate(rc.restypes     ):
      __lowerCAmelCase							     =     rc.restype_atoa[restype_letter]
      __lowerCAmelCase							     =     rc.residue_atoms[restype_name]
      for atom_name in atom_names:
         __lowerCAmelCase							     =     rc.atom_order[atom_name]
         __lowerCAmelCase							     =     1

   __lowerCAmelCase							     =     restype_atomaa_mask[protein_aatype]
   __lowerCAmelCase							     =     residx_atomaa_mask

   return protein




def   a_   (			lowerCAmelCase_				:      Dict[str, torch.Tensor]     ):
   __lowerCAmelCase							     =     tree_map(lambda lowerCAmelCase_     : torch.tensor(lowerCAmelCase_,     device=batch['aatype'].device     ),     lowerCAmelCase_,     np.ndarray     )
   __lowerCAmelCase							     =     tensor_tree_map(lambda lowerCAmelCase_     : np.array(lowerCAmelCase_     ),     make_atomaa_masks(lowerCAmelCase_     )     )
   return out

