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 	__snake_case	( __lowerCAmelCase		):





      '''simple docstring'''






      _snake_case    		=       ['image_processor', 'tokenizer']
      _snake_case    		=       'LayoutLMv3ImageProcessor'
      _snake_case    		=       ('LayoutLMv3Tokenizer', 'LayoutLMv3TokenizerFast')



      def __init__(					self     :				Dict    ,  _UpperCamelCase     :				Tuple=None    ,  _UpperCamelCase     :				Optional[Any]=None    ,  **_UpperCamelCase     :				Tuple)				->List[Any]:


          """simple docstring"""


          _lowerCamelCase     :		Dict 						=						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    ,  )
              _lowerCamelCase     :		List[Any] 						=						kwargs.pop("""feature_extractor""")

          _lowerCamelCase     :		Tuple 						=						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     :				Optional[int]    ,  _UpperCamelCase     :				int    ,  _UpperCamelCase     :				Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None    ,  _UpperCamelCase     :				Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None    ,  _UpperCamelCase     :				Union[List[List[int]], List[List[List[int]]]] = None    ,  _UpperCamelCase     :				Optional[Union[List[int], List[List[int]]]] = None    ,  _UpperCamelCase     :				bool = True    ,  _UpperCamelCase     :				Union[bool, str, PaddingStrategy] = False    ,  _UpperCamelCase     :				Union[bool, str, TruncationStrategy] = None    ,  _UpperCamelCase     :				Optional[int] = None    ,  _UpperCamelCase     :				int = 0    ,  _UpperCamelCase     :				Optional[int] = None    ,  _UpperCamelCase     :				Optional[bool] = None    ,  _UpperCamelCase     :				Optional[bool] = None    ,  _UpperCamelCase     :				bool = False    ,  _UpperCamelCase     :				bool = False    ,  _UpperCamelCase     :				bool = False    ,  _UpperCamelCase     :				bool = False    ,  _UpperCamelCase     :				bool = True    ,  _UpperCamelCase     :				Optional[Union[str, TensorType]] = None    ,  **_UpperCamelCase     :				Union[str, Any]    ,  )				->BatchEncoding:


          """simple docstring"""


          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.""")

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

          _lowerCamelCase     :		Optional[Any] 						=						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
          _lowerCamelCase     :		List[Any] 						=						features.pop("""pixel_values""")
          if return_overflowing_tokens is True:
              _lowerCamelCase     :		List[str] 						=						self.get_overflowing_images(_UpperCamelCase    ,  encoded_inputs["""overflow_to_sample_mapping"""])
          _lowerCamelCase     :		int 						=						images

          return encoded_inputs



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Union[str, Any]    ,  _UpperCamelCase     :				Any    ,  _UpperCamelCase     :				List[Any])				->Any:


          """simple docstring"""


          _lowerCamelCase     :		Tuple 						=						[]
          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 	_SCREAMING_SNAKE_CASE    (					self     :				List[str]    ,  *_UpperCamelCase     :				str    ,  **_UpperCamelCase     :				Optional[Any])				->str:


          """simple docstring"""


          return self.tokenizer.batch_decode(*_UpperCamelCase    ,  **_UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[Any]    ,  *_UpperCamelCase     :				int    ,  **_UpperCamelCase     :				Any)				->int:


          """simple docstring"""


          return self.tokenizer.decode(*_UpperCamelCase    ,  **_UpperCamelCase)



      @property
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Dict)				->Tuple:


          """simple docstring"""


          return ["input_ids", "bbox", "attention_mask", "pixel_values"]



      @property
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Tuple)				->str:


          """simple docstring"""


          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 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int])				->Optional[int]:


          """simple docstring"""


          warnings.warn(
              """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""    ,  _UpperCamelCase    ,  )
          return self.image_processor





import json
import os
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase			:							Dict     =logging.get_logger(__name__)

lowerCAmelCase			:							Dict     ={"vocab_file": "vocab.json"}

lowerCAmelCase			:							List[str]     ={
    "vocab_file": {
        "mgp-str": "https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json",
    }
}

lowerCAmelCase			:							int     ={"mgp-str": 27}




class 	__snake_case	( __lowerCAmelCase		):





      '''simple docstring'''






      _snake_case    		=       VOCAB_FILES_NAMES
      _snake_case    		=       PRETRAINED_VOCAB_FILES_MAP
      _snake_case    		=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES



      def __init__(					self     :				List[Any]    ,  _UpperCamelCase     :				str    ,  _UpperCamelCase     :				int="[GO]"    ,  _UpperCamelCase     :				Any="[GO]"    ,  _UpperCamelCase     :				Optional[Any]="[s]"    ,  _UpperCamelCase     :				List[str]="[GO]"    ,  **_UpperCamelCase     :				Dict)				->Union[str, Any]:


          """simple docstring"""


          super().__init__(
              unk_token=_UpperCamelCase    ,  bos_token=_UpperCamelCase    ,  eos_token=_UpperCamelCase    ,  pad_token=_UpperCamelCase    ,  **_UpperCamelCase    ,  )

          with open(_UpperCamelCase    ,  encoding="""utf-8""") as vocab_handle:
              _lowerCamelCase     :		Optional[Any] 						=						json.load(_UpperCamelCase)
          _lowerCamelCase     :		Optional[Any] 						=						{v: k for k, v in self.vocab.items()}



      @property
      def 	_SCREAMING_SNAKE_CASE    (					self     :				str)				->Any:


          """simple docstring"""


          return len(self.vocab)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Any)				->List[Any]:


          """simple docstring"""


          return dict(self.vocab    ,  **self.added_tokens_encoder)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int]    ,  _UpperCamelCase     :				Union[str, Any])				->Any:


          """simple docstring"""


          _lowerCamelCase     :		Tuple 						=						[]
          for s in text:
              char_tokens.extend(_UpperCamelCase)
          return char_tokens



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Union[str, Any]    ,  _UpperCamelCase     :				int)				->Optional[int]:


          """simple docstring"""


          return self.vocab.get(_UpperCamelCase    ,  self.vocab.get(self.unk_token))



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int]    ,  _UpperCamelCase     :				Optional[Any])				->Dict:


          """simple docstring"""


          return self.decoder.get(_UpperCamelCase)




      def 	_SCREAMING_SNAKE_CASE    (					self     :				int    ,  _UpperCamelCase     :				str    ,  _UpperCamelCase     :				Optional[str] = None)				->Tuple[str]:


          """simple docstring"""


          if not os.path.isdir(_UpperCamelCase):
              logger.error("""Vocabulary path ({}) should be a directory""".format(_UpperCamelCase))
              return
          _lowerCamelCase     :		Tuple 						=						os.path.join(
              _UpperCamelCase    ,  (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""])

          with open(_UpperCamelCase    ,  """w"""    ,  encoding="""utf-8""") as f:
              f.write(json.dumps(self.vocab    ,  indent=2    ,  sort_keys=_UpperCamelCase    ,  ensure_ascii=_UpperCamelCase) + """\n""")

          return (vocab_file,)




