


from dataclasses import dataclass, field
from typing import Optional

@dataclass
class 		__a				:
		_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=1_00_00   ,				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=7_50   ,				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=UpperCAmelCase   ,				metadata={'help': 'Use gradient checkpointing to reduce memory footprint.'}      )
		_a     :  Optional[int]           =     field(default=5_00_00   ,				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=10_24   ,				metadata={'help': 'Sequence lengths used for training.'}      )
		_a     :  Optional[int]           =     field(default=1   ,				metadata={'help': 'Training seed.'}      )
		_a     :  Optional[int]           =     field(
		    default=10_24   ,				metadata={'help': 'Interval to save checkpoints. Measured as number of forward passes not training steps.'}   ,				)
		_a     :  Optional[str]           =     field(
		    default=UpperCAmelCase   ,				metadata={'help': 'States path if the training should continue from a checkpoint folder.'}      )
		_a     :  Optional[bool]           =     field(default=UpperCAmelCase   ,				metadata={'help': 'If True the data is pretokenized.'}      )



@dataclass
class 		__a				:
		_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=10_24   ,				metadata={'help': 'Length of sequences to be evaluated.'}      )
		_a     :  Optional[int]           =     field(default=1   ,				metadata={'help': 'Random seed used for evaluation.'}      )



@dataclass
class 		__a				:
		_a     :  Optional[str]           =     field(
		    default='codeparrot/codeparrot'   ,				metadata={'help': 'Model name or path of model to be evaluated.'}      )
		_a     :  Optional[int]           =     field(default=UpperCAmelCase   ,				metadata={'help': 'Number of workers used for code evaluation.'}      )
		_a     :  Optional[int]           =     field(
		    default=UpperCAmelCase   ,				metadata={'help': 'The number of human-eval tasks to run. If not included all tasks are evaluated.'}   ,				)
		_a     :  Optional[bool]           =     field(
		    default=UpperCAmelCase   ,				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=2_56   ,				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=2_00   ,				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 		__a				:
		_a     :  Optional[int]           =     field(
		    default=UpperCAmelCase   ,				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=10_00_00   ,				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=10_00   ,				metadata={'help': 'Maximum line length in file, otherwise file is filtered.'}      )
		_a     :  Optional[float]           =     field(
		    default=1_00   ,				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=UpperCAmelCase   ,				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 		__a				:
		_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=20_00_00   ,				metadata={'help': 'Number of examples to train tokenizer on.'}      )
		_a     :  Optional[int]           =     field(
		    default=3_27_68   ,				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=UpperCAmelCase   ,				metadata={'help': 'Push saved tokenizer to the hub.'}      )



@dataclass
class 		__a				:
		_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=UpperCAmelCase   ,				metadata={'help': 'Number of workers used for code evaluation.'}      )



@dataclass
class 		__a				:
		_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=UpperCAmelCase   ,				metadata={'help': 'Push saved tokenizer to the hub.'}      )




from importlib import import_module

from .logging import get_logger


lowerCAmelCase__  :Optional[Any]             =							get_logger(__name__)

class 		__a				:
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE=None						)			->	List[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      attrs or []
									if module is not None:
																for key in module.__dict__:
																							if key in attrs or not key.startswith('__'						):
																														setattr(self      ,   _SCREAMING_SNAKE_CASE      ,   getattr(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)						)
									_UpperCAmelCase					=      module._original_module if isinstance(_SCREAMING_SNAKE_CASE      ,   _PatchedModuleObj						) else module



class 		__a				:
		_a     :  Any           =     []
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE=None						)			->	Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      obj
									_UpperCAmelCase					=      target
									_UpperCAmelCase					=      new
									_UpperCAmelCase					=      target.split('.'						)[0]
									_UpperCAmelCase					=      {}
									_UpperCAmelCase					=      attrs or []
		def __enter__(      self						)			->	int:




									"""simple docstring"""





									*_UpperCAmelCase		,	_UpperCAmelCase					=      self.target.split('.'						)

									# Patch modules:
									# it's used to patch attributes of submodules like "os.path.join";
									# in this case we need to patch "os" and "os.path"

									for i in range(len(_SCREAMING_SNAKE_CASE						)						):
																try:
																							_UpperCAmelCase					=      import_module('.'.join(submodules[: i + 1]						)						)
																except ModuleNotFoundError:
																							continue
																# We iterate over all the globals in self.obj in case we find "os" or "os.path"
																for attr in self.obj.__dir__():
																							_UpperCAmelCase					=      getattr(self.obj      ,   _SCREAMING_SNAKE_CASE						)
																							# We don't check for the name of the global, but rather if its value *is* "os" or "os.path".
																							# This allows to patch renamed modules like "from os import path as ospath".
																							if obj_attr is submodule or (
																							    (isinstance(_SCREAMING_SNAKE_CASE      ,   _PatchedModuleObj						) and obj_attr._original_module is submodule)
																							):
																														_UpperCAmelCase					=      obj_attr
																														# patch at top level
																														setattr(self.obj      ,   _SCREAMING_SNAKE_CASE      ,   _PatchedModuleObj(_SCREAMING_SNAKE_CASE      ,   attrs=self.attrs						)						)
																														_UpperCAmelCase					=      getattr(self.obj      ,   _SCREAMING_SNAKE_CASE						)
																														# construct lower levels patches
																														for key in submodules[i + 1 :]:
																																					setattr(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _PatchedModuleObj(getattr(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)      ,   attrs=self.attrs						)						)
																																					_UpperCAmelCase					=      getattr(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
																														# finally set the target attribute
																														setattr(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   self.new						)

        # Patch attribute itself:
        # it's used for builtins like "open",
        # and also to patch "os.path.join" we may also need to patch "join"
        # itself if it was imported as "from os.path import join".

									if submodules:  # if it's an attribute of a submodule like "os.path.join"
																try:
																							_UpperCAmelCase					=      getattr(import_module('.'.join(_SCREAMING_SNAKE_CASE						)						)      ,   _SCREAMING_SNAKE_CASE						)
																except (AttributeError, ModuleNotFoundError):
																							return
																# We iterate over all the globals in self.obj in case we find "os.path.join"
																for attr in self.obj.__dir__():
																							# We don't check for the name of the global, but rather if its value *is* "os.path.join".
																							# This allows to patch renamed attributes like "from os.path import join as pjoin".
																							if getattr(self.obj      ,   _SCREAMING_SNAKE_CASE						) is attr_value:
																														_UpperCAmelCase					=      getattr(self.obj      ,   _SCREAMING_SNAKE_CASE						)
																														setattr(self.obj      ,   _SCREAMING_SNAKE_CASE      ,   self.new						)
									elif target_attr in globals()["__builtins__"]:  # if it'a s builtin like "open"
																_UpperCAmelCase					=      globals()['__builtins__'][target_attr]
																setattr(self.obj      ,   _SCREAMING_SNAKE_CASE      ,   self.new						)
									else:
																raise RuntimeError(f'''Tried to patch attribute {target_attr} instead of a submodule.'''						)
		def __exit__(      self      ,   *_SCREAMING_SNAKE_CASE						)			->	Union[str, Any]:




									"""simple docstring"""





									for attr in list(self.original						):
																setattr(self.obj      ,   _SCREAMING_SNAKE_CASE      ,   self.original.pop(_SCREAMING_SNAKE_CASE						)						)
		def      UpperCAmelCase__    (      self						)			->	Optional[int]:




									"""simple docstring"""





									self.__enter__()
									self._active_patches.append(self						)






		def      UpperCAmelCase__    (      self						)			->	str:




									"""simple docstring"""





									try:
																self._active_patches.remove(self						)
									except ValueError:
																# If the patch hasn't been started this will fail
																return None

									return self.__exit__()
