



"""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


_lowerCAmelCase    :Dict     =					logging.get_logger(__name__)

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

_lowerCAmelCase    :Any     =					{
    '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    :Union[str, Any]     =					{
    'Salesforce/codegen-350M-mono': 2_048,
}
class 							_UpperCAmelCase							( a      ):





     '''simple docstring'''



     a__									=VOCAB_FILES_NAMES
     a__									=PRETRAINED_VOCAB_FILES_MAP
     a__									=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     a__									=['''input_ids''', '''attention_mask''']
     a__									=CodeGenTokenizer





     def __init__(					self       ,	A=None       ,	A=None       ,	A=None       ,	A="<|endoftext|>"       ,	A="<|endoftext|>"       ,	A="<|endoftext|>"       ,	A=False       ,	**A       ,	)							->					Optional[int]:
         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							:  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.'''		)

         _UpperCAmelCase							:  Optional[Any]											=    json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
         if pre_tok_state.get('''add_prefix_space'''       ,	A		) != add_prefix_space:
             _UpperCAmelCase							:  Optional[Any]											=    getattr(A       ,	pre_tok_state.pop('''type'''		)		)
             _UpperCAmelCase							:  Any											=    add_prefix_space
             _UpperCAmelCase							:  Any											=    pre_tok_class(**A		)

         _UpperCAmelCase							:  Any											=    add_prefix_space





     def  __lowerCAmelCase	(					self       ,	*A       ,	**A		)							->					BatchEncoding:
         _UpperCAmelCase							:  Optional[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()._batch_encode_plus(*A       ,	**A		)





     def  __lowerCAmelCase	(					self       ,	*A       ,	**A		)							->					BatchEncoding:
         _UpperCAmelCase							:  Optional[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()._encode_plus(*A       ,	**A		)





     def  __lowerCAmelCase	(					self       ,	A       ,	A = None		)							->					Tuple[str]:
         _UpperCAmelCase							:  Optional[int]											=    self._tokenizer.model.save(A       ,	name=A		)
         return tuple(A		)





     def  __lowerCAmelCase	(					self       ,	A       ,	A = False       ,	A = None       ,	A = None       ,	**A       ,	)							->					str:
         _UpperCAmelCase							:  Dict											=    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							:  Tuple											=    self.truncate(A       ,	A		)

         return decoded_text





     def  __lowerCAmelCase	(					self       ,	A       ,	A		)							->					Optional[Any]:
         def find_re(A       ,	A       ,	A		):
             _UpperCAmelCase							:  Any											=    pattern.search(A       ,	A		)
             return m.start() if m else -1

         _UpperCAmelCase							:  Any											=    [re.compile(A       ,	re.MULTILINE		) for pattern in truncate_before_pattern]

         _UpperCAmelCase							:  str											=    list(re.finditer('''^print'''       ,	A       ,	re.MULTILINE		)		)

         if len(A		) > 1:
             _UpperCAmelCase							:  Tuple											=    completion[: prints[1].start()]

         _UpperCAmelCase							:  List[Any]											=    list(re.finditer('''^def'''       ,	A       ,	re.MULTILINE		)		)

         if len(A		) > 1:
             _UpperCAmelCase							:  Optional[int]											=    completion[: defs[1].start()]

         _UpperCAmelCase							:  Optional[Any]											=    0

         _UpperCAmelCase							:  Optional[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







"""simple docstring"""
import math

from numpy import inf
from scipy.integrate import quad





def  lowerCamelCase_						(UpperCamelCase__		:		float							):
    if num <= 0:
        raise ValueError('''math domain error'''							)

    return quad(UpperCamelCase__     ,       0     ,       UpperCamelCase__     ,       args=(UpperCamelCase__)							)[0]





def  lowerCamelCase_						(UpperCamelCase__		:		float     ,       UpperCamelCase__		:		float							):
    return math.pow(UpperCamelCase__     ,       z - 1							) * math.exp(-x							)


if __name__ == "__main__":
  from doctest import testmod

  testmod()


