
import multiprocessing
import os
from typing import BinaryIO, Optional, Union

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.csv.csv import Csv
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader





class   __UpperCamelCase				(    lowercase_   ):






     """simple docstring"""







     def __init__(							self    :    Union[str, Any]      ,		_A    :    NestedDataStructureLike[PathLike]      ,		_A    :    Optional[NamedSplit] = None      ,		_A    :    Optional[Features] = None      ,		_A    :    str = None      ,		_A    :    bool = False      ,		_A    :    bool = False      ,		_A    :    Optional[int] = None      ,		**_A    :    Tuple      ,		):







       """simple docstring"""




       super().__init__(
           __UpperCamelCase      ,		split=__UpperCamelCase      ,		features=__UpperCamelCase      ,		cache_dir=__UpperCamelCase      ,		keep_in_memory=__UpperCamelCase      ,		streaming=__UpperCamelCase      ,		num_proc=__UpperCamelCase      ,		**__UpperCamelCase      ,		)
       __SCREAMING_SNAKE_CASE     :					List[str]			=						path_or_paths if isinstance(__UpperCamelCase      ,		__UpperCamelCase				) else {self.split: path_or_paths}
       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						Csv(
           cache_dir=__UpperCamelCase      ,		data_files=__UpperCamelCase      ,		features=__UpperCamelCase      ,		**__UpperCamelCase      ,		)




     def 				UpperCAmelCase__    (							self    :    Optional[int]				):







       """simple docstring"""




       if self.streaming:
         __SCREAMING_SNAKE_CASE     :					Tuple			=						self.builder.as_streaming_dataset(split=self.split				)
       # Build regular (map-style) dataset
       else:
         __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						None
         __SCREAMING_SNAKE_CASE     :					str			=						None
         __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						None
         __SCREAMING_SNAKE_CASE     :					str			=						None

         self.builder.download_and_prepare(
             download_config=__UpperCamelCase      ,		download_mode=__UpperCamelCase      ,		verification_mode=__UpperCamelCase      ,		base_path=__UpperCamelCase      ,		num_proc=self.num_proc      ,		)
         __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						self.builder.as_dataset(
             split=self.split      ,		verification_mode=__UpperCamelCase      ,		in_memory=self.keep_in_memory				)
       return dataset






class   __UpperCamelCase				:






     """simple docstring"""







     def __init__(							self    :    Optional[int]      ,		_A    :    Dataset      ,		_A    :    Union[PathLike, BinaryIO]      ,		_A    :    Optional[int] = None      ,		_A    :    Optional[int] = None      ,		**_A    :    str      ,		):







       """simple docstring"""




       if num_proc is not None and num_proc <= 0:
         raise ValueError(F'''num_proc {num_proc} must be an integer > 0.'''				)

       __SCREAMING_SNAKE_CASE     :					List[str]			=						dataset
       __SCREAMING_SNAKE_CASE     :					List[Any]			=						path_or_buf
       __SCREAMING_SNAKE_CASE     :					Any			=						batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						num_proc
       __SCREAMING_SNAKE_CASE     :					List[Any]			=						'''utf-8'''
       __SCREAMING_SNAKE_CASE     :					List[Any]			=						to_csv_kwargs



     def 				UpperCAmelCase__    (							self    :    Optional[int]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					str			=						self.to_csv_kwargs.pop('''path_or_buf'''      ,		__UpperCamelCase				)
       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						self.to_csv_kwargs.pop('''header'''      ,		__UpperCamelCase				)
       __SCREAMING_SNAKE_CASE     :					str			=						self.to_csv_kwargs.pop('''index'''      ,		__UpperCamelCase				)

       if isinstance(self.path_or_buf      ,		(str, bytes, os.PathLike)				):
         with open(self.path_or_buf      ,		'''wb+'''				) as buffer:
           __SCREAMING_SNAKE_CASE     :					List[Any]			=						self._write(file_obj=__UpperCamelCase      ,		header=__UpperCamelCase      ,		index=__UpperCamelCase      ,		**self.to_csv_kwargs				)
       else:
         __SCREAMING_SNAKE_CASE     :					Dict			=						self._write(file_obj=self.path_or_buf      ,		header=__UpperCamelCase      ,		index=__UpperCamelCase      ,		**self.to_csv_kwargs				)
       return written



     def 				UpperCAmelCase__    (							self    :    Union[str, Any]      ,		_A    :    str				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE     :					Dict			=						args

       __SCREAMING_SNAKE_CASE     :					str			=						query_table(
           table=self.dataset.data      ,		key=slice(__UpperCamelCase      ,		offset + self.batch_size				)      ,		indices=self.dataset._indices      ,		)
       __SCREAMING_SNAKE_CASE     :					List[Any]			=						batch.to_pandas().to_csv(
           path_or_buf=__UpperCamelCase      ,		header=header if (offset == 0) else False      ,		index=__UpperCamelCase      ,		**__UpperCamelCase				)
       return csv_str.encode(self.encoding				)




     def 				UpperCAmelCase__    (							self    :    str      ,		_A    :    BinaryIO      ,		_A    :    Tuple      ,		_A    :    Dict      ,		**_A    :    Optional[int]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					List[str]			=						0

       if self.num_proc is None or self.num_proc == 1:
         for offset in logging.tqdm(
             range(0      ,		len(self.dataset				)      ,		self.batch_size				)      ,		unit='''ba'''      ,		disable=not logging.is_progress_bar_enabled()      ,		desc='''Creating CSV from Arrow format'''      ,		):
           __SCREAMING_SNAKE_CASE     :					Tuple			=						self._batch_csv((offset, header, index, to_csv_kwargs)				)
           written += file_obj.write(__UpperCamelCase				)

       else:
         __SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE     :					Optional[Any]			=						len(self.dataset				), self.batch_size
         with multiprocessing.Pool(self.num_proc				) as pool:
           for csv_str in logging.tqdm(
               pool.imap(
                   self._batch_csv      ,		[(offset, header, index, to_csv_kwargs) for offset in range(0      ,		__UpperCamelCase      ,		__UpperCamelCase				)]      ,		)      ,		total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size      ,		unit='''ba'''      ,		disable=not logging.is_progress_bar_enabled()      ,		desc='''Creating CSV from Arrow format'''      ,		):
             written += file_obj.write(__UpperCamelCase				)

       return written


import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_deberta import DebertaTokenizer


if TYPE_CHECKING:
   from transformers.pipelines.conversational import Conversation


lowercase_											=	logging.get_logger(__name__)

lowercase_											=	{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

lowercase_											=	{
    """vocab_file""": {
        """microsoft/deberta-base""": """https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json""",
        """microsoft/deberta-large""": """https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json""",
        """microsoft/deberta-xlarge""": """https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json""",
        """microsoft/deberta-base-mnli""": """https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json""",
        """microsoft/deberta-large-mnli""": """https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json""",
        """microsoft/deberta-xlarge-mnli""": (
            """https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json"""
        ),
    },
    """merges_file""": {
        """microsoft/deberta-base""": """https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt""",
        """microsoft/deberta-large""": """https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt""",
        """microsoft/deberta-xlarge""": """https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt""",
        """microsoft/deberta-base-mnli""": """https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt""",
        """microsoft/deberta-large-mnli""": """https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt""",
        """microsoft/deberta-xlarge-mnli""": (
            """https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt"""
        ),
    },
}

lowercase_											=	{
    """microsoft/deberta-base""": 512,
    """microsoft/deberta-large""": 512,
    """microsoft/deberta-xlarge""": 512,
    """microsoft/deberta-base-mnli""": 512,
    """microsoft/deberta-large-mnli""": 512,
    """microsoft/deberta-xlarge-mnli""": 512,
}

lowercase_											=	{
    """microsoft/deberta-base""": {"""do_lower_case""": False},
    """microsoft/deberta-large""": {"""do_lower_case""": False},
}





class   __UpperCamelCase				(    lowerCAmelCase__   ):






     """simple docstring"""







     lowerCAmelCase_     =	VOCAB_FILES_NAMES
     lowerCAmelCase_     =	PRETRAINED_VOCAB_FILES_MAP
     lowerCAmelCase_     =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCAmelCase_     =	['''input_ids''', '''attention_mask''', '''token_type_ids''']
     lowerCAmelCase_     =	DebertaTokenizer



     def __init__(							self    :    List[str]      ,		_A    :    Tuple=None      ,		_A    :    List[Any]=None      ,		_A    :    Optional[int]=None      ,		_A    :    List[str]="replace"      ,		_A    :    List[Any]="[CLS]"      ,		_A    :    Optional[Any]="[SEP]"      ,		_A    :    Union[str, Any]="[SEP]"      ,		_A    :    List[str]="[CLS]"      ,		_A    :    Optional[Any]="[UNK]"      ,		_A    :    str="[PAD]"      ,		_A    :    List[str]="[MASK]"      ,		_A    :    Dict=False      ,		**_A    :    Optional[int]      ,		):







       """simple docstring"""




       super().__init__(
           _A      ,		_A      ,		tokenizer_file=_A      ,		errors=_A      ,		bos_token=_A      ,		eos_token=_A      ,		unk_token=_A      ,		sep_token=_A      ,		cls_token=_A      ,		pad_token=_A      ,		mask_token=_A      ,		add_prefix_space=_A      ,		**_A      ,		)
       __SCREAMING_SNAKE_CASE     :					List[str]			=						kwargs.pop('''add_bos_token'''      ,		_A				)

       __SCREAMING_SNAKE_CASE     :					int			=						json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()				)
       if pre_tok_state.get('''add_prefix_space'''      ,		_A				) != add_prefix_space:
         __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						getattr(_A      ,		pre_tok_state.pop('''type'''				)				)
         __SCREAMING_SNAKE_CASE     :					Dict			=						add_prefix_space
         __SCREAMING_SNAKE_CASE     :					Any			=						pre_tok_class(**_A				)

       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						add_prefix_space



     @property
     def 				UpperCAmelCase__    (							self    :    List[Any]				):







       """simple docstring"""




       if self._mask_token is None:
         if self.verbose:
           logger.error('''Using mask_token, but it is not set yet.'''				)
         return None
       return str(self._mask_token				)



     @mask_token.setter
     def 				UpperCAmelCase__    (							self    :    Any      ,		_A    :    Union[str, Any]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Dict			=						AddedToken(_A      ,		lstrip=_A      ,		rstrip=_A				) if isinstance(_A      ,		_A				) else value
       __SCREAMING_SNAKE_CASE     :					Tuple			=						value



     def 				UpperCAmelCase__    (							self    :    List[Any]      ,		_A    :    List[int]      ,		_A    :    Optional[List[int]] = None				):







       """simple docstring"""




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



     def 				UpperCAmelCase__    (							self    :    int      ,		_A    :    List[int]      ,		_A    :    Optional[List[int]] = None				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Tuple			=						[self.sep_token_id]
       __SCREAMING_SNAKE_CASE     :					List[str]			=						[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    :    Tuple      ,		*_A    :    Optional[Any]      ,		**_A    :    Optional[Any]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					List[str]			=						kwargs.get('''is_split_into_words'''      ,		_A				)
       assert self.add_prefix_space or not is_split_into_words, (
           F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
           "to use it with pretokenized inputs."
       )

       return super()._batch_encode_plus(*_A      ,		**_A				)



     def 				UpperCAmelCase__    (							self    :    Tuple      ,		*_A    :    Tuple      ,		**_A    :    int				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Dict			=						kwargs.get('''is_split_into_words'''      ,		_A				)

       assert self.add_prefix_space or not is_split_into_words, (
           F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
           "to use it with pretokenized inputs."
       )

       return super()._encode_plus(*_A      ,		**_A				)



     def 				UpperCAmelCase__    (							self    :    Tuple      ,		_A    :    str      ,		_A    :    Optional[str] = None				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						self._tokenizer.model.save(_A      ,		name=_A				)
       return tuple(_A				)




     def 				UpperCAmelCase__    (							self    :    Tuple      ,		_A    :    "Conversation"				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					List[Any]			=						[]
       for is_user, text in conversation.iter_texts():
         input_ids.extend(self.encode(_A      ,		add_special_tokens=_A				) + [self.eos_token_id]				)

       if len(_A				) > self.model_max_length:
         __SCREAMING_SNAKE_CASE     :					Any			=						input_ids[-self.model_max_length :]
       return input_ids
