






import json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin

class 		_snake_case  (  A__   ,    unittest.TestCase					):
			_lowercase		:    Tuple			     =							XLMTokenizer
			_lowercase		:    int			     =							False






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Tuple:
						super().setUp()

						# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
						SCREAMING_SNAKE_CASE						= [
						    'l',
						    'o',
						    'w',
						    'e',
						    'r',
						    's',
						    't',
						    'i',
						    'd',
						    'n',
						    'w</w>',
						    'r</w>',
						    't</w>',
						    'lo',
						    'low',
						    'er</w>',
						    'low</w>',
						    'lowest</w>',
						    'newer</w>',
						    'wider</w>',
						    '<unk>',
						]
						SCREAMING_SNAKE_CASE						= dict(zip(a		,					range(len(a))))
						SCREAMING_SNAKE_CASE						= ['l o 123', 'lo w 1456', 'e r</w> 1789', '']

						SCREAMING_SNAKE_CASE						= os.path.join(self.tmpdirname		,					VOCAB_FILES_NAMES['vocab_file'])
						SCREAMING_SNAKE_CASE						= os.path.join(self.tmpdirname		,					VOCAB_FILES_NAMES['merges_file'])
						with open(self.vocab_file		,					'w') as fp:
									fp.write(json.dumps(a))
						with open(self.merges_file		,					'w') as fp:
									fp.write('\n'.join(a))






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       int:
						SCREAMING_SNAKE_CASE						= 'lower newer'
						SCREAMING_SNAKE_CASE						= 'lower newer'
						return input_text, output_text






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[Any]:
						SCREAMING_SNAKE_CASE						= XLMTokenizer(self.vocab_file		,					self.merges_file)

						SCREAMING_SNAKE_CASE						= 'lower'
						SCREAMING_SNAKE_CASE						= ['low', 'er</w>']
						SCREAMING_SNAKE_CASE						= tokenizer.tokenize(a)
						self.assertListEqual(a		,					a)

						SCREAMING_SNAKE_CASE						= tokens + ['<unk>']
						SCREAMING_SNAKE_CASE						= [14, 15, 20]
						self.assertListEqual(tokenizer.convert_tokens_to_ids(a)		,					a)







			@slow
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[Any]:
						SCREAMING_SNAKE_CASE						= XLMTokenizer.from_pretrained('xlm-mlm-en-2048')

						SCREAMING_SNAKE_CASE						= tokenizer.encode('sequence builders'		,					add_special_tokens=a)
						SCREAMING_SNAKE_CASE						= tokenizer.encode('multi-sequence build'		,					add_special_tokens=a)

						SCREAMING_SNAKE_CASE						= tokenizer.build_inputs_with_special_tokens(a)
						SCREAMING_SNAKE_CASE						= tokenizer.build_inputs_with_special_tokens(a		,					a)

						assert encoded_sentence == [0] + text + [1]
						assert encoded_pair == [0] + text + [1] + text_a + [1]












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 		_snake_case  (  A__					):
			_lowercase		:    Union[str, Any]			     =							['''image_processor''', '''tokenizer''']
			_lowercase		:    Optional[int]			     =							'''Pix2StructImageProcessor'''
			_lowercase		:    str			     =							('''T5Tokenizer''', '''T5TokenizerFast''')






			def __init__(       self		,					a		,					a)      ->       Any:
						SCREAMING_SNAKE_CASE						= False
						super().__init__(a		,					a)






			def __call__(       self		,					a=None		,					a = None		,					a = True		,					a = False		,					a = None		,					a = None		,					a = 2048		,					a = 0		,					a = None		,					a = None		,					a = False		,					a = False		,					a = False		,					a = False		,					a = False		,					a = True		,					a = None		,					**a		,					)      ->       BatchEncoding:
						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:
									SCREAMING_SNAKE_CASE						= self.tokenizer
									SCREAMING_SNAKE_CASE						= self.tokenizer(
									    text=a		,					add_special_tokens=a		,					padding=a		,					truncation=a		,					max_length=a		,					stride=a		,					pad_to_multiple_of=a		,					return_attention_mask=a		,					return_overflowing_tokens=a		,					return_special_tokens_mask=a		,					return_offsets_mapping=a		,					return_token_type_ids=a		,					return_length=a		,					verbose=a		,					return_tensors=a		,					**a		,					)
									return text_encoding

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

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

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

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

						return encoding_image_processor






			def    SCREAMING_SNAKE_CASE__    (       self		,					*a		,					**a)      ->       List[Any]:
						return self.tokenizer.batch_decode(*a		,					**a)






			def    SCREAMING_SNAKE_CASE__    (       self		,					*a		,					**a)      ->       Optional[int]:
						return self.tokenizer.decode(*a		,					**a)







			@property
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Tuple:
						SCREAMING_SNAKE_CASE						= self.tokenizer.model_input_names
						SCREAMING_SNAKE_CASE						= self.image_processor.model_input_names
						return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))




