
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_   )		->	str:


  '''simple docstring'''






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

def 		UpperCamelCase	(      lowercase_   )		->	Union[str, Any]:


  '''simple docstring'''






  lowercase__  :   Optional[Any]    		=      np.max(_outputs , axis=-1 , keepdims=lowercase_   )
  lowercase__  :   Optional[int]    		=      np.exp(_outputs - maxes   )
  return shifted_exp / shifted_exp.sum(axis=-1 , keepdims=lowercase_   )



class       _snake_case   (	UpperCAmelCase_							):
 __lowerCAmelCase  :     Dict        =			'sigmoid'
 __lowerCAmelCase  :     List[str]        =			'softmax'
 __lowerCAmelCase  :     Optional[int]        =			'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       _snake_case   (	UpperCAmelCase_							):
 __lowerCAmelCase  :     Optional[int]        =			False
 __lowerCAmelCase  :     str        =			ClassificationFunction.NONE


 def __init__(					self					,  **SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   super().__init__(**SCREAMING_SNAKE_CASE_)

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


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_=None					,  SCREAMING_SNAKE_CASE_=None					,  SCREAMING_SNAKE_CASE_=""					,  **SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__  :   Any    		=      tokenizer_kwargs

   lowercase__  :   Any    		=      {}
   if hasattr(self.model.config					,  """return_all_scores""") and return_all_scores is None:
     lowercase__  :   List[str]    		=      self.model.config.return_all_scores

   if isinstance(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_) or top_k is None:
     lowercase__  :   Union[str, Any]    		=      top_k
     lowercase__  :   Dict    		=      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`."""					,  SCREAMING_SNAKE_CASE_					,  )
     if return_all_scores:
       lowercase__  :   Tuple    		=      None
     else:
       lowercase__  :   Dict    		=      1

   if isinstance(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_):
     lowercase__  :   str    		=      ClassificationFunction[function_to_apply.upper()]

   if function_to_apply is not None:
     lowercase__  :   List[str]    		=      function_to_apply
   return preprocess_params, {}, postprocess_params


 def __call__(					self					,  *SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




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


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__  :   Optional[Any]    		=      self.framework
   if isinstance(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_):
     return self.tokenizer(**SCREAMING_SNAKE_CASE_					,  return_tensors=SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_)
   elif isinstance(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_) and len(SCREAMING_SNAKE_CASE_) == 1 and isinstance(inputs[0]					,  SCREAMING_SNAKE_CASE_) 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=SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_)
   elif isinstance(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_):
     # 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(SCREAMING_SNAKE_CASE_					,  return_tensors=SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_)


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   return self.model(**SCREAMING_SNAKE_CASE_)



 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_=None					,  SCREAMING_SNAKE_CASE_=1					,  SCREAMING_SNAKE_CASE_=True):


   '''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__  :   Dict    		=      ClassificationFunction.SIGMOID
     elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
       lowercase__  :   List[str]    		=      ClassificationFunction.SOFTMAX
     elif hasattr(self.model.config					,  """function_to_apply""") and function_to_apply is None:
       lowercase__  :   Union[str, Any]    		=      self.model.config.function_to_apply
     else:
       lowercase__  :   Any    		=      ClassificationFunction.NONE

   lowercase__  :   List[str]    		=      model_outputs["""logits"""][0]
   lowercase__  :   Optional[int]    		=      outputs.numpy()

   if function_to_apply == ClassificationFunction.SIGMOID:
     lowercase__  :   Union[str, Any]    		=      sigmoid(SCREAMING_SNAKE_CASE_)
   elif function_to_apply == ClassificationFunction.SOFTMAX:
     lowercase__  :   int    		=      softmax(SCREAMING_SNAKE_CASE_)
   elif function_to_apply == ClassificationFunction.NONE:
     lowercase__  :   int    		=      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(SCREAMING_SNAKE_CASE_)
   ]
   if not _legacy:
     dict_scores.sort(key=lambda SCREAMING_SNAKE_CASE_: x["score"]					,  reverse=SCREAMING_SNAKE_CASE_)
     if top_k is not None:
       lowercase__  :   Union[str, Any]    		=      dict_scores[:top_k]
   return dict_scores








import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig


lowerCamelCase__    :		List[str]          =   logging.get_logger(__name__)

lowerCamelCase__    :		str          =   {
    """Intel/dpt-large""": """https://huggingface.co/Intel/dpt-large/resolve/main/config.json""",
    # See all DPT models at https://huggingface.co/models?filter=dpt
}



class       _snake_case   (	UpperCAmelCase_							):
 __lowerCAmelCase  :     Optional[int]        =			'dpt'


 def __init__(					self					,  SCREAMING_SNAKE_CASE_=7_68					,  SCREAMING_SNAKE_CASE_=12					,  SCREAMING_SNAKE_CASE_=12					,  SCREAMING_SNAKE_CASE_=30_72					,  SCREAMING_SNAKE_CASE_="gelu"					,  SCREAMING_SNAKE_CASE_=0.0					,  SCREAMING_SNAKE_CASE_=0.0					,  SCREAMING_SNAKE_CASE_=0.0_2					,  SCREAMING_SNAKE_CASE_=1E-12					,  SCREAMING_SNAKE_CASE_=3_84					,  SCREAMING_SNAKE_CASE_=16					,  SCREAMING_SNAKE_CASE_=3					,  SCREAMING_SNAKE_CASE_=False					,  SCREAMING_SNAKE_CASE_=True					,  SCREAMING_SNAKE_CASE_=[2, 5, 8, 11]					,  SCREAMING_SNAKE_CASE_="project"					,  SCREAMING_SNAKE_CASE_=[4, 2, 1, 0.5]					,  SCREAMING_SNAKE_CASE_=[96, 1_92, 3_84, 7_68]					,  SCREAMING_SNAKE_CASE_=2_56					,  SCREAMING_SNAKE_CASE_=-1					,  SCREAMING_SNAKE_CASE_=False					,  SCREAMING_SNAKE_CASE_=True					,  SCREAMING_SNAKE_CASE_=0.4					,  SCREAMING_SNAKE_CASE_=2_55					,  SCREAMING_SNAKE_CASE_=0.1					,  SCREAMING_SNAKE_CASE_=[1, 10_24, 24, 24]					,  SCREAMING_SNAKE_CASE_=[0, 1]					,  SCREAMING_SNAKE_CASE_=None					,  **SCREAMING_SNAKE_CASE_					,  ):


   '''simple docstring'''




   super().__init__(**SCREAMING_SNAKE_CASE_)

   lowercase__  :   List[str]    		=      hidden_size
   lowercase__  :   str    		=      is_hybrid

   if self.is_hybrid:
     if backbone_config is None:
       logger.info("""Initializing the config with a `BiT` backbone.""")
       lowercase__  :   Any    		=      {
           """global_padding""": """same""",
           """layer_type""": """bottleneck""",
           """depths""": [3, 4, 9],
           """out_features""": ["""stage1""", """stage2""", """stage3"""],
           """embedding_dynamic_padding""": True,
       }
       lowercase__  :   Optional[Any]    		=      BitConfig(**SCREAMING_SNAKE_CASE_)
     elif isinstance(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_):
       logger.info("""Initializing the config with a `BiT` backbone.""")
       lowercase__  :   List[Any]    		=      BitConfig(**SCREAMING_SNAKE_CASE_)
     elif isinstance(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_):
       lowercase__  :   int    		=      backbone_config
     else:
       raise ValueError(
           f'backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}.')

     lowercase__  :   Optional[Any]    		=      backbone_featmap_shape
     lowercase__  :   List[str]    		=      neck_ignore_stages

     if readout_type != "project":
       raise ValueError("""Readout type must be 'project' when using `DPT-hybrid` mode.""")
   else:
     lowercase__  :   Optional[int]    		=      None
     lowercase__  :   Union[str, Any]    		=      None
     lowercase__  :   Tuple    		=      []

   lowercase__  :   List[str]    		=      num_hidden_layers
   lowercase__  :   Union[str, Any]    		=      num_attention_heads
   lowercase__  :   List[Any]    		=      intermediate_size
   lowercase__  :   Optional[Any]    		=      hidden_act
   lowercase__  :   int    		=      hidden_dropout_prob
   lowercase__  :   str    		=      attention_probs_dropout_prob
   lowercase__  :   Dict    		=      initializer_range
   lowercase__  :   Tuple    		=      layer_norm_eps
   lowercase__  :   List[str]    		=      image_size
   lowercase__  :   Tuple    		=      patch_size
   lowercase__  :   Tuple    		=      num_channels
   lowercase__  :   Dict    		=      qkv_bias
   lowercase__  :   List[Any]    		=      backbone_out_indices
   if readout_type not in ["ignore", "add", "project"]:
     raise ValueError("""Readout_type must be one of ['ignore', 'add', 'project']""")
   lowercase__  :   Dict    		=      readout_type
   lowercase__  :   Tuple    		=      reassemble_factors
   lowercase__  :   Any    		=      neck_hidden_sizes
   lowercase__  :   Dict    		=      fusion_hidden_size
   lowercase__  :   Tuple    		=      head_in_index
   lowercase__  :   Tuple    		=      use_batch_norm_in_fusion_residual
   # auxiliary head attributes (semantic segmentation)
   lowercase__  :   Tuple    		=      use_auxiliary_head
   lowercase__  :   int    		=      auxiliary_loss_weight
   lowercase__  :   Union[str, Any]    		=      semantic_loss_ignore_index
   lowercase__  :   str    		=      semantic_classifier_dropout



 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   Optional[int]    		=      copy.deepcopy(self.__dict__)

   if output["backbone_config"] is not None:
     lowercase__  :   Optional[Any]    		=      self.backbone_config.to_dict()

   lowercase__  :   Dict    		=      self.__class__.model_type
   return output






