



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

import sentencepiece as spm

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


_lowercase		:				str      		=logging.get_logger(__name__)

_lowercase		:				Union[str, Any]      		="▁"

_lowercase		:				List[Any]      		={"vocab_file": "sentencepiece.bpe.model"}

_lowercase		:				Optional[Any]      		={
    "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"
        ),
    }
}

_lowercase		:				Dict      		={
    "facebook/mbart-large-50-one-to-many-mmt": 1024,
}

# fmt: off
_lowercase		:				List[Any]      		=["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        snake_case__    (A__		):







						"""simple docstring"""




						__lowerCAmelCase					:Optional[int]      =					VOCAB_FILES_NAMES
						__lowerCAmelCase					:Any      =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						__lowerCAmelCase					:List[Any]      =					PRETRAINED_VOCAB_FILES_MAP
						__lowerCAmelCase					:Optional[int]      =					["input_ids", "attention_mask"]

						__lowerCAmelCase					:List[int]      =					[]
						__lowerCAmelCase					:List[int]      =					[]

						def __init__(       self ,       __lowercase ,       __lowercase=None ,       __lowercase=None ,       __lowercase="</s>" ,       __lowercase="</s>" ,       __lowercase="<s>" ,       __lowercase="<unk>" ,       __lowercase="<pad>" ,       __lowercase="<mask>" ,       __lowercase = None ,       **__lowercase ,       ) ->				None:
									"""simple docstring"""




									a__       :						Union[str, Any]							=  AddedToken(__lowercase ,       lstrip=__lowercase ,       rstrip=__lowercase  ) if isinstance(__lowercase ,       __lowercase  ) else mask_token

									a__       :						Optional[Any]							=  {} if sp_model_kwargs is None else sp_model_kwargs

									a__       :						Tuple							=  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__(
									    src_lang=__lowercase ,       tgt_lang=__lowercase ,       eos_token=__lowercase ,       unk_token=__lowercase ,       sep_token=__lowercase ,       cls_token=__lowercase ,       pad_token=__lowercase ,       mask_token=__lowercase ,       sp_model_kwargs=self.sp_model_kwargs ,       **__lowercase ,       )

									a__       :						str							=  spm.SentencePieceProcessor(**self.sp_model_kwargs  )
									self.sp_model.Load(str(__lowercase  )  )
									a__       :						Optional[Any]							=  vocab_file

									# Original fairseq vocab and spm vocab must be "aligned":
									# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
									# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
									# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
									# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

									# Mimic fairseq token-to-id alignment for the first 4 token
									a__       :						str							=  {"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

									# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
									a__       :						Tuple							=  1

									a__       :						Optional[Any]							=  len(self.sp_model  )
									a__       :						Tuple							=  {
									    code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(__lowercase  )
									}
									a__       :						str							=  {v: k for k, v in self.lang_code_to_id.items()}
									a__       :						int							=  len(self.sp_model  ) + len(self.lang_code_to_id  ) + self.fairseq_offset

									self.fairseq_tokens_to_ids.update(self.lang_code_to_id  )
									a__       :						Tuple							=  {v: k for k, v in self.fairseq_tokens_to_ids.items()}

									a__       :						Optional[Any]							=  src_lang if src_lang is not None else """en_XX"""
									a__       :						str							=  self.lang_code_to_id[self._src_lang]
									a__       :						Any							=  tgt_lang
									self.set_src_lang_special_tokens(self._src_lang  )

						@property
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				int:
									"""simple docstring"""




									return len(self.sp_model  ) + len(self.lang_code_to_id  ) + self.fairseq_offset + 1  # Plus 1 for the mask token

						@property
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				str:
									"""simple docstring"""




									return self._src_lang

						@src_lang.setter
						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase  ) ->				None:
									"""simple docstring"""




									a__       :						List[Any]							=  new_src_lang
									self.set_src_lang_special_tokens(self._src_lang  )

						def __getstate__(       self  ) ->				Dict:
									"""simple docstring"""




									a__       :						List[Any]							=  self.__dict__.copy()
									a__       :						Union[str, Any]							=  None
									return state

						def __setstate__(       self ,       __lowercase  ) ->				None:
									"""simple docstring"""




									a__       :						Union[str, Any]							=  d

									# for backward compatibility
									if not hasattr(self ,       """sp_model_kwargs"""  ):
												a__       :						Union[str, Any]							=  {}

									a__       :						List[str]							=  spm.SentencePieceProcessor(**self.sp_model_kwargs  )
									self.sp_model.Load(self.vocab_file  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Dict:
									"""simple docstring"""




									a__       :						List[Any]							=  {self.convert_ids_to_tokens(__lowercase  ): i for i in range(self.vocab_size  )}
									vocab.update(self.added_tokens_encoder  )
									return vocab

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase  ) ->				List[str]:
									"""simple docstring"""




									return self.sp_model.encode(__lowercase ,       out_type=__lowercase  )

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase  ) ->				int:
									"""simple docstring"""




									if token in self.fairseq_tokens_to_ids:
												return self.fairseq_tokens_to_ids[token]
									a__       :						List[Any]							=  self.sp_model.PieceToId(__lowercase  )

									# Need to return unknown token if the SP model returned 0
									return spm_id + self.fairseq_offset if spm_id else self.unk_token_id

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase  ) ->				str:
									"""simple docstring"""




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

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase  ) ->				int:
									"""simple docstring"""




									a__       :						List[str]							=  []
									a__       :						List[str]							=  """"""
									a__       :						Optional[int]							=  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(__lowercase  ) + token
															a__       :						List[str]							=  True
															a__       :						str							=  []
												else:
															current_sub_tokens.append(__lowercase  )
															a__       :						Any							=  False
									out_string += self.sp_model.decode(__lowercase  )
									return out_string.strip()

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase = None  ) ->				Tuple[str]:
									"""simple docstring"""




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

									if os.path.abspath(self.vocab_file  ) != os.path.abspath(__lowercase  ) and os.path.isfile(self.vocab_file  ):
												copyfile(self.vocab_file ,       __lowercase  )
									elif not os.path.isfile(self.vocab_file  ):
												with open(__lowercase ,       """wb"""  ) as fi:
															a__       :						Tuple							=  self.sp_model.serialized_model_proto()
															fi.write(__lowercase  )

									return (out_vocab_file,)

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase = None ,       __lowercase = False  ) ->				List[int]:
									"""simple docstring"""




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

									a__       :						Tuple							=  [1] * len(self.prefix_tokens  )
									a__       :						Optional[int]							=  [1] * len(self.suffix_tokens  )
									if token_ids_a is None:
												return prefix_ones + ([0] * len(__lowercase  )) + suffix_ones
									return prefix_ones + ([0] * len(__lowercase  )) + ([0] * len(__lowercase  )) + suffix_ones

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase = None  ) ->				List[int]:
									"""simple docstring"""




									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 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase ,       **__lowercase  ) ->				str:
									"""simple docstring"""




									if src_lang is None or tgt_lang is None:
												raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model"""  )
									a__       :						Any							=  src_lang
									a__       :						int							=  self(__lowercase ,       add_special_tokens=__lowercase ,       return_tensors=__lowercase ,       **__lowercase  )
									a__       :						int							=  self.convert_tokens_to_ids(__lowercase  )
									a__       :						str							=  tgt_lang_id
									return inputs

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase = "en_XX" ,       __lowercase = None ,       __lowercase = "ro_RO" ,       **__lowercase ,       ) ->				BatchEncoding:
									"""simple docstring"""




									a__       :						str							=  src_lang
									a__       :						Union[str, Any]							=  tgt_lang
									return super().prepare_seqaseq_batch(__lowercase ,       __lowercase ,       **__lowercase  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Optional[int]:
									"""simple docstring"""




									return self.set_src_lang_special_tokens(self.src_lang  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Optional[int]:
									"""simple docstring"""




									return self.set_tgt_lang_special_tokens(self.tgt_lang  )

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase  ) ->				None:
									"""simple docstring"""




									a__       :						List[str]							=  self.lang_code_to_id[src_lang]
									a__       :						str							=  [self.cur_lang_code_id]
									a__       :						Optional[int]							=  [self.eos_token_id]

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase  ) ->				None:
									"""simple docstring"""




									a__       :						List[str]							=  self.lang_code_to_id[tgt_lang]
									a__       :						Optional[int]							=  [self.cur_lang_code_id]
									a__       :						Union[str, Any]							=  [self.eos_token_id]









import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


_lowercase		:				Union[str, Any]      		=16
_lowercase		:				Dict      		=32

def      lowerCAmelCase_					(  _lowercase    :       Accelerator						,       _lowercase    :       int = 16						,       _lowercase    :       str = "bert-base-cased")							->	Union[str, Any]:







			"""simple docstring"""
			a__       :						Union[str, Any]							=  AutoTokenizer.from_pretrained(_lowercase)
			a__       :						Any							=  load_dataset("""glue"""						,       """mrpc""")

			def tokenize_function(_lowercase    :       str):
						# max_length=None => use the model max length (it's actually the default)
						a__       :						List[str]							=  tokenizer(examples["""sentence1"""]						,       examples["""sentence2"""]						,       truncation=_lowercase						,       max_length=_lowercase)
						return outputs

			# Apply the method we just defined to all the examples in all the splits of the dataset
			a__       :						Any							=  datasets.map(
			    _lowercase						,       batched=_lowercase						,       remove_columns=["""idx""", """sentence1""", """sentence2"""]						,       load_from_cache_file=_lowercase)

			# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
			# transformers library
			a__       :						Tuple							=  tokenized_datasets.rename_column("""label"""						,       """labels""")

			def collate_fn(_lowercase    :       Optional[Any]):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						if accelerator.distributed_type == DistributedType.TPU:
									return tokenizer.pad(_lowercase						,       padding="""max_length"""						,       max_length=128						,       return_tensors="""pt""")
						return tokenizer.pad(_lowercase						,       padding="""longest"""						,       return_tensors="""pt""")

			# Instantiate dataloaders.
			a__       :						int							=  DataLoader(
			    tokenized_datasets["""train"""]						,       shuffle=_lowercase						,       collate_fn=_lowercase						,       batch_size=_lowercase)
			a__       :						Union[str, Any]							=  DataLoader(
			    tokenized_datasets["""validation"""]						,       shuffle=_lowercase						,       collate_fn=_lowercase						,       batch_size=_lowercase)

			return train_dataloader, eval_dataloader

def      lowerCAmelCase_					(  _lowercase    :       Any						,       _lowercase    :       Dict)							->	int:







			"""simple docstring"""
			# Initialize accelerator
			a__       :						Optional[int]							=  Accelerator()

			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			a__       :						Union[str, Any]							=  config["""lr"""]
			a__       :						List[str]							=  int(config["""num_epochs"""])
			a__       :						List[str]							=  int(config["""seed"""])
			a__       :						Tuple							=  int(config["""batch_size"""])
			a__       :						int							=  args.model_name_or_path

			set_seed(_lowercase)
			a__	,						a__       :						int							=  get_dataloaders(_lowercase						,       _lowercase						,       _lowercase)

			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			a__       :						Optional[Any]							=  AutoModelForSequenceClassification.from_pretrained(_lowercase						,       return_dict=_lowercase)

			# Instantiate optimizer
			a__       :						int							=  (
			    AdamW
			    if accelerator.state.deepspeed_plugin is None
			    or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
			    else DummyOptim
			)
			a__       :						Optional[Any]							=  optimizer_cls(params=model.parameters()						,       lr=_lowercase)

			if accelerator.state.deepspeed_plugin is not None:
						a__       :						Dict							=  accelerator.state.deepspeed_plugin.deepspeed_config[
						    """gradient_accumulation_steps"""
						]
			else:
						a__       :						List[str]							=  1
			a__       :						List[Any]							=  (len(_lowercase) * num_epochs) // gradient_accumulation_steps

			# Instantiate scheduler
			if (
			    accelerator.state.deepspeed_plugin is None
			    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
			):
						a__       :						Dict							=  get_linear_schedule_with_warmup(
						    optimizer=_lowercase						,       num_warmup_steps=0						,       num_training_steps=_lowercase						,       )
			else:
						a__       :						Dict							=  DummyScheduler(_lowercase						,       total_num_steps=_lowercase						,       warmup_num_steps=0)

			# Prepare everything
			# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
			# prepare method.
			a__	,						a__	,						a__	,						a__	,						a__       :						List[Any]							=  accelerator.prepare(
			    _lowercase						,       _lowercase						,       _lowercase						,       _lowercase						,       _lowercase)

			# We need to keep track of how many total steps we have iterated over
			a__       :						Optional[int]							=  0
			# We also need to keep track of the stating epoch so files are named properly
			a__       :						Optional[int]							=  0

			# Now we train the model
			a__       :						Tuple							=  evaluate.load("""glue"""						,       """mrpc""")
			a__       :						List[Any]							=  0
			a__       :						Tuple							=  {}
			for epoch in range(_lowercase						,       _lowercase):
						model.train()
						for step, batch in enumerate(_lowercase):
									a__       :						Union[str, Any]							=  model(**_lowercase)
									a__       :						Tuple							=  outputs.loss
									a__       :						Any							=  loss / gradient_accumulation_steps
									accelerator.backward(_lowercase)
									if step % gradient_accumulation_steps == 0:
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()

									overall_step += 1

						model.eval()
						a__       :						int							=  0
						for step, batch in enumerate(_lowercase):
									# We could avoid this line since we set the accelerator with `device_placement=True`.
									batch.to(accelerator.device)
									with torch.no_grad():
												a__       :						str							=  model(**_lowercase)
									a__       :						Union[str, Any]							=  outputs.logits.argmax(dim=-1)
									# It is slightly faster to call this once, than multiple times
									a__	,						a__       :						Optional[int]							=  accelerator.gather(
									    (predictions, batch["""labels"""]))  # If we are in a multiprocess environment, the last batch has duplicates
									if accelerator.use_distributed:
												if step == len(_lowercase) - 1:
															a__       :						Union[str, Any]							=  predictions[: len(eval_dataloader.dataset) - samples_seen]
															a__       :						Any							=  references[: len(eval_dataloader.dataset) - samples_seen]
												else:
															samples_seen += references.shape[0]
									metric.add_batch(
									    predictions=_lowercase						,       references=_lowercase						,       )

						a__       :						Optional[Any]							=  metric.compute()
						# Use accelerator.print to print only on the main process.
						accelerator.print(F'''epoch {epoch}:'''						,       _lowercase)
						a__       :						Any							=  eval_metric["""accuracy"""]

						if best_performance < eval_metric["accuracy"]:
									a__       :						List[str]							=  eval_metric["""accuracy"""]

			if args.performance_lower_bound is not None:
						assert (
						    args.performance_lower_bound <= best_performance
						), F'''Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}'''

			accelerator.wait_for_everyone()
			if accelerator.is_main_process:
						with open(os.path.join(args.output_dir						,       """all_results.json""")						,       """w""") as f:
									json.dump(_lowercase						,       _lowercase)

def      lowerCAmelCase_					(  )							->	Tuple:







			"""simple docstring"""
			a__       :						Optional[Any]							=  argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage.""")
			parser.add_argument(
			    """--model_name_or_path"""						,       type=_lowercase						,       default="""bert-base-cased"""						,       help="""Path to pretrained model or model identifier from huggingface.co/models."""						,       required=_lowercase						,       )
			parser.add_argument(
			    """--output_dir"""						,       type=_lowercase						,       default="""."""						,       help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""						,       )
			parser.add_argument(
			    """--performance_lower_bound"""						,       type=_lowercase						,       default=_lowercase						,       help="""Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value."""						,       )
			parser.add_argument(
			    """--num_epochs"""						,       type=_lowercase						,       default=3						,       help="""Number of train epochs."""						,       )
			a__       :						Any							=  parser.parse_args()
			a__       :						Dict							=  {"""lr""": 2e-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
			training_function(_lowercase						,       _lowercase)


if __name__ == "__main__":
		main()




