

from typing import Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
					from PIL import Image

					from ..image_utils import load_image

if is_torch_available():
					from ..models.auto.modeling_auto import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING

lowerCAmelCase_       	=	logging.get_logger(__name__)





@add_end_docstrings(__A					)
class 	snake_case_   (		__A					):







			'''simple docstring'''

			def __init__(     self :				Any							,   *_UpperCamelCase :				int							,   **_UpperCamelCase :				Optional[Any]							)      ->Union[str, Any]:
									super().__init__(*_UpperCamelCase							,   **_UpperCamelCase							)
									self.check_model_type(_UpperCamelCase							)







			def 				snake_case__(     self :				str							,   _UpperCamelCase :				Dict=None							,   _UpperCamelCase :				Dict=None							,   _UpperCamelCase :				Optional[int]=None							,   **_UpperCamelCase :				List[str]							)      ->str:
									snake_case_,    snake_case_   =	{}, {}
									if padding is not None:
															snake_case_   =	padding
									if truncation is not None:
															snake_case_   =	truncation
									if top_k is not None:
															snake_case_   =	top_k
									return preprocess_params, {}, postprocess_params







			def __call__(     self :				Optional[Any]							,   _UpperCamelCase :				Union["Image.Image", str]							,   _UpperCamelCase :				str = None							,   **_UpperCamelCase :				Any							)      ->Any:
									if isinstance(_UpperCamelCase							,   (Image.Image, str)							) and isinstance(_UpperCamelCase							,   _UpperCamelCase							):
															snake_case_   =	{'''image''': image, '''question''': question}
									else:
															snake_case_   =	image
									snake_case_   =	super().__call__(_UpperCamelCase							,   **_UpperCamelCase							)
									return results







			def 				snake_case__(     self :				Union[str, Any]							,   _UpperCamelCase :				Union[str, Any]							,   _UpperCamelCase :				Tuple=False							,   _UpperCamelCase :				List[Any]=False							)      ->Optional[Any]:
									snake_case_   =	load_image(inputs['''image''']							)
									snake_case_   =	self.tokenizer(
									    inputs['''question''']							,   return_tensors=self.framework							,   padding=_UpperCamelCase							,   truncation=_UpperCamelCase							)
									snake_case_   =	self.image_processor(images=_UpperCamelCase							,   return_tensors=self.framework							)
									model_inputs.update(_UpperCamelCase							)
									return model_inputs







			def 				snake_case__(     self :				Union[str, Any]							,   _UpperCamelCase :				List[Any]							)      ->List[Any]:
									snake_case_   =	self.model(**_UpperCamelCase							)
									return model_outputs







			def 				snake_case__(     self :				Tuple							,   _UpperCamelCase :				Union[str, Any]							,   _UpperCamelCase :				Optional[int]=5							)      ->Any:
									if top_k > self.model.config.num_labels:
															snake_case_   =	self.model.config.num_labels

									if self.framework == "pt":
															snake_case_   =	model_outputs.logits.sigmoid()[0]
															snake_case_,    snake_case_   =	probs.topk(_UpperCamelCase							)
									else:
															raise ValueError(f'''Unsupported framework: {self.framework}'''							)

									snake_case_   =	scores.tolist()
									snake_case_   =	ids.tolist()
									return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(_UpperCamelCase							,   _UpperCamelCase							)]


'''simple docstring'''



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


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







class 						lowerCAmelCase__   (      a ,					a					):



 """simple docstring"""



 lowerCAmelCase__  					=      "maskformer-swin"

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


      """simple docstring"""




      super().__init__(**__SCREAMING_SNAKE_CASE			)

      __SCREAMING_SNAKE_CASE     =      image_size
      __SCREAMING_SNAKE_CASE     =      patch_size
      __SCREAMING_SNAKE_CASE     =      num_channels
      __SCREAMING_SNAKE_CASE     =      embed_dim
      __SCREAMING_SNAKE_CASE     =      depths
      __SCREAMING_SNAKE_CASE     =      len(__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      num_heads
      __SCREAMING_SNAKE_CASE     =      window_size
      __SCREAMING_SNAKE_CASE     =      mlp_ratio
      __SCREAMING_SNAKE_CASE     =      qkv_bias
      __SCREAMING_SNAKE_CASE     =      hidden_dropout_prob
      __SCREAMING_SNAKE_CASE     =      attention_probs_dropout_prob
      __SCREAMING_SNAKE_CASE     =      drop_path_rate
      __SCREAMING_SNAKE_CASE     =      hidden_act
      __SCREAMING_SNAKE_CASE     =      use_absolute_embeddings
      __SCREAMING_SNAKE_CASE     =      layer_norm_eps
      __SCREAMING_SNAKE_CASE     =      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
      __SCREAMING_SNAKE_CASE     =      int(embed_dim * 2 ** (len(__SCREAMING_SNAKE_CASE			) - 1)			)
      __SCREAMING_SNAKE_CASE     =      ["""stem"""] + [f'stage{idx}' for idx in range(1					,							len(__SCREAMING_SNAKE_CASE			) + 1			)]
      __SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE     =      get_aligned_output_features_output_indices(
          out_features=__SCREAMING_SNAKE_CASE					,							out_indices=__SCREAMING_SNAKE_CASE					,							stage_names=self.stage_names			)






