
"""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


SCREAMING_SNAKE_CASE_      					=						logging.getLogger(__name__)
@dataclass
class      _UpperCAmelCase							:
      __SCREAMING_SNAKE_CASE   :							str
      __SCREAMING_SNAKE_CASE   :							List[str]
      __SCREAMING_SNAKE_CASE   :							Optional[List[str]]

@dataclass
class      _UpperCAmelCase							:
      __SCREAMING_SNAKE_CASE   :							List[int]
      __SCREAMING_SNAKE_CASE   :							List[int]
      __SCREAMING_SNAKE_CASE   :							Optional[List[int]]							      =	None
      __SCREAMING_SNAKE_CASE   :							Optional[List[int]]							      =	None

class      _UpperCAmelCase							(		A_							):
      __SCREAMING_SNAKE_CASE   :							List[str]							      =	'''train'''
      __SCREAMING_SNAKE_CASE   :							Dict							      =	'''dev'''
      __SCREAMING_SNAKE_CASE   :							List[str]							      =	'''test'''

class      _UpperCAmelCase							:




      @staticmethod
      def     a_      (							lowercase_   ,       lowercase_							)						->					Optional[Any]:
          raise NotImplementedError




      @staticmethod
      def     a_      (							lowercase_							)						->					List[Any]:
          raise NotImplementedError




      @staticmethod
      def     a_      (							lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_=False   ,       lowercase_="[CLS]"   ,       lowercase_=1   ,       lowercase_="[SEP]"   ,       lowercase_=False   ,       lowercase_=False   ,       lowercase_=0   ,       lowercase_=0   ,       lowercase_=-1_0_0   ,       lowercase_=0   ,       lowercase_=True   ,       )						->					Union[str, Any]:
          UpperCAmelCase									=					{label: i for i, label in enumerate(lowercase_							)}

          UpperCAmelCase									=					[]
          for ex_index, example in enumerate(lowercase_							):
              if ex_index % 1_0_0_0_0 == 0:
                  logger.info('Writing example %d of %d'   ,       lowercase_   ,       len(lowercase_							)							)

              UpperCAmelCase									=					[]
              UpperCAmelCase									=					[]
              for word, label in zip(example.words   ,       example.labels							):
                  UpperCAmelCase									=					tokenizer.tokenize(lowercase_							)

                  # bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
                  if len(lowercase_							) > 0:
                      tokens.extend(lowercase_							)
                      # 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(lowercase_							) - 1)							)

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
              UpperCAmelCase									=					tokenizer.num_special_tokens_to_add()
              if len(lowercase_							) > max_seq_length - special_tokens_count:
                  UpperCAmelCase									=					tokens[: (max_seq_length - special_tokens_count)]
                  UpperCAmelCase									=					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									=					[sequence_a_segment_id] * len(lowercase_							)

              if cls_token_at_end:
                  tokens += [cls_token]
                  label_ids += [pad_token_label_id]
                  segment_ids += [cls_token_segment_id]
              else:
                  UpperCAmelCase									=					[cls_token] + tokens
                  UpperCAmelCase									=					[pad_token_label_id] + label_ids
                  UpperCAmelCase									=					[cls_token_segment_id] + segment_ids

              UpperCAmelCase									=					tokenizer.convert_tokens_to_ids(lowercase_							)

              # The mask has 1 for real tokens and 0 for padding tokens. Only real
              # tokens are attended to.
              UpperCAmelCase									=					[1 if mask_padding_with_zero else 0] * len(lowercase_							)

              # Zero-pad up to the sequence length.
              UpperCAmelCase									=					max_seq_length - len(lowercase_							)
              if pad_on_left:
                  UpperCAmelCase									=					([pad_token] * padding_length) + input_ids
                  UpperCAmelCase									=					([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
                  UpperCAmelCase									=					([pad_token_segment_id] * padding_length) + segment_ids
                  UpperCAmelCase									=					([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(lowercase_							) == max_seq_length
              assert len(lowercase_							) == max_seq_length
              assert len(lowercase_							) == max_seq_length
              assert len(lowercase_							) == max_seq_length

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

              if "token_type_ids" not in tokenizer.model_input_names:
                  UpperCAmelCase									=					None

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


if is_torch_available():
   import torch
   from torch import nn
   from torch.utils.data import Dataset
   class      _UpperCAmelCase							(		A_							):
         __SCREAMING_SNAKE_CASE   :							List[InputFeatures]
         __SCREAMING_SNAKE_CASE   :							int							      =	nn.CrossEntropyLoss().ignore_index




         def __init__(							self   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_ = None   ,       lowercase_=False   ,       lowercase_ = Split.train   ,       )						->					Tuple:
             # Load data features from cache or dataset file
             UpperCAmelCase									=					os.path.join(
                 lowercase_   ,       'cached_{}_{}_{}'.format(mode.value   ,       tokenizer.__class__.__name__   ,       str(lowercase_							)							)   ,       )

             # Make sure only the first process in distributed training processes the dataset,
             # and the others will use the cache.
             UpperCAmelCase									=					cached_features_file + """.lock"""
             with FileLock(lowercase_							):
                 if os.path.exists(lowercase_							) and not overwrite_cache:
                     logger.info(F"Loading features from cached file {cached_features_file}"							)
                     UpperCAmelCase									=					torch.load(lowercase_							)
                 else:
                     logger.info(F"Creating features from dataset file at {data_dir}"							)
                     UpperCAmelCase									=					token_classification_task.read_examples_from_file(lowercase_   ,       lowercase_							)
                     # TODO clean up all this to leverage built-in features of tokenizers
                     UpperCAmelCase									=					token_classification_task.convert_examples_to_features(
                         lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       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=lowercase_   ,       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   ,       lowercase_							)




         def __len__(							self							)						->					List[str]:
             return len(self.features							)




         def __getitem__(							self   ,       lowercase_							)						->					int:
             return self.features[i]


if is_tf_available():
   import tensorflow as tf
   class      _UpperCAmelCase							:
         __SCREAMING_SNAKE_CASE   :							List[InputFeatures]
         __SCREAMING_SNAKE_CASE   :							int							      =	-1_0_0




         def __init__(							self   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_ = None   ,       lowercase_=False   ,       lowercase_ = Split.train   ,       )						->					Optional[int]:
             UpperCAmelCase									=					token_classification_task.read_examples_from_file(lowercase_   ,       lowercase_							)
             # TODO clean up all this to leverage built-in features of tokenizers
             UpperCAmelCase									=					token_classification_task.convert_examples_to_features(
                 lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       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=lowercase_   ,       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									=					tf.data.Dataset.from_generator(
                     lowercase_   ,       ({'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									=					tf.data.Dataset.from_generator(
                     lowercase_   ,       ({'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     a_      (							self							)						->					List[str]:
             UpperCAmelCase									=					self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features							)							)							)

             return self.dataset




         def __len__(							self							)						->					Any:
             return len(self.features							)




         def __getitem__(							self   ,       lowercase_							)						->					str:
             return self.features[i]





"""simple docstring"""

import sys
import turtle




def     lowercase__						(					lowerCAmelCase				:					tuple[float, float] ,				lowerCAmelCase				:					tuple[float, float] ) ->      tuple[float, float]:






    """simple docstring"""




    return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2




def     lowercase__						(					lowerCAmelCase				:					tuple[float, float] ,				lowerCAmelCase				:					tuple[float, float] ,				lowerCAmelCase				:					tuple[float, float] ,				lowerCAmelCase				:					int ,				) ->      None:






    """simple docstring"""




    my_pen.up()
    my_pen.goto(vertexa[0] ,				vertexa[1] )
    my_pen.down()
    my_pen.goto(vertexa[0] ,				vertexa[1] )
    my_pen.goto(vertexa[0] ,				vertexa[1] )
    my_pen.goto(vertexa[0] ,				vertexa[1] )

    if depth == 0:
        return

    triangle(lowerCAmelCase ,				get_mid(lowerCAmelCase ,				lowerCAmelCase ) ,				get_mid(lowerCAmelCase ,				lowerCAmelCase ) ,				depth - 1 )
    triangle(lowerCAmelCase ,				get_mid(lowerCAmelCase ,				lowerCAmelCase ) ,				get_mid(lowerCAmelCase ,				lowerCAmelCase ) ,				depth - 1 )
    triangle(lowerCAmelCase ,				get_mid(lowerCAmelCase ,				lowerCAmelCase ) ,				get_mid(lowerCAmelCase ,				lowerCAmelCase ) ,				depth - 1 )


if __name__ == "__main__":
   if len(sys.argv) != 2:
      raise ValueError(
          '''Correct format for using this script: '''
          '''python fractals.py <int:depth_for_fractal>'''
      )
   SCREAMING_SNAKE_CASE_    					=						turtle.Turtle()
   my_pen.ht()
   my_pen.speed(5)
   my_pen.pencolor('''red''')

   SCREAMING_SNAKE_CASE_    					=						[(-175, -125), (0, 175), (175, -125)]  # vertices of triangle
   triangle(vertices[0], vertices[1], vertices[2], int(sys.argv[1]))



