





from __future__ import annotations


class   __SCREAMING_SNAKE_CASE     :







       def __init__( self	:      Dict						,   A	:      list[list[int]]     )  ->int:
          lowerCamelCase__  :		List[str]     =				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(__lowerCamelCase     ) != 0:
             lowerCamelCase__  :		Any     =				len(rows[0]     )
             if cols == 0:
                raise error
             for row in rows:
                if len(__lowerCamelCase     ) != cols:
                   raise error
                for value in row:
                   if not isinstance(__lowerCamelCase						,   (int, float)     ):
                      raise error
             lowerCamelCase__  :		Optional[int]     =				rows
          else:
             lowerCamelCase__  :		Any     =				[]







       def 		__lowerCamelCase       ( self	:      Any     )  ->Union[str, Any]:
          return [[row[i] for row in self.rows] for i in range(len(self.rows[0]     )     )]







       @property
       def 		__lowerCamelCase       ( self	:      Tuple     )  ->List[Any]:
          return len(self.rows     )







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







       @property
       def 		__lowerCamelCase       ( self	:      str     )  ->List[Any]:
          return (self.num_rows, self.num_columns)







       @property
       def 		__lowerCamelCase       ( self	:      Optional[int]     )  ->Union[str, Any]:
          return self.order[0] == self.order[1]







       def 		__lowerCamelCase       ( self	:      Union[str, Any]     )  ->Dict:
          lowerCamelCase__  :		List[Any]     =				[
              [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(__lowerCamelCase     )







       def 		__lowerCamelCase       ( self	:      Tuple     )  ->Tuple:
          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 		__lowerCamelCase       ( self	:      Optional[Any]     )  ->Optional[Any]:
          return bool(self.determinant()     )







       def 		__lowerCamelCase       ( self	:      Union[str, Any]						,   A	:      int						,   A	:      int     )  ->Optional[int]:
          lowerCamelCase__  :		List[Any]     =				[
              [
                  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(__lowerCamelCase     ).determinant()







       def 		__lowerCamelCase       ( self	:      Dict						,   A	:      int						,   A	:      int     )  ->List[Any]:
          if (row + column) % 2 == 0:
             return self.get_minor(__lowerCamelCase						,   __lowerCamelCase     )
          return -1 * self.get_minor(__lowerCamelCase						,   __lowerCamelCase     )







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







       def 		__lowerCamelCase       ( self	:      Dict     )  ->int:
          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 		__lowerCamelCase       ( self	:      int     )  ->str:
          lowerCamelCase__  :		Any     =				[
              [self.cofactors().rows[column][row] for column in range(self.num_columns     )]
              for row in range(self.num_rows     )
          ]
          return Matrix(__lowerCamelCase     )







       def 		__lowerCamelCase       ( self	:      List[str]     )  ->Union[str, Any]:
          lowerCamelCase__  :		int     =				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	:      Tuple     )  ->Tuple:
          return str(self.rows     )







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







       def 		__lowerCamelCase       ( self	:      Dict						,   A	:      list[int]						,   A	:      int | None = None     )  ->int:
          lowerCamelCase__  :		Union[str, Any]     =				TypeError('''Row must be a list containing all ints and/or floats'''     )
          if not isinstance(__lowerCamelCase						,   __lowerCamelCase     ):
             raise type_error
          for value in row:
             if not isinstance(__lowerCamelCase						,   (int, float)     ):
                raise type_error
          if len(__lowerCamelCase     ) != 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(__lowerCamelCase     )
          else:
             lowerCamelCase__  :		Dict     =				self.rows[0:position] + [row] + self.rows[position:]







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







       def __eq__( self	:      Dict						,   A	:      object     )  ->Any:
          if not isinstance(__lowerCamelCase						,   __lowerCamelCase     ):
             return NotImplemented
          return self.rows == other.rows







       def __ne__( self	:      List[Any]						,   A	:      object     )  ->Union[str, Any]:
          return not self == other







       def __neg__( self	:      Any     )  ->Optional[Any]:
          return self * -1







       def __add__( self	:      Dict						,   A	:      Matrix     )  ->Union[str, Any]:
          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	:      Union[str, Any]						,   A	:      Matrix     )  ->Optional[Any]:
          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	:      Any						,   A	:      Matrix | int | float     )  ->int:
          if isinstance(__lowerCamelCase						,   (int, float)     ):
             return Matrix(
                 [[int(element * other     ) for element in row] for row in self.rows]     )
          elif isinstance(__lowerCamelCase						,   __lowerCamelCase     ):
             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(__lowerCamelCase						,   __lowerCamelCase     ) 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	:      str						,   A	:      int     )  ->str:
          if not isinstance(__lowerCamelCase						,   __lowerCamelCase     ):
             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'''     )
          lowerCamelCase__  :		str     =				self
          for _ in range(other - 1     ):
             result *= self
          return result







       @classmethod
       def 		__lowerCamelCase       ( cls	:      List[str]						,   A	:      list[int]						,   A	:      list[int]     )  ->Tuple:
          return sum(row[i] * column[i] for i in range(len(__lowerCamelCase     )     )     )


if __name__ == "__main__":
       import doctest

       doctest.testmod()







'''simple docstring'''





import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
    import torch


if is_vision_available():
    from PIL import Image

    from transformers import MgpstrProcessor, ViTImageProcessor




@require_torch
@require_vision
class 				UpperCAmelCase			(			unittest.TestCase						):
 UpperCAmelCase				=					ViTImageProcessor if is_vision_available() else None


 @property
 def        __SCREAMING_SNAKE_CASE					(      self							:	List[Any] ):
    return self.image_processor_tester.prepare_image_processor_dict()


 def        __SCREAMING_SNAKE_CASE					(      self							:	List[str] ):
    UpperCAmelCase__	:Any      =      (3, 3_2, 1_2_8)
    UpperCAmelCase__	:Optional[int]      =      tempfile.mkdtemp()

    # fmt: off
    UpperCAmelCase__	:Union[str, Any]      =      ['''[GO]''', '''[s]''', '''0''', '''1''', '''2''', '''3''', '''4''', '''5''', '''6''', '''7''', '''8''', '''9''', '''a''', '''b''', '''c''', '''d''', '''e''', '''f''', '''g''', '''h''', '''i''', '''j''', '''k''', '''l''', '''m''', '''n''', '''o''', '''p''', '''q''', '''r''', '''s''', '''t''', '''u''', '''v''', '''w''', '''x''', '''y''', '''z''']
    # fmt: on
    UpperCAmelCase__	:str      =      dict(zip(__lowerCamelCase			,       range(len(__lowerCamelCase ) ) ) )

    UpperCAmelCase__	:str      =      os.path.join(self.tmpdirname			,       VOCAB_FILES_NAMES['''vocab_file'''] )
    with open(self.vocab_file			,       '''w'''			,       encoding='''utf-8''' ) as fp:
       fp.write(json.dumps(__lowerCamelCase ) + '''\n''' )

    UpperCAmelCase__	:Dict      =      {
        '''do_normalize''': False,
        '''do_resize''': True,
        '''image_processor_type''': '''ViTImageProcessor''',
        '''resample''': 3,
        '''size''': {'''height''': 3_2, '''width''': 1_2_8},
    }
    UpperCAmelCase__	:Optional[Any]      =      os.path.join(self.tmpdirname			,       __lowerCamelCase )
    with open(self.image_processor_file			,       '''w'''			,       encoding='''utf-8''' ) as fp:
       json.dump(__lowerCamelCase			,       __lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Tuple			,       **__lowerCamelCase							:	Any ):
    return MgpstrTokenizer.from_pretrained(self.tmpdirname			,       **__lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	int			,       **__lowerCamelCase							:	str ):
    return ViTImageProcessor.from_pretrained(self.tmpdirname			,       **__lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	int ):
    shutil.rmtree(self.tmpdirname )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Union[str, Any] ):
    UpperCAmelCase__	:Optional[int]      =      np.random.randint(2_5_5			,       size=(3, 3_0, 4_0_0)			,       dtype=np.uinta )

    UpperCAmelCase__	:Union[str, Any]      =      Image.fromarray(np.moveaxis(__lowerCamelCase			,       0			,       -1 ) )

    return image_input


 def        __SCREAMING_SNAKE_CASE					(      self							:	int ):
    UpperCAmelCase__	:Optional[Any]      =      self.get_tokenizer()
    UpperCAmelCase__	:str      =      self.get_image_processor()

    UpperCAmelCase__	:List[str]      =      MgpstrProcessor(tokenizer=__lowerCamelCase			,       image_processor=__lowerCamelCase )
    processor.save_pretrained(self.tmpdirname )
    UpperCAmelCase__	:Optional[int]      =      MgpstrProcessor.from_pretrained(self.tmpdirname			,       use_fast=__lowerCamelCase )

    self.assertEqual(processor.char_tokenizer.get_vocab()			,       tokenizer.get_vocab() )
    self.assertIsInstance(processor.char_tokenizer			,       __lowerCamelCase )

    self.assertEqual(processor.image_processor.to_json_string()			,       image_processor.to_json_string() )
    self.assertIsInstance(processor.image_processor			,       __lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Union[str, Any] ):
    UpperCAmelCase__	:Any      =      self.get_tokenizer()
    UpperCAmelCase__	:int      =      self.get_image_processor()

    UpperCAmelCase__	:Union[str, Any]      =      MgpstrProcessor(tokenizer=__lowerCamelCase			,       image_processor=__lowerCamelCase )
    processor.save_pretrained(self.tmpdirname )

    UpperCAmelCase__	:List[Any]      =      self.get_tokenizer(bos_token='''(BOS)'''			,       eos_token='''(EOS)''' )
    UpperCAmelCase__	:List[Any]      =      self.get_image_processor(do_normalize=__lowerCamelCase			,       padding_value=1.0 )

    UpperCAmelCase__	:Any      =      MgpstrProcessor.from_pretrained(
        self.tmpdirname			,       bos_token='''(BOS)'''			,       eos_token='''(EOS)'''			,       do_normalize=__lowerCamelCase			,       padding_value=1.0 )

    self.assertEqual(processor.char_tokenizer.get_vocab()			,       tokenizer_add_kwargs.get_vocab() )
    self.assertIsInstance(processor.char_tokenizer			,       __lowerCamelCase )

    self.assertEqual(processor.image_processor.to_json_string()			,       image_processor_add_kwargs.to_json_string() )
    self.assertIsInstance(processor.image_processor			,       __lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	List[Any] ):
    UpperCAmelCase__	:Tuple      =      self.get_image_processor()
    UpperCAmelCase__	:Any      =      self.get_tokenizer()

    UpperCAmelCase__	:Any      =      MgpstrProcessor(tokenizer=__lowerCamelCase			,       image_processor=__lowerCamelCase )

    UpperCAmelCase__	:Optional[Any]      =      self.prepare_image_inputs()

    UpperCAmelCase__	:str      =      image_processor(__lowerCamelCase			,       return_tensors='''np''' )
    UpperCAmelCase__	:Tuple      =      processor(images=__lowerCamelCase			,       return_tensors='''np''' )

    for key in input_image_proc.keys():
       self.assertAlmostEqual(input_image_proc[key].sum()			,       input_processor[key].sum()			,       delta=1e-2 )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[int] ):
    UpperCAmelCase__	:Optional[Any]      =      self.get_image_processor()
    UpperCAmelCase__	:List[Any]      =      self.get_tokenizer()

    UpperCAmelCase__	:List[str]      =      MgpstrProcessor(tokenizer=__lowerCamelCase			,       image_processor=__lowerCamelCase )

    UpperCAmelCase__	:Union[str, Any]      =      '''test'''

    UpperCAmelCase__	:Tuple      =      processor(text=__lowerCamelCase )

    UpperCAmelCase__	:Tuple      =      tokenizer(__lowerCamelCase )
    for key in encoded_tok.keys():
       self.assertListEqual(encoded_tok[key]			,       encoded_processor[key] )


 def        __SCREAMING_SNAKE_CASE					(      self							:	str ):
    UpperCAmelCase__	:int      =      self.get_image_processor()
    UpperCAmelCase__	:Dict      =      self.get_tokenizer()

    UpperCAmelCase__	:int      =      MgpstrProcessor(tokenizer=__lowerCamelCase			,       image_processor=__lowerCamelCase )

    UpperCAmelCase__	:List[Any]      =      '''test'''
    UpperCAmelCase__	:List[Any]      =      self.prepare_image_inputs()

    UpperCAmelCase__	:Any      =      processor(text=__lowerCamelCase			,       images=__lowerCamelCase )

    self.assertListEqual(list(inputs.keys() )			,       ['''pixel_values''', '''labels'''] )

    # test if it raises when no input is passed
    with pytest.raises(__lowerCamelCase ):
       processor()


 def        __SCREAMING_SNAKE_CASE					(      self							:	List[Any] ):
    UpperCAmelCase__	:int      =      self.get_image_processor()
    UpperCAmelCase__	:Optional[int]      =      self.get_tokenizer()

    UpperCAmelCase__	:Union[str, Any]      =      MgpstrProcessor(tokenizer=__lowerCamelCase			,       image_processor=__lowerCamelCase )

    UpperCAmelCase__	:Any      =      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

    UpperCAmelCase__	:Optional[Any]      =      processor.char_decode(__lowerCamelCase )
    UpperCAmelCase__	:int      =      tokenizer.batch_decode(__lowerCamelCase )
    UpperCAmelCase__	:int      =      [seq.replace(''' '''			,       '''''' ) for seq in decoded_tok]

    self.assertListEqual(__lowerCamelCase			,       __lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Union[str, Any] ):
    UpperCAmelCase__	:int      =      self.get_image_processor()
    UpperCAmelCase__	:Optional[int]      =      self.get_tokenizer()

    UpperCAmelCase__	:Dict      =      MgpstrProcessor(tokenizer=__lowerCamelCase			,       image_processor=__lowerCamelCase )

    UpperCAmelCase__	:List[str]      =      None
    UpperCAmelCase__	:Optional[int]      =      self.prepare_image_inputs()

    UpperCAmelCase__	:Tuple      =      processor(text=__lowerCamelCase			,       images=__lowerCamelCase )

    self.assertListEqual(list(inputs.keys() )			,       processor.model_input_names )





 def        __SCREAMING_SNAKE_CASE					(      self							:	Tuple ):
    UpperCAmelCase__	:Dict      =      self.get_image_processor()
    UpperCAmelCase__	:int      =      self.get_tokenizer()

    UpperCAmelCase__	:Any      =      MgpstrProcessor(tokenizer=__lowerCamelCase			,       image_processor=__lowerCamelCase )

    UpperCAmelCase__	:Union[str, Any]      =      torch.randn(1			,       2_7			,       3_8 )
    UpperCAmelCase__	:Any      =      torch.randn(1			,       2_7			,       5_0_2_5_7 )
    UpperCAmelCase__	:Union[str, Any]      =      torch.randn(1			,       2_7			,       3_0_5_2_2 )

    UpperCAmelCase__	:Optional[Any]      =      processor.batch_decode([char_input, bpe_input, wp_input] )

    self.assertListEqual(list(results.keys() )			,       ['''generated_text''', '''scores''', '''char_preds''', '''bpe_preds''', '''wp_preds'''] )



