'''simple docstring'''
from collections import defaultdict
from typing import Optional

from ..image_utils import load_image
from ..utils import (
    add_end_docstrings,
    is_torch_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_torch_available():
      import torch

      from ..models.auto.modeling_auto import MODEL_FOR_MASK_GENERATION_MAPPING

lowercase__	:   List[Any]										=       logging.get_logger(__name__)







@add_end_docstrings(lowerCamelCase       )
class lowerCamelCase				(						lowerCamelCase       ):




  '''simple docstring'''



  def __init__(   self      :							Optional[int]	,				**UpperCAmelCase__      :							str	)					->int:
        super().__init__(**_lowercase	)
        requires_backends(self	,				'''vision'''	)
        requires_backends(self	,				'''torch'''	)

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

        self.check_model_type(_lowercase	)






  def 			lowerCAmelCase__			(   self      :							List[Any]	,				**UpperCAmelCase__      :							Any	)					->str:
        UpperCAmelCase_      =	{}
        UpperCAmelCase_      =	{}
        UpperCAmelCase_      =	{}
        # preprocess args
        if "points_per_batch" in kwargs:
              UpperCAmelCase_      =	kwargs['''points_per_batch''']
        if "points_per_crop" in kwargs:
              UpperCAmelCase_      =	kwargs['''points_per_crop''']
        if "crops_n_layers" in kwargs:
              UpperCAmelCase_      =	kwargs['''crops_n_layers''']
        if "crop_overlap_ratio" in kwargs:
              UpperCAmelCase_      =	kwargs['''crop_overlap_ratio''']
        if "crop_n_points_downscale_factor" in kwargs:
              UpperCAmelCase_      =	kwargs['''crop_n_points_downscale_factor''']
        # postprocess args
        if "pred_iou_thresh" in kwargs:
              UpperCAmelCase_      =	kwargs['''pred_iou_thresh''']
        if "stability_score_offset" in kwargs:
              UpperCAmelCase_      =	kwargs['''stability_score_offset''']
        if "mask_threshold" in kwargs:
              UpperCAmelCase_      =	kwargs['''mask_threshold''']
        if "stability_score_thresh" in kwargs:
              UpperCAmelCase_      =	kwargs['''stability_score_thresh''']
        if "crops_nms_thresh" in kwargs:
              UpperCAmelCase_      =	kwargs['''crops_nms_thresh''']
        if "output_rle_mask" in kwargs:
              UpperCAmelCase_      =	kwargs['''output_rle_mask''']
        if "output_bboxes_mask" in kwargs:
              UpperCAmelCase_      =	kwargs['''output_bboxes_mask''']
        return preprocess_kwargs, forward_params, postprocess_kwargs






  def __call__(   self      :							Optional[Any]	,				UpperCAmelCase__      :							List[Any]	,				*UpperCAmelCase__      :							Optional[int]	,				UpperCAmelCase__      :							Optional[int]=None	,				UpperCAmelCase__      :							Dict=None	,				**UpperCAmelCase__      :							Tuple	)					->List[str]:
        return super().__call__(_lowercase	,				*_lowercase	,				num_workers=_lowercase	,				batch_size=_lowercase	,				**_lowercase	)






  def 			lowerCAmelCase__			(   self      :							int	,				UpperCAmelCase__      :							int	,				UpperCAmelCase__      :							int=64	,				UpperCAmelCase__      :							int = 0	,				UpperCAmelCase__      :							float = 512 / 1500	,				UpperCAmelCase__      :							Optional[int] = 32	,				UpperCAmelCase__      :							Optional[int] = 1	,				)					->Optional[Any]:
        UpperCAmelCase_      =	load_image(_lowercase	)
        UpperCAmelCase_      =	self.image_processor.size['''longest_edge''']
        UpperCAmelCase_   ,		UpperCAmelCase_   ,		UpperCAmelCase_   ,		UpperCAmelCase_      =	self.image_processor.generate_crop_boxes(
            _lowercase	,				_lowercase	,				_lowercase	,				_lowercase	,				_lowercase	,				_lowercase	)
        UpperCAmelCase_      =	self.image_processor(images=_lowercase	,				return_tensors='''pt'''	)

        with self.device_placement():
              if self.framework == "pt":
                    UpperCAmelCase_      =	self.get_inference_context()
                    with inference_context():
                          UpperCAmelCase_      =	self._ensure_tensor_on_device(_lowercase	,				device=self.device	)
                          UpperCAmelCase_      =	self.model.get_image_embeddings(model_inputs.pop('''pixel_values'''	)	)
                          UpperCAmelCase_      =	image_embeddings

        UpperCAmelCase_      =	grid_points.shape[1]
        UpperCAmelCase_      =	points_per_batch if points_per_batch is not None else n_points

        if points_per_batch <= 0:
              raise ValueError(
                  '''Cannot have points_per_batch<=0. Must be >=1 to returned batched outputs. '''
                  '''To return all points at once, set points_per_batch to None'''	)

        for i in range(0	,				_lowercase	,				_lowercase	):
              UpperCAmelCase_      =	grid_points[:, i : i + points_per_batch, :, :]
              UpperCAmelCase_      =	input_labels[:, i : i + points_per_batch]
              UpperCAmelCase_      =	i == n_points - points_per_batch
              yield {
                  "input_points": batched_points,
                  "input_labels": labels,
                  "input_boxes": crop_boxes,
                  "is_last": is_last,
                  **model_inputs,
              }






  def 			lowerCAmelCase__			(   self      :							List[str]	,				UpperCAmelCase__      :							List[Any]	,				UpperCAmelCase__      :							List[str]=0.88	,				UpperCAmelCase__      :							Dict=0.95	,				UpperCAmelCase__      :							str=0	,				UpperCAmelCase__      :							Dict=1	,				)					->Tuple:
        UpperCAmelCase_      =	model_inputs.pop('''input_boxes'''	)
        UpperCAmelCase_      =	model_inputs.pop('''is_last'''	)
        UpperCAmelCase_      =	model_inputs.pop('''original_sizes'''	).tolist()
        UpperCAmelCase_      =	model_inputs.pop('''reshaped_input_sizes'''	).tolist()

        UpperCAmelCase_      =	self.model(**_lowercase	)

        # post processing happens here in order to avoid CPU GPU copies of ALL the masks
        UpperCAmelCase_      =	model_outputs['''pred_masks''']
        UpperCAmelCase_      =	self.image_processor.post_process_masks(
            _lowercase	,				_lowercase	,				_lowercase	,				_lowercase	,				binarize=_lowercase	)
        UpperCAmelCase_      =	model_outputs['''iou_scores''']
        UpperCAmelCase_   ,		UpperCAmelCase_   ,		UpperCAmelCase_      =	self.image_processor.filter_masks(
            masks[0]	,				iou_scores[0]	,				original_sizes[0]	,				input_boxes[0]	,				_lowercase	,				_lowercase	,				_lowercase	,				_lowercase	,				)
        return {
            "masks": masks,
            "is_last": is_last,
            "boxes": boxes,
            "iou_scores": iou_scores,
        }






  def 			lowerCAmelCase__			(   self      :							List[Any]	,				UpperCAmelCase__      :							List[Any]	,				UpperCAmelCase__      :							Tuple=False	,				UpperCAmelCase__      :							Any=False	,				UpperCAmelCase__      :							str=0.7	,				)					->Dict:
        UpperCAmelCase_      =	[]
        UpperCAmelCase_      =	[]
        UpperCAmelCase_      =	[]
        for model_output in model_outputs:
              all_scores.append(model_output.pop('''iou_scores'''	)	)
              all_masks.extend(model_output.pop('''masks'''	)	)
              all_boxes.append(model_output.pop('''boxes'''	)	)

        UpperCAmelCase_      =	torch.cat(_lowercase	)
        UpperCAmelCase_      =	torch.cat(_lowercase	)
        UpperCAmelCase_   ,		UpperCAmelCase_   ,		UpperCAmelCase_   ,		UpperCAmelCase_      =	self.image_processor.post_process_for_mask_generation(
            _lowercase	,				_lowercase	,				_lowercase	,				_lowercase	)

        UpperCAmelCase_      =	defaultdict(_lowercase	)
        for output in model_outputs:
              for k, v in output.items():
                    extra[k].append(_lowercase	)

        UpperCAmelCase_      =	{}
        if output_rle_mask:
              UpperCAmelCase_      =	rle_mask

        if output_bboxes_mask:
              UpperCAmelCase_      =	bounding_boxes

        return {"masks": output_masks, "scores": iou_scores, **optional, **extra}











'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE			=						logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE			=						{
    "facebook/dpr-ctx_encoder-single-nq-base": (
        "https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json"
    ),
    "facebook/dpr-question_encoder-single-nq-base": (
        "https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json"
    ),
    "facebook/dpr-reader-single-nq-base": (
        "https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json"
    ),
    "facebook/dpr-ctx_encoder-multiset-base": (
        "https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json"
    ),
    "facebook/dpr-question_encoder-multiset-base": (
        "https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json"
    ),
    "facebook/dpr-reader-multiset-base": (
        "https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json"
    ),
}
class   __UpperCAmelCase				(     lowerCAmelCase				):



					'''simple docstring'''





					_UpperCamelCase							=		"""dpr"""



					def __init__(	self     :						Optional[Any]      ,       _lowercase     :						Dict=30_522      ,       _lowercase     :						Tuple=768      ,       _lowercase     :						int=12      ,       _lowercase     :						List[str]=12      ,       _lowercase     :						Union[str, Any]=3_072      ,       _lowercase     :						Dict="gelu"      ,       _lowercase     :						Dict=0.1      ,       _lowercase     :						Any=0.1      ,       _lowercase     :						Union[str, Any]=512      ,       _lowercase     :						Optional[int]=2      ,       _lowercase     :						Union[str, Any]=0.02      ,       _lowercase     :						List[Any]=1E-12      ,       _lowercase     :						List[str]=0      ,       _lowercase     :						Dict="absolute"      ,       _lowercase     :						int = 0      ,       **_lowercase     :						int      ,       )    ->							int:
								super().__init__(pad_token_id=_lowercase      ,       **_lowercase)

								A_									= vocab_size
								A_									= hidden_size
								A_									= num_hidden_layers
								A_									= num_attention_heads
								A_									= hidden_act
								A_									= intermediate_size
								A_									= hidden_dropout_prob
								A_									= attention_probs_dropout_prob
								A_									= max_position_embeddings
								A_									= type_vocab_size
								A_									= initializer_range
								A_									= layer_norm_eps
								A_									= projection_dim
								A_									= position_embedding_type




