
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 		UpperCamelCase	(      lowercase_   )		->	str:


		'''simple docstring'''






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

def 		UpperCamelCase	(      lowercase_   )		->	Union[str, Any]:


		'''simple docstring'''






		lowercase__  :   Optional[Any]    		=      np.max(_outputs , axis=-1 , keepdims=lowercase_   )
		lowercase__  :   Optional[int]    		=      np.exp(_outputs - maxes   )
		return shifted_exp / shifted_exp.sum(axis=-1 , keepdims=lowercase_   )



class       _snake_case   (	UpperCAmelCase_							):
	__lowerCAmelCase  :     Dict        =			'sigmoid'
	__lowerCAmelCase  :     List[str]        =			'softmax'
	__lowerCAmelCase  :     Optional[int]        =			'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  :     Optional[int]        =			False
	__lowerCAmelCase  :     str        =			ClassificationFunction.NONE


	def __init__(					self					,  **SCREAMING_SNAKE_CASE_):


			'''simple docstring'''




			super().__init__(**SCREAMING_SNAKE_CASE_)

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


	def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_=None					,  SCREAMING_SNAKE_CASE_=None					,  SCREAMING_SNAKE_CASE_=""					,  **SCREAMING_SNAKE_CASE_):


			'''simple docstring'''




			lowercase__  :   Any    		=      tokenizer_kwargs

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

			if isinstance(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_) or top_k is None:
					lowercase__  :   Union[str, Any]    		=      top_k
					lowercase__  :   Dict    		=      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`."""					,  SCREAMING_SNAKE_CASE_					,  )
					if return_all_scores:
							lowercase__  :   Tuple    		=      None
					else:
							lowercase__  :   Dict    		=      1

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

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


	def __call__(					self					,  *SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_):


			'''simple docstring'''




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


	def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_):


			'''simple docstring'''




			lowercase__  :   Optional[Any]    		=      self.framework
			if isinstance(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_):
					return self.tokenizer(**SCREAMING_SNAKE_CASE_					,  return_tensors=SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_)
			elif isinstance(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_) and len(SCREAMING_SNAKE_CASE_) == 1 and isinstance(inputs[0]					,  SCREAMING_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=SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_)
			elif isinstance(SCREAMING_SNAKE_CASE_					,  SCREAMING_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(SCREAMING_SNAKE_CASE_					,  return_tensors=SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_)


	def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_):


			'''simple docstring'''




			return self.model(**SCREAMING_SNAKE_CASE_)



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


			'''simple docstring'''




			if function_to_apply is None:
					if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
							lowercase__  :   Dict    		=      ClassificationFunction.SIGMOID
					elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
							lowercase__  :   List[str]    		=      ClassificationFunction.SOFTMAX
					elif hasattr(self.model.config					,  """function_to_apply""") and function_to_apply is None:
							lowercase__  :   Union[str, Any]    		=      self.model.config.function_to_apply
					else:
							lowercase__  :   Any    		=      ClassificationFunction.NONE

			lowercase__  :   List[str]    		=      model_outputs["""logits"""][0]
			lowercase__  :   Optional[int]    		=      outputs.numpy()

			if function_to_apply == ClassificationFunction.SIGMOID:
					lowercase__  :   Union[str, Any]    		=      sigmoid(SCREAMING_SNAKE_CASE_)
			elif function_to_apply == ClassificationFunction.SOFTMAX:
					lowercase__  :   int    		=      softmax(SCREAMING_SNAKE_CASE_)
			elif function_to_apply == ClassificationFunction.NONE:
					lowercase__  :   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()}

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








from ..utils import DummyObject, requires_backends



class       _snake_case   (	metaclass=UpperCAmelCase_							):
 __lowerCAmelCase  :     int        =			['torch', 'transformers', 'onnx']


 def __init__(					self					,  *SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   requires_backends(self					,  ["""torch""", """transformers""", """onnx"""])


 @classmethod
 def      lowercase__     (					cls					,  *SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   requires_backends(cls					,  ["""torch""", """transformers""", """onnx"""])



 @classmethod
 def      lowercase__     (					cls					,  *SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   requires_backends(cls					,  ["""torch""", """transformers""", """onnx"""])



class       _snake_case   (	metaclass=UpperCAmelCase_							):
 __lowerCAmelCase  :     Any        =			['torch', 'transformers', 'onnx']


 def __init__(					self					,  *SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   requires_backends(self					,  ["""torch""", """transformers""", """onnx"""])


 @classmethod
 def      lowercase__     (					cls					,  *SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   requires_backends(cls					,  ["""torch""", """transformers""", """onnx"""])



 @classmethod
 def      lowercase__     (					cls					,  *SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   requires_backends(cls					,  ["""torch""", """transformers""", """onnx"""])



class       _snake_case   (	metaclass=UpperCAmelCase_							):
 __lowerCAmelCase  :     Union[str, Any]        =			['torch', 'transformers', 'onnx']


 def __init__(					self					,  *SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   requires_backends(self					,  ["""torch""", """transformers""", """onnx"""])


 @classmethod
 def      lowercase__     (					cls					,  *SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   requires_backends(cls					,  ["""torch""", """transformers""", """onnx"""])



 @classmethod
 def      lowercase__     (					cls					,  *SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   requires_backends(cls					,  ["""torch""", """transformers""", """onnx"""])



class       _snake_case   (	metaclass=UpperCAmelCase_							):
 __lowerCAmelCase  :     List[Any]        =			['torch', 'transformers', 'onnx']


 def __init__(					self					,  *SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   requires_backends(self					,  ["""torch""", """transformers""", """onnx"""])


 @classmethod
 def      lowercase__     (					cls					,  *SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   requires_backends(cls					,  ["""torch""", """transformers""", """onnx"""])



 @classmethod
 def      lowercase__     (					cls					,  *SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   requires_backends(cls					,  ["""torch""", """transformers""", """onnx"""])



class       _snake_case   (	metaclass=UpperCAmelCase_							):
 __lowerCAmelCase  :     Dict        =			['torch', 'transformers', 'onnx']


 def __init__(					self					,  *SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   requires_backends(self					,  ["""torch""", """transformers""", """onnx"""])


 @classmethod
 def      lowercase__     (					cls					,  *SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   requires_backends(cls					,  ["""torch""", """transformers""", """onnx"""])



 @classmethod
 def      lowercase__     (					cls					,  *SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   requires_backends(cls					,  ["""torch""", """transformers""", """onnx"""])



class       _snake_case   (	metaclass=UpperCAmelCase_							):
 __lowerCAmelCase  :     List[str]        =			['torch', 'transformers', 'onnx']


 def __init__(					self					,  *SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   requires_backends(self					,  ["""torch""", """transformers""", """onnx"""])


 @classmethod
 def      lowercase__     (					cls					,  *SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   requires_backends(cls					,  ["""torch""", """transformers""", """onnx"""])



 @classmethod
 def      lowercase__     (					cls					,  *SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   requires_backends(cls					,  ["""torch""", """transformers""", """onnx"""])






