
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
			import torch

			from transformers.generation import DisjunctiveConstraint




@require_torch
class     _SCREAMING_SNAKE_CASE     (      unittest.TestCase						):



	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[int]:
					# For consistency across different places the DisjunctiveConstraint is called,
					# dc.token_ids is a list of integers. It is also initialized only by integers.

					lowerCamelCase_						=			[[1, 2, 4], [1, 2, 3, 4]]
					lowerCamelCase_						=			DisjunctiveConstraint(lowercase )
					self.assertTrue(isinstance(dc.token_ids						,  lowercase ) )

					with self.assertRaises(lowercase ):
									DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]] ) )

					with self.assertRaises(lowercase ):
									DisjunctiveConstraint([torch.LongTensor([1, 2, 4] ), torch.LongTensor([1, 2, 3, 4, 5] )] )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Union[str, Any]:
					# We can't have constraints that are complete subsets of another. This leads to a preverse
					# interpretation of "constraint fulfillment": does generating [1,2,3] fulfill the constraint?
					# It would mean that it generated [1,2] which fulfills it, but it's in the middle of potentially
					# fulfilling [1,2,3,4]. If we believe that [1,2,3] does fulfill the constraint, then the algorithm
					# will necessarily never reach [1,2,3,4], giving users a false sense of control (better to just not allow it).
					lowerCamelCase_						=			[[1, 2], [1, 2, 3, 4]]

					with self.assertRaises(lowercase ):
									DisjunctiveConstraint(lowercase )  # fails here



	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[Any]:
					lowerCamelCase_						=			[[1, 2, 3], [1, 2, 4]]

					lowerCamelCase_						=			DisjunctiveConstraint(lowercase )

					lowerCamelCase_	,    lowerCamelCase_	,    lowerCamelCase_						=			dc.update(1 )
					lowerCamelCase_						=			stepped is True and completed is False and reset is False
					self.assertTrue(lowercase )
					self.assertTrue(not dc.completed )
					self.assertTrue(dc.current_seq == [1] )

					lowerCamelCase_	,    lowerCamelCase_	,    lowerCamelCase_						=			dc.update(2 )
					lowerCamelCase_						=			stepped is True and completed is False and reset is False
					self.assertTrue(lowercase )
					self.assertTrue(not dc.completed )
					self.assertTrue(dc.current_seq == [1, 2] )

					lowerCamelCase_	,    lowerCamelCase_	,    lowerCamelCase_						=			dc.update(3 )
					lowerCamelCase_						=			stepped is True and completed is True and reset is False
					self.assertTrue(lowercase )
					self.assertTrue(dc.completed )  # Completed!
					self.assertTrue(dc.current_seq == [1, 2, 3] )



	def     SCREAMING_SNAKE_CASE_(    self )				->       List[Any]:
					lowerCamelCase_						=			[[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

					lowerCamelCase_						=			DisjunctiveConstraint(lowercase )

					lowerCamelCase_	,    lowerCamelCase_	,    lowerCamelCase_						=			dc.update(1 )
					self.assertTrue(not dc.completed )
					self.assertTrue(dc.current_seq == [1] )

					lowerCamelCase_	,    lowerCamelCase_	,    lowerCamelCase_						=			dc.update(2 )
					self.assertTrue(not dc.completed )
					self.assertTrue(dc.current_seq == [1, 2] )

					lowerCamelCase_	,    lowerCamelCase_	,    lowerCamelCase_						=			dc.update(4 )
					self.assertTrue(not dc.completed )
					self.assertTrue(dc.current_seq == [1, 2, 4] )

					lowerCamelCase_	,    lowerCamelCase_	,    lowerCamelCase_						=			dc.update(5 )
					self.assertTrue(dc.completed )  # Completed!
					self.assertTrue(dc.current_seq == [1, 2, 4, 5] )

					dc.reset()

					lowerCamelCase_	,    lowerCamelCase_	,    lowerCamelCase_						=			dc.update(1 )
					self.assertTrue(not dc.completed )
					self.assertTrue(dc.remaining() == 3 )
					self.assertTrue(dc.current_seq == [1] )

					lowerCamelCase_	,    lowerCamelCase_	,    lowerCamelCase_						=			dc.update(2 )
					self.assertTrue(not dc.completed )
					self.assertTrue(dc.remaining() == 2 )
					self.assertTrue(dc.current_seq == [1, 2] )

					lowerCamelCase_	,    lowerCamelCase_	,    lowerCamelCase_						=			dc.update(5 )
					self.assertTrue(dc.completed )  # Completed!
					self.assertTrue(dc.remaining() == 0 )
					self.assertTrue(dc.current_seq == [1, 2, 5] )




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					(    __lowerCamelCase     : List[Any]    ):
       return 1.0 / (1.0 + np.exp(-_outputs    ))


def 			UpperCamelCase					(    __lowerCamelCase     : int    ):
       snake_case       :   Tuple							=      np.max(_outputs				,					axis=-1				,					keepdims=__lowerCamelCase    )
       snake_case       :   int							=      np.exp(_outputs - maxes    )
       return shifted_exp / shifted_exp.sum(axis=-1				,					keepdims=__lowerCamelCase    )






class   UpperCAmelCase		(						A_ ):
   A__					:		Any						     =     "sigmoid"
   A__					:		str						     =     "softmax"
   A__					:		int						     =     "none"






@add_end_docstrings(
    A_       ,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   UpperCAmelCase		(						A_ ):
   A__					:		int						     =     False
   A__					:		Union[str, Any]						     =     ClassificationFunction.NONE



   def __init__(self		:					List[str]    ,						**snake_case__		:					int )		->	str:


          '''simple docstring'''


          super().__init__(**snake_case__ )

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



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[Any]    ,						snake_case__		:					List[str]=None    ,						snake_case__		:					Optional[Any]=None    ,						snake_case__		:					Union[str, Any]=""    ,						**snake_case__		:					List[str] )		->	Union[str, Any]:


          '''simple docstring'''


          snake_case       :   Dict							=      tokenizer_kwargs

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

          if isinstance(snake_case__    ,						snake_case__ ) or top_k is None:
                 snake_case       :   List[Any]							=      top_k
                 snake_case       :   str							=      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`."    ,						snake_case__    ,						)
                 if return_all_scores:
                        snake_case       :   List[str]							=      None
                 else:
                        snake_case       :   Optional[int]							=      1

          if isinstance(snake_case__    ,						snake_case__ ):
                 snake_case       :   Dict							=      ClassificationFunction[function_to_apply.upper()]

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



   def __call__(self		:					Dict    ,						*snake_case__		:					List[str]    ,						**snake_case__		:					int )		->	Optional[int]:


          '''simple docstring'''


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



   def 				_SCREAMING_SNAKE_CASE   (self		:					Dict    ,						snake_case__		:					Tuple    ,						**snake_case__		:					Union[str, Any] )		->	Dict[str, GenericTensor]:


          '''simple docstring'''


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



   def 				_SCREAMING_SNAKE_CASE   (self		:					int    ,						snake_case__		:					Union[str, Any] )		->	int:


          '''simple docstring'''


          return self.model(**snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[str]    ,						snake_case__		:					Optional[Any]    ,						snake_case__		:					List[str]=None    ,						snake_case__		:					Dict=1    ,						snake_case__		:					Tuple=True )		->	str:


          '''simple docstring'''


          if function_to_apply is None:
                 if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
                        snake_case       :   Tuple							=      ClassificationFunction.SIGMOID
                 elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
                        snake_case       :   Tuple							=      ClassificationFunction.SOFTMAX
                 elif hasattr(self.model.config    ,						"function_to_apply" ) and function_to_apply is None:
                        snake_case       :   Tuple							=      self.model.config.function_to_apply
                 else:
                        snake_case       :   int							=      ClassificationFunction.NONE

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

          if function_to_apply == ClassificationFunction.SIGMOID:
                 snake_case       :   Optional[Any]							=      sigmoid(snake_case__ )
          elif function_to_apply == ClassificationFunction.SOFTMAX:
                 snake_case       :   Union[str, Any]							=      softmax(snake_case__ )
          elif function_to_apply == ClassificationFunction.NONE:
                 snake_case       :   Optional[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()}

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





