





"""simple docstring"""




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

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


if TYPE_CHECKING:
   from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


lowerCamelCase_	:		int         =   logging.get_logger(__name__)

lowerCamelCase_	:		int         =   {
    'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/config.json',
    'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/config.json',
    'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/config.json',
    'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/config.json',
    'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/config.json',
    'microsoft/deberta-xlarge-mnli': 'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/config.json',
}

class 					_UpperCAmelCase    (  UpperCAmelCase__						):



      '''simple docstring'''
      lowercase_ :					Optional[int]             =  """deberta"""

      def __init__(	self   , snake_case_=5_0_2_6_5   , snake_case_=7_6_8   , snake_case_=1_2   , snake_case_=1_2   , snake_case_=3_0_7_2   , snake_case_="gelu"   , snake_case_=0.1   , snake_case_=0.1   , snake_case_=5_1_2   , snake_case_=0   , snake_case_=0.02   , snake_case_=1E-7   , snake_case_=False   , snake_case_=-1   , snake_case_=0   , snake_case_=True   , snake_case_=None   , snake_case_=0   , snake_case_="gelu"   , **snake_case_   , ):

        """simple docstring"""

        super().__init__(**snake_case_    )

        A_    :			Tuple									=    hidden_size
        A_    :			List[Any]									=    num_hidden_layers
        A_    :			Union[str, Any]									=    num_attention_heads
        A_    :			int									=    intermediate_size
        A_    :			Any									=    hidden_act
        A_    :			List[str]									=    hidden_dropout_prob
        A_    :			Dict									=    attention_probs_dropout_prob
        A_    :			List[Any]									=    max_position_embeddings
        A_    :			Tuple									=    type_vocab_size
        A_    :			Optional[int]									=    initializer_range
        A_    :			Union[str, Any]									=    relative_attention
        A_    :			Optional[int]									=    max_relative_positions
        A_    :			Any									=    pad_token_id
        A_    :			Optional[int]									=    position_biased_input

        # Backwards compatibility
        if type(snake_case_    ) == str:
          A_    :			Tuple									=    [x.strip() for x in pos_att_type.lower().split('|'    )]

        A_    :			Any									=    pos_att_type
        A_    :			Optional[int]									=    vocab_size
        A_    :			Any									=    layer_norm_eps

        A_    :			str									=    kwargs.get('pooler_hidden_size'   , snake_case_    )
        A_    :			str									=    pooler_dropout
        A_    :			Dict									=    pooler_hidden_act


class 					_UpperCAmelCase    (  UpperCAmelCase__						):



      '''simple docstring'''

      @property
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        if self.task == "multiple-choice":
          A_    :			Any									=    {0: 'batch', 1: 'choice', 2: 'sequence'}
        else:
          A_    :			Dict									=    {0: 'batch', 1: 'sequence'}
        if self._config.type_vocab_size > 0:
          return OrderedDict(
              [('input_ids', dynamic_axis), ('attention_mask', dynamic_axis), ('token_type_ids', dynamic_axis)]    )
        else:
          return OrderedDict([('input_ids', dynamic_axis), ('attention_mask', dynamic_axis)]    )

      @property
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        return 1_2

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_ = -1   , snake_case_ = -1   , snake_case_ = -1   , snake_case_ = False   , snake_case_ = None   , snake_case_ = 3   , snake_case_ = 4_0   , snake_case_ = 4_0   , snake_case_ = None   , ):

        """simple docstring"""

        A_    :			Tuple									=    super().generate_dummy_inputs(preprocessor=snake_case_   , framework=snake_case_    )
        if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
          del dummy_inputs["token_type_ids"]
        return dummy_inputs



import argparse
import logging

import datasets
from tokenizers import Tokenizer, decoders, normalizers, pre_tokenizers, processors
from tokenizers.models import Unigram
from tokenizers.trainers import UnigramTrainer

from transformers import AlbertTokenizerFast


UpperCamelCase			:					List[str]         =							logging.getLogger(__name__)
def 				UpperCamelCase_	(					)->   Dict:




						'''simple docstring'''
						__magic_name__					=      argparse.ArgumentParser(description='Train a unigram tokenizer on the wikitext dataset.'  )
						parser.add_argument(
						    '--dataset_name'						,      type=__snake_case						,      default='wikitext'						,      help='Name of the training. Explore datasets at: hf.co/datasets.'						,      )
						parser.add_argument(
						    '--dataset_config'						,      type=__snake_case						,      default='wikitext-103-raw-v1'						,      help='Configuration name of the dataset.'  )
						parser.add_argument(
						    '--batch_size'						,      type=__snake_case						,      default=10_00						,      help='Batch size during training.'						,      )
						parser.add_argument(
						    '--vocab_size'						,      type=__snake_case						,      default=1_00_48						,      help='Size of the desired vocabulary.'						,      )
						parser.add_argument(
						    '--limit'						,      default=__snake_case						,      type=__snake_case						,      help='Limit the number of shards (used for debugging).'						,      )
						parser.add_argument(
						    '--export_to_hub'						,      action='store_true'						,      )

						__magic_name__					=      parser.parse_args()
						return args
def 				UpperCamelCase_	(					__snake_case      :    Any  )->   Optional[int]:




						'''simple docstring'''
						__magic_name__					=      datasets.load_dataset(args.dataset_name						,      args.dataset_config						,      split='train'  )

						if args.limit is not None:
												__magic_name__					=      min(len(__snake_case  )						,      args.limit  )
												__magic_name__					=      dataset.select(range(__snake_case  )  )
												logger.info(F'''Limiting the dataset to {args.limit} entries.'''  )

						def batch_iterator():
												for i in range(0						,      len(__snake_case  )						,      args.batch_size  ):
																		yield dataset[i : i + args.batch_size]["text"]

    # Prepare the tokenizer.
						__magic_name__					=      Tokenizer(Unigram()  )
						__magic_name__					=      normalizers.Sequence([normalizers.Replace('``'						,      '"'  ), normalizers.Replace('\'\''						,      '"'  )]  )
						__magic_name__					=      pre_tokenizers.Metaspace()

						# Prepare the trainer.
						__magic_name__					=      UnigramTrainer(
						    unk_token='<unk>'						,      special_tokens=['[CLS]', '[SEP]', '<unk>', '<pad>', '[MASK]']						,      vocab_size=args.vocab_size						,      )

						logger.info('Training the tokenizer.'  )
						tokenizer.train_from_iterator(batch_iterator()						,      trainer=__snake_case  )
						logger.info('Tokenizer training complete!'  )

						__magic_name__					=      tokenizer.token_to_id('[CLS]'  )
						__magic_name__					=      tokenizer.token_to_id('[SEP]'  )
						__magic_name__					=      processors.TemplateProcessing(
						    single='[CLS]:0 $A:0 [SEP]:0'						,      pair='[CLS]:0 $A:0 [SEP]:0 $B:1 [SEP]:1'						,      special_tokens=[
						        ('[CLS]', cls_token_id),
						        ('[SEP]', sep_token_id),
						    ]						,      )
						__magic_name__					=      decoders.Metaspace()

						if args.export_to_hub:
												logger.info('Exporting the trained tokenzier to Hub.'  )
												__magic_name__					=      AlbertTokenizerFast(tokenizer_object=__snake_case  )
												new_tokenizer.push_to_hub('unigram-tokenizer-dataset'  )


if __name__ == "__main__":
					UpperCamelCase			:					Dict         =							parse_args()
					main(args)

