

from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_vision_available():
					from PIL import Image

					from ..image_utils import load_image

if is_torch_available():
					import torch

					from transformers.modeling_outputs import BaseModelOutput

					from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING

_lowerCAmelCase  :      int			 =logging.get_logger(__name__)

@add_end_docstrings(A__		)
class __UpperCamelCase     (				A__		):






					'''simple docstring'''



					def __init__(				self				,      **lowerCamelCase__						):
						super().__init__(**snake_case_						)

						if self.framework == "tf":
							raise ValueError(F"The {self.__class__} is only available in PyTorch."						)

						requires_backends(self				,      "vision"						)
						self.check_model_type(snake_case_						)





					def __call__(				self				,      lowerCamelCase__				,      lowerCamelCase__ = None				,      **lowerCamelCase__				,      ):
						if "text_queries" in kwargs:
							UpperCAmelCase__:	str									= kwargs.pop("text_queries"						)

						if isinstance(snake_case_				,      (str, Image.Image)						):
							UpperCAmelCase__:	List[str]									= {"image": image, "candidate_labels": candidate_labels}
						else:
							UpperCAmelCase__:	List[Any]									= image
						UpperCAmelCase__:	List[Any]									= super().__call__(snake_case_				,      **snake_case_						)
						return results





					def    _UpperCAmelCase    (				self				,      **lowerCamelCase__						):
						UpperCAmelCase__:	Union[str, Any]									= {}
						if "threshold" in kwargs:
							UpperCAmelCase__:	Tuple									= kwargs["threshold"]
						if "top_k" in kwargs:
							UpperCAmelCase__:	List[Any]									= kwargs["top_k"]
						return {}, {}, postprocess_params





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__						):
						UpperCAmelCase__:	Tuple									= load_image(inputs["image"]						)
						UpperCAmelCase__:	int									= inputs["candidate_labels"]
						if isinstance(snake_case_				,      snake_case_						):
							UpperCAmelCase__:	Union[str, Any]									= candidate_labels.split(","						)

						UpperCAmelCase__:	str									= torch.tensor([[image.height, image.width]]				,      dtype=torch.intaa						)
						for i, candidate_label in enumerate(snake_case_						):
							UpperCAmelCase__:	Optional[Any]									= self.tokenizer(snake_case_				,      return_tensors=self.framework						)
							UpperCAmelCase__:	Any									= self.image_processor(snake_case_				,      return_tensors=self.framework						)
							yield {
							    "is_last": i == len(snake_case_						) - 1,
							    "target_size": target_size,
							    "candidate_label": candidate_label,
							    **text_inputs,
							    **image_features,
							}





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__						):
						UpperCAmelCase__:	Optional[int]									= model_inputs.pop("target_size"						)
						UpperCAmelCase__:	int									= model_inputs.pop("candidate_label"						)
						UpperCAmelCase__:	Optional[Any]									= model_inputs.pop("is_last"						)

						UpperCAmelCase__:	Dict									= self.model(**snake_case_						)

						UpperCAmelCase__:	Optional[Any]									= {"target_size": target_size, "candidate_label": candidate_label, "is_last": is_last, **outputs}
						return model_outputs





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__=0.1				,      lowerCamelCase__=None						):
						UpperCAmelCase__:	Any									= []
						for model_output in model_outputs:
							UpperCAmelCase__:	List[Any]									= model_output["candidate_label"]
							UpperCAmelCase__:	int									= BaseModelOutput(snake_case_						)
							UpperCAmelCase__:	Optional[Any]									= self.image_processor.post_process_object_detection(
							    outputs=snake_case_				,      threshold=snake_case_				,      target_sizes=model_output["target_size"]						)[0]

							for index in outputs["scores"].nonzero():
								UpperCAmelCase__:	List[str]									= outputs["scores"][index].item()
								UpperCAmelCase__:	Tuple									= self._get_bounding_box(outputs["boxes"][index][0]						)

								UpperCAmelCase__:	List[Any]									= {"score": score, "label": label, "box": box}
								results.append(snake_case_						)

						UpperCAmelCase__:	List[Any]									= sorted(snake_case_				,      key=lambda lowerCamelCase__						: x["score"]				,      reverse=snake_case_						)
						if top_k:
							UpperCAmelCase__:	Optional[int]									= results[:top_k]

						return results





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__						):
						if self.framework != "pt":
							raise ValueError("The ZeroShotObjectDetectionPipeline is only available in PyTorch."						)
						UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__:	Dict									= box.int().tolist()
						UpperCAmelCase__:	Dict									= {
						    "xmin": xmin,
						    "ymin": ymin,
						    "xmax": xmax,
						    "ymax": ymax,
						}
						return bbox

"""simple docstring"""
from typing import List

from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_					     =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_					     =	{
    '''snap-research/efficientformer-l1-300''': (
        '''https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json'''
    ),
}
class       lowerCAmelCase_		(				A__ ):



							'''simple docstring'''





							_snake_case						      =		'''efficientformer'''




							def __init__( self    ,				snake_case_ = [3, 2, 6, 4]    ,				snake_case_ = [48, 96, 224, 448]    ,				snake_case_ = [True, True, True, True]    ,				snake_case_ = 448    ,				snake_case_ = 32    ,				snake_case_ = 4    ,				snake_case_ = 7    ,				snake_case_ = 5    ,				snake_case_ = 8    ,				snake_case_ = 4    ,				snake_case_ = 0.0    ,				snake_case_ = 16    ,				snake_case_ = 3    ,				snake_case_ = 3    ,				snake_case_ = 3    ,				snake_case_ = 2    ,				snake_case_ = 1    ,				snake_case_ = 0.0    ,				snake_case_ = 1    ,				snake_case_ = True    ,				snake_case_ = True    ,				snake_case_ = 1e-5    ,				snake_case_ = "gelu"    ,				snake_case_ = 0.02    ,				snake_case_ = 1e-1_2    ,				snake_case_ = 224    ,				snake_case_ = 1e-0_5    ,				**snake_case_    ,				)       ->  None:
													super().__init__(**snake_case_				)

													__lowerCAmelCase     =  hidden_act
													__lowerCAmelCase     =  hidden_dropout_prob
													__lowerCAmelCase     =  hidden_sizes
													__lowerCAmelCase     =  num_hidden_layers
													__lowerCAmelCase     =  num_attention_heads
													__lowerCAmelCase     =  initializer_range
													__lowerCAmelCase     =  layer_norm_eps
													__lowerCAmelCase     =  patch_size
													__lowerCAmelCase     =  num_channels
													__lowerCAmelCase     =  depths
													__lowerCAmelCase     =  mlp_expansion_ratio
													__lowerCAmelCase     =  downsamples
													__lowerCAmelCase     =  dim
													__lowerCAmelCase     =  key_dim
													__lowerCAmelCase     =  attention_ratio
													__lowerCAmelCase     =  resolution
													__lowerCAmelCase     =  pool_size
													__lowerCAmelCase     =  downsample_patch_size
													__lowerCAmelCase     =  downsample_stride
													__lowerCAmelCase     =  downsample_pad
													__lowerCAmelCase     =  drop_path_rate
													__lowerCAmelCase     =  num_metaad_blocks
													__lowerCAmelCase     =  distillation
													__lowerCAmelCase     =  use_layer_scale
													__lowerCAmelCase     =  layer_scale_init_value
													__lowerCAmelCase     =  image_size
													__lowerCAmelCase     =  batch_norm_eps
