




'''simple docstring'''







from dataclasses import dataclass, field
from typing import Optional







@dataclass
class 					_a :





     '''simple docstring'''



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







@dataclass
class 					_a :





     '''simple docstring'''



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







@dataclass
class 					_a :





     '''simple docstring'''



     lowerCAmelCase_  :     Optional[str]          =					field(
         default="""codeparrot/codeparrot""",		metadata={"""help""": """Model name or path of model to be evaluated."""}	)
     lowerCAmelCase_  :     Optional[int]          =					field(default=a__,		metadata={"""help""": """Number of workers used for code evaluation."""}	)
     lowerCAmelCase_  :     Optional[int]          =					field(
         default=a__,		metadata={"""help""": """The number of human-eval tasks to run. If not included all tasks are evaluated."""},		)
     lowerCAmelCase_  :     Optional[bool]          =					field(
         default=a__,		metadata={"""help""": """Sample from the language model's output distribution."""}	)
     lowerCAmelCase_  :     Optional[float]          =					field(default=0.2,		metadata={"""help""": """Sampling temperature used for generation."""}	)
     lowerCAmelCase_  :     Optional[int]          =					field(default=256,		metadata={"""help""": """Maximum number of newly generated tokens."""}	)
     lowerCAmelCase_  :     Optional[int]          =					field(default=0,		metadata={"""help""": """Top-k parameter used for generation."""}	)
     lowerCAmelCase_  :     Optional[float]          =					field(default=0.95,		metadata={"""help""": """Top-p parameter used for nucleus sampling."""}	)
     lowerCAmelCase_  :     Optional[int]          =					field(default=10,		metadata={"""help""": """Number of generations to run in parallel."""}	)
     lowerCAmelCase_  :     Optional[int]          =					field(
         default=200,		metadata={"""help""": """Number of completions to generate for each sample."""}	)
     lowerCAmelCase_  :     Optional[int]          =					field(default=1,		metadata={"""help""": """Random seed used for evaluation."""}	)
     lowerCAmelCase_  :     Optional[str]          =					field(
         default="""eval_results.json""",		metadata={"""help""": """Random seed used for evaluation."""}	)
     lowerCAmelCase_  :     Optional[str]          =					field(
         default="""0""",		metadata={"""help""": """Allow `code_eval` to execute Python code on machine"""}	)
     lowerCAmelCase_  :     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 :





     '''simple docstring'''



     lowerCAmelCase_  :     Optional[int]          =					field(
         default=a__,		metadata={
             """help""": """The number of CPU cores to use for parallel preprocessing. Default uses the maximum available."""
         },		)
     lowerCAmelCase_  :     Optional[str]          =					field(
         default="""transformersbook/codeparrot""",		metadata={"""help""": """Folder or name of dataset to process."""}	)
     lowerCAmelCase_  :     Optional[str]          =					field(
         default="""codeparrot-clean""",		metadata={"""help""": """Folder to save processed processed dataset."""}	)
     lowerCAmelCase_  :     Optional[int]          =					field(
         default=100000,		metadata={"""help""": """Number of files to save per JSON output file."""}	)
     lowerCAmelCase_  :     Optional[str]          =					field(default="""content""",		metadata={"""help""": """Column containing text data to process."""}	)
     lowerCAmelCase_  :     Optional[float]          =					field(
         default=1000,		metadata={"""help""": """Maximum line length in file, otherwise file is filtered."""}	)
     lowerCAmelCase_  :     Optional[float]          =					field(
         default=100,		metadata={"""help""": """Maximum mean line length in file, otherwise file is filtered."""}	)
     lowerCAmelCase_  :     Optional[float]          =					field(
         default=0.25,		metadata={"""help""": """Maximum fraction of non-alphanumeric characters, otherwise file is filtered."""}	)
     lowerCAmelCase_  :     Optional[float]          =					field(
         default=1.5,		metadata={"""help""": """Minimum character token ratio for the file, otherwise file is filtered."""}	)
     lowerCAmelCase_  :     Optional[float]          =					field(
         default=0.7,		metadata={"""help""": """Probability for filtering config, test and uncommon files."""}	)
     lowerCAmelCase_  :     Optional[str]          =					field(
         default="""codeparrot/codeparrot""",		metadata={"""help""": """Name or path to the tokenizer."""},		)
     lowerCAmelCase_  :     Optional[bool]          =					field(
         default=a__,		metadata={"""help""": """If True, near-duplicate samples are removed."""}	)
     lowerCAmelCase_  :     Optional[float]          =					field(
         default=0.85,		metadata={"""help""": """Jaccard threshold for near-duplicate samples."""}	)







@dataclass
class 					_a :





     '''simple docstring'''



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







@dataclass
class 					_a :





     '''simple docstring'''



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







@dataclass
class 					_a :





     '''simple docstring'''



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









'''simple docstring'''







import os
import time

import pytest

from datasets.utils.filelock import FileLock, Timeout



def        lowerCamelCase__				(      __lowercase				):
     snake_case			:      List[Any]        =			FileLock(str(tmpdir / """foo.lock"""				)				)
     snake_case			:      str        =			FileLock(str(tmpdir / """foo.lock"""				)				)
     snake_case			:      Any        =			0.01
     with locka.acquire():
          with pytest.raises(__lowercase				):
               snake_case			:      Union[str, Any]        =			time.time()
               locka.acquire(__lowercase				)
               assert time.time() - _start > timeout







def        lowerCamelCase__				(      __lowercase				):
     snake_case			:      List[Any]        =			"""a""" * 1_000 + """.lock"""
     snake_case			:      List[str]        =			FileLock(str(tmpdir / filename				)				)
     assert locka._lock_file.endswith(""".lock"""				)
     assert not locka._lock_file.endswith(__lowercase				)
     assert len(os.path.basename(locka._lock_file				)				) <= 255
     snake_case			:      Any        =			FileLock(tmpdir / filename				)
     with locka.acquire():
          with pytest.raises(__lowercase				):
               locka.acquire(0				)



