





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     (						a    ):
				__A      =				["""image_processor"""]
				__A      =				"""SamImageProcessor"""


				def __init__(		self   , UpperCAmelCase_							):
								super().__init__(UpperCAmelCase_							)
								lowerCamelCase	 =self.image_processor
								lowerCamelCase	 =-10
								lowerCamelCase	 =self.image_processor.size["""longest_edge"""]


				def __call__(		self   , UpperCAmelCase_=None   , UpperCAmelCase_=None   , UpperCAmelCase_=None   , UpperCAmelCase_=None   , UpperCAmelCase_ = None   , **UpperCAmelCase_   , ):
								lowerCamelCase	 =self.image_processor(
								    UpperCAmelCase_   , return_tensors=UpperCAmelCase_   , **UpperCAmelCase_   , )

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

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

								lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase	 =self._check_and_preprocess_points(
								    input_points=UpperCAmelCase_   , input_labels=UpperCAmelCase_   , input_boxes=UpperCAmelCase_   , )

								lowerCamelCase	 =self._normalize_and_convert(
								    UpperCAmelCase_   , UpperCAmelCase_   , input_points=UpperCAmelCase_   , input_labels=UpperCAmelCase_   , input_boxes=UpperCAmelCase_   , return_tensors=UpperCAmelCase_   , )

								return encoding_image_processor


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_=None   , UpperCAmelCase_=None   , UpperCAmelCase_=None   , UpperCAmelCase_="pt"   , ):
								if input_points is not None:
												if len(UpperCAmelCase_							) != len(UpperCAmelCase_							):
																lowerCamelCase	 =[
																    self._normalize_coordinates(self.target_size   , UpperCAmelCase_   , original_sizes[0]							) for point in input_points
																]
												else:
																lowerCamelCase	 =[
																    self._normalize_coordinates(self.target_size   , UpperCAmelCase_   , UpperCAmelCase_							)
																    for point, original_size in zip(UpperCAmelCase_   , UpperCAmelCase_							)
																]
												# 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(UpperCAmelCase_   , UpperCAmelCase_							)

												lowerCamelCase	 =np.array(UpperCAmelCase_							)

								if input_labels is not None:
												lowerCamelCase	 =np.array(UpperCAmelCase_							)

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

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

								return encoding_image_processor


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_							):
								lowerCamelCase	 =max([point.shape[0] for point in input_points]							)
								lowerCamelCase	 =[]
								for i, point in enumerate(UpperCAmelCase_							):
												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(UpperCAmelCase_							)
								lowerCamelCase	 =processed_input_points
								return input_points, input_labels


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_=False							):
								lowerCamelCase ,  lowerCamelCase	 =original_size
								lowerCamelCase ,  lowerCamelCase	 =self.image_processor._get_preprocess_shape(UpperCAmelCase_   , longest_edge=UpperCAmelCase_							)
								lowerCamelCase	 =deepcopy(UpperCAmelCase_							).astype(UpperCAmelCase_							)

								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 _snake_case       (		self   , UpperCAmelCase_=None   , UpperCAmelCase_=None   , UpperCAmelCase_=None   , ):
								if input_points is not None:
												if hasattr(UpperCAmelCase_   , """numpy"""							):  # Checks for TF or Torch tensor
																lowerCamelCase	 =input_points.numpy().tolist()

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

								if input_labels is not None:
												if hasattr(UpperCAmelCase_   , """numpy"""							):
																lowerCamelCase	 =input_labels.numpy().tolist()

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

								if input_boxes is not None:
												if hasattr(UpperCAmelCase_   , """numpy"""							):
																lowerCamelCase	 =input_boxes.numpy().tolist()

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

								return input_points, input_labels, input_boxes


				@property
				def _snake_case       (		self							):
								lowerCamelCase	 =self.image_processor.model_input_names
								return list(dict.fromkeys(UpperCAmelCase_							)							)


				def _snake_case       (		self   , *UpperCAmelCase_   , **UpperCAmelCase_							):
								return self.image_processor.post_process_masks(*UpperCAmelCase_   , **UpperCAmelCase_							)












from typing import TYPE_CHECKING

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


UpperCAmelCase__						:			str									={'''configuration_fnet''': ['''FNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FNetConfig''']}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__						:			List[Any]									=['''FNetTokenizer''']

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__						:			Any									=['''FNetTokenizerFast''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__						:			Dict									=[
							    '''FNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''FNetForMaskedLM''',
							    '''FNetForMultipleChoice''',
							    '''FNetForNextSentencePrediction''',
							    '''FNetForPreTraining''',
							    '''FNetForQuestionAnswering''',
							    '''FNetForSequenceClassification''',
							    '''FNetForTokenClassification''',
							    '''FNetLayer''',
							    '''FNetModel''',
							    '''FNetPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_fnet import FNetTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_fnet_fast import FNetTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_fnet import (
														    FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
														    FNetForMaskedLM,
														    FNetForMultipleChoice,
														    FNetForNextSentencePrediction,
														    FNetForPreTraining,
														    FNetForQuestionAnswering,
														    FNetForSequenceClassification,
														    FNetForTokenClassification,
														    FNetLayer,
														    FNetModel,
														    FNetPreTrainedModel,
														)


else:
							import sys

							UpperCAmelCase__						:			Optional[int]									=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





