

import json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin






class 	__magic_name__    (			lowerCAmelCase_  ,    unittest.TestCase      ):
			SCREAMING_SNAKE_CASE							   =						BlenderbotSmallTokenizer
			SCREAMING_SNAKE_CASE							   =						False





			def 		__magic_name__    (		self					)   -> Any:




						'''simple docstring'''
						super().setUp()

						__a        =['__start__', 'adapt', 'act', 'ap@@', 'te', '__end__', '__unk__']
						__a        =dict(zip(__snake_case					,  range(len(__snake_case					)					)					)					)

						__a        =['#version: 0.2', 'a p', 't e</w>', 'ap t</w>', 'a d', 'ad apt</w>', 'a c', 'ac t</w>', '']
						__a        ={'unk_token': '__unk__', 'bos_token': '__start__', 'eos_token': '__end__'}

						__a        =os.path.join(self.tmpdirname					,  VOCAB_FILES_NAMES['vocab_file']					)
						__a        =os.path.join(self.tmpdirname					,  VOCAB_FILES_NAMES['merges_file']					)
						with open(self.vocab_file					,  'w'					,  encoding='utf-8'					) as fp:
									fp.write(json.dumps(__snake_case					) + '\n'					)
						with open(self.merges_file					,  'w'					,  encoding='utf-8'					) as fp:
									fp.write('\n'.join(__snake_case					)					)





			def 		__magic_name__    (		self					,  **__snake_case					)   -> List[Any]:




						'''simple docstring'''
						kwargs.update(self.special_tokens_map					)
						return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname					,  **__snake_case					)





			def 		__magic_name__    (		self					,  __snake_case					)   -> Dict:




						'''simple docstring'''
						__a        ='adapt act apte'
						__a        ='adapt act apte'
						return input_text, output_text





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




						'''simple docstring'''
						__a        =BlenderbotSmallTokenizer(self.vocab_file					,  self.merges_file					,  **self.special_tokens_map					)
						__a        ='adapt act apte'
						__a        =['adapt', 'act', 'ap@@', 'te']
						__a        =tokenizer.tokenize(__snake_case					)
						self.assertListEqual(__snake_case					,  __snake_case					)

						__a        =[tokenizer.bos_token] + tokens + [tokenizer.eos_token]

						__a        =[0, 1, 2, 3, 4, 5]
						self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case					)					,  __snake_case					)





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




						'''simple docstring'''
						__a        =BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot-90M'					)
						assert tok('sam'					).input_ids == [1384]
						__a        ='I am a small frog.'
						__a        =tok([src_text]					,  padding=__snake_case					,  truncation=__snake_case					)['input_ids']
						__a        =tok.batch_decode(__snake_case					,  skip_special_tokens=__snake_case					,  clean_up_tokenization_spaces=__snake_case					)[0]
						assert src_text != decoded  # I wish it did!
						assert decoded == "i am a small frog ."





			def 		__magic_name__    (		self					)   -> int:




						'''simple docstring'''
						__a        =BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot-90M'					)
						__a        ='I am a small frog .'
						__a        ='.'
						__a        =tok(__snake_case					)['input_ids']
						__a        =tok(__snake_case					)['input_ids']

						assert encoded[-1] == encoded_dot[0]









import argparse
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.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to 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)
#
# 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					:			Union[str, Any]  				=			16
_lowerCAmelCase					:			List[str]  				=			32






def        UpperCamelCase_(							_snake_case   :  Accelerator       ,     _snake_case   :  int = 16	):




			"""simple docstring"""






			__a        =AutoTokenizer.from_pretrained('bert-base-cased'	)
			__a        =load_dataset('glue'       ,     'mrpc'	)

			def tokenize_function(_snake_case   :  Optional[int]	):
						# max_length=None => use the model max length (it's actually the default)
						__a        =tokenizer(examples['sentence1']       ,     examples['sentence2']       ,     truncation=_snake_case       ,     max_length=_snake_case	)
						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():
						__a        =datasets.map(
						    _snake_case       ,     batched=_snake_case       ,     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
			__a        =tokenized_datasets.rename_column('label'       ,     'labels'	)

			def collate_fn(_snake_case   :  List[Any]	):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						__a        =128 if accelerator.distributed_type == DistributedType.TPU else None
						# When using mixed precision we want round multiples of 8/16
						if accelerator.mixed_precision == "fp8":
									__a        =16
						elif accelerator.mixed_precision != "no":
									__a        =8
						else:
									__a        =None

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

			# Instantiate dataloaders.
			__a        =DataLoader(
			    tokenized_datasets['train']       ,     shuffle=_snake_case       ,     collate_fn=_snake_case       ,     batch_size=_snake_case	)
			__a        =DataLoader(
			    tokenized_datasets['validation']       ,     shuffle=_snake_case       ,     collate_fn=_snake_case       ,     batch_size=_snake_case	)

			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					:			List[Any]  				=			mocked_dataloaders  # noqa: F811






def        UpperCamelCase_(							_snake_case   :  Tuple       ,     _snake_case   :  Union[str, Any]	):




			"""simple docstring"""






			if os.environ.get('TESTING_MOCKED_DATALOADERS'       ,     _snake_case	) == "1":
						__a        =2
			# New Code #
			__a        =int(args.gradient_accumulation_steps	)
			__a        =int(args.local_sgd_steps	)
			# Initialize accelerator
			__a        =Accelerator(
			    cpu=args.cpu       ,     mixed_precision=args.mixed_precision       ,     gradient_accumulation_steps=_snake_case	)
			if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
						raise NotImplementedError('LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'	)
			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			__a        =config['lr']
			__a        =int(config['num_epochs']	)
			__a        =int(config['seed']	)
			__a        =int(config['batch_size']	)

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

			set_seed(_snake_case	)
			__a      ,    __a        =get_dataloaders(_snake_case       ,     _snake_case	)
			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			__a        =AutoModelForSequenceClassification.from_pretrained('bert-base-cased'       ,     return_dict=_snake_case	)

			# 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).
			__a        =model.to(accelerator.device	)

			# Instantiate optimizer
			__a        =AdamW(params=model.parameters()       ,     lr=_snake_case	)

			# Instantiate scheduler
			__a        =get_linear_schedule_with_warmup(
			    optimizer=_snake_case       ,     num_warmup_steps=100       ,     num_training_steps=(len(_snake_case	) * 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.
			__a      ,    __a      ,    __a      ,    __a      ,    __a        =accelerator.prepare(
			    _snake_case       ,     _snake_case       ,     _snake_case       ,     _snake_case       ,     _snake_case	)

			# Now we train the model
			for epoch in range(_snake_case	):
						model.train()
						with LocalSGD(
						    accelerator=_snake_case       ,     model=_snake_case       ,     local_sgd_steps=_snake_case       ,     enabled=local_sgd_steps is not None	) as local_sgd:
									for step, batch in enumerate(_snake_case	):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device	)
												# New code #
												# We use the new `accumulate` context manager to perform gradient accumulation
												# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
												with accelerator.accumulate(_snake_case	):
															__a        =model(**_snake_case	)
															__a        =output.loss
															accelerator.backward(_snake_case	)
															optimizer.step()
															lr_scheduler.step()
															optimizer.zero_grad()
															# LocalSGD-specific line
															local_sgd.step()

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

						__a        =metric.compute()
						# Use accelerator.print to print only on the main process.
						accelerator.print(F'epoch {epoch}:'       ,     _snake_case	)






def        UpperCamelCase_(							):




			"""simple docstring"""






			__a        =argparse.ArgumentParser(description='Simple example of training script.'	)
			parser.add_argument(
			    '--mixed_precision'       ,     type=_snake_case       ,     default=_snake_case       ,     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.'       ,     )
			# New Code #
			parser.add_argument(
			    '--gradient_accumulation_steps'       ,     type=_snake_case       ,     default=1       ,     help='The number of minibatches to be ran before gradients are accumulated.'       ,     )
			parser.add_argument(
			    '--local_sgd_steps'       ,     type=_snake_case       ,     default=8       ,     help='Number of local SGD steps or None to disable local SGD'	)
			parser.add_argument('--cpu'       ,     action='store_true'       ,     help='If passed, will train on the CPU.'	)
			__a        =parser.parse_args()
			__a        ={'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
			training_function(_snake_case       ,     _snake_case	)


if __name__ == "__main__":
						main()






