
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				(					_A  ):






			'''simple docstring'''

			__UpperCamelCase      :						List[Any]					      = ["""image_processor"""]
			__UpperCamelCase      :						Any					      = """SamImageProcessor"""

			def __init__(       self :       Tuple						,	snake_case_ :       Dict      ):
							super().__init__(snake_case_      )
							UpperCamelCase_:							int	=						self.image_processor
							UpperCamelCase_:							Optional[Any]	=						-10
							UpperCamelCase_:							Optional[Any]	=						self.image_processor.size["""longest_edge"""]

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

							# pop arguments that are not used in the foward but used nevertheless
							UpperCamelCase_:							Optional[Any]	=						encoding_image_processor["""original_sizes"""]

							if hasattr(snake_case_						,	"""numpy"""      ):  # Checks if Torch or TF tensor
											UpperCamelCase_:							Optional[int]	=						original_sizes.numpy()

							UpperCamelCase_,   UpperCamelCase_,   UpperCamelCase_:							Optional[int]	=						self._check_and_preprocess_points(
							    input_points=snake_case_						,	input_labels=snake_case_						,	input_boxes=snake_case_						,	)

							UpperCamelCase_:							Any	=						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       lowerCAmelCase__	(       self :       Dict						,	snake_case_ :       Optional[int]						,	snake_case_ :       List[str]						,	snake_case_ :       Optional[Any]=None						,	snake_case_ :       Union[str, Any]=None						,	snake_case_ :       Optional[Any]=None						,	snake_case_ :       Dict="pt"						,	):
							if input_points is not None:
											if len(snake_case_      ) != len(snake_case_      ):
															UpperCamelCase_:							int	=						[
															    self._normalize_coordinates(self.target_size						,	snake_case_						,	original_sizes[0]      ) for point in input_points
															]
											else:
															UpperCamelCase_:							Dict	=						[
															    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_,   UpperCamelCase_:							Union[str, Any]	=						self._pad_points_and_labels(snake_case_						,	snake_case_      )

											UpperCamelCase_:							Optional[int]	=						np.array(snake_case_      )

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

							if input_boxes is not None:
											if len(snake_case_      ) != len(snake_case_      ):
															UpperCamelCase_:							int	=						[
															    self._normalize_coordinates(self.target_size						,	snake_case_						,	original_sizes[0]						,	is_bounding_box=snake_case_      )
															    for box in input_boxes
															]
											else:
															UpperCamelCase_:							Dict	=						[
															    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_:							Tuple	=						np.array(snake_case_      )

							if input_boxes is not None:
											if return_tensors == "pt":
															UpperCamelCase_:							List[Any]	=						torch.from_numpy(snake_case_      )
															# boxes batch size of 1 by default
															UpperCamelCase_:							Union[str, Any]	=						input_boxes.unsqueeze(1      ) if len(input_boxes.shape      ) != 3 else input_boxes
											elif return_tensors == "tf":
															UpperCamelCase_:							Dict	=						tf.convert_to_tensor(snake_case_      )
															# boxes batch size of 1 by default
															UpperCamelCase_:							str	=						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_:							Optional[int]	=						torch.from_numpy(snake_case_      )
															# point batch size of 1 by default
															UpperCamelCase_:							Union[str, Any]	=						input_points.unsqueeze(1      ) if len(input_points.shape      ) != 4 else input_points
											elif return_tensors == "tf":
															UpperCamelCase_:							str	=						tf.convert_to_tensor(snake_case_      )
															# point batch size of 1 by default
															UpperCamelCase_:							Optional[Any]	=						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_:							Union[str, Any]	=						torch.from_numpy(snake_case_      )
															# point batch size of 1 by default
															UpperCamelCase_:							str	=						input_labels.unsqueeze(1      ) if len(input_labels.shape      ) != 3 else input_labels
											elif return_tensors == "tf":
															UpperCamelCase_:							List[Any]	=						tf.convert_to_tensor(snake_case_      )
															# point batch size of 1 by default
															UpperCamelCase_:							Optional[Any]	=						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       lowerCAmelCase__	(       self :       Tuple						,	snake_case_ :       List[Any]						,	snake_case_ :       Optional[Any]      ):
							UpperCamelCase_:							Tuple	=						max([point.shape[0] for point in input_points]      )
							UpperCamelCase_:							int	=						[]
							for i, point in enumerate(snake_case_      ):
											if point.shape[0] != expected_nb_points:
															UpperCamelCase_:							Optional[int]	=						np.concatenate(
															    [point, np.zeros((expected_nb_points - point.shape[0], 2)      ) + self.point_pad_value]						,	axis=0      )
															UpperCamelCase_:							int	=						np.append(input_labels[i]						,	[self.point_pad_value]      )
											processed_input_points.append(snake_case_      )
							UpperCamelCase_:							List[Any]	=						processed_input_points
							return input_points, input_labels

			def       lowerCAmelCase__	(       self :       int						,	snake_case_ :       int						,	snake_case_ :       np.ndarray						,	snake_case_ :       List[Any]						,	snake_case_ :       Optional[int]=False      ):
							UpperCamelCase_,   UpperCamelCase_:							Dict	=						original_size
							UpperCamelCase_,   UpperCamelCase_:							str	=						self.image_processor._get_preprocess_shape(snake_case_						,	longest_edge=snake_case_      )
							UpperCamelCase_:							Tuple	=						deepcopy(snake_case_      ).astype(snake_case_      )

							if is_bounding_box:
											UpperCamelCase_:							Optional[Any]	=						coords.reshape(-1						,	2						,	2      )

							UpperCamelCase_:							List[str]	=						coords[..., 0] * (new_w / old_w)
							UpperCamelCase_:							int	=						coords[..., 1] * (new_h / old_h)

							if is_bounding_box:
											UpperCamelCase_:							List[Any]	=						coords.reshape(-1						,	4      )

							return coords

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

							if input_labels is not None:
											if hasattr(snake_case_						,	"""numpy"""      ):
															UpperCamelCase_:							Any	=						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_:							Optional[Any]	=						[np.array(snake_case_      ) for label in input_labels]
							else:
											UpperCamelCase_:							Optional[Any]	=						None

							if input_boxes is not None:
											if hasattr(snake_case_						,	"""numpy"""      ):
															UpperCamelCase_:							Union[str, Any]	=						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_:							List[Any]	=						[np.array(snake_case_      ).astype(np.floataa      ) for box in input_boxes]
							else:
											UpperCamelCase_:							Optional[Any]	=						None

							return input_points, input_labels, input_boxes

			@property
			def       lowerCAmelCase__	(       self :       Dict      ):
							UpperCamelCase_:							Tuple	=						self.image_processor.model_input_names
							return list(dict.fromkeys(snake_case_      )      )




			def       lowerCAmelCase__	(       self :       Optional[int]						,	*snake_case_ :       Tuple						,	**snake_case_ :       Tuple      ):
							return self.image_processor.post_process_masks(*snake_case_						,	**snake_case_      )








import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class        _UpperCamelCase				(					_A     ,       unittest.TestCase  ):






			'''simple docstring'''

			__UpperCamelCase      :						Optional[int]					      = BarthezTokenizer
			__UpperCamelCase      :						str					      = BarthezTokenizerFast
			__UpperCamelCase      :						str					      = True
			__UpperCamelCase      :						List[Any]					      = True

			def       lowerCAmelCase__	(       self :       Optional[int]      ):
							super().setUp()

							UpperCamelCase_:							Tuple	=						BarthezTokenizerFast.from_pretrained("""moussaKam/mbarthez"""      )
							tokenizer.save_pretrained(self.tmpdirname      )
							tokenizer.save_pretrained(self.tmpdirname						,	legacy_format=snake_case_      )
							UpperCamelCase_:							Dict	=						tokenizer

			def       lowerCAmelCase__	(       self :       List[str]      ):
							UpperCamelCase_:							str	=						"""<pad>"""
							UpperCamelCase_:							int	=						1

							self.assertEqual(self.get_tokenizer()._convert_token_to_id(snake_case_      )						,	snake_case_      )
							self.assertEqual(self.get_tokenizer()._convert_id_to_token(snake_case_      )						,	snake_case_      )

			def       lowerCAmelCase__	(       self :       Dict      ):
							UpperCamelCase_:							Tuple	=						list(self.get_tokenizer().get_vocab().keys()      )

							self.assertEqual(vocab_keys[0]						,	"""<s>"""      )
							self.assertEqual(vocab_keys[1]						,	"""<pad>"""      )
							self.assertEqual(vocab_keys[-1]						,	"""<mask>"""      )
							self.assertEqual(len(snake_case_      )						,	10_1122      )

			def       lowerCAmelCase__	(       self :       Dict      ):
							self.assertEqual(self.get_tokenizer().vocab_size						,	10_1122      )

			@require_torch
			def       lowerCAmelCase__	(       self :       int      ):
							UpperCamelCase_:							Dict	=						["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
							UpperCamelCase_:							Union[str, Any]	=						[0, 57, 3018, 7_0307, 91, 2]

							UpperCamelCase_:							Union[str, Any]	=						self.tokenizer(
							    snake_case_						,	max_length=len(snake_case_      )						,	padding=snake_case_						,	truncation=snake_case_						,	return_tensors="""pt"""      )
							self.assertIsInstance(snake_case_						,	snake_case_      )

							self.assertEqual((2, 6)						,	batch.input_ids.shape      )
							self.assertEqual((2, 6)						,	batch.attention_mask.shape      )
							UpperCamelCase_:							Any	=						batch.input_ids.tolist()[0]
							self.assertListEqual(snake_case_						,	snake_case_      )

			def       lowerCAmelCase__	(       self :       Any      ):
							if not self.test_rust_tokenizer:
											return

							UpperCamelCase_:							Optional[Any]	=						self.get_tokenizer()
							UpperCamelCase_:							Union[str, Any]	=						self.get_rust_tokenizer()

							UpperCamelCase_:							str	=						"""I was born in 92000, and this is falsé."""

							UpperCamelCase_:							str	=						tokenizer.tokenize(snake_case_      )
							UpperCamelCase_:							int	=						rust_tokenizer.tokenize(snake_case_      )
							self.assertListEqual(snake_case_						,	snake_case_      )

							UpperCamelCase_:							int	=						tokenizer.encode(snake_case_						,	add_special_tokens=snake_case_      )
							UpperCamelCase_:							int	=						rust_tokenizer.encode(snake_case_						,	add_special_tokens=snake_case_      )
							self.assertListEqual(snake_case_						,	snake_case_      )

							UpperCamelCase_:							List[str]	=						self.get_rust_tokenizer()
							UpperCamelCase_:							Tuple	=						tokenizer.encode(snake_case_      )
							UpperCamelCase_:							Tuple	=						rust_tokenizer.encode(snake_case_      )
							self.assertListEqual(snake_case_						,	snake_case_      )




			@slow
			def       lowerCAmelCase__	(       self :       int      ):
							# fmt: off
							UpperCamelCase_:							Optional[Any]	=						{"""input_ids""": [[0, 490, 1_4328, 4507, 354, 47, 4_3669, 95, 25, 7_8117, 2_0215, 1_9779, 190, 22, 400, 4, 3_5343, 8_0310, 603, 86, 2_4937, 105, 3_3438, 9_4762, 196, 3_9642, 7, 15, 1_5933, 173, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 1_0534, 87, 25, 66, 3358, 196, 5_5289, 8, 8_2961, 81, 2204, 7_5203, 7, 15, 763, 1_2956, 216, 178, 1_4328, 9595, 1377, 6_9693, 7, 448, 7_1021, 196, 1_8106, 1437, 1_3974, 108, 9083, 4, 4_9315, 7, 39, 86, 1326, 2793, 4_6333, 4, 448, 196, 7_4588, 7, 4_9315, 7, 39, 21, 822, 3_8470, 74, 21, 6_6723, 6_2480, 8, 2_2050, 5, 2]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
							# fmt: on

							# moussaKam/mbarthez is a french model. So we also use french texts.
							UpperCamelCase_:							str	=						[
							    """Le transformeur est un modèle d'apprentissage profond introduit en 2017, """
							    """utilisé principalement dans le domaine du traitement automatique des langues (TAL).""",
							    """À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus """
							    """pour gérer des données séquentielles, telles que le langage naturel, pour des tâches """
							    """telles que la traduction et la synthèse de texte.""",
							]

							self.tokenizer_integration_test_util(
							    expected_encoding=snake_case_						,	model_name="""moussaKam/mbarthez"""						,	revision="""c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6"""						,	sequences=snake_case_						,	)






