




from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


UpperCamelCase__						=			logging.get_logger(__name__)




class   a__			(       UpperCamelCase_					,	UpperCamelCase_  ):
						snake_case__             = '''maskformer-swin'''

						snake_case__             = {
						    '''num_attention_heads''': '''num_heads''',
						    '''num_hidden_layers''': '''num_layers''',
						}





						def __init__(							self      :	Optional[Any]     ,a__      :	Tuple=224     ,a__      :	List[str]=4     ,a__      :	Dict=3     ,a__      :	Dict=96     ,a__      :	Dict=[2, 2, 6, 2]     ,a__      :	List[str]=[3, 6, 12, 24]     ,a__      :	List[Any]=7     ,a__      :	int=4.0     ,a__      :	int=True     ,a__      :	Tuple=0.0     ,a__      :	Dict=0.0     ,a__      :	List[str]=0.1     ,a__      :	Any="gelu"     ,a__      :	Optional[Any]=False     ,a__      :	Any=0.02     ,a__      :	int=1E-5     ,a__      :	Tuple=None     ,a__      :	Optional[int]=None     ,**a__      :	List[str]     ,)						->       Any:




											"""simple docstring"""







											super().__init__(**a__)

											_lowerCAmelCase:List[str]									=  image_size
											_lowerCAmelCase:Optional[int]									=  patch_size
											_lowerCAmelCase:Dict									=  num_channels
											_lowerCAmelCase:Any									=  embed_dim
											_lowerCAmelCase:Union[str, Any]									=  depths
											_lowerCAmelCase:Dict									=  len(a__)
											_lowerCAmelCase:List[str]									=  num_heads
											_lowerCAmelCase:List[str]									=  window_size
											_lowerCAmelCase:Any									=  mlp_ratio
											_lowerCAmelCase:Optional[int]									=  qkv_bias
											_lowerCAmelCase:List[Any]									=  hidden_dropout_prob
											_lowerCAmelCase:Optional[int]									=  attention_probs_dropout_prob
											_lowerCAmelCase:str									=  drop_path_rate
											_lowerCAmelCase:Any									=  hidden_act
											_lowerCAmelCase:Any									=  use_absolute_embeddings
											_lowerCAmelCase:Union[str, Any]									=  layer_norm_eps
											_lowerCAmelCase:Union[str, Any]									=  initializer_range
											# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
											# this indicates the channel dimension after the last stage of the model
											_lowerCAmelCase:Union[str, Any]									=  int(embed_dim * 2 ** (len(a__) - 1))
											_lowerCAmelCase:Union[str, Any]									=  ['''stem'''] + [F'stage{idx}' for idx in range(1     ,len(a__) + 1)]
											_lowerCAmelCase:str									=  get_aligned_output_features_output_indices(
											    out_features=a__     ,out_indices=a__     ,stage_names=self.stage_names)












"""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






