




"""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		(		snake_case			:					Any    ):
					return 1.0 / (1.0 + np.exp(-_outputs    ))





def 							UpperCAmelCase		(		snake_case			:					Optional[Any]    ):
					_lowerCAmelCase:Optional[Any]									=  np.max(_outputs      ,       axis=-1      ,       keepdims=snake_case    )
					_lowerCAmelCase:List[Any]									=  np.exp(_outputs - maxes    )
					return shifted_exp / shifted_exp.sum(axis=-1      ,       keepdims=snake_case    )




class   a__			(       UpperCamelCase_  ):
						snake_case__             = '''sigmoid'''
						snake_case__             = '''softmax'''
						snake_case__             = '''none'''




@add_end_docstrings(
    UpperCamelCase_					,	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   a__			(       UpperCamelCase_  ):
						snake_case__             = False
						snake_case__             = ClassificationFunction.NONE





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




											"""simple docstring"""







											super().__init__(**a__)

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





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




											"""simple docstring"""







											_lowerCAmelCase:List[Any]									=  tokenizer_kwargs

											_lowerCAmelCase:Union[str, Any]									=  {}
											if hasattr(self.model.config     ,'''return_all_scores''') and return_all_scores is None:
																_lowerCAmelCase:str									=  self.model.config.return_all_scores

											if isinstance(a__     ,a__) or top_k is None:
																_lowerCAmelCase:Union[str, Any]									=  top_k
																_lowerCAmelCase: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`.'''     ,a__     ,)
																if return_all_scores:
																					_lowerCAmelCase:Tuple									=  None
																else:
																					_lowerCAmelCase:Union[str, Any]									=  1

											if isinstance(a__     ,a__):
																_lowerCAmelCase:Union[str, Any]									=  ClassificationFunction[function_to_apply.upper()]

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





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




											"""simple docstring"""







											_lowerCAmelCase:Dict									=  super().__call__(*a__     ,**a__)
											# TODO try and retrieve it in a nicer way from _sanitize_parameters.
											_lowerCAmelCase:str									=  '''top_k''' not in kwargs
											if isinstance(args[0]     ,a__) and _legacy:
																# This pipeline is odd, and return a list when single item is run
																return [result]
											else:
																return result





						def       __UpperCamelCase     (							self      :	Union[str, Any]     ,a__      :	Optional[Any]     ,**a__      :	Optional[int])						->       Dict[str, GenericTensor]:




											"""simple docstring"""







											_lowerCAmelCase:Dict									=  self.framework
											if isinstance(a__     ,a__):
																return self.tokenizer(**a__     ,return_tensors=a__     ,**a__)
											elif isinstance(a__     ,a__) and len(a__) == 1 and isinstance(inputs[0]     ,a__) 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=a__     ,**a__)
											elif isinstance(a__     ,a__):
																# 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(a__     ,return_tensors=a__     ,**a__)





						def       __UpperCamelCase     (							self      :	List[Any]     ,a__      :	List[str])						->       Optional[Any]:




											"""simple docstring"""







											return self.model(**a__)







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

											_lowerCAmelCase:List[Any]									=  model_outputs['''logits'''][0]
											_lowerCAmelCase:Optional[Any]									=  outputs.numpy()

											if function_to_apply == ClassificationFunction.SIGMOID:
																_lowerCAmelCase:Dict									=  sigmoid(a__)
											elif function_to_apply == ClassificationFunction.SOFTMAX:
																_lowerCAmelCase:Dict									=  softmax(a__)
											elif function_to_apply == ClassificationFunction.NONE:
																_lowerCAmelCase:Union[str, 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()}

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












"""simple docstring"""






# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin




class   a__			(       UpperCamelCase_					,	UpperCamelCase_  ):
						snake_case__             = 1





						@register_to_config
						def __init__(							self      :	Union[str, Any]     ,a__      :	List[str]=2000     ,a__      :	Tuple=0.1     ,a__      :	Union[str, Any]=20     ,a__      :	int=1E-3)						->       Any:




											"""simple docstring"""







											_lowerCAmelCase:Optional[Any]									=  None
											_lowerCAmelCase:List[Any]									=  None
											_lowerCAmelCase:Dict									=  None





						def       __UpperCamelCase     (							self      :	int     ,a__      :	Any     ,a__      :	Union[str, torch.device] = None)						->       List[str]:




											"""simple docstring"""







											_lowerCAmelCase:str									=  torch.linspace(1     ,self.config.sampling_eps     ,a__     ,device=a__)





						def       __UpperCamelCase     (							self      :	Union[str, Any]     ,a__      :	str     ,a__      :	List[Any]     ,a__      :	Union[str, Any]     ,a__      :	Any=None)						->       Tuple:




											"""simple docstring"""







											if self.timesteps is None:
																raise ValueError(
																    '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler''')

											# TODO(Patrick) better comments + non-PyTorch
											# postprocess model score
											_lowerCAmelCase:Union[str, Any]									=  (
											    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
											)
											_lowerCAmelCase:Dict									=  torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff))
											_lowerCAmelCase:Optional[Any]									=  std.flatten()
											while len(std.shape) < len(score.shape):
																_lowerCAmelCase:str									=  std.unsqueeze(-1)
											_lowerCAmelCase:Optional[int]									=  -score / std

											# compute
											_lowerCAmelCase:Optional[Any]									=  -1.0 / len(self.timesteps)

											_lowerCAmelCase:Union[str, Any]									=  self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
											_lowerCAmelCase:Any									=  beta_t.flatten()
											while len(beta_t.shape) < len(x.shape):
																_lowerCAmelCase:List[str]									=  beta_t.unsqueeze(-1)
											_lowerCAmelCase:Tuple									=  -0.5 * beta_t * x

											_lowerCAmelCase:str									=  torch.sqrt(a__)
											_lowerCAmelCase:Union[str, Any]									=  drift - diffusion**2 * score
											_lowerCAmelCase:List[str]									=  x + drift * dt

											# add noise
											_lowerCAmelCase:Any									=  randn_tensor(x.shape     ,layout=x.layout     ,generator=a__     ,device=x.device     ,dtype=x.dtype)
											_lowerCAmelCase:Union[str, Any]									=  x_mean + diffusion * math.sqrt(-dt) * noise

											return x, x_mean







						def __len__(							self      :	int)						->       Tuple:




											"""simple docstring"""







											return self.config.num_train_timesteps






