






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


__a :    Any								=				logging.get_logger(__name__)

__a :    Union[str, Any]								=				{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

__a :    Optional[Any]								=				{
    "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"
        ),
    },
}

__a :    Optional[int]								=				{
    "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,
}

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


class  __lowercase (		lowercase_					):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE				  =			VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE				  =			PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE				  =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     SCREAMING_SNAKE_CASE				  =			["input_ids", "attention_mask", "token_type_ids"]
     SCREAMING_SNAKE_CASE				  =			DebertaTokenizer




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


         """simple docstring"""



         super().__init__(
             UpperCamelCase_	,		UpperCamelCase_	,		tokenizer_file=UpperCamelCase_	,		errors=UpperCamelCase_	,		bos_token=UpperCamelCase_	,		eos_token=UpperCamelCase_	,		unk_token=UpperCamelCase_	,		sep_token=UpperCamelCase_	,		cls_token=UpperCamelCase_	,		pad_token=UpperCamelCase_	,		mask_token=UpperCamelCase_	,		add_prefix_space=UpperCamelCase_	,		**UpperCamelCase_	,		)
         __A             =		kwargs.pop("""add_bos_token"""	,		UpperCamelCase_			)

         __A             =		json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()			)
         if pre_tok_state.get("""add_prefix_space"""	,		UpperCamelCase_			) != add_prefix_space:
             __A             =		getattr(UpperCamelCase_	,		pre_tok_state.pop("""type"""			)			)
             __A             =		add_prefix_space
             __A             =		pre_tok_class(**UpperCamelCase_			)

         __A             =		add_prefix_space




     @property
     def 						lowerCAmelCase_       (			self    :     Optional[int]			):


         """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 						lowerCAmelCase_       (			self    :     List[str]	,		UpperCamelCase_    :     Tuple			):


         """simple docstring"""



         __A             =		AddedToken(UpperCamelCase_	,		lstrip=UpperCamelCase_	,		rstrip=UpperCamelCase_			) if isinstance(UpperCamelCase_	,		UpperCamelCase_			) else value
         __A             =		value




     def 						lowerCAmelCase_       (			self    :     Dict	,		UpperCamelCase_    :     List[int]	,		UpperCamelCase_    :     Optional[List[int]] = None			):


         """simple docstring"""



         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 						lowerCAmelCase_       (			self    :     int	,		UpperCamelCase_    :     List[int]	,		UpperCamelCase_    :     Optional[List[int]] = None			):


         """simple docstring"""



         __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 						lowerCAmelCase_       (			self    :     Any	,		*UpperCamelCase_    :     List[str]	,		**UpperCamelCase_    :     Tuple			):


         """simple docstring"""



         __A             =		kwargs.get("""is_split_into_words"""	,		UpperCamelCase_			)
         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(*UpperCamelCase_	,		**UpperCamelCase_			)




     def 						lowerCAmelCase_       (			self    :     Optional[Any]	,		*UpperCamelCase_    :     Any	,		**UpperCamelCase_    :     Dict			):


         """simple docstring"""



         __A             =		kwargs.get("""is_split_into_words"""	,		UpperCamelCase_			)

         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(*UpperCamelCase_	,		**UpperCamelCase_			)




     def 						lowerCAmelCase_       (			self    :     Dict	,		UpperCamelCase_    :     str	,		UpperCamelCase_    :     Optional[str] = None			):


         """simple docstring"""



         __A             =		self._tokenizer.model.save(UpperCamelCase_	,		name=UpperCamelCase_			)
         return tuple(UpperCamelCase_			)





     def 						lowerCAmelCase_       (			self    :     Dict	,		UpperCamelCase_    :     "Conversation"			):


         """simple docstring"""



         __A             =		[]
         for is_user, text in conversation.iter_texts():
             input_ids.extend(self.encode(UpperCamelCase_	,		add_special_tokens=UpperCamelCase_			) + [self.eos_token_id]			)

         if len(UpperCamelCase_			) > self.model_max_length:
             __A             =		input_ids[-self.model_max_length :]
         return input_ids












# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from typing import List



def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     List[str]     ,			__lowercase				:     str      )  -> Optional[int]:





    """simple docstring"""

    if name != "training_function" and name != "main":
        raise ValueError(f"Incorrect function name passed: {name}, choose either 'main' or 'training_function'"      )
    __A      ,		__A             =		[], False
    for line in lines:
        if not found_start and f"def {name}" in line:
            __A             =		True
            good_lines.append(__lowercase      )
            continue
        if found_start:
            if name == "training_function" and "def main" in line:
                return good_lines
            if name == "main" and "if __name__" in line:
                return good_lines
            good_lines.append(__lowercase      )



def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     List[str]      )  -> Optional[int]:





    """simple docstring"""

    return [line for line in lines if not line.lstrip().startswith("""#"""      ) and line != "\n"]






def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     str     ,			__lowercase				:     str     ,			__lowercase				:     bool     ,			__lowercase				:     str = None      )  -> List[Any]:





    """simple docstring"""

    with open(__lowercase     ,			"""r"""      ) as f:
        __A             =		f.readlines()
    with open(os.path.abspath(os.path.join("""examples"""     ,			"""nlp_example.py"""      )      )     ,			"""r"""      ) as f:
        __A             =		f.readlines()
    with open(__lowercase     ,			"""r"""      ) as f:
        __A             =		f.readlines()
    if secondary_filename is not None:
        with open(__lowercase     ,			"""r"""      ) as f:
            __A             =		f.readlines()

    # This is our base, we remove all the code from here in our `full_filename` and `feature_filename` to find the new content
    if parser_only:
        __A             =		clean_lines(get_function_contents_by_name(__lowercase     ,			"""main"""      )      )
        __A             =		clean_lines(get_function_contents_by_name(__lowercase     ,			"""main"""      )      )
        __A             =		clean_lines(get_function_contents_by_name(__lowercase     ,			"""main"""      )      )
        if secondary_filename is not None:
            __A             =		clean_lines(get_function_contents_by_name(__lowercase     ,			"""main"""      )      )
    else:
        __A             =		clean_lines(get_function_contents_by_name(__lowercase     ,			"""training_function"""      )      )
        __A             =		clean_lines(get_function_contents_by_name(__lowercase     ,			"""training_function"""      )      )
        __A             =		clean_lines(get_function_contents_by_name(__lowercase     ,			"""training_function"""      )      )
        if secondary_filename is not None:
            __A             =		clean_lines(
                get_function_contents_by_name(__lowercase     ,			"""training_function"""      )      )

    __A             =		"""train_dataloader, eval_dataloader = get_dataloaders(accelerator, batch_size)\n"""

    # Specific code in our script that differs from the full version, aka what is new
    __A             =		[]
    __A             =		[]  # We keep track of the idxs just in case it's a repeated statement
    __A             =		iter(__lowercase      )
    for i in range(len(__lowercase      ) - 1      ):
        if i not in passed_idxs:
            __A             =		next(__lowercase      )
            if (line not in full_file_func) and (line.lstrip() != _dl_line):
                if "TESTING_MOCKED_DATALOADERS" not in line:
                    new_feature_code.append(__lowercase      )
                    passed_idxs.append(__lowercase      )
                else:
                    # Skip over the `config['num_epochs'] = 2` statement
                    __A             =		next(__lowercase      )

    # Extract out just the new parts from the full_file_training_func
    __A             =		[]
    __A             =		[]  # We keep track of the idxs just in case it's a repeated statement
    for i, line in enumerate(__lowercase      ):
        if i not in passed_idxs:
            if (line not in full_file_func) and (line.lstrip() != _dl_line):
                if "TESTING_MOCKED_DATALOADERS" not in line:
                    new_full_example_parts.append(__lowercase      )
                    passed_idxs.append(__lowercase      )

    # Finally, get the overall diff
    __A             =		[line for line in new_feature_code if line not in new_full_example_parts]
    if secondary_filename is not None:
        __A             =		[line for line in full_file_contents if line not in secondary_file_func]
        __A             =		[line for line in diff_from_example if line not in diff_from_two]

    return diff_from_example




