




'''simple docstring'''





import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

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


_SCREAMING_SNAKE_CASE							:       str						=			logging.get_logger(__name__)


_SCREAMING_SNAKE_CASE							:       List[str]						=			{"vocab_file": "vocab.json", "merges_file": "merges.txt"}

_SCREAMING_SNAKE_CASE							:       Dict						=			{
    "vocab_file": {
        "allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json",
        "allenai/longformer-large-4096": (
            "https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json"
        ),
        "allenai/longformer-large-4096-finetuned-triviaqa": (
            "https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json"
        ),
        "allenai/longformer-base-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json"
        ),
        "allenai/longformer-large-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt",
        "allenai/longformer-large-4096": (
            "https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt"
        ),
        "allenai/longformer-large-4096-finetuned-triviaqa": (
            "https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt"
        ),
        "allenai/longformer-base-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt"
        ),
        "allenai/longformer-large-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt"
        ),
    },
}

_SCREAMING_SNAKE_CASE							:       List[Any]						=			{
    "allenai/longformer-base-4096": 4096,
    "allenai/longformer-large-4096": 4096,
    "allenai/longformer-large-4096-finetuned-triviaqa": 4096,
    "allenai/longformer-base-4096-extra.pos.embd.only": 4096,
    "allenai/longformer-large-4096-extra.pos.embd.only": 4096,
}


@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def 					UpperCamelCase_(     ):

       '''simple docstring'''






       snake_case_      =				(
           list(range(ord("!"					)							,    ord("~"					) + 1					)					) + list(range(ord("¡"					)							,    ord("¬"					) + 1					)					) + list(range(ord("®"					)							,    ord("ÿ"					) + 1					)					)
       )
       snake_case_      =				bs[:]
       snake_case_      =				0
       for b in range(2**8					):
              if b not in bs:
                     bs.append(snake_case					)
                     cs.append(2**8 + n					)
                     n += 1
       snake_case_      =				[chr(snake_case					) for n in cs]
       return dict(zip(snake_case							,    snake_case					)					)


def 					UpperCamelCase_(     snake_case							:	Tuple					):

       '''simple docstring'''






       snake_case_      =				set()
       snake_case_      =				word[0]
       for char in word[1:]:
              pairs.add((prev_char, char)					)
              snake_case_      =				char
       return pairs




class 			_snake_case		(     lowercase_							):
   lowerCAmelCase_					:				Optional[int]							=   VOCAB_FILES_NAMES
   lowerCAmelCase_					:				str							=   PRETRAINED_VOCAB_FILES_MAP
   lowerCAmelCase_					:				int							=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCAmelCase_					:				Optional[Any]							=   ["input_ids", "attention_mask"]




   def __init__( self    ,							a__    ,							a__    ,							a__="replace"    ,							a__="<s>"    ,							a__="</s>"    ,							a__="</s>"    ,							a__="<s>"    ,							a__="<unk>"    ,							a__="<pad>"    ,							a__="<mask>"    ,							a__=False    ,							**a__    ,							)    ->    Tuple:

          '''simple docstring'''




          snake_case_      =				AddedToken(a__    ,							lstrip=a__    ,							rstrip=a__							) if isinstance(a__    ,							a__							) else bos_token
          snake_case_      =				AddedToken(a__    ,							lstrip=a__    ,							rstrip=a__							) if isinstance(a__    ,							a__							) else eos_token
          snake_case_      =				AddedToken(a__    ,							lstrip=a__    ,							rstrip=a__							) if isinstance(a__    ,							a__							) else sep_token
          snake_case_      =				AddedToken(a__    ,							lstrip=a__    ,							rstrip=a__							) if isinstance(a__    ,							a__							) else cls_token
          snake_case_      =				AddedToken(a__    ,							lstrip=a__    ,							rstrip=a__							) if isinstance(a__    ,							a__							) else unk_token
          snake_case_      =				AddedToken(a__    ,							lstrip=a__    ,							rstrip=a__							) if isinstance(a__    ,							a__							) else pad_token

          # Mask token behave like a normal word, i.e. include the space before it
          snake_case_      =				AddedToken(a__    ,							lstrip=a__    ,							rstrip=a__							) if isinstance(a__    ,							a__							) else mask_token

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

          with open(a__    ,							encoding="utf-8"							) as vocab_handle:
                 snake_case_      =				json.load(a__							)
          snake_case_      =				{v: k for k, v in self.encoder.items()}
          snake_case_      =				errors  # how to handle errors in decoding
          snake_case_      =				bytes_to_unicode()
          snake_case_      =				{v: k for k, v in self.byte_encoder.items()}
          with open(a__    ,							encoding="utf-8"							) as merges_handle:
                 snake_case_      =				merges_handle.read().split("\n"							)[1:-1]
          snake_case_      =				[tuple(merge.split()							) for merge in bpe_merges]
          snake_case_      =				dict(zip(a__    ,							range(len(a__							)							)							)							)
          snake_case_      =				{}
          snake_case_      =				add_prefix_space

          # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
          snake_case_      =				re.compile(r"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"							)




   @property
   def        lowerCAmelCase__    ( self							)    ->    Dict:

          '''simple docstring'''




          return len(self.encoder							)




   def        lowerCAmelCase__    ( self							)    ->    List[str]:

          '''simple docstring'''




          return dict(self.encoder    ,							**self.added_tokens_encoder							)




   def        lowerCAmelCase__    ( self    ,							a__							)    ->    List[Any]:

          '''simple docstring'''




          if token in self.cache:
                 return self.cache[token]
          snake_case_      =				tuple(a__							)
          snake_case_      =				get_pairs(a__							)

          if not pairs:
                 return token

          while True:
                 snake_case_      =				min(a__    ,							key=lambda a__							: self.bpe_ranks.get(a__    ,							float("inf"							)							)							)
                 if bigram not in self.bpe_ranks:
                        break
                 snake_case_ ,				snake_case_      =				bigram
                 snake_case_      =				[]
                 snake_case_      =				0
                 while i < len(a__							):
                        try:
                               snake_case_      =				word.index(a__    ,							a__							)
                        except ValueError:
                               new_word.extend(word[i:]							)
                               break
                        else:
                               new_word.extend(word[i:j]							)
                               snake_case_      =				j

                        if word[i] == first and i < len(a__							) - 1 and word[i + 1] == second:
                               new_word.append(first + second							)
                               i += 2
                        else:
                               new_word.append(word[i]							)
                               i += 1
                 snake_case_      =				tuple(a__							)
                 snake_case_      =				new_word
                 if len(a__							) == 1:
                        break
                 else:
                        snake_case_      =				get_pairs(a__							)
          snake_case_      =				" ".join(a__							)
          snake_case_      =				word
          return word




   def        lowerCAmelCase__    ( self    ,							a__							)    ->    int:

          '''simple docstring'''




          snake_case_      =				[]
          for token in re.findall(self.pat    ,							a__							):
                 snake_case_      =				"".join(
                     self.byte_encoder[b] for b in token.encode("utf-8"							)							)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
                 bpe_tokens.extend(bpe_token for bpe_token in self.bpe(a__							).split(" "							)							)
          return bpe_tokens




   def        lowerCAmelCase__    ( self    ,							a__							)    ->    Union[str, Any]:

          '''simple docstring'''




          return self.encoder.get(a__    ,							self.encoder.get(self.unk_token							)							)




   def        lowerCAmelCase__    ( self    ,							a__							)    ->    Union[str, Any]:

          '''simple docstring'''




          return self.decoder.get(a__							)




   def        lowerCAmelCase__    ( self    ,							a__							)    ->    int:

          '''simple docstring'''




          snake_case_      =				"".join(a__							)
          snake_case_      =				bytearray([self.byte_decoder[c] for c in text]							).decode("utf-8"    ,							errors=self.errors							)
          return text




   def        lowerCAmelCase__    ( self    ,							a__    ,							a__ = None							)    ->    Tuple[str]:

          '''simple docstring'''




          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"]							)
          snake_case_      =				os.path.join(
              a__    ,							(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]							)

          with open(a__    ,							"w"    ,							encoding="utf-8"							) as f:
                 f.write(json.dumps(self.encoder    ,							indent=2    ,							sort_keys=a__    ,							ensure_ascii=a__							) + "\n"							)

          snake_case_      =				0
          with open(a__    ,							"w"    ,							encoding="utf-8"							) as writer:
                 writer.write("#version: 0.2\n"							)
                 for bpe_tokens, token_index in sorted(self.bpe_ranks.items()    ,							key=lambda a__							: kv[1]							):
                        if index != token_index:
                               logger.warning(
                                   F'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
                                   " Please check that the tokenizer is not corrupted!"							)
                               snake_case_      =				token_index
                        writer.write(" ".join(a__							) + "\n"							)
                        index += 1

          return vocab_file, merge_file




   def        lowerCAmelCase__    ( self    ,							a__    ,							a__ = None							)    ->    List[int]:

          '''simple docstring'''




          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        lowerCAmelCase__    ( self    ,							a__    ,							a__ = None    ,							a__ = False							)    ->    List[int]:

          '''simple docstring'''




          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        lowerCAmelCase__    ( self    ,							a__    ,							a__ = None							)    ->    List[int]:

          '''simple docstring'''




          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]




   def        lowerCAmelCase__    ( self    ,							a__    ,							a__=False    ,							**a__							)    ->    int:

          '''simple docstring'''




          snake_case_      =				kwargs.pop("add_prefix_space"    ,							self.add_prefix_space							)
          if (is_split_into_words or add_prefix_space) and (len(a__							) > 0 and not text[0].isspace()):
                 snake_case_      =				" " + text
          return (text, kwargs)










'''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


_SCREAMING_SNAKE_CASE							:       Tuple						=			get_logger(__name__)


def 					UpperCamelCase_(     snake_case							:	Optional[Any]							,    snake_case							:	Optional[Any]							,    snake_case							:	Optional[Any]							,    snake_case							:	int							,    snake_case							:	List[Any]=0					):

							'''simple docstring'''






							os.makedirs(snake_case							,    exist_ok=snake_case					)
							with FSDP.state_dict_type(
							    snake_case							,    fsdp_plugin.state_dict_type							,    fsdp_plugin.state_dict_config							,    fsdp_plugin.optim_state_dict_config					):
														snake_case_      =				model.state_dict()
														if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
																					snake_case_      =				f'{MODEL_NAME}.bin' if model_index == 0 else f'{MODEL_NAME}_{model_index}.bin'
																					snake_case_      =				os.path.join(snake_case							,    snake_case					)
																					if accelerator.process_index == 0:
																												logger.info(f'Saving model to {output_model_file}'					)
																												torch.save(snake_case							,    snake_case					)
																												logger.info(f'Model saved to {output_model_file}'					)
														elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
																					snake_case_      =				(
																					    f'{MODEL_NAME}_rank{accelerator.process_index}.bin'
																					    if model_index == 0
																					    else f'{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin'
																					)
																					snake_case_      =				os.path.join(snake_case							,    snake_case					)
																					logger.info(f'Saving model to {output_model_file}'					)
																					torch.save(snake_case							,    snake_case					)
																					logger.info(f'Model saved to {output_model_file}'					)
														elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
																					snake_case_      =				os.path.join(snake_case							,    f'{MODEL_NAME}_{model_index}'					)
																					os.makedirs(snake_case							,    exist_ok=snake_case					)
																					logger.info(f'Saving model to {ckpt_dir}'					)
																					snake_case_      =				{"model": state_dict}

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


def 					UpperCamelCase_(     snake_case							:	Optional[int]							,    snake_case							:	Optional[Any]							,    snake_case							:	int							,    snake_case							:	Union[str, Any]							,    snake_case							:	Any=0					):

							'''simple docstring'''






							accelerator.wait_for_everyone()
							with FSDP.state_dict_type(
							    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(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
																					snake_case_      =				f'{MODEL_NAME}.bin' if model_index == 0 else f'{MODEL_NAME}_{model_index}.bin'
																					snake_case_      =				os.path.join(snake_case							,    snake_case					)
																					logger.info(f'Loading model from {input_model_file}'					)
																					snake_case_      =				torch.load(snake_case					)
																					logger.info(f'Model loaded from {input_model_file}'					)
														elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
																					snake_case_      =				(
																					    f'{MODEL_NAME}_rank{accelerator.process_index}.bin'
																					    if model_index == 0
																					    else f'{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin'
																					)
																					snake_case_      =				os.path.join(snake_case							,    snake_case					)
																					logger.info(f'Loading model from {input_model_file}'					)
																					snake_case_      =				torch.load(snake_case					)
																					logger.info(f'Model loaded from {input_model_file}'					)
														elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
																					snake_case_      =				(
																					    os.path.join(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}'					)
																					snake_case_      =				{"model": model.state_dict()}
																					dist_cp.load_state_dict(
																					    state_dict=snake_case							,    storage_reader=dist_cp.FileSystemReader(snake_case					)							,    planner=DefaultLoadPlanner()							,    )
																					snake_case_      =				state_dict["model"]
																					logger.info(f'Model loaded from {ckpt_dir}'					)
														model.load_state_dict(snake_case					)


def 					UpperCamelCase_(     snake_case							:	str							,    snake_case							:	List[str]							,    snake_case							:	Any							,    snake_case							:	Tuple							,    snake_case							:	Optional[Any]							,    snake_case							:	Tuple=0					):

							'''simple docstring'''






							os.makedirs(snake_case							,    exist_ok=snake_case					)
							with FSDP.state_dict_type(
							    snake_case							,    fsdp_plugin.state_dict_type							,    fsdp_plugin.state_dict_config							,    fsdp_plugin.optim_state_dict_config					):
														snake_case_      =				FSDP.optim_state_dict(snake_case							,    snake_case					)
														if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
																					if accelerator.process_index == 0:
																												snake_case_      =				(
																												    f'{OPTIMIZER_NAME}.bin' if optimizer_index == 0 else f'{OPTIMIZER_NAME}_{optimizer_index}.bin'
																												)
																												snake_case_      =				os.path.join(snake_case							,    snake_case					)
																												logger.info(f'Saving Optimizer state to {output_optimizer_file}'					)
																												torch.save(snake_case							,    snake_case					)
																												logger.info(f'Optimizer state saved in {output_optimizer_file}'					)
														else:
																					snake_case_      =				os.path.join(snake_case							,    f'{OPTIMIZER_NAME}_{optimizer_index}'					)
																					os.makedirs(snake_case							,    exist_ok=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(snake_case					)							,    planner=DefaultSavePlanner()							,    )
																					logger.info(f'Optimizer state saved in {ckpt_dir}'					)




def 					UpperCamelCase_(     snake_case							:	Optional[Any]							,    snake_case							:	List[str]							,    snake_case							:	Union[str, Any]							,    snake_case							:	int							,    snake_case							:	Optional[int]							,    snake_case							:	Union[str, Any]=0					):

							'''simple docstring'''






							accelerator.wait_for_everyone()
							with FSDP.state_dict_type(
							    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:
																					snake_case_      =				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:
																					snake_case_      =				(
																					    f'{OPTIMIZER_NAME}.bin' if optimizer_index == 0 else f'{OPTIMIZER_NAME}_{optimizer_index}.bin'
																					)
																					snake_case_      =				os.path.join(snake_case							,    snake_case					)
																					logger.info(f'Loading Optimizer state from {input_optimizer_file}'					)
																					snake_case_      =				torch.load(snake_case					)
																					logger.info(f'Optimizer state loaded from {input_optimizer_file}'					)
														else:
																					snake_case_      =				(
																					    os.path.join(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}'					)
																					snake_case_      =				load_sharded_optimizer_state_dict(
																					    model_state_dict=model.state_dict()							,    optimizer_key="optimizer"							,    storage_reader=dist_cp.FileSystemReader(snake_case					)							,    )
																					snake_case_      =				optim_state["optimizer"]
																					logger.info(f'Optimizer loaded from {ckpt_dir}'					)
														snake_case_      =				FSDP.optim_state_dict_to_load(snake_case							,    snake_case							,    snake_case					)
														optimizer.load_state_dict(snake_case					)




