
from copy import deepcopy
from typing import Optional, Union

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available


if is_torch_available():
      import torch

if is_tf_available():
      import tensorflow as tf


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







  lowerCamelCase__     =						['''image_processor''']
  lowerCamelCase__     =						'''SamImageProcessor'''







  def __init__(							self :       Union[str, Any]  ,     __magic_name__ :       Optional[int]   )  ->	List[str]:
        super().__init__(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  self.image_processor
        SCREAMING_SNAKE_CASE_          =  -10
        SCREAMING_SNAKE_CASE_          =  self.image_processor.size["longest_edge"]







  def __call__(							self :       Any  ,     __magic_name__ :       Optional[int]=None  ,     __magic_name__ :       Dict=None  ,     __magic_name__ :       Union[str, Any]=None  ,     __magic_name__ :       List[str]=None  ,     __magic_name__ :       Optional[Union[str, TensorType]] = None  ,     **__magic_name__ :       List[str]  ,     )  ->	BatchEncoding:
        SCREAMING_SNAKE_CASE_          =  self.image_processor(
            __magic_name__  ,     return_tensors=__magic_name__  ,     **__magic_name__  ,     )

        # pop arguments that are not used in the foward but used nevertheless
        SCREAMING_SNAKE_CASE_          =  encoding_image_processor["original_sizes"]

        if hasattr(__magic_name__  ,     "numpy"   ):  # Checks if Torch or TF tensor
              SCREAMING_SNAKE_CASE_          =  original_sizes.numpy()

        SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  self._check_and_preprocess_points(
            input_points=__magic_name__  ,     input_labels=__magic_name__  ,     input_boxes=__magic_name__  ,     )

        SCREAMING_SNAKE_CASE_          =  self._normalize_and_convert(
            __magic_name__  ,     __magic_name__  ,     input_points=__magic_name__  ,     input_labels=__magic_name__  ,     input_boxes=__magic_name__  ,     return_tensors=__magic_name__  ,     )

        return encoding_image_processor







  def    __A     (							self :       Tuple  ,     __magic_name__ :       Union[str, Any]  ,     __magic_name__ :       int  ,     __magic_name__ :       List[str]=None  ,     __magic_name__ :       Dict=None  ,     __magic_name__ :       List[str]=None  ,     __magic_name__ :       Optional[int]="pt"  ,     )  ->	int:
        if input_points is not None:
              if len(__magic_name__   ) != len(__magic_name__   ):
                    SCREAMING_SNAKE_CASE_          =  [
                        self._normalize_coordinates(self.target_size  ,     __magic_name__  ,     original_sizes[0]   ) for point in input_points
                    ]
              else:
                    SCREAMING_SNAKE_CASE_          =  [
                        self._normalize_coordinates(self.target_size  ,     __magic_name__  ,     __magic_name__   )
                        for point, original_size in zip(__magic_name__  ,     __magic_name__   )
                    ]
              # check that all arrays have the same shape
              if not all(point.shape == input_points[0].shape for point in input_points   ):
                    if input_labels is not None:
                          SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  self._pad_points_and_labels(__magic_name__  ,     __magic_name__   )

              SCREAMING_SNAKE_CASE_          =  np.array(__magic_name__   )

        if input_labels is not None:
              SCREAMING_SNAKE_CASE_          =  np.array(__magic_name__   )

        if input_boxes is not None:
              if len(__magic_name__   ) != len(__magic_name__   ):
                    SCREAMING_SNAKE_CASE_          =  [
                        self._normalize_coordinates(self.target_size  ,     __magic_name__  ,     original_sizes[0]  ,     is_bounding_box=__magic_name__   )
                        for box in input_boxes
                    ]
              else:
                    SCREAMING_SNAKE_CASE_          =  [
                        self._normalize_coordinates(self.target_size  ,     __magic_name__  ,     __magic_name__  ,     is_bounding_box=__magic_name__   )
                        for box, original_size in zip(__magic_name__  ,     __magic_name__   )
                    ]
              SCREAMING_SNAKE_CASE_          =  np.array(__magic_name__   )

        if input_boxes is not None:
              if return_tensors == "pt":
                    SCREAMING_SNAKE_CASE_          =  torch.from_numpy(__magic_name__   )
                    # boxes batch size of 1 by default
                    SCREAMING_SNAKE_CASE_          =  input_boxes.unsqueeze(1   ) if len(input_boxes.shape   ) != 3 else input_boxes
              elif return_tensors == "tf":
                    SCREAMING_SNAKE_CASE_          =  tf.convert_to_tensor(__magic_name__   )
                    # boxes batch size of 1 by default
                    SCREAMING_SNAKE_CASE_          =  tf.expand_dims(__magic_name__  ,     1   ) if len(input_boxes.shape   ) != 3 else input_boxes
              encoding_image_processor.update({"input_boxes": input_boxes}   )
        if input_points is not None:
              if return_tensors == "pt":
                    SCREAMING_SNAKE_CASE_          =  torch.from_numpy(__magic_name__   )
                    # point batch size of 1 by default
                    SCREAMING_SNAKE_CASE_          =  input_points.unsqueeze(1   ) if len(input_points.shape   ) != 4 else input_points
              elif return_tensors == "tf":
                    SCREAMING_SNAKE_CASE_          =  tf.convert_to_tensor(__magic_name__   )
                    # point batch size of 1 by default
                    SCREAMING_SNAKE_CASE_          =  tf.expand_dims(__magic_name__  ,     1   ) if len(input_points.shape   ) != 4 else input_points
              encoding_image_processor.update({"input_points": input_points}   )
        if input_labels is not None:
              if return_tensors == "pt":
                    SCREAMING_SNAKE_CASE_          =  torch.from_numpy(__magic_name__   )
                    # point batch size of 1 by default
                    SCREAMING_SNAKE_CASE_          =  input_labels.unsqueeze(1   ) if len(input_labels.shape   ) != 3 else input_labels
              elif return_tensors == "tf":
                    SCREAMING_SNAKE_CASE_          =  tf.convert_to_tensor(__magic_name__   )
                    # point batch size of 1 by default
                    SCREAMING_SNAKE_CASE_          =  tf.expand_dims(__magic_name__  ,     1   ) if len(input_labels.shape   ) != 3 else input_labels
              encoding_image_processor.update({"input_labels": input_labels}   )

        return encoding_image_processor







  def    __A     (							self :       str  ,     __magic_name__ :       List[str]  ,     __magic_name__ :       Optional[int]   )  ->	Optional[int]:
        SCREAMING_SNAKE_CASE_          =  max([point.shape[0] for point in input_points]   )
        SCREAMING_SNAKE_CASE_          =  []
        for i, point in enumerate(__magic_name__   ):
              if point.shape[0] != expected_nb_points:
                    SCREAMING_SNAKE_CASE_          =  np.concatenate(
                        [point, np.zeros((expected_nb_points - point.shape[0], 2)   ) + self.point_pad_value]  ,     axis=0   )
                    SCREAMING_SNAKE_CASE_          =  np.append(input_labels[i]  ,     [self.point_pad_value]   )
              processed_input_points.append(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  processed_input_points
        return input_points, input_labels







  def    __A     (							self :       Union[str, Any]  ,     __magic_name__ :       int  ,     __magic_name__ :       np.ndarray  ,     __magic_name__ :       List[str]  ,     __magic_name__ :       str=False   )  ->	np.ndarray:
        SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  original_size
        SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  self.image_processor._get_preprocess_shape(__magic_name__  ,     longest_edge=__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  deepcopy(__magic_name__   ).astype(__magic_name__   )

        if is_bounding_box:
              SCREAMING_SNAKE_CASE_          =  coords.reshape(-1  ,     2  ,     2   )

        SCREAMING_SNAKE_CASE_          =  coords[..., 0] * (new_w / old_w)
        SCREAMING_SNAKE_CASE_          =  coords[..., 1] * (new_h / old_h)

        if is_bounding_box:
              SCREAMING_SNAKE_CASE_          =  coords.reshape(-1  ,     4   )

        return coords







  def    __A     (							self :       str  ,     __magic_name__ :       Any=None  ,     __magic_name__ :       Optional[Any]=None  ,     __magic_name__ :       Tuple=None  ,     )  ->	List[Any]:
        if input_points is not None:
              if hasattr(__magic_name__  ,     "numpy"   ):  # Checks for TF or Torch tensor
                    SCREAMING_SNAKE_CASE_          =  input_points.numpy().tolist()

              if not isinstance(__magic_name__  ,     __magic_name__   ) or not isinstance(input_points[0]  ,     __magic_name__   ):
                    raise ValueError("Input points must be a list of list of floating points."   )
              SCREAMING_SNAKE_CASE_          =  [np.array(__magic_name__   ) for input_point in input_points]
        else:
              SCREAMING_SNAKE_CASE_          =  None

        if input_labels is not None:
              if hasattr(__magic_name__  ,     "numpy"   ):
                    SCREAMING_SNAKE_CASE_          =  input_labels.numpy().tolist()

              if not isinstance(__magic_name__  ,     __magic_name__   ) or not isinstance(input_labels[0]  ,     __magic_name__   ):
                    raise ValueError("Input labels must be a list of list integers."   )
              SCREAMING_SNAKE_CASE_          =  [np.array(__magic_name__   ) for label in input_labels]
        else:
              SCREAMING_SNAKE_CASE_          =  None

        if input_boxes is not None:
              if hasattr(__magic_name__  ,     "numpy"   ):
                    SCREAMING_SNAKE_CASE_          =  input_boxes.numpy().tolist()

              if (
                  not isinstance(__magic_name__  ,     __magic_name__   )
                  or not isinstance(input_boxes[0]  ,     __magic_name__   )
                  or not isinstance(input_boxes[0][0]  ,     __magic_name__   )
              ):
                    raise ValueError("Input boxes must be a list of list of list of floating points."   )
              SCREAMING_SNAKE_CASE_          =  [np.array(__magic_name__   ).astype(np.floataa   ) for box in input_boxes]
        else:
              SCREAMING_SNAKE_CASE_          =  None

        return input_points, input_labels, input_boxes







  @property
  def    __A     (							self :       Optional[int]   )  ->	int:
        SCREAMING_SNAKE_CASE_          =  self.image_processor.model_input_names
        return list(dict.fromkeys(__magic_name__   )   )







  def    __A     (							self :       Optional[Any]  ,     *__magic_name__ :       Dict  ,     **__magic_name__ :       int   )  ->	int:
        return self.image_processor.post_process_masks(*__magic_name__  ,     **__magic_name__   )



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__)


