


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    (       lowerCamelCase__							):
					'''simple docstring'''






					A__			= ['image_processor']
					A__			= 'SamImageProcessor'

					def __init__(self    :						Optional[Any]      ,					_UpperCAmelCase    :						Any		)						->   Tuple:




							"""simple docstring"""


							super().__init__(lowercase_		)
							lowercase__     			=		self.image_processor
							lowercase__     			=		-10
							lowercase__     			=		self.image_processor.size["""longest_edge"""]

					def __call__(self    :						Any      ,					_UpperCAmelCase    :						Any=None      ,					_UpperCAmelCase    :						Tuple=None      ,					_UpperCAmelCase    :						List[Any]=None      ,					_UpperCAmelCase    :						List[str]=None      ,					_UpperCAmelCase    :						Union[str, Any] = None      ,					**_UpperCAmelCase    :						Optional[int]      ,					)						->   BatchEncoding:




							"""simple docstring"""


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

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

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

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

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

							return encoding_image_processor

					def   lowerCamelCase__     (self    :						Union[str, Any]      ,					_UpperCAmelCase    :						List[Any]      ,					_UpperCAmelCase    :						Dict      ,					_UpperCAmelCase    :						List[Any]=None      ,					_UpperCAmelCase    :						List[Any]=None      ,					_UpperCAmelCase    :						str=None      ,					_UpperCAmelCase    :						List[Any]="pt"      ,					)						->   int:




							"""simple docstring"""


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

									lowercase__     			=		np.array(lowercase_		)

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

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

							if input_boxes is not None:
									if return_tensors == "pt":
											lowercase__     			=		torch.from_numpy(lowercase_		)
											# boxes batch size of 1 by default
											lowercase__     			=		input_boxes.unsqueeze(1		) if len(input_boxes.shape		) != 3 else input_boxes
									elif return_tensors == "tf":
											lowercase__     			=		tf.convert_to_tensor(lowercase_		)
											# boxes batch size of 1 by default
											lowercase__     			=		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":
											lowercase__     			=		torch.from_numpy(lowercase_		)
											# point batch size of 1 by default
											lowercase__     			=		input_points.unsqueeze(1		) if len(input_points.shape		) != 4 else input_points
									elif return_tensors == "tf":
											lowercase__     			=		tf.convert_to_tensor(lowercase_		)
											# point batch size of 1 by default
											lowercase__     			=		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":
											lowercase__     			=		torch.from_numpy(lowercase_		)
											# point batch size of 1 by default
											lowercase__     			=		input_labels.unsqueeze(1		) if len(input_labels.shape		) != 3 else input_labels
									elif return_tensors == "tf":
											lowercase__     			=		tf.convert_to_tensor(lowercase_		)
											# point batch size of 1 by default
											lowercase__     			=		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   lowerCamelCase__     (self    :						Dict      ,					_UpperCAmelCase    :						str      ,					_UpperCAmelCase    :						Optional[Any]		)						->   str:




							"""simple docstring"""


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

					def   lowerCamelCase__     (self    :						Dict      ,					_UpperCAmelCase    :						Union[str, Any]      ,					_UpperCAmelCase    :						Dict      ,					_UpperCAmelCase    :						Any      ,					_UpperCAmelCase    :						List[Any]=False		)						->   np.ndarray:




							"""simple docstring"""


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

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

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

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

							return coords

					def   lowerCamelCase__     (self    :						Optional[int]      ,					_UpperCAmelCase    :						List[str]=None      ,					_UpperCAmelCase    :						Union[str, Any]=None      ,					_UpperCAmelCase    :						Tuple=None      ,					)						->   List[Any]:




							"""simple docstring"""


							if input_points is not None:
									if hasattr(lowercase_      ,					"""numpy"""		):  # Checks for TF or Torch tensor
											lowercase__     			=		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."""		)
									lowercase__     			=		[np.array(lowercase_		) for input_point in input_points]
							else:
									lowercase__     			=		None

							if input_labels is not None:
									if hasattr(lowercase_      ,					"""numpy"""		):
											lowercase__     			=		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."""		)
									lowercase__     			=		[np.array(lowercase_		) for label in input_labels]
							else:
									lowercase__     			=		None

							if input_boxes is not None:
									if hasattr(lowercase_      ,					"""numpy"""		):
											lowercase__     			=		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."""		)
									lowercase__     			=		[np.array(lowercase_		).astype(np.floataa		) for box in input_boxes]
							else:
									lowercase__     			=		None

							return input_points, input_labels, input_boxes

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




							"""simple docstring"""


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


					def   lowerCamelCase__     (self    :						Optional[Any]      ,					*_UpperCAmelCase    :						Optional[int]      ,					**_UpperCAmelCase    :						Dict		)						->   Tuple:




							"""simple docstring"""


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




from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    LineByLineWithSOPTextDataset,
    TextDataset,
    TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments
