


'''simple docstring'''







import os

import torch

from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version


if is_torch_version('''>=''', FSDP_PYTORCH_VERSION):
   import torch.distributed.checkpoint as dist_cp
   from torch.distributed.checkpoint.default_planner import DefaultLoadPlanner, DefaultSavePlanner
   from torch.distributed.checkpoint.optimizer import load_sharded_optimizer_state_dict
   from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel as FSDP
   from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType


A				:					List[Any]    						=			get_logger(__name__)






def 				lowerCAmelCase__	(    lowerCamelCase					:      Union[str, Any]  ,lowerCamelCase					:      str  ,lowerCamelCase					:      List[str]  ,lowerCamelCase					:      Tuple  ,lowerCamelCase					:      Tuple=0       ):
       os.makedirs(lowerCamelCase  ,exist_ok=lowerCamelCase       )
       with FSDP.state_dict_type(
           lowerCamelCase  ,fsdp_plugin.state_dict_type  ,fsdp_plugin.state_dict_config  ,fsdp_plugin.optim_state_dict_config       ):
              _A					:     Optional[Any]     = model.state_dict()
              if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
                     _A					:     Any     = F'{MODEL_NAME}.bin' if model_index == 0 else F'{MODEL_NAME}_{model_index}.bin'
                     _A					:     List[str]     = os.path.join(lowerCamelCase  ,lowerCamelCase       )
                     if accelerator.process_index == 0:
                            logger.info(F'Saving model to {output_model_file}'       )
                            torch.save(lowerCamelCase  ,lowerCamelCase       )
                            logger.info(F'Model saved to {output_model_file}'       )
              elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
                     _A					:     int     = (
                         F'{MODEL_NAME}_rank{accelerator.process_index}.bin'
                         if model_index == 0
                         else F'{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin'
                     )
                     _A					:     List[Any]     = os.path.join(lowerCamelCase  ,lowerCamelCase       )
                     logger.info(F'Saving model to {output_model_file}'       )
                     torch.save(lowerCamelCase  ,lowerCamelCase       )
                     logger.info(F'Model saved to {output_model_file}'       )
              elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
                     _A					:     List[Any]     = os.path.join(lowerCamelCase  ,F'{MODEL_NAME}_{model_index}'       )
                     os.makedirs(lowerCamelCase  ,exist_ok=lowerCamelCase       )
                     logger.info(F'Saving model to {ckpt_dir}'       )
                     _A					:     List[str]     = {'model': state_dict}

                     dist_cp.save_state_dict(
                         state_dict=lowerCamelCase  ,storage_writer=dist_cp.FileSystemWriter(lowerCamelCase       )  ,planner=DefaultSavePlanner()  ,)
                     logger.info(F'Model saved to {ckpt_dir}'       )






def 				lowerCAmelCase__	(    lowerCamelCase					:      Tuple  ,lowerCamelCase					:      Optional[Any]  ,lowerCamelCase					:      Optional[int]  ,lowerCamelCase					:      Any  ,lowerCamelCase					:      str=0       ):
       accelerator.wait_for_everyone()
       with FSDP.state_dict_type(
           lowerCamelCase  ,fsdp_plugin.state_dict_type  ,fsdp_plugin.state_dict_config  ,fsdp_plugin.optim_state_dict_config       ):
              if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
                     if type(lowerCamelCase       ) != FSDP and accelerator.process_index != 0:
                            if not fsdp_plugin.sync_module_states:
                                   raise ValueError(
                                       'Set the `sync_module_states` flag to `True` so that model states are synced across processes when '
                                       'initializing FSDP object'       )
                            return
                     _A					:     Optional[Any]     = F'{MODEL_NAME}.bin' if model_index == 0 else F'{MODEL_NAME}_{model_index}.bin'
                     _A					:     Any     = os.path.join(lowerCamelCase  ,lowerCamelCase       )
                     logger.info(F'Loading model from {input_model_file}'       )
                     _A					:     Dict     = torch.load(lowerCamelCase       )
                     logger.info(F'Model loaded from {input_model_file}'       )
              elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
                     _A					:     Dict     = (
                         F'{MODEL_NAME}_rank{accelerator.process_index}.bin'
                         if model_index == 0
                         else F'{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin'
                     )
                     _A					:     int     = os.path.join(lowerCamelCase  ,lowerCamelCase       )
                     logger.info(F'Loading model from {input_model_file}'       )
                     _A					:     Union[str, Any]     = torch.load(lowerCamelCase       )
                     logger.info(F'Model loaded from {input_model_file}'       )
              elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
                     _A					:     int     = (
                         os.path.join(lowerCamelCase  ,F'{MODEL_NAME}_{model_index}'       )
                         if F'{MODEL_NAME}' not in input_dir
                         else input_dir
                     )
                     logger.info(F'Loading model from {ckpt_dir}'       )
                     _A					:     Dict     = {'model': model.state_dict()}
                     dist_cp.load_state_dict(
                         state_dict=lowerCamelCase  ,storage_reader=dist_cp.FileSystemReader(lowerCamelCase       )  ,planner=DefaultLoadPlanner()  ,)
                     _A					:     List[str]     = state_dict['model']
                     logger.info(F'Model loaded from {ckpt_dir}'       )
              model.load_state_dict(lowerCamelCase       )






def 				lowerCAmelCase__	(    lowerCamelCase					:      Any  ,lowerCamelCase					:      List[Any]  ,lowerCamelCase					:      int  ,lowerCamelCase					:      Tuple  ,lowerCamelCase					:      Union[str, Any]  ,lowerCamelCase					:      Optional[int]=0       ):
       os.makedirs(lowerCamelCase  ,exist_ok=lowerCamelCase       )
       with FSDP.state_dict_type(
           lowerCamelCase  ,fsdp_plugin.state_dict_type  ,fsdp_plugin.state_dict_config  ,fsdp_plugin.optim_state_dict_config       ):
              _A					:     List[str]     = FSDP.optim_state_dict(lowerCamelCase  ,lowerCamelCase       )
              if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
                     if accelerator.process_index == 0:
                            _A					:     Tuple     = (
                                F'{OPTIMIZER_NAME}.bin' if optimizer_index == 0 else F'{OPTIMIZER_NAME}_{optimizer_index}.bin'
                            )
                            _A					:     Optional[int]     = os.path.join(lowerCamelCase  ,lowerCamelCase       )
                            logger.info(F'Saving Optimizer state to {output_optimizer_file}'       )
                            torch.save(lowerCamelCase  ,lowerCamelCase       )
                            logger.info(F'Optimizer state saved in {output_optimizer_file}'       )
              else:
                     _A					:     Dict     = os.path.join(lowerCamelCase  ,F'{OPTIMIZER_NAME}_{optimizer_index}'       )
                     os.makedirs(lowerCamelCase  ,exist_ok=lowerCamelCase       )
                     logger.info(F'Saving Optimizer state to {ckpt_dir}'       )
                     dist_cp.save_state_dict(
                         state_dict={'optimizer': optim_state}  ,storage_writer=dist_cp.FileSystemWriter(lowerCamelCase       )  ,planner=DefaultSavePlanner()  ,)
                     logger.info(F'Optimizer state saved in {ckpt_dir}'       )






def 				lowerCAmelCase__	(    lowerCamelCase					:      List[str]  ,lowerCamelCase					:      List[str]  ,lowerCamelCase					:      List[str]  ,lowerCamelCase					:      Optional[int]  ,lowerCamelCase					:      Union[str, Any]  ,lowerCamelCase					:      int=0       ):
       accelerator.wait_for_everyone()
       with FSDP.state_dict_type(
           lowerCamelCase  ,fsdp_plugin.state_dict_type  ,fsdp_plugin.state_dict_config  ,fsdp_plugin.optim_state_dict_config       ):
              if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
                     _A					:     Any     = None
                     # below check should work but currently it isn't working (mostly opytorch issue),
                     # in the meantime disabling it at the cost of excess memory usage
                     # if accelerator.process_index == 0 or not fsdp_plugin.optim_state_dict_config.rank0_only:
                     _A					:     int     = (
                         F'{OPTIMIZER_NAME}.bin' if optimizer_index == 0 else F'{OPTIMIZER_NAME}_{optimizer_index}.bin'
                     )
                     _A					:     int     = os.path.join(lowerCamelCase  ,lowerCamelCase       )
                     logger.info(F'Loading Optimizer state from {input_optimizer_file}'       )
                     _A					:     Any     = torch.load(lowerCamelCase       )
                     logger.info(F'Optimizer state loaded from {input_optimizer_file}'       )
              else:
                     _A					:     List[str]     = (
                         os.path.join(lowerCamelCase  ,F'{OPTIMIZER_NAME}_{optimizer_index}'       )
                         if F'{OPTIMIZER_NAME}' not in input_dir
                         else input_dir
                     )
                     logger.info(F'Loading Optimizer from {ckpt_dir}'       )
                     _A					:     Union[str, Any]     = load_sharded_optimizer_state_dict(
                         model_state_dict=model.state_dict()  ,optimizer_key='optimizer'  ,storage_reader=dist_cp.FileSystemReader(lowerCamelCase       )  ,)
                     _A					:     Optional[int]     = optim_state['optimizer']
                     logger.info(F'Optimizer loaded from {ckpt_dir}'       )
              _A					:     str     = FSDP.optim_state_dict_to_load(lowerCamelCase  ,lowerCamelCase  ,lowerCamelCase       )
              optimizer.load_state_dict(lowerCamelCase       )










'''simple docstring'''







from dataclasses import dataclass, field
from typing import Optional


@dataclass
class 			__lowerCamelCase			:
    """simple docstring"""
    a													=   field(
        default="codeparrot/codeparrot"    , metadata={"help": "Model name or path of model to be trained."}	)
    a													=   field(
        default="./"    , metadata={"help": "Save dir where model repo is cloned and models updates are saved to."}	)
    a													=   field(
        default="codeparrot/codeparrot-clean-train"    , metadata={"help": "Name or path of training dataset."}	)
    a													=   field(
        default="codeparrot/codeparrot-clean-valid"    , metadata={"help": "Name or path of validation dataset."}	)
    a													=   field(default=2    , metadata={"help": "Batch size for training."}	)
    a													=   field(default=2    , metadata={"help": "Batch size for evaluation."}	)
    a													=   field(default=0.1    , metadata={"help": "Value of weight decay."}	)
    a													=   field(
        default=1_0000    , metadata={"help": "Size of buffer used to shuffle streaming dataset."}	)
    a													=   field(default=2E-4    , metadata={"help": "Learning rate fo training."}	)
    a													=   field(default="cosine"    , metadata={"help": "Learning rate."}	)
    a													=   field(
        default=750    , metadata={"help": "Number of warmup steps in the learning rate schedule."}	)
    a													=   field(
        default=16    , metadata={"help": "Number of gradient accumulation steps."}	)
    a													=   field(
        default=a_    , metadata={"help": "Use gradient checkpointing to reduce memory footprint."}	)
    a													=   field(default=5_0000    , metadata={"help": "Maximum number of training steps."}	)
    a													=   field(
        default=-1    , metadata={"help": "Maximum number of evaluation steps. If -1 the full dataset is evaluated."}	)
    a													=   field(default=1024    , metadata={"help": "Sequence lengths used for training."}	)
    a													=   field(default=1    , metadata={"help": "Training seed."}	)
    a													=   field(
        default=1024    , metadata={"help": "Interval to save checkpoints. Measured as number of forward passes not training steps."}    , )
    a													=   field(
        default=a_    , metadata={"help": "States path if the training should continue from a checkpoint folder."}	)
    a													=   field(default=a_    , metadata={"help": "If True the data is pretokenized."}	)




@dataclass
class 			__lowerCamelCase			:
    """simple docstring"""
    a													=   field(
        default="codeparrot/codeparrot"    , metadata={"help": "Model name or path of model to be evaluated."}	)
    a													=   field(
        default="codeparrot/codeparrot-clean-valid"    , metadata={"help": "Name or path of validation dataset."}	)
    a													=   field(default=2    , metadata={"help": "Batch size used for evaluation."}	)
    a													=   field(
        default=-1    , metadata={"help": "Maximum number of evaluation steps. If -1 the full dataset is evaluated."}	)
    a													=   field(default=1024    , metadata={"help": "Length of sequences to be evaluated."}	)
    a													=   field(default=1    , metadata={"help": "Random seed used for evaluation."}	)




@dataclass
class 			__lowerCamelCase			:
    """simple docstring"""
    a													=   field(
        default="codeparrot/codeparrot"    , metadata={"help": "Model name or path of model to be evaluated."}	)
    a													=   field(default=a_    , metadata={"help": "Number of workers used for code evaluation."}	)
    a													=   field(
        default=a_    , metadata={"help": "The number of human-eval tasks to run. If not included all tasks are evaluated."}    , )
    a													=   field(
        default=a_    , metadata={"help": "Sample from the language model's output distribution."}	)
    a													=   field(default=0.2    , metadata={"help": "Sampling temperature used for generation."}	)
    a													=   field(default=256    , metadata={"help": "Maximum number of newly generated tokens."}	)
    a													=   field(default=0    , metadata={"help": "Top-k parameter used for generation."}	)
    a													=   field(default=0.95    , metadata={"help": "Top-p parameter used for nucleus sampling."}	)
    a													=   field(default=10    , metadata={"help": "Number of generations to run in parallel."}	)
    a													=   field(
        default=200    , metadata={"help": "Number of completions to generate for each sample."}	)
    a													=   field(default=1    , metadata={"help": "Random seed used for evaluation."}	)
    a													=   field(
        default="eval_results.json"    , metadata={"help": "Random seed used for evaluation."}	)
    a													=   field(
        default="0"    , metadata={"help": "Allow `code_eval` to execute Python code on machine"}	)
    a													=   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													=   field(
        default=a_    , metadata={
            "help": "The number of CPU cores to use for parallel preprocessing. Default uses the maximum available."
        }    , )
    a													=   field(
        default="transformersbook/codeparrot"    , metadata={"help": "Folder or name of dataset to process."}	)
    a													=   field(
        default="codeparrot-clean"    , metadata={"help": "Folder to save processed processed dataset."}	)
    a													=   field(
        default=10_0000    , metadata={"help": "Number of files to save per JSON output file."}	)
    a													=   field(default="content"    , metadata={"help": "Column containing text data to process."}	)
    a													=   field(
        default=1000    , metadata={"help": "Maximum line length in file, otherwise file is filtered."}	)
    a													=   field(
        default=100    , metadata={"help": "Maximum mean line length in file, otherwise file is filtered."}	)
    a													=   field(
        default=0.25    , metadata={"help": "Maximum fraction of non-alphanumeric characters, otherwise file is filtered."}	)
    a													=   field(
        default=1.5    , metadata={"help": "Minimum character token ratio for the file, otherwise file is filtered."}	)
    a													=   field(
        default=0.7    , metadata={"help": "Probability for filtering config, test and uncommon files."}	)
    a													=   field(
        default="codeparrot/codeparrot"    , metadata={"help": "Name or path to the tokenizer."}    , )
    a													=   field(
        default=a_    , metadata={"help": "If True, near-duplicate samples are removed."}	)
    a													=   field(
        default=0.85    , metadata={"help": "Jaccard threshold for near-duplicate samples."}	)




@dataclass
class 			__lowerCamelCase			:
    """simple docstring"""
    a													=   field(
        default="gpt2"    , metadata={"help": "Base tokenizer to build new tokenizer from."}	)
    a													=   field(
        default="transformersbook/codeparrot-train"    , metadata={"help": "Dataset to train tokenizer on."}	)
    a													=   field(default="content"    , metadata={"help": "Column containing text data to process."}	)
    a													=   field(default=20_0000    , metadata={"help": "Number of examples to train tokenizer on."}	)
    a													=   field(
        default=3_2768    , metadata={"help": "Number of examples to train the tokenizer on."}	)
    a													=   field(default="codeparrot"    , metadata={"help": "Name of new tokenizer."}	)
    a													=   field(default=a_    , metadata={"help": "Push saved tokenizer to the hub."}	)




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




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






