





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 ):


   def 			SCREAMING_SNAKE_CASE_				(       self     :							Union[str, Any]			)->  Tuple:





     """simple docstring"""







     lowercase__           =    tempfile.mkdtemp()

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

     lowercase__           =    BlipaProcessor(_SCREAMING_SNAKE_CASE     ,	_SCREAMING_SNAKE_CASE			)

     processor.save_pretrained(self.tmpdirname			)


   def 			SCREAMING_SNAKE_CASE_				(       self     :							List[Any]     ,	**a     :							Tuple			)->  str:





     """simple docstring"""







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


   def 			SCREAMING_SNAKE_CASE_				(       self     :							List[str]     ,	**a     :							Optional[Any]			)->  Optional[Any]:





     """simple docstring"""







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


   def 			SCREAMING_SNAKE_CASE_				(       self     :							Optional[int]			)->  Optional[int]:





     """simple docstring"""







     shutil.rmtree(self.tmpdirname			)


   def 			SCREAMING_SNAKE_CASE_				(       self     :							str			)->  Optional[int]:





     """simple docstring"""







     lowercase__           =    [np.random.randint(255     ,	size=(3, 30, 400)     ,	dtype=np.uinta			)]

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

     return image_inputs


   def 			SCREAMING_SNAKE_CASE_				(       self     :							Union[str, Any]			)->  Tuple:





     """simple docstring"""







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

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

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

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

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


   def 			SCREAMING_SNAKE_CASE_				(       self     :							Optional[Any]			)->  List[Any]:





     """simple docstring"""







     lowercase__           =    self.get_image_processor()
     lowercase__           =    self.get_tokenizer()

     lowercase__           =    BlipaProcessor(tokenizer=_SCREAMING_SNAKE_CASE     ,	image_processor=_SCREAMING_SNAKE_CASE			)

     lowercase__           =    self.prepare_image_inputs()

     lowercase__           =    image_processor(_SCREAMING_SNAKE_CASE     ,	return_tensors='np'			)
     lowercase__           =    processor(images=_SCREAMING_SNAKE_CASE     ,	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 			SCREAMING_SNAKE_CASE_				(       self     :							Optional[int]			)->  Any:





     """simple docstring"""







     lowercase__           =    self.get_image_processor()
     lowercase__           =    self.get_tokenizer()

     lowercase__           =    BlipaProcessor(tokenizer=_SCREAMING_SNAKE_CASE     ,	image_processor=_SCREAMING_SNAKE_CASE			)

     lowercase__           =    "lower newer"

     lowercase__           =    processor(text=_SCREAMING_SNAKE_CASE			)

     lowercase__           =    tokenizer(_SCREAMING_SNAKE_CASE     ,	return_token_type_ids=_SCREAMING_SNAKE_CASE			)

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


   def 			SCREAMING_SNAKE_CASE_				(       self     :							List[str]			)->  Any:





     """simple docstring"""







     lowercase__           =    self.get_image_processor()
     lowercase__           =    self.get_tokenizer()

     lowercase__           =    BlipaProcessor(tokenizer=_SCREAMING_SNAKE_CASE     ,	image_processor=_SCREAMING_SNAKE_CASE			)

     lowercase__           =    "lower newer"
     lowercase__           =    self.prepare_image_inputs()

     lowercase__           =    processor(text=_SCREAMING_SNAKE_CASE     ,	images=_SCREAMING_SNAKE_CASE			)

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

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


   def 			SCREAMING_SNAKE_CASE_				(       self     :							Optional[Any]			)->  Tuple:





     """simple docstring"""







     lowercase__           =    self.get_image_processor()
     lowercase__           =    self.get_tokenizer()

     lowercase__           =    BlipaProcessor(tokenizer=_SCREAMING_SNAKE_CASE     ,	image_processor=_SCREAMING_SNAKE_CASE			)

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

     lowercase__           =    processor.batch_decode(_SCREAMING_SNAKE_CASE			)
     lowercase__           =    tokenizer.batch_decode(_SCREAMING_SNAKE_CASE			)

     self.assertListEqual(_SCREAMING_SNAKE_CASE     ,	_SCREAMING_SNAKE_CASE			)


   def 			SCREAMING_SNAKE_CASE_				(       self     :							str			)->  Union[str, Any]:





     """simple docstring"""







     lowercase__           =    self.get_image_processor()
     lowercase__           =    self.get_tokenizer()

     lowercase__           =    BlipaProcessor(tokenizer=_SCREAMING_SNAKE_CASE     ,	image_processor=_SCREAMING_SNAKE_CASE			)

     lowercase__           =    "lower newer"
     lowercase__           =    self.prepare_image_inputs()

     lowercase__           =    processor(text=_SCREAMING_SNAKE_CASE     ,	images=_SCREAMING_SNAKE_CASE			)

     # 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"""






from math import factorial



class      A__  :




	'''simple docstring'''





	def __init__(	self:						str			,     _SCREAMING_SNAKE_CASE:						Tuple			,     _SCREAMING_SNAKE_CASE:						Any)							->			str:

				"""simple docstring"""

				__lowerCAmelCase :    str				=			real
				if isinstance(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE):
							__lowerCAmelCase :    Optional[int]				=			[1] * rank
				else:
							__lowerCAmelCase :    Optional[Any]				=			rank

	def __repr__(	self:						Optional[int])							->			List[str]:

				"""simple docstring"""

				return (
				    F"""{self.real}+"""
				    F"""{'+'.join(str(_SCREAMING_SNAKE_CASE)+'E'+str(n+1)for n,dual in enumerate(self.duals))}"""
				)

	def 				_SCREAMING_SNAKE_CASE  (	self:						List[str])							->			Union[str, Any]:

				"""simple docstring"""

				__lowerCAmelCase :    List[str]				=			self.duals.copy()
				while cur[-1] == 0:
							cur.pop(-1)
				return Dual(self.real			,     _SCREAMING_SNAKE_CASE)

	def __add__(	self:						Optional[int]			,     _SCREAMING_SNAKE_CASE:						Tuple)							->			Union[str, Any]:

				"""simple docstring"""

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

	SCREAMING_SNAKE_CASE        =      __add__

	def __sub__(	self:						Dict			,     _SCREAMING_SNAKE_CASE:						str)							->			Optional[Any]:

				"""simple docstring"""

				return self + other * -1

	def __mul__(	self:						str			,     _SCREAMING_SNAKE_CASE:						Tuple)							->			Optional[int]:

				"""simple docstring"""

				if not isinstance(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE):
							__lowerCAmelCase :    List[Any]				=			[]
							for i in self.duals:
										new_duals.append(i * other)
							return Dual(self.real * other			,     _SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    List[str]				=			[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			,     _SCREAMING_SNAKE_CASE)

	SCREAMING_SNAKE_CASE        =      __mul__

	def __truediv__(	self:						Any			,     _SCREAMING_SNAKE_CASE:						str)							->			Dict:

				"""simple docstring"""

				if not isinstance(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE):
							__lowerCAmelCase :    List[str]				=			[]
							for i in self.duals:
										new_duals.append(i / other)
							return Dual(self.real / other			,     _SCREAMING_SNAKE_CASE)
				raise ValueError

	def __floordiv__(	self:						List[Any]			,     _SCREAMING_SNAKE_CASE:						Tuple)							->			str:

				"""simple docstring"""

				if not isinstance(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE):
							__lowerCAmelCase :    str				=			[]
							for i in self.duals:
										new_duals.append(i // other)
							return Dual(self.real // other			,     _SCREAMING_SNAKE_CASE)
				raise ValueError







	def __pow__(	self:						Optional[Any]			,     _SCREAMING_SNAKE_CASE:						Tuple)							->			List[Any]:

				"""simple docstring"""

				if n < 0 or isinstance(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE):
							raise ValueError("power must be a positive integer")
				if n == 0:
							return 1
				if n == 1:
							return self
				__lowerCAmelCase :    List[Any]				=			self
				for _ in range(n - 1):
							x *= self
				return x






def      _lowercase							(    __snake_case   ,__snake_case   ,__snake_case      )		->  Any:
			if not callable(__snake_case      ):
						raise ValueError("differentiate() requires a function as input for func"      )
			if not isinstance(__snake_case   ,(float, int)      ):
						raise ValueError("differentiate() requires a float as input for position"      )
			if not isinstance(__snake_case   ,__snake_case      ):
						raise ValueError("differentiate() requires an int as input for order"      )
			__lowerCAmelCase :    Any				=			Dual(__snake_case   ,1      )
			__lowerCAmelCase :    Tuple				=			func(__snake_case      )
			if order == 0:
						return result.real
			return result.duals[order - 1] * factorial(__snake_case      )


if __name__ == "__main__":
	import doctest

	doctest.testmod()


	def      _lowercase							(    __snake_case      )		->  Tuple:
				return y**2 * y**4





	print(differentiate(f, 9, 2))