



"""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      lowerCAmelCase__		(   lowerCamelCase_		:		List[Any]):





				'''simple docstring'''

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






def      lowerCAmelCase__		(   lowerCamelCase_		:		Union[str, Any]):





				'''simple docstring'''

				lowerCAmelCase__					:    Optional[Any]												=					np.max(_outputs	,axis=-1	,keepdims=lowerCamelCase_)
				lowerCAmelCase__					:    Union[str, Any]												=					np.exp(_outputs - maxes)
				return shifted_exp / shifted_exp.sum(axis=-1	,keepdims=lowerCamelCase_)



class 	lowerCamelCase__      (							lowerCamelCase__):




		'''simple docstring'''



		snake_case_     ="""sigmoid"""
		snake_case_     ="""softmax"""
		snake_case_     ="""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__):




		'''simple docstring'''



		snake_case_     =False
		snake_case_     =ClassificationFunction.NONE



		def __init__(self		,**__lowerCamelCase			)					->      List[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 		lowerCAmelCase__				(self		,__lowerCamelCase=None		,__lowerCamelCase=None		,__lowerCamelCase=""		,**__lowerCamelCase			)					->      str:

						"""simple docstring"""






						lowerCAmelCase__					:    Optional[Any]												=					tokenizer_kwargs

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

						if isinstance(__lowerCamelCase		,__lowerCamelCase			) or top_k is None:
										lowerCAmelCase__					:    List[str]												=					top_k
										lowerCAmelCase__					:    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`.'''		,__lowerCamelCase		,)
										if return_all_scores:
														lowerCAmelCase__					:    Optional[int]												=					None
										else:
														lowerCAmelCase__					:    Union[str, Any]												=					1

						if isinstance(__lowerCamelCase		,__lowerCamelCase			):
										lowerCAmelCase__					:    Dict												=					ClassificationFunction[function_to_apply.upper()]

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



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

						"""simple docstring"""






						lowerCAmelCase__					:    Dict												=					super().__call__(*__lowerCamelCase		,**__lowerCamelCase			)
						# TODO try and retrieve it in a nicer way from _sanitize_parameters.
						lowerCAmelCase__					:    int												=					'''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 		lowerCAmelCase__				(self		,__lowerCamelCase		,**__lowerCamelCase			)					->      Dict[str, GenericTensor]:

						"""simple docstring"""






						lowerCAmelCase__					:    Optional[Any]												=					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 		lowerCAmelCase__				(self		,__lowerCamelCase			)					->      str:

						"""simple docstring"""






						return self.model(**__lowerCamelCase			)



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase=None		,__lowerCamelCase=1		,__lowerCamelCase=True			)					->      Tuple:

						"""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__					:    Optional[int]												=					ClassificationFunction.SIGMOID
										elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
														lowerCAmelCase__					:    Tuple												=					ClassificationFunction.SOFTMAX
										elif hasattr(self.model.config		,'''function_to_apply'''			) and function_to_apply is None:
														lowerCAmelCase__					:    Optional[int]												=					self.model.config.function_to_apply
										else:
														lowerCAmelCase__					:    int												=					ClassificationFunction.NONE

						lowerCAmelCase__					:    Optional[int]												=					model_outputs['''logits'''][0]
						lowerCAmelCase__					:    str												=					outputs.numpy()

						if function_to_apply == ClassificationFunction.SIGMOID:
										lowerCAmelCase__					:    Optional[Any]												=					sigmoid(__lowerCamelCase			)
						elif function_to_apply == ClassificationFunction.SOFTMAX:
										lowerCAmelCase__					:    str												=					softmax(__lowerCamelCase			)
						elif function_to_apply == ClassificationFunction.NONE:
										lowerCAmelCase__					:    List[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__					:    str												=					[
						    {'''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:
														lowerCAmelCase__					:    int												=					dict_scores[:top_k]
						return dict_scores






import flax.linen as nn
import jax
import jax.numpy as jnp



class 	lowerCamelCase__      (							nn.Module):




		'''simple docstring'''



		snake_case_     =42
		snake_case_     =jnp.floataa



		def 		lowerCAmelCase__				(self			)					->      List[str]:

						"""simple docstring"""






						lowerCAmelCase__					:    Dict												=					nn.Conv(
						    self.out_channels		,kernel_size=(3, 3)		,strides=(1, 1)		,padding=((1, 1), (1, 1))		,dtype=self.dtype		,)



		def __call__(self		,__lowerCamelCase			)					->      Union[str, Any]:

						"""simple docstring"""






						lowerCAmelCase__						,		lowerCAmelCase__						,		lowerCAmelCase__						,		lowerCAmelCase__					:    Dict												=					hidden_states.shape
						lowerCAmelCase__					:    Dict												=					jax.image.resize(
						    __lowerCamelCase		,shape=(batch, height * 2, width * 2, channels)		,method='''nearest'''		,)
						lowerCAmelCase__					:    Dict												=					self.conv(__lowerCamelCase			)
						return hidden_states



class 	lowerCamelCase__      (							nn.Module):




		'''simple docstring'''



		snake_case_     =42
		snake_case_     =jnp.floataa



		def 		lowerCAmelCase__				(self			)					->      int:

						"""simple docstring"""






						lowerCAmelCase__					:    List[Any]												=					nn.Conv(
						    self.out_channels		,kernel_size=(3, 3)		,strides=(2, 2)		,padding=((1, 1), (1, 1))		,dtype=self.dtype		,)



		def __call__(self		,__lowerCamelCase			)					->      Any:

						"""simple docstring"""






						lowerCAmelCase__					:    Optional[int]												=					self.conv(__lowerCamelCase			)
						return hidden_states



class 	lowerCamelCase__      (							nn.Module):




		'''simple docstring'''



		snake_case_     =42
		snake_case_     =None
		snake_case_     =0.0
		snake_case_     =None
		snake_case_     =jnp.floataa



		def 		lowerCAmelCase__				(self			)					->      int:

						"""simple docstring"""






						lowerCAmelCase__					:    List[str]												=					self.in_channels if self.out_channels is None else self.out_channels

						lowerCAmelCase__					:    Union[str, Any]												=					nn.GroupNorm(num_groups=32		,epsilon=1e-5			)
						lowerCAmelCase__					:    Union[str, Any]												=					nn.Conv(
						    __lowerCamelCase		,kernel_size=(3, 3)		,strides=(1, 1)		,padding=((1, 1), (1, 1))		,dtype=self.dtype		,)

						lowerCAmelCase__					:    int												=					nn.Dense(__lowerCamelCase		,dtype=self.dtype			)

						lowerCAmelCase__					:    List[Any]												=					nn.GroupNorm(num_groups=32		,epsilon=1e-5			)
						lowerCAmelCase__					:    Union[str, Any]												=					nn.Dropout(self.dropout_prob			)
						lowerCAmelCase__					:    Optional[int]												=					nn.Conv(
						    __lowerCamelCase		,kernel_size=(3, 3)		,strides=(1, 1)		,padding=((1, 1), (1, 1))		,dtype=self.dtype		,)

						lowerCAmelCase__					:    str												=					self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut

						lowerCAmelCase__					:    Union[str, Any]												=					None
						if use_nin_shortcut:
										lowerCAmelCase__					:    Optional[Any]												=					nn.Conv(
										    __lowerCamelCase		,kernel_size=(1, 1)		,strides=(1, 1)		,padding='''VALID'''		,dtype=self.dtype		,)



		def __call__(self		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase=True			)					->      Union[str, Any]:

						"""simple docstring"""






						lowerCAmelCase__					:    Tuple												=					hidden_states
						lowerCAmelCase__					:    Dict												=					self.norma(__lowerCamelCase			)
						lowerCAmelCase__					:    Union[str, Any]												=					nn.swish(__lowerCamelCase			)
						lowerCAmelCase__					:    List[Any]												=					self.conva(__lowerCamelCase			)

						lowerCAmelCase__					:    List[Any]												=					self.time_emb_proj(nn.swish(__lowerCamelCase			)			)
						lowerCAmelCase__					:    Dict												=					jnp.expand_dims(jnp.expand_dims(__lowerCamelCase		,1			)		,1			)
						lowerCAmelCase__					:    Optional[int]												=					hidden_states + temb

						lowerCAmelCase__					:    Optional[int]												=					self.norma(__lowerCamelCase			)
						lowerCAmelCase__					:    Optional[int]												=					nn.swish(__lowerCamelCase			)
						lowerCAmelCase__					:    int												=					self.dropout(__lowerCamelCase		,__lowerCamelCase			)
						lowerCAmelCase__					:    List[Any]												=					self.conva(__lowerCamelCase			)

						if self.conv_shortcut is not None:
										lowerCAmelCase__					:    Optional[int]												=					self.conv_shortcut(__lowerCamelCase			)

						return hidden_states + residual

