





from dataclasses import dataclass, field
from typing import Optional

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


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


@dataclass
class   lowerCAmelCase				:
 UpperCAmelCase__						=						field(
     default="""codeparrot/codeparrot""",    metadata={"""help""": """Model name or path of model to be evaluated."""}     )
 UpperCAmelCase__						=						field(default=__UpperCamelCase,    metadata={"""help""": """Number of workers used for code evaluation."""}     )
 UpperCAmelCase__						=						field(
     default=__UpperCamelCase,    metadata={"""help""": """The number of human-eval tasks to run. If not included all tasks are evaluated."""},    )
 UpperCAmelCase__						=						field(
     default=__UpperCamelCase,    metadata={"""help""": """Sample from the language model's output distribution."""}     )
 UpperCAmelCase__						=						field(default=0.2,    metadata={"""help""": """Sampling temperature used for generation."""}     )
 UpperCAmelCase__						=						field(default=2_56,    metadata={"""help""": """Maximum number of newly generated tokens."""}     )
 UpperCAmelCase__						=						field(default=0,    metadata={"""help""": """Top-k parameter used for generation."""}     )
 UpperCAmelCase__						=						field(default=0.95,    metadata={"""help""": """Top-p parameter used for nucleus sampling."""}     )
 UpperCAmelCase__						=						field(default=10,    metadata={"""help""": """Number of generations to run in parallel."""}     )
 UpperCAmelCase__						=						field(
     default=2_00,    metadata={"""help""": """Number of completions to generate for each sample."""}     )
 UpperCAmelCase__						=						field(default=1,    metadata={"""help""": """Random seed used for evaluation."""}     )
 UpperCAmelCase__						=						field(
     default="""eval_results.json""",    metadata={"""help""": """Random seed used for evaluation."""}     )
 UpperCAmelCase__						=						field(
     default="""0""",    metadata={"""help""": """Allow `code_eval` to execute Python code on machine"""}     )
 UpperCAmelCase__						=						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				:
 UpperCAmelCase__						=						field(
     default=__UpperCamelCase,    metadata={
         """help""": """The number of CPU cores to use for parallel preprocessing. Default uses the maximum available."""
     },    )
 UpperCAmelCase__						=						field(
     default="""transformersbook/codeparrot""",    metadata={"""help""": """Folder or name of dataset to process."""}     )
 UpperCAmelCase__						=						field(
     default="""codeparrot-clean""",    metadata={"""help""": """Folder to save processed processed dataset."""}     )
 UpperCAmelCase__						=						field(
     default=10_00_00,    metadata={"""help""": """Number of files to save per JSON output file."""}     )
 UpperCAmelCase__						=						field(default="""content""",    metadata={"""help""": """Column containing text data to process."""}     )
 UpperCAmelCase__						=						field(
     default=10_00,    metadata={"""help""": """Maximum line length in file, otherwise file is filtered."""}     )
 UpperCAmelCase__						=						field(
     default=1_00,    metadata={"""help""": """Maximum mean line length in file, otherwise file is filtered."""}     )
 UpperCAmelCase__						=						field(
     default=0.25,    metadata={"""help""": """Maximum fraction of non-alphanumeric characters, otherwise file is filtered."""}     )
 UpperCAmelCase__						=						field(
     default=1.5,    metadata={"""help""": """Minimum character token ratio for the file, otherwise file is filtered."""}     )
 UpperCAmelCase__						=						field(
     default=0.7,    metadata={"""help""": """Probability for filtering config, test and uncommon files."""}     )
 UpperCAmelCase__						=						field(
     default="""codeparrot/codeparrot""",    metadata={"""help""": """Name or path to the tokenizer."""},    )
 UpperCAmelCase__						=						field(
     default=__UpperCamelCase,    metadata={"""help""": """If True, near-duplicate samples are removed."""}     )
 UpperCAmelCase__						=						field(
     default=0.85,    metadata={"""help""": """Jaccard threshold for near-duplicate samples."""}     )


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


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


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












import gc
import threading
import time

import psutil
import torch

class   lowerCAmelCase				:
 def __init__( self    : str      )     ->      Union[str, Any]:
     lowerCamelCase__       : Optional[Any]   					=						psutil.Process()
     lowerCamelCase__       : Union[str, Any]   					=						False
 def 	A_      ( self    : Optional[int]      )     ->      int:
     lowerCamelCase__       : Optional[Any]   					=						-1

     while True:
         lowerCamelCase__       : Dict   					=						max(self.process.memory_info().rss     ,	self.cpu_memory_peak      )

         # can't sleep or will not catch the peak right (this comment is here on purpose)
         if not self.peak_monitoring:
             break
 def 	A_      ( self    : Tuple      )     ->      Dict:
     lowerCamelCase__       : List[Any]   					=						True
     lowerCamelCase__       : List[str]   					=						threading.Thread(target=self.peak_monitor      )
     lowerCamelCase__       : Union[str, Any]   					=						True
     self.thread.start()







 def 	A_      ( self    : str      )     ->      Dict:
     lowerCamelCase__       : int   					=						False
     self.thread.join()
     return self.cpu_memory_peak


_UpperCAmelCase							:			Dict        =							PeakCPUMemory()


def 			SCREAMING_SNAKE_CASE (			)     ->	Union[str, Any]:
    # Time
    lowerCamelCase__       : List[Any]   					=						{'time': time.time()}

    gc.collect()
    torch.cuda.empty_cache()

    # CPU mem
    lowerCamelCase__       : List[str]   					=						psutil.Process().memory_info().rss
    cpu_peak_tracker.start()

    # GPU mem
    for i in range(torch.cuda.device_count()					):
        lowerCamelCase__       : Union[str, Any]   					=						torch.cuda.memory_allocated(_UpperCAmelCase					)
    torch.cuda.reset_peak_memory_stats()

    return measures


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase					)     ->	Tuple:
    # Time
    lowerCamelCase__       : Optional[int]   					=						{'time': time.time() - start_measures['time']}

    gc.collect()
    torch.cuda.empty_cache()

    # CPU mem
    lowerCamelCase__       : Dict   					=						(psutil.Process().memory_info().rss - start_measures['cpu']) / 2**20
    lowerCamelCase__       : int   					=						(cpu_peak_tracker.stop() - start_measures['cpu']) / 2**20

    # GPU mem
    for i in range(torch.cuda.device_count()					):
        lowerCamelCase__       : List[str]   					=						(torch.cuda.memory_allocated(_UpperCAmelCase					) - start_measures[str(_UpperCAmelCase					)]) / 2**20
        lowerCamelCase__       : Optional[Any]   					=						(torch.cuda.max_memory_allocated(_UpperCAmelCase					) - start_measures[str(_UpperCAmelCase					)]) / 2**20

    return measures


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase					)     ->	Dict:
    print(F"""{description}:"""					)
    print(F"""- Time: {measures["time"]:.2f}s"""					)
    for i in range(torch.cuda.device_count()					):
        print(F"""- GPU {i} allocated: {measures[str(_UpperCAmelCase					)]:.2f}MiB"""					)
        lowerCamelCase__       : List[str]   					=						measures[F"""{i}-peak"""]
        print(F"""- GPU {i} peak: {peak:.2f}MiB"""					)
    print(F"""- CPU RAM allocated: {measures["cpu"]:.2f}MiB"""					)
    print(F"""- CPU RAM peak: {measures["cpu-peak"]:.2f}MiB"""					)





