"""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_   (  __UpperCamelCase      ):
					UpperCamelCase_			:						List[Any]      =     ["image_processor"]
					UpperCamelCase_			:						List[str]      =     "SamImageProcessor"




					def __init__(						self    :  Any	,    snake_case__    :  Any  ):
							super().__init__(snake_case__  )
							lowerCAmelCase__   = self.image_processor
							lowerCAmelCase__   = -10
							lowerCAmelCase__   = self.image_processor.size["""longest_edge"""]




					def __call__(						self    :  List[str]	,    snake_case__    :  Tuple=None	,    snake_case__    :  List[str]=None	,    snake_case__    :  int=None	,    snake_case__    :  Tuple=None	,    snake_case__    :  Optional[Union[str, TensorType]] = None	,    **snake_case__    :  Optional[Any]	,    ):
							lowerCAmelCase__   = self.image_processor(
							    snake_case__	,    return_tensors=snake_case__	,    **snake_case__	,    )

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

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

							lowerCAmelCase__ ,       lowerCAmelCase__ ,       lowerCAmelCase__   = self._check_and_preprocess_points(
							    input_points=snake_case__	,    input_labels=snake_case__	,    input_boxes=snake_case__	,    )

							lowerCAmelCase__   = 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     _SCREAMING_SNAKE_CASE    (						self    :  List[Any]	,    snake_case__    :  Tuple	,    snake_case__    :  Optional[Any]	,    snake_case__    :  Any=None	,    snake_case__    :  Tuple=None	,    snake_case__    :  Any=None	,    snake_case__    :  Any="pt"	,    ):
							if input_points is not None:
									if len(snake_case__  ) != len(snake_case__  ):
											lowerCAmelCase__   = [
											    self._normalize_coordinates(self.target_size	,    snake_case__	,    original_sizes[0]  ) for point in input_points
											]
									else:
											lowerCAmelCase__   = [
											    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:
													lowerCAmelCase__ ,       lowerCAmelCase__   = self._pad_points_and_labels(snake_case__	,    snake_case__  )

									lowerCAmelCase__   = np.array(snake_case__  )

							if input_labels is not None:
									lowerCAmelCase__   = np.array(snake_case__  )

							if input_boxes is not None:
									if len(snake_case__  ) != len(snake_case__  ):
											lowerCAmelCase__   = [
											    self._normalize_coordinates(self.target_size	,    snake_case__	,    original_sizes[0]	,    is_bounding_box=snake_case__  )
											    for box in input_boxes
											]
									else:
											lowerCAmelCase__   = [
											    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__  )
											]
									lowerCAmelCase__   = np.array(snake_case__  )

							if input_boxes is not None:
									if return_tensors == "pt":
											lowerCAmelCase__   = torch.from_numpy(snake_case__  )
											# 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(snake_case__  )
											# boxes batch size of 1 by default
											lowerCAmelCase__   = 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":
											lowerCAmelCase__   = torch.from_numpy(snake_case__  )
											# 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(snake_case__  )
											# point batch size of 1 by default
											lowerCAmelCase__   = 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":
											lowerCAmelCase__   = torch.from_numpy(snake_case__  )
											# 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(snake_case__  )
											# point batch size of 1 by default
											lowerCAmelCase__   = 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     _SCREAMING_SNAKE_CASE    (						self    :  str	,    snake_case__    :  Any	,    snake_case__    :  str  ):
							lowerCAmelCase__   = max([point.shape[0] for point in input_points]  )
							lowerCAmelCase__   = []
							for i, point in enumerate(snake_case__  ):
									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(snake_case__  )
							lowerCAmelCase__   = processed_input_points
							return input_points, input_labels




					def     _SCREAMING_SNAKE_CASE    (						self    :  Tuple	,    snake_case__    :  int	,    snake_case__    :  np.ndarray	,    snake_case__    :  Dict	,    snake_case__    :  Any=False  ):
							lowerCAmelCase__ ,       lowerCAmelCase__   = original_size
							lowerCAmelCase__ ,       lowerCAmelCase__   = self.image_processor._get_preprocess_shape(snake_case__	,    longest_edge=snake_case__  )
							lowerCAmelCase__   = deepcopy(snake_case__  ).astype(snake_case__  )

							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     _SCREAMING_SNAKE_CASE    (						self    :  str	,    snake_case__    :  int=None	,    snake_case__    :  Union[str, Any]=None	,    snake_case__    :  Union[str, Any]=None	,    ):
							if input_points is not None:
									if hasattr(snake_case__	,    """numpy"""  ):  # Checks for TF or Torch tensor
											lowerCAmelCase__   = 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."""  )
									lowerCAmelCase__   = [np.array(snake_case__  ) for input_point in input_points]
							else:
									lowerCAmelCase__   = None

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

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

							return input_points, input_labels, input_boxes




					@property
					def     _SCREAMING_SNAKE_CASE    (						self    :  Optional[int]  ):
							lowerCAmelCase__   = self.image_processor.model_input_names
							return list(dict.fromkeys(snake_case__  )  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  List[str]	,    *snake_case__    :  str	,    **snake_case__    :  List[Any]  ):
							return self.image_processor.post_process_masks(*snake_case__	,    **snake_case__  )





"""simple docstring"""




import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class    a_   (  __UpperCamelCase			,		unittest.TestCase      ):
					UpperCamelCase_			:						Optional[int]      =     MgpstrTokenizer
					UpperCamelCase_			:						Optional[int]      =     False
					UpperCamelCase_			:						Tuple      =     {}
					UpperCamelCase_			:						List[str]      =     False




					def     _SCREAMING_SNAKE_CASE    (						self    :  Dict  ):
							super().setUp()

							# fmt: off
							lowerCAmelCase__   = ["""[GO]""", """[s]""", """0""", """1""", """2""", """3""", """4""", """5""", """6""", """7""", """8""", """9""", """a""", """b""", """c""", """d""", """e""", """f""", """g""", """h""", """i""", """j""", """k""", """l""", """m""", """n""", """o""", """p""", """q""", """r""", """s""", """t""", """u""", """v""", """w""", """x""", """y""", """z"""]
							# fmt: on
							lowerCAmelCase__   = dict(zip(snake_case__	,    range(len(snake_case__  )  )  )  )

							lowerCAmelCase__   = os.path.join(self.tmpdirname	,    VOCAB_FILES_NAMES["""vocab_file"""]  )
							with open(self.vocab_file	,    """w"""	,    encoding="""utf-8"""  ) as fp:
									fp.write(json.dumps(snake_case__  ) + """\n"""  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  int	,    **snake_case__    :  Tuple  ):
							return MgpstrTokenizer.from_pretrained(self.tmpdirname	,    **snake_case__  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  List[str]	,    snake_case__    :  Dict  ):
							lowerCAmelCase__   = """tester"""
							lowerCAmelCase__   = """tester"""
							return input_text, output_text




					@unittest.skip("""MGP-STR always lower cases letters."""  )
					def     _SCREAMING_SNAKE_CASE    (						self    :  Optional[int]  ):
							pass




					def     _SCREAMING_SNAKE_CASE    (						self    :  Optional[int]  ):
							lowerCAmelCase__   = self.get_tokenizers(do_lower_case=snake_case__  )
							for tokenizer in tokenizers:
									with self.subTest(F"""{tokenizer.__class__.__name__}"""  ):
											lowerCAmelCase__   = """[SPECIAL_TOKEN]"""

											tokenizer.add_special_tokens({"""cls_token""": special_token}  )
											lowerCAmelCase__   = tokenizer.encode([special_token]	,    add_special_tokens=snake_case__  )
											self.assertEqual(len(snake_case__  )	,    1  )

											lowerCAmelCase__   = tokenizer.decode(snake_case__	,    skip_special_tokens=snake_case__  )
											self.assertTrue(special_token not in decoded  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  List[str]  ):
							lowerCAmelCase__   = self.get_tokenizers()
							for tokenizer in tokenizers:
									with self.subTest(F"""{tokenizer.__class__.__name__}"""  ):
											lowerCAmelCase__ ,       lowerCAmelCase__   = self.get_input_output_texts(snake_case__  )

											lowerCAmelCase__   = tokenizer.tokenize(snake_case__  )
											lowerCAmelCase__   = tokenizer.convert_tokens_to_ids(snake_case__  )
											lowerCAmelCase__   = tokenizer.encode(snake_case__	,    add_special_tokens=snake_case__  )
											self.assertListEqual(snake_case__	,    snake_case__  )

											lowerCAmelCase__   = tokenizer.convert_ids_to_tokens(snake_case__  )
											self.assertNotEqual(len(snake_case__  )	,    0  )
											lowerCAmelCase__   = tokenizer.decode(snake_case__  )
											self.assertIsInstance(snake_case__	,    snake_case__  )

											self.assertEqual(text_a.replace(""" """	,    """"""  )	,    snake_case__  )




					@unittest.skip("""MGP-STR tokenizer only handles one sequence."""  )
					def     _SCREAMING_SNAKE_CASE    (						self    :  Optional[Any]  ):
							pass




					@unittest.skip("""inputs cannot be pretokenized in MgpstrTokenizer"""  )
					def     _SCREAMING_SNAKE_CASE    (						self    :  List[Any]  ):
							pass




