




import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    CLIPConfig,
    CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER


sys.path.append(str(Path(__file__).parent.parent.parent.parent / """utils"""))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_image_processing import CustomImageProcessor  # noqa E402


class 							UpperCamelCase_       (					unittest.TestCase	):

    '''simple docstring'''






    def 			SCREAMING_SNAKE_CASE				(     self :      Dict)		->int:





           '''simple docstring'''

           A__ 					=       0


    def 			SCREAMING_SNAKE_CASE				(     self :      Any)		->Tuple:





           '''simple docstring'''

           A__ 					=       AutoImageProcessor.from_pretrained('''openai/clip-vit-base-patch32''')
           self.assertIsInstance(UpperCAmelCase__   ,					UpperCAmelCase__)


    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[Any])		->Optional[int]:





           '''simple docstring'''

           with tempfile.TemporaryDirectory() as tmpdirname:
                  A__ 					=       Path(UpperCAmelCase__) / '''preprocessor_config.json'''
                  A__ 					=       Path(UpperCAmelCase__) / '''config.json'''
                  json.dump(
                      {'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''}   ,					open(UpperCAmelCase__   ,					'''w''')   ,					)
                  json.dump({'''model_type''': '''clip'''}   ,					open(UpperCAmelCase__   ,					'''w'''))

                  A__ 					=       AutoImageProcessor.from_pretrained(UpperCAmelCase__)
                  self.assertIsInstance(UpperCAmelCase__   ,					UpperCAmelCase__)


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





           '''simple docstring'''

           with tempfile.TemporaryDirectory() as tmpdirname:
                  A__ 					=       Path(UpperCAmelCase__) / '''preprocessor_config.json'''
                  A__ 					=       Path(UpperCAmelCase__) / '''config.json'''
                  json.dump(
                      {'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''}   ,					open(UpperCAmelCase__   ,					'''w''')   ,					)
                  json.dump({'''model_type''': '''clip'''}   ,					open(UpperCAmelCase__   ,					'''w'''))

                  A__ 					=       AutoImageProcessor.from_pretrained(UpperCAmelCase__)
                  self.assertIsInstance(UpperCAmelCase__   ,					UpperCAmelCase__)


    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[Any])		->Optional[Any]:





           '''simple docstring'''

           with tempfile.TemporaryDirectory() as tmpdirname:
                  A__ 					=       CLIPConfig()

                  # Create a dummy config file with image_proceesor_type
                  A__ 					=       Path(UpperCAmelCase__) / '''preprocessor_config.json'''
                  A__ 					=       Path(UpperCAmelCase__) / '''config.json'''
                  json.dump(
                      {'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''}   ,					open(UpperCAmelCase__   ,					'''w''')   ,					)
                  json.dump({'''model_type''': '''clip'''}   ,					open(UpperCAmelCase__   ,					'''w'''))

                  # remove image_processor_type to make sure config.json alone is enough to load image processor locally
                  A__ 					=       AutoImageProcessor.from_pretrained(UpperCAmelCase__).to_dict()

                  config_dict.pop('''image_processor_type''')
                  A__ 					=       CLIPImageProcessor(**UpperCAmelCase__)

                  # save in new folder
                  model_config.save_pretrained(UpperCAmelCase__)
                  config.save_pretrained(UpperCAmelCase__)

                  A__ 					=       AutoImageProcessor.from_pretrained(UpperCAmelCase__)

                  # make sure private variable is not incorrectly saved
                  A__ 					=       json.loads(config.to_json_string())
                  self.assertTrue('''_processor_class''' not in dict_as_saved)

           self.assertIsInstance(UpperCAmelCase__   ,					UpperCAmelCase__)


    def 			SCREAMING_SNAKE_CASE				(     self :      List[Any])		->Tuple:





           '''simple docstring'''

           with tempfile.TemporaryDirectory() as tmpdirname:
                  A__ 					=       Path(UpperCAmelCase__) / '''preprocessor_config.json'''
                  json.dump(
                      {'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''}   ,					open(UpperCAmelCase__   ,					'''w''')   ,					)

                  A__ 					=       AutoImageProcessor.from_pretrained(UpperCAmelCase__)
                  self.assertIsInstance(UpperCAmelCase__   ,					UpperCAmelCase__)


    def 			SCREAMING_SNAKE_CASE				(     self :      int)		->str:





           '''simple docstring'''

           with self.assertRaisesRegex(
               UpperCAmelCase__   ,					'''clip-base is not a local folder and is not a valid model identifier'''):
                  A__ 					=       AutoImageProcessor.from_pretrained('''clip-base''')


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





           '''simple docstring'''

           with self.assertRaisesRegex(
               UpperCAmelCase__   ,					R'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''):
                  A__ 					=       AutoImageProcessor.from_pretrained(UpperCAmelCase__   ,					revision='''aaaaaa''')


    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[int])		->Union[str, Any]:





           '''simple docstring'''

           with self.assertRaisesRegex(
               UpperCAmelCase__   ,					'''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.'''   ,					):
                  A__ 					=       AutoImageProcessor.from_pretrained('''hf-internal-testing/config-no-model''')


    def 			SCREAMING_SNAKE_CASE				(     self :      List[str])		->Tuple:





           '''simple docstring'''

           with self.assertRaises(UpperCAmelCase__):
                  A__ 					=       AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor''')
           # If remote code is disabled, we can't load this config.
           with self.assertRaises(UpperCAmelCase__):
                  A__ 					=       AutoImageProcessor.from_pretrained(
                      '''hf-internal-testing/test_dynamic_image_processor'''   ,					trust_remote_code=UpperCAmelCase__)

           A__ 					=       AutoImageProcessor.from_pretrained(
               '''hf-internal-testing/test_dynamic_image_processor'''   ,					trust_remote_code=UpperCAmelCase__)
           self.assertEqual(image_processor.__class__.__name__   ,					'''NewImageProcessor''')

           # Test image processor can be reloaded.
           with tempfile.TemporaryDirectory() as tmp_dir:
                  image_processor.save_pretrained(UpperCAmelCase__)
                  A__ 					=       AutoImageProcessor.from_pretrained(UpperCAmelCase__   ,					trust_remote_code=UpperCAmelCase__)
           self.assertEqual(reloaded_image_processor.__class__.__name__   ,					'''NewImageProcessor''')


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





           '''simple docstring'''

           try:
                  AutoConfig.register('''custom'''   ,					UpperCAmelCase__)
                  AutoImageProcessor.register(UpperCAmelCase__   ,					UpperCAmelCase__)
                  # Trying to register something existing in the Transformers library will raise an error
                  with self.assertRaises(UpperCAmelCase__):
                         AutoImageProcessor.register(UpperCAmelCase__   ,					UpperCAmelCase__)

                  with tempfile.TemporaryDirectory() as tmpdirname:
                         A__ 					=       Path(UpperCAmelCase__) / '''preprocessor_config.json'''
                         A__ 					=       Path(UpperCAmelCase__) / '''config.json'''
                         json.dump(
                             {'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''}   ,					open(UpperCAmelCase__   ,					'''w''')   ,					)
                         json.dump({'''model_type''': '''clip'''}   ,					open(UpperCAmelCase__   ,					'''w'''))

                         A__ 					=       CustomImageProcessor.from_pretrained(UpperCAmelCase__)

                  # Now that the config is registered, it can be used as any other config with the auto-API
                  with tempfile.TemporaryDirectory() as tmp_dir:
                         image_processor.save_pretrained(UpperCAmelCase__)
                         A__ 					=       AutoImageProcessor.from_pretrained(UpperCAmelCase__)
                         self.assertIsInstance(UpperCAmelCase__   ,					UpperCAmelCase__)

           finally:
                  if "custom" in CONFIG_MAPPING._extra_content:
                         del CONFIG_MAPPING._extra_content["custom"]
                  if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
                         del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]





    def 			SCREAMING_SNAKE_CASE				(     self :      Tuple)		->List[str]:





           '''simple docstring'''

           class 							UpperCamelCase_       (					UpperCAmelCase__	):

               '''simple docstring'''






               UpperCAmelCase__				    = True

           try:
                  AutoConfig.register('''custom'''   ,					UpperCAmelCase__)
                  AutoImageProcessor.register(UpperCAmelCase__   ,					UpperCAmelCase__)
                  # If remote code is not set, the default is to use local
                  A__ 					=       AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor''')
                  self.assertEqual(image_processor.__class__.__name__   ,					'''NewImageProcessor''')
                  self.assertTrue(image_processor.is_local)

                  # If remote code is disabled, we load the local one.
                  A__ 					=       AutoImageProcessor.from_pretrained(
                      '''hf-internal-testing/test_dynamic_image_processor'''   ,					trust_remote_code=UpperCAmelCase__)
                  self.assertEqual(image_processor.__class__.__name__   ,					'''NewImageProcessor''')
                  self.assertTrue(image_processor.is_local)

                  # If remote is enabled, we load from the Hub
                  A__ 					=       AutoImageProcessor.from_pretrained(
                      '''hf-internal-testing/test_dynamic_image_processor'''   ,					trust_remote_code=UpperCAmelCase__)
                  self.assertEqual(image_processor.__class__.__name__   ,					'''NewImageProcessor''')
                  self.assertTrue(not hasattr(UpperCAmelCase__   ,					'''is_local'''))

           finally:
                  if "custom" in CONFIG_MAPPING._extra_content:
                         del CONFIG_MAPPING._extra_content["custom"]
                  if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
                         del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]







"""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))