
"""simple docstring"""





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									=				None

_a									=				logging.get_logger(__name__)

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

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

_a									=				{
    '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_      (lowercase__ ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__     :		Optional[int]    	=						VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE__     :		Dict    	=						PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE__     :		List[str]    	=						PRETRAINED_INIT_CONFIGURATION
  SCREAMING_SNAKE_CASE__     :		Tuple    	=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE__     :		Optional[Any]    	=						DebertaVaTokenizer







  def __init__(						self							,  lowercase_=None							,  lowercase_=None							,  lowercase_=False							,  lowercase_=False							,  lowercase_="[CLS]"							,  lowercase_="[SEP]"							,  lowercase_="[UNK]"							,  lowercase_="[SEP]"							,  lowercase_="[PAD]"							,  lowercase_="[CLS]"							,  lowercase_="[MASK]"							,  **lowercase_							,  ):

     """simple docstring"""
     super().__init__(
         lowercase_							,  tokenizer_file=lowercase_							,  do_lower_case=lowercase_							,  bos_token=lowercase_							,  eos_token=lowercase_							,  unk_token=lowercase_							,  sep_token=lowercase_							,  pad_token=lowercase_							,  cls_token=lowercase_							,  mask_token=lowercase_							,  split_by_punct=lowercase_							,  **lowercase_							,  )

     UpperCAmelCase_   :   Optional[Any]     	=       do_lower_case
     UpperCAmelCase_   :   Optional[int]     	=       split_by_punct
     UpperCAmelCase_   :   List[str]     	=       vocab_file
     UpperCAmelCase_   :   Optional[Any]     	=       False if not self.vocab_file else True







  def 			UpperCamelCase__   (						self							,  lowercase_							,  lowercase_=None    ):

     """simple docstring"""

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







  def 			UpperCamelCase__   (						self							,  lowercase_							,  lowercase_=None							,  lowercase_=False    ):

     """simple docstring"""

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

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







  def 			UpperCamelCase__   (						self							,  lowercase_							,  lowercase_=None    ):

     """simple docstring"""
     UpperCAmelCase_   :   Union[str, Any]     	=       [self.sep_token_id]
     UpperCAmelCase_   :   List[Any]     	=       [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 			UpperCamelCase__   (						self							,  lowercase_							,  lowercase_ = None    ):

     """simple docstring"""
     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(lowercase_    ):
        logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""    )
        return
     UpperCAmelCase_   :   Tuple     	=       os.path.join(
         lowercase_							,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]    )

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

     return (out_vocab_file,)









'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase__      :   Any      	=			{
    "configuration_open_llama": ["OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "OpenLlamaConfig"],
}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__      :   Optional[int]      	=			["LlamaTokenizer"]

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__      :   int      	=			["LlamaTokenizerFast"]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__      :   int      	=			[
			    "OpenLlamaForCausalLM",
			    "OpenLlamaModel",
			    "OpenLlamaPreTrainedModel",
			    "OpenLlamaForSequenceClassification",
			]


if TYPE_CHECKING:
			from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from transformers import LlamaTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from transformers import LlamaTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_open_llama import (
						    OpenLlamaForCausalLM,
						    OpenLlamaForSequenceClassification,
						    OpenLlamaModel,
						    OpenLlamaPreTrainedModel,
						)


else:
			import sys

			lowerCAmelCase__      :   Dict      	=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




