




from typing import Dict, Optional, Tuple

import torch







def lowerCamelCase__   (			_lowercase      ):

     '''simple docstring'''


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







def lowerCamelCase__   (			_lowercase ,						_lowercase ,						):

     '''simple docstring'''


     UpperCAmelCase_			:							List[Any]   =			_calculate_bin_centers(__lowercase      )
     return (
         torch.sum(aligned_distance_error_probs * bin_centers ,						dim=-1      ),
         bin_centers[-1],
     )







def lowerCamelCase__   (			_lowercase ,						_lowercase = 31 ,						_lowercase = 64 ,						**_lowercase ,						):

     '''simple docstring'''


     UpperCAmelCase_			:							Optional[Any]   =			torch.linspace(0 ,						__lowercase ,						steps=(no_bins - 1) ,						device=logits.device      )

     UpperCAmelCase_			:							Optional[Any]   =			torch.nn.functional.softmax(__lowercase ,						dim=-1      )
     UpperCAmelCase_,    UpperCAmelCase_			:							List[Any]   =			_calculate_expected_aligned_error(
         alignment_confidence_breaks=__lowercase ,						aligned_distance_error_probs=__lowercase ,						)

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







def lowerCamelCase__   (			_lowercase ,						_lowercase = None ,						_lowercase = 31 ,						_lowercase = 64 ,						_lowercase = 1E-8 ,						**_lowercase ,						):

     '''simple docstring'''


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

     UpperCAmelCase_			:							Union[str, Any]   =			torch.linspace(0 ,						__lowercase ,						steps=(no_bins - 1) ,						device=logits.device      )

     UpperCAmelCase_			:							Optional[Any]   =			_calculate_bin_centers(__lowercase      )
     torch.sum(__lowercase      )
     UpperCAmelCase_			:							str   =			logits.shape[-2]
     UpperCAmelCase_			:							Dict   =			max(__lowercase ,						19      )

     UpperCAmelCase_			:							List[Any]   =			1.24 * (clipped_n - 15) ** (1.0 / 3) - 1.8

     UpperCAmelCase_			:							Dict   =			torch.nn.functional.softmax(__lowercase ,						dim=-1      )

     UpperCAmelCase_			:							Dict   =			1.0 / (1 + (bin_centers**2) / (da**2))
     UpperCAmelCase_			:							str   =			torch.sum(probs * tm_per_bin ,						dim=-1      )

     UpperCAmelCase_			:							List[str]   =			residue_weights / (eps + residue_weights.sum())
     UpperCAmelCase_			:							Dict   =			torch.sum(predicted_tm_term * normed_residue_mask ,						dim=-1      )

     UpperCAmelCase_			:							Optional[int]   =			per_alignment * residue_weights

     UpperCAmelCase_			:							int   =			(weighted == torch.max(__lowercase      )).nonzero()[0]
     return per_alignment[tuple(__lowercase      )]



"""simple docstring"""


import os
from shutil import copyfile
from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_reformer import ReformerTokenizer
else:
		a_											=			None


a_											=			logging.get_logger(__name__)


a_											=			"▁"

a_											=			{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

a_											=			{
    "vocab_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
        )
    },
    "tokenizer_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json"
        )
    },
}

a_											=			{
    "google/reformer-crime-and-punishment": 52_42_88,
}
class snake_case					(     _UpperCamelCase):
		__UpperCamelCase							=	VOCAB_FILES_NAMES
		__UpperCamelCase							=	PRETRAINED_VOCAB_FILES_MAP
		__UpperCamelCase							=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__UpperCamelCase							=	['input_ids', 'attention_mask']
		__UpperCamelCase							=	ReformerTokenizer







		def __init__(    self					:				Dict			,      a__					:				List[Any]=None			,      a__					:				int=None			,      a__					:				int="</s>"			,      a__					:				List[Any]="<unk>"			,      a__					:				Optional[Any]=[]			,      **a__					:				Tuple			,      )				->  Tuple:





						'''simple docstring'''







						super().__init__(
						    a__			,      tokenizer_file=a__			,      eos_token=a__			,      unk_token=a__			,      additional_special_tokens=a__			,      **a__			,      )

						_A         =	vocab_file
						_A         =	False if not self.vocab_file else True







		def        a_       (    self					:				Any			,      a__					:				str			,      a__					:				Optional[str] = None						)				->  Tuple[str]:





						'''simple docstring'''







						if not self.can_save_slow_tokenizer:
										raise ValueError(
										    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
										    "tokenizer."						)

						if not os.path.isdir(a__						):
										logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""						)
										return
						_A         =	os.path.join(
						    a__			,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]						)

						if os.path.abspath(self.vocab_file						) != os.path.abspath(a__						):
										copyfile(self.vocab_file			,      a__						)

						return (out_vocab_file,)