



"""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 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  List[str]			):
					return 1.0 / (1.0 + np.exp(-_outputs			))







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Any			):
					snake_case_					:		Union[str, Any] 					=					np.max(_outputs				,    axis=-1				,    keepdims=_lowercase			)
					snake_case_					:		List[Any] 					=					np.exp(_outputs - maxes			)
					return shifted_exp / shifted_exp.sum(axis=-1				,    keepdims=_lowercase			)







class    __lowercase     (					_UpperCAmelCase):







						"""simple docstring"""







						_A		:							str							       =		"""sigmoid"""
						_A		:							Union[str, Any]							       =		"""softmax"""
						_A		:							int							       =		"""none"""







@add_end_docstrings(
    _UpperCAmelCase					,    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    __lowercase     (					_UpperCAmelCase):







						"""simple docstring"""







						_A		:							Optional[Any]							       =		False
						_A		:							Optional[int]							       =		ClassificationFunction.NONE







						def __init__(self   ,		**lowercase__       ):
											super().__init__(**UpperCamelCase_       )

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







						def 						__UpperCamelCase							(self   ,		lowercase__=None   ,		lowercase__=None   ,		lowercase__=""   ,		**lowercase__       ):
											snake_case_					:		Optional[int] 					=					tokenizer_kwargs

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

											if isinstance(UpperCamelCase_   ,		UpperCamelCase_       ) or top_k is None:
																snake_case_					:		List[Any] 					=					top_k
																snake_case_					:		Tuple 					=					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:
																					snake_case_					:		Optional[int] 					=					None
																else:
																					snake_case_					:		int 					=					1

											if isinstance(UpperCamelCase_   ,		UpperCamelCase_       ):
																snake_case_					:		Dict 					=					ClassificationFunction[function_to_apply.upper()]

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







						def __call__(self   ,		*lowercase__   ,		**lowercase__       ):
											snake_case_					:		Optional[int] 					=					super().__call__(*UpperCamelCase_   ,		**UpperCamelCase_       )
											# TODO try and retrieve it in a nicer way from _sanitize_parameters.
											snake_case_					:		Union[str, Any] 					=					'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 						__UpperCamelCase							(self   ,		lowercase__   ,		**lowercase__       ):
											snake_case_					:		Optional[Any] 					=					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 						__UpperCamelCase							(self   ,		lowercase__       ):
											return self.model(**UpperCamelCase_       )







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__=None   ,		lowercase__=1   ,		lowercase__=True       ):





											if function_to_apply is None:
																if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
																					snake_case_					:		Any 					=					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[Any] 					=					self.model.config.function_to_apply
																else:
																					snake_case_					:		List[str] 					=					ClassificationFunction.NONE

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

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









"""simple docstring"""

from copy import deepcopy







class    __lowercase     :







						"""simple docstring"""







						def __init__(self   ,		lowercase__ = None   ,		lowercase__ = None       ):

											if arr is None and size is not None:
																snake_case_					:		str 					=					size
																snake_case_					:		Optional[Any] 					=					[0] * size
											elif arr is not None:
																self.init(lowercase__       )
											else:
																raise ValueError("""Either arr or size must be specified"""       )







						def 						__UpperCamelCase							(self   ,		lowercase__       ):
											snake_case_					:		Optional[Any] 					=					len(lowercase__       )
											snake_case_					:		int 					=					deepcopy(lowercase__       )
											for i in range(1   ,		self.size       ):
																snake_case_					:		Optional[Any] 					=					self.next_(lowercase__       )
																if j < self.size:
																					self.tree[j] += self.tree[i]







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Dict 					=					self.tree[:]
											for i in range(self.size - 1   ,		0   ,		-1       ):
																snake_case_					:		Optional[int] 					=					self.next_(lowercase__       )
																if j < self.size:
																					arr[j] -= arr[i]
											return arr







						@staticmethod
						def 						__UpperCamelCase							(lowercase__       ):
											return index + (index & (-index))







						@staticmethod
						def 						__UpperCamelCase							(lowercase__       ):
											return index - (index & (-index))







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__       ):
											if index == 0:
																self.tree[0] += value
																return
											while index < self.size:
																self.tree[index] += value
																snake_case_					:		Tuple 					=					self.next_(lowercase__       )







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__       ):
											self.add(lowercase__   ,		value - self.get(lowercase__       )       )







						def 						__UpperCamelCase							(self   ,		lowercase__       ):
											if right == 0:
																return 0
											snake_case_					:		List[str] 					=					self.tree[0]
											right -= 1  # make right inclusive
											while right > 0:
																result += self.tree[right]
																snake_case_					:		Optional[int] 					=					self.prev(lowercase__       )
											return result







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__       ):
											return self.prefix(lowercase__       ) - self.prefix(lowercase__       )







						def 						__UpperCamelCase							(self   ,		lowercase__       ):
											return self.query(lowercase__   ,		index + 1       )







						def 						__UpperCamelCase							(self   ,		lowercase__       ):
											value -= self.tree[0]
											if value < 0:
																return -1

											snake_case_					:		Tuple 					=					1  # Largest power of 2 <= size
											while j * 2 < self.size:
																j *= 2

											snake_case_					:		Tuple 					=					0

											while j > 0:
																if i + j < self.size and self.tree[i + j] <= value:
																					value -= self.tree[i + j]
																					i += j
																j //= 2
											return i


if __name__ == "__main__":
			import doctest

			doctest.testmod()




