





"""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     _UpperCAmelCase   (   unittest.TestCase   ):

  """simple docstring"""


  def     lowercase		(    self			:		List[Any]					)							->						str:
     __lowerCAmelCase							     =     tempfile.mkdtemp()

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

     __lowerCAmelCase							     =     BlipaProcessor(lowerCAmelCase_ ,  lowerCAmelCase_					)

     processor.save_pretrained(self.tmpdirname					)






  def     lowercase		(    self			:		Union[str, Any] ,  **lowerCAmelCase_			:		Optional[Any]					)							->						List[Any]:
     return AutoProcessor.from_pretrained(self.tmpdirname ,  **lowerCAmelCase_					).tokenizer






  def     lowercase		(    self			:		Optional[int] ,  **lowerCAmelCase_			:		List[str]					)							->						str:
     return AutoProcessor.from_pretrained(self.tmpdirname ,  **lowerCAmelCase_					).image_processor






  def     lowercase		(    self			:		Union[str, Any]					)							->						str:
     shutil.rmtree(self.tmpdirname					)






  def     lowercase		(    self			:		Any					)							->						Optional[int]:
     __lowerCAmelCase							     =     [np.random.randint(2_5_5 ,  size=(3, 3_0, 4_0_0) ,  dtype=np.uinta					)]

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

     return image_inputs






  def     lowercase		(    self			:		Any					)							->						int:
     __lowerCAmelCase							     =     BlipaProcessor(tokenizer=self.get_tokenizer() ,  image_processor=self.get_image_processor()					)
     processor.save_pretrained(self.tmpdirname					)

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

     __lowerCAmelCase							     =     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     lowercase		(    self			:		Optional[int]					)							->						Optional[int]:
     __lowerCAmelCase							     =     self.get_image_processor()
     __lowerCAmelCase							     =     self.get_tokenizer()

     __lowerCAmelCase							     =     BlipaProcessor(tokenizer=lowerCAmelCase_ ,  image_processor=lowerCAmelCase_					)

     __lowerCAmelCase							     =     self.prepare_image_inputs()

     __lowerCAmelCase							     =     image_processor(lowerCAmelCase_ ,  return_tensors='np'					)
     __lowerCAmelCase							     =     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     lowercase		(    self			:		Any					)							->						Union[str, Any]:
     __lowerCAmelCase							     =     self.get_image_processor()
     __lowerCAmelCase							     =     self.get_tokenizer()

     __lowerCAmelCase							     =     BlipaProcessor(tokenizer=lowerCAmelCase_ ,  image_processor=lowerCAmelCase_					)

     __lowerCAmelCase							     =     'lower newer'

     __lowerCAmelCase							     =     processor(text=lowerCAmelCase_					)

     __lowerCAmelCase							     =     tokenizer(lowerCAmelCase_ ,  return_token_type_ids=lowerCAmelCase_					)

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






  def     lowercase		(    self			:		int					)							->						List[Any]:
     __lowerCAmelCase							     =     self.get_image_processor()
     __lowerCAmelCase							     =     self.get_tokenizer()

     __lowerCAmelCase							     =     BlipaProcessor(tokenizer=lowerCAmelCase_ ,  image_processor=lowerCAmelCase_					)

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

     __lowerCAmelCase							     =     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     lowercase		(    self			:		int					)							->						Optional[Any]:
     __lowerCAmelCase							     =     self.get_image_processor()
     __lowerCAmelCase							     =     self.get_tokenizer()

     __lowerCAmelCase							     =     BlipaProcessor(tokenizer=lowerCAmelCase_ ,  image_processor=lowerCAmelCase_					)

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

     __lowerCAmelCase							     =     processor.batch_decode(lowerCAmelCase_					)
     __lowerCAmelCase							     =     tokenizer.batch_decode(lowerCAmelCase_					)

     self.assertListEqual(lowerCAmelCase_ ,  lowerCAmelCase_					)






  def     lowercase		(    self			:		List[Any]					)							->						List[str]:
     __lowerCAmelCase							     =     self.get_image_processor()
     __lowerCAmelCase							     =     self.get_tokenizer()

     __lowerCAmelCase							     =     BlipaProcessor(tokenizer=lowerCAmelCase_ ,  image_processor=lowerCAmelCase_					)

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

     __lowerCAmelCase							     =     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']					)








from math import factorial



class     _UpperCAmelCase   :

  """simple docstring"""


  def __init__(    self			:		Dict ,  lowerCAmelCase_			:		Optional[int] ,  lowerCAmelCase_			:		str					)							->						Union[str, Any]:
     __lowerCAmelCase							     =     real
     if isinstance(lowerCAmelCase_ ,  lowerCAmelCase_					):
        __lowerCAmelCase							     =     [1] * rank
     else:
        __lowerCAmelCase							     =     rank






  def __repr__(    self			:		Optional[Any]					)							->						Tuple:
     return (
         f"""{self.real}+"""
         f"""{"+".join(str(lowerCAmelCase_					)+"E"+str(n+1					)for n,dual in enumerate(self.duals					)					)}"""
     )






  def     lowercase		(    self			:		str					)							->						Dict:
     __lowerCAmelCase							     =     self.duals.copy()
     while cur[-1] == 0:
        cur.pop(-1					)
     return Dual(self.real ,  lowerCAmelCase_					)






  def __add__(    self			:		Union[str, Any] ,  lowerCAmelCase_			:		Tuple					)							->						Optional[Any]:
     if not isinstance(lowerCAmelCase_ ,  lowerCAmelCase_					):
        return Dual(self.real + other ,  self.duals					)
     __lowerCAmelCase							     =     self.duals.copy()
     __lowerCAmelCase							     =     other.duals.copy()
     if len(lowerCAmelCase_					) > len(lowerCAmelCase_					):
        o_dual.extend([1] * (len(lowerCAmelCase_					) - len(lowerCAmelCase_					))					)
     elif len(lowerCAmelCase_					) < len(lowerCAmelCase_					):
        s_dual.extend([1] * (len(lowerCAmelCase_					) - len(lowerCAmelCase_					))					)
     __lowerCAmelCase							     =     []
     for i in range(len(lowerCAmelCase_					)					):
        new_duals.append(s_dual[i] + o_dual[i]					)
     return Dual(self.real + other.real ,  lowerCAmelCase_					)

  a_      =			__add__






  def __sub__(    self			:		int ,  lowerCAmelCase_			:		Dict					)							->						Optional[Any]:
     return self + other * -1






  def __mul__(    self			:		int ,  lowerCAmelCase_			:		Optional[int]					)							->						Dict:
     if not isinstance(lowerCAmelCase_ ,  lowerCAmelCase_					):
        __lowerCAmelCase							     =     []
        for i in self.duals:
           new_duals.append(i * other					)
        return Dual(self.real * other ,  lowerCAmelCase_					)
     __lowerCAmelCase							     =     [0] * (len(self.duals					) + len(other.duals					) + 1)
     for i, item in enumerate(self.duals					):
        for j, jtem in enumerate(other.duals					):
           new_duals[i + j + 1] += item * jtem
     for k in range(len(self.duals					)					):
        new_duals[k] += self.duals[k] * other.real
     for index in range(len(other.duals					)					):
        new_duals[index] += other.duals[index] * self.real
     return Dual(self.real * other.real ,  lowerCAmelCase_					)

  a_      =			__mul__






  def __truediv__(    self			:		Union[str, Any] ,  lowerCAmelCase_			:		str					)							->						Dict:
     if not isinstance(lowerCAmelCase_ ,  lowerCAmelCase_					):
        __lowerCAmelCase							     =     []
        for i in self.duals:
           new_duals.append(i / other					)
        return Dual(self.real / other ,  lowerCAmelCase_					)
     raise ValueError






  def __floordiv__(    self			:		str ,  lowerCAmelCase_			:		List[Any]					)							->						Union[str, Any]:
     if not isinstance(lowerCAmelCase_ ,  lowerCAmelCase_					):
        __lowerCAmelCase							     =     []
        for i in self.duals:
           new_duals.append(i // other					)
        return Dual(self.real // other ,  lowerCAmelCase_					)
     raise ValueError






  def __pow__(    self			:		Tuple ,  lowerCAmelCase_			:		Dict					)							->						List[str]:
     if n < 0 or isinstance(lowerCAmelCase_ ,  lowerCAmelCase_					):
        raise ValueError('power must be a positive integer'					)
     if n == 0:
        return 1
     if n == 1:
        return self
     __lowerCAmelCase							     =     self
     for _ in range(n - 1					):
        x *= self
     return x





def   a_   (			lowerCAmelCase_				:      List[str],     lowerCAmelCase_				:      Optional[Any],     lowerCAmelCase_				:      Optional[Any]     ):
   if not callable(lowerCAmelCase_     ):
      raise ValueError('differentiate() requires a function as input for func'     )
   if not isinstance(lowerCAmelCase_,     (float, int)     ):
      raise ValueError('differentiate() requires a float as input for position'     )
   if not isinstance(lowerCAmelCase_,     lowerCAmelCase_     ):
      raise ValueError('differentiate() requires an int as input for order'     )
   __lowerCAmelCase							     =     Dual(lowerCAmelCase_,     1     )
   __lowerCAmelCase							     =     func(lowerCAmelCase_     )
   if order == 0:
      return result.real
   return result.duals[order - 1] * factorial(lowerCAmelCase_     )


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     def   a_   (			lowerCAmelCase_				:      int     ):
        return y**2 * y**4




     print(differentiate(f, 9, 2))

