

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

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


__a						:Any            = logging.get_logger(__name__)

__a						:List[str]            = {
    'gpt2': 'https://huggingface.co/gpt2/resolve/main/config.json',
    'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/config.json',
    'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/config.json',
    'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/config.json',
    'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/config.json',
}

class      _a				( snake_case_							):




      """simple docstring"""

      _lowerCamelCase					: List[str]					=					'gpt2'
      _lowerCamelCase					: Optional[Any]					=					['past_key_values']
      _lowerCamelCase					: int					=					{
          'hidden_size': 'n_embd',
          'max_position_embeddings': 'n_positions',
          'num_attention_heads': 'n_head',
          'num_hidden_layers': 'n_layer',
      }





      def __init__(      self       :							Tuple	, UpperCAmelCase       :							str=50257	, UpperCAmelCase       :							List[Any]=1024	, UpperCAmelCase       :							Any=768	, UpperCAmelCase       :							Dict=12	, UpperCAmelCase       :							int=12	, UpperCAmelCase       :							Any=None	, UpperCAmelCase       :							str="gelu_new"	, UpperCAmelCase       :							Any=0.1	, UpperCAmelCase       :							Optional[int]=0.1	, UpperCAmelCase       :							Any=0.1	, UpperCAmelCase       :							Tuple=1E-5	, UpperCAmelCase       :							Tuple=0.02	, UpperCAmelCase       :							Optional[int]="cls_index"	, UpperCAmelCase       :							List[Any]=True	, UpperCAmelCase       :							Optional[int]=None	, UpperCAmelCase       :							List[str]=True	, UpperCAmelCase       :							List[Any]=0.1	, UpperCAmelCase       :							List[str]=True	, UpperCAmelCase       :							Dict=True	, UpperCAmelCase       :							int=50256	, UpperCAmelCase       :							Optional[int]=50256	, UpperCAmelCase       :							str=False	, UpperCAmelCase       :							Union[str, Any]=False	, **UpperCAmelCase       :							Union[str, Any]	, ):
            A_	     =							vocab_size
            A_	     =							n_positions
            A_	     =							n_embd
            A_	     =							n_layer
            A_	     =							n_head
            A_	     =							n_inner
            A_	     =							activation_function
            A_	     =							resid_pdrop
            A_	     =							embd_pdrop
            A_	     =							attn_pdrop
            A_	     =							layer_norm_epsilon
            A_	     =							initializer_range
            A_	     =							summary_type
            A_	     =							summary_use_proj
            A_	     =							summary_activation
            A_	     =							summary_first_dropout
            A_	     =							summary_proj_to_labels
            A_	     =							scale_attn_weights
            A_	     =							use_cache
            A_	     =							scale_attn_by_inverse_layer_idx
            A_	     =							reorder_and_upcast_attn

            A_	     =							bos_token_id
            A_	     =							eos_token_id

            super().__init__(bos_token_id=UpperCAmelCase	, eos_token_id=UpperCAmelCase	, **UpperCAmelCase  )




class      _a				( snake_case_							):




      """simple docstring"""

      def __init__(      self       :							Dict	, UpperCAmelCase       :							PretrainedConfig	, UpperCAmelCase       :							str = "default"	, UpperCAmelCase       :							List[PatchingSpec] = None	, UpperCAmelCase       :							bool = False	, ):
            super().__init__(UpperCAmelCase	, task=UpperCAmelCase	, patching_specs=UpperCAmelCase	, use_past=UpperCAmelCase  )
            if not getattr(self._config	, "pad_token_id"	, UpperCAmelCase  ):
                  # TODO: how to do that better?
                  A_	     =							0





      @property
      def 		__A  (      self       :							str  ):
            A_	     =							OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}  )
            if self.use_past:
                  self.fill_with_past_key_values_(UpperCAmelCase	, direction="inputs"  )
                  A_	     =							{0: "batch", 1: "past_sequence + sequence"}
            else:
                  A_	     =							{0: "batch", 1: "sequence"}

            return common_inputs





      @property
      def 		__A  (      self       :							Union[str, Any]  ):
            return self._config.n_layer





      @property
      def 		__A  (      self       :							str  ):
            return self._config.n_head





      def 		__A  (      self       :							Optional[int]	, UpperCAmelCase       :							PreTrainedTokenizer	, UpperCAmelCase       :							int = -1	, UpperCAmelCase       :							int = -1	, UpperCAmelCase       :							bool = False	, UpperCAmelCase       :							Optional[TensorType] = None	, ):
            A_	     =							super(UpperCAmelCase	, self  ).generate_dummy_inputs(
                UpperCAmelCase	, batch_size=UpperCAmelCase	, seq_length=UpperCAmelCase	, is_pair=UpperCAmelCase	, framework=UpperCAmelCase  )

            # We need to order the input in the way they appears in the forward()
            A_	     =							OrderedDict({"input_ids": common_inputs["input_ids"]}  )

            # Need to add the past_keys
            if self.use_past:
                  if not is_torch_available():
                        raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."  )
                  else:
                        import torch

                        A_     ,    A_	     =							common_inputs["input_ids"].shape
                        # Not using the same length for past_key_values
                        A_	     =							seqlen + 2
                        A_	     =							(
                            batch,
                            self.num_attention_heads,
                            past_key_values_length,
                            self._config.hidden_size // self.num_attention_heads,
                        )
                        A_	     =							[
                            (torch.zeros(UpperCAmelCase  ), torch.zeros(UpperCAmelCase  )) for _ in range(self.num_layers  )
                        ]

            A_	     =							common_inputs["attention_mask"]
            if self.use_past:
                  A_	     =							ordered_inputs["attention_mask"].dtype
                  A_	     =							torch.cat(
                      [ordered_inputs["attention_mask"], torch.ones(UpperCAmelCase	, UpperCAmelCase	, dtype=UpperCAmelCase  )]	, dim=1  )

            return ordered_inputs







      @property
      def 		__A  (      self       :							Any  ):
            return 13


import os
from shutil import copyfile
from typing import Optional, Tuple

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


if is_sentencepiece_available():
      from .tokenization_deberta_va import DebertaVaTokenizer
else:
      __a						:int            = None

__a						:List[str]            = logging.get_logger(__name__)

__a						:str            = {'vocab_file': 'spm.model', 'tokenizer_file': 'tokenizer.json'}

__a						:List[Any]            = {
    'vocab_file': {
        'microsoft/deberta-v2-xlarge': 'https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/spm.model',
        'microsoft/deberta-v2-xxlarge': 'https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/spm.model',
        'microsoft/deberta-v2-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/spm.model'
        ),
        'microsoft/deberta-v2-xxlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/spm.model'
        ),
    }
}

__a						:Dict            = {
    'microsoft/deberta-v2-xlarge': 512,
    'microsoft/deberta-v2-xxlarge': 512,
    'microsoft/deberta-v2-xlarge-mnli': 512,
    'microsoft/deberta-v2-xxlarge-mnli': 512,
}

__a						:List[Any]            = {
    'microsoft/deberta-v2-xlarge': {'do_lower_case': False},
    'microsoft/deberta-v2-xxlarge': {'do_lower_case': False},
    'microsoft/deberta-v2-xlarge-mnli': {'do_lower_case': False},
    'microsoft/deberta-v2-xxlarge-mnli': {'do_lower_case': False},
}

class      _a				( snake_case_							):




      """simple docstring"""

      _lowerCamelCase					: Dict					=					VOCAB_FILES_NAMES
      _lowerCamelCase					: Optional[Any]					=					PRETRAINED_VOCAB_FILES_MAP
      _lowerCamelCase					: Tuple					=					PRETRAINED_INIT_CONFIGURATION
      _lowerCamelCase					: Tuple					=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _lowerCamelCase					: Optional[int]					=					DebertaVaTokenizer





      def __init__(      self       :							List[Any]	, UpperCAmelCase       :							str=None	, UpperCAmelCase       :							int=None	, UpperCAmelCase       :							List[str]=False	, UpperCAmelCase       :							Optional[Any]=False	, UpperCAmelCase       :							List[str]="[CLS]"	, UpperCAmelCase       :							str="[SEP]"	, UpperCAmelCase       :							List[str]="[UNK]"	, UpperCAmelCase       :							Tuple="[SEP]"	, UpperCAmelCase       :							Union[str, Any]="[PAD]"	, UpperCAmelCase       :							Union[str, Any]="[CLS]"	, UpperCAmelCase       :							Any="[MASK]"	, **UpperCAmelCase       :							Dict	, ):
            super().__init__(
                UpperCAmelCase	, tokenizer_file=UpperCAmelCase	, do_lower_case=UpperCAmelCase	, bos_token=UpperCAmelCase	, eos_token=UpperCAmelCase	, unk_token=UpperCAmelCase	, sep_token=UpperCAmelCase	, pad_token=UpperCAmelCase	, cls_token=UpperCAmelCase	, mask_token=UpperCAmelCase	, split_by_punct=UpperCAmelCase	, **UpperCAmelCase	, )

            A_	     =							do_lower_case
            A_	     =							split_by_punct
            A_	     =							vocab_file
            A_	     =							False if not self.vocab_file else True





      def 		__A  (      self       :							Optional[int]	, UpperCAmelCase       :							Tuple	, UpperCAmelCase       :							List[str]=None  ):

            if token_ids_a is None:
                  return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
            A_	     =							[self.cls_token_id]
            A_	     =							[self.sep_token_id]
            return cls + token_ids_a + sep + token_ids_a + sep





      def 		__A  (      self       :							List[Any]	, UpperCAmelCase       :							Any	, UpperCAmelCase       :							Optional[int]=None	, UpperCAmelCase       :							int=False  ):

            if already_has_special_tokens:
                  return super().get_special_tokens_mask(
                      token_ids_a=UpperCAmelCase	, token_ids_a=UpperCAmelCase	, already_has_special_tokens=UpperCAmelCase  )

            if token_ids_a is not None:
                  return [1] + ([0] * len(UpperCAmelCase  )) + [1] + ([0] * len(UpperCAmelCase  )) + [1]
            return [1] + ([0] * len(UpperCAmelCase  )) + [1]





      def 		__A  (      self       :							List[Any]	, UpperCAmelCase       :							Union[str, Any]	, UpperCAmelCase       :							Optional[int]=None  ):
            A_	     =							[self.sep_token_id]
            A_	     =							[self.cls_token_id]
            if token_ids_a is None:
                  return len(cls + token_ids_a + sep  ) * [0]
            return len(cls + token_ids_a + sep  ) * [0] + len(token_ids_a + sep  ) * [1]







      def 		__A  (      self       :							str	, UpperCAmelCase       :							str	, UpperCAmelCase       :							Optional[str] = None  ):
            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(UpperCAmelCase  ):
                  logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''  )
                  return
            A_	     =							os.path.join(
                UpperCAmelCase	, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]  )

            if os.path.abspath(self.vocab_file  ) != os.path.abspath(UpperCAmelCase  ):
                  copyfile(self.vocab_file	, UpperCAmelCase  )

            return (out_vocab_file,)