


'''simple docstring'''






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

import numpy as np

from ...utils import is_tf_available, is_torch_available, logging


if TYPE_CHECKING:
 if is_torch_available():
  import torch
 if is_tf_available():
  import tensorflow as tf

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_codegen import CodeGenTokenizer


a_   :      Any				    =      logging.get_logger(__name__)

a_   :      Optional[int]				    =      {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

a_   :      str				    =      {
    "vocab_file": {
        "Salesforce/codegen-350M-mono": "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json",
    },
    "merges_file": {
        "Salesforce/codegen-350M-mono": "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "Salesforce/codegen-350M-mono": (
            "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/tokenizer.json"
        ),
    },
}

a_   :      Union[str, Any]				    =      {
    "Salesforce/codegen-350M-mono": 2_0_4_8,
}




class 	a       (       _SCREAMING_SNAKE_CASE    ):
 _lowerCAmelCase	   =		VOCAB_FILES_NAMES
 _lowerCAmelCase	   =		PRETRAINED_VOCAB_FILES_MAP
 _lowerCAmelCase	   =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _lowerCAmelCase	   =		["""input_ids""", """attention_mask"""]
 _lowerCAmelCase	   =		CodeGenTokenizer
 def __init__(	self ,			__magic_name__=None ,			__magic_name__=None ,			__magic_name__=None ,			__magic_name__="<|endoftext|>" ,			__magic_name__="<|endoftext|>" ,			__magic_name__="<|endoftext|>" ,			__magic_name__=False ,			**__magic_name__ ,			)   ->							int:
      super().__init__(
          __magic_name__ ,			__magic_name__ ,			tokenizer_file=__magic_name__ ,			unk_token=__magic_name__ ,			bos_token=__magic_name__ ,			eos_token=__magic_name__ ,			add_prefix_space=__magic_name__ ,			**__magic_name__ ,			)

      if kwargs.pop('add_bos_token' ,			__magic_name__  ):
           _a      =							kwargs.pop('name_or_path' ,			''  )
           raise ValueError(
               'Currenty GPT2\'s fast tokenizer does NOT support adding a BOS token.'
               'Instead you should use GPT2\'s slow tokenizer class `CodeGenTokenizer` as follows: \n'
               f'`CodeGenTokenizer.from_pretrained(\'{model_id}\')`\nor\n'
               f'`AutoTokenizer.from_pretrained(\'{model_id}\', use_fast=False)`\n'
               'This issue will be fixed soon, see: https://github.com/huggingface/tokenizers/pull/1005.'
               ' so that the fast tokenizer works correctly.'  )

      _a      =							json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()  )
      if pre_tok_state.get('add_prefix_space' ,			__magic_name__  ) != add_prefix_space:
           _a      =							getattr(__magic_name__ ,			pre_tok_state.pop('type'  )  )
           _a      =							add_prefix_space
           _a      =							pre_tok_class(**__magic_name__  )

      _a      =							add_prefix_space
 def 							__UpperCAmelCase						(	self ,			*__magic_name__ ,			**__magic_name__  )   ->							BatchEncoding:
      _a      =							kwargs.get('is_split_into_words' ,			__magic_name__  )
      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(*__magic_name__ ,			**__magic_name__  )
 def 							__UpperCAmelCase						(	self ,			*__magic_name__ ,			**__magic_name__  )   ->							BatchEncoding:
      _a      =							kwargs.get('is_split_into_words' ,			__magic_name__  )

      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(*__magic_name__ ,			**__magic_name__  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ = None  )   ->							Tuple[str]:
      _a      =							self._tokenizer.model.save(__magic_name__ ,			name=__magic_name__  )
      return tuple(__magic_name__  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ = False ,			__magic_name__ = None ,			__magic_name__ = None ,			**__magic_name__ ,			)   ->							str:
      _a      =							super().decode(
          token_ids=__magic_name__ ,			skip_special_tokens=__magic_name__ ,			clean_up_tokenization_spaces=__magic_name__ ,			**__magic_name__ ,			)

      if truncate_before_pattern is not None and len(__magic_name__  ) > 0:
           _a      =							self.truncate(__magic_name__ ,			__magic_name__  )

      return decoded_text




 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__  )   ->							int:
      def find_re(__magic_name__ ,			__magic_name__ ,			__magic_name__  ):
           _a      =							pattern.search(__magic_name__ ,			__magic_name__  )
           return m.start() if m else -1

      _a      =							[re.compile(__magic_name__ ,			re.MULTILINE  ) for pattern in truncate_before_pattern]

      _a      =							list(re.finditer('^print' ,			__magic_name__ ,			re.MULTILINE  )  )

      if len(__magic_name__  ) > 1:
           _a      =							completion[: prints[1].start()]

      _a      =							list(re.finditer('^def' ,			__magic_name__ ,			re.MULTILINE  )  )

      if len(__magic_name__  ) > 1:
           _a      =							completion[: defs[1].start()]

      _a      =							0

      _a      =							[
          pos for pos in [find_re(__magic_name__ ,			__magic_name__ ,			__magic_name__  ) for terminal in terminals] if pos != -1
      ]

      if len(__magic_name__  ) > 0:
           return completion[: min(__magic_name__  )]
      else:
           return completion





'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


a_   :      Optional[int]				    =      {"configuration_deit": ["DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeiTConfig", "DeiTOnnxConfig"]}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :      Dict				    =      ["DeiTFeatureExtractor"]
 a_   :      List[Any]				    =      ["DeiTImageProcessor"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :      Tuple				    =      [
     "DEIT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "DeiTForImageClassification",
     "DeiTForImageClassificationWithTeacher",
     "DeiTForMaskedImageModeling",
     "DeiTModel",
     "DeiTPreTrainedModel",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :      Optional[Any]				    =      [
     "TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFDeiTForImageClassification",
     "TFDeiTForImageClassificationWithTeacher",
     "TFDeiTForMaskedImageModeling",
     "TFDeiTModel",
     "TFDeiTPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_deit import DeiTFeatureExtractor
  from .image_processing_deit import DeiTImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_deit import (
      DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
      DeiTForImageClassification,
      DeiTForImageClassificationWithTeacher,
      DeiTForMaskedImageModeling,
      DeiTModel,
      DeiTPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_deit import (
      TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFDeiTForImageClassification,
      TFDeiTForImageClassificationWithTeacher,
      TFDeiTForMaskedImageModeling,
      TFDeiTModel,
      TFDeiTPreTrainedModel,
  )


else:
 import sys

 a_   :      List[Any]				    =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

