



"""simple docstring"""
import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


A:   List[Any]								=		logging.getLogger(__name__)



@dataclass
class       SCREAMING_SNAKE_CASE__  :
   __lowerCAmelCase  :   str
   __lowerCAmelCase  :   List[str]
   __lowerCAmelCase  :   Optional[List[str]]



@dataclass
class       SCREAMING_SNAKE_CASE__  :
   __lowerCAmelCase  :   List[int]
   __lowerCAmelCase  :   List[int]
   __lowerCAmelCase  :   Optional[List[int]]						=						None
   __lowerCAmelCase  :   Optional[List[int]]						=						None



class       SCREAMING_SNAKE_CASE__  (					UpperCAmelCase__ ):
   __lowerCAmelCase  :   List[str]						=						'train'
   __lowerCAmelCase  :   int						=						'dev'
   __lowerCAmelCase  :   str						=						'test'



class       SCREAMING_SNAKE_CASE__  :

   @staticmethod
   def 	SCREAMING_SNAKE_CASE     (   _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)  ->      List[InputExample]:





    '''simple docstring'''




    raise NotImplementedError

   @staticmethod
   def 	SCREAMING_SNAKE_CASE     (   _SCREAMING_SNAKE_CASE						)  ->      List[str]:





    '''simple docstring'''




    raise NotImplementedError




   @staticmethod
   def 	SCREAMING_SNAKE_CASE     (   _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE=False      ,     _SCREAMING_SNAKE_CASE="[CLS]"      ,     _SCREAMING_SNAKE_CASE=1      ,     _SCREAMING_SNAKE_CASE="[SEP]"      ,     _SCREAMING_SNAKE_CASE=False      ,     _SCREAMING_SNAKE_CASE=False      ,     _SCREAMING_SNAKE_CASE=0      ,     _SCREAMING_SNAKE_CASE=0      ,     _SCREAMING_SNAKE_CASE=-100      ,     _SCREAMING_SNAKE_CASE=0      ,     _SCREAMING_SNAKE_CASE=True      ,     )  ->      List[InputFeatures]:





    '''simple docstring'''




    UpperCAmelCase	:					int	=	{label: i for i, label in enumerate(_SCREAMING_SNAKE_CASE						)}

    UpperCAmelCase	:					int	=	[]
    for ex_index, example in enumerate(_SCREAMING_SNAKE_CASE						):
     if ex_index % 10000 == 0:
      logger.info("""Writing example %d of %d"""      ,     _SCREAMING_SNAKE_CASE      ,     len(_SCREAMING_SNAKE_CASE						)						)

     UpperCAmelCase	:					Tuple	=	[]
     UpperCAmelCase	:					Tuple	=	[]
     for word, label in zip(example.words      ,     example.labels						):
      UpperCAmelCase	:					Optional[int]	=	tokenizer.tokenize(_SCREAMING_SNAKE_CASE						)

      # bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
      if len(_SCREAMING_SNAKE_CASE						) > 0:
       tokens.extend(_SCREAMING_SNAKE_CASE						)
       # Use the real label id for the first token of the word, and padding ids for the remaining tokens
       label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(_SCREAMING_SNAKE_CASE						) - 1)						)

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
     UpperCAmelCase	:					Dict	=	tokenizer.num_special_tokens_to_add()
     if len(_SCREAMING_SNAKE_CASE						) > max_seq_length - special_tokens_count:
      UpperCAmelCase	:					str	=	tokens[: (max_seq_length - special_tokens_count)]
      UpperCAmelCase	:					Optional[int]	=	label_ids[: (max_seq_length - special_tokens_count)]

     # The convention in BERT is:
     # (a) For sequence pairs:
     #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
     #  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
     # (b) For single sequences:
     #  tokens:   [CLS] the dog is hairy . [SEP]
     #  type_ids:   0   0   0   0  0     0   0
     #
     # Where "type_ids" are used to indicate whether this is the first
     # sequence or the second sequence. The embedding vectors for `type=0` and
     # `type=1` were learned during pre-training and are added to the wordpiece
     # embedding vector (and position vector). This is not *strictly* necessary
     # since the [SEP] token unambiguously separates the sequences, but it makes
     # it easier for the model to learn the concept of sequences.
     #
     # For classification tasks, the first vector (corresponding to [CLS]) is
     # used as the "sentence vector". Note that this only makes sense because
     # the entire model is fine-tuned.
     tokens += [sep_token]
     label_ids += [pad_token_label_id]
     if sep_token_extra:
      # roberta uses an extra separator b/w pairs of sentences
      tokens += [sep_token]
      label_ids += [pad_token_label_id]
     UpperCAmelCase	:					Optional[Any]	=	[sequence_a_segment_id] * len(_SCREAMING_SNAKE_CASE						)

     if cls_token_at_end:
      tokens += [cls_token]
      label_ids += [pad_token_label_id]
      segment_ids += [cls_token_segment_id]
     else:
      UpperCAmelCase	:					str	=	[cls_token] + tokens
      UpperCAmelCase	:					int	=	[pad_token_label_id] + label_ids
      UpperCAmelCase	:					List[Any]	=	[cls_token_segment_id] + segment_ids

     UpperCAmelCase	:					Optional[int]	=	tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE						)

     # The mask has 1 for real tokens and 0 for padding tokens. Only real
     # tokens are attended to.
     UpperCAmelCase	:					Union[str, Any]	=	[1 if mask_padding_with_zero else 0] * len(_SCREAMING_SNAKE_CASE						)

     # Zero-pad up to the sequence length.
     UpperCAmelCase	:					Tuple	=	max_seq_length - len(_SCREAMING_SNAKE_CASE						)
     if pad_on_left:
      UpperCAmelCase	:					Any	=	([pad_token] * padding_length) + input_ids
      UpperCAmelCase	:					Union[str, Any]	=	([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
      UpperCAmelCase	:					Optional[Any]	=	([pad_token_segment_id] * padding_length) + segment_ids
      UpperCAmelCase	:					Dict	=	([pad_token_label_id] * padding_length) + label_ids
     else:
      input_ids += [pad_token] * padding_length
      input_mask += [0 if mask_padding_with_zero else 1] * padding_length
      segment_ids += [pad_token_segment_id] * padding_length
      label_ids += [pad_token_label_id] * padding_length

     assert len(_SCREAMING_SNAKE_CASE						) == max_seq_length
     assert len(_SCREAMING_SNAKE_CASE						) == max_seq_length
     assert len(_SCREAMING_SNAKE_CASE						) == max_seq_length
     assert len(_SCREAMING_SNAKE_CASE						) == max_seq_length

     if ex_index < 5:
      logger.info("""*** Example ***"""						)
      logger.info("""guid: %s"""      ,     example.guid						)
      logger.info("""tokens: %s"""      ,     """ """.join([str(_SCREAMING_SNAKE_CASE						) for x in tokens]						)						)
      logger.info("""input_ids: %s"""      ,     """ """.join([str(_SCREAMING_SNAKE_CASE						) for x in input_ids]						)						)
      logger.info("""input_mask: %s"""      ,     """ """.join([str(_SCREAMING_SNAKE_CASE						) for x in input_mask]						)						)
      logger.info("""segment_ids: %s"""      ,     """ """.join([str(_SCREAMING_SNAKE_CASE						) for x in segment_ids]						)						)
      logger.info("""label_ids: %s"""      ,     """ """.join([str(_SCREAMING_SNAKE_CASE						) for x in label_ids]						)						)

     if "token_type_ids" not in tokenizer.model_input_names:
      UpperCAmelCase	:					List[Any]	=	None

     features.append(
         InputFeatures(
             input_ids=_SCREAMING_SNAKE_CASE      ,     attention_mask=_SCREAMING_SNAKE_CASE      ,     token_type_ids=_SCREAMING_SNAKE_CASE      ,     label_ids=_SCREAMING_SNAKE_CASE						)						)
    return features


if is_torch_available():
      import torch
      from torch import nn
      from torch.utils.data import Dataset



      class       SCREAMING_SNAKE_CASE__  (					UpperCAmelCase__ ):
         __lowerCAmelCase  :   List[InputFeatures]
         __lowerCAmelCase  :   int						=						nn.CrossEntropyLoss().ignore_index

         def __init__(   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE = None      ,     _SCREAMING_SNAKE_CASE=False      ,     _SCREAMING_SNAKE_CASE = Split.train      ,     )  ->      Optional[Any]:





          '''simple docstring'''




          UpperCAmelCase	:					List[str]	=	os.path.join(
              _SCREAMING_SNAKE_CASE      ,     """cached_{}_{}_{}""".format(mode.value      ,     tokenizer.__class__.__name__      ,     str(_SCREAMING_SNAKE_CASE						)						)      ,     )

          # Make sure only the first process in distributed training processes the dataset,
          # and the others will use the cache.
          UpperCAmelCase	:					List[Any]	=	cached_features_file + """.lock"""
          with FileLock(_SCREAMING_SNAKE_CASE						):
           if os.path.exists(_SCREAMING_SNAKE_CASE						) and not overwrite_cache:
            logger.info(F"Loading features from cached file {cached_features_file}"						)
            UpperCAmelCase	:					Tuple	=	torch.load(_SCREAMING_SNAKE_CASE						)
           else:
            logger.info(F"Creating features from dataset file at {data_dir}"						)
            UpperCAmelCase	:					Union[str, Any]	=	token_classification_task.read_examples_from_file(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)
            # TODO clean up all this to leverage built-in features of tokenizers
            UpperCAmelCase	:					Any	=	token_classification_task.convert_examples_to_features(
                _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     cls_token_at_end=bool(model_type in ["""xlnet"""]						)      ,     cls_token=tokenizer.cls_token      ,     cls_token_segment_id=2 if model_type in ["""xlnet"""] else 0      ,     sep_token=tokenizer.sep_token      ,     sep_token_extra=_SCREAMING_SNAKE_CASE      ,     pad_on_left=bool(tokenizer.padding_side == """left"""						)      ,     pad_token=tokenizer.pad_token_id      ,     pad_token_segment_id=tokenizer.pad_token_type_id      ,     pad_token_label_id=self.pad_token_label_id      ,     )
            logger.info(F"Saving features into cached file {cached_features_file}"						)
            torch.save(self.features      ,     _SCREAMING_SNAKE_CASE						)

         def __len__(   self						)  ->      Tuple:





          '''simple docstring'''




          return len(self.features						)




         def __getitem__(   self      ,     _SCREAMING_SNAKE_CASE						)  ->      InputFeatures:





          '''simple docstring'''




          return self.features[i]


if is_tf_available():
      import tensorflow as tf



      class       SCREAMING_SNAKE_CASE__  :
         __lowerCAmelCase  :   List[InputFeatures]
         __lowerCAmelCase  :   int						=						-100

         def __init__(   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE = None      ,     _SCREAMING_SNAKE_CASE=False      ,     _SCREAMING_SNAKE_CASE = Split.train      ,     )  ->      Dict:





          '''simple docstring'''




          UpperCAmelCase	:					Tuple	=	token_classification_task.read_examples_from_file(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)
          # TODO clean up all this to leverage built-in features of tokenizers
          UpperCAmelCase	:					Union[str, Any]	=	token_classification_task.convert_examples_to_features(
              _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     cls_token_at_end=bool(model_type in ["""xlnet"""]						)      ,     cls_token=tokenizer.cls_token      ,     cls_token_segment_id=2 if model_type in ["""xlnet"""] else 0      ,     sep_token=tokenizer.sep_token      ,     sep_token_extra=_SCREAMING_SNAKE_CASE      ,     pad_on_left=bool(tokenizer.padding_side == """left"""						)      ,     pad_token=tokenizer.pad_token_id      ,     pad_token_segment_id=tokenizer.pad_token_type_id      ,     pad_token_label_id=self.pad_token_label_id      ,     )

          def gen():
           for ex in self.features:
            if ex.token_type_ids is None:
             yield (
                 {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
                 ex.label_ids,
             )
            else:
             yield (
                 {
                     "input_ids": ex.input_ids,
                     "attention_mask": ex.attention_mask,
                     "token_type_ids": ex.token_type_ids,
                 },
                 ex.label_ids,
             )

          if "token_type_ids" not in tokenizer.model_input_names:
           UpperCAmelCase	:					Tuple	=	tf.data.Dataset.from_generator(
               _SCREAMING_SNAKE_CASE      ,     ({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa}, tf.intaa)      ,     (
                   {"""input_ids""": tf.TensorShape([None]						), """attention_mask""": tf.TensorShape([None]						)},
                   tf.TensorShape([None]						),
               )      ,     )
          else:
           UpperCAmelCase	:					str	=	tf.data.Dataset.from_generator(
               _SCREAMING_SNAKE_CASE      ,     ({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa, """token_type_ids""": tf.intaa}, tf.intaa)      ,     (
                   {
                       """input_ids""": tf.TensorShape([None]						),
                       """attention_mask""": tf.TensorShape([None]						),
                       """token_type_ids""": tf.TensorShape([None]						),
                   },
                   tf.TensorShape([None]						),
               )      ,     )

         def 	SCREAMING_SNAKE_CASE     (   self						)  ->      List[Any]:





          '''simple docstring'''




          UpperCAmelCase	:					List[str]	=	self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features						)						)						)

          return self.dataset

         def __len__(   self						)  ->      Any:





          '''simple docstring'''




          return len(self.features						)




         def __getitem__(   self      ,     _SCREAMING_SNAKE_CASE						)  ->      InputFeatures:





          '''simple docstring'''




          return self.features[i]





"""simple docstring"""
from collections import defaultdict
from math import ceil, sqrt

def       _snake_case						(						UpperCamelCase						:		int = 1000000			,		UpperCamelCase						:		int = 10						):
 UpperCAmelCase	:					defaultdict	=	defaultdict(UpperCamelCase						)

 for outer_width in range(3			,		(t_limit // 4) + 2						):
  if outer_width * outer_width > t_limit:
   UpperCAmelCase	:					str	=	max(
       ceil(sqrt(outer_width * outer_width - t_limit						)						)			,		1						)
  else:
   UpperCAmelCase	:					Optional[Any]	=	1

  hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2

  for hole_width in range(UpperCamelCase			,		outer_width - 1			,		2						):
   count[outer_width * outer_width - hole_width * hole_width] += 1

 return sum(1 for n in count.values() if 1 <= n <= 10						)


if __name__ == "__main__":
      print(f"""{solution() = }""")
