'''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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to combine both the gradient accumulation
# and automatic batch size finder utilities of Accelerate to perfrom
# automatic gradient accumulation
#
# 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
#
########################################################################

_UpperCamelCase				:		str       			=		32



def 						__snake_case (       lowerCAmelCase       :     Accelerator      ,  lowerCAmelCase       :     int = 16			):
	__UpperCAmelCase         =					AutoTokenizer.from_pretrained('bert-base-cased'			)
	__UpperCAmelCase         =					load_dataset('glue'      ,  'mrpc'			)

	def tokenize_function(lowerCAmelCase       :     List[str]			):
		# max_length=None => use the model max length (it's actually the default)
		__UpperCAmelCase         =					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():
		__UpperCAmelCase         =					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
	__UpperCAmelCase         =					tokenized_datasets.rename_column('label'      ,  'labels'			)

	def collate_fn(lowerCAmelCase       :     List[str]			):
		# When using mixed precision we want round multiples of 8/16
		if accelerator.mixed_precision == "fp8":
			__UpperCAmelCase         =					16
		elif accelerator.mixed_precision != "no":
			__UpperCAmelCase         =					8
		else:
			__UpperCAmelCase         =					None

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

	# Instantiate dataloaders.
	__UpperCAmelCase         =					DataLoader(
	    tokenized_datasets['train']      ,  shuffle=lowerCAmelCase      ,  collate_fn=lowerCAmelCase      ,  batch_size=lowerCAmelCase			)
	__UpperCAmelCase         =					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

					_UpperCamelCase				:		int       			=		mocked_dataloaders  # noqa: F811



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

	__UpperCAmelCase         =					evaluate.load('glue'      ,  'mrpc'			)

	# New Code #
	# We use the `find_executable_batch_size` decorator, passing in the desired observed batch size
	# to train on. If a CUDA OOM error occurs, it will retry this loop cutting the batch size in
	# half each time. From this, we can calculate the number of gradient accumulation steps needed
	# and modify the Accelerator object as a result
	@find_executable_batch_size(starting_batch_size=int(lowerCAmelCase			)			)
	def inner_training_loop(lowerCAmelCase       :     Tuple			):
		# Since we need to modify the outside accelerator object, we need to bring it
		# to the local scope
		nonlocal accelerator

		# We can calculate the number of gradient accumulation steps based on the current
		# batch size vs the starting batch size
		__UpperCAmelCase         =					observed_batch_size // batch_size

		# And then set it in the Accelerator directly:
		__UpperCAmelCase         =					num_gradient_accumulation_steps

		# Next we need to free all of the stored model references in the Accelerator each time
		accelerator.free_memory()

		# And set the seed so our results are reproducable each reset
		set_seed(lowerCAmelCase			)

		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		__UpperCAmelCase         =					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).
		__UpperCAmelCase         =					model.to(accelerator.device			)

		# Instantiate optimizer
		__UpperCAmelCase         =					AdamW(params=model.parameters()      ,  lr=lowerCAmelCase			)
		__UpperCAmelCase  ,			__UpperCAmelCase         =					get_dataloaders(lowerCAmelCase      ,  lowerCAmelCase			)

		# Instantiate scheduler
		__UpperCAmelCase         =					get_linear_schedule_with_warmup(
		    optimizer=lowerCAmelCase      ,  num_warmup_steps=100      ,  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.
		__UpperCAmelCase  ,			__UpperCAmelCase  ,			__UpperCAmelCase  ,			__UpperCAmelCase  ,			__UpperCAmelCase         =					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			):
				# And perform gradient accumulation
				with accelerator.accumulate(lowerCAmelCase			):
					# We could avoid this line since we set the accelerator with `device_placement=True`.
					batch.to(accelerator.device			)
					__UpperCAmelCase         =					model(**lowerCAmelCase			)
					__UpperCAmelCase         =					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():
					__UpperCAmelCase         =					model(**lowerCAmelCase			)
				__UpperCAmelCase         =					outputs.logits.argmax(dim=-1			)
				__UpperCAmelCase  ,			__UpperCAmelCase         =					accelerator.gather_for_metrics((predictions, batch['labels'])			)
				metric.add_batch(
				    predictions=lowerCAmelCase      ,  references=lowerCAmelCase      ,  )

			__UpperCAmelCase         =					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 						__snake_case (       ):
	__UpperCAmelCase         =					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.'			)
	__UpperCAmelCase         =					parser.parse_args()
	# New Code #
	# We modify the starting batch size to be an observed batch size of 256, to guarentee an initial CUDA OOM
	__UpperCAmelCase         =					{'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 256}
	training_function(lowerCAmelCase      ,  lowerCAmelCase			)


if __name__ == "__main__":
					main()




'''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_longformer import LongformerTokenizer


_UpperCamelCase				:		int       			=		logging.get_logger(__name__)

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

_UpperCamelCase				:		int       			=		{
    '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'
        ),
    },
    'tokenizer_file': {
        'allenai/longformer-base-4096': (
            'https://huggingface.co/allenai/longformer-base-4096/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/tokenizer.json'
        ),
    },
}

_UpperCamelCase				:		List[str]       			=		{
    'allenai/longformer-base-4096': 40_96,
    'allenai/longformer-large-4096': 40_96,
    'allenai/longformer-large-4096-finetuned-triviaqa': 40_96,
    'allenai/longformer-base-4096-extra.pos.embd.only': 40_96,
    'allenai/longformer-large-4096-extra.pos.embd.only': 40_96,
}

class   _lowercase(     _lowerCamelCase				):



   """simple docstring"""





   __lowerCamelCase    =  VOCAB_FILES_NAMES
   __lowerCamelCase    =  PRETRAINED_VOCAB_FILES_MAP
   __lowerCamelCase    =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __lowerCamelCase    =  ['''input_ids''', '''attention_mask''']
   __lowerCamelCase    =  LongformerTokenizer





   def __init__(							self:    Any			,a:    Optional[int]=None			,a:    Optional[int]=None			,a:    int=None			,a:    Any="replace"			,a:    Optional[int]="<s>"			,a:    Any="</s>"			,a:    Optional[int]="</s>"			,a:    Dict="<s>"			,a:    Dict="<unk>"			,a:    int="<pad>"			,a:    List[str]="<mask>"			,a:    Union[str, Any]=False			,a:    Any=True			,**a:    str			,):
    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         =					json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()	)
    if pre_tok_state.get('add_prefix_space'			,a	) != add_prefix_space:
     __UpperCAmelCase         =					getattr(a			,pre_tok_state.pop('type'	)	)
     __UpperCAmelCase         =					add_prefix_space
     __UpperCAmelCase         =					pre_tok_class(**a	)

    __UpperCAmelCase         =					add_prefix_space

    __UpperCAmelCase         =					'post_processor'
    __UpperCAmelCase         =					getattr(self.backend_tokenizer			,a			,a	)
    if tokenizer_component_instance:
     __UpperCAmelCase         =					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         =					tuple(state['sep']	)
     if "cls" in state:
      __UpperCAmelCase         =					tuple(state['cls']	)

     __UpperCAmelCase         =					False

     if state.get('add_prefix_space'			,a	) != add_prefix_space:
      __UpperCAmelCase         =					add_prefix_space
      __UpperCAmelCase         =					True

     if state.get('trim_offsets'			,a	) != trim_offsets:
      __UpperCAmelCase         =					trim_offsets
      __UpperCAmelCase         =					True

     if changes_to_apply:
      __UpperCAmelCase         =					getattr(a			,state.pop('type'	)	)
      __UpperCAmelCase         =					component_class(**a	)
      setattr(self.backend_tokenizer			,a			,a	)





   @property
   def 			snake_case	(							self:    List[Any]	):
    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 			snake_case	(							self:    int			,a:    Optional[int]	):
    __UpperCAmelCase         =					AddedToken(a			,lstrip=a			,rstrip=a	) if isinstance(a			,a	) else value
    __UpperCAmelCase         =					value





   def 			snake_case	(							self:    Optional[int]			,*a:    Dict			,**a:    Dict	):
    __UpperCAmelCase         =					kwargs.get('is_split_into_words'			,a	)
    assert self.add_prefix_space or not is_split_into_words, (
        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 			snake_case	(							self:    str			,*a:    Dict			,**a:    str	):
    __UpperCAmelCase         =					kwargs.get('is_split_into_words'			,a	)

    assert self.add_prefix_space or not is_split_into_words, (
        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 			snake_case	(							self:    List[str]			,a:    str			,a:    Optional[str] = None	):
    __UpperCAmelCase         =					self._tokenizer.model.save(a			,name=a	)
    return tuple(a	)





   def 			snake_case	(							self:    Tuple			,a:    Dict			,a:    Tuple=None	):
    __UpperCAmelCase         =					[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 			snake_case	(							self:    int			,a:    List[int]			,a:    Optional[List[int]] = None	):
    __UpperCAmelCase         =					[self.sep_token_id]
    __UpperCAmelCase         =					[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]



