




"""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 							lowercase					(snake_case__   : Optional[int] )     ->  Optional[int]:


				'''simple docstring'''






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





def 							lowercase					(snake_case__   : Optional[int] )     ->  str:


				'''simple docstring'''






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







class       SCREAMING_SNAKE_CASE__					(					__UpperCAmelCase					):
						_a    						=						"""sigmoid"""
						_a    						=						"""softmax"""
						_a    						=						"""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       SCREAMING_SNAKE_CASE__					(					__UpperCAmelCase					):
						_a    						=						False
						_a    						=						ClassificationFunction.NONE


						def __init__(							self				:			int						,						**lowerCAmelCase				:			Tuple					):
										super().__init__(**UpperCAmelCase_					)

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


						def   __lowercase (							self				:			Optional[Any]						,						lowerCAmelCase				:			str=None						,						lowerCAmelCase				:			Union[str, Any]=None						,						lowerCAmelCase				:			Union[str, Any]=""						,						**lowerCAmelCase				:			Dict					):
										# Using "" as default argument because we're going to use `top_k=None` in user code to declare
										# "No top_k"
										lowerCAmelCase  =				tokenizer_kwargs

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

										if isinstance(UpperCAmelCase_						,						UpperCAmelCase_					) or top_k is None:
														lowerCAmelCase  =				top_k
														lowerCAmelCase  =				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:
																		lowerCAmelCase  =				None
														else:
																		lowerCAmelCase  =				1

										if isinstance(UpperCAmelCase_						,						UpperCAmelCase_					):
														lowerCAmelCase  =				ClassificationFunction[function_to_apply.upper()]

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


						def __call__(							self				:			Dict						,						*lowerCAmelCase				:			str						,						**lowerCAmelCase				:			str					):
										lowerCAmelCase  =				super().__call__(*UpperCAmelCase_						,						**UpperCAmelCase_					)
										# TODO try and retrieve it in a nicer way from _sanitize_parameters.
										lowerCAmelCase  =				"""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   __lowercase (							self				:			Dict						,						lowerCAmelCase				:			Union[str, Any]						,						**lowerCAmelCase				:			str					):
										lowerCAmelCase  =				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   __lowercase (							self				:			Any						,						lowerCAmelCase				:			Optional[Any]					):
										return self.model(**UpperCAmelCase_					)




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

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

										if function_to_apply == ClassificationFunction.SIGMOID:
														lowerCAmelCase  =				sigmoid(UpperCAmelCase_					)
										elif function_to_apply == ClassificationFunction.SOFTMAX:
														lowerCAmelCase  =				softmax(UpperCAmelCase_					)
										elif function_to_apply == ClassificationFunction.NONE:
														lowerCAmelCase  =				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()}

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











"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


a      =							{
    'configuration_owlvit': [
        'OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'OwlViTConfig',
        'OwlViTOnnxConfig',
        'OwlViTTextConfig',
        'OwlViTVisionConfig',
    ],
    'processing_owlvit': ['OwlViTProcessor'],
}


try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a      =							['OwlViTFeatureExtractor']
		a      =							['OwlViTImageProcessor']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a      =							[
		    'OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'OwlViTModel',
		    'OwlViTPreTrainedModel',
		    'OwlViTTextModel',
		    'OwlViTVisionModel',
		    'OwlViTForObjectDetection',
		]

if TYPE_CHECKING:
		from .configuration_owlvit import (
		    OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    OwlViTConfig,
		    OwlViTOnnxConfig,
		    OwlViTTextConfig,
		    OwlViTVisionConfig,
		)
		from .processing_owlvit import OwlViTProcessor

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_owlvit import OwlViTFeatureExtractor
				from .image_processing_owlvit import OwlViTImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_owlvit import (
				    OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    OwlViTForObjectDetection,
				    OwlViTModel,
				    OwlViTPreTrainedModel,
				    OwlViTTextModel,
				    OwlViTVisionModel,
				)

else:
		import sys

		a      =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





