




'''simple docstring'''

import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


a  :				Tuple									=  logging.get_logger(__name__)

a  :				List[str]									=  {'vocab_file': 'sentencepiece.bpe.model'}

a  :				Union[str, Any]									=  {
    'vocab_file': {
        'moussaKam/mbarthez': 'https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez': 'https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez-orangesum-title': (
            'https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'
        ),
    },
}

a  :				List[Any]									=  {
    'moussaKam/mbarthez': 1024,
    'moussaKam/barthez': 1024,
    'moussaKam/barthez-orangesum-title': 1024,
}

a  :				Dict									=  '▁'






class 						a				(   snake_case__     ):
				snake_case_      =		VOCAB_FILES_NAMES
				snake_case_      =		PRETRAINED_VOCAB_FILES_MAP
				snake_case_      =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				snake_case_      =		["""input_ids""", """attention_mask"""]


				def __init__(		self    :					Optional[int]       ,				lowercase_    :					List[str]       ,				lowercase_    :					List[Any]="<s>"       ,				lowercase_    :					Optional[Any]="</s>"       ,				lowercase_    :					List[Any]="</s>"       ,				lowercase_    :					List[Any]="<s>"       ,				lowercase_    :					int="<unk>"       ,				lowercase_    :					str="<pad>"       ,				lowercase_    :					List[Any]="<mask>"       ,				lowercase_    :					List[str] = None       ,				**lowercase_    :					List[Any]       ,				):
									snake_case_									=				AddedToken(_A       ,				lstrip=_A       ,				rstrip=_A					) if isinstance(_A       ,				_A					) else mask_token

									snake_case_									=				{} if sp_model_kwargs is None else sp_model_kwargs

									super().__init__(
									    bos_token=_A       ,				eos_token=_A       ,				unk_token=_A       ,				sep_token=_A       ,				cls_token=_A       ,				pad_token=_A       ,				mask_token=_A       ,				sp_model_kwargs=self.sp_model_kwargs       ,				**_A       ,				)

									snake_case_									=				vocab_file
									snake_case_									=				spm.SentencePieceProcessor(**self.sp_model_kwargs					)
									self.sp_model.Load(str(_A					)					)

									snake_case_									=				{'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

									snake_case_									=				len(self.sp_model					) - 1
									snake_case_									=				{v: k for k, v in self.fairseq_tokens_to_ids.items()}


				def   A_ (		self    :					str       ,				lowercase_    :					List[Any]       ,				lowercase_    :					int = None					):



									if token_ids_a is None:
														return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
									snake_case_									=				[self.cls_token_id]
									snake_case_									=				[self.sep_token_id]
									return cls + token_ids_a + sep + sep + token_ids_a + sep


				def   A_ (		self    :					Optional[int]       ,				lowercase_    :					List[str]       ,				lowercase_    :					int = None       ,				lowercase_    :					int = False					):



									if already_has_special_tokens:
														return super().get_special_tokens_mask(
														    token_ids_a=_A       ,				token_ids_a=_A       ,				already_has_special_tokens=_A					)

									if token_ids_a is None:
														return [1] + ([0] * len(_A					)) + [1]
									return [1] + ([0] * len(_A					)) + [1, 1] + ([0] * len(_A					)) + [1]


				def   A_ (		self    :					int       ,				lowercase_    :					Tuple       ,				lowercase_    :					str = None					):
									snake_case_									=				[self.sep_token_id]
									snake_case_									=				[self.cls_token_id]

									if token_ids_a is None:
														return len(cls + token_ids_a + sep					) * [0]
									return len(cls + token_ids_a + sep + sep + token_ids_a + sep					) * [0]


				@property
				def   A_ (		self    :					List[Any]					):
									return len(self.sp_model					)


				def   A_ (		self    :					Optional[int]					):
									snake_case_									=				{self.convert_ids_to_tokens(_A					): i for i in range(self.vocab_size					)}
									vocab.update(self.added_tokens_encoder					)
									return vocab


				def   A_ (		self    :					Tuple       ,				lowercase_    :					Optional[Any]					):
									return self.sp_model.encode(_A       ,				out_type=_A					)


				def   A_ (		self    :					int       ,				lowercase_    :					Tuple					):



									if token in self.fairseq_tokens_to_ids:
														return self.fairseq_tokens_to_ids[token]
									snake_case_									=				self.sp_model.PieceToId(_A					)

									return spm_id if spm_id else self.unk_token_id


				def   A_ (		self    :					Dict       ,				lowercase_    :					Tuple					):



									if index in self.fairseq_ids_to_tokens:
														return self.fairseq_ids_to_tokens[index]
									return self.sp_model.IdToPiece(_A					)


				def   A_ (		self    :					int       ,				lowercase_    :					Dict					):
									snake_case_									=				[]
									snake_case_									=				''''''
									snake_case_									=				False
									for token in tokens:
														# make sure that special tokens are not decoded using sentencepiece model
														if token in self.all_special_tokens:
																			if not prev_is_special:
																								out_string += " "
																			out_string += self.sp_model.decode(_A					) + token
																			snake_case_									=				True
																			snake_case_									=				[]
														else:
																			current_sub_tokens.append(_A					)
																			snake_case_									=				False
									out_string += self.sp_model.decode(_A					)
									return out_string.strip()


				def __getstate__(		self    :					Union[str, Any]					):
									snake_case_									=				self.__dict__.copy()
									snake_case_									=				None
									return state


				def __setstate__(		self    :					Optional[int]       ,				lowercase_    :					Union[str, Any]					):
									snake_case_									=				d

									# for backward compatibility
									if not hasattr(self       ,				'''sp_model_kwargs'''					):
														snake_case_									=				{}

									snake_case_									=				spm.SentencePieceProcessor(**self.sp_model_kwargs					)
									self.sp_model.Load(self.vocab_file					)


				def   A_ (		self    :					Optional[int]       ,				lowercase_    :					Union[str, Any]       ,				lowercase_    :					Optional[int] = None					):



									if not os.path.isdir(_A					):
														logger.error(F"Vocabulary path ({save_directory}) should be a directory"					)
														return
									snake_case_									=				os.path.join(
									    _A       ,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']					)

									if os.path.abspath(self.vocab_file					) != os.path.abspath(_A					) and os.path.isfile(self.vocab_file					):
														copyfile(self.vocab_file       ,				_A					)
									elif not os.path.isfile(self.vocab_file					):
														with open(_A       ,				'''wb'''					) as fi:
																			snake_case_									=				self.sp_model.serialized_model_proto()
																			fi.write(_A					)

									return (out_vocab_file,)







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


UpperCamelCase__    			=			get_logger(__name__)







def 	_a      (						SCREAMING_SNAKE_CASE_							:				List[str]      ,      SCREAMING_SNAKE_CASE_							:				Tuple      ,      SCREAMING_SNAKE_CASE_							:				Tuple      ,      SCREAMING_SNAKE_CASE_							:				Optional[Any]      ,      SCREAMING_SNAKE_CASE_							:				str=0     ):
					os.makedirs(SCREAMING_SNAKE_CASE_      ,      exist_ok=SCREAMING_SNAKE_CASE_     )
					with FSDP.state_dict_type(
					    SCREAMING_SNAKE_CASE_      ,      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(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )
															if accelerator.process_index == 0:
																				logger.info(F"""Saving model to {output_model_file}"""     )
																				torch.save(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )
																				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(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )
															logger.info(F"""Saving model to {output_model_file}"""     )
															torch.save(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )
															logger.info(F"""Model saved to {output_model_file}"""     )
										elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
															__lowerCAmelCase				   =       os.path.join(SCREAMING_SNAKE_CASE_      ,      F"""{MODEL_NAME}_{model_index}"""     )
															os.makedirs(SCREAMING_SNAKE_CASE_      ,      exist_ok=SCREAMING_SNAKE_CASE_     )
															logger.info(F"""Saving model to {ckpt_dir}"""     )
															__lowerCAmelCase				   =       {"model": state_dict}

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







def 	_a      (						SCREAMING_SNAKE_CASE_							:				Dict      ,      SCREAMING_SNAKE_CASE_							:				int      ,      SCREAMING_SNAKE_CASE_							:				Optional[int]      ,      SCREAMING_SNAKE_CASE_							:				List[str]      ,      SCREAMING_SNAKE_CASE_							:				Any=0     ):
					accelerator.wait_for_everyone()
					with FSDP.state_dict_type(
					    SCREAMING_SNAKE_CASE_      ,      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(SCREAMING_SNAKE_CASE_     ) != 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(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )
															logger.info(F"""Loading model from {input_model_file}"""     )
															__lowerCAmelCase				   =       torch.load(SCREAMING_SNAKE_CASE_     )
															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(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )
															logger.info(F"""Loading model from {input_model_file}"""     )
															__lowerCAmelCase				   =       torch.load(SCREAMING_SNAKE_CASE_     )
															logger.info(F"""Model loaded from {input_model_file}"""     )
										elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
															__lowerCAmelCase				   =       (
															    os.path.join(SCREAMING_SNAKE_CASE_      ,      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=SCREAMING_SNAKE_CASE_      ,      storage_reader=dist_cp.FileSystemReader(SCREAMING_SNAKE_CASE_     )      ,      planner=DefaultLoadPlanner()      ,      )
															__lowerCAmelCase				   =       state_dict["model"]
															logger.info(F"""Model loaded from {ckpt_dir}"""     )
										model.load_state_dict(SCREAMING_SNAKE_CASE_     )







def 	_a      (						SCREAMING_SNAKE_CASE_							:				Dict      ,      SCREAMING_SNAKE_CASE_							:				int      ,      SCREAMING_SNAKE_CASE_							:				List[str]      ,      SCREAMING_SNAKE_CASE_							:				Optional[Any]      ,      SCREAMING_SNAKE_CASE_							:				Optional[int]      ,      SCREAMING_SNAKE_CASE_							:				str=0     ):
					os.makedirs(SCREAMING_SNAKE_CASE_      ,      exist_ok=SCREAMING_SNAKE_CASE_     )
					with FSDP.state_dict_type(
					    SCREAMING_SNAKE_CASE_      ,      fsdp_plugin.state_dict_type      ,      fsdp_plugin.state_dict_config      ,      fsdp_plugin.optim_state_dict_config     ):
										__lowerCAmelCase				   =       FSDP.optim_state_dict(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )
										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(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )
																				logger.info(F"""Saving Optimizer state to {output_optimizer_file}"""     )
																				torch.save(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )
																				logger.info(F"""Optimizer state saved in {output_optimizer_file}"""     )
										else:
															__lowerCAmelCase				   =       os.path.join(SCREAMING_SNAKE_CASE_      ,      F"""{OPTIMIZER_NAME}_{optimizer_index}"""     )
															os.makedirs(SCREAMING_SNAKE_CASE_      ,      exist_ok=SCREAMING_SNAKE_CASE_     )
															logger.info(F"""Saving Optimizer state to {ckpt_dir}"""     )
															dist_cp.save_state_dict(
															    state_dict={"optimizer": optim_state}      ,      storage_writer=dist_cp.FileSystemWriter(SCREAMING_SNAKE_CASE_     )      ,      planner=DefaultSavePlanner()      ,      )
															logger.info(F"""Optimizer state saved in {ckpt_dir}"""     )







def 	_a      (						SCREAMING_SNAKE_CASE_							:				Dict      ,      SCREAMING_SNAKE_CASE_							:				Dict      ,      SCREAMING_SNAKE_CASE_							:				Tuple      ,      SCREAMING_SNAKE_CASE_							:				List[str]      ,      SCREAMING_SNAKE_CASE_							:				str      ,      SCREAMING_SNAKE_CASE_							:				Dict=0     ):
					accelerator.wait_for_everyone()
					with FSDP.state_dict_type(
					    SCREAMING_SNAKE_CASE_      ,      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(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )
															logger.info(F"""Loading Optimizer state from {input_optimizer_file}"""     )
															__lowerCAmelCase				   =       torch.load(SCREAMING_SNAKE_CASE_     )
															logger.info(F"""Optimizer state loaded from {input_optimizer_file}"""     )
										else:
															__lowerCAmelCase				   =       (
															    os.path.join(SCREAMING_SNAKE_CASE_      ,      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(SCREAMING_SNAKE_CASE_     )      ,      )
															__lowerCAmelCase				   =       optim_state["optimizer"]
															logger.info(F"""Optimizer loaded from {ckpt_dir}"""     )
										__lowerCAmelCase				   =       FSDP.optim_state_dict_to_load(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )
										optimizer.load_state_dict(SCREAMING_SNAKE_CASE_     )




