

# 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 			__snake_case	(   __UpperCamelCase  :   List[Any]							,__UpperCamelCase  :   Optional[int]						)     ->   List[Any]:



      """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(UpperCamelCase_						)
                  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(UpperCamelCase_						)

def 			__snake_case	(   __UpperCamelCase  :   List[Any]						)     ->   Dict:



      """simple docstring"""

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



def 			__snake_case	(   __UpperCamelCase  :   int							,__UpperCamelCase  :   int							,__UpperCamelCase  :   List[str]							,__UpperCamelCase  :   str = None						)     ->   str:



      """simple docstring"""

      with open(UpperCamelCase_							,"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(UpperCamelCase_							,"r"						) as f:
            A_	     =							f.readlines()
      if secondary_filename is not None:
            with open(UpperCamelCase_							,"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(UpperCamelCase_							,"main"						)						)
            A_	     =							clean_lines(get_function_contents_by_name(UpperCamelCase_							,"main"						)						)
            A_	     =							clean_lines(get_function_contents_by_name(UpperCamelCase_							,"main"						)						)
            if secondary_filename is not None:
                  A_	     =							clean_lines(get_function_contents_by_name(UpperCamelCase_							,"main"						)						)
      else:
            A_	     =							clean_lines(get_function_contents_by_name(UpperCamelCase_							,"training_function"						)						)
            A_	     =							clean_lines(get_function_contents_by_name(UpperCamelCase_							,"training_function"						)						)
            A_	     =							clean_lines(get_function_contents_by_name(UpperCamelCase_							,"training_function"						)						)
            if secondary_filename is not None:
                  A_	     =							clean_lines(
                      get_function_contents_by_name(UpperCamelCase_							,"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(UpperCamelCase_						)
      for i in range(len(UpperCamelCase_						) - 1						):
            if i not in passed_idxs:
                  A_	     =							next(UpperCamelCase_						)
                  if (line not in full_file_func) and (line.lstrip() != _dl_line):
                        if "TESTING_MOCKED_DATALOADERS" not in line:
                              new_feature_code.append(UpperCamelCase_						)
                              passed_idxs.append(UpperCamelCase_						)
                        else:
                              # Skip over the `config['num_epochs'] = 2` statement
                              A_	     =							next(UpperCamelCase_						)

    # 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(UpperCamelCase_						):
            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(UpperCamelCase_						)
                              passed_idxs.append(UpperCamelCase_						)

    # 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


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,)