



"""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 __lowercase    (							_a					):
				return 1.0 / (1.0 + np.exp(-_outputs					))




def __lowercase    (							_a					):
				snake_case_				:  Optional[int]       =			np.max(_outputs	,    axis=-1	,    keepdims=_a					)
				snake_case_				:  int       =			np.exp(_outputs - maxes					)
				return shifted_exp / shifted_exp.sum(axis=-1	,    keepdims=_a					)




class  _UpperCAmelCase (    lowerCAmelCase__):
	_lowerCAmelCase     :    Tuple	      =	"""sigmoid"""
	_lowerCAmelCase     :    List[str]	      =	"""softmax"""
	_lowerCAmelCase     :    Optional[int]	      =	"""none"""





@add_end_docstrings(
    lowerCAmelCase__					,  R"""
        return_all_scores (`bool`, *optional*, defaults to `False`):
            Whether to return all prediction scores or just the one of the predicted class.
        function_to_apply (`str`, *optional*, defaults to `\"default\"`):
            The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:

            - `\"default\"`: if the model has a single label, will apply the sigmoid function on the output. If the model
              has several labels, will apply the softmax function on the output.
            - `\"sigmoid\"`: Applies the sigmoid function on the output.
            - `\"softmax\"`: Applies the softmax function on the output.
            - `\"none\"`: Does not apply any function on the output.
    """					,  )
class  _UpperCAmelCase (    lowerCAmelCase__):
	_lowerCAmelCase     :    Dict	      =	False
	_lowerCAmelCase     :    List[str]	      =	ClassificationFunction.NONE

	def __init__(  self	: Dict						,  **lowercase_	: Optional[Any]       ):
					super().__init__(**lowercase_       )

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

	def 		_snake_case      (  self	: List[Any]						,  lowercase_	: str=None						,  lowercase_	: Tuple=None						,  lowercase_	: Tuple=""						,  **lowercase_	: str       ):
					# Using "" as default argument because we're going to use `top_k=None` in user code to declare
					# "No top_k"
					snake_case_				:  Tuple       =			tokenizer_kwargs

					snake_case_				:  Union[str, Any]       =			{}
					if hasattr(self.model.config						,  '''return_all_scores'''       ) and return_all_scores is None:
									snake_case_				:  str       =			self.model.config.return_all_scores

					if isinstance(lowercase_						,  lowercase_       ) or top_k is None:
									snake_case_				:  Optional[int]       =			top_k
									snake_case_				:  Optional[int]       =			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`.'''						,  lowercase_						,  )
									if return_all_scores:
													snake_case_				:  Dict       =			None
									else:
													snake_case_				:  str       =			1

					if isinstance(lowercase_						,  lowercase_       ):
									snake_case_				:  List[Any]       =			ClassificationFunction[function_to_apply.upper()]

					if function_to_apply is not None:
									snake_case_				:  List[str]       =			function_to_apply
					return preprocess_params, {}, postprocess_params

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

	def 		_snake_case      (  self	: List[Any]						,  lowercase_	: Union[str, Any]						,  **lowercase_	: int       ):
					snake_case_				:  Dict       =			self.framework
					if isinstance(lowercase_						,  lowercase_       ):
									return self.tokenizer(**lowercase_						,  return_tensors=lowercase_						,  **lowercase_       )
					elif isinstance(lowercase_						,  lowercase_       ) and len(lowercase_       ) == 1 and isinstance(inputs[0]						,  lowercase_       ) 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=lowercase_						,  **lowercase_       )
					elif isinstance(lowercase_						,  lowercase_       ):
									# 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(lowercase_						,  return_tensors=lowercase_						,  **lowercase_       )

	def 		_snake_case      (  self	: List[str]						,  lowercase_	: Optional[int]       ):
					return self.model(**lowercase_       )






	def 		_snake_case      (  self	: int						,  lowercase_	: str						,  lowercase_	: str=None						,  lowercase_	: str=1						,  lowercase_	: Any=True       ):
					# `_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:
													snake_case_				:  int       =			ClassificationFunction.SIGMOID
									elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
													snake_case_				:  str       =			ClassificationFunction.SOFTMAX
									elif hasattr(self.model.config						,  '''function_to_apply'''       ) and function_to_apply is None:
													snake_case_				:  List[str]       =			self.model.config.function_to_apply
									else:
													snake_case_				:  Union[str, Any]       =			ClassificationFunction.NONE

					snake_case_				:  Optional[Any]       =			model_outputs['''logits'''][0]
					snake_case_				:  Dict       =			outputs.numpy()

					if function_to_apply == ClassificationFunction.SIGMOID:
									snake_case_				:  List[Any]       =			sigmoid(lowercase_       )
					elif function_to_apply == ClassificationFunction.SOFTMAX:
									snake_case_				:  Any       =			softmax(lowercase_       )
					elif function_to_apply == ClassificationFunction.NONE:
									snake_case_				:  int       =			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_				:  Dict       =			[
					    {'''label''': self.model.config.idalabel[i], '''score''': score.item()} for i, score in enumerate(lowercase_       )
					]
					if not _legacy:
									dict_scores.sort(key=lambda lowercase_       : x["score"]						,  reverse=lowercase_       )
									if top_k is not None:
													snake_case_				:  Dict       =			dict_scores[:top_k]
					return dict_scores










"""simple docstring"""

def __lowercase    (							_a	,    _a					):
    snake_case_				:  str       =			[0 for i in range(r + 1					)]
    # nc0 = 1
    snake_case_				:  int       =			1
    for i in range(1	,    n + 1					):
        # to compute current row from previous row.
        snake_case_				:  Any       =			min(_a	,    _a					)
        while j > 0:
            c[j] += c[j - 1]
            j -= 1
    return c[r]






print(binomial_coefficient(n=10, r=5))





