


"""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


snake_case__						:   List[Any]			 = logging.get_logger(__name__)

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

snake_case__						:   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'''
        ),
    },
}

snake_case__						:   Union[str, Any]			 = {
    '''Salesforce/codegen-350M-mono''': 2_048,
}



class 						snake_case_(				a__	):
     __UpperCamelCase     =    VOCAB_FILES_NAMES
     __UpperCamelCase     =    PRETRAINED_VOCAB_FILES_MAP
     __UpperCamelCase     =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __UpperCamelCase     =    ['''input_ids''', '''attention_mask''']
     __UpperCamelCase     =    CodeGenTokenizer






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

           if kwargs.pop('''add_bos_token'''    , UpperCamelCase_ ):
                 lowerCAmelCase    :		str				=      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.''' )

           lowerCAmelCase    :		str				=      json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
           if pre_tok_state.get('''add_prefix_space'''    , UpperCamelCase_ ) != add_prefix_space:
                 lowerCAmelCase    :		List[str]				=      getattr(UpperCamelCase_    , pre_tok_state.pop('''type''' ) )
                 lowerCAmelCase    :		Optional[Any]				=      add_prefix_space
                 lowerCAmelCase    :		Tuple				=      pre_tok_class(**UpperCamelCase_ )

           lowerCAmelCase    :		Optional[int]				=      add_prefix_space






     def    lowerCamelCase__   (		self     :    int    , *UpperCamelCase_     :    Union[str, Any]    , **UpperCamelCase_     :    Any ):
           lowerCAmelCase    :		Dict				=      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[int]    , *UpperCamelCase_     :    Optional[Any]    , **UpperCamelCase_     :    int ):
           lowerCAmelCase    :		List[str]				=      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     :    int    , UpperCamelCase_     :    str    , UpperCamelCase_     :    Optional[str] = None ):
           lowerCAmelCase    :		Tuple				=      self._tokenizer.model.save(UpperCamelCase_    , name=UpperCamelCase_ )
           return tuple(UpperCamelCase_ )






     def    lowerCamelCase__   (		self     :    List[str]    , UpperCamelCase_     :    Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"]    , UpperCamelCase_     :    bool = False    , UpperCamelCase_     :    bool = None    , UpperCamelCase_     :    Optional[List[str]] = None    , **UpperCamelCase_     :    Tuple    , ):
           lowerCAmelCase    :		List[str]				=      super().decode(
               token_ids=UpperCamelCase_    , skip_special_tokens=UpperCamelCase_    , clean_up_tokenization_spaces=UpperCamelCase_    , **UpperCamelCase_    , )

           if truncate_before_pattern is not None and len(UpperCamelCase_ ) > 0:
                 lowerCAmelCase    :		str				=      self.truncate(UpperCamelCase_    , UpperCamelCase_ )

           return decoded_text






     def    lowerCamelCase__   (		self     :    Tuple    , UpperCamelCase_     :    Union[str, Any]    , UpperCamelCase_     :    Optional[Any] ):
           def find_re(UpperCamelCase_     :    Optional[Any]    , UpperCamelCase_     :    Optional[Any]    , UpperCamelCase_     :    List[str] ):
                 lowerCAmelCase    :		Dict				=      pattern.search(UpperCamelCase_    , UpperCamelCase_ )
                 return m.start() if m else -1

           lowerCAmelCase    :		Union[str, Any]				=      [re.compile(UpperCamelCase_    , re.MULTILINE ) for pattern in truncate_before_pattern]

           lowerCAmelCase    :		Optional[Any]				=      list(re.finditer('''^print'''    , UpperCamelCase_    , re.MULTILINE ) )

           if len(UpperCamelCase_ ) > 1:
                 lowerCAmelCase    :		int				=      completion[: prints[1].start()]

           lowerCAmelCase    :		List[str]				=      list(re.finditer('''^def'''    , UpperCamelCase_    , re.MULTILINE ) )

           if len(UpperCamelCase_ ) > 1:
                 lowerCAmelCase    :		Union[str, Any]				=      completion[: defs[1].start()]

           lowerCAmelCase    :		Optional[Any]				=      0

           lowerCAmelCase    :		Tuple				=      [
               pos for pos in [find_re(UpperCamelCase_    , UpperCamelCase_    , UpperCamelCase_ ) for terminal in terminals] if pos != -1
           ]

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






"""simple docstring"""





from datetime import datetime

import matplotlib.pyplot as plt
import torch


def    _snake_case  (  _snake_case :	int	):
      for param in module.parameters():
            lowerCAmelCase    :		Optional[int]				=      False


def    _snake_case  (  ):
      lowerCAmelCase    :		List[str]				=      '''cuda''' if torch.cuda.is_available() else '''cpu'''
      if torch.backends.mps.is_available() and torch.backends.mps.is_built():
            lowerCAmelCase    :		Any				=      '''mps'''
      if device == "mps":
            print(
                '''WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'''
                ''' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'''
                ''' with generations.'''	)
      return device


def    _snake_case  (  _snake_case :	Dict	):
      lowerCAmelCase    :		Optional[int]				=      plt.imshow(_snake_case	)
      fig.axes.get_xaxis().set_visible(_snake_case	)
      fig.axes.get_yaxis().set_visible(_snake_case	)
      plt.show()






def    _snake_case  (  ):
      lowerCAmelCase    :		List[str]				=      datetime.now()
      lowerCAmelCase    :		Union[str, Any]				=      current_time.strftime('''%H:%M:%S'''	)
      return timestamp


