






"""simple docstring"""





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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer


if TYPE_CHECKING:
  from ...tokenization_utils_base import TextInput
from ...utils import logging


A__					: Dict=	logging.get_logger(__name__)

A__					: str=	{"""vocab_file""": """spiece.model"""}

A__					: Union[str, Any]=	{
    """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""",
    }
}


# TODO(PVP) - this should be removed in Transformers v5
A__					: Union[str, Any]=	{
    """t5-small""": 5_12,
    """t5-base""": 5_12,
    """t5-large""": 5_12,
    """t5-3b""": 5_12,
    """t5-11b""": 5_12,
}

A__					: Optional[Any]=	"""▁"""
class    __lowerCamelCase    (  _a  ):
   a      :				Dict 						=VOCAB_FILES_NAMES
   a      :				str 						=PRETRAINED_VOCAB_FILES_MAP
   a      :				Union[str, Any] 						=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   a      :				List[str] 						=["""input_ids""", """attention_mask"""]

   def __init__(					self ,				snake_case_ ,				snake_case_="</s>" ,				snake_case_="<unk>" ,				snake_case_="<pad>" ,				snake_case_=100 ,				snake_case_=None ,				snake_case_ = None ,				snake_case_=True ,				**snake_case_ ,				)		->       None:
       # Add extra_ids to the special token list
       if extra_ids > 0 and additional_special_tokens is None:
           UpperCamelCase__         =    [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_id special tokens
           UpperCamelCase__         =    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'      )
       if legacy:
           logger.warning_once(
               F'You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to'
               ' read the related pull request available at https://github.com/huggingface/transformers/pull/24565'      )

       UpperCamelCase__         =    legacy
       UpperCamelCase__         =    {} if sp_model_kwargs is None else sp_model_kwargs

       super().__init__(
           eos_token=snake_case_ ,				unk_token=snake_case_ ,				pad_token=snake_case_ ,				extra_ids=snake_case_ ,				additional_special_tokens=snake_case_ ,				sp_model_kwargs=self.sp_model_kwargs ,				legacy=snake_case_ ,				**snake_case_ ,				)

       UpperCamelCase__         =    vocab_file
       UpperCamelCase__         =    extra_ids

       UpperCamelCase__         =    spm.SentencePieceProcessor(**self.sp_model_kwargs      )
       self.sp_model.Load(snake_case_      )

   @staticmethod
   def 						SCREAMING_SNAKE_CASE__ (					snake_case_ ,				snake_case_ ,				snake_case_      )		->       Optional[Any]:
       if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
           UpperCamelCase__         =    TaTokenizer.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

   @property
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Any:
       return self.sp_model.get_piece_size() + self._extra_ids

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[int]:
       UpperCamelCase__         =    {self.convert_ids_to_tokens(snake_case_      ): i for i in range(self.vocab_size      )}
       vocab.update(self.added_tokens_encoder      )
       return vocab

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ = None ,				snake_case_ = False      )		->       List[int]:
       if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=snake_case_ ,				token_ids_a=snake_case_ ,				already_has_special_tokens=snake_case_      )

       # normal case: some special tokens
       if token_ids_a is None:
           return ([0] * len(snake_case_      )) + [1]
       return ([0] * len(snake_case_      )) + [1] + ([0] * len(snake_case_      )) + [1]

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[Any]:
       return list(
           set(filter(lambda snake_case_      : bool(re.search(r'<extra_id_\d+>' ,				snake_case_      )      ) is not None ,				self.additional_special_tokens      )      )      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       int:
       return [self._convert_token_to_id(snake_case_      ) for token in self.get_sentinel_tokens()]

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       List[int]:
       if len(snake_case_      ) > 0 and token_ids[-1] == self.eos_token_id:
           warnings.warn(
               F'This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated'
               ' eos tokens being added.'      )
           return token_ids
       else:
           return token_ids + [self.eos_token_id]

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ = None      )		->       List[int]:
       UpperCamelCase__         =    [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 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ = None      )		->       List[int]:
       UpperCamelCase__         =    self._add_eos_if_not_present(snake_case_      )
       if token_ids_a is None:
           return token_ids_a
       else:
           UpperCamelCase__         =    self._add_eos_if_not_present(snake_case_      )
           return token_ids_a + token_ids_a

   def __getstate__(					self      )		->       str:
       UpperCamelCase__         =    self.__dict__.copy()
       UpperCamelCase__         =    None
       return state

   def __setstate__(					self ,				snake_case_      )		->       Any:
       UpperCamelCase__         =    d

       # for backward compatibility
       if not hasattr(self ,				'sp_model_kwargs'      ):
           UpperCamelCase__         =    {}

       UpperCamelCase__         =    spm.SentencePieceProcessor(**self.sp_model_kwargs      )
       self.sp_model.Load(self.vocab_file      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				**snake_case_      )		->       List[str]:
       # Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
       # the beginning of the text
       if not self.legacy:
           UpperCamelCase__         =    SPIECE_UNDERLINE + text.replace(snake_case_ ,				' '      )
       return super().tokenize(snake_case_ ,				**snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				**snake_case_      )		->       List[Any]:
       if not self.legacy:
           UpperCamelCase__         =    text.startswith(snake_case_      )
           if is_first:
               UpperCamelCase__         =    text[1:]

       UpperCamelCase__         =    self.sp_model.encode(snake_case_ ,				out_type=snake_case_      )

       if not self.legacy and not is_first and not text.startswith(' '      ) and tokens[0].startswith(snake_case_      ):
           UpperCamelCase__         =    ([tokens[0][1:]] if len(tokens[0]      ) > 1 else []) + tokens[1:]
       return tokens

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       List[str]:
       if token.startswith('<extra_id_'      ):
           UpperCamelCase__         =    re.match(r'<extra_id_(\d+)>' ,				snake_case_      )
           UpperCamelCase__         =    int(match.group(1      )      )
           return self.vocab_size - num - 1
       return self.sp_model.piece_to_id(snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       Optional[int]:
       if index < self.sp_model.get_piece_size():
           UpperCamelCase__         =    self.sp_model.IdToPiece(snake_case_      )
       else:
           UpperCamelCase__         =    F'<extra_id_{self.vocab_size - 1 - index}>'
       return token

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       List[str]:
       UpperCamelCase__         =    []
       UpperCamelCase__         =    ''
       UpperCamelCase__         =    False
       for token in tokens:
           # make sure that special tokens are not decoded using sentencepiece model
           if token in self.all_special_tokens:
               if not prev_is_special:
                   out_string += " "
               out_string += self.sp_model.decode(snake_case_      ) + token
               UpperCamelCase__         =    True
               UpperCamelCase__         =    []
           else:
               current_sub_tokens.append(snake_case_      )
               UpperCamelCase__         =    False
       out_string += self.sp_model.decode(snake_case_      )
       return out_string.strip()







   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ = None      )		->       Tuple[str]:
       if not os.path.isdir(snake_case_      ):
           logger.error(F'Vocabulary path ({save_directory}) should be a directory'      )
           return
       UpperCamelCase__         =    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_      ) and os.path.isfile(self.vocab_file      ):
           copyfile(self.vocab_file ,				snake_case_      )
       elif not os.path.isfile(self.vocab_file      ):
           with open(snake_case_ ,				'wb'      ) as fi:
               UpperCamelCase__         =    self.sp_model.serialized_model_proto()
               fi.write(snake_case_      )

       return (out_vocab_file,)












"""simple docstring"""





from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


if TYPE_CHECKING:
  from ...processing_utils import ProcessorMixin
  from ...utils import TensorType


A__					: Any=	logging.get_logger(__name__)

A__					: str=	{
    """microsoft/layoutlmv3-base""": """https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json""",
}
class    __lowerCamelCase    (  _a  ):
   a      :				List[str] 						="""layoutlmv3"""

   def __init__(					self ,				snake_case_=5_0265 ,				snake_case_=768 ,				snake_case_=12 ,				snake_case_=12 ,				snake_case_=3072 ,				snake_case_="gelu" ,				snake_case_=0.1 ,				snake_case_=0.1 ,				snake_case_=512 ,				snake_case_=2 ,				snake_case_=0.02 ,				snake_case_=1E-5 ,				snake_case_=1 ,				snake_case_=0 ,				snake_case_=2 ,				snake_case_=1024 ,				snake_case_=128 ,				snake_case_=128 ,				snake_case_=True ,				snake_case_=32 ,				snake_case_=128 ,				snake_case_=64 ,				snake_case_=256 ,				snake_case_=True ,				snake_case_=True ,				snake_case_=True ,				snake_case_=224 ,				snake_case_=3 ,				snake_case_=16 ,				snake_case_=None ,				**snake_case_ ,				)		->       Union[str, Any]:
       super().__init__(
           vocab_size=snake_case_ ,				hidden_size=snake_case_ ,				num_hidden_layers=snake_case_ ,				num_attention_heads=snake_case_ ,				intermediate_size=snake_case_ ,				hidden_act=snake_case_ ,				hidden_dropout_prob=snake_case_ ,				attention_probs_dropout_prob=snake_case_ ,				max_position_embeddings=snake_case_ ,				type_vocab_size=snake_case_ ,				initializer_range=snake_case_ ,				layer_norm_eps=snake_case_ ,				pad_token_id=snake_case_ ,				bos_token_id=snake_case_ ,				eos_token_id=snake_case_ ,				**snake_case_ ,				)
       UpperCamelCase__         =    max_ad_position_embeddings
       UpperCamelCase__         =    coordinate_size
       UpperCamelCase__         =    shape_size
       UpperCamelCase__         =    has_relative_attention_bias
       UpperCamelCase__         =    rel_pos_bins
       UpperCamelCase__         =    max_rel_pos
       UpperCamelCase__         =    has_spatial_attention_bias
       UpperCamelCase__         =    rel_ad_pos_bins
       UpperCamelCase__         =    max_rel_ad_pos
       UpperCamelCase__         =    text_embed
       UpperCamelCase__         =    visual_embed
       UpperCamelCase__         =    input_size
       UpperCamelCase__         =    num_channels
       UpperCamelCase__         =    patch_size
       UpperCamelCase__         =    classifier_dropout







class    __lowerCamelCase    (  _a  ):
   a      :				Tuple 						=version.parse("""1.12"""  )

   @property
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Mapping[str, Mapping[int, str]]:
       # The order of inputs is different for question answering and sequence classification
       if self.task in ["question-answering", "sequence-classification"]:
           return OrderedDict(
               [
                   ('input_ids', {0: 'batch', 1: 'sequence'}),
                   ('attention_mask', {0: 'batch', 1: 'sequence'}),
                   ('bbox', {0: 'batch', 1: 'sequence'}),
                   ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
               ]      )
       else:
           return OrderedDict(
               [
                   ('input_ids', {0: 'batch', 1: 'sequence'}),
                   ('bbox', {0: 'batch', 1: 'sequence'}),
                   ('attention_mask', {0: 'batch', 1: 'sequence'}),
                   ('pixel_values', {0: 'batch', 1: 'num_channels'}),
               ]      )

   @property
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       float:
       return 1E-5

   @property
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       int:
       return 12







   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ = -1 ,				snake_case_ = -1 ,				snake_case_ = False ,				snake_case_ = None ,				snake_case_ = 3 ,				snake_case_ = 40 ,				snake_case_ = 40 ,				)		->       Mapping[str, Any]:
       setattr(processor.image_processor ,				'apply_ocr' ,				snake_case_      )

       # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
       UpperCamelCase__         =    compute_effective_axis_dimension(
           snake_case_ ,				fixed_dimension=OnnxConfig.default_fixed_batch ,				num_token_to_add=0      )
       # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
       UpperCamelCase__         =    processor.tokenizer.num_special_tokens_to_add(snake_case_      )
       UpperCamelCase__         =    compute_effective_axis_dimension(
           snake_case_ ,				fixed_dimension=OnnxConfig.default_fixed_sequence ,				num_token_to_add=snake_case_      )
       # Generate dummy inputs according to compute batch and sequence
       UpperCamelCase__         =    [[' '.join([processor.tokenizer.unk_token]      ) * seq_length]] * batch_size

       # Generate dummy bounding boxes
       UpperCamelCase__         =    [[[48, 84, 73, 128]]] * batch_size

       # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
       # batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
       UpperCamelCase__         =    self._generate_dummy_images(snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_      )

       UpperCamelCase__         =    dict(
           processor(
               snake_case_ ,				text=snake_case_ ,				boxes=snake_case_ ,				return_tensors=snake_case_ ,				)      )

       return inputs




