


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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_A		       =						logging.get_logger(__name__)

_A		       =						'▁'

_A		       =						{'vocab_file': 'sentencepiece.bpe.model'}

_A		       =						{
    'vocab_file': {
        'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model',
    }
}

_A		       =						{
    'facebook/xglm-564M': 2048,
}






class     UpperCAmelCase__       (					A_		):



    """simple docstring"""






    UpperCAmelCase__						:		List[Any]      =  VOCAB_FILES_NAMES
    UpperCAmelCase__						:		Any      =  PRETRAINED_VOCAB_FILES_MAP
    UpperCAmelCase__						:		str      =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCAmelCase__						:		int      =  ["input_ids", "attention_mask"]






    def __init__(			self					,   A_					,   A_="<s>"					,   A_="</s>"					,   A_="</s>"					,   A_="<s>"					,   A_="<unk>"					,   A_="<pad>"					,   A_ = None					,   **A_					,   )  ->       None:
         __UpperCamelCase    							={} if sp_model_kwargs is None else sp_model_kwargs

         # Compatibility with the original tokenizer
         __UpperCamelCase    							=7
         __UpperCamelCase    							=[f'<madeupword{i}>' for i in range(self.num_madeup_words    )]

         __UpperCamelCase    							=kwargs.get('additional_special_tokens'					,   []    )
         kwargs["additional_special_tokens"] += [
             word for word in madeup_words if word not in kwargs["additional_special_tokens"]
         ]

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

         __UpperCamelCase    							=spm.SentencePieceProcessor(**self.sp_model_kwargs    )
         self.sp_model.Load(str(_snake_case    )    )
         __UpperCamelCase    							=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'

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

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

         __UpperCamelCase    							=len(self.sp_model    )
         __UpperCamelCase    							={f'<madeupword{i}>': sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words    )}
         self.fairseq_tokens_to_ids.update(_snake_case    )

         __UpperCamelCase    							={v: k for k, v in self.fairseq_tokens_to_ids.items()}






    def __getstate__(			self    )  ->       Optional[int]:
         __UpperCamelCase    							=self.__dict__.copy()
         __UpperCamelCase    							=None
         __UpperCamelCase    							=self.sp_model.serialized_model_proto()
         return state






    def __setstate__(			self					,   A_    )  ->       Any:
         __UpperCamelCase    							=d

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

         __UpperCamelCase    							=spm.SentencePieceProcessor(**self.sp_model_kwargs    )
         self.sp_model.LoadFromSerializedProto(self.sp_model_proto    )






    def _a					(			self					,   A_					,   A_ = None    )  ->       List[int]:



         if token_ids_a is None:
              return [self.sep_token_id] + token_ids_a
         __UpperCamelCase    							=[self.sep_token_id]
         return sep + token_ids_a + sep + sep + token_ids_a






    def _a					(			self					,   A_					,   A_ = None					,   A_ = False    )  ->       List[int]:



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

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






    def _a					(			self					,   A_					,   A_ = None    )  ->       List[int]:
         __UpperCamelCase    							=[self.sep_token_id]

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






    @property
    def _a					(			self    )  ->       Tuple:
         return len(self.sp_model    ) + self.fairseq_offset + self.num_madeup_words






    def _a					(			self    )  ->       Optional[Any]:
         __UpperCamelCase    							={self.convert_ids_to_tokens(_snake_case    ): i for i in range(self.vocab_size    )}
         vocab.update(self.added_tokens_encoder    )
         return vocab






    def _a					(			self					,   A_    )  ->       List[str]:
         return self.sp_model.encode(_snake_case					,   out_type=_snake_case    )






    def _a					(			self					,   A_    )  ->       List[Any]:



         if token in self.fairseq_tokens_to_ids:
              return self.fairseq_tokens_to_ids[token]
         __UpperCamelCase    							=self.sp_model.PieceToId(_snake_case    )

         # 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 _a					(			self					,   A_    )  ->       Any:



         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 _a					(			self					,   A_    )  ->       Union[str, Any]:
         __UpperCamelCase    							=''''''.join(_snake_case    ).replace(_snake_case					,   ' '    ).strip()
         return out_string






    def _a					(			self					,   A_					,   A_ = None    )  ->       Tuple[str]:



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

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

         return (out_vocab_file,)










"""simple docstring"""







import argparse
import os

# New Code #
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 import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


lowerCAmelCase_   						=   16
lowerCAmelCase_   						=   32



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase = 16	)							->      Optional[Any]:
		lowercase__   :    Optional[Any]				=			AutoTokenizer.from_pretrained('''bert-base-cased'''	)
		lowercase__   :    int				=			load_dataset('''glue'''						,     '''mrpc'''	)

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

		# Apply the method we just defined to all the examples in all the splits of the dataset
		# starting with the main process first:
		with accelerator.main_process_first():
				lowercase__   :    str				=			datasets.map(
				    __lowerCamelCase						,     batched=__lowerCamelCase						,     remove_columns=['''idx''', '''sentence1''', '''sentence2''']						,     )

		# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
		# transformers library
		lowercase__   :    Union[str, Any]				=			tokenized_datasets.rename_column('''label'''						,     '''labels'''	)

		def collate_fn(__lowerCamelCase	):
				# On TPU it's best to pad everything to the same length or training will be very slow.
				lowercase__   :    List[str]				=			1_28 if accelerator.distributed_type == DistributedType.TPU else None
				# When using mixed precision we want round multiples of 8/16
				if accelerator.mixed_precision == "fp8":
						lowercase__   :    Optional[int]				=			16
				elif accelerator.mixed_precision != "no":
						lowercase__   :    List[Any]				=			8
				else:
						lowercase__   :    int				=			None

				return tokenizer.pad(
				    __lowerCamelCase						,     padding='''longest'''						,     max_length=__lowerCamelCase						,     pad_to_multiple_of=__lowerCamelCase						,     return_tensors='''pt'''						,     )

		# Instantiate dataloaders.
		lowercase__   :    List[Any]				=			DataLoader(
		    tokenized_datasets['''train''']						,     shuffle=__lowerCamelCase						,     collate_fn=__lowerCamelCase						,     batch_size=__lowerCamelCase	)
		lowercase__   :    str				=			DataLoader(
		    tokenized_datasets['''validation''']						,     shuffle=__lowerCamelCase						,     collate_fn=__lowerCamelCase						,     batch_size=__lowerCamelCase	)

		return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
							from accelerate.test_utils.training import mocked_dataloaders

							lowerCAmelCase_   						=   mocked_dataloaders  # noqa: F811



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase	)							->      str:
		# For testing only
		if os.environ.get('''TESTING_MOCKED_DATALOADERS'''						,     __lowerCamelCase	) == "1":
				lowercase__   :    List[Any]				=			2
		# Initialize accelerator
		lowercase__   :    Optional[int]				=			Accelerator(cpu=args.cpu						,     mixed_precision=args.mixed_precision	)
		# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
		lowercase__   :    str				=			config['''lr''']
		lowercase__   :    str				=			int(config['''num_epochs''']	)
		lowercase__   :    Optional[int]				=			int(config['''seed''']	)
		lowercase__   :    Tuple				=			int(config['''batch_size''']	)

		lowercase__   :    List[Any]				=			evaluate.load('''glue'''						,     '''mrpc'''	)

		# New Code #
		# We now can define an inner training loop function. It should take a batch size as the only parameter,
		# and build the dataloaders in there.
		# It also gets our decorator
		@find_executable_batch_size(starting_batch_size=__lowerCamelCase	)
		def inner_training_loop(__lowerCamelCase	):
				# And now just move everything below under this function
				# We need to bring in the Accelerator object from earlier
				nonlocal accelerator
				# And reset all of its attributes that could hold onto any memory:
				accelerator.free_memory()

				# Then we can declare the model, optimizer, and everything else:
				set_seed(__lowerCamelCase	)

				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				lowercase__   :    List[str]				=			AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''						,     return_dict=__lowerCamelCase	)

				# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
				# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
				# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
				lowercase__   :    Tuple				=			model.to(accelerator.device	)

				# Instantiate optimizer
				lowercase__   :    List[str]				=			AdamW(params=model.parameters()						,     lr=__lowerCamelCase	)
				lowercase__		,			lowercase__   :    List[Any]				=			get_dataloaders(__lowerCamelCase						,     __lowerCamelCase	)

				# Instantiate scheduler
				lowercase__   :    Optional[int]				=			get_linear_schedule_with_warmup(
				    optimizer=__lowerCamelCase						,     num_warmup_steps=1_00						,     num_training_steps=(len(__lowerCamelCase	) * num_epochs)						,     )

				# 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.
				lowercase__		,			lowercase__		,			lowercase__		,			lowercase__		,			lowercase__   :    Optional[int]				=			accelerator.prepare(
				    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase	)

				# Now we train the model
				for epoch in range(__lowerCamelCase	):
						model.train()
						for step, batch in enumerate(__lowerCamelCase	):
								# We could avoid this line since we set the accelerator with `device_placement=True`.
								batch.to(accelerator.device	)
								lowercase__   :    Dict				=			model(**__lowerCamelCase	)
								lowercase__   :    List[Any]				=			outputs.loss
								accelerator.backward(__lowerCamelCase	)
								optimizer.step()
								lr_scheduler.step()
								optimizer.zero_grad()

						model.eval()
						for step, batch in enumerate(__lowerCamelCase	):
								# We could avoid this line since we set the accelerator with `device_placement=True`.
								batch.to(accelerator.device	)
								with torch.no_grad():
										lowercase__   :    Tuple				=			model(**__lowerCamelCase	)
								lowercase__   :    Any				=			outputs.logits.argmax(dim=-1	)
								lowercase__		,			lowercase__   :    int				=			accelerator.gather_for_metrics((predictions, batch['''labels'''])	)
								metric.add_batch(
								    predictions=__lowerCamelCase						,     references=__lowerCamelCase						,     )

						lowercase__   :    List[Any]				=			metric.compute()
						# Use accelerator.print to print only on the main process.
						accelerator.print(f"""epoch {epoch}:"""						,     __lowerCamelCase	)

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
		inner_training_loop()



def      __UpperCAmelCase      (    )							->      Dict:
		lowercase__   :    Optional[int]				=			argparse.ArgumentParser(description='''Simple example of training script.'''	)
		parser.add_argument(
		    '''--mixed_precision'''						,     type=__lowerCamelCase						,     default=__lowerCamelCase						,     choices=['''no''', '''fp16''', '''bf16''', '''fp8''']						,     help='''Whether to use mixed precision. Choose'''
		    '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
		    '''and an Nvidia Ampere GPU.'''						,     )
		parser.add_argument('''--cpu'''						,     action='''store_true'''						,     help='''If passed, will train on the CPU.'''	)
		lowercase__   :    int				=			parser.parse_args()
		lowercase__   :    Union[str, Any]				=			{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
		training_function(__lowerCamelCase						,     __lowerCamelCase	)


if __name__ == "__main__":
							main()
