






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       ):




	'''simple docstring'''







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





def 					snake_case	(  lowerCamelCase       ):




	'''simple docstring'''







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


class 							__UpperCamelCase     (  _lowerCAmelCase ):
	__snake_case						:Optional[Any] =							'sigmoid'
	__snake_case						:List[Any] =							'softmax'
	__snake_case						:Optional[int] =							'none'


@add_end_docstrings(
    _lowerCAmelCase ,      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     (  _lowerCAmelCase ):
	__snake_case						:Any =							False
	__snake_case						:int =							ClassificationFunction.NONE







	def __init__(	self   :				Any							,					**_lowerCAmelCase   :				Dict      )		->			Any:
		"""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   :				Tuple							,					_lowerCAmelCase   :				List[str]=None							,					_lowerCAmelCase   :				Union[str, Any]=None							,					_lowerCAmelCase   :				Any=""							,					**_lowerCAmelCase   :				Dict      )		->			Dict:
		"""simple docstring"""







		__lowercase   							=	tokenizer_kwargs

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

		if isinstance(_lowerCAmelCase							,					_lowerCAmelCase      ) or top_k is None:
			__lowercase   							=	top_k
			__lowercase   							=	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:
				__lowercase   							=	None
			else:
				__lowercase   							=	1

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

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







	def __call__(	self   :				str							,					*_lowerCAmelCase   :				List[Any]							,					**_lowerCAmelCase   :				str      )		->			str:
		"""simple docstring"""







		__lowercase   							=	super().__call__(*_lowerCAmelCase							,					**_lowerCAmelCase      )
		# TODO try and retrieve it in a nicer way from _sanitize_parameters.
		__lowercase   							=	"""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   :				str							,					_lowerCAmelCase   :				str							,					**_lowerCAmelCase   :				Optional[Any]      )		->			Dict[str, GenericTensor]:
		"""simple docstring"""







		__lowercase   							=	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   :				Optional[Any]							,					_lowerCAmelCase   :				Tuple      )		->			Dict:
		"""simple docstring"""







		return self.model(**_lowerCAmelCase      )







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

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

		if function_to_apply == ClassificationFunction.SIGMOID:
			__lowercase   							=	sigmoid(_lowerCAmelCase      )
		elif function_to_apply == ClassificationFunction.SOFTMAX:
			__lowercase   							=	softmax(_lowerCAmelCase      )
		elif function_to_apply == ClassificationFunction.NONE:
			__lowercase   							=	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   							=	[
		    {"""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:
				__lowercase   							=	dict_scores[:top_k]
		return dict_scores




from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available





@dataclass
class 					__lowerCAmelCase	(   SCREAMING_SNAKE_CASE				):
  _a		     =    42
  _a		     =    42


if is_transformers_available() and is_torch_available():
  from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline


