



'''simple docstring'''


import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
   from PIL import Image

   from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast

@require_vision
class  SCREAMING_SNAKE_CASE (  unittest.TestCase		):






    """simple docstring"""





    def  A       (  self      :					List[str] ):


       """simple docstring"""







       UpperCamelCase					=  tempfile.mkdtemp()

       UpperCamelCase					=  BlipImageProcessor()
       UpperCamelCase					=  GPTaTokenizer.from_pretrained('hf-internal-testing/tiny-random-GPT2Model' )

       UpperCamelCase					=  BlipaProcessor(_lowerCamelCase  ,						_lowerCamelCase )

       processor.save_pretrained(self.tmpdirname )






    def  A       (  self      :					Tuple  ,						**UpperCamelCase__      :					List[Any] ):


       """simple docstring"""







       return AutoProcessor.from_pretrained(self.tmpdirname  ,						**_lowerCamelCase ).tokenizer






    def  A       (  self      :					Union[str, Any]  ,						**UpperCamelCase__      :					str ):


       """simple docstring"""







       return AutoProcessor.from_pretrained(self.tmpdirname  ,						**_lowerCamelCase ).image_processor






    def  A       (  self      :					Optional[Any] ):


       """simple docstring"""







       shutil.rmtree(self.tmpdirname )






    def  A       (  self      :					int ):


       """simple docstring"""







       UpperCamelCase					=  [np.random.randint(2_5_5  ,						size=(3, 3_0, 4_0_0)  ,						dtype=np.uinta )]

       UpperCamelCase					=  [Image.fromarray(np.moveaxis(_lowerCamelCase  ,						0  ,						-1 ) ) for x in image_inputs]

       return image_inputs






    def  A       (  self      :					List[str] ):


       """simple docstring"""







       UpperCamelCase					=  BlipaProcessor(tokenizer=self.get_tokenizer()  ,						image_processor=self.get_image_processor() )
       processor.save_pretrained(self.tmpdirname )

       UpperCamelCase					=  self.get_tokenizer(bos_token='(BOS)'  ,						eos_token='(EOS)' )
       UpperCamelCase					=  self.get_image_processor(do_normalize=_lowerCamelCase  ,						padding_value=1.0 )

       UpperCamelCase					=  BlipaProcessor.from_pretrained(
           self.tmpdirname  ,						bos_token='(BOS)'  ,						eos_token='(EOS)'  ,						do_normalize=_lowerCamelCase  ,						padding_value=1.0 )

       self.assertEqual(processor.tokenizer.get_vocab()  ,						tokenizer_add_kwargs.get_vocab() )
       self.assertIsInstance(processor.tokenizer  ,						_lowerCamelCase )

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






    def  A       (  self      :					Union[str, Any] ):


       """simple docstring"""







       UpperCamelCase					=  self.get_image_processor()
       UpperCamelCase					=  self.get_tokenizer()

       UpperCamelCase					=  BlipaProcessor(tokenizer=_lowerCamelCase  ,						image_processor=_lowerCamelCase )

       UpperCamelCase					=  self.prepare_image_inputs()

       UpperCamelCase					=  image_processor(_lowerCamelCase  ,						return_tensors='np' )
       UpperCamelCase					=  processor(images=_lowerCamelCase  ,						return_tensors='np' )

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






    def  A       (  self      :					Tuple ):


       """simple docstring"""







       UpperCamelCase					=  self.get_image_processor()
       UpperCamelCase					=  self.get_tokenizer()

       UpperCamelCase					=  BlipaProcessor(tokenizer=_lowerCamelCase  ,						image_processor=_lowerCamelCase )

       UpperCamelCase					=  'lower newer'

       UpperCamelCase					=  processor(text=_lowerCamelCase )

       UpperCamelCase					=  tokenizer(_lowerCamelCase  ,						return_token_type_ids=_lowerCamelCase )

       for key in encoded_tok.keys():
          self.assertListEqual(encoded_tok[key]  ,						encoded_processor[key] )






    def  A       (  self      :					str ):


       """simple docstring"""







       UpperCamelCase					=  self.get_image_processor()
       UpperCamelCase					=  self.get_tokenizer()

       UpperCamelCase					=  BlipaProcessor(tokenizer=_lowerCamelCase  ,						image_processor=_lowerCamelCase )

       UpperCamelCase					=  'lower newer'
       UpperCamelCase					=  self.prepare_image_inputs()

       UpperCamelCase					=  processor(text=_lowerCamelCase  ,						images=_lowerCamelCase )

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

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






    def  A       (  self      :					Union[str, Any] ):


       """simple docstring"""







       UpperCamelCase					=  self.get_image_processor()
       UpperCamelCase					=  self.get_tokenizer()

       UpperCamelCase					=  BlipaProcessor(tokenizer=_lowerCamelCase  ,						image_processor=_lowerCamelCase )

       UpperCamelCase					=  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

       UpperCamelCase					=  processor.batch_decode(_lowerCamelCase )
       UpperCamelCase					=  tokenizer.batch_decode(_lowerCamelCase )

       self.assertListEqual(_lowerCamelCase  ,						_lowerCamelCase )






    def  A       (  self      :					Dict ):


       """simple docstring"""







       UpperCamelCase					=  self.get_image_processor()
       UpperCamelCase					=  self.get_tokenizer()

       UpperCamelCase					=  BlipaProcessor(tokenizer=_lowerCamelCase  ,						image_processor=_lowerCamelCase )

       UpperCamelCase					=  'lower newer'
       UpperCamelCase					=  self.prepare_image_inputs()

       UpperCamelCase					=  processor(text=_lowerCamelCase  ,						images=_lowerCamelCase )

       # For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
       self.assertListEqual(list(inputs.keys() )  ,						['pixel_values', 'input_ids', 'attention_mask'] )









"""simple docstring"""


def 					lowerCAmelCase				(    __UpperCamelCase ):




  """simple docstring"""







  __A									= current_set.copy()
  for row_index, row in enumerate(__UpperCamelCase ):
    __A									= row[0]
    for column_index, column in enumerate(__UpperCamelCase ):
      if magnitude == 0:
        __A									= column
        continue
      __A									= column / magnitude
    # Subtract to cancel term
  __A									= current_set[0]
  __A									= [first_row]
  __A									= current_set[1::]
  for row in current_set:
    __A									= []
    # If first term is 0, it is already in form we want, so we preserve it
    if row[0] == 0:
      final_set.append(__UpperCamelCase )
      continue
    for column_index in range(len(__UpperCamelCase ) ):
      temp_row.append(first_row[column_index] - row[column_index] )
    final_set.append(__UpperCamelCase )
  # Create next recursion iteration set
  if len(final_set[0] ) != 3:
    __A									= final_set[0]
    __A									= []
    __A									= []
    for row in final_set[1::]:
      current_first_column.append(row[0] )
      next_iteration.append(row[1::] )
    __A									= simplify(__UpperCamelCase )
    for i in range(len(__UpperCamelCase ) ):
      resultant[i].insert(0		,       current_first_column[i] )
    resultant.insert(0		,       __UpperCamelCase )
    __A									= resultant
  return final_set



def 					lowerCAmelCase				(    __UpperCamelCase ):




  """simple docstring"""







  if len(__UpperCamelCase ) == 0:
    raise IndexError('''solve_simultaneous() requires n lists of length n+1''' )
  __A									= len(__UpperCamelCase ) + 1
  if any(len(__UpperCamelCase ) != _length for item in equations ):
    raise IndexError('''solve_simultaneous() requires n lists of length n+1''' )
  for row in equations:
    if any(not isinstance(__UpperCamelCase		,       (int, float) ) for column in row ):
      raise ValueError('''solve_simultaneous() requires lists of integers''' )
  if len(__UpperCamelCase ) == 1:
    return [equations[0][-1] / equations[0][0]]
  __A									= equations.copy()
  if any(0 in row for row in data_set ):
    __A									= data_set.copy()
    __A									= []
    for row_index, row in enumerate(__UpperCamelCase ):
      if 0 not in row:
        __A									= data_set.pop(__UpperCamelCase )
        break
    if not full_row:
      raise ValueError('''solve_simultaneous() requires at least 1 full equation''' )
    data_set.insert(0		,       __UpperCamelCase )
  __A									= data_set.copy()
  __A									= simplify(__UpperCamelCase )
  __A									= simplified[::-1]
  __A									= []
  for row in simplified:
    __A									= row[-1]
    if not solutions:
      if row[-2] == 0:
        solutions.append(0 )
        continue
      solutions.append(current_solution / row[-2] )
      continue
    __A									= row.copy()[: len(__UpperCamelCase ) - 1 :]
    while temp_row[0] == 0:
      temp_row.pop(0 )
    if len(__UpperCamelCase ) == 0:
      solutions.append(0 )
      continue
    __A									= temp_row[1::]
    __A									= temp_row[::-1]
    for column_index, column in enumerate(__UpperCamelCase ):
      current_solution -= column * solutions[column_index]
    solutions.append(__UpperCamelCase )
  __A									= []
  for item in solutions:
    final.append(float(round(__UpperCamelCase		,       5 ) ) )
  return final[::-1]


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       lowercase_				     =     [
           [2, 1, 1, 1, 1, 4],
           [1, 2, 1, 1, 1, 5],
           [1, 1, 2, 1, 1, 6],
           [1, 1, 1, 2, 1, 7],
           [1, 1, 1, 1, 2, 8],
       ]
       print(solve_simultaneous(eq))
       print(solve_simultaneous([[4, 2]]))




