




'''simple docstring'''






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    A						(					SCREAMING_SNAKE_CASE__						):




				'''simple docstring'''





				A       =							["image_processor", "tokenizer"]
				A       =							"LayoutLMv2ImageProcessor"
				A       =							("LayoutLMv2Tokenizer", "LayoutLMv2TokenizerFast")
				def __init__(self					,    _UpperCAmelCase=None					,    _UpperCAmelCase=None					,    **_UpperCAmelCase     )      ->			Tuple:
							__UpperCamelCase :     Tuple										=  None
							if "feature_extractor" in kwargs:
										warnings.warn(
										    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
										    " instead."					,    _UpperCAmelCase					,    )
										__UpperCamelCase :     str										=  kwargs.pop("feature_extractor"     )

							__UpperCamelCase :     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__(_UpperCAmelCase					,    _UpperCAmelCase     )
				def __call__(self					,    _UpperCAmelCase					,    _UpperCAmelCase = None					,    _UpperCAmelCase = None					,    _UpperCAmelCase = None					,    _UpperCAmelCase = None					,    _UpperCAmelCase = True					,    _UpperCAmelCase = False					,    _UpperCAmelCase = False					,    _UpperCAmelCase = None					,    _UpperCAmelCase = 0					,    _UpperCAmelCase = None					,    _UpperCAmelCase = None					,    _UpperCAmelCase = None					,    _UpperCAmelCase = False					,    _UpperCAmelCase = False					,    _UpperCAmelCase = False					,    _UpperCAmelCase = False					,    _UpperCAmelCase = True					,    _UpperCAmelCase = None					,    **_UpperCAmelCase					,    )      ->			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
							__UpperCamelCase :     Dict										=  self.image_processor(images=_UpperCAmelCase					,    return_tensors=_UpperCAmelCase     )

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

							__UpperCamelCase :     str										=  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=_UpperCAmelCase					,    add_special_tokens=_UpperCAmelCase					,    padding=_UpperCAmelCase					,    truncation=_UpperCAmelCase					,    max_length=_UpperCAmelCase					,    stride=_UpperCAmelCase					,    pad_to_multiple_of=_UpperCAmelCase					,    return_token_type_ids=_UpperCAmelCase					,    return_attention_mask=_UpperCAmelCase					,    return_overflowing_tokens=_UpperCAmelCase					,    return_special_tokens_mask=_UpperCAmelCase					,    return_offsets_mapping=_UpperCAmelCase					,    return_length=_UpperCAmelCase					,    verbose=_UpperCAmelCase					,    return_tensors=_UpperCAmelCase					,    **_UpperCAmelCase					,    )

							# add pixel values
							__UpperCamelCase :     int										=  features.pop("pixel_values"     )
							if return_overflowing_tokens is True:
										__UpperCamelCase :     List[str]										=  self.get_overflowing_images(_UpperCAmelCase					,    encoded_inputs["overflow_to_sample_mapping"]     )
							__UpperCamelCase :     Any										=  images

							return encoded_inputs
				def 			a_			(self					,    _UpperCAmelCase					,    _UpperCAmelCase     )      ->			str:
							# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
							__UpperCamelCase :     Any										=  []
							for sample_idx in overflow_to_sample_mapping:
										images_with_overflow.append(images[sample_idx]     )

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

							return images_with_overflow
				def 			a_			(self					,    *_UpperCAmelCase					,    **_UpperCAmelCase     )      ->			int:
							return self.tokenizer.batch_decode(*_UpperCAmelCase					,    **_UpperCAmelCase     )
				def 			a_			(self					,    *_UpperCAmelCase					,    **_UpperCAmelCase     )      ->			Dict:
							return self.tokenizer.decode(*_UpperCAmelCase					,    **_UpperCAmelCase     )
				@property
				def 			a_			(self     )      ->			Any:
							return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]
				@property
				def 			a_			(self     )      ->			Optional[Any]:
							warnings.warn(
							    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."					,    _UpperCAmelCase					,    )
							return self.image_processor_class





				@property
				def 			a_			(self     )      ->			List[Any]:
							warnings.warn(
							    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."					,    _UpperCAmelCase					,    )
							return self.image_processor









'''simple docstring'''






import os
import unittest

from transformers.models.bertweet.tokenization_bertweet import VOCAB_FILES_NAMES, BertweetTokenizer

from ...test_tokenization_common import TokenizerTesterMixin



class    A						(					SCREAMING_SNAKE_CASE__     ,				unittest.TestCase						):




				'''simple docstring'''





				A       =							BertweetTokenizer
				A       =							False
				def 			a_			(self     )      ->			Any:
							super().setUp()

							# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
							__UpperCamelCase :     str										=  ["I", "m", "V@@", "R@@", "r", "e@@"]
							__UpperCamelCase :     int										=  dict(zip(_UpperCAmelCase					,    range(len(_UpperCAmelCase     )     )     )     )
							__UpperCamelCase :     List[Any]										=  ["#version: 0.2", "a m</w>"]
							__UpperCamelCase :     str										=  {"unk_token": "<unk>"}

							__UpperCamelCase :     Any										=  os.path.join(self.tmpdirname					,    VOCAB_FILES_NAMES["vocab_file"]     )
							__UpperCamelCase :     Tuple										=  os.path.join(self.tmpdirname					,    VOCAB_FILES_NAMES["merges_file"]     )
							with open(self.vocab_file					,    "w"					,    encoding="utf-8"     ) as fp:
										for token in vocab_tokens:
													fp.write(f"{token} {vocab_tokens[token]}\n"     )
							with open(self.merges_file					,    "w"					,    encoding="utf-8"     ) as fp:
										fp.write("\n".join(_UpperCAmelCase     )     )
				def 			a_			(self					,    **_UpperCAmelCase     )      ->			str:
							kwargs.update(self.special_tokens_map     )
							return BertweetTokenizer.from_pretrained(self.tmpdirname					,    **_UpperCAmelCase     )
				def 			a_			(self					,    _UpperCAmelCase     )      ->			Dict:
							__UpperCamelCase :     Dict										=  "I am VinAI Research"
							__UpperCamelCase :     Any										=  "I <unk> m V<unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"
							return input_text, output_text





				def 			a_			(self     )      ->			str:
							__UpperCamelCase :     Tuple										=  BertweetTokenizer(self.vocab_file					,    self.merges_file					,    **self.special_tokens_map     )
							__UpperCamelCase :     Union[str, Any]										=  "I am VinAI Research"
							__UpperCamelCase :     List[str]										=  "I a@@ m V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h".split()
							__UpperCamelCase :     Optional[int]										=  tokenizer.tokenize(_UpperCAmelCase     )
							self.assertListEqual(_UpperCAmelCase					,    _UpperCAmelCase     )

							__UpperCamelCase :     Optional[Any]										=  tokens + [tokenizer.unk_token]

							__UpperCamelCase :     Any										=  [4, 3, 5, 6, 3, 3, 3, 4, 7, 9, 3, 9, 3, 3, 3, 3, 3]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase     )					,    _UpperCAmelCase     )



