


'''simple docstring'''
import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer


UpperCamelCase__          = logging.get_logger(__name__)


UpperCamelCase__          = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

# See all BART models at https://huggingface.co/models?filter=bart
UpperCamelCase__          = {
    '''vocab_file''': {
        '''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/vocab.json''',
        '''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/vocab.json''',
        '''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json''',
        '''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json''',
        '''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json''',
        '''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/merges.txt''',
        '''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/merges.txt''',
        '''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt''',
        '''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt''',
        '''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt''',
        '''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json''',
        '''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json''',
        '''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json''',
        '''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json''',
        '''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json''',
        '''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json''',
    },
}

UpperCamelCase__          = {
    '''facebook/bart-base''': 1_0_2_4,
    '''facebook/bart-large''': 1_0_2_4,
    '''facebook/bart-large-mnli''': 1_0_2_4,
    '''facebook/bart-large-cnn''': 1_0_2_4,
    '''facebook/bart-large-xsum''': 1_0_2_4,
    '''yjernite/bart_eli5''': 1_0_2_4,
}




class      lowerCamelCase_  (      __a ):
      lowerCAmelCase__							=	VOCAB_FILES_NAMES
      lowerCAmelCase__							=	PRETRAINED_VOCAB_FILES_MAP
      lowerCAmelCase__							=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      lowerCAmelCase__							=	['input_ids', 'attention_mask']
      lowerCAmelCase__							=	BartTokenizer






      def __init__(   self							:       Tuple		,		_A							:       List[str]=None		,		_A							:       Optional[Any]=None		,		_A							:       Union[str, Any]=None		,		_A							:       Tuple="replace"		,		_A							:       Optional[Any]="<s>"		,		_A							:       int="</s>"		,		_A							:       Optional[Any]="</s>"		,		_A							:       List[str]="<s>"		,		_A							:       Optional[int]="<unk>"		,		_A							:       Optional[int]="<pad>"		,		_A							:       str="<mask>"		,		_A							:       Dict=False		,		_A							:       int=True		,		**_A							:       Optional[Any]		,		):


          '''simple docstring'''


          super().__init__(
              _A		,		_A		,		tokenizer_file=_A		,		errors=_A		,		bos_token=_A		,		eos_token=_A		,		sep_token=_A		,		cls_token=_A		,		unk_token=_A		,		pad_token=_A		,		mask_token=_A		,		add_prefix_space=_A		,		trim_offsets=_A		,		**_A		,		)

          UpperCAmelCase__     :						Optional[int]						=							json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()					)
          if pre_tok_state.get('''add_prefix_space'''		,		_A					) != add_prefix_space:
              UpperCAmelCase__     :						str						=							getattr(_A		,		pre_tok_state.pop('''type'''					)					)
              UpperCAmelCase__     :						Any						=							add_prefix_space
              UpperCAmelCase__     :						str						=							pre_tok_class(**_A					)

          UpperCAmelCase__     :						Dict						=							add_prefix_space

          # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
          UpperCAmelCase__     :						Optional[Any]						=							'''post_processor'''
          UpperCAmelCase__     :						List[Any]						=							getattr(self.backend_tokenizer		,		_A		,		_A					)
          if tokenizer_component_instance:
              UpperCAmelCase__     :						Tuple						=							json.loads(tokenizer_component_instance.__getstate__()					)

              # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
              if "sep" in state:
                  UpperCAmelCase__     :						Union[str, Any]						=							tuple(state['''sep''']					)
              if "cls" in state:
                  UpperCAmelCase__     :						Union[str, Any]						=							tuple(state['''cls''']					)

              UpperCAmelCase__     :						Dict						=							False

              if state.get('''add_prefix_space'''		,		_A					) != add_prefix_space:
                  UpperCAmelCase__     :						Union[str, Any]						=							add_prefix_space
                  UpperCAmelCase__     :						Dict						=							True

              if state.get('''trim_offsets'''		,		_A					) != trim_offsets:
                  UpperCAmelCase__     :						List[Any]						=							trim_offsets
                  UpperCAmelCase__     :						List[Any]						=							True

              if changes_to_apply:
                  UpperCAmelCase__     :						Dict						=							getattr(_A		,		state.pop('''type'''					)					)
                  UpperCAmelCase__     :						Union[str, Any]						=							component_class(**_A					)
                  setattr(self.backend_tokenizer		,		_A		,		_A					)






      @property
      def lowercase_				(   self							:       Dict					):


          '''simple docstring'''


          if self._mask_token is None:
              if self.verbose:
                  logger.error('''Using mask_token, but it is not set yet.'''					)
              return None
          return str(self._mask_token					)






      @mask_token.setter
      def lowercase_				(   self							:       Dict		,		_A							:       List[Any]					):


          '''simple docstring'''


          UpperCAmelCase__     :						Tuple						=							AddedToken(_A		,		lstrip=_A		,		rstrip=_A					) if isinstance(_A		,		_A					) else value
          UpperCAmelCase__     :						str						=							value






      def lowercase_				(   self							:       Optional[int]		,		*_A							:       List[str]		,		**_A							:       Dict					):


          '''simple docstring'''


          UpperCAmelCase__     :						Any						=							kwargs.get('''is_split_into_words'''		,		_A					)

          if is_split_into_words and not self.add_prefix_space:
              raise ValueError(
                  f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
                  '''to use it with pretokenized inputs.'''					)

          return super()._batch_encode_plus(*_A		,		**_A					)






      def lowercase_				(   self							:       Optional[Any]		,		*_A							:       Union[str, Any]		,		**_A							:       List[Any]					):


          '''simple docstring'''


          UpperCAmelCase__     :						Optional[Any]						=							kwargs.get('''is_split_into_words'''		,		_A					)

          if is_split_into_words and not self.add_prefix_space:
              raise ValueError(
                  f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
                  '''to use it with pretokenized inputs.'''					)

          return super()._encode_plus(*_A		,		**_A					)






      def lowercase_				(   self							:       Optional[int]		,		_A							:       str		,		_A							:       Optional[str] = None					):


          '''simple docstring'''


          UpperCAmelCase__     :						str						=							self._tokenizer.model.save(_A		,		name=_A					)
          return tuple(_A					)






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


          '''simple docstring'''


          UpperCAmelCase__     :						Any						=							[self.bos_token_id] + token_ids_a + [self.eos_token_id]
          if token_ids_a is None:
              return output

          return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]






      def lowercase_				(   self							:       int		,		_A							:       List[int]		,		_A							:       Optional[List[int]] = None					):


          '''simple docstring'''


          UpperCAmelCase__     :						Optional[int]						=							[self.sep_token_id]
          UpperCAmelCase__     :						int						=							[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]




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


SCREAMING_SNAKE_CASE__     					=			get_logger(__name__)


def     lowercase	(    a     , a     , a     , a     , a=0 ):



		'''simple docstring'''




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

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


def     lowercase	(    a     , a     , a     , a     , a=0 ):



		'''simple docstring'''




		accelerator.wait_for_everyone()
		with FSDP.state_dict_type(
		    a     , 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(a ) != 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
						SCREAMING_SNAKE_CASE_				:List[Any]				     =						F"{MODEL_NAME}.bin" if model_index == 0 else F"{MODEL_NAME}_{model_index}.bin"
						SCREAMING_SNAKE_CASE_				:int				     =						os.path.join(a     , a )
						logger.info(F"Loading model from {input_model_file}" )
						SCREAMING_SNAKE_CASE_				:Dict				     =						torch.load(a )
						logger.info(F"Model loaded from {input_model_file}" )
				elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
						SCREAMING_SNAKE_CASE_				:int				     =						(
						    F"{MODEL_NAME}_rank{accelerator.process_index}.bin"
						    if model_index == 0
						    else F"{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin"
						)
						SCREAMING_SNAKE_CASE_				:int				     =						os.path.join(a     , a )
						logger.info(F"Loading model from {input_model_file}" )
						SCREAMING_SNAKE_CASE_				:Dict				     =						torch.load(a )
						logger.info(F"Model loaded from {input_model_file}" )
				elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
						SCREAMING_SNAKE_CASE_				:List[str]				     =						(
						    os.path.join(a     , F"{MODEL_NAME}_{model_index}" )
						    if F"{MODEL_NAME}" not in input_dir
						    else input_dir
						)
						logger.info(F"Loading model from {ckpt_dir}" )
						SCREAMING_SNAKE_CASE_				:Any				     =						{"model": model.state_dict()}
						dist_cp.load_state_dict(
						    state_dict=a     , storage_reader=dist_cp.FileSystemReader(a )     , planner=DefaultLoadPlanner()     , )
						SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						state_dict["model"]
						logger.info(F"Model loaded from {ckpt_dir}" )
				model.load_state_dict(a )


def     lowercase	(    a     , a     , a     , a     , a     , a=0 ):



		'''simple docstring'''




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


def     lowercase	(    a     , a     , a     , a     , a     , a=0 ):



		'''simple docstring'''




		accelerator.wait_for_everyone()
		with FSDP.state_dict_type(
		    a     , 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:
						SCREAMING_SNAKE_CASE_				:List[str]				     =						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:
						SCREAMING_SNAKE_CASE_				:List[Any]				     =						(
						    F"{OPTIMIZER_NAME}.bin" if optimizer_index == 0 else F"{OPTIMIZER_NAME}_{optimizer_index}.bin"
						)
						SCREAMING_SNAKE_CASE_				:Dict				     =						os.path.join(a     , a )
						logger.info(F"Loading Optimizer state from {input_optimizer_file}" )
						SCREAMING_SNAKE_CASE_				:str				     =						torch.load(a )
						logger.info(F"Optimizer state loaded from {input_optimizer_file}" )
				else:
						SCREAMING_SNAKE_CASE_				:Any				     =						(
						    os.path.join(a     , F"{OPTIMIZER_NAME}_{optimizer_index}" )
						    if F"{OPTIMIZER_NAME}" not in input_dir
						    else input_dir
						)
						logger.info(F"Loading Optimizer from {ckpt_dir}" )
						SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						load_sharded_optimizer_state_dict(
						    model_state_dict=model.state_dict()     , optimizer_key="optimizer"     , storage_reader=dist_cp.FileSystemReader(a )     , )
						SCREAMING_SNAKE_CASE_				:List[str]				     =						optim_state["optimizer"]
						logger.info(F"Optimizer loaded from {ckpt_dir}" )
				SCREAMING_SNAKE_CASE_				:int				     =						FSDP.optim_state_dict_to_load(a     , a     , a )
				optimizer.load_state_dict(a )






