
"""simple docstring"""

from __future__ import annotations






def 							A						(    snake_case				:str    , snake_case				:list[str] | None = None    , snake_case				:dict[str, float] | None = None    , snake_case				:bool = False    , )				->     tuple[int, float, str]:
 __UpperCamelCase      =	cipher_alphabet or [chr(snake_case		) for i in range(9_7    , 1_2_3		)]

 # If the argument is None or the user provided an empty dictionary
 if not frequencies_dict:
  # Frequencies of letters in the english language (how much they show up)
  __UpperCamelCase      =	{
      'a': 0.08_497,
      'b': 0.01_492,
      'c': 0.02_202,
      'd': 0.04_253,
      'e': 0.11_162,
      'f': 0.02_228,
      'g': 0.02_015,
      'h': 0.06_094,
      'i': 0.07_546,
      'j': 0.00_153,
      'k': 0.01_292,
      'l': 0.04_025,
      'm': 0.02_406,
      'n': 0.06_749,
      'o': 0.07_507,
      'p': 0.01_929,
      'q': 0.00_095,
      'r': 0.07_587,
      's': 0.06_327,
      't': 0.09_356,
      'u': 0.02_758,
      'v': 0.00_978,
      'w': 0.02_560,
      'x': 0.00_150,
      'y': 0.01_994,
      'z': 0.00_077,
  }
 else:
  # Custom frequencies dictionary
  __UpperCamelCase      =	frequencies_dict

 if not case_sensitive:
  __UpperCamelCase      =	ciphertext.lower()

 # Chi squared statistic values
 __UpperCamelCase      =	{}

 # cycle through all of the shifts
 for shift in range(len(snake_case		)		):
  __UpperCamelCase      =	''

  # decrypt the message with the shift
  for letter in ciphertext:
   try:
    # Try to index the letter in the alphabet
    __UpperCamelCase      =	(alphabet_letters.index(letter.lower()		) - shift) % len(
        snake_case		)
    decrypted_with_shift += (
        alphabet_letters[new_key].upper()
        if case_sensitive and letter.isupper()
        else alphabet_letters[new_key]
    )
   except ValueError:
    # Append the character if it isn't in the alphabet
    decrypted_with_shift += letter

  __UpperCamelCase      =	0.0

  # Loop through each letter in the decoded message with the shift
  for letter in decrypted_with_shift:
   if case_sensitive:
    __UpperCamelCase      =	letter.lower()
    if letter in frequencies:
     # Get the amount of times the letter occurs in the message
     __UpperCamelCase      =	decrypted_with_shift.lower().count(snake_case		)

     # Get the excepcted amount of times the letter should appear based
     # on letter frequencies
     __UpperCamelCase      =	frequencies[letter] * occurrences

     # Complete the chi squared statistic formula
     __UpperCamelCase      =	((occurrences - expected) ** 2) / expected

     # Add the margin of error to the total chi squared statistic
     chi_squared_statistic += chi_letter_value
   else:
    if letter.lower() in frequencies:
     # Get the amount of times the letter occurs in the message
     __UpperCamelCase      =	decrypted_with_shift.count(snake_case		)

     # Get the excepcted amount of times the letter should appear based
     # on letter frequencies
     __UpperCamelCase      =	frequencies[letter] * occurrences

     # Complete the chi squared statistic formula
     __UpperCamelCase      =	((occurrences - expected) ** 2) / expected

     # Add the margin of error to the total chi squared statistic
     chi_squared_statistic += chi_letter_value

        # Add the data to the chi_squared_statistic_values dictionary
  __UpperCamelCase      =	(
      chi_squared_statistic,
      decrypted_with_shift,
  )

 # Get the most likely cipher by finding the cipher with the smallest chi squared
 # statistic
 def chi_squared_statistic_values_sorting_key(snake_case				:int		) -> tuple[float, str]:
  return chi_squared_statistic_values[key]

 __UpperCamelCase      =	min(
     snake_case    , key=snake_case    , )

 # Get all the data from the most likely cipher (key, decoded message)
 (
     (
     __UpperCamelCase
 )  ,     (
     __UpperCamelCase
 )  ,     
 )      =	chi_squared_statistic_values[most_likely_cipher]

 # Return the data on the most likely shift
 return (
     most_likely_cipher,
     most_likely_cipher_chi_squared_value,
     decoded_most_likely_cipher,
 )





"""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


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

UpperCamelCase     :    List[str]       =			{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

UpperCamelCase     :    List[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"
        ),
    },
}

UpperCamelCase     :    List[Any]       =			{
    "Salesforce/codegen-350M-mono": 2_0_4_8,
}

class __lowerCAmelCase		(				__SCREAMING_SNAKE_CASE      ):
     lowercase						       =						VOCAB_FILES_NAMES
     lowercase						       =						PRETRAINED_VOCAB_FILES_MAP
     lowercase						       =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowercase						       =						["input_ids", "attention_mask"]
     lowercase						       =						CodeGenTokenizer





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

      '''simple docstring'''




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

      __UpperCamelCase      =	add_prefix_space





     def 						UpperCAmelCase (     self     ,					*__UpperCAmelCase     ,					**__UpperCAmelCase				):

      '''simple docstring'''




      __UpperCamelCase      =	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 						UpperCAmelCase (     self     ,					*__UpperCAmelCase     ,					**__UpperCAmelCase				):

      '''simple docstring'''




      __UpperCamelCase      =	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 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase = None				):

      '''simple docstring'''




      __UpperCamelCase      =	self._tokenizer.model.save(__UpperCAmelCase     ,					name=__UpperCAmelCase				)
      return tuple(__UpperCAmelCase				)





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase = False     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = None     ,					**__UpperCAmelCase     ,					):

      '''simple docstring'''




      __UpperCamelCase      =	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:
       __UpperCamelCase      =	self.truncate(__UpperCAmelCase     ,					__UpperCAmelCase				)

      return decoded_text







     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase				):

      '''simple docstring'''




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

      __UpperCamelCase      =	[re.compile(__UpperCAmelCase     ,					re.MULTILINE				) for pattern in truncate_before_pattern]

      __UpperCamelCase      =	list(re.finditer('^print'     ,					__UpperCAmelCase     ,					re.MULTILINE				)				)

      if len(__UpperCAmelCase				) > 1:
       __UpperCamelCase      =	completion[: prints[1].start()]

      __UpperCamelCase      =	list(re.finditer('^def'     ,					__UpperCAmelCase     ,					re.MULTILINE				)				)

      if len(__UpperCAmelCase				) > 1:
       __UpperCamelCase      =	completion[: defs[1].start()]

      __UpperCamelCase      =	0

      __UpperCamelCase      =	[
          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



