





"""simple docstring"""





from dataclasses import dataclass, field
from typing import Optional



@dataclass
class __lowerCAmelCase				:




			"""simple docstring"""







			snake_case	      =				field(
			    default="codeparrot/codeparrot"     ,				metadata={"help": "Model name or path of model to be trained."}      )
			snake_case	      =				field(
			    default="./"     ,				metadata={"help": "Save dir where model repo is cloned and models updates are saved to."}      )
			snake_case	      =				field(
			    default="codeparrot/codeparrot-clean-train"     ,				metadata={"help": "Name or path of training dataset."}      )
			snake_case	      =				field(
			    default="codeparrot/codeparrot-clean-valid"     ,				metadata={"help": "Name or path of validation dataset."}      )
			snake_case	      =				field(default=2     ,				metadata={"help": "Batch size for training."}      )
			snake_case	      =				field(default=2     ,				metadata={"help": "Batch size for evaluation."}      )
			snake_case	      =				field(default=0.1     ,				metadata={"help": "Value of weight decay."}      )
			snake_case	      =				field(
			    default=1_00_00     ,				metadata={"help": "Size of buffer used to shuffle streaming dataset."}      )
			snake_case	      =				field(default=2E-4     ,				metadata={"help": "Learning rate fo training."}      )
			snake_case	      =				field(default="cosine"     ,				metadata={"help": "Learning rate."}      )
			snake_case	      =				field(
			    default=7_50     ,				metadata={"help": "Number of warmup steps in the learning rate schedule."}      )
			snake_case	      =				field(
			    default=16     ,				metadata={"help": "Number of gradient accumulation steps."}      )
			snake_case	      =				field(
			    default=_lowercase     ,				metadata={"help": "Use gradient checkpointing to reduce memory footprint."}      )
			snake_case	      =				field(default=5_00_00     ,				metadata={"help": "Maximum number of training steps."}      )
			snake_case	      =				field(
			    default=-1     ,				metadata={"help": "Maximum number of evaluation steps. If -1 the full dataset is evaluated."}      )
			snake_case	      =				field(default=10_24     ,				metadata={"help": "Sequence lengths used for training."}      )
			snake_case	      =				field(default=1     ,				metadata={"help": "Training seed."}      )
			snake_case	      =				field(
			    default=10_24     ,				metadata={"help": "Interval to save checkpoints. Measured as number of forward passes not training steps."}     ,				)
			snake_case	      =				field(
			    default=_lowercase     ,				metadata={"help": "States path if the training should continue from a checkpoint folder."}      )
			snake_case	      =				field(default=_lowercase     ,				metadata={"help": "If True the data is pretokenized."}      )





@dataclass
class __lowerCAmelCase				:




			"""simple docstring"""







			snake_case	      =				field(
			    default="codeparrot/codeparrot"     ,				metadata={"help": "Model name or path of model to be evaluated."}      )
			snake_case	      =				field(
			    default="codeparrot/codeparrot-clean-valid"     ,				metadata={"help": "Name or path of validation dataset."}      )
			snake_case	      =				field(default=2     ,				metadata={"help": "Batch size used for evaluation."}      )
			snake_case	      =				field(
			    default=-1     ,				metadata={"help": "Maximum number of evaluation steps. If -1 the full dataset is evaluated."}      )
			snake_case	      =				field(default=10_24     ,				metadata={"help": "Length of sequences to be evaluated."}      )
			snake_case	      =				field(default=1     ,				metadata={"help": "Random seed used for evaluation."}      )





@dataclass
class __lowerCAmelCase				:




			"""simple docstring"""







			snake_case	      =				field(
			    default="codeparrot/codeparrot"     ,				metadata={"help": "Model name or path of model to be evaluated."}      )
			snake_case	      =				field(default=_lowercase     ,				metadata={"help": "Number of workers used for code evaluation."}      )
			snake_case	      =				field(
			    default=_lowercase     ,				metadata={"help": "The number of human-eval tasks to run. If not included all tasks are evaluated."}     ,				)
			snake_case	      =				field(
			    default=_lowercase     ,				metadata={"help": "Sample from the language model\'s output distribution."}      )
			snake_case	      =				field(default=0.2     ,				metadata={"help": "Sampling temperature used for generation."}      )
			snake_case	      =				field(default=2_56     ,				metadata={"help": "Maximum number of newly generated tokens."}      )
			snake_case	      =				field(default=0     ,				metadata={"help": "Top-k parameter used for generation."}      )
			snake_case	      =				field(default=0.95     ,				metadata={"help": "Top-p parameter used for nucleus sampling."}      )
			snake_case	      =				field(default=10     ,				metadata={"help": "Number of generations to run in parallel."}      )
			snake_case	      =				field(
			    default=2_00     ,				metadata={"help": "Number of completions to generate for each sample."}      )
			snake_case	      =				field(default=1     ,				metadata={"help": "Random seed used for evaluation."}      )
			snake_case	      =				field(
			    default="eval_results.json"     ,				metadata={"help": "Random seed used for evaluation."}      )
			snake_case	      =				field(
			    default="0"     ,				metadata={"help": "Allow `code_eval` to execute Python code on machine"}      )
			snake_case	      =				field(
			    default=-1     ,				metadata={
			        "help": (
			            "Determine which device to run the `text-generation` Pipeline on. -1 is CPU and any zero or positive"
			            " number corresponds to which GPU device id to run on."
			        )
			    }     ,				)





@dataclass
class __lowerCAmelCase				:




			"""simple docstring"""







			snake_case	      =				field(
			    default=_lowercase     ,				metadata={
			        "help": "The number of CPU cores to use for parallel preprocessing. Default uses the maximum available."
			    }     ,				)
			snake_case	      =				field(
			    default="transformersbook/codeparrot"     ,				metadata={"help": "Folder or name of dataset to process."}      )
			snake_case	      =				field(
			    default="codeparrot-clean"     ,				metadata={"help": "Folder to save processed processed dataset."}      )
			snake_case	      =				field(
			    default=10_00_00     ,				metadata={"help": "Number of files to save per JSON output file."}      )
			snake_case	      =				field(default="content"     ,				metadata={"help": "Column containing text data to process."}      )
			snake_case	      =				field(
			    default=10_00     ,				metadata={"help": "Maximum line length in file, otherwise file is filtered."}      )
			snake_case	      =				field(
			    default=1_00     ,				metadata={"help": "Maximum mean line length in file, otherwise file is filtered."}      )
			snake_case	      =				field(
			    default=0.25     ,				metadata={"help": "Maximum fraction of non-alphanumeric characters, otherwise file is filtered."}      )
			snake_case	      =				field(
			    default=1.5     ,				metadata={"help": "Minimum character token ratio for the file, otherwise file is filtered."}      )
			snake_case	      =				field(
			    default=0.7     ,				metadata={"help": "Probability for filtering config, test and uncommon files."}      )
			snake_case	      =				field(
			    default="codeparrot/codeparrot"     ,				metadata={"help": "Name or path to the tokenizer."}     ,				)
			snake_case	      =				field(
			    default=_lowercase     ,				metadata={"help": "If True, near-duplicate samples are removed."}      )
			snake_case	      =				field(
			    default=0.85     ,				metadata={"help": "Jaccard threshold for near-duplicate samples."}      )





@dataclass
class __lowerCAmelCase				:




			"""simple docstring"""







			snake_case	      =				field(
			    default="gpt2"     ,				metadata={"help": "Base tokenizer to build new tokenizer from."}      )
			snake_case	      =				field(
			    default="transformersbook/codeparrot-train"     ,				metadata={"help": "Dataset to train tokenizer on."}      )
			snake_case	      =				field(default="content"     ,				metadata={"help": "Column containing text data to process."}      )
			snake_case	      =				field(default=20_00_00     ,				metadata={"help": "Number of examples to train tokenizer on."}      )
			snake_case	      =				field(
			    default=3_27_68     ,				metadata={"help": "Number of examples to train the tokenizer on."}      )
			snake_case	      =				field(default="codeparrot"     ,				metadata={"help": "Name of new tokenizer."}      )
			snake_case	      =				field(default=_lowercase     ,				metadata={"help": "Push saved tokenizer to the hub."}      )





@dataclass
class __lowerCAmelCase				:




			"""simple docstring"""







			snake_case	      =				field(
			    default="codeparrot/codeparrot"     ,				metadata={"help": "Name or path to the tokenizer."}      )
			snake_case	      =				field(
			    default="codeparrot/codeparrot-clean-train"     ,				metadata={"help": "Name or path to the dataset to pretokenize."}      )
			snake_case	      =				field(
			    default="tokenized-codeparrot-train"     ,				metadata={"help": "Repo name of the pretokenized data."}      )
			snake_case	      =				field(default=_lowercase     ,				metadata={"help": "Number of workers used for code evaluation."}      )





@dataclass
class __lowerCAmelCase				:




			"""simple docstring"""







			snake_case	      =				field(
			    default="gpt2-large"     ,				metadata={"help": "Configuration to use for model initialization."}      )
			snake_case	      =				field(
			    default="codeparrot/codeparrot"     ,				metadata={"help": "Tokenizer attached to model."}      )
			snake_case	      =				field(default="codeparrot"     ,				metadata={"help": "Name of the created model."}      )
			snake_case	      =				field(default=_lowercase     ,				metadata={"help": "Push saved tokenizer to the hub."}      )






"""simple docstring"""





from __future__ import annotations
def 							A_     (__a				,   __a = None				,   __a = None				,   __a = False				,   ):

					'''simple docstring'''





					A_				 =						cipher_alphabet or [chr(__a				) for i in range(97				,   123				)]

					# 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)
										A_				 =						{
										    "a": 0.08497,
										    "b": 0.01492,
										    "c": 0.02202,
										    "d": 0.04253,
										    "e": 0.11162,
										    "f": 0.02228,
										    "g": 0.02015,
										    "h": 0.06094,
										    "i": 0.07546,
										    "j": 0.00153,
										    "k": 0.01292,
										    "l": 0.04025,
										    "m": 0.02406,
										    "n": 0.06749,
										    "o": 0.07507,
										    "p": 0.01929,
										    "q": 0.00095,
										    "r": 0.07587,
										    "s": 0.06327,
										    "t": 0.09356,
										    "u": 0.02758,
										    "v": 0.00978,
										    "w": 0.02560,
										    "x": 0.00150,
										    "y": 0.01994,
										    "z": 0.00077,
										}
					else:
										# Custom frequencies dictionary
										A_				 =						frequencies_dict

					if not case_sensitive:
										A_				 =						ciphertext.lower()

					# Chi squared statistic values
					A_				 =						{}

					# cycle through all of the shifts
					for shift in range(len(__a				)				):
										A_				 =						""

										# decrypt the message with the shift
										for letter in ciphertext:
															try:
																				# Try to index the letter in the alphabet
																				A_				 =						(alphabet_letters.index(letter.lower()				) - shift) % len(
																				    __a				)
																				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

										A_				 =						0.0

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

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

																									# Complete the chi squared statistic formula
																									A_				 =						((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
																									A_				 =						decrypted_with_shift.count(__a				)

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

																									# Complete the chi squared statistic formula
																									A_				 =						((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
										A_				 =						(
										    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(__a				) -> tuple[float, str]:
										return chi_squared_statistic_values[key]

					A_				 =						min(
					    __a				,   key=__a				,   )

					# Get all the data from the most likely cipher (key, decoded message)
					(
					    (
					    A_
					) ,    (
					    A_
					) ,    
					)				 =						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,
					)




