



'''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 						__UpperCamelCase				(							_lowercase					) ->			int:
	return 1.0 / (1.0 + np.exp(-_outputs					))







def 						__UpperCamelCase				(							_lowercase					) ->			List[str]:
	_lowercase					:       Union[str, Any]       =		np.max(_outputs,				axis=-1,				keepdims=_lowercase					)
	_lowercase					:       List[Any]       =		np.exp(_outputs - maxes					)
	return shifted_exp / shifted_exp.sum(axis=-1,				keepdims=_lowercase					)



class 		lowerCamelCase__      (						A     ):




					'''simple docstring'''



					A_						    =						"""sigmoid"""
					A_						    =						"""softmax"""
					A_						    =						"""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_						    =						False
					A_						    =						ClassificationFunction.NONE




					def __init__(  self				:	List[Any]     ,  **UpperCamelCase_				:	Tuple					)  ->       int:



						'''simple docstring'''





						super().__init__(**UpperCamelCase_					)

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




					def 						__UpperCAmelCase     (  self				:	Dict     ,  UpperCamelCase_				:	str=None     ,  UpperCamelCase_				:	Union[str, Any]=None     ,  UpperCamelCase_				:	Any=""     ,  **UpperCamelCase_				:	Optional[Any]					)  ->       Optional[int]:



						'''simple docstring'''





						_lowercase					:       Optional[int]       =		tokenizer_kwargs

						_lowercase					:       Optional[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(UpperCamelCase_     ,  UpperCamelCase_					) or top_k is None:
							_lowercase					:       List[Any]       =		top_k
							_lowercase					:       Tuple       =		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:
								_lowercase					:       Optional[int]       =		None
							else:
								_lowercase					:       int       =		1

						if isinstance(UpperCamelCase_     ,  UpperCamelCase_					):
							_lowercase					:       Dict       =		ClassificationFunction[function_to_apply.upper()]

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




					def __call__(  self				:	Tuple     ,  *UpperCamelCase_				:	List[str]     ,  **UpperCamelCase_				:	Union[str, Any]					)  ->       str:



						'''simple docstring'''





						_lowercase					:       Optional[int]       =		super().__call__(*UpperCamelCase_     ,  **UpperCamelCase_					)
						# TODO try and retrieve it in a nicer way from _sanitize_parameters.
						_lowercase					:       Union[str, Any]       =		'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 						__UpperCAmelCase     (  self				:	int     ,  UpperCamelCase_				:	Union[str, Any]     ,  **UpperCamelCase_				:	Union[str, Any]					)  ->       Dict[str, GenericTensor]:



						'''simple docstring'''





						_lowercase					:       Optional[Any]       =		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 						__UpperCAmelCase     (  self				:	List[str]     ,  UpperCamelCase_				:	List[Any]					)  ->       Tuple:



						'''simple docstring'''





						return self.model(**UpperCamelCase_					)




					def 						__UpperCAmelCase     (  self				:	Tuple     ,  UpperCamelCase_				:	Optional[int]     ,  UpperCamelCase_				:	List[Any]=None     ,  UpperCamelCase_				:	Optional[int]=1     ,  UpperCamelCase_				:	List[Any]=True					)  ->       Union[str, 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					:       Any       =		ClassificationFunction.SIGMOID
							elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
								_lowercase					:       str       =		ClassificationFunction.SOFTMAX
							elif hasattr(self.model.config     ,  'function_to_apply'					) and function_to_apply is None:
								_lowercase					:       List[Any]       =		self.model.config.function_to_apply
							else:
								_lowercase					:       List[str]       =		ClassificationFunction.NONE

						_lowercase					:       Optional[Any]       =		model_outputs['logits'][0]
						_lowercase					:       int       =		outputs.numpy()

						if function_to_apply == ClassificationFunction.SIGMOID:
							_lowercase					:       Union[str, Any]       =		sigmoid(UpperCamelCase_					)
						elif function_to_apply == ClassificationFunction.SOFTMAX:
							_lowercase					:       List[Any]       =		softmax(UpperCamelCase_					)
						elif function_to_apply == ClassificationFunction.NONE:
							_lowercase					:       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()}

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





'''simple docstring'''

from collections import UserDict
from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
		from PIL import Image

		from ..image_utils import load_image

if is_torch_available():
		from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING

if is_tf_available():
		from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
		from ..tf_utils import stable_softmax

_A				:    Optional[int]     						=logging.get_logger(__name__)



@add_end_docstrings(A     )
class 		lowerCamelCase__      (						A     ):




					'''simple docstring'''



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



						'''simple docstring'''





						super().__init__(**UpperCamelCase_					)

						requires_backends(self     ,  'vision'					)
						self.check_model_type(
						    TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
						    if self.framework == 'tf'
						    else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING					)




					def __call__(  self				:	int     ,  UpperCamelCase_				:	Union[str, List[str], "Image", List["Image"]]     ,  **UpperCamelCase_				:	Tuple					)  ->       List[Any]:



						'''simple docstring'''





						return super().__call__(UpperCamelCase_     ,  **UpperCamelCase_					)




					def 						__UpperCAmelCase     (  self				:	List[Any]     ,  **UpperCamelCase_				:	str					)  ->       List[str]:



						'''simple docstring'''





						_lowercase					:       Optional[int]       =		{}
						if "candidate_labels" in kwargs:
							_lowercase					:       Union[str, Any]       =		kwargs['candidate_labels']
						if "hypothesis_template" in kwargs:
							_lowercase					:       int       =		kwargs['hypothesis_template']

						return preprocess_params, {}, {}




					def 						__UpperCAmelCase     (  self				:	Tuple     ,  UpperCamelCase_				:	Dict     ,  UpperCamelCase_				:	Optional[Any]=None     ,  UpperCamelCase_				:	str="This is a photo of {}."					)  ->       Union[str, Any]:



						'''simple docstring'''





						_lowercase					:       Dict       =		load_image(UpperCamelCase_					)
						_lowercase					:       List[str]       =		self.image_processor(images=[image]     ,  return_tensors=self.framework					)
						_lowercase					:       Optional[Any]       =		candidate_labels
						_lowercase					:       List[Any]       =		[hypothesis_template.format(UpperCamelCase_					) for x in candidate_labels]
						_lowercase					:       Union[str, Any]       =		self.tokenizer(UpperCamelCase_     ,  return_tensors=self.framework     ,  padding=UpperCamelCase_					)
						_lowercase					:       Any       =		[text_inputs]
						return inputs




					def 						__UpperCAmelCase     (  self				:	str     ,  UpperCamelCase_				:	Union[str, Any]					)  ->       Optional[Any]:



						'''simple docstring'''





						_lowercase					:       Optional[Any]       =		model_inputs.pop('candidate_labels'					)
						_lowercase					:       List[str]       =		model_inputs.pop('text_inputs'					)
						if isinstance(text_inputs[0]     ,  UpperCamelCase_					):
							_lowercase					:       Optional[int]       =		text_inputs[0]
						else:
							# Batching case.
							_lowercase					:       List[str]       =		text_inputs[0][0]

						_lowercase					:       Optional[Any]       =		self.model(**UpperCamelCase_     ,  **UpperCamelCase_					)

						_lowercase					:       Optional[Any]       =		{
						    'candidate_labels': candidate_labels,
						    'logits': outputs.logits_per_image,
						}
						return model_outputs




					def 						__UpperCAmelCase     (  self				:	Optional[int]     ,  UpperCamelCase_				:	int					)  ->       List[str]:



						'''simple docstring'''





						_lowercase					:       Optional[int]       =		model_outputs.pop('candidate_labels'					)
						_lowercase					:       Optional[int]       =		model_outputs['logits'][0]
						if self.framework == "pt":
							_lowercase					:       List[Any]       =		logits.softmax(dim=-1					).squeeze(-1					)
							_lowercase					:       Tuple       =		probs.tolist()
							if not isinstance(UpperCamelCase_     ,  UpperCamelCase_					):
								_lowercase					:       List[Any]       =		[scores]
						elif self.framework == "tf":
							_lowercase					:       Optional[int]       =		stable_softmax(UpperCamelCase_     ,  axis=-1					)
							_lowercase					:       List[Any]       =		probs.numpy().tolist()
						else:
							raise ValueError(F'''Unsupported framework: {self.framework}'''					)

						_lowercase					:       List[Any]       =		[
						    {'score': score, 'label': candidate_label}
						    for score, candidate_label in sorted(zip(UpperCamelCase_     ,  UpperCamelCase_					)     ,  key=lambda UpperCamelCase_					: -x[0]					)
						]
						return result
