






from dataclasses import dataclass, field
from typing import Optional

@dataclass
class 						lowerCAmelCase   :

					'''simple docstring'''

					_A  :	Optional[str]     =					field(
					    default='''codeparrot/codeparrot'''					,				metadata={'''help''': '''Model name or path of model to be trained.'''}	)
					_A  :	Optional[str]     =					field(
					    default='''./'''					,				metadata={'''help''': '''Save dir where model repo is cloned and models updates are saved to.'''}	)
					_A  :	Optional[str]     =					field(
					    default='''codeparrot/codeparrot-clean-train'''					,				metadata={'''help''': '''Name or path of training dataset.'''}	)
					_A  :	Optional[str]     =					field(
					    default='''codeparrot/codeparrot-clean-valid'''					,				metadata={'''help''': '''Name or path of validation dataset.'''}	)
					_A  :	Optional[int]     =					field(default=2					,				metadata={'''help''': '''Batch size for training.'''}	)
					_A  :	Optional[int]     =					field(default=2					,				metadata={'''help''': '''Batch size for evaluation.'''}	)
					_A  :	Optional[float]     =					field(default=0.1					,				metadata={'''help''': '''Value of weight decay.'''}	)
					_A  :	Optional[int]     =					field(
					    default=10000					,				metadata={'''help''': '''Size of buffer used to shuffle streaming dataset.'''}	)
					_A  :	Optional[float]     =					field(default=2e-4					,				metadata={'''help''': '''Learning rate fo training.'''}	)
					_A  :	Optional[str]     =					field(default='''cosine'''					,				metadata={'''help''': '''Learning rate.'''}	)
					_A  :	Optional[int]     =					field(
					    default=750					,				metadata={'''help''': '''Number of warmup steps in the learning rate schedule.'''}	)
					_A  :	Optional[int]     =					field(
					    default=16					,				metadata={'''help''': '''Number of gradient accumulation steps.'''}	)
					_A  :	Optional[bool]     =					field(
					    default=__a					,				metadata={'''help''': '''Use gradient checkpointing to reduce memory footprint.'''}	)
					_A  :	Optional[int]     =					field(default=50000					,				metadata={'''help''': '''Maximum number of training steps.'''}	)
					_A  :	Optional[int]     =					field(
					    default=-1					,				metadata={'''help''': '''Maximum number of evaluation steps. If -1 the full dataset is evaluated.'''}	)
					_A  :	Optional[int]     =					field(default=1024					,				metadata={'''help''': '''Sequence lengths used for training.'''}	)
					_A  :	Optional[int]     =					field(default=1					,				metadata={'''help''': '''Training seed.'''}	)
					_A  :	Optional[int]     =					field(
					    default=1024					,				metadata={'''help''': '''Interval to save checkpoints. Measured as number of forward passes not training steps.'''}					,				)
					_A  :	Optional[str]     =					field(
					    default=__a					,				metadata={'''help''': '''States path if the training should continue from a checkpoint folder.'''}	)
					_A  :	Optional[bool]     =					field(default=__a					,				metadata={'''help''': '''If True the data is pretokenized.'''}	)







@dataclass
class 						lowerCAmelCase   :

					'''simple docstring'''

					_A  :	Optional[str]     =					field(
					    default='''codeparrot/codeparrot'''					,				metadata={'''help''': '''Model name or path of model to be evaluated.'''}	)
					_A  :	Optional[str]     =					field(
					    default='''codeparrot/codeparrot-clean-valid'''					,				metadata={'''help''': '''Name or path of validation dataset.'''}	)
					_A  :	Optional[int]     =					field(default=2					,				metadata={'''help''': '''Batch size used for evaluation.'''}	)
					_A  :	Optional[int]     =					field(
					    default=-1					,				metadata={'''help''': '''Maximum number of evaluation steps. If -1 the full dataset is evaluated.'''}	)
					_A  :	Optional[int]     =					field(default=1024					,				metadata={'''help''': '''Length of sequences to be evaluated.'''}	)
					_A  :	Optional[int]     =					field(default=1					,				metadata={'''help''': '''Random seed used for evaluation.'''}	)







@dataclass
class 						lowerCAmelCase   :

					'''simple docstring'''

					_A  :	Optional[str]     =					field(
					    default='''codeparrot/codeparrot'''					,				metadata={'''help''': '''Model name or path of model to be evaluated.'''}	)
					_A  :	Optional[int]     =					field(default=__a					,				metadata={'''help''': '''Number of workers used for code evaluation.'''}	)
					_A  :	Optional[int]     =					field(
					    default=__a					,				metadata={'''help''': '''The number of human-eval tasks to run. If not included all tasks are evaluated.'''}					,				)
					_A  :	Optional[bool]     =					field(
					    default=__a					,				metadata={'''help''': '''Sample from the language model\'s output distribution.'''}	)
					_A  :	Optional[float]     =					field(default=0.2					,				metadata={'''help''': '''Sampling temperature used for generation.'''}	)
					_A  :	Optional[int]     =					field(default=256					,				metadata={'''help''': '''Maximum number of newly generated tokens.'''}	)
					_A  :	Optional[int]     =					field(default=0					,				metadata={'''help''': '''Top-k parameter used for generation.'''}	)
					_A  :	Optional[float]     =					field(default=0.9_5					,				metadata={'''help''': '''Top-p parameter used for nucleus sampling.'''}	)
					_A  :	Optional[int]     =					field(default=10					,				metadata={'''help''': '''Number of generations to run in parallel.'''}	)
					_A  :	Optional[int]     =					field(
					    default=200					,				metadata={'''help''': '''Number of completions to generate for each sample.'''}	)
					_A  :	Optional[int]     =					field(default=1					,				metadata={'''help''': '''Random seed used for evaluation.'''}	)
					_A  :	Optional[str]     =					field(
					    default='''eval_results.json'''					,				metadata={'''help''': '''Random seed used for evaluation.'''}	)
					_A  :	Optional[str]     =					field(
					    default='''0'''					,				metadata={'''help''': '''Allow `code_eval` to execute Python code on machine'''}	)
					_A  :	Optional[int]     =					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'''

					_A  :	Optional[int]     =					field(
					    default=__a					,				metadata={
					        '''help''': '''The number of CPU cores to use for parallel preprocessing. Default uses the maximum available.'''
					    }					,				)
					_A  :	Optional[str]     =					field(
					    default='''transformersbook/codeparrot'''					,				metadata={'''help''': '''Folder or name of dataset to process.'''}	)
					_A  :	Optional[str]     =					field(
					    default='''codeparrot-clean'''					,				metadata={'''help''': '''Folder to save processed processed dataset.'''}	)
					_A  :	Optional[int]     =					field(
					    default=100000					,				metadata={'''help''': '''Number of files to save per JSON output file.'''}	)
					_A  :	Optional[str]     =					field(default='''content'''					,				metadata={'''help''': '''Column containing text data to process.'''}	)
					_A  :	Optional[float]     =					field(
					    default=1000					,				metadata={'''help''': '''Maximum line length in file, otherwise file is filtered.'''}	)
					_A  :	Optional[float]     =					field(
					    default=100					,				metadata={'''help''': '''Maximum mean line length in file, otherwise file is filtered.'''}	)
					_A  :	Optional[float]     =					field(
					    default=0.2_5					,				metadata={'''help''': '''Maximum fraction of non-alphanumeric characters, otherwise file is filtered.'''}	)
					_A  :	Optional[float]     =					field(
					    default=1.5					,				metadata={'''help''': '''Minimum character token ratio for the file, otherwise file is filtered.'''}	)
					_A  :	Optional[float]     =					field(
					    default=0.7					,				metadata={'''help''': '''Probability for filtering config, test and uncommon files.'''}	)
					_A  :	Optional[str]     =					field(
					    default='''codeparrot/codeparrot'''					,				metadata={'''help''': '''Name or path to the tokenizer.'''}					,				)
					_A  :	Optional[bool]     =					field(
					    default=__a					,				metadata={'''help''': '''If True, near-duplicate samples are removed.'''}	)
					_A  :	Optional[float]     =					field(
					    default=0.8_5					,				metadata={'''help''': '''Jaccard threshold for near-duplicate samples.'''}	)







@dataclass
class 						lowerCAmelCase   :

					'''simple docstring'''

					_A  :	Optional[str]     =					field(
					    default='''gpt2'''					,				metadata={'''help''': '''Base tokenizer to build new tokenizer from.'''}	)
					_A  :	Optional[str]     =					field(
					    default='''transformersbook/codeparrot-train'''					,				metadata={'''help''': '''Dataset to train tokenizer on.'''}	)
					_A  :	Optional[str]     =					field(default='''content'''					,				metadata={'''help''': '''Column containing text data to process.'''}	)
					_A  :	Optional[int]     =					field(default=200000					,				metadata={'''help''': '''Number of examples to train tokenizer on.'''}	)
					_A  :	Optional[int]     =					field(
					    default=32768					,				metadata={'''help''': '''Number of examples to train the tokenizer on.'''}	)
					_A  :	Optional[str]     =					field(default='''codeparrot'''					,				metadata={'''help''': '''Name of new tokenizer.'''}	)
					_A  :	Optional[bool]     =					field(default=__a					,				metadata={'''help''': '''Push saved tokenizer to the hub.'''}	)







@dataclass
class 						lowerCAmelCase   :

					'''simple docstring'''

					_A  :	Optional[str]     =					field(
					    default='''codeparrot/codeparrot'''					,				metadata={'''help''': '''Name or path to the tokenizer.'''}	)
					_A  :	Optional[str]     =					field(
					    default='''codeparrot/codeparrot-clean-train'''					,				metadata={'''help''': '''Name or path to the dataset to pretokenize.'''}	)
					_A  :	Optional[str]     =					field(
					    default='''tokenized-codeparrot-train'''					,				metadata={'''help''': '''Repo name of the pretokenized data.'''}	)
					_A  :	Optional[int]     =					field(default=__a					,				metadata={'''help''': '''Number of workers used for code evaluation.'''}	)







@dataclass
class 						lowerCAmelCase   :

					'''simple docstring'''

					_A  :	Optional[str]     =					field(
					    default='''gpt2-large'''					,				metadata={'''help''': '''Configuration to use for model initialization.'''}	)
					_A  :	Optional[str]     =					field(
					    default='''codeparrot/codeparrot'''					,				metadata={'''help''': '''Tokenizer attached to model.'''}	)
					_A  :	Optional[str]     =					field(default='''codeparrot'''					,				metadata={'''help''': '''Name of the created model.'''}	)
					_A  :	Optional[bool]     =					field(default=__a					,				metadata={'''help''': '''Push saved tokenizer to the hub.'''}	)







import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def 					snake_case_						(   lowerCAmelCase_				:  int = 8					):
							__lowercase					:							str			=   ascii_letters + digits + punctuation
							return "".join(secrets.choice(lowerCAmelCase_					) for _ in range(lowerCAmelCase_					)					)
def 					snake_case_						(   lowerCAmelCase_				:  str				,				lowerCAmelCase_				:  int					):
							# Password Generator = full boot with random_number, random_letters, and
							# random_character FUNCTIONS
							# Put your code here...
							i -= len(lowerCAmelCase_					)
							__lowercase					:							List[Any]			=   i // 3
							__lowercase					:							int			=   i % 3
							# chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
							#     random_number(digits, i / 3) + random_characters(punctuation, i / 3)
							__lowercase					:							str			=   (
							    chars_incl
							    + random(lowerCAmelCase_				,				quotient + remainder					)
							    + random(lowerCAmelCase_				,				lowerCAmelCase_					)
							    + random(lowerCAmelCase_				,				lowerCAmelCase_					)
							)
							__lowercase					:							int			=   list(lowerCAmelCase_					)
							shuffle(lowerCAmelCase_					)
							return "".join(lowerCAmelCase_					)

							# random is a generalised function for letters, characters and numbers
def 					snake_case_						(   lowerCAmelCase_				:  str				,				lowerCAmelCase_				:  int					):
							return "".join(secrets.choice(lowerCAmelCase_					) for _ in range(lowerCAmelCase_					)					)
def 					snake_case_						(   lowerCAmelCase_				:  Optional[Any]				,				lowerCAmelCase_				:  int					):
							pass  # Put your code here...
def 					snake_case_						(   lowerCAmelCase_				:  Union[str, Any]				,				lowerCAmelCase_				:  List[Any]					):
							pass  # Put your code here...
def 					snake_case_						(   lowerCAmelCase_				:  Dict				,				lowerCAmelCase_				:  Optional[int]					):
							pass  # Put your code here...
def 					snake_case_						(   lowerCAmelCase_				:  str				,				lowerCAmelCase_				:  int = 8					):

							if len(lowerCAmelCase_					) < min_length:
														# Your Password must be at least 8 characters long
														return False

							__lowercase					:							Tuple			=   any(char in ascii_uppercase for char in password					)
							__lowercase					:							Union[str, Any]			=   any(char in ascii_lowercase for char in password					)
							__lowercase					:							Dict			=   any(char in digits for char in password					)
							__lowercase					:							Tuple			=   any(char in punctuation for char in password					)

							return upper and lower and num and spec_char
							# Passwords should contain UPPERCASE, lowerase
							# numbers, and special characters
def 					snake_case_						(   ):
							__lowercase					:							Union[str, Any]			=   int(input("""Please indicate the max length of your password: """					).strip()					)
							__lowercase					:							List[str]			=   input(
							    """Please indicate the characters that must be in your password: """					).strip()
							print("""Password generated:"""				,				password_generator(lowerCAmelCase_					)					)
							print(
							    """Alternative Password generated:"""				,				alternative_password_generator(lowerCAmelCase_				,				lowerCAmelCase_					)				,				)
							print("""[If you are thinking of using this passsword, You better save it.]"""					)


if __name__ == "__main__":
	main()