
"""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     _UpperCAmelCase(    __lowercase	):
		lowercase__						=      ["image_processor"]
		lowercase__						=      "SamImageProcessor"



		def __init__(					self				,		__a)       ->       List[Any]:






							'''simple docstring'''
							super().__init__(snake_case_)
							_UpperCamelCase							 =  self.image_processor
							_UpperCamelCase							 =  -10
							_UpperCamelCase							 =  self.image_processor.size['''longest_edge''']



		def __call__(					self				,		__a=None				,		__a=None				,		__a=None				,		__a=None				,		__a = None				,		**__a				,		)       ->       Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase							 =  self.image_processor(
							    snake_case_				,		return_tensors=snake_case_				,		**snake_case_				,		)

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

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

							_UpperCamelCase							 =  self._check_and_preprocess_points(
							    input_points=snake_case_				,		input_labels=snake_case_				,		input_boxes=snake_case_				,		)

							_UpperCamelCase							 =  self._normalize_and_convert(
							    snake_case_				,		snake_case_				,		input_points=snake_case_				,		input_labels=snake_case_				,		input_boxes=snake_case_				,		return_tensors=snake_case_				,		)

							return encoding_image_processor



		def 						UpperCAmelCase							(					self				,		__a				,		__a				,		__a=None				,		__a=None				,		__a=None				,		__a="pt"				,		)       ->       List[Any]:






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

												_UpperCamelCase							 =  np.array(snake_case_)

							if input_labels is not None:
												_UpperCamelCase							 =  np.array(snake_case_)

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

							if input_boxes is not None:
												if return_tensors == "pt":
																	_UpperCamelCase							 =  torch.from_numpy(snake_case_)
																	# boxes batch size of 1 by default
																	_UpperCamelCase							 =  input_boxes.unsqueeze(1) if len(input_boxes.shape) != 3 else input_boxes
												elif return_tensors == "tf":
																	_UpperCamelCase							 =  tf.convert_to_tensor(snake_case_)
																	# boxes batch size of 1 by default
																	_UpperCamelCase							 =  tf.expand_dims(snake_case_				,		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":
																	_UpperCamelCase							 =  torch.from_numpy(snake_case_)
																	# point batch size of 1 by default
																	_UpperCamelCase							 =  input_points.unsqueeze(1) if len(input_points.shape) != 4 else input_points
												elif return_tensors == "tf":
																	_UpperCamelCase							 =  tf.convert_to_tensor(snake_case_)
																	# point batch size of 1 by default
																	_UpperCamelCase							 =  tf.expand_dims(snake_case_				,		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":
																	_UpperCamelCase							 =  torch.from_numpy(snake_case_)
																	# point batch size of 1 by default
																	_UpperCamelCase							 =  input_labels.unsqueeze(1) if len(input_labels.shape) != 3 else input_labels
												elif return_tensors == "tf":
																	_UpperCamelCase							 =  tf.convert_to_tensor(snake_case_)
																	# point batch size of 1 by default
																	_UpperCamelCase							 =  tf.expand_dims(snake_case_				,		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				,		__a				,		__a)       ->       Union[str, Any]:






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



		def 						UpperCAmelCase							(					self				,		__a				,		__a				,		__a				,		__a=False)       ->       Dict:






							'''simple docstring'''
							_UpperCamelCase							 =  original_size
							_UpperCamelCase							 =  self.image_processor._get_preprocess_shape(snake_case_				,		longest_edge=snake_case_)
							_UpperCamelCase							 =  deepcopy(snake_case_).astype(snake_case_)

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

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

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

							return coords



		def 						UpperCAmelCase							(					self				,		__a=None				,		__a=None				,		__a=None				,		)       ->       Optional[Any]:






							'''simple docstring'''
							if input_points is not None:
												if hasattr(snake_case_				,		'''numpy'''):  # Checks for TF or Torch tensor
																	_UpperCamelCase							 =  input_points.numpy().tolist()

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

							if input_labels is not None:
												if hasattr(snake_case_				,		'''numpy'''):
																	_UpperCamelCase							 =  input_labels.numpy().tolist()

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

							if input_boxes is not None:
												if hasattr(snake_case_				,		'''numpy'''):
																	_UpperCamelCase							 =  input_boxes.numpy().tolist()

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

							return input_points, input_labels, input_boxes



		@property
		def 						UpperCAmelCase							(					self)       ->       List[Any]:






							'''simple docstring'''
							_UpperCamelCase							 =  self.image_processor.model_input_names
							return list(dict.fromkeys(snake_case_))




		def 						UpperCAmelCase							(					self				,		*__a				,		**__a)       ->       str:






							'''simple docstring'''
							return self.image_processor.post_process_masks(*snake_case_				,		**snake_case_)








from typing import TYPE_CHECKING

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


lowercase__       ={
    'configuration_roformer': ['ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoFormerConfig', 'RoFormerOnnxConfig'],
    'tokenization_roformer': ['RoFormerTokenizer'],
}

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

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__       =[
          'ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
          'RoFormerForCausalLM',
          'RoFormerForMaskedLM',
          'RoFormerForMultipleChoice',
          'RoFormerForQuestionAnswering',
          'RoFormerForSequenceClassification',
          'RoFormerForTokenClassification',
          'RoFormerLayer',
          'RoFormerModel',
          'RoFormerPreTrainedModel',
          'load_tf_weights_in_roformer',
      ]


try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__       =[
          'TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
          'TFRoFormerForCausalLM',
          'TFRoFormerForMaskedLM',
          'TFRoFormerForMultipleChoice',
          'TFRoFormerForQuestionAnswering',
          'TFRoFormerForSequenceClassification',
          'TFRoFormerForTokenClassification',
          'TFRoFormerLayer',
          'TFRoFormerModel',
          'TFRoFormerPreTrainedModel',
      ]


try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__       =[
          'FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
          'FlaxRoFormerForMaskedLM',
          'FlaxRoFormerForMultipleChoice',
          'FlaxRoFormerForQuestionAnswering',
          'FlaxRoFormerForSequenceClassification',
          'FlaxRoFormerForTokenClassification',
          'FlaxRoFormerModel',
          'FlaxRoFormerPreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
      from .tokenization_roformer import RoFormerTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_roformer_fast import RoFormerTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_roformer import (
                ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                RoFormerForCausalLM,
                RoFormerForMaskedLM,
                RoFormerForMultipleChoice,
                RoFormerForQuestionAnswering,
                RoFormerForSequenceClassification,
                RoFormerForTokenClassification,
                RoFormerLayer,
                RoFormerModel,
                RoFormerPreTrainedModel,
                load_tf_weights_in_roformer,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_roformer import (
                TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFRoFormerForCausalLM,
                TFRoFormerForMaskedLM,
                TFRoFormerForMultipleChoice,
                TFRoFormerForQuestionAnswering,
                TFRoFormerForSequenceClassification,
                TFRoFormerForTokenClassification,
                TFRoFormerLayer,
                TFRoFormerModel,
                TFRoFormerPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_roformer import (
                FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                FlaxRoFormerForMaskedLM,
                FlaxRoFormerForMultipleChoice,
                FlaxRoFormerForQuestionAnswering,
                FlaxRoFormerForSequenceClassification,
                FlaxRoFormerForTokenClassification,
                FlaxRoFormerModel,
                FlaxRoFormerPreTrainedModel,
            )


else:
      import sys

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