



"""simple docstring"""




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

from ... import PretrainedConfig, PreTrainedTokenizer
from ...onnx import OnnxConfig, PatchingSpec
from ...utils import TensorType, is_torch_available, logging


_lowerCAmelCase       :      Any							=							logging.get_logger(__name__)

_lowerCAmelCase       :      Tuple							=							{
    "microsoft/layoutlm-base-uncased": (
        "https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/config.json"
    ),
    "microsoft/layoutlm-large-uncased": (
        "https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/config.json"
    ),
}






class 			UpperCAmelCase_						(       _UpperCamelCase		):
      __SCREAMING_SNAKE_CASE		:     List[str]				  =				'layoutlm'



      def __init__(  self  :			List[str]  ,			A  :			int=3_0_5_2_2  ,			A  :			Any=7_6_8  ,			A  :			List[str]=1_2  ,			A  :			List[str]=1_2  ,			A  :			List[Any]=3_0_7_2  ,			A  :			int="gelu"  ,			A  :			str=0.1  ,			A  :			Any=0.1  ,			A  :			Union[str, Any]=5_1_2  ,			A  :			Optional[int]=2  ,			A  :			Any=0.02  ,			A  :			Optional[Any]=1e-12  ,			A  :			Tuple=0  ,			A  :			Dict="absolute"  ,			A  :			Optional[int]=True  ,			A  :			List[str]=1_0_2_4  ,			**A  :			Union[str, Any]  ,			):
           super().__init__(pad_token_id=A  ,			**A		)
           _UpperCAmelCase    :			Any              =					vocab_size
           _UpperCAmelCase    :			Optional[int]              =					hidden_size
           _UpperCAmelCase    :			int              =					num_hidden_layers
           _UpperCAmelCase    :			Tuple              =					num_attention_heads
           _UpperCAmelCase    :			Optional[Any]              =					hidden_act
           _UpperCAmelCase    :			Dict              =					intermediate_size
           _UpperCAmelCase    :			Union[str, Any]              =					hidden_dropout_prob
           _UpperCAmelCase    :			Dict              =					attention_probs_dropout_prob
           _UpperCAmelCase    :			Union[str, Any]              =					max_position_embeddings
           _UpperCAmelCase    :			List[Any]              =					type_vocab_size
           _UpperCAmelCase    :			List[Any]              =					initializer_range
           _UpperCAmelCase    :			List[str]              =					layer_norm_eps
           _UpperCAmelCase    :			Dict              =					position_embedding_type
           _UpperCAmelCase    :			List[Any]              =					use_cache
           _UpperCAmelCase    :			Optional[Any]              =					max_ad_position_embeddings







class 			UpperCAmelCase_						(       _UpperCamelCase		):



      def __init__(  self  :			List[Any]  ,			A  :			PretrainedConfig  ,			A  :			str = "default"  ,			A  :			List[PatchingSpec] = None  ,			):
           super().__init__(A  ,			task=A  ,			patching_specs=A		)
           _UpperCAmelCase    :			Any              =					config.max_ad_position_embeddings - 1



      @property
      def 		snake_case_      (  self  :			int		):
           return OrderedDict(
               [
                   ("input_ids", {0: "batch", 1: "sequence"}),
                   ("bbox", {0: "batch", 1: "sequence"}),
                   ("attention_mask", {0: "batch", 1: "sequence"}),
                   ("token_type_ids", {0: "batch", 1: "sequence"}),
               ]		)



      def 		snake_case_      (  self  :			Dict  ,			A  :			PreTrainedTokenizer  ,			A  :			int = -1  ,			A  :			int = -1  ,			A  :			bool = False  ,			A  :			Optional[TensorType] = None  ,			):
           _UpperCAmelCase    :			int              =					super().generate_dummy_inputs(
               A  ,			batch_size=A  ,			seq_length=A  ,			is_pair=A  ,			framework=A		)

           # Generate a dummy bbox
           _UpperCAmelCase    :			Any              =					[4_8, 8_4, 7_3, 1_2_8]

           if not framework == TensorType.PYTORCH:
                raise NotImplementedError("Exporting LayoutLM to ONNX is currently only supported for PyTorch."		)

           if not is_torch_available():
                raise ValueError("Cannot generate dummy inputs without PyTorch installed."		)
           import torch

           _UpperCAmelCase       , _UpperCAmelCase    :			Optional[int]              =					input_dict["input_ids"].shape
           _UpperCAmelCase    :			Tuple              =					torch.tensor([*[box] * seq_length]		).tile(A  ,			1  ,			1		)
           return input_dict





"""simple docstring"""




from arguments import TokenizerTrainingArguments
from datasets import load_dataset
from tqdm import tqdm

from transformers import AutoTokenizer, HfArgumentParser
from transformers.models.gpta.tokenization_gpta import bytes_to_unicode

def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		str=10       )						->     List[Any]:





     '''simple docstring'''

     for _ in tqdm(range(0		,						args.n_examples		,						SCREAMING_SNAKE_CASE__       )       ):
          yield [next(SCREAMING_SNAKE_CASE__       )[args.text_column] for _ in range(SCREAMING_SNAKE_CASE__       )]


# Configuration
_lowerCAmelCase       :      Optional[Any]							=							HfArgumentParser(TokenizerTrainingArguments)
_lowerCAmelCase       :      Dict							=							parser.parse_args()

# Base tokenizer
_lowerCAmelCase       :      int							=							AutoTokenizer.from_pretrained(args.base_tokenizer)
_lowerCAmelCase       :      List[str]							=							list(bytes_to_unicode().values())

# Load dataset
_lowerCAmelCase       :      Dict							=							load_dataset(args.dataset_name, split="train", streaming=True)
_lowerCAmelCase       :      List[Any]							=							iter(dataset)


# Training and saving
_lowerCAmelCase       :      List[str]							=							tokenizer.train_new_from_iterator(
    batch_iterator(), vocab_size=args.vocab_size, initial_alphabet=base_vocab
)
new_tokenizer.save_pretrained(args.tokenizer_name, push_to_hub=args.push_to_hub)
