



'''simple docstring'''





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	(					_snake_case	:    Optional[int]							):





    '''simple docstring'''


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




def 							lowerCamelCase	(					_snake_case	:    List[Any]							):





    '''simple docstring'''


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


class 					snake_case			(UpperCamelCase ):
       lowerCAmelCase__	:Union[str, Any]     =       "sigmoid"
       lowerCAmelCase__	:Any     =       "softmax"
       lowerCAmelCase__	:Dict     =       "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 					snake_case			(UpperCamelCase ):
       lowerCAmelCase__	:int     =       False
       lowerCAmelCase__	:str     =       ClassificationFunction.NONE



       def __init__(					self  ,**UpperCAmelCase_   )	->				str:
           super().__init__(**UpperCAmelCase_   )

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



       def 				_a   (					self  ,UpperCAmelCase_=None  ,UpperCAmelCase_=None  ,UpperCAmelCase_=""  ,**UpperCAmelCase_   )	->				str:
           # Using "" as default argument because we're going to use `top_k=None` in user code to declare
           # "No top_k"
           lowercase__									=    tokenizer_kwargs

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

           if isinstance(UpperCAmelCase_  ,UpperCAmelCase_   ) or top_k is None:
               lowercase__									=    top_k
               lowercase__									=    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`."  ,UpperCAmelCase_  ,)
               if return_all_scores:
                   lowercase__									=    None
               else:
                   lowercase__									=    1

           if isinstance(UpperCAmelCase_  ,UpperCAmelCase_   ):
               lowercase__									=    ClassificationFunction[function_to_apply.upper()]

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



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



       def 				_a   (					self  ,UpperCAmelCase_  ,**UpperCAmelCase_   )	->				Dict[str, GenericTensor]:
           lowercase__									=    self.framework
           if isinstance(UpperCAmelCase_  ,UpperCAmelCase_   ):
               return self.tokenizer(**UpperCAmelCase_  ,return_tensors=UpperCAmelCase_  ,**UpperCAmelCase_   )
           elif isinstance(UpperCAmelCase_  ,UpperCAmelCase_   ) and len(UpperCAmelCase_   ) == 1 and isinstance(inputs[0]  ,UpperCAmelCase_   ) 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=UpperCAmelCase_  ,**UpperCAmelCase_   )
           elif isinstance(UpperCAmelCase_  ,UpperCAmelCase_   ):
               # 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(UpperCAmelCase_  ,return_tensors=UpperCAmelCase_  ,**UpperCAmelCase_   )



       def 				_a   (					self  ,UpperCAmelCase_   )	->				Any:
           return self.model(**UpperCAmelCase_   )







       def 				_a   (					self  ,UpperCAmelCase_  ,UpperCAmelCase_=None  ,UpperCAmelCase_=1  ,UpperCAmelCase_=True   )	->				List[Any]:
           # `_legacy` is used to determine if we're running the naked pipeline and in backward
           # compatibility mode, or if running the pipeline with `pipeline(..., top_k=1)` we're running
           # the more natural result containing the list.
           # Default value before `set_parameters`
           if function_to_apply is None:
               if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
                   lowercase__									=    ClassificationFunction.SIGMOID
               elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
                   lowercase__									=    ClassificationFunction.SOFTMAX
               elif hasattr(self.model.config  ,"function_to_apply"   ) and function_to_apply is None:
                   lowercase__									=    self.model.config.function_to_apply
               else:
                   lowercase__									=    ClassificationFunction.NONE

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

           if function_to_apply == ClassificationFunction.SIGMOID:
               lowercase__									=    sigmoid(UpperCAmelCase_   )
           elif function_to_apply == ClassificationFunction.SOFTMAX:
               lowercase__									=    softmax(UpperCAmelCase_   )
           elif function_to_apply == ClassificationFunction.NONE:
               lowercase__									=    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()}

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









'''simple docstring'''





from __future__ import annotations




def 							lowerCamelCase	(					_snake_case	:    int  ,_snake_case	:    int							):





    '''simple docstring'''


    if b == 0:
        return (1, 0)
    ((lowercase__)							,							(lowercase__))									=    extended_euclid(_snake_case  ,a % b							)
    lowercase__									=    a // b
    return (y, x - k * y)




def 							lowerCamelCase	(					_snake_case	:    int  ,_snake_case	:    int  ,_snake_case	:    int  ,_snake_case	:    int							):





    '''simple docstring'''


    ((lowercase__)							,							(lowercase__))									=    extended_euclid(_snake_case  ,_snake_case							)
    lowercase__									=    na * na
    lowercase__									=    ra * x * na + ra * y * na
    return (n % m + m) % m




def 							lowerCamelCase	(					_snake_case	:    int  ,_snake_case	:    int							):





    '''simple docstring'''


    ((lowercase__)							,							(lowercase__))									=    extended_euclid(_snake_case  ,_snake_case							)
    if b < 0:
        lowercase__									=    (b % n + n) % n
    return b




def 							lowerCamelCase	(					_snake_case	:    int  ,_snake_case	:    int  ,_snake_case	:    int  ,_snake_case	:    int							):





    '''simple docstring'''


    lowercase__							,							lowercase__									=    invert_modulo(_snake_case  ,_snake_case							), invert_modulo(_snake_case  ,_snake_case							)
    lowercase__									=    na * na
    lowercase__									=    ra * x * na + ra * y * na
    return (n % m + m) % m


if __name__ == "__main__":
 from doctest import testmod

 testmod(name="chinese_remainder_theorem", verbose=True)
 testmod(name="chinese_remainder_theorem2", verbose=True)
 testmod(name="invert_modulo", verbose=True)
 testmod(name="extended_euclid", verbose=True)




