






"""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 _a			(		_snake_case							):







      """simple docstring"""






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

def _a			(		_snake_case							):







      """simple docstring"""






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







class  lowerCamelCase__ (	__lowerCamelCase			):
    SCREAMING_SNAKE_CASE		=      'sigmoid'
    SCREAMING_SNAKE_CASE		=      'softmax'
    SCREAMING_SNAKE_CASE		=      'none'







@add_end_docstrings(
    __lowerCamelCase   ,					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  lowerCamelCase__ (	__lowerCamelCase			):
    SCREAMING_SNAKE_CASE		=      False
    SCREAMING_SNAKE_CASE		=      ClassificationFunction.NONE






    def __init__(			self     ,**A ):
          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     ,A=None     ,A=None     ,A=""     ,**A ):
          # Using "" as default argument because we're going to use `top_k=None` in user code to declare
          # "No top_k"
          UpperCAmelCase          =	tokenizer_kwargs

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

          if isinstance(UpperCamelCase_     ,UpperCamelCase_ ) or top_k is None:
                UpperCAmelCase          =	top_k
                UpperCAmelCase          =	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:
                      UpperCAmelCase          =	None
                else:
                      UpperCAmelCase          =	1

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

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






    def __call__(			self     ,*A     ,**A ):
          UpperCAmelCase          =	super().__call__(*UpperCamelCase_     ,**UpperCamelCase_ )
          # TODO try and retrieve it in a nicer way from _sanitize_parameters.
          UpperCAmelCase          =	"""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     ,A     ,**A ):
          UpperCAmelCase          =	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     ,A ):
          return self.model(**UpperCamelCase_ )






    def     _UpperCamelCase							(			self     ,A     ,A=None     ,A=1     ,A=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:
                      UpperCAmelCase          =	ClassificationFunction.SIGMOID
                elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
                      UpperCAmelCase          =	ClassificationFunction.SOFTMAX
                elif hasattr(self.model.config     ,"""function_to_apply""" ) and function_to_apply is None:
                      UpperCAmelCase          =	self.model.config.function_to_apply
                else:
                      UpperCAmelCase          =	ClassificationFunction.NONE

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

          if function_to_apply == ClassificationFunction.SIGMOID:
                UpperCAmelCase          =	sigmoid(UpperCamelCase_ )
          elif function_to_apply == ClassificationFunction.SOFTMAX:
                UpperCAmelCase          =	softmax(UpperCamelCase_ )
          elif function_to_apply == ClassificationFunction.NONE:
                UpperCAmelCase          =	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()}

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











"""simple docstring"""







_UpperCamelCase					      =  [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
_UpperCamelCase					      =  [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
_UpperCamelCase					      =  {
    0: """Sunday""",
    1: """Monday""",
    2: """Tuesday""",
    3: """Wednesday""",
    4: """Thursday""",
    5: """Friday""",
    6: """Saturday""",
}

def _a			(		_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






      assert len(str(_snake_case							)							) > 2, "year should be in YYYY format"
      assert 1 <= month <= 12, "month should be between 1 to 12"
      assert 1 <= day <= 31, "day should be between 1 to 31"

      # Doomsday algorithm:
      UpperCAmelCase          =	year // 100
      UpperCAmelCase          =	(5 * (century % 4) + 2) % 7
      UpperCAmelCase          =	year % 100
      UpperCAmelCase          =	centurian % 12
      UpperCAmelCase          =	(
          (centurian // 12) + centurian_m + (centurian_m // 4) + century_anchor
      ) % 7
      UpperCAmelCase          =	(
          DOOMSDAY_NOT_LEAP[month - 1]
          if (year % 4 != 0) or (centurian == 0 and (year % 400) == 0)
          else DOOMSDAY_LEAP[month - 1]
      )
      UpperCAmelCase          =	(dooms_day + day - day_anchor) % 7
      return WEEK_DAY_NAMES[week_day]


if __name__ == "__main__":
  import doctest

  doctest.testmod()



