


"""simple docstring"""


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 			lowerCamelCase__						(   UpperCAmelCase_			):
						lowerCAmelCase       =     ["""image_processor""", """tokenizer"""]
						lowerCAmelCase       =     """Pix2StructImageProcessor"""
						lowerCAmelCase       =     ("""T5Tokenizer""", """T5TokenizerFast""")







						def __init__(							self   :    int			,			_lowercase   :    int			,			_lowercase   :    Dict		):
								A					       = False
								super().__init__(_lowercase			,			_lowercase		)







						def __call__(							self   :    Any			,			_lowercase   :    int=None			,			_lowercase   :    Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None			,			_lowercase   :    bool = True			,			_lowercase   :    Union[bool, str, PaddingStrategy] = False			,			_lowercase   :    Union[bool, str, TruncationStrategy] = None			,			_lowercase   :    Optional[int] = None			,			_lowercase   :    Optional[int] = 2_048			,			_lowercase   :    int = 0			,			_lowercase   :    Optional[int] = None			,			_lowercase   :    Optional[bool] = None			,			_lowercase   :    bool = False			,			_lowercase   :    bool = False			,			_lowercase   :    bool = False			,			_lowercase   :    bool = False			,			_lowercase   :    bool = False			,			_lowercase   :    bool = True			,			_lowercase   :    Optional[Union[str, TensorType]] = None			,			**_lowercase   :    List[Any]			,			):
								if images is None and text is None:
										raise ValueError('You have to specify either images or text.'		)

								# Get only text
								if images is None and not self.image_processor.is_vqa:
										A					       = self.tokenizer
										A					       = self.tokenizer(
										    text=_lowercase			,			add_special_tokens=_lowercase			,			padding=_lowercase			,			truncation=_lowercase			,			max_length=_lowercase			,			stride=_lowercase			,			pad_to_multiple_of=_lowercase			,			return_attention_mask=_lowercase			,			return_overflowing_tokens=_lowercase			,			return_special_tokens_mask=_lowercase			,			return_offsets_mapping=_lowercase			,			return_token_type_ids=_lowercase			,			return_length=_lowercase			,			verbose=_lowercase			,			return_tensors=_lowercase			,			**_lowercase			,			)
										return text_encoding

								if not self.image_processor.is_vqa:
										# add pixel_values
										A					       = self.image_processor(
										    _lowercase			,			return_tensors=_lowercase			,			max_patches=_lowercase			,			**_lowercase		)
								else:
										# add pixel_values and bbox
										A					       = self.image_processor(
										    _lowercase			,			return_tensors=_lowercase			,			max_patches=_lowercase			,			header_text=_lowercase			,			**_lowercase		)

								if text is not None and not self.image_processor.is_vqa:
										A					       = self.tokenizer(
										    text=_lowercase			,			add_special_tokens=_lowercase			,			padding=_lowercase			,			truncation=_lowercase			,			max_length=_lowercase			,			stride=_lowercase			,			pad_to_multiple_of=_lowercase			,			return_attention_mask=_lowercase			,			return_overflowing_tokens=_lowercase			,			return_special_tokens_mask=_lowercase			,			return_offsets_mapping=_lowercase			,			return_token_type_ids=_lowercase			,			return_length=_lowercase			,			verbose=_lowercase			,			return_tensors=_lowercase			,			**_lowercase			,			)

										if "attention_mask" in text_encoding:
												A					       = text_encoding.pop('attention_mask'		)
										if "input_ids" in text_encoding:
												A					       = text_encoding.pop('input_ids'		)
								else:
										A					       = None

								if text_encoding is not None:
										encoding_image_processor.update(_lowercase		)

								return encoding_image_processor







						def __a						(							self   :    Optional[Any]			,			*_lowercase   :    Optional[int]			,			**_lowercase   :    Any		):
								return self.tokenizer.batch_decode(*_lowercase			,			**_lowercase		)







						def __a						(							self   :    Union[str, Any]			,			*_lowercase   :    Dict			,			**_lowercase   :    Union[str, Any]		):
								return self.tokenizer.decode(*_lowercase			,			**_lowercase		)







						@property
						def __a						(							self   :    List[str]		):
								A					       = self.tokenizer.model_input_names
								A					       = self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)









"""simple docstring"""


import os
import unittest

from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin



class 			lowerCamelCase__						(   UpperCAmelCase_							,    unittest.TestCase			):
						lowerCAmelCase       =     TransfoXLTokenizer
						lowerCAmelCase       =     False
						lowerCAmelCase       =     False







						def __a						(							self   :    Union[str, Any]		):
								super().setUp()

								A					       = [
								    '<unk>',
								    '[CLS]',
								    '[SEP]',
								    'want',
								    'unwanted',
								    'wa',
								    'un',
								    'running',
								    ',',
								    'low',
								    'l',
								]
								A					       = os.path.join(self.tmpdirname			,			VOCAB_FILES_NAMES['vocab_file']		)
								with open(self.vocab_file			,			'w'			,			encoding='utf-8'		) as vocab_writer:
										vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]		)		)







						def __a						(							self   :    int			,			**_lowercase   :    List[str]		):
								A					       = True
								return TransfoXLTokenizer.from_pretrained(self.tmpdirname			,			**_lowercase		)







						def __a						(							self   :    int			,			_lowercase   :    List[Any]		):
								A					       = '<unk> UNwanted , running'
								A					       = '<unk> unwanted, running'
								return input_text, output_text







						def __a						(							self   :    List[str]		):
								A					       = TransfoXLTokenizer(vocab_file=self.vocab_file			,			lower_case=_lowercase		)

								A					       = tokenizer.tokenize('<unk> UNwanted , running'		)
								self.assertListEqual(_lowercase			,			['<unk>', 'unwanted', ',', 'running']		)

								self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowercase		)			,			[0, 4, 8, 7]		)







						def __a						(							self   :    int		):
								A					       = TransfoXLTokenizer(lower_case=_lowercase		)

								self.assertListEqual(
								    tokenizer.tokenize(' \tHeLLo ! how  \n Are yoU ?  '		)			,			['hello', '!', 'how', 'are', 'you', '?']		)







						def __a						(							self   :    Optional[Any]		):
								A					       = TransfoXLTokenizer(lower_case=_lowercase		)

								self.assertListEqual(
								    tokenizer.tokenize(' \tHeLLo ! how  \n Are yoU ?  '		)			,			['HeLLo', '!', 'how', 'Are', 'yoU', '?']		)







						def __a						(							self   :    str		):
								A					       = TransfoXLTokenizer(lower_case=_lowercase		)
								A					       = 'Hello (bracket) and side-scrolled [and] Henry\'s $5,000 with 3.34 m. What\'s up!?'
								A					       = [
								    'Hello',
								    '(',
								    'bracket',
								    ')',
								    'and',
								    'side',
								    '@-@',
								    'scrolled',
								    '[',
								    'and',
								    ']',
								    'Henry',
								    '\'s',
								    '$',
								    '5',
								    '@,@',
								    '000',
								    'with',
								    '3',
								    '@.@',
								    '34',
								    'm',
								    '.',
								    'What',
								    '\'s',
								    'up',
								    '!',
								    '?',
								]

								self.assertListEqual(tokenizer.tokenize(_lowercase		)			,			_lowercase		)

								self.assertEqual(tokenizer.convert_tokens_to_string(_lowercase		)			,			_lowercase		)







						def __a						(							self   :    Dict		):
								A					       = self.get_tokenizer()
								A					       = len(_lowercase		)

								tokenizer.add_tokens(['new1', 'new2']		)
								tokenizer.move_added_token('new1'			,			1		)

								# Check that moved token is not copied (duplicate)
								self.assertEqual(len(_lowercase		)			,			original_len + 2		)
								# Check that token is moved to specified id
								self.assertEqual(tokenizer.encode('new1'		)			,			[1]		)
								self.assertEqual(tokenizer.decode([1]		)			,			'new1'		)





