


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     lowercase__  (					__SCREAMING_SNAKE_CASE		):
		A__=  ['image_processor']
		A__=  'SamImageProcessor'






		def __init__(					self      :					int		,						_lowercase      :					List[Any] ):




									"""simple docstring"""







									super().__init__(_lowercase )
									UpperCAmelCase__										=     self.image_processor
									UpperCAmelCase__										=     -10
									UpperCAmelCase__										=     self.image_processor.size["longest_edge"]






		def __call__(					self      :					Union[str, Any]		,						_lowercase      :					str=None		,						_lowercase      :					Union[str, Any]=None		,						_lowercase      :					List[Any]=None		,						_lowercase      :					Dict=None		,						_lowercase      :					Optional[Union[str, TensorType]] = None		,						**_lowercase      :					Union[str, Any]		,						):




									"""simple docstring"""







									UpperCAmelCase__										=     self.image_processor(
									    _lowercase		,						return_tensors=_lowercase		,						**_lowercase		,						)

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

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

									UpperCAmelCase__						,			UpperCAmelCase__						,			UpperCAmelCase__										=     self._check_and_preprocess_points(
									    input_points=_lowercase		,						input_labels=_lowercase		,						input_boxes=_lowercase		,						)

									UpperCAmelCase__										=     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      :					Dict		,						_lowercase      :					int		,						_lowercase      :					Dict		,						_lowercase      :					Optional[Any]=None		,						_lowercase      :					Any=None		,						_lowercase      :					Union[str, Any]=None		,						_lowercase      :					List[Any]="pt"		,						):




									"""simple docstring"""







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

																UpperCAmelCase__										=     np.array(_lowercase )

									if input_labels is not None:
																UpperCAmelCase__										=     np.array(_lowercase )

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

									if input_boxes is not None:
																if return_tensors == "pt":
																							UpperCAmelCase__										=     torch.from_numpy(_lowercase )
																							# 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(_lowercase )
																							# boxes batch size of 1 by default
																							UpperCAmelCase__										=     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":
																							UpperCAmelCase__										=     torch.from_numpy(_lowercase )
																							# 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(_lowercase )
																							# point batch size of 1 by default
																							UpperCAmelCase__										=     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":
																							UpperCAmelCase__										=     torch.from_numpy(_lowercase )
																							# 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(_lowercase )
																							# point batch size of 1 by default
																							UpperCAmelCase__										=     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      :					Union[str, Any]		,						_lowercase      :					Any		,						_lowercase      :					Optional[Any] ):




									"""simple docstring"""







									UpperCAmelCase__										=     max([point.shape[0] for point in input_points] )
									UpperCAmelCase__										=     []
									for i, point in enumerate(_lowercase ):
																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(_lowercase )
									UpperCAmelCase__										=     processed_input_points
									return input_points, input_labels






		def 			_UpperCAmelCase  (					self      :					Any		,						_lowercase      :					int		,						_lowercase      :					np.ndarray		,						_lowercase      :					Optional[Any]		,						_lowercase      :					Any=False ):




									"""simple docstring"""







									UpperCAmelCase__						,			UpperCAmelCase__										=     original_size
									UpperCAmelCase__						,			UpperCAmelCase__										=     self.image_processor._get_preprocess_shape(_lowercase		,						longest_edge=_lowercase )
									UpperCAmelCase__										=     deepcopy(_lowercase ).astype(_lowercase )

									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      :					int		,						_lowercase      :					Tuple=None		,						_lowercase      :					Optional[Any]=None		,						_lowercase      :					List[Any]=None		,						):




									"""simple docstring"""







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

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

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

									return input_points, input_labels, input_boxes






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




									"""simple docstring"""







									UpperCAmelCase__										=     self.image_processor.model_input_names
									return list(dict.fromkeys(_lowercase ) )






		def 			_UpperCAmelCase  (					self      :					int		,						*_lowercase      :					List[str]		,						**_lowercase      :					Any ):




									"""simple docstring"""







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









import os
import unittest

from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir

from ...test_tokenization_common import TokenizerTesterMixin


A      = get_tests_dir("fixtures/test_sentencepiece_bpe.model")


class     lowercase__  (					__SCREAMING_SNAKE_CASE					,							unittest.TestCase		):
  A__=  BartphoTokenizer
  A__=  False
  A__=  True






  def 			_UpperCAmelCase  (					self      :					Dict ):




         """simple docstring"""







         super().setUp()

         UpperCAmelCase__										=     ["▁This", "▁is", "▁a", "▁t", "est"]
         UpperCAmelCase__										=     dict(zip(_lowercase		,						range(len(_lowercase ) ) ) )
         UpperCAmelCase__										=     {"unk_token": "<unk>"}

         UpperCAmelCase__										=     os.path.join(self.tmpdirname		,						VOCAB_FILES_NAMES["monolingual_vocab_file"] )
         with open(self.monolingual_vocab_file		,						"w"		,						encoding="utf-8" ) as fp:
                for token in vocab_tokens:
                       fp.write(F"""{token} {vocab_tokens[token]}\n""" )

         UpperCAmelCase__										=     BartphoTokenizer(_lowercase		,						self.monolingual_vocab_file		,						**self.special_tokens_map )
         tokenizer.save_pretrained(self.tmpdirname )






  def 			_UpperCAmelCase  (					self      :					Dict		,						**_lowercase      :					Optional[Any] ):




         """simple docstring"""







         kwargs.update(self.special_tokens_map )
         return BartphoTokenizer.from_pretrained(self.tmpdirname		,						**_lowercase )






  def 			_UpperCAmelCase  (					self      :					List[Any]		,						_lowercase      :					Union[str, Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     "This is a là test"
         UpperCAmelCase__										=     "This is a<unk><unk> test"
         return input_text, output_text






  def 			_UpperCAmelCase  (					self      :					Optional[Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     BartphoTokenizer(_lowercase		,						self.monolingual_vocab_file		,						**self.special_tokens_map )
         UpperCAmelCase__										=     "This is a là test"
         UpperCAmelCase__										=     "▁This ▁is ▁a ▁l à ▁t est".split()
         UpperCAmelCase__										=     tokenizer.tokenize(_lowercase )
         self.assertListEqual(_lowercase		,						_lowercase )

         UpperCAmelCase__										=     tokens + [tokenizer.unk_token]
         UpperCAmelCase__										=     [4, 5, 6, 3, 3, 7, 8, 3]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowercase )		,						_lowercase )





