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

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
						from .tokenization_mbartaa import MBartaaTokenizer
else:
						lowerCAmelCase__  	=      None


lowerCAmelCase__  	=      logging.get_logger(__name__)

lowerCAmelCase__  	=      {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

lowerCAmelCase__  	=      {
    "vocab_file": {
        "facebook/mbart-large-50-one-to-many-mmt": (
            "https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model"
        ),
    },
    "tokenizer_file": {
        "facebook/mbart-large-50-one-to-many-mmt": (
            "https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/tokenizer.json"
        ),
    },
}

lowerCAmelCase__  	=      {
    "facebook/mbart-large-50-one-to-many-mmt": 1_0_2_4,
}

# fmt: off
lowerCAmelCase__  	=      ["ar_AR", "cs_CZ", "de_DE", "en_XX", "es_XX", "et_EE", "fi_FI", "fr_XX", "gu_IN", "hi_IN", "it_IT", "ja_XX", "kk_KZ", "ko_KR", "lt_LT", "lv_LV", "my_MM", "ne_NP", "nl_XX", "ro_RO", "ru_RU", "si_LK", "tr_TR", "vi_VN", "zh_CN", "af_ZA", "az_AZ", "bn_IN", "fa_IR", "he_IL", "hr_HR", "id_ID", "ka_GE", "km_KH", "mk_MK", "ml_IN", "mn_MN", "mr_IN", "pl_PL", "ps_AF", "pt_XX", "sv_SE", "sw_KE", "ta_IN", "te_IN", "th_TH", "tl_XX", "uk_UA", "ur_PK", "xh_ZA", "gl_ES", "sl_SI"]
class 	_a  (							lowerCamelCase_					):



	"""simple docstring"""



	__SCREAMING_SNAKE_CASE              =					VOCAB_FILES_NAMES
	__SCREAMING_SNAKE_CASE              =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__SCREAMING_SNAKE_CASE              =					PRETRAINED_VOCAB_FILES_MAP
	__SCREAMING_SNAKE_CASE              =					['input_ids', 'attention_mask']
	__SCREAMING_SNAKE_CASE              =					MBartaaTokenizer

	__SCREAMING_SNAKE_CASE              =					[]
	__SCREAMING_SNAKE_CASE              =					[]


	def __init__(   self       ,  lowerCAmelCase_=None       ,  lowerCAmelCase_=None       ,  lowerCAmelCase_=None       ,  lowerCAmelCase_=None       ,  lowerCAmelCase_="</s>"       ,  lowerCAmelCase_="</s>"       ,  lowerCAmelCase_="<s>"       ,  lowerCAmelCase_="<unk>"       ,  lowerCAmelCase_="<pad>"       ,  lowerCAmelCase_="<mask>"       ,  **lowerCAmelCase_       ,  ):
		# Mask token behave like a normal word, i.e. include the space before it
		_lowercase   =AddedToken(lowerCAmelCase_       ,  lstrip=lowerCAmelCase_       ,  rstrip=lowerCAmelCase_       ) if isinstance(lowerCAmelCase_       ,  lowerCAmelCase_       ) else mask_token

		_lowercase   =kwargs.get("additional_special_tokens"       ,  []       )
		kwargs["additional_special_tokens"] += [
		    code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
		]

		super().__init__(
		    lowerCAmelCase_       ,  src_lang=lowerCAmelCase_       ,  tgt_lang=lowerCAmelCase_       ,  tokenizer_file=lowerCAmelCase_       ,  eos_token=lowerCAmelCase_       ,  sep_token=lowerCAmelCase_       ,  cls_token=lowerCAmelCase_       ,  unk_token=lowerCAmelCase_       ,  pad_token=lowerCAmelCase_       ,  mask_token=lowerCAmelCase_       ,  **lowerCAmelCase_       ,  )

		_lowercase   =vocab_file
		_lowercase   =False if not self.vocab_file else True

		_lowercase   ={
		    lang_code: self.convert_tokens_to_ids(lowerCAmelCase_       ) for lang_code in FAIRSEQ_LANGUAGE_CODES
		}

		_lowercase   =src_lang if src_lang is not None else "en_XX"
		_lowercase   =tgt_lang
		_lowercase   =self.lang_code_to_id[self._src_lang]
		self.set_src_lang_special_tokens(self._src_lang       )


	@property
	def __lowerCAmelCase      (   self       ):
		return self._src_lang


	@src_lang.setter
	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ):
		_lowercase   =new_src_lang
		self.set_src_lang_special_tokens(self._src_lang       )


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_ = None       ):
		if token_ids_a is None:
			return self.prefix_tokens + token_ids_a + self.suffix_tokens
		# We don't expect to process pairs, but leave the pair logic for API consistency
		return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_ = "en_XX"       ,  lowerCAmelCase_ = None       ,  lowerCAmelCase_ = "ro_RO"       ,  **lowerCAmelCase_       ,  ):
		_lowercase   =src_lang
		_lowercase   =tgt_lang
		return super().prepare_seqaseq_batch(lowerCAmelCase_       ,  lowerCAmelCase_       ,  **lowerCAmelCase_       )


	def __lowerCAmelCase      (   self       ):
		return self.set_src_lang_special_tokens(self.src_lang       )


	def __lowerCAmelCase      (   self       ):
		return self.set_tgt_lang_special_tokens(self.tgt_lang       )


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ):
		_lowercase   =self.convert_tokens_to_ids(lowerCAmelCase_       )
		_lowercase   =[self.cur_lang_code_id]
		_lowercase   =[self.eos_token_id]

		_lowercase   =self.convert_ids_to_tokens(self.prefix_tokens       )
		_lowercase   =self.convert_ids_to_tokens(self.suffix_tokens       )

		_lowercase   =processors.TemplateProcessing(
		    single=prefix_tokens_str + ["$A"] + suffix_tokens_str       ,  pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str       ,  special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str       ,  self.prefix_tokens + self.suffix_tokens       )       )       ,  )


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ):
		_lowercase   =self.convert_tokens_to_ids(lowerCAmelCase_       )
		_lowercase   =[self.cur_lang_code_id]
		_lowercase   =[self.eos_token_id]

		_lowercase   =self.convert_ids_to_tokens(self.prefix_tokens       )
		_lowercase   =self.convert_ids_to_tokens(self.suffix_tokens       )

		_lowercase   =processors.TemplateProcessing(
		    single=prefix_tokens_str + ["$A"] + suffix_tokens_str       ,  pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str       ,  special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str       ,  self.prefix_tokens + self.suffix_tokens       )       )       ,  )


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  **lowerCAmelCase_       ):
		if src_lang is None or tgt_lang is None:
			raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model"       )
		_lowercase   =src_lang
		_lowercase   =self(lowerCAmelCase_       ,  add_special_tokens=lowerCAmelCase_       ,  return_tensors=lowerCAmelCase_       ,  **lowerCAmelCase_       )
		_lowercase   =self.convert_tokens_to_ids(lowerCAmelCase_       )
		_lowercase   =tgt_lang_id
		return inputs


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_ = None       ):
		if not self.can_save_slow_tokenizer:
			raise ValueError(
			    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
			    "tokenizer."       )

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

		if os.path.abspath(self.vocab_file       ) != os.path.abspath(lowerCAmelCase_       ):
			copyfile(self.vocab_file       ,  lowerCAmelCase_       )

		return (out_vocab_file,)








import os
import random
from pathlib import Path
from typing import List

import numpy as np
import torch
from torch.cuda.amp import GradScaler

from .utils import (
    MODEL_NAME,
    OPTIMIZER_NAME,
    RNG_STATE_NAME,
    SCALER_NAME,
    SCHEDULER_NAME,
    get_pretty_name,
    is_tpu_available,
    is_xpu_available,
    save,
)


if is_tpu_available(check_device=False):
       import torch_xla.core.xla_model as xm

from .logging import get_logger
from .state import PartialState


__UpperCamelCase					:	int          =				get_logger(__name__)
def   snake_case_    (			__lowercase      ,							__lowercase      ,							__lowercase      ,							__lowercase      ,							__lowercase      ,							__lowercase = None      ,							):
   # Model states
   for i, state in enumerate(__lowercase							):
      UpperCAmelCase_				:   Dict							= F'''{MODEL_NAME}.bin''' if i == 0 else F'''{MODEL_NAME}_{i}.bin'''
      UpperCAmelCase_				:   List[Any]							= os.path.join(__lowercase      ,							__lowercase							)
      save(__lowercase      ,							__lowercase							)
      logger.info(F'''Model weights saved in {output_model_file}'''							)
   # Optimizer states
   for i, opt in enumerate(__lowercase							):
      UpperCAmelCase_				:   Tuple							= opt.state_dict()
      UpperCAmelCase_				:   Any							= F'''{OPTIMIZER_NAME}.bin''' if i == 0 else F'''{OPTIMIZER_NAME}_{i}.bin'''
      UpperCAmelCase_				:   List[Any]							= os.path.join(__lowercase      ,							__lowercase							)
      save(__lowercase      ,							__lowercase							)
      logger.info(F'''Optimizer state saved in {output_optimizer_file}'''							)
   # Scheduler states
   for i, scheduler in enumerate(__lowercase							):
      UpperCAmelCase_				:   Tuple							= scheduler.state_dict()
      UpperCAmelCase_				:   Tuple							= F'''{SCHEDULER_NAME}.bin''' if i == 0 else F'''{SCHEDULER_NAME}_{i}.bin'''
      UpperCAmelCase_				:   Tuple							= os.path.join(__lowercase      ,							__lowercase							)
      save(__lowercase      ,							__lowercase							)
      logger.info(F'''Scheduler state saved in {output_scheduler_file}'''							)
   # GradScaler state
   if scaler is not None:
      UpperCAmelCase_				:   Union[str, Any]							= scaler.state_dict()
      UpperCAmelCase_				:   Union[str, Any]							= os.path.join(__lowercase      ,							__lowercase							)
      torch.save(__lowercase      ,							__lowercase							)
      logger.info(F'''Gradient scaler state saved in {output_scaler_file}'''							)
   # Random number generator states
   UpperCAmelCase_				:   Union[str, Any]							= {}
   UpperCAmelCase_				:   Tuple							= F'''{RNG_STATE_NAME}_{process_index}.pkl'''
   UpperCAmelCase_				:   List[Any]							= random.getstate()
   UpperCAmelCase_				:   Any							= np.random.get_state()
   UpperCAmelCase_				:   List[Any]							= torch.get_rng_state()
   if is_xpu_available():
      UpperCAmelCase_				:   Optional[int]							= torch.xpu.get_rng_state_all()
   else:
      UpperCAmelCase_				:   Any							= torch.cuda.get_rng_state_all()
   if is_tpu_available():
      UpperCAmelCase_				:   List[str]							= xm.get_rng_state()
   UpperCAmelCase_				:   Any							= os.path.join(__lowercase      ,							__lowercase							)
   torch.save(__lowercase      ,							__lowercase							)
   logger.info(F'''Random states saved in {output_states_file}'''							)
   return output_dir
def   snake_case_    (			__lowercase      ,							__lowercase      ,							__lowercase      ,							__lowercase      ,							__lowercase      ,							__lowercase=None      ,							__lowercase=None      ,							**__lowercase      ,							):
   if map_location not in [None, "cpu", "on_device"]:
      raise TypeError(
          '''Unsupported optimizer map location passed, please choose one of `None`, `\'cpu\'`, or `\'on_device\'`'''							)
   if map_location is None:
      UpperCAmelCase_				:   Optional[Any]							= '''cpu'''
   elif map_location == "on_device":
      UpperCAmelCase_				:   Optional[int]							= PartialState().device
   # Model states
   for i, model in enumerate(__lowercase							):
      UpperCAmelCase_				:   List[Any]							= F'''{MODEL_NAME}.bin''' if i == 0 else F'''{MODEL_NAME}_{i}.bin'''
      UpperCAmelCase_				:   int							= os.path.join(__lowercase      ,							__lowercase							)
      models[i].load_state_dict(torch.load(__lowercase      ,							map_location=__lowercase							)      ,							**__lowercase							)
   logger.info('''All model weights loaded successfully'''							)

   # Optimizer states
   for i, opt in enumerate(__lowercase							):
      UpperCAmelCase_				:   List[str]							= F'''{OPTIMIZER_NAME}.bin''' if i == 0 else F'''{OPTIMIZER_NAME}_{i}.bin'''
      UpperCAmelCase_				:   List[Any]							= os.path.join(__lowercase      ,							__lowercase							)
      UpperCAmelCase_				:   Tuple							= torch.load(__lowercase      ,							map_location=__lowercase							)
      optimizers[i].load_state_dict(__lowercase							)
   logger.info('''All optimizer states loaded successfully'''							)

   # Scheduler states
   for i, scheduler in enumerate(__lowercase							):
      UpperCAmelCase_				:   Dict							= F'''{SCHEDULER_NAME}.bin''' if i == 0 else F'''{SCHEDULER_NAME}_{i}.bin'''
      UpperCAmelCase_				:   int							= os.path.join(__lowercase      ,							__lowercase							)
      scheduler.load_state_dict(torch.load(__lowercase							)							)
   logger.info('''All scheduler states loaded successfully'''							)

   # GradScaler state
   if scaler is not None:
      UpperCAmelCase_				:   List[str]							= os.path.join(__lowercase      ,							__lowercase							)
      scaler.load_state_dict(torch.load(__lowercase							)							)
      logger.info('''GradScaler state loaded successfully'''							)

   # Random states
   try:
      UpperCAmelCase_				:   Optional[Any]							= torch.load(os.path.join(__lowercase      ,							F'''{RNG_STATE_NAME}_{process_index}.pkl'''							)							)
      random.setstate(states['''random_state''']							)
      np.random.set_state(states['''numpy_random_seed''']							)
      torch.set_rng_state(states['''torch_manual_seed''']							)
      if is_xpu_available():
         torch.xpu.set_rng_state_all(states['''torch_xpu_manual_seed''']							)
      else:
         torch.cuda.set_rng_state_all(states['''torch_cuda_manual_seed''']							)
      if is_tpu_available():
         xm.set_rng_state(states['''xm_seed''']							)
      logger.info('''All random states loaded successfully'''							)
   except Exception:
      logger.info('''Could not load random states'''							)
def   snake_case_    (			__lowercase      ,							__lowercase      ,							__lowercase = 0							):
   UpperCAmelCase_				:   Optional[int]							= Path(__lowercase							) / F'''custom_checkpoint_{index}.pkl'''
   logger.info(F'''Saving the state of {get_pretty_name(__lowercase							)} to {save_location}'''							)
   torch.save(obj.state_dict()      ,							__lowercase							)


def   snake_case_    (			__lowercase      ,							__lowercase      ,							__lowercase = 0							):
   UpperCAmelCase_				:   Dict							= F'''{path}/custom_checkpoint_{index}.pkl'''
   logger.info(F'''Loading the state of {get_pretty_name(__lowercase							)} from {load_location}'''							)
   obj.load_state_dict(torch.load(__lowercase      ,							map_location='''cpu'''							)							)