





import unittest

from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


_SCREAMING_SNAKE_CASE 		=						get_tests_dir("""fixtures/test_sentencepiece_with_bytefallback.model""")
@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE_  (   snake_case_    ,			unittest.TestCase   ):
						__magic_name__:     Any	 =    GPTSwaTokenizer
						__magic_name__:     List[str]	 =    False
						__magic_name__:     Any	 =    True
						__magic_name__:     Union[str, Any]	 =    False






						def UpperCAmelCase_			( self : Optional[int]		)							->      Dict:







							"""simple docstring"""


							super().setUp()

							# We have a SentencePiece fixture for testing
							snake_case_						:     Optional[Any]     =		GPTSwaTokenizer(_A						,				eos_token='<unk>'						,				bos_token='<unk>'						,				pad_token='<unk>'		)

							tokenizer.save_pretrained(self.tmpdirname		)






						def UpperCAmelCase_			( self : int						,				_A : Any		)							->      str:







							"""simple docstring"""


							snake_case_						:     Tuple     =		'This is a test'
							snake_case_						:     Union[str, Any]     =		'This is a test'
							return input_text, output_text






						def UpperCAmelCase_			( self : List[Any]		)							->      Optional[Any]:







							"""simple docstring"""


							snake_case_						:     Optional[int]     =		'<s>'
							snake_case_						:     int     =		1

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






						def UpperCAmelCase_			( self : Dict		)							->      List[Any]:







							"""simple docstring"""


							snake_case_						:     int     =		list(self.get_tokenizer().get_vocab().keys()		)

							self.assertEqual(vocab_keys[0]						,				'<unk>'		)
							self.assertEqual(vocab_keys[1]						,				'<s>'		)
							self.assertEqual(vocab_keys[-1]						,				'j'		)
							self.assertEqual(len(_A		)						,				2000		)






						def UpperCAmelCase_			( self : int		)							->      int:







							"""simple docstring"""


							self.assertEqual(self.get_tokenizer().vocab_size						,				2000		)






						def UpperCAmelCase_			( self : List[str]		)							->      Optional[Any]:







							"""simple docstring"""


							snake_case_						:     Optional[Any]     =		GPTSwaTokenizer(_A		)

							snake_case_						:     Union[str, Any]     =		tokenizer.tokenize('This is a test'		)
							self.assertListEqual(_A						,				['▁This', '▁is', '▁a', '▁t', 'est']		)

							self.assertListEqual(tokenizer.convert_tokens_to_ids(_A		)						,				[465, 287, 265, 631, 842]		)

							snake_case_						:     Optional[int]     =		tokenizer.tokenize('I was born in 92000, and this is falsé.'		)
							# fmt: off
							self.assertListEqual(
							    _A						,				['▁I', '▁was', '▁bor', 'n', '▁in', '▁', '<0x39>', '2', '0', '0', '0', ',', '▁and', '▁this', '▁is', '▁f', 'al', 's', '<0xC3>', '<0xA9>', '.']						,				)
							# fmt: on

							snake_case_						:     Optional[int]     =		tokenizer.convert_tokens_to_ids(_A		)
							self.assertListEqual(
							    _A						,				[262, 272, 1525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260]						,				)

							snake_case_						:     List[str]     =		tokenizer.convert_ids_to_tokens(_A		)
							# fmt: off
							self.assertListEqual(
							    _A						,				['▁I', '▁was', '▁bor', 'n', '▁in', '▁', '<0x39>', '2', '0', '0', '0', ',', '▁and', '▁this', '▁is', '▁f', 'al', 's', '<0xC3>', '<0xA9>', '.']		)
							# fmt: on






						def UpperCAmelCase_			( self : Union[str, Any]		)							->      List[str]:







							"""simple docstring"""


							snake_case_						:     Optional[Any]     =		GPTSwaTokenizer(_A		)
							snake_case_						:     int     =		['This is a test', 'I was born in 92000, and this is falsé.']
							snake_case_						:     int     =		[
							    [465, 287, 265, 631, 842],
							    [262, 272, 1525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260],
							]

							# Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
							for text, expected_ids in zip(_A						,				_A		):
								self.assertListEqual(tokenizer.encode_fast(_A		)						,				_A		)

							# Test that decode_fast returns the input text
							for text, token_ids in zip(_A						,				_A		):
								self.assertEqual(tokenizer.decode_fast(_A		)						,				_A		)






						@slow
						def UpperCAmelCase_			( self : Any		)							->      str:







							"""simple docstring"""


							snake_case_						:     Optional[int]     =		[
							    '<|python|>def fibonacci(n)\n    if n < 0:\n        print(\'Incorrect input\')',
							    'Hey there, how are you doing this fine day?',
							    'This is a text with a trailing spaces followed by a dot     .',
							    'Häj sväjs lillebrör! =)',
							    'Det är inget fel på Mr. Cool',
							]

							# fmt: off
							snake_case_						:     Dict     =		{'input_ids': [[63423, 5, 6811, 14954, 282, 816, 3821, 63466, 63425, 63462, 18, 63978, 678, 301, 1320, 63423, 63455, 63458, 18, 63982, 4246, 3940, 1901, 47789, 5547, 18994], [19630, 1100, 63446, 1342, 633, 544, 4488, 593, 5102, 2416, 63495, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1652, 428, 268, 1936, 515, 268, 58593, 22413, 9106, 546, 268, 33213, 63979, 698, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [55130, 63450, 924, 63449, 2249, 4062, 1558, 318, 63504, 21498, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [509, 377, 2827, 2559, 332, 6575, 63443, 26801, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'token_type_ids': [[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, 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, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '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, 1, 1, 1, 1, 1, 1, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [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], [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]]}
							# fmt: on
							self.tokenizer_integration_test_util(
							    expected_encoding=_A						,				model_name='AI-Sweden/gpt-sw3-126m'						,				sequences=_A						,				)








import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class SCREAMING_SNAKE_CASE_  (   snake_case_   ):
						__magic_name__:     Optional[Any]	 =    ["image_processor", "tokenizer"]
						__magic_name__:     Optional[Any]	 =    "LayoutLMv3ImageProcessor"
						__magic_name__:     str	 =    ("LayoutLMv3Tokenizer", "LayoutLMv3TokenizerFast")






						def __init__( self : int						,				_A : List[str]=None						,				_A : Dict=None						,				**_A : int		)							->      List[str]:







							"""simple docstring"""


							snake_case_						:     Any     =		None
							if "feature_extractor" in kwargs:
								warnings.warn(
								    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
								    ' instead.'						,				_A						,				)
								snake_case_						:     Any     =		kwargs.pop('feature_extractor'		)

							snake_case_						:     Optional[int]     =		image_processor if image_processor is not None else feature_extractor
							if image_processor is None:
								raise ValueError('You need to specify an `image_processor`.'		)
							if tokenizer is None:
								raise ValueError('You need to specify a `tokenizer`.'		)

							super().__init__(_A						,				_A		)






						def __call__( self : List[str]						,				_A : Optional[Any]						,				_A : Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None						,				_A : Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None						,				_A : Union[List[List[int]], List[List[List[int]]]] = None						,				_A : Optional[Union[List[int], List[List[int]]]] = None						,				_A : bool = True						,				_A : Union[bool, str, PaddingStrategy] = False						,				_A : Union[bool, str, TruncationStrategy] = None						,				_A : Optional[int] = None						,				_A : int = 0						,				_A : Optional[int] = None						,				_A : Optional[bool] = None						,				_A : Optional[bool] = None						,				_A : bool = False						,				_A : bool = False						,				_A : bool = False						,				_A : bool = False						,				_A : bool = True						,				_A : Optional[Union[str, TensorType]] = None						,				**_A : str						,				)							->      BatchEncoding:







							"""simple docstring"""


							if self.image_processor.apply_ocr and (boxes is not None):
								raise ValueError(
								    'You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True.'		)

							if self.image_processor.apply_ocr and (word_labels is not None):
								raise ValueError(
								    'You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'		)

							# first, apply the image processor
							snake_case_						:     str     =		self.image_processor(images=_A						,				return_tensors=_A		)

							# second, apply the tokenizer
							if text is not None and self.image_processor.apply_ocr and text_pair is None:
								if isinstance(_A						,				_A		):
									snake_case_						:     List[Any]     =		[text]  # add batch dimension (as the image processor always adds a batch dimension)
								snake_case_						:     str     =		features['words']

							snake_case_						:     Optional[int]     =		self.tokenizer(
							    text=text if text is not None else features['words']						,				text_pair=text_pair if text_pair is not None else None						,				boxes=boxes if boxes is not None else features['boxes']						,				word_labels=_A						,				add_special_tokens=_A						,				padding=_A						,				truncation=_A						,				max_length=_A						,				stride=_A						,				pad_to_multiple_of=_A						,				return_token_type_ids=_A						,				return_attention_mask=_A						,				return_overflowing_tokens=_A						,				return_special_tokens_mask=_A						,				return_offsets_mapping=_A						,				return_length=_A						,				verbose=_A						,				return_tensors=_A						,				**_A						,				)

							# add pixel values
							snake_case_						:     List[str]     =		features.pop('pixel_values'		)
							if return_overflowing_tokens is True:
								snake_case_						:     Dict     =		self.get_overflowing_images(_A						,				encoded_inputs['overflow_to_sample_mapping']		)
							snake_case_						:     Optional[Any]     =		images

							return encoded_inputs






						def UpperCAmelCase_			( self : Dict						,				_A : Tuple						,				_A : Dict		)							->      Union[str, Any]:







							"""simple docstring"""


							snake_case_						:     List[str]     =		[]
							for sample_idx in overflow_to_sample_mapping:
								images_with_overflow.append(images[sample_idx]		)

							if len(_A		) != len(_A		):
								raise ValueError(
								    'Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'
								    F""" {len(_A		)} and {len(_A		)}"""		)

							return images_with_overflow






						def UpperCAmelCase_			( self : Optional[Any]						,				*_A : Optional[Any]						,				**_A : List[Any]		)							->      List[str]:







							"""simple docstring"""


							return self.tokenizer.batch_decode(*_A						,				**_A		)






						def UpperCAmelCase_			( self : Union[str, Any]						,				*_A : Dict						,				**_A : str		)							->      Any:







							"""simple docstring"""


							return self.tokenizer.decode(*_A						,				**_A		)






						@property
						def UpperCAmelCase_			( self : Optional[int]		)							->      int:







							"""simple docstring"""


							return ["input_ids", "bbox", "attention_mask", "pixel_values"]






						@property
						def UpperCAmelCase_			( self : Any		)							->      Any:







							"""simple docstring"""


							warnings.warn(
							    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'						,				_A						,				)
							return self.image_processor_class






						@property
						def UpperCAmelCase_			( self : List[Any]		)							->      int:







							"""simple docstring"""


							warnings.warn(
							    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'						,				_A						,				)
							return self.image_processor

