



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


UpperCamelCase_										=					logging.get_logger(__name__)

UpperCamelCase_										=					{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCamelCase_										=					{
    '''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'''
        ),
    },
}

UpperCamelCase_										=					{
    '''Salesforce/codegen-350M-mono''': 2048,
}







class 	_snake_case							(			_UpperCAmelCase      ):






   '''simple docstring'''






   A__	:      Dict    =		VOCAB_FILES_NAMES
   A__	:      int    =		PRETRAINED_VOCAB_FILES_MAP
   A__	:      Union[str, Any]    =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A__	:      Union[str, Any]    =		['input_ids', 'attention_mask']
   A__	:      Any    =		CodeGenTokenizer


   def __init__(  self:			Optional[int]   ,lowerCamelCase_:			Any=None   ,lowerCamelCase_:			List[Any]=None   ,lowerCamelCase_:			Optional[int]=None   ,lowerCamelCase_:			Tuple="<|endoftext|>"   ,lowerCamelCase_:			Tuple="<|endoftext|>"   ,lowerCamelCase_:			Any="<|endoftext|>"   ,lowerCamelCase_:			Any=False   ,**lowerCamelCase_:			Dict   ,)     ->	List[Any]:
       super().__init__(
           a__   ,a__   ,tokenizer_file=a__   ,unk_token=a__   ,bos_token=a__   ,eos_token=a__   ,add_prefix_space=a__   ,**a__   ,)

       if kwargs.pop("""add_bos_token"""   ,a__				):
           UpperCAmelCase_			:    Union[str, Any]   						= 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."""				)

       UpperCAmelCase_			:    Any   						= json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()				)
       if pre_tok_state.get("""add_prefix_space"""   ,a__				) != add_prefix_space:
           UpperCAmelCase_			:    Union[str, Any]   						= getattr(a__   ,pre_tok_state.pop("""type"""				)				)
           UpperCAmelCase_			:    str   						= add_prefix_space
           UpperCAmelCase_			:    Optional[Any]   						= pre_tok_class(**a__				)

       UpperCAmelCase_			:    Optional[Any]   						= add_prefix_space


   def 	A__  (  self:			Tuple   ,*lowerCamelCase_:			Any   ,**lowerCamelCase_:			Optional[int]				)     ->	str:
       UpperCAmelCase_			:    int   						= 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 	A__  (  self:			Optional[int]   ,*lowerCamelCase_:			List[str]   ,**lowerCamelCase_:			Optional[Any]				)     ->	str:
       UpperCAmelCase_			:    List[Any]   						= 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 	A__  (  self:			List[str]   ,lowerCamelCase_:			str   ,lowerCamelCase_:			Optional[str] = None				)     ->	Dict:
       UpperCAmelCase_			:    Union[str, Any]   						= self._tokenizer.model.save(a__   ,name=a__				)
       return tuple(a__				)


   def 	A__  (  self:			Dict   ,lowerCamelCase_:			Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"]   ,lowerCamelCase_:			bool = False   ,lowerCamelCase_:			bool = None   ,lowerCamelCase_:			Optional[List[str]] = None   ,**lowerCamelCase_:			List[str]   ,)     ->	Union[str, Any]:
       UpperCAmelCase_			:    Union[str, Any]   						= super().decode(
           token_ids=a__   ,skip_special_tokens=a__   ,clean_up_tokenization_spaces=a__   ,**a__   ,)

       if truncate_before_pattern is not None and len(a__				) > 0:
           UpperCAmelCase_			:    Any   						= self.truncate(a__   ,a__				)

       return decoded_text






   def 	A__  (  self:			Any   ,lowerCamelCase_:			Any   ,lowerCamelCase_:			Optional[int]				)     ->	List[str]:



       def find_re(lowerCamelCase_:			str   ,lowerCamelCase_:			Any   ,lowerCamelCase_:			Any				):
           UpperCAmelCase_			:    Tuple   						= pattern.search(a__   ,a__				)
           return m.start() if m else -1

       UpperCAmelCase_			:    str   						= [re.compile(a__   ,re.MULTILINE				) for pattern in truncate_before_pattern]

       UpperCAmelCase_			:    Union[str, Any]   						= list(re.finditer("""^print"""   ,a__   ,re.MULTILINE				)				)

       if len(a__				) > 1:
           UpperCAmelCase_			:    List[Any]   						= completion[: prints[1].start()]

       UpperCAmelCase_			:    Optional[Any]   						= list(re.finditer("""^def"""   ,a__   ,re.MULTILINE				)				)

       if len(a__				) > 1:
           UpperCAmelCase_			:    Any   						= completion[: defs[1].start()]

       UpperCAmelCase_			:    Any   						= 0

       UpperCAmelCase_			:    List[Any]   						= [
           pos for pos in [find_re(a__   ,a__   ,a__				) for terminal in terminals] if pos != -1
       ]

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







from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case_				=			logging.get_logger(__name__)

snake_case_				=			{
    'sayakpaul/vit-msn-base': 'https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json',
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}

class 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase  ):
      A_						:				List[Any]							=    'vit_msn'


      def __init__(self       :      Union[str, Any]				,			a__       :      Optional[Any]=768				,			a__       :      Optional[Any]=12				,			a__       :      Optional[int]=12				,			a__       :      Optional[int]=3072				,			a__       :      Union[str, Any]="gelu"				,			a__       :      str=0.0				,			a__       :      int=0.0				,			a__       :      Optional[Any]=0.0_2				,			a__       :      List[Any]=1E-06				,			a__       :      Optional[int]=224				,			a__       :      str=16				,			a__       :      Optional[Any]=3				,			a__       :      int=True				,			**a__       :      List[Any]				,			):





         """simple docstring"""



         super().__init__(**a__      )

         __snake_case							       =   hidden_size
         __snake_case							       =   num_hidden_layers
         __snake_case							       =   num_attention_heads
         __snake_case							       =   intermediate_size
         __snake_case							       =   hidden_act
         __snake_case							       =   hidden_dropout_prob
         __snake_case							       =   attention_probs_dropout_prob
         __snake_case							       =   initializer_range
         __snake_case							       =   layer_norm_eps
         __snake_case							       =   image_size
         __snake_case							       =   patch_size
         __snake_case							       =   num_channels
         __snake_case							       =   qkv_bias

