

"""simple docstring"""





import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin




class 					_lowerCAmelCase  (	lowercase   ):







 """simple docstring"""







 __UpperCAmelCase	:					Optional[int]   		=	["image_processor", "tokenizer"]
 __UpperCAmelCase	:					List[str]   		=	"AutoImageProcessor"
 __UpperCAmelCase	:					List[Any]   		=	"AutoTokenizer"


 def __init__(			self							:			List[Any],					UpperCAmelCase__							:			int=None,					UpperCAmelCase__							:			Any=None,					**UpperCAmelCase__							:			Tuple	):
    __lowercase								=						None
    if "feature_extractor" in kwargs:
       warnings.warn(
           "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
           " instead.",					UpperCAmelCase__,					)
       __lowercase								=						kwargs.pop("feature_extractor"	)

    __lowercase								=						image_processor if image_processor is not None else feature_extractor
    if image_processor is None:
       raise ValueError("You need to specify an `image_processor`."	)
    if tokenizer is None:
       raise ValueError("You need to specify a `tokenizer`."	)

    super().__init__(UpperCAmelCase__,					UpperCAmelCase__	)
    __lowercase								=						self.image_processor
    __lowercase								=						False


 def __call__(			self							:			Optional[Any],					*UpperCAmelCase__							:			int,					**UpperCAmelCase__							:			Union[str, Any]	):
    # For backward compatibility
    if self._in_target_context_manager:
       return self.current_processor(*UpperCAmelCase__,					**UpperCAmelCase__	)

    __lowercase								=						kwargs.pop("images",					UpperCAmelCase__	)
    __lowercase								=						kwargs.pop("text",					UpperCAmelCase__	)
    if len(UpperCAmelCase__	) > 0:
       __lowercase								=						args[0]
       __lowercase								=						args[1:]

    if images is None and text is None:
       raise ValueError("You need to specify either an `images` or `text` input to process."	)

    if images is not None:
       __lowercase								=						self.image_processor(UpperCAmelCase__,					*UpperCAmelCase__,					**UpperCAmelCase__	)
    if text is not None:
       __lowercase								=						self.tokenizer(UpperCAmelCase__,					**UpperCAmelCase__	)

    if text is None:
       return inputs
    elif images is None:
       return encodings
    else:
       __lowercase								=						encodings["input_ids"]
       return inputs


 def 					_lowercase (			self							:			Union[str, Any],					*UpperCAmelCase__							:			int,					**UpperCAmelCase__							:			int	):
    return self.tokenizer.batch_decode(*UpperCAmelCase__,					**UpperCAmelCase__	)


 def 					_lowercase (			self							:			Any,					*UpperCAmelCase__							:			str,					**UpperCAmelCase__							:			Optional[Any]	):
    return self.tokenizer.decode(*UpperCAmelCase__,					**UpperCAmelCase__	)


 @contextmanager
 def 					_lowercase (			self							:			str	):
    warnings.warn(
        "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
        "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
        "your images inputs, or in a separate call."	)
    __lowercase								=						True
    __lowercase								=						self.tokenizer
    yield
    __lowercase								=						self.image_processor
    __lowercase								=						False


 def 					_lowercase (			self							:			Union[str, Any],					UpperCAmelCase__							:			Union[str, Any],					UpperCAmelCase__							:			Optional[Any]=False,					UpperCAmelCase__							:			List[Any]=None	):
    if added_vocab is None:
       __lowercase								=						self.tokenizer.get_added_vocab()

    __lowercase								=						{}

    while tokens:
       __lowercase								=						re.search(r"<s_(.*?)>",					UpperCAmelCase__,					re.IGNORECASE	)
       if start_token is None:
          break
       __lowercase								=						start_token.group(1	)
       __lowercase								=						re.search(rF"""</s_{key}>""",					UpperCAmelCase__,					re.IGNORECASE	)
       __lowercase								=						start_token.group()
       if end_token is None:
          __lowercase								=						tokens.replace(UpperCAmelCase__,					""	)
       else:
          __lowercase								=						end_token.group()
          __lowercase								=						re.escape(UpperCAmelCase__	)
          __lowercase								=						re.escape(UpperCAmelCase__	)
          __lowercase								=						re.search(F"""{start_token_escaped}(.*?){end_token_escaped}""",					UpperCAmelCase__,					re.IGNORECASE	)
          if content is not None:
             __lowercase								=						content.group(1	).strip()
             if r"<s_" in content and r"</s_" in content:  # non-leaf node
                __lowercase								=						self.tokenajson(UpperCAmelCase__,					is_inner_value=UpperCAmelCase__,					added_vocab=UpperCAmelCase__	)
                if value:
                   if len(UpperCAmelCase__	) == 1:
                      __lowercase								=						value[0]
                   __lowercase								=						value
             else:  # leaf nodes
                __lowercase								=						[]
                for leaf in content.split(r"<sep/>"	):
                   __lowercase								=						leaf.strip()
                   if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
                      __lowercase								=						leaf[1:-2]  # for categorical special tokens
                   output[key].append(UpperCAmelCase__	)
                if len(output[key]	) == 1:
                   __lowercase								=						output[key][0]

          __lowercase								=						tokens[tokens.find(UpperCAmelCase__	) + len(UpperCAmelCase__	) :].strip()
          if tokens[:6] == r"<sep/>":  # non-leaf nodes
             return [output] + self.tokenajson(tokens[6:],					is_inner_value=UpperCAmelCase__,					added_vocab=UpperCAmelCase__	)

    if len(UpperCAmelCase__	):
       return [output] if is_inner_value else output
    else:
       return [] if is_inner_value else {"text_sequence": tokens}


 @property
 def 					_lowercase (			self							:			Union[str, Any]	):
    warnings.warn(
        "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.",					UpperCAmelCase__,					)
    return self.image_processor_class



 @property
 def 					_lowercase (			self							:			List[str]	):
    warnings.warn(
        "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.",					UpperCAmelCase__,					)
    return self.image_processor











'''simple docstring'''




from sympy import diff, lambdify, symbols
from sympy.functions import *  # noqa: F403






def        _lowerCAmelCase			(  __snake_case  : str ,							__snake_case  : complex ,							__snake_case  : str = "x" ,							__snake_case  : float = 10**-10 ,							__snake_case  : int = 1 ,							)     ->     complex:
     __A						: int      =       symbols(__snake_case			)
     __A						: Tuple      =       lambdify(__snake_case ,							__snake_case			)
     __A						: Any      =       lambdify(__snake_case ,							diff(__snake_case ,							__snake_case			)			)

     __A						: str      =       starting_point

     while True:
          if diff_function(__snake_case			) != 0:
               __A						: Optional[Any]      =       prev_guess - multiplicity * func(__snake_case			) / diff_function(
                   __snake_case			)
          else:
               raise ZeroDivisionError('Could not find root'			) from None

          # Precision is checked by comparing the difference of consecutive guesses
          if abs(next_guess - prev_guess			) < precision:
               return next_guess

          __A						: Dict      =       next_guess


# Let's Execute
if __name__ == "__main__":
  # Find root of trigonometric function
  # Find value of pi
  print(f"""The root of sin(x) = 0 is {newton_raphson("sin(x)", 2)}""")

  # Find root of polynomial
  # Find fourth Root of 5
  print(f"""The root of x**4 - 5 = 0 is {newton_raphson("x**4 -5", 0.4 +5j)}""")

  # Find value of e
  print(
      '''The root of log(y) - 1 = 0 is ''',
      f"""{newton_raphson("log(y) - 1", 2, variable="y")}""",
  )

  # Exponential Roots
  print(
      '''The root of exp(x) - 1 = 0 is''',
      f"""{newton_raphson("exp(x) - 1", 10, precision=0.005)}""",
  )

  # Find root of cos(x)
  print(f"""The root of cos(x) = 0 is {newton_raphson("cos(x)", 0)}""")