




"""simple docstring"""






import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors






@require_tokenizers
class lowerCAmelCase__			(  lowercase,			unittest.TestCase		):






			'''simple docstring'''



			lowerCamelCase__   =    MvpTokenizer
			lowerCamelCase__   =    MvpTokenizerFast
			lowerCamelCase__   =    True
			lowerCamelCase__   =    filter_roberta_detectors
			def       A_					(						self						):
							super().setUp()
							_lowerCamelCase  :    str					=					[
							    'l',
							    'o',
							    'w',
							    'e',
							    'r',
							    's',
							    't',
							    'i',
							    'd',
							    'n',
							    '\u0120',
							    '\u0120l',
							    '\u0120n',
							    '\u0120lo',
							    '\u0120low',
							    'er',
							    '\u0120lowest',
							    '\u0120newer',
							    '\u0120wider',
							    '<unk>',
							]
							_lowerCamelCase  :    Optional[int]					=					dict(zip(lowercase						,   range(len(lowercase						)						)						)						)
							_lowerCamelCase  :    Union[str, Any]					=					['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
							_lowerCamelCase  :    Dict					=					{'unk_token': '<unk>'}

							_lowerCamelCase  :    Any					=					os.path.join(self.tmpdirname						,   VOCAB_FILES_NAMES['vocab_file']						)
							_lowerCamelCase  :    List[Any]					=					os.path.join(self.tmpdirname						,   VOCAB_FILES_NAMES['merges_file']						)
							with open(self.vocab_file						,   'w'						,   encoding='utf-8'						) as fp:
											fp.write(json.dumps(lowercase						) + '\n'						)
							with open(self.merges_file						,   'w'						,   encoding='utf-8'						) as fp:
											fp.write('\n'.join(lowercase						)						)
			def       A_					(						self						,   **lowercase						):
							kwargs.update(self.special_tokens_map						)
							return self.tokenizer_class.from_pretrained(self.tmpdirname						,   **lowercase						)
			def       A_					(						self						,   **lowercase						):
							kwargs.update(self.special_tokens_map						)
							return self.rust_tokenizer_class.from_pretrained(self.tmpdirname						,   **lowercase						)
			def       A_					(						self						,   lowercase						):
							return "lower newer", "lower newer"
			@cached_property
			def       A_					(						self						):
							return MvpTokenizer.from_pretrained('RUCAIBox/mvp'						)
			@cached_property
			def       A_					(						self						):
							return MvpTokenizerFast.from_pretrained('RUCAIBox/mvp'						)
			@require_torch
			def       A_					(						self						):
							_lowerCamelCase  :    Union[str, Any]					=					['A long paragraph for summarization.', 'Another paragraph for summarization.']
							_lowerCamelCase  :    str					=					[0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											_lowerCamelCase  :    Dict					=					tokenizer(lowercase						,   max_length=len(lowercase						)						,   padding=lowercase						,   return_tensors='pt'						)
											self.assertIsInstance(lowercase						,   lowercase						)

											self.assertEqual((2, 9)						,   batch.input_ids.shape						)
											self.assertEqual((2, 9)						,   batch.attention_mask.shape						)
											_lowerCamelCase  :    Union[str, Any]					=					batch.input_ids.tolist()[0]
											self.assertListEqual(lowercase						,   lowercase						)
											# Test that special tokens are reset
			@require_torch
			def       A_					(						self						):
							_lowerCamelCase  :    str					=					['A long paragraph for summarization.', 'Another paragraph for summarization.']
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											_lowerCamelCase  :    str					=					tokenizer(lowercase						,   padding=lowercase						,   return_tensors='pt'						)
											# check if input_ids are returned and no labels
											self.assertIn('input_ids'						,   lowercase						)
											self.assertIn('attention_mask'						,   lowercase						)
											self.assertNotIn('labels'						,   lowercase						)
											self.assertNotIn('decoder_attention_mask'						,   lowercase						)
			@require_torch
			def       A_					(						self						):
							_lowerCamelCase  :    Tuple					=					[
							    'Summary of the text.',
							    'Another summary.',
							]
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											_lowerCamelCase  :    Any					=					tokenizer(text_target=lowercase						,   max_length=32						,   padding='max_length'						,   return_tensors='pt'						)
											self.assertEqual(32						,   targets['input_ids'].shape[1]						)
			@require_torch
			def       A_					(						self						):
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											_lowerCamelCase  :    Union[str, Any]					=					tokenizer(
											    ['I am a small frog' * 1024, 'I am a small frog']						,   padding=lowercase						,   truncation=lowercase						,   return_tensors='pt'						)
											self.assertIsInstance(lowercase						,   lowercase						)
											self.assertEqual(batch.input_ids.shape						,   (2, 1024)						)
			@require_torch
			def       A_					(						self						):
							_lowerCamelCase  :    str					=					['A long paragraph for summarization.']
							_lowerCamelCase  :    Any					=					[
							    'Summary of the text.',
							]
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											_lowerCamelCase  :    Any					=					tokenizer(lowercase						,   text_target=lowercase						,   return_tensors='pt'						)
											_lowerCamelCase  :    Optional[Any]					=					inputs['input_ids']
											_lowerCamelCase  :    Optional[Any]					=					inputs['labels']
											self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()						)
											self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()						)
											self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()						)
											self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()						)
			def       A_					(						self						):
							pass






			def       A_					(						self						):
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''						):
															_lowerCamelCase  :    Optional[int]					=					self.rust_tokenizer_class.from_pretrained(lowercase						,   **lowercase						)
															_lowerCamelCase  :    Union[str, Any]					=					self.tokenizer_class.from_pretrained(lowercase						,   **lowercase						)
															_lowerCamelCase  :    List[Any]					=					'A, <mask> AllenNLP sentence.'
															_lowerCamelCase  :    Optional[int]					=					tokenizer_r.encode_plus(lowercase						,   add_special_tokens=lowercase						,   return_token_type_ids=lowercase						)
															_lowerCamelCase  :    Optional[int]					=					tokenizer_p.encode_plus(lowercase						,   add_special_tokens=lowercase						,   return_token_type_ids=lowercase						)

															# token_type_ids should put 0 everywhere
															self.assertEqual(sum(tokens_r['token_type_ids']						)						,   sum(tokens_p['token_type_ids']						)						)

															# attention_mask should put 1 everywhere, so sum over length should be 1
															self.assertEqual(
															    sum(tokens_r['attention_mask']						) / len(tokens_r['attention_mask']						)						,   sum(tokens_p['attention_mask']						) / len(tokens_p['attention_mask']						)						,   )

															_lowerCamelCase  :    Tuple					=					tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']						)
															_lowerCamelCase  :    Union[str, Any]					=					tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']						)

															# Rust correctly handles the space before the mask while python doesnt
															self.assertSequenceEqual(tokens_p['input_ids']						,   [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]						)
															self.assertSequenceEqual(tokens_r['input_ids']						,   [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]						)

															self.assertSequenceEqual(
															    lowercase						,   ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']						)
															self.assertSequenceEqual(
															    lowercase						,   ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']						)





"""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 lowerCAmelCase__			(  lowercase		):






			'''simple docstring'''



			lowerCamelCase__   =    ["""image_processor"""]
			lowerCamelCase__   =    """SamImageProcessor"""
			def __init__(						self						,   lowercase						):
							super().__init__(lowercase						)
							_lowerCamelCase  :    str					=					self.image_processor
							_lowerCamelCase  :    Optional[int]					=					-10
							_lowerCamelCase  :    Optional[Any]					=					self.image_processor.size['longest_edge']
			def __call__(						self						,   lowercase=None						,   lowercase=None						,   lowercase=None						,   lowercase=None						,   lowercase = None						,   **lowercase						,   ):
							_lowerCamelCase  :    int					=					self.image_processor(
							    lowercase						,   return_tensors=lowercase						,   **lowercase						,   )

							# pop arguments that are not used in the foward but used nevertheless
							_lowerCamelCase  :    int					=					encoding_image_processor['original_sizes']

							if hasattr(lowercase						,   'numpy'						):  # Checks if Torch or TF tensor
											_lowerCamelCase  :    Tuple					=					original_sizes.numpy()

							_lowerCamelCase,				_lowerCamelCase,				_lowerCamelCase  :    Optional[Any]					=					self._check_and_preprocess_points(
							    input_points=lowercase						,   input_labels=lowercase						,   input_boxes=lowercase						,   )

							_lowerCamelCase  :    Tuple					=					self._normalize_and_convert(
							    lowercase						,   lowercase						,   input_points=lowercase						,   input_labels=lowercase						,   input_boxes=lowercase						,   return_tensors=lowercase						,   )

							return encoding_image_processor
			def       A_					(						self						,   lowercase						,   lowercase						,   lowercase=None						,   lowercase=None						,   lowercase=None						,   lowercase="pt"						,   ):
							if input_points is not None:
											if len(lowercase						) != len(lowercase						):
															_lowerCamelCase  :    str					=					[
															    self._normalize_coordinates(self.target_size						,   lowercase						,   original_sizes[0]						) for point in input_points
															]
											else:
															_lowerCamelCase  :    Optional[Any]					=					[
															    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:
																			_lowerCamelCase,				_lowerCamelCase  :    List[str]					=					self._pad_points_and_labels(lowercase						,   lowercase						)

											_lowerCamelCase  :    Optional[int]					=					np.array(lowercase						)

							if input_labels is not None:
											_lowerCamelCase  :    List[Any]					=					np.array(lowercase						)

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

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

							if is_bounding_box:
											_lowerCamelCase  :    List[str]					=					coords.reshape(-1						,   2						,   2						)

							_lowerCamelCase  :    Optional[Any]					=					coords[..., 0] * (new_w / old_w)
							_lowerCamelCase  :    List[Any]					=					coords[..., 1] * (new_h / old_h)

							if is_bounding_box:
											_lowerCamelCase  :    Union[str, Any]					=					coords.reshape(-1						,   4						)

							return coords
			def       A_					(						self						,   lowercase=None						,   lowercase=None						,   lowercase=None						,   ):
							if input_points is not None:
											if hasattr(lowercase						,   'numpy'						):  # Checks for TF or Torch tensor
															_lowerCamelCase  :    int					=					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.'						)
											_lowerCamelCase  :    Optional[int]					=					[np.array(lowercase						) for input_point in input_points]
							else:
											_lowerCamelCase  :    Optional[Any]					=					None

							if input_labels is not None:
											if hasattr(lowercase						,   'numpy'						):
															_lowerCamelCase  :    Any					=					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.'						)
											_lowerCamelCase  :    List[str]					=					[np.array(lowercase						) for label in input_labels]
							else:
											_lowerCamelCase  :    List[str]					=					None

							if input_boxes is not None:
											if hasattr(lowercase						,   'numpy'						):
															_lowerCamelCase  :    Optional[int]					=					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.'						)
											_lowerCamelCase  :    int					=					[np.array(lowercase						).astype(np.floataa						) for box in input_boxes]
							else:
											_lowerCamelCase  :    str					=					None

							return input_points, input_labels, input_boxes
			@property
			def       A_					(						self						):
							_lowerCamelCase  :    Optional[Any]					=					self.image_processor.model_input_names
							return list(dict.fromkeys(lowercase						)						)






			def       A_					(						self						,   *lowercase						,   **lowercase						):
							return self.image_processor.post_process_masks(*lowercase						,   **lowercase						)