

"""simple docstring"""




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 		_A       (			__lowercase					):
      __a								=	['image_processor']
      __a								=	'SamImageProcessor'





      def __init__(  self    ,						SCREAMING_SNAKE_CASE__		)   ->					List[str]:
         super().__init__(_A		)
         lowerCamelCase__						=			self.image_processor
         lowerCamelCase__						=			-10
         lowerCamelCase__						=			self.image_processor.size["longest_edge"]





      def __call__(  self    ,						SCREAMING_SNAKE_CASE__=None    ,						SCREAMING_SNAKE_CASE__=None    ,						SCREAMING_SNAKE_CASE__=None    ,						SCREAMING_SNAKE_CASE__=None    ,						SCREAMING_SNAKE_CASE__ = None    ,						**SCREAMING_SNAKE_CASE__    ,						)   ->					Optional[int]:
         lowerCamelCase__						=			self.image_processor(
             _A    ,						return_tensors=_A    ,						**_A    ,						)

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

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

         lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__						=			self._check_and_preprocess_points(
             input_points=_A    ,						input_labels=_A    ,						input_boxes=_A    ,						)

         lowerCamelCase__						=			self._normalize_and_convert(
             _A    ,						_A    ,						input_points=_A    ,						input_labels=_A    ,						input_boxes=_A    ,						return_tensors=_A    ,						)

         return encoding_image_processor





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__=None    ,						SCREAMING_SNAKE_CASE__=None    ,						SCREAMING_SNAKE_CASE__=None    ,						SCREAMING_SNAKE_CASE__="pt"    ,						)   ->					Optional[int]:
         if input_points is not None:
            if len(_A		) != len(_A		):
               lowerCamelCase__						=			[
                   self._normalize_coordinates(self.target_size    ,						_A    ,						original_sizes[0]		) for point in input_points
               ]
            else:
               lowerCamelCase__						=			[
                   self._normalize_coordinates(self.target_size    ,						_A    ,						_A		)
                   for point, original_size in zip(_A    ,						_A		)
               ]
            # 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:
                  lowerCamelCase__					,       lowerCamelCase__						=			self._pad_points_and_labels(_A    ,						_A		)

            lowerCamelCase__						=			np.array(_A		)

         if input_labels is not None:
            lowerCamelCase__						=			np.array(_A		)

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

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

         return encoding_image_processor





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)   ->					List[Any]:
         lowerCamelCase__						=			max([point.shape[0] for point in input_points]		)
         lowerCamelCase__						=			[]
         for i, point in enumerate(_A		):
            if point.shape[0] != expected_nb_points:
               lowerCamelCase__						=			np.concatenate(
                   [point, np.zeros((expected_nb_points - point.shape[0], 2)		) + self.point_pad_value]    ,						axis=0		)
               lowerCamelCase__						=			np.append(input_labels[i]    ,						[self.point_pad_value]		)
            processed_input_points.append(_A		)
         lowerCamelCase__						=			processed_input_points
         return input_points, input_labels





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__=False		)   ->					Union[str, Any]:
         lowerCamelCase__					,       lowerCamelCase__						=			original_size
         lowerCamelCase__					,       lowerCamelCase__						=			self.image_processor._get_preprocess_shape(_A    ,						longest_edge=_A		)
         lowerCamelCase__						=			deepcopy(_A		).astype(_A		)

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

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

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

         return coords





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__=None    ,						SCREAMING_SNAKE_CASE__=None    ,						SCREAMING_SNAKE_CASE__=None    ,						)   ->					Dict:
         if input_points is not None:
            if hasattr(_A    ,						"numpy"		):  # Checks for TF or Torch tensor
               lowerCamelCase__						=			input_points.numpy().tolist()

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

         if input_labels is not None:
            if hasattr(_A    ,						"numpy"		):
               lowerCamelCase__						=			input_labels.numpy().tolist()

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

         if input_boxes is not None:
            if hasattr(_A    ,						"numpy"		):
               lowerCamelCase__						=			input_boxes.numpy().tolist()

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

         return input_points, input_labels, input_boxes





      @property
      def 					_lowerCamelCase  (  self		)   ->					Optional[int]:
         lowerCamelCase__						=			self.image_processor.model_input_names
         return list(dict.fromkeys(_A		)		)





      def 					_lowerCamelCase  (  self    ,						*SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__		)   ->					int:
         return self.image_processor.post_process_masks(*_A    ,						**_A		)







"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


SCREAMING_SNAKE_CASE_     :			List[Any]	  =			{
    '''configuration_rag''': ['''RagConfig'''],
    '''retrieval_rag''': ['''RagRetriever'''],
    '''tokenization_rag''': ['''RagTokenizer'''],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE_     :			Union[str, Any]	  =			[
         '''RagModel''',
         '''RagPreTrainedModel''',
         '''RagSequenceForGeneration''',
         '''RagTokenForGeneration''',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE_     :			str	  =			[
         '''TFRagModel''',
         '''TFRagPreTrainedModel''',
         '''TFRagSequenceForGeneration''',
         '''TFRagTokenForGeneration''',
     ]


if TYPE_CHECKING:
     from .configuration_rag import RagConfig
     from .retrieval_rag import RagRetriever
     from .tokenization_rag import RagTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_rag import (
              TFRagModel,
              TFRagPreTrainedModel,
              TFRagSequenceForGeneration,
              TFRagTokenForGeneration,
          )

else:
     import sys

     SCREAMING_SNAKE_CASE_     :			List[str]	  =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




