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

A     :  Optional[int]					     =       logging.get_logger(__name__)


@add_end_docstrings(SCREAMING_SNAKE_CASE__					)
class   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







  def __init__(							self :       List[Any]  ,     **__magic_name__ :       int   )  ->	Union[str, Any]:
        super().__init__(**__magic_name__   )
        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(__magic_name__   )







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







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







  def    __A     (							self :       Union[str, Any]  ,     __magic_name__ :       Optional[Any]  ,     __magic_name__ :       Dict=64  ,     __magic_name__ :       int = 0  ,     __magic_name__ :       float = 512 / 1_500  ,     __magic_name__ :       Optional[int] = 32  ,     __magic_name__ :       Optional[int] = 1  ,     )  ->	List[Any]:
        SCREAMING_SNAKE_CASE_          =  load_image(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  self.image_processor.size["longest_edge"]
        SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  self.image_processor.generate_crop_boxes(
            __magic_name__  ,     __magic_name__  ,     __magic_name__  ,     __magic_name__  ,     __magic_name__  ,     __magic_name__   )
        SCREAMING_SNAKE_CASE_          =  self.image_processor(images=__magic_name__  ,     return_tensors="pt"   )

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

        SCREAMING_SNAKE_CASE_          =  grid_points.shape[1]
        SCREAMING_SNAKE_CASE_          =  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  ,     __magic_name__  ,     __magic_name__   ):
              SCREAMING_SNAKE_CASE_          =  grid_points[:, i : i + points_per_batch, :, :]
              SCREAMING_SNAKE_CASE_          =  input_labels[:, i : i + points_per_batch]
              SCREAMING_SNAKE_CASE_          =  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    __A     (							self :       Tuple  ,     __magic_name__ :       Optional[int]  ,     __magic_name__ :       Union[str, Any]=0.88  ,     __magic_name__ :       Union[str, Any]=0.95  ,     __magic_name__ :       Optional[Any]=0  ,     __magic_name__ :       Any=1  ,     )  ->	Dict:
        SCREAMING_SNAKE_CASE_          =  model_inputs.pop("input_boxes"   )
        SCREAMING_SNAKE_CASE_          =  model_inputs.pop("is_last"   )
        SCREAMING_SNAKE_CASE_          =  model_inputs.pop("original_sizes"   ).tolist()
        SCREAMING_SNAKE_CASE_          =  model_inputs.pop("reshaped_input_sizes"   ).tolist()

        SCREAMING_SNAKE_CASE_          =  self.model(**__magic_name__   )

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







  def    __A     (							self :       int  ,     __magic_name__ :       List[str]  ,     __magic_name__ :       Any=False  ,     __magic_name__ :       Any=False  ,     __magic_name__ :       Tuple=0.7  ,     )  ->	int:
        SCREAMING_SNAKE_CASE_          =  []
        SCREAMING_SNAKE_CASE_          =  []
        SCREAMING_SNAKE_CASE_          =  []
        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"   )   )

        SCREAMING_SNAKE_CASE_          =  torch.cat(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  torch.cat(__magic_name__   )
        SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  self.image_processor.post_process_for_mask_generation(
            __magic_name__  ,     __magic_name__  ,     __magic_name__  ,     __magic_name__   )

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

        SCREAMING_SNAKE_CASE_          =  {}
        if output_rle_mask:
              SCREAMING_SNAKE_CASE_          =  rle_mask

        if output_bboxes_mask:
              SCREAMING_SNAKE_CASE_          =  bounding_boxes

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



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A     :  str					     =       {
    "configuration_rembert": ["REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RemBertConfig", "RemBertOnnxConfig"]
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  Tuple					     =       ["RemBertTokenizer"]

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  Tuple					     =       ["RemBertTokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  Tuple					     =       [
          "REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "RemBertForCausalLM",
          "RemBertForMaskedLM",
          "RemBertForMultipleChoice",
          "RemBertForQuestionAnswering",
          "RemBertForSequenceClassification",
          "RemBertForTokenClassification",
          "RemBertLayer",
          "RemBertModel",
          "RemBertPreTrainedModel",
          "load_tf_weights_in_rembert",
      ]


try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  List[str]					     =       [
          "TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFRemBertForCausalLM",
          "TFRemBertForMaskedLM",
          "TFRemBertForMultipleChoice",
          "TFRemBertForQuestionAnswering",
          "TFRemBertForSequenceClassification",
          "TFRemBertForTokenClassification",
          "TFRemBertLayer",
          "TFRemBertModel",
          "TFRemBertPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_rembert import RemBertTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_rembert_fast import RemBertTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_rembert import (
                REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                RemBertForCausalLM,
                RemBertForMaskedLM,
                RemBertForMultipleChoice,
                RemBertForQuestionAnswering,
                RemBertForSequenceClassification,
                RemBertForTokenClassification,
                RemBertLayer,
                RemBertModel,
                RemBertPreTrainedModel,
                load_tf_weights_in_rembert,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_rembert import (
                TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFRemBertForCausalLM,
                TFRemBertForMaskedLM,
                TFRemBertForMultipleChoice,
                TFRemBertForQuestionAnswering,
                TFRemBertForSequenceClassification,
                TFRemBertForTokenClassification,
                TFRemBertLayer,
                TFRemBertModel,
                TFRemBertPreTrainedModel,
            )


else:
      import sys

      A     :  Tuple					     =       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


