





'''simple docstring'''




import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
 from .tokenization_ta import TaTokenizer
else:
 _SCREAMING_SNAKE_CASE							     =			None


_SCREAMING_SNAKE_CASE							     =			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE							     =			{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

_SCREAMING_SNAKE_CASE							     =			{
    "vocab_file": {
        "t5-small": "https://huggingface.co/t5-small/resolve/main/spiece.model",
        "t5-base": "https://huggingface.co/t5-base/resolve/main/spiece.model",
        "t5-large": "https://huggingface.co/t5-large/resolve/main/spiece.model",
        "t5-3b": "https://huggingface.co/t5-3b/resolve/main/spiece.model",
        "t5-11b": "https://huggingface.co/t5-11b/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "t5-small": "https://huggingface.co/t5-small/resolve/main/tokenizer.json",
        "t5-base": "https://huggingface.co/t5-base/resolve/main/tokenizer.json",
        "t5-large": "https://huggingface.co/t5-large/resolve/main/tokenizer.json",
        "t5-3b": "https://huggingface.co/t5-3b/resolve/main/tokenizer.json",
        "t5-11b": "https://huggingface.co/t5-11b/resolve/main/tokenizer.json",
    },
}


# TODO(PVP) - this should be removed in Transformers v5
_SCREAMING_SNAKE_CASE							     =			{
    "t5-small": 512,
    "t5-base": 512,
    "t5-large": 512,
    "t5-3b": 512,
    "t5-11b": 512,
}
class 			_lowerCAmelCase		(			A__	):






     """simple docstring"""






     snake_case_						      =   VOCAB_FILES_NAMES
     snake_case_						      =   PRETRAINED_VOCAB_FILES_MAP
     snake_case_						      =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     snake_case_						      =   ["input_ids", "attention_mask"]
     snake_case_						      =   TaTokenizer

     snake_case_						      =   []



     def __init__(   self							:			List[Any]   ,       __snake_case							:			List[Any]=None   ,       __snake_case							:			List[Any]=None   ,       __snake_case							:			List[Any]="</s>"   ,       __snake_case							:			Any="<unk>"   ,       __snake_case							:			Any="<pad>"   ,       __snake_case							:			Tuple=1_00   ,       __snake_case							:			Any=None   ,       **__snake_case							:			int   ,       )->	Any:
        # Add extra_ids to the special token list
        if extra_ids > 0 and additional_special_tokens is None:
           snake_case 						=   [f'''<extra_id_{i}>''' for i in range(__snake_case						)]
        elif extra_ids > 0 and additional_special_tokens is not None:
           # Check that we have the right number of extra special tokens
           snake_case 						=   len(set(filter(lambda __snake_case						: bool("""extra_id_""" in str(__snake_case						)						)   ,       __snake_case						)						)						)
           if extra_tokens != extra_ids:
              raise ValueError(
                  f'''Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'''
                  """ provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids"""
                  """ tokens"""						)

        super().__init__(
            __snake_case   ,       tokenizer_file=__snake_case   ,       eos_token=__snake_case   ,       unk_token=__snake_case   ,       pad_token=__snake_case   ,       extra_ids=__snake_case   ,       additional_special_tokens=__snake_case   ,       **__snake_case   ,       )

        snake_case 						=   vocab_file
        snake_case 						=   False if not self.vocab_file else True
        snake_case 						=   extra_ids



     @staticmethod
     def      lowerCAmelCase			(   __snake_case							:			List[str]   ,       __snake_case							:			Tuple   ,       __snake_case							:			Any						)->	str:
        if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
           snake_case 						=   TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
           if init_max_model_length is not None and init_max_model_length != max_model_length:
              return init_max_model_length
           elif init_max_model_length is None:
              warnings.warn(
                  """This tokenizer was incorrectly instantiated with a model max length of"""
                  f''' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'''
                  """ behavior is kept to avoid breaking backwards compatibility when padding/encoding with"""
                  """ `truncation is True`.\n- Be aware that you SHOULD NOT rely on"""
                  f''' {pretrained_model_name_or_path} automatically truncating your input to'''
                  f''' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'''
                  f''' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'''
                  """ `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please"""
                  """ instantiate this tokenizer with `model_max_length` set to your preferred value."""   ,       __snake_case   ,       )

        return max_model_length



     def      lowerCAmelCase			(   self							:			str   ,       __snake_case							:			str   ,       __snake_case							:			Optional[str] = None						)->	Tuple[str]:
        if not self.can_save_slow_tokenizer:
           raise ValueError(
               """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
               """tokenizer."""						)

        if not os.path.isdir(__snake_case						):
           logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''						)
           return
        snake_case 						=   os.path.join(
            __snake_case   ,       (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]						)

        if os.path.abspath(self.vocab_file						) != os.path.abspath(__snake_case						):
           copyfile(self.vocab_file   ,       __snake_case						)
           logger.info(f'''Copy vocab file to {out_vocab_file}'''						)

        return (out_vocab_file,)



     def      lowerCAmelCase			(   self							:			Tuple   ,       __snake_case							:			List[int]   ,       __snake_case							:			Optional[List[int]] = None						)->	List[int]:
        snake_case 						=   token_ids_a + [self.eos_token_id]
        if token_ids_a is None:
           return self.prefix_tokens + token_ids_a
        else:
           snake_case 						=   token_ids_a + [self.eos_token_id]
           return self.prefix_tokens + token_ids_a + token_ids_a



     def      lowerCAmelCase			(   self							:			int   ,       __snake_case							:			List[int]   ,       __snake_case							:			Optional[List[int]] = None						)->	List[int]:
        snake_case 						=   [self.eos_token_id]

        if token_ids_a is None:
           return len(token_ids_a + eos						) * [0]
        return len(token_ids_a + eos + token_ids_a + eos						) * [0]



     def      lowerCAmelCase			(   self							:			List[str]						)->	str:
        return list(
            set(filter(lambda __snake_case						: bool(re.search(r"""<extra_id_\d+>"""   ,       __snake_case						)						) is not None   ,       self.additional_special_tokens						)						)						)



     def      lowerCAmelCase			(   self							:			List[str]						)->	int:
        return [self.convert_tokens_to_ids(__snake_case						) for token in self.get_sentinel_tokens()]








'''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 			_lowerCAmelCase		(			A__	):






     """simple docstring"""






     snake_case_						      =   ["image_processor", "tokenizer"]
     snake_case_						      =   "LayoutLMv2ImageProcessor"
     snake_case_						      =   ("LayoutXLMTokenizer", "LayoutXLMTokenizerFast")



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

        snake_case 						=   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__(__snake_case   ,       __snake_case						)



     def __call__(   self							:			Optional[Any]   ,       __snake_case							:			Dict   ,       __snake_case							:			Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None   ,       __snake_case							:			Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None   ,       __snake_case							:			Union[List[List[int]], List[List[List[int]]]] = None   ,       __snake_case							:			Optional[Union[List[int], List[List[int]]]] = None   ,       __snake_case							:			bool = True   ,       __snake_case							:			Union[bool, str, PaddingStrategy] = False   ,       __snake_case							:			Union[bool, str, TruncationStrategy] = None   ,       __snake_case							:			Optional[int] = None   ,       __snake_case							:			int = 0   ,       __snake_case							:			Optional[int] = None   ,       __snake_case							:			Optional[bool] = None   ,       __snake_case							:			Optional[bool] = None   ,       __snake_case							:			bool = False   ,       __snake_case							:			bool = False   ,       __snake_case							:			bool = False   ,       __snake_case							:			bool = False   ,       __snake_case							:			bool = True   ,       __snake_case							:			Optional[Union[str, TensorType]] = None   ,       **__snake_case							:			List[str]   ,       )->	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
        snake_case 						=   self.image_processor(images=__snake_case   ,       return_tensors=__snake_case						)

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

        snake_case 						=   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=__snake_case   ,       add_special_tokens=__snake_case   ,       padding=__snake_case   ,       truncation=__snake_case   ,       max_length=__snake_case   ,       stride=__snake_case   ,       pad_to_multiple_of=__snake_case   ,       return_token_type_ids=__snake_case   ,       return_attention_mask=__snake_case   ,       return_overflowing_tokens=__snake_case   ,       return_special_tokens_mask=__snake_case   ,       return_offsets_mapping=__snake_case   ,       return_length=__snake_case   ,       verbose=__snake_case   ,       return_tensors=__snake_case   ,       **__snake_case   ,       )

        # add pixel values
        snake_case 						=   features.pop("""pixel_values"""						)
        if return_overflowing_tokens is True:
           snake_case 						=   self.get_overflowing_images(__snake_case   ,       encoded_inputs["""overflow_to_sample_mapping"""]						)
        snake_case 						=   images

        return encoded_inputs



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

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

        return images_with_overflow



     def      lowerCAmelCase			(   self							:			int   ,       *__snake_case							:			Optional[int]   ,       **__snake_case							:			Tuple						)->	str:
        return self.tokenizer.batch_decode(*__snake_case   ,       **__snake_case						)



     def      lowerCAmelCase			(   self							:			Dict   ,       *__snake_case							:			Tuple   ,       **__snake_case							:			Optional[int]						)->	List[Any]:
        return self.tokenizer.decode(*__snake_case   ,       **__snake_case						)



     @property
     def      lowerCAmelCase			(   self							:			str						)->	int:
        return ["input_ids", "bbox", "attention_mask", "image"]



     @property
     def      lowerCAmelCase			(   self							:			Union[str, Any]						)->	List[str]:
        warnings.warn(
            """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""   ,       __snake_case   ,       )
        return self.image_processor_class



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

