
"""simple docstring"""

from __future__ import annotations
class      _UpperCAmelCase							:




      def __init__(							self   ,       lowercase_							)						->					List[Any]:
          UpperCAmelCase									=					TypeError(
              'Matrices must be formed from a list of zero or more lists containing at '
              'least one and the same number of values, each of which must be of type '
              'int or float.'							)
          if len(lowercase_							) != 0:
              UpperCAmelCase									=					len(rows[0]							)
              if cols == 0:
                  raise error
              for row in rows:
                  if len(lowercase_							) != cols:
                      raise error
                  for value in row:
                      if not isinstance(lowercase_   ,       (int, float)							):
                          raise error
              UpperCAmelCase									=					rows
          else:
              UpperCAmelCase									=					[]




      def     a_      (							self							)						->					list[list[int]]:
          return [[row[i] for row in self.rows] for i in range(len(self.rows[0]							)							)]




      @property
      def     a_      (							self							)						->					int:
          return len(self.rows							)




      @property
      def     a_      (							self							)						->					int:
          return len(self.rows[0]							)




      @property
      def     a_      (							self							)						->					tuple[int, int]:
          return (self.num_rows, self.num_columns)




      @property
      def     a_      (							self							)						->					bool:
          return self.order[0] == self.order[1]




      def     a_      (							self							)						->					Matrix:
          UpperCAmelCase									=					[
              [0 if column_num != row_num else 1 for column_num in range(self.num_rows							)]
              for row_num in range(self.num_rows							)
          ]
          return Matrix(lowercase_							)




      def     a_      (							self							)						->					int:
          if not self.is_square:
              return 0
          if self.order == (0, 0):
              return 1
          if self.order == (1, 1):
              return int(self.rows[0][0]							)
          if self.order == (2, 2):
              return int(
                  (self.rows[0][0] * self.rows[1][1])
                  - (self.rows[0][1] * self.rows[1][0])							)
          else:
              return sum(
                  self.rows[0][column] * self.cofactors().rows[0][column]
                  for column in range(self.num_columns							)							)




      def     a_      (							self							)						->					bool:
          return bool(self.determinant()							)




      def     a_      (							self   ,       lowercase_   ,       lowercase_							)						->					int:
          UpperCAmelCase									=					[
              [
                  self.rows[other_row][other_column]
                  for other_column in range(self.num_columns							)
                  if other_column != column
              ]
              for other_row in range(self.num_rows							)
              if other_row != row
          ]
          return Matrix(lowercase_							).determinant()




      def     a_      (							self   ,       lowercase_   ,       lowercase_							)						->					int:
          if (row + column) % 2 == 0:
              return self.get_minor(lowercase_   ,       lowercase_							)
          return -1 * self.get_minor(lowercase_   ,       lowercase_							)




      def     a_      (							self							)						->					Matrix:
          return Matrix(
              [
                  [self.get_minor(lowercase_   ,       lowercase_							) for column in range(self.num_columns							)]
                  for row in range(self.num_rows							)
              ]							)




      def     a_      (							self							)						->					Matrix:
          return Matrix(
              [
                  [
                      self.minors().rows[row][column]
                      if (row + column) % 2 == 0
                      else self.minors().rows[row][column] * -1
                      for column in range(self.minors().num_columns							)
                  ]
                  for row in range(self.minors().num_rows							)
              ]							)




      def     a_      (							self							)						->					Matrix:
          UpperCAmelCase									=					[
              [self.cofactors().rows[column][row] for column in range(self.num_columns							)]
              for row in range(self.num_rows							)
          ]
          return Matrix(lowercase_							)




      def     a_      (							self							)						->					Matrix:
          UpperCAmelCase									=					self.determinant()
          if not determinant:
              raise TypeError('Only matrices with a non-zero determinant have an inverse'							)
          return self.adjugate() * (1 / determinant)




      def __repr__(							self							)						->					str:
          return str(self.rows							)




      def __str__(							self							)						->					str:
          if self.num_rows == 0:
              return "[]"
          if self.num_rows == 1:
              return "[[" + ". ".join(str(self.rows[0]							)							) + "]]"
          return (
              "["
              + "\n ".join(
                  [
                      '[' + '. '.join([str(lowercase_							) for value in row]							) + '.]'
                      for row in self.rows
                  ]							)
              + "]"
          )




      def     a_      (							self   ,       lowercase_   ,       lowercase_ = None							)						->					None:
          UpperCAmelCase									=					TypeError('Row must be a list containing all ints and/or floats'							)
          if not isinstance(lowercase_   ,       lowercase_							):
              raise type_error
          for value in row:
              if not isinstance(lowercase_   ,       (int, float)							):
                  raise type_error
          if len(lowercase_							) != self.num_columns:
              raise ValueError(
                  'Row must be equal in length to the other rows in the matrix'							)
          if position is None:
              self.rows.append(lowercase_							)
          else:
              UpperCAmelCase									=					self.rows[0:position] + [row] + self.rows[position:]




      def     a_      (							self   ,       lowercase_   ,       lowercase_ = None							)						->					None:
          UpperCAmelCase									=					TypeError(
              'Column must be a list containing all ints and/or floats'							)
          if not isinstance(lowercase_   ,       lowercase_							):
              raise type_error
          for value in column:
              if not isinstance(lowercase_   ,       (int, float)							):
                  raise type_error
          if len(lowercase_							) != self.num_rows:
              raise ValueError(
                  'Column must be equal in length to the other columns in the matrix'							)
          if position is None:
              UpperCAmelCase									=					[self.rows[i] + [column[i]] for i in range(self.num_rows							)]
          else:
              UpperCAmelCase									=					[
                  self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
                  for i in range(self.num_rows							)
              ]




      def __eq__(							self   ,       lowercase_							)						->					bool:
          if not isinstance(lowercase_   ,       lowercase_							):
              return NotImplemented
          return self.rows == other.rows




      def __ne__(							self   ,       lowercase_							)						->					bool:
          return not self == other




      def __neg__(							self							)						->					Matrix:
          return self * -1




      def __add__(							self   ,       lowercase_							)						->					Matrix:
          if self.order != other.order:
              raise ValueError('Addition requires matrices of the same order'							)
          return Matrix(
              [
                  [self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns							)]
                  for i in range(self.num_rows							)
              ]							)




      def __sub__(							self   ,       lowercase_							)						->					Matrix:
          if self.order != other.order:
              raise ValueError('Subtraction requires matrices of the same order'							)
          return Matrix(
              [
                  [self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns							)]
                  for i in range(self.num_rows							)
              ]							)




      def __mul__(							self   ,       lowercase_							)						->					Matrix:
          if isinstance(lowercase_   ,       (int, float)							):
              return Matrix(
                  [[int(element * other							) for element in row] for row in self.rows]							)
          elif isinstance(lowercase_   ,       lowercase_							):
              if self.num_columns != other.num_rows:
                  raise ValueError(
                      'The number of columns in the first matrix must '
                      'be equal to the number of rows in the second'							)
              return Matrix(
                  [
                      [Matrix.dot_product(lowercase_   ,       lowercase_							) for column in other.columns()]
                      for row in self.rows
                  ]							)
          else:
              raise TypeError(
                  'A Matrix can only be multiplied by an int, float, or another matrix'							)




      def __pow__(							self   ,       lowercase_							)						->					Matrix:
          if not isinstance(lowercase_   ,       lowercase_							):
              raise TypeError('A Matrix can only be raised to the power of an int'							)
          if not self.is_square:
              raise ValueError('Only square matrices can be raised to a power'							)
          if other == 0:
              return self.identity()
          if other < 0:
              if self.is_invertable():
                  return self.inverse() ** (-other)
              raise ValueError(
                  'Only invertable matrices can be raised to a negative power'							)
          UpperCAmelCase									=					self
          for _ in range(other - 1							):
              result *= self
          return result




      @classmethod
      def     a_      (							cls   ,       lowercase_   ,       lowercase_							)						->					int:
          return sum(row[i] * column[i] for i in range(len(lowercase_							)							)							)


if __name__ == "__main__":
   import doctest

   doctest.testmod()





"""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							(		SCREAMING_SNAKE_CASE_							):




      @slow
      @require_torch
      def     a_      (							self							)						->					List[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=lowercase_   ,       max_length=5_1_2							)
              UpperCAmelCase									=					tokenizer(batch['highlights']   ,       padding='max_length'   ,       truncation=lowercase_   ,       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(lowercase_							) == 5_1_2 for x in inputs.input_ids							)
              assert all(len(lowercase_							) == 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(lowercase_   ,       skip_special_tokens=lowercase_							)
              UpperCAmelCase									=					tokenizer.batch_decode(lowercase_   ,       skip_special_tokens=lowercase_							)

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

              return {"accuracy": accuracy}

          # map train dataset
          UpperCAmelCase									=					train_dataset.map(
              _map_to_encoder_decoder_inputs   ,       batched=lowercase_   ,       batch_size=lowercase_   ,       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=lowercase_   ,       batch_size=lowercase_   ,       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=lowercase_   ,       per_device_train_batch_size=lowercase_   ,       per_device_eval_batch_size=lowercase_   ,       predict_with_generate=lowercase_   ,       evaluation_strategy='steps'   ,       do_train=lowercase_   ,       do_eval=lowercase_   ,       warmup_steps=0   ,       eval_steps=2   ,       logging_steps=2   ,       )

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

          # start training
          trainer.train()



