
"""simple docstring"""

from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
   import datasets
class      _UpperCAmelCase							(		UpperCAmelCase_							):




      @slow
      @require_torch
      def     a_      (							self							)						->					Any:
          UpperCAmelCase									=					EncoderDecoderModel.from_encoder_decoder_pretrained('prajjwal1/bert-tiny'   ,       'prajjwal1/bert-tiny'							)
          UpperCAmelCase									=					BertTokenizer.from_pretrained('bert-base-uncased'							)

          UpperCAmelCase									=					bertabert.config.encoder.vocab_size
          UpperCAmelCase									=					tokenizer.sep_token_id
          UpperCAmelCase									=					tokenizer.cls_token_id
          UpperCAmelCase									=					1_2_8

          UpperCAmelCase									=					datasets.load_dataset('cnn_dailymail'   ,       '3.0.0'   ,       split='train[:1%]'							)
          UpperCAmelCase									=					datasets.load_dataset('cnn_dailymail'   ,       '3.0.0'   ,       split='validation[:1%]'							)

          UpperCAmelCase									=					train_dataset.select(range(3_2							)							)
          UpperCAmelCase									=					val_dataset.select(range(1_6							)							)

          UpperCAmelCase									=					4

          def _map_to_encoder_decoder_inputs(lowercase_							):
              # Tokenizer will automatically set [BOS] <text> [EOS]
              UpperCAmelCase									=					tokenizer(batch['article']   ,       padding='max_length'   ,       truncation=_snake_case   ,       max_length=5_1_2							)
              UpperCAmelCase									=					tokenizer(batch['highlights']   ,       padding='max_length'   ,       truncation=_snake_case   ,       max_length=1_2_8							)
              UpperCAmelCase									=					inputs.input_ids
              UpperCAmelCase									=					inputs.attention_mask

              UpperCAmelCase									=					outputs.input_ids
              UpperCAmelCase									=					outputs.input_ids.copy()
              UpperCAmelCase									=					[
                  [-1_0_0 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['labels']
              ]
              UpperCAmelCase									=					outputs.attention_mask

              assert all(len(_snake_case							) == 5_1_2 for x in inputs.input_ids							)
              assert all(len(_snake_case							) == 1_2_8 for x in outputs.input_ids							)

              return batch

          def _compute_metrics(lowercase_							):
              UpperCAmelCase									=					pred.label_ids
              UpperCAmelCase									=					pred.predictions

              # all unnecessary tokens are removed
              UpperCAmelCase									=					tokenizer.batch_decode(_snake_case   ,       skip_special_tokens=_snake_case							)
              UpperCAmelCase									=					tokenizer.batch_decode(_snake_case   ,       skip_special_tokens=_snake_case							)

              UpperCAmelCase									=					sum([int(pred_str[i] == label_str[i]							) for i in range(len(_snake_case							)							)]							) / len(_snake_case							)

              return {"accuracy": accuracy}

          # map train dataset
          UpperCAmelCase									=					train_dataset.map(
              _map_to_encoder_decoder_inputs   ,       batched=_snake_case   ,       batch_size=_snake_case   ,       remove_columns=['article', 'highlights']   ,       )
          train_dataset.set_format(
              type='torch'   ,       columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels']   ,       )

          # same for validation dataset
          UpperCAmelCase									=					val_dataset.map(
              _map_to_encoder_decoder_inputs   ,       batched=_snake_case   ,       batch_size=_snake_case   ,       remove_columns=['article', 'highlights']   ,       )
          val_dataset.set_format(
              type='torch'   ,       columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels']   ,       )

          UpperCAmelCase									=					self.get_auto_remove_tmp_dir()

          UpperCAmelCase									=					SeqaSeqTrainingArguments(
              output_dir=_snake_case   ,       per_device_train_batch_size=_snake_case   ,       per_device_eval_batch_size=_snake_case   ,       predict_with_generate=_snake_case   ,       evaluation_strategy='steps'   ,       do_train=_snake_case   ,       do_eval=_snake_case   ,       warmup_steps=0   ,       eval_steps=2   ,       logging_steps=2   ,       )

          # instantiate trainer
          UpperCAmelCase									=					SeqaSeqTrainer(
              model=_snake_case   ,       args=_snake_case   ,       compute_metrics=_compute_metrics   ,       train_dataset=_snake_case   ,       eval_dataset=_snake_case   ,       tokenizer=_snake_case   ,       )

          # start training
          trainer.train()





"""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]))



