

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 							UpperCAmelCase_						(     __lowercase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		List[Any]             =       ["image_processor", "tokenizer"]
							UpperCAmelCase__			:		List[str]             =       "LayoutLMv2ImageProcessor"
							UpperCAmelCase__			:		Any             =       ("LayoutXLMTokenizer", "LayoutXLMTokenizerFast")


							def __init__(     self			,				_a=None			,				_a=None			,				**_a			)       ->       str:
												if "feature_extractor" in kwargs:
																	warnings.warn(
																	    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
																	    ''' instead.'''			,				_a			,				)
																	_a      :       Union[str, Any]   =						kwargs.pop('''feature_extractor'''			)

												_a      :       str   =						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			,				_a			,				_a = None			,				_a = None			,				_a = None			,				_a = None			,				_a = True			,				_a = False			,				_a = None			,				_a = None			,				_a = 0			,				_a = None			,				_a = None			,				_a = None			,				_a = False			,				_a = False			,				_a = False			,				_a = False			,				_a = True			,				_a = None			,				**_a			,				)       ->       BatchEncoding:
												# verify input
												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.'''			)

												if return_overflowing_tokens is True and return_offsets_mapping is False:
																	raise ValueError('''You cannot return overflowing tokens without returning the offsets mapping.'''			)

												# first, apply the image processor
												_a      :       List[Any]   =						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			):
																						_a      :       Dict   =						[text]  # add batch dimension (as the image processor always adds a batch dimension)
																	_a      :       List[str]   =						features['''words''']

												_a      :       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
												_a      :       int   =						features.pop('''pixel_values'''			)
												if return_overflowing_tokens is True:
																	_a      :       int   =						self.get_overflowing_images(_a			,				encoded_inputs['''overflow_to_sample_mapping''']			)
												_a      :       Optional[int]   =						images

												return encoded_inputs


							def 							__lowercase							(     self			,				_a			,				_a			)       ->       Any:
												# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
												_a      :       Optional[int]   =						[]
												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 							__lowercase							(     self			,				*_a			,				**_a			)       ->       List[str]:
												return self.tokenizer.batch_decode(*_a			,				**_a			)


							def 							__lowercase							(     self			,				*_a			,				**_a			)       ->       Union[str, Any]:
												return self.tokenizer.decode(*_a			,				**_a			)


							@property
							def 							__lowercase							(     self			)       ->       List[Any]:
												return ["input_ids", "bbox", "attention_mask", "image"]


							@property
							def 							__lowercase							(     self			)       ->       str:
												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 							__lowercase							(     self			)       ->       Union[str, Any]:
												warnings.warn(
												    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''			,				_a			,				)
												return self.image_processor






import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 							UpperCAmelCase_						(     __lowercase							,			unittest.TestCase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		List[Any]             =       MgpstrTokenizer
							UpperCAmelCase__			:		int             =       False
							UpperCAmelCase__			:		Union[str, Any]             =       {}
							UpperCAmelCase__			:		List[Any]             =       False


							def 							__lowercase							(     self			)       ->       Any:
												super().setUp()

												# fmt: off
												_a      :       Tuple   =						['''[GO]''', '''[s]''', '''0''', '''1''', '''2''', '''3''', '''4''', '''5''', '''6''', '''7''', '''8''', '''9''', '''a''', '''b''', '''c''', '''d''', '''e''', '''f''', '''g''', '''h''', '''i''', '''j''', '''k''', '''l''', '''m''', '''n''', '''o''', '''p''', '''q''', '''r''', '''s''', '''t''', '''u''', '''v''', '''w''', '''x''', '''y''', '''z''']
												# fmt: on
												_a      :       Optional[int]   =						dict(zip(_a			,				range(len(_a			)			)			)			)

												_a      :       Any   =						os.path.join(self.tmpdirname			,				VOCAB_FILES_NAMES['''vocab_file''']			)
												with open(self.vocab_file			,				'''w'''			,				encoding='''utf-8'''			) as fp:
																	fp.write(json.dumps(_a			) + '''\n'''			)


							def 							__lowercase							(     self			,				**_a			)       ->       Dict:
												return MgpstrTokenizer.from_pretrained(self.tmpdirname			,				**_a			)


							def 							__lowercase							(     self			,				_a			)       ->       Tuple:
												_a      :       List[str]   =						'''tester'''
												_a      :       Optional[Any]   =						'''tester'''
												return input_text, output_text


							@unittest.skip('''MGP-STR always lower cases letters.'''			)
							def 							__lowercase							(     self			)       ->       Any:
												pass


							def 							__lowercase							(     self			)       ->       Any:
												_a      :       Union[str, Any]   =						self.get_tokenizers(do_lower_case=_a			)
												for tokenizer in tokenizers:
																	with self.subTest(F"""{tokenizer.__class__.__name__}"""			):
																						_a      :       int   =						'''[SPECIAL_TOKEN]'''

																						tokenizer.add_special_tokens({'''cls_token''': special_token}			)
																						_a      :       Tuple   =						tokenizer.encode([special_token]			,				add_special_tokens=_a			)
																						self.assertEqual(len(_a			)			,				1			)

																						_a      :       Tuple   =						tokenizer.decode(_a			,				skip_special_tokens=_a			)
																						self.assertTrue(special_token not in decoded			)


							def 							__lowercase							(     self			)       ->       Tuple:
												_a      :       List[Any]   =						self.get_tokenizers()
												for tokenizer in tokenizers:
																	with self.subTest(F"""{tokenizer.__class__.__name__}"""			):
																						_a ,   _a      :       int   =						self.get_input_output_texts(_a			)

																						_a      :       List[str]   =						tokenizer.tokenize(_a			)
																						_a      :       Optional[int]   =						tokenizer.convert_tokens_to_ids(_a			)
																						_a      :       Tuple   =						tokenizer.encode(_a			,				add_special_tokens=_a			)
																						self.assertListEqual(_a			,				_a			)

																						_a      :       Optional[int]   =						tokenizer.convert_ids_to_tokens(_a			)
																						self.assertNotEqual(len(_a			)			,				0			)
																						_a      :       int   =						tokenizer.decode(_a			)
																						self.assertIsInstance(_a			,				_a			)

																						self.assertEqual(text_a.replace(''' '''			,				''''''			)			,				_a			)


							@unittest.skip('''MGP-STR tokenizer only handles one sequence.'''			)
							def 							__lowercase							(     self			)       ->       List[str]:
												pass




							@unittest.skip('''inputs cannot be pretokenized in MgpstrTokenizer'''			)
							def 							__lowercase							(     self			)       ->       Optional[Any]:
												pass



