

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


_lowerCamelCase      :					Dict					    =      logging.get_logger(__name__)

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

_lowerCamelCase      :					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'''
        ),
    },
}

_lowerCamelCase      :					Optional[int]					    =      {
    '''Salesforce/codegen-350M-mono''': 2048,
}





class        lowerCAmelCase__  (					__magic_name__			):



       '''simple docstring'''







       lowercase_						=    VOCAB_FILES_NAMES
       lowercase_						=    PRETRAINED_VOCAB_FILES_MAP
       lowercase_						=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowercase_						=    ["""input_ids""", """attention_mask"""]
       lowercase_						=    CodeGenTokenizer

       def __init__(							self ,   lowercase__=None ,   lowercase__=None ,   lowercase__=None ,   lowercase__="<|endoftext|>" ,   lowercase__="<|endoftext|>" ,   lowercase__="<|endoftext|>" ,   lowercase__=False ,   **lowercase__ ,   ):







             '''simple docstring'''



             super().__init__(
                 lowercase__ ,   lowercase__ ,   tokenizer_file=lowercase__ ,   unk_token=lowercase__ ,   bos_token=lowercase__ ,   eos_token=lowercase__ ,   add_prefix_space=lowercase__ ,   **lowercase__ ,   )

             if kwargs.pop('''add_bos_token''' ,   lowercase__     ):
                   __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''' ,   lowercase__     ) != add_prefix_space:
                   __A    =getattr(lowercase__ ,   pre_tok_state.pop('''type'''     )     )
                   __A    =add_prefix_space
                   __A    =pre_tok_class(**lowercase__     )

             __A    =add_prefix_space

       def     __UpperCamelCase     (							self ,   *lowercase__ ,   **lowercase__     ):







             '''simple docstring'''



             __A    =kwargs.get('''is_split_into_words''' ,   lowercase__     )
             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(*lowercase__ ,   **lowercase__     )

       def     __UpperCamelCase     (							self ,   *lowercase__ ,   **lowercase__     ):







             '''simple docstring'''



             __A    =kwargs.get('''is_split_into_words''' ,   lowercase__     )

             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(*lowercase__ ,   **lowercase__     )

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ = None     ):







             '''simple docstring'''



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

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ = False ,   lowercase__ = None ,   lowercase__ = None ,   **lowercase__ ,   ):







             '''simple docstring'''



             __A    =super().decode(
                 token_ids=lowercase__ ,   skip_special_tokens=lowercase__ ,   clean_up_tokenization_spaces=lowercase__ ,   **lowercase__ ,   )

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

             return decoded_text


       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__     ):







             '''simple docstring'''



             def find_re(lowercase__ ,   lowercase__ ,   lowercase__     ):
                   __A    =pattern.search(lowercase__ ,   lowercase__     )
                   return m.start() if m else -1

             __A    =[re.compile(lowercase__ ,   re.MULTILINE     ) for pattern in truncate_before_pattern]

             __A    =list(re.finditer('''^print''' ,   lowercase__ ,   re.MULTILINE     )     )

             if len(lowercase__     ) > 1:
                   __A    =completion[: prints[1].start()]

             __A    =list(re.finditer('''^def''' ,   lowercase__ ,   re.MULTILINE     )     )

             if len(lowercase__     ) > 1:
                   __A    =completion[: defs[1].start()]

             __A    =0

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

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





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available


_lowerCamelCase      :					int					    =      {
    '''configuration_audio_spectrogram_transformer''': [
        '''AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''ASTConfig''',
    ]
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowerCamelCase      :					Tuple					    =      [
       '''AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''ASTForAudioClassification''',
       '''ASTModel''',
       '''ASTPreTrainedModel''',
   ]

try:
   if not is_speech_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowerCamelCase      :					Optional[Any]					    =      ['''ASTFeatureExtractor''']

if TYPE_CHECKING:
   from .configuration_audio_spectrogram_transformer import (
       AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
       ASTConfig,
   )

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_audio_spectrogram_transformer import (
          AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          ASTForAudioClassification,
          ASTModel,
          ASTPreTrainedModel,
      )

   try:
      if not is_speech_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_audio_spectrogram_transformer import ASTFeatureExtractor


else:
   import sys

   _lowerCamelCase      :					Optional[int]					    =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


