from dataclasses import dataclass, field
from typing import Optional







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







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







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







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







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







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

import os
from argparse import ArgumentParser
from typing import List

import torch.utils.data

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node


__lowerCamelCase      :			int							       =		4
__lowerCamelCase      :			Dict							       =		3







class __snake_case    (						lowerCamelCase_		):
   pass





def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	List[str]     )							-> List[str]:







  """simple docstring"""





  for shard in shards:
    for i in range(__UpperCamelCase     ):
      yield {"i": i, "shard": shard}



def      __SCREAMING_SNAKE_CASE  ( )							-> Union[str, Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				int(os.environ["""RANK"""]     )
  SCREAMING_SNAKE_CASE__      							=				int(os.environ["""WORLD_SIZE"""]     )

  SCREAMING_SNAKE_CASE__      							=				ArgumentParser()
  parser.add_argument("""--streaming"""			,					type=__UpperCamelCase     )
  parser.add_argument("""--local_rank"""			,					type=__UpperCamelCase     )
  parser.add_argument("""--num_workers"""			,					type=__UpperCamelCase			,					default=0     )
  SCREAMING_SNAKE_CASE__      							=				parser.parse_args()
  SCREAMING_SNAKE_CASE__      							=				args.streaming
  SCREAMING_SNAKE_CASE__      							=				args.num_workers

  SCREAMING_SNAKE_CASE__      							=				{"""shards""": [f"""shard_{shard_idx}""" for shard_idx in range(__UpperCamelCase     )]}
  SCREAMING_SNAKE_CASE__      							=				IterableDataset.from_generator(__UpperCamelCase			,					gen_kwargs=__UpperCamelCase     )
  if not streaming:
    SCREAMING_SNAKE_CASE__      							=				Dataset.from_list(list(__UpperCamelCase     )     )

  SCREAMING_SNAKE_CASE__      							=				split_dataset_by_node(__UpperCamelCase			,					rank=__UpperCamelCase			,					world_size=__UpperCamelCase     )
  SCREAMING_SNAKE_CASE__      							=				torch.utils.data.DataLoader(__UpperCamelCase			,					num_workers=__UpperCamelCase     )

  SCREAMING_SNAKE_CASE__      							=				NUM_SHARDS * NUM_ITEMS_PER_SHARD
  SCREAMING_SNAKE_CASE__      							=				full_size // world_size
  expected_local_size += int(rank < (full_size % world_size)     )

  SCREAMING_SNAKE_CASE__      							=				sum(1 for _ in dataloader     )
  if local_size != expected_local_size:
    raise FailedTestError(f"""local_size {local_size} != expected_local_size {expected_local_size}"""     )


if __name__ == "__main__":
   main()
