


import warnings
from typing import Dict

import numpy as np

from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline


if is_tf_available():
						from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

if is_torch_available():
						from ..models.auto.modeling_auto import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING





def 	_lowerCAmelCase       (    _lowerCAmelCase )						->	str:


						'''simple docstring'''






						return 1.0 / (1.0 + np.exp(-_outputs ))





def 	_lowerCAmelCase       (    _lowerCAmelCase )						->	Any:


						'''simple docstring'''






						__snake_case				=		np.max(_outputs      ,	axis=-1      ,	keepdims=__UpperCamelCase )
						__snake_case				=		np.exp(_outputs - maxes )
						return shifted_exp / shifted_exp.sum(axis=-1      ,	keepdims=__UpperCamelCase )






class     UpperCamelCase(  UpperCAmelCase_   ):
					snake_case_		:				List[Any]										=	"sigmoid"
					snake_case_		:				Tuple										=	"softmax"
					snake_case_		:				Any										=	"none"






@add_end_docstrings(
    UpperCAmelCase_     ,							R"""\n        return_all_scores (`bool`, *optional*, defaults to `False`):\n            Whether to return all prediction scores or just the one of the predicted class.\n        function_to_apply (`str`, *optional*, defaults to `\"default\"`):\n            The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:\n\n            - `\"default\"`: if the model has a single label, will apply the sigmoid function on the output. If the model\n              has several labels, will apply the softmax function on the output.\n            - `\"sigmoid\"`: Applies the sigmoid function on the output.\n            - `\"softmax\"`: Applies the softmax function on the output.\n            - `\"none\"`: Does not apply any function on the output.\n    """     ,							)
class     UpperCamelCase(  UpperCAmelCase_   ):
					snake_case_		:				str										=	False
					snake_case_		:				Tuple										=	ClassificationFunction.NONE


					def __init__(						self	:		Optional[int]     ,    **SCREAMING_SNAKE_CASE	:		List[Any]						)			->	List[str]:




											'''simple docstring'''

											super().__init__(**_snake_case						)

											self.check_model_type(
											    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
											    if self.framework == "tf"
											    else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Tuple     ,    SCREAMING_SNAKE_CASE	:		int=None     ,    SCREAMING_SNAKE_CASE	:		Dict=None     ,    SCREAMING_SNAKE_CASE	:		List[Any]=""     ,    **SCREAMING_SNAKE_CASE	:		Dict						)			->	List[str]:




											'''simple docstring'''

											__snake_case				=		tokenizer_kwargs

											__snake_case				=		{}
											if hasattr(self.model.config     ,    "return_all_scores"						) and return_all_scores is None:
																	__snake_case				=		self.model.config.return_all_scores

											if isinstance(_snake_case     ,    _snake_case						) or top_k is None:
																	__snake_case				=		top_k
																	__snake_case				=		False
											elif return_all_scores is not None:
																	warnings.warn(
																	    "`return_all_scores` is now deprecated,  if want a similar functionality use `top_k=None` instead of"
																	    " `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`."     ,    _snake_case     ,    )
																	if return_all_scores:
																							__snake_case				=		None
																	else:
																							__snake_case				=		1

											if isinstance(_snake_case     ,    _snake_case						):
																	__snake_case				=		ClassificationFunction[function_to_apply.upper()]

											if function_to_apply is not None:
																	__snake_case				=		function_to_apply
											return preprocess_params, {}, postprocess_params


					def __call__(						self	:		int     ,    *SCREAMING_SNAKE_CASE	:		List[str]     ,    **SCREAMING_SNAKE_CASE	:		Tuple						)			->	List[Any]:




											'''simple docstring'''

											__snake_case				=		super().__call__(*_snake_case     ,    **_snake_case						)
											# TODO try and retrieve it in a nicer way from _sanitize_parameters.
											__snake_case				=		"top_k" not in kwargs
											if isinstance(args[0]     ,    _snake_case						) and _legacy:
																	# This pipeline is odd, and return a list when single item is run
																	return [result]
											else:
																	return result


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




											'''simple docstring'''

											__snake_case				=		self.framework
											if isinstance(_snake_case     ,    _snake_case						):
																	return self.tokenizer(**_snake_case     ,    return_tensors=_snake_case     ,    **_snake_case						)
											elif isinstance(_snake_case     ,    _snake_case						) and len(_snake_case						) == 1 and isinstance(inputs[0]     ,    _snake_case						) and len(inputs[0]						) == 2:
																	# It used to be valid to use a list of list of list for text pairs, keeping this path for BC
																	return self.tokenizer(
																	    text=inputs[0][0]     ,    text_pair=inputs[0][1]     ,    return_tensors=_snake_case     ,    **_snake_case						)
											elif isinstance(_snake_case     ,    _snake_case						):
																	# This is likely an invalid usage of the pipeline attempting to pass text pairs.
																	raise ValueError(
																	    "The pipeline received invalid inputs, if you are trying to send text pairs, you can try to send a"
																	    " dictionary `{\"text\": \"My text\", \"text_pair\": \"My pair\"}` in order to send a text pair."						)
											return self.tokenizer(_snake_case     ,    return_tensors=_snake_case     ,    **_snake_case						)


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




											'''simple docstring'''

											return self.model(**_snake_case						)






					def 							SCREAMING_SNAKE_CASE_  (						self	:		Any     ,    SCREAMING_SNAKE_CASE	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=None     ,    SCREAMING_SNAKE_CASE	:		int=1     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=True						)			->	List[str]:




											'''simple docstring'''

											if function_to_apply is None:
																	if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
																							__snake_case				=		ClassificationFunction.SIGMOID
																	elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
																							__snake_case				=		ClassificationFunction.SOFTMAX
																	elif hasattr(self.model.config     ,    "function_to_apply"						) and function_to_apply is None:
																							__snake_case				=		self.model.config.function_to_apply
																	else:
																							__snake_case				=		ClassificationFunction.NONE

											__snake_case				=		model_outputs["logits"][0]
											__snake_case				=		outputs.numpy()

											if function_to_apply == ClassificationFunction.SIGMOID:
																	__snake_case				=		sigmoid(_snake_case						)
											elif function_to_apply == ClassificationFunction.SOFTMAX:
																	__snake_case				=		softmax(_snake_case						)
											elif function_to_apply == ClassificationFunction.NONE:
																	__snake_case				=		outputs
											else:
																	raise ValueError(f'''Unrecognized `function_to_apply` argument: {function_to_apply}'''						)

											if top_k == 1 and _legacy:
																	return {"label": self.model.config.idalabel[scores.argmax().item()], "score": scores.max().item()}

											__snake_case				=		[
											    {"label": self.model.config.idalabel[i], "score": score.item()} for i, score in enumerate(_snake_case						)
											]
											if not _legacy:
																	dict_scores.sort(key=lambda SCREAMING_SNAKE_CASE						: x["score"]     ,    reverse=_snake_case						)
																	if top_k is not None:
																							__snake_case				=		dict_scores[:top_k]
											return dict_scores









from __future__ import annotations

from decimal import Decimal
from math import *  # noqa: F403

from sympy import diff





def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase = 10**-10 )						->	float:


						'''simple docstring'''






						__snake_case				=		a
						while True:
												__snake_case				=		Decimal(_lowerCAmelCase ) - (
												    Decimal(eval(_lowerCAmelCase ) ) / Decimal(eval(str(diff(_lowerCAmelCase ) ) ) )  # noqa: S307
												)
												# This number dictates the accuracy of the answer
												if abs(eval(_lowerCAmelCase ) ) < precision:  # noqa: S307
																		return float(_lowerCAmelCase )


# 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
	print(f'''The root of x**2 - 5*x + 2 = 0 is {newton_raphson("x**2 - 5*x + 2", 0.4)}''')
	# Find Square Root of 5
	print(f'''The root of log(x) - 1 = 0 is {newton_raphson("log(x) - 1", 2)}''')
	# Exponential Roots
	print(f'''The root of exp(x) - 1 = 0 is {newton_raphson("exp(x) - 1", 0)}''')





