

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 						__magic_name__			(							lowerCamelCase__							):

   '''simple docstring'''




   lowerCamelCase__ :	Optional[int]    =   ['image_processor']
   lowerCamelCase__ :	List[Any]    =   'SamImageProcessor'



   def __init__(				self,    lowercase_       )							->    Tuple:






     """simple docstring"""






     super().__init__(lowercase_       )
     a__  =self.image_processor
     a__  =-10
     a__  =self.image_processor.size['''longest_edge''']



   def __call__(				self,    lowercase_=None,    lowercase_=None,    lowercase_=None,    lowercase_=None,    lowercase_ = None,    **lowercase_,    )							->    BatchEncoding:






     """simple docstring"""






     a__  =self.image_processor(
         lowercase_,    return_tensors=lowercase_,    **lowercase_,    )

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

     if hasattr(lowercase_,    '''numpy'''       ):  # Checks if Torch or TF tensor
       a__  =original_sizes.numpy()

     a__,      a__,      a__  =self._check_and_preprocess_points(
         input_points=lowercase_,    input_labels=lowercase_,    input_boxes=lowercase_,    )

     a__  =self._normalize_and_convert(
         lowercase_,    lowercase_,    input_points=lowercase_,    input_labels=lowercase_,    input_boxes=lowercase_,    return_tensors=lowercase_,    )

     return encoding_image_processor



   def  _UpperCAmelCase (				self,    lowercase_,    lowercase_,    lowercase_=None,    lowercase_=None,    lowercase_=None,    lowercase_="pt",    )							->    int:






     """simple docstring"""






     if input_points is not None:
       if len(lowercase_       ) != len(lowercase_       ):
         a__  =[
             self._normalize_coordinates(self.target_size,    lowercase_,    original_sizes[0]       ) for point in input_points
         ]
       else:
         a__  =[
             self._normalize_coordinates(self.target_size,    lowercase_,    lowercase_       )
             for point, original_size in zip(lowercase_,    lowercase_       )
         ]
       # 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:
           a__,      a__  =self._pad_points_and_labels(lowercase_,    lowercase_       )

       a__  =np.array(lowercase_       )

     if input_labels is not None:
       a__  =np.array(lowercase_       )

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

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

     return encoding_image_processor



   def  _UpperCAmelCase (				self,    lowercase_,    lowercase_       )							->    str:






     """simple docstring"""






     a__  =max([point.shape[0] for point in input_points]       )
     a__  =[]
     for i, point in enumerate(lowercase_       ):
       if point.shape[0] != expected_nb_points:
         a__  =np.concatenate(
             [point, np.zeros((expected_nb_points - point.shape[0], 2)       ) + self.point_pad_value],    axis=0       )
         a__  =np.append(input_labels[i],    [self.point_pad_value]       )
       processed_input_points.append(lowercase_       )
     a__  =processed_input_points
     return input_points, input_labels



   def  _UpperCAmelCase (				self,    lowercase_,    lowercase_,    lowercase_,    lowercase_=False       )							->    np.ndarray:






     """simple docstring"""






     a__,      a__  =original_size
     a__,      a__  =self.image_processor._get_preprocess_shape(lowercase_,    longest_edge=lowercase_       )
     a__  =deepcopy(lowercase_       ).astype(lowercase_       )

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

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

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

     return coords



   def  _UpperCAmelCase (				self,    lowercase_=None,    lowercase_=None,    lowercase_=None,    )							->    List[Any]:






     """simple docstring"""






     if input_points is not None:
       if hasattr(lowercase_,    '''numpy'''       ):  # Checks for TF or Torch tensor
         a__  =input_points.numpy().tolist()

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

     if input_labels is not None:
       if hasattr(lowercase_,    '''numpy'''       ):
         a__  =input_labels.numpy().tolist()

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

     if input_boxes is not None:
       if hasattr(lowercase_,    '''numpy'''       ):
         a__  =input_boxes.numpy().tolist()

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

     return input_points, input_labels, input_boxes



   @property
   def  _UpperCAmelCase (				self       )							->    Optional[Any]:






     """simple docstring"""






     a__  =self.image_processor.model_input_names
     return list(dict.fromkeys(lowercase_       )       )



   def  _UpperCAmelCase (				self,    *lowercase_,    **lowercase_       )							->    Tuple:






     """simple docstring"""






     return self.image_processor.post_process_masks(*lowercase_,    **lowercase_       )



from typing import TYPE_CHECKING

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


lowerCamelCase           =					{
    '''configuration_deberta''': ['''DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DebertaConfig''', '''DebertaOnnxConfig'''],
    '''tokenization_deberta''': ['''DebertaTokenizer'''],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase           =					['''DebertaTokenizerFast''']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase           =					[
           '''DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''DebertaForMaskedLM''',
           '''DebertaForQuestionAnswering''',
           '''DebertaForSequenceClassification''',
           '''DebertaForTokenClassification''',
           '''DebertaModel''',
           '''DebertaPreTrainedModel''',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase           =					[
           '''TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''TFDebertaForMaskedLM''',
           '''TFDebertaForQuestionAnswering''',
           '''TFDebertaForSequenceClassification''',
           '''TFDebertaForTokenClassification''',
           '''TFDebertaModel''',
           '''TFDebertaPreTrainedModel''',
       ]


if TYPE_CHECKING:
       from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
       from .tokenization_deberta import DebertaTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_deberta_fast import DebertaTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_deberta import (
                  DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  DebertaForMaskedLM,
                  DebertaForQuestionAnswering,
                  DebertaForSequenceClassification,
                  DebertaForTokenClassification,
                  DebertaModel,
                  DebertaPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_deberta import (
                  TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFDebertaForMaskedLM,
                  TFDebertaForQuestionAnswering,
                  TFDebertaForSequenceClassification,
                  TFDebertaForTokenClassification,
                  TFDebertaModel,
                  TFDebertaPreTrainedModel,
              )


else:
       import sys

       lowerCamelCase           =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
