






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      __lowercase					( snake_case ):







				"""simple docstring"""






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

def      __lowercase					( snake_case ):







				"""simple docstring"""






				__magic_name__		:str        =      np.max(_outputs,  axis=-1,  keepdims=snake_case )
				__magic_name__		:Any        =      np.exp(_outputs - maxes )
				return shifted_exp / shifted_exp.sum(axis=-1,  keepdims=snake_case )
class 						lowerCamelCase_				(      lowerCamelCase ):
				a__					       =  '''sigmoid'''
				a__					       =  '''softmax'''
				a__					       =  '''none'''







@add_end_docstrings(
    lowerCamelCase			,				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_				(      lowerCamelCase ):
				a__					       =  False
				a__					       =  ClassificationFunction.NONE







				def __init__(						self       ,					**__lowerCAmelCase     ):
								"""simple docstring"""
								super().__init__(**__lowerCAmelCase     )

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







				def 			A	(						self       ,					__lowerCAmelCase=None       ,					__lowerCAmelCase=None       ,					__lowerCAmelCase=""       ,					**__lowerCAmelCase     ):
								"""simple docstring"""
								__magic_name__		:Dict        =      tokenizer_kwargs

								__magic_name__		:List[str]        =      {}
								if hasattr(self.model.config       ,					'''return_all_scores'''     ) and return_all_scores is None:
												__magic_name__		:int        =      self.model.config.return_all_scores

								if isinstance(__lowerCAmelCase       ,					__lowerCAmelCase     ) or top_k is None:
												__magic_name__		:str        =      top_k
												__magic_name__		:Any        =      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`.'''       ,					__lowerCAmelCase       ,					)
												if return_all_scores:
																__magic_name__		:int        =      None
												else:
																__magic_name__		:Tuple        =      1

								if isinstance(__lowerCAmelCase       ,					__lowerCAmelCase     ):
												__magic_name__		:str        =      ClassificationFunction[function_to_apply.upper()]

								if function_to_apply is not None:
												__magic_name__		:Union[str, Any]        =      function_to_apply
								return preprocess_params, {}, postprocess_params







				def __call__(						self       ,					*__lowerCAmelCase       ,					**__lowerCAmelCase     ):
								"""simple docstring"""
								__magic_name__		:Union[str, Any]        =      super().__call__(*__lowerCAmelCase       ,					**__lowerCAmelCase     )
								# TODO try and retrieve it in a nicer way from _sanitize_parameters.
								__magic_name__		:Optional[Any]        =      '''top_k''' not in kwargs
								if isinstance(args[0]       ,					__lowerCAmelCase     ) and _legacy:
												# This pipeline is odd, and return a list when single item is run
												return [result]
								else:
												return result







				def 			A	(						self       ,					__lowerCAmelCase       ,					**__lowerCAmelCase     ):
								"""simple docstring"""
								__magic_name__		:List[str]        =      self.framework
								if isinstance(__lowerCAmelCase       ,					__lowerCAmelCase     ):
												return self.tokenizer(**__lowerCAmelCase       ,					return_tensors=__lowerCAmelCase       ,					**__lowerCAmelCase     )
								elif isinstance(__lowerCAmelCase       ,					__lowerCAmelCase     ) and len(__lowerCAmelCase     ) == 1 and isinstance(inputs[0]       ,					__lowerCAmelCase     ) 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=__lowerCAmelCase       ,					**__lowerCAmelCase     )
								elif isinstance(__lowerCAmelCase       ,					__lowerCAmelCase     ):
												# 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(__lowerCAmelCase       ,					return_tensors=__lowerCAmelCase       ,					**__lowerCAmelCase     )







				def 			A	(						self       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								return self.model(**__lowerCAmelCase     )







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase=None       ,					__lowerCAmelCase=1       ,					__lowerCAmelCase=True     ):
								"""simple docstring"""
								# `_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:
																__magic_name__		:Union[str, Any]        =      ClassificationFunction.SIGMOID
												elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
																__magic_name__		:Tuple        =      ClassificationFunction.SOFTMAX
												elif hasattr(self.model.config       ,					'''function_to_apply'''     ) and function_to_apply is None:
																__magic_name__		:Dict        =      self.model.config.function_to_apply
												else:
																__magic_name__		:Tuple        =      ClassificationFunction.NONE

								__magic_name__		:List[Any]        =      model_outputs['''logits'''][0]
								__magic_name__		:Union[str, Any]        =      outputs.numpy()

								if function_to_apply == ClassificationFunction.SIGMOID:
												__magic_name__		:int        =      sigmoid(__lowerCAmelCase     )
								elif function_to_apply == ClassificationFunction.SOFTMAX:
												__magic_name__		:Optional[int]        =      softmax(__lowerCAmelCase     )
								elif function_to_apply == ClassificationFunction.NONE:
												__magic_name__		: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()}

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














from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE__      :			Tuple							=		{
    """configuration_table_transformer""": [
        """TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """TableTransformerConfig""",
        """TableTransformerOnnxConfig""",
    ]
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__      :			List[str]							=		[
					    """TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """TableTransformerForObjectDetection""",
					    """TableTransformerModel""",
					    """TableTransformerPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_table_transformer import (
					    TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    TableTransformerConfig,
					    TableTransformerOnnxConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_table_transformer import (
										    TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TableTransformerForObjectDetection,
										    TableTransformerModel,
										    TableTransformerPreTrainedModel,
										)

else:
					import sys

					SCREAMING_SNAKE_CASE__      :			Union[str, Any]							=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






