






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       snake_case_		(     lowerCAmelCase_       :						List[Any]      ):
       return 1.0 / (1.0 + np.exp(-_outputs      ))
def       snake_case_		(     lowerCAmelCase_       :						int      ):
       __lowercase      :							Dict				=     np.max(_outputs       ,       axis=-1       ,       keepdims=lowerCAmelCase_      )
       __lowercase      :							Tuple				=     np.exp(_outputs - maxes      )
       return shifted_exp / shifted_exp.sum(axis=-1       ,       keepdims=lowerCAmelCase_      )

class 		lowerCAmelCase     ( __a			):

     '''simple docstring'''

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







@add_end_docstrings(
    __a      ,       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 		lowerCAmelCase     ( __a			):

     '''simple docstring'''

     _A						:			Any									=      False
     _A						:			Dict									=      ClassificationFunction.NONE



     def __init__(  self				:	Dict		,     **__a				:	int							)			->  str:


            """simple docstring"""

            super().__init__(**__a							)

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



     def       lowerCAmelCase				(  self				:	Any		,     __a				:	Tuple=None		,     __a				:	Any=None		,     __a				:	Dict=""		,     **__a				:	Dict							)			->  Optional[int]:


            """simple docstring"""

            __lowercase      :							Union[str, Any]				=     tokenizer_kwargs

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

            if isinstance(__a		,     __a							) or top_k is None:
                   __lowercase      :							List[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`."""		,     __a		,     )
                   if return_all_scores:
                          __lowercase      :							Union[str, Any]				=     None
                   else:
                          __lowercase      :							Tuple				=     1

            if isinstance(__a		,     __a							):
                   __lowercase      :							str				=     ClassificationFunction[function_to_apply.upper()]

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



     def __call__(  self				:	str		,     *__a				:	str		,     **__a				:	List[Any]							)			->  List[Any]:


            """simple docstring"""

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



     def       lowerCAmelCase				(  self				:	Union[str, Any]		,     __a				:	List[Any]		,     **__a				:	Union[str, Any]							)			->  Dict[str, GenericTensor]:


            """simple docstring"""

            __lowercase      :							int				=     self.framework
            if isinstance(__a		,     __a							):
                   return self.tokenizer(**__a		,     return_tensors=__a		,     **__a							)
            elif isinstance(__a		,     __a							) and len(__a							) == 1 and isinstance(inputs[0]		,     __a							) 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=__a		,     **__a							)
            elif isinstance(__a		,     __a							):
                   # 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(__a		,     return_tensors=__a		,     **__a							)



     def       lowerCAmelCase				(  self				:	str		,     __a				:	List[str]							)			->  Dict:


            """simple docstring"""

            return self.model(**__a							)



     def       lowerCAmelCase				(  self				:	int		,     __a				:	Optional[int]		,     __a				:	Optional[int]=None		,     __a				:	Tuple=1		,     __a				:	List[str]=True							)			->  Optional[Any]:


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

            __lowercase      :							List[str]				=     model_outputs["""logits"""][0]
            __lowercase      :							Any				=     outputs.numpy()

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







from __future__ import annotations
def       snake_case_		(     lowerCAmelCase_       :						str       ,       lowerCAmelCase_       :						list[str] | None = None      ):
							__lowercase      :							Tuple				=     word_bank or []
							# create a table
							__lowercase      :							int				=     len(lowerCAmelCase_      ) + 1

							__lowercase      :							list[list[list[str]]]				=     []
							for _ in range(lowerCAmelCase_      ):
														table.append([]      )
							# seed value
							__lowercase      :							Dict				=     [[]]  # because empty string has empty combination

							# iterate through the indices
							for i in range(lowerCAmelCase_      ):
														# condition
														if table[i] != []:
																					for word in word_bank:
																												# slice condition
																												if target[i : i + len(lowerCAmelCase_      )] == word:
																																			__lowercase      :							list[list[str]]				=     [
																																			    [word, *way] for way in table[i]
																																			]
																																			# adds the word to every combination the current position holds
																																			# now,push that combination to the table[i+len(word)]
																																			table[i + len(lowerCAmelCase_      )] += new_combinations

    # combinations are in reverse order so reverse for better output
							for combination in table[len(lowerCAmelCase_      )]:
														combination.reverse()

							return table[len(lowerCAmelCase_      )]


if __name__ == "__main__":
	print(all_construct('''jwajalapa''', ['''jwa''', '''j''', '''w''', '''a''', '''la''', '''lapa''']))
	print(all_construct('''rajamati''', ['''s''', '''raj''', '''amat''', '''raja''', '''ma''', '''i''', '''t''']))
	print(
	    all_construct(
	        '''hexagonosaurus''',
	        ['''h''', '''ex''', '''hex''', '''ag''', '''ago''', '''ru''', '''auru''', '''rus''', '''go''', '''no''', '''o''', '''s'''],
	    )
	)