





"""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__							(  SCREAMING_SNAKE_CASE     :						List[Any]			):







      '''simple docstring'''




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


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						Dict			):







      '''simple docstring'''




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

class 		lowercase					(					_UpperCAmelCase					):
      _SCREAMING_SNAKE_CASE					=      'sigmoid'
      _SCREAMING_SNAKE_CASE					=      'softmax'
      _SCREAMING_SNAKE_CASE					=      '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 		lowercase					(					_UpperCAmelCase					):
      _SCREAMING_SNAKE_CASE					=      False
      _SCREAMING_SNAKE_CASE					=      ClassificationFunction.NONE





      def __init__(       self      ,						**lowercase )		->     Tuple:
            super().__init__(**lowercase )

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





      def     _snake_case			(       self      ,						lowercase=None      ,						lowercase=None      ,						lowercase=""      ,						**lowercase )		->     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(lowercase      ,						lowercase ) 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`."""      ,						lowercase      ,						)
                  if return_all_scores:
                        lowerCAmelCase  						=       None
                  else:
                        lowerCAmelCase  						=       1

            if isinstance(lowercase      ,						lowercase ):
                  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      ,						*lowercase      ,						**lowercase )		->     int:
            lowerCAmelCase  						=       super().__call__(*lowercase      ,						**lowercase )
            # TODO try and retrieve it in a nicer way from _sanitize_parameters.
            lowerCAmelCase  						=       """top_k""" not in kwargs
            if isinstance(args[0]      ,						lowercase ) and _legacy:
                  # This pipeline is odd, and return a list when single item is run
                  return [result]
            else:
                  return result





      def     _snake_case			(       self      ,						lowercase      ,						**lowercase )		->     Dict[str, GenericTensor]:
            lowerCAmelCase  						=       self.framework
            if isinstance(lowercase      ,						lowercase ):
                  return self.tokenizer(**lowercase      ,						return_tensors=lowercase      ,						**lowercase )
            elif isinstance(lowercase      ,						lowercase ) and len(lowercase ) == 1 and isinstance(inputs[0]      ,						lowercase ) 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=lowercase      ,						**lowercase )
            elif isinstance(lowercase      ,						lowercase ):
                  # 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(lowercase      ,						return_tensors=lowercase      ,						**lowercase )





      def     _snake_case			(       self      ,						lowercase )		->     Optional[Any]:
            return self.model(**lowercase )





      def     _snake_case			(       self      ,						lowercase      ,						lowercase=None      ,						lowercase=1      ,						lowercase=True )		->     Optional[int]:
            # `_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(lowercase )
            elif function_to_apply == ClassificationFunction.SOFTMAX:
                  lowerCAmelCase  						=       softmax(lowercase )
            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(lowercase )
            ]
            if not _legacy:
                  dict_scores.sort(key=lambda lowercase : x["score"]      ,						reverse=lowercase )
                  if top_k is not None:
                        lowerCAmelCase  						=       dict_scores[:top_k]
            return dict_scores













"""simple docstring"""





from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer

@dataclass
class 		lowercase					(					_UpperCAmelCase					):
      _SCREAMING_SNAKE_CASE					=      42







class 		lowercase					(					_UpperCAmelCase     ,  _UpperCAmelCase					):





      @register_to_config
      def __init__(       self      ,						lowercase = 3      ,						lowercase = 3      ,						lowercase = ("DownEncoderBlock2D",)      ,						lowercase = ("UpDecoderBlock2D",)      ,						lowercase = (64,)      ,						lowercase = 1      ,						lowercase = "silu"      ,						lowercase = 3      ,						lowercase = 32      ,						lowercase = 256      ,						lowercase = 32      ,						lowercase = None      ,						lowercase = 0.18_215      ,						lowercase = "group"      ,						)		->     Union[str, Any]:
            super().__init__()

            # pass init params to Encoder
            lowerCAmelCase  						=       Encoder(
                in_channels=lowercase      ,						out_channels=lowercase      ,						down_block_types=lowercase      ,						block_out_channels=lowercase      ,						layers_per_block=lowercase      ,						act_fn=lowercase      ,						norm_num_groups=lowercase      ,						double_z=lowercase      ,						)

            lowerCAmelCase  						=       vq_embed_dim if vq_embed_dim is not None else latent_channels

            lowerCAmelCase  						=       nn.Convad(lowercase      ,						lowercase      ,						1 )
            lowerCAmelCase  						=       VectorQuantizer(lowercase      ,						lowercase      ,						beta=0.25      ,						remap=lowercase      ,						sane_index_shape=lowercase )
            lowerCAmelCase  						=       nn.Convad(lowercase      ,						lowercase      ,						1 )

            # pass init params to Decoder
            lowerCAmelCase  						=       Decoder(
                in_channels=lowercase      ,						out_channels=lowercase      ,						up_block_types=lowercase      ,						block_out_channels=lowercase      ,						layers_per_block=lowercase      ,						act_fn=lowercase      ,						norm_num_groups=lowercase      ,						norm_type=lowercase      ,						)





      @apply_forward_hook
      def     _snake_case			(       self      ,						lowercase      ,						lowercase = True )		->     VQEncoderOutput:
            lowerCAmelCase  						=       self.encoder(lowercase )
            lowerCAmelCase  						=       self.quant_conv(lowercase )

            if not return_dict:
                  return (h,)

            return VQEncoderOutput(latents=lowercase )





      @apply_forward_hook
      def     _snake_case			(       self      ,						lowercase      ,						lowercase = False      ,						lowercase = True )		->     Union[DecoderOutput, torch.FloatTensor]:
            # also go through quantization layer
            if not force_not_quantize:
                  lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase  						=       self.quantize(lowercase )
            else:
                  lowerCAmelCase  						=       h
            lowerCAmelCase  						=       self.post_quant_conv(lowercase )
            lowerCAmelCase  						=       self.decoder(lowercase      ,						quant if self.config.norm_type == """spatial""" else None )

            if not return_dict:
                  return (dec,)

            return DecoderOutput(sample=lowercase )





      def     _snake_case			(       self      ,						lowercase      ,						lowercase = True )		->     Union[DecoderOutput, torch.FloatTensor]:
            lowerCAmelCase  						=       sample
            lowerCAmelCase  						=       self.encode(lowercase ).latents
            lowerCAmelCase  						=       self.decode(lowercase ).sample

            if not return_dict:
                  return (dec,)

            return DecoderOutput(sample=lowercase )






