


'''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


__snake_case       		=get_logger(__name__)





def       a_   (					lowerCamelCase      :	Optional[Any]      ,					lowerCamelCase      :	int      ,					lowerCamelCase      :	Tuple      ,					lowerCamelCase      :	str      ,					lowerCamelCase      :	Dict=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							):
      lowerCAmelCase	=     model.state_dict()
      if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
         lowerCAmelCase	=     f'''{MODEL_NAME}.bin''' if model_index == 0 else f'''{MODEL_NAME}_{model_index}.bin'''
         lowerCAmelCase	=     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:
         lowerCAmelCase	=     (
             f'''{MODEL_NAME}_rank{accelerator.process_index}.bin'''
             if model_index == 0
             else f'''{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin'''
         )
         lowerCAmelCase	=     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:
         lowerCAmelCase	=     os.path.join(lowerCamelCase      ,					f'''{MODEL_NAME}_{model_index}'''							)
         os.makedirs(lowerCamelCase      ,					exist_ok=lowerCamelCase							)
         logger.info(f'''Saving model to {ckpt_dir}'''							)
         lowerCAmelCase	=     {"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       a_   (					lowerCamelCase      :	Tuple      ,					lowerCamelCase      :	List[str]      ,					lowerCamelCase      :	str      ,					lowerCamelCase      :	int      ,					lowerCamelCase      :	Union[str, Any]=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
         lowerCAmelCase	=     f'''{MODEL_NAME}.bin''' if model_index == 0 else f'''{MODEL_NAME}_{model_index}.bin'''
         lowerCAmelCase	=     os.path.join(lowerCamelCase      ,					lowerCamelCase							)
         logger.info(f'''Loading model from {input_model_file}'''							)
         lowerCAmelCase	=     torch.load(lowerCamelCase							)
         logger.info(f'''Model loaded from {input_model_file}'''							)
      elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
         lowerCAmelCase	=     (
             f'''{MODEL_NAME}_rank{accelerator.process_index}.bin'''
             if model_index == 0
             else f'''{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin'''
         )
         lowerCAmelCase	=     os.path.join(lowerCamelCase      ,					lowerCamelCase							)
         logger.info(f'''Loading model from {input_model_file}'''							)
         lowerCAmelCase	=     torch.load(lowerCamelCase							)
         logger.info(f'''Model loaded from {input_model_file}'''							)
      elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
         lowerCAmelCase	=     (
             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}'''							)
         lowerCAmelCase	=     {"model": model.state_dict()}
         dist_cp.load_state_dict(
             state_dict=lowerCamelCase      ,					storage_reader=dist_cp.FileSystemReader(lowerCamelCase							)      ,					planner=DefaultLoadPlanner()      ,					)
         lowerCAmelCase	=     state_dict["model"]
         logger.info(f'''Model loaded from {ckpt_dir}'''							)
      model.load_state_dict(lowerCamelCase							)





def       a_   (					lowerCamelCase      :	Optional[int]      ,					lowerCamelCase      :	int      ,					lowerCamelCase      :	int      ,					lowerCamelCase      :	int      ,					lowerCamelCase      :	Tuple      ,					lowerCamelCase      :	Dict=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							):
      lowerCAmelCase	=     FSDP.optim_state_dict(lowerCamelCase      ,					lowerCamelCase							)
      if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
         if accelerator.process_index == 0:
            lowerCAmelCase	=     (
                f'''{OPTIMIZER_NAME}.bin''' if optimizer_index == 0 else f'''{OPTIMIZER_NAME}_{optimizer_index}.bin'''
            )
            lowerCAmelCase	=     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:
         lowerCAmelCase	=     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       a_   (					lowerCamelCase      :	Any      ,					lowerCamelCase      :	List[Any]      ,					lowerCamelCase      :	Dict      ,					lowerCamelCase      :	int      ,					lowerCamelCase      :	Union[str, Any]      ,					lowerCamelCase      :	Tuple=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:
         lowerCAmelCase	=     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:
         lowerCAmelCase	=     (
             f'''{OPTIMIZER_NAME}.bin''' if optimizer_index == 0 else f'''{OPTIMIZER_NAME}_{optimizer_index}.bin'''
         )
         lowerCAmelCase	=     os.path.join(lowerCamelCase      ,					lowerCamelCase							)
         logger.info(f'''Loading Optimizer state from {input_optimizer_file}'''							)
         lowerCAmelCase	=     torch.load(lowerCamelCase							)
         logger.info(f'''Optimizer state loaded from {input_optimizer_file}'''							)
      else:
         lowerCAmelCase	=     (
             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}'''							)
         lowerCAmelCase	=     load_sharded_optimizer_state_dict(
             model_state_dict=model.state_dict()      ,					optimizer_key='optimizer'      ,					storage_reader=dist_cp.FileSystemReader(lowerCamelCase							)      ,					)
         lowerCAmelCase	=     optim_state["optimizer"]
         logger.info(f'''Optimizer loaded from {ckpt_dir}'''							)
      lowerCAmelCase	=     FSDP.optim_state_dict_to_load(lowerCamelCase      ,					lowerCamelCase      ,					lowerCamelCase							)
      optimizer.load_state_dict(lowerCamelCase							)









from collections import Counter
from pathlib import Path
from typing import Optional, Tuple

import yaml



class 						_UpperCAmelCase      (     yaml.SafeLoader							):






		def _snake_case		(						self				:     Dict   ,  UpperCAmelCase				:     Union[str, Any]):
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						[self.constructed_objects[key_node] for key_node, _ in node.value]
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						[tuple(UpperCAmelCase) if isinstance(UpperCAmelCase   ,  UpperCAmelCase) else key for key in keys]
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						Counter(UpperCAmelCase)
				SCREAMING_SNAKE_CASE_				:str				     =						[key for key in counter if counter[key] > 1]
				if duplicate_keys:
						raise TypeError(F"Got duplicate yaml keys: {duplicate_keys}")







		def _snake_case		(						self				:     int   ,  UpperCAmelCase				:     Union[str, Any]   ,  UpperCAmelCase				:     Union[str, Any]=False):
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						super().construct_mapping(UpperCAmelCase   ,  deep=UpperCAmelCase)
				self._check_no_duplicates_on_constructed_node(UpperCAmelCase)
				return mapping




def     lowercase	(    a ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:Any				     =						list(readme_content.splitlines() )
		if full_content and full_content[0] == "---" and "---" in full_content[1:]:
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						full_content[1:].index("---" ) + 1
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						"\n".join(full_content[1:sep_idx] )
				return yamlblock, "\n".join(full_content[sep_idx + 1 :] )

		return None, "\n".join(a )



class 						_UpperCAmelCase      (     lowercase							):
		# class attributes
		lowerCamelCase_	:			List[Any]       			=  {"""train_eval_index"""}  # train-eval-index in the YAML metadata






		@classmethod
		def _snake_case		(						cls				:     Optional[int]   ,  UpperCAmelCase				:     Path):
				with open(UpperCAmelCase   ,  encoding="utf-8") as readme_file:
						SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						_split_yaml_from_readme(readme_file.read())
				if yaml_string is not None:
						return cls.from_yaml_string(UpperCAmelCase)
				else:
						return cls()






		def _snake_case		(						self				:     Dict   ,  UpperCAmelCase				:     Path):
				if path.exists():
						with open(UpperCAmelCase   ,  encoding="utf-8") as readme_file:
								SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						readme_file.read()
				else:
						SCREAMING_SNAKE_CASE_				:List[str]				     =						None
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						self._to_readme(UpperCAmelCase)
				with open(UpperCAmelCase   ,  "w"   ,  encoding="utf-8") as readme_file:
						readme_file.write(UpperCAmelCase)






		def _snake_case		(						self				:     Union[str, Any]   ,  UpperCAmelCase				:     Optional[str] = None):
				if readme_content is not None:
						SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:List[str]				     =						_split_yaml_from_readme(UpperCAmelCase)
						SCREAMING_SNAKE_CASE_				:int				     =						"---\n" + self.to_yaml_string() + "---\n" + content
				else:
						SCREAMING_SNAKE_CASE_				:Any				     =						"---\n" + self.to_yaml_string() + "---\n"
				return full_content






		@classmethod
		def _snake_case		(						cls				:     List[str]   ,  UpperCAmelCase				:     str):
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						yaml.load(UpperCAmelCase   ,  Loader=_NoDuplicateSafeLoader) or {}

				# Convert the YAML keys to DatasetMetadata fields
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						{
				    (key.replace("-"   ,  "_") if key.replace("-"   ,  "_") in cls._FIELDS_WITH_DASHES else key): value
				    for key, value in metadata_dict.items()
				}
				return cls(**UpperCAmelCase)







		def _snake_case		(						self				:     str):
				return yaml.safe_dump(
				    {
				        (key.replace("_"   ,  "-") if key in self._FIELDS_WITH_DASHES else key): value
				        for key, value in self.items()
				    }   ,  sort_keys=UpperCAmelCase   ,  allow_unicode=UpperCAmelCase   ,  encoding="utf-8"   ,  ).decode("utf-8")




SCREAMING_SNAKE_CASE__     					=			{
    "image-classification": [],
    "translation": [],
    "image-segmentation": [],
    "fill-mask": [],
    "automatic-speech-recognition": [],
    "token-classification": [],
    "sentence-similarity": [],
    "audio-classification": [],
    "question-answering": [],
    "summarization": [],
    "zero-shot-classification": [],
    "table-to-text": [],
    "feature-extraction": [],
    "other": [],
    "multiple-choice": [],
    "text-classification": [],
    "text-to-image": [],
    "text2text-generation": [],
    "zero-shot-image-classification": [],
    "tabular-classification": [],
    "tabular-regression": [],
    "image-to-image": [],
    "tabular-to-text": [],
    "unconditional-image-generation": [],
    "text-retrieval": [],
    "text-to-speech": [],
    "object-detection": [],
    "audio-to-audio": [],
    "text-generation": [],
    "conversational": [],
    "table-question-answering": [],
    "visual-question-answering": [],
    "image-to-text": [],
    "reinforcement-learning": [],
    "voice-activity-detection": [],
    "time-series-forecasting": [],
    "document-question-answering": [],
}


if __name__ == "__main__":
		from argparse import ArgumentParser

		SCREAMING_SNAKE_CASE__     					=			ArgumentParser(usage="Validate the yaml metadata block of a README.md file.")
		ap.add_argument("readme_filepath")
		SCREAMING_SNAKE_CASE__     					=			ap.parse_args()

		SCREAMING_SNAKE_CASE__     					=			Path(args.readme_filepath)
		SCREAMING_SNAKE_CASE__     					=			DatasetMetadata.from_readme(readme_filepath)
		print(dataset_metadata)
		dataset_metadata.to_readme(readme_filepath)






