

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


lowerCAmelCase__								=		1_6
lowerCAmelCase__								=		3_2






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Accelerator							,       UpperCAmelCase_							:     int = 1_6							,       UpperCAmelCase_							:     str = "bert-base-cased"	)						->				Tuple:




	'''simple docstring'''




	_UpperCamelCase   :		Union[str, Any]			=						AutoTokenizer.from_pretrained(UpperCAmelCase_	)
	_UpperCamelCase   :		Optional[int]			=						load_dataset('glue'							,       'mrpc'	)

	def tokenize_function(UpperCAmelCase_							:     Optional[int]	):
		# max_length=None => use the model max length (it's actually the default)
		_UpperCamelCase   :		Optional[Any]			=						tokenizer(examples['sentence1']							,       examples['sentence2']							,       truncation=UpperCAmelCase_							,       max_length=UpperCAmelCase_	)
		return outputs

	# Apply the method we just defined to all the examples in all the splits of the dataset
	_UpperCamelCase   :		int			=						datasets.map(
	    UpperCAmelCase_							,       batched=UpperCAmelCase_							,       remove_columns=['idx', 'sentence1', 'sentence2']							,       load_from_cache_file=UpperCAmelCase_	)

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

	def collate_fn(UpperCAmelCase_							:     List[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(UpperCAmelCase_							,       padding='max_length'							,       max_length=1_2_8							,       return_tensors='pt'	)
		return tokenizer.pad(UpperCAmelCase_							,       padding='longest'							,       return_tensors='pt'	)

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

	return train_dataloader, eval_dataloader






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Union[str, Any]							,       UpperCAmelCase_							:     str	)						->				str:




	'''simple docstring'''




	_UpperCamelCase   :		int			=						Accelerator()

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

	set_seed(UpperCAmelCase_	)
	_UpperCamelCase	,		_UpperCamelCase   :		Optional[Any]			=						get_dataloaders(UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_	)

	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	_UpperCamelCase   :		Any			=						AutoModelForSequenceClassification.from_pretrained(UpperCAmelCase_							,       return_dict=UpperCAmelCase_	)

	# Instantiate optimizer
	_UpperCamelCase   :		List[str]			=						(
	    AdamW
	    if accelerator.state.deepspeed_plugin is None
	    or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
	    else DummyOptim
	)
	_UpperCamelCase   :		int			=						optimizer_cls(params=model.parameters()							,       lr=UpperCAmelCase_	)

	if accelerator.state.deepspeed_plugin is not None:
		_UpperCamelCase   :		List[str]			=						accelerator.state.deepspeed_plugin.deepspeed_config[
		    'gradient_accumulation_steps'
		]
	else:
		_UpperCamelCase   :		Tuple			=						1
	_UpperCamelCase   :		Union[str, Any]			=						(len(UpperCAmelCase_	) * num_epochs) // gradient_accumulation_steps

	# Instantiate scheduler
	if (
	    accelerator.state.deepspeed_plugin is None
	    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
	):
		_UpperCamelCase   :		Optional[int]			=						get_linear_schedule_with_warmup(
		    optimizer=UpperCAmelCase_							,       num_warmup_steps=0							,       num_training_steps=UpperCAmelCase_							,       )
	else:
		_UpperCamelCase   :		Any			=						DummyScheduler(UpperCAmelCase_							,       total_num_steps=UpperCAmelCase_							,       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.
	_UpperCamelCase	,		_UpperCamelCase	,		_UpperCamelCase	,		_UpperCamelCase	,		_UpperCamelCase   :		str			=						accelerator.prepare(
	    UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_	)

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

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

			overall_step += 1

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

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

		if best_performance < eval_metric["accuracy"]:
			_UpperCamelCase   :		Optional[Any]			=						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(UpperCAmelCase_							,       UpperCAmelCase_	)






def 					lowerCamelCase_     (							)						->				Optional[int]:




	'''simple docstring'''




	_UpperCamelCase   :		int			=						argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'	)
	parser.add_argument(
	    '--model_name_or_path'							,       type=UpperCAmelCase_							,       default='bert-base-cased'							,       help='Path to pretrained model or model identifier from huggingface.co/models.'							,       required=UpperCAmelCase_							,       )
	parser.add_argument(
	    '--output_dir'							,       type=UpperCAmelCase_							,       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=UpperCAmelCase_							,       default=UpperCAmelCase_							,       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=UpperCAmelCase_							,       default=3							,       help='Number of train epochs.'							,       )
	_UpperCamelCase   :		Tuple			=						parser.parse_args()
	_UpperCamelCase   :		Optional[int]			=						{'lr': 2e-5, 'num_epochs': args.num_epochs, 'seed': 4_2, 'batch_size': 1_6}
	training_function(UpperCAmelCase_							,       UpperCAmelCase_	)


if __name__ == "__main__":
							main()







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCAmelCase__								=		logging.get_logger(__name__)

lowerCAmelCase__								=		{
    """bert-base-uncased""": """https://huggingface.co/bert-base-uncased/resolve/main/config.json""",
    """bert-large-uncased""": """https://huggingface.co/bert-large-uncased/resolve/main/config.json""",
    """bert-base-cased""": """https://huggingface.co/bert-base-cased/resolve/main/config.json""",
    """bert-large-cased""": """https://huggingface.co/bert-large-cased/resolve/main/config.json""",
    """bert-base-multilingual-uncased""": """https://huggingface.co/bert-base-multilingual-uncased/resolve/main/config.json""",
    """bert-base-multilingual-cased""": """https://huggingface.co/bert-base-multilingual-cased/resolve/main/config.json""",
    """bert-base-chinese""": """https://huggingface.co/bert-base-chinese/resolve/main/config.json""",
    """bert-base-german-cased""": """https://huggingface.co/bert-base-german-cased/resolve/main/config.json""",
    """bert-large-uncased-whole-word-masking""": (
        """https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/config.json"""
    ),
    """bert-large-cased-whole-word-masking""": (
        """https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/config.json"""
    ),
    """bert-large-uncased-whole-word-masking-finetuned-squad""": (
        """https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/config.json"""
    ),
    """bert-large-cased-whole-word-masking-finetuned-squad""": (
        """https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/config.json"""
    ),
    """bert-base-cased-finetuned-mrpc""": """https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/config.json""",
    """bert-base-german-dbmdz-cased""": """https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/config.json""",
    """bert-base-german-dbmdz-uncased""": """https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/config.json""",
    """cl-tohoku/bert-base-japanese""": """https://huggingface.co/cl-tohoku/bert-base-japanese/resolve/main/config.json""",
    """cl-tohoku/bert-base-japanese-whole-word-masking""": (
        """https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking/resolve/main/config.json"""
    ),
    """cl-tohoku/bert-base-japanese-char""": (
        """https://huggingface.co/cl-tohoku/bert-base-japanese-char/resolve/main/config.json"""
    ),
    """cl-tohoku/bert-base-japanese-char-whole-word-masking""": (
        """https://huggingface.co/cl-tohoku/bert-base-japanese-char-whole-word-masking/resolve/main/config.json"""
    ),
    """TurkuNLP/bert-base-finnish-cased-v1""": (
        """https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/config.json"""
    ),
    """TurkuNLP/bert-base-finnish-uncased-v1""": (
        """https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/config.json"""
    ),
    """wietsedv/bert-base-dutch-cased""": """https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/config.json""",
    # See all BERT models at https://huggingface.co/models?filter=bert
}







class 	lowercase    (				_lowercase							):


		"""simple docstring"""




		a__					       =  "bert"


		def __init__(				self	,       __snake_case=3_05_22	,       __snake_case=7_68	,       __snake_case=12	,       __snake_case=12	,       __snake_case=30_72	,       __snake_case="gelu"	,       __snake_case=0.1	,       __snake_case=0.1	,       __snake_case=5_12	,       __snake_case=2	,       __snake_case=0.0_2	,       __snake_case=1e-12	,       __snake_case=0	,       __snake_case="absolute"	,       __snake_case=True	,       __snake_case=None	,       **__snake_case	,       ):
			super().__init__(pad_token_id=__snake_case	,       **__snake_case)

			_UpperCamelCase   :		int			=						vocab_size
			_UpperCamelCase   :		Optional[Any]			=						hidden_size
			_UpperCamelCase   :		Optional[Any]			=						num_hidden_layers
			_UpperCamelCase   :		List[str]			=						num_attention_heads
			_UpperCamelCase   :		int			=						hidden_act
			_UpperCamelCase   :		Optional[Any]			=						intermediate_size
			_UpperCamelCase   :		Union[str, Any]			=						hidden_dropout_prob
			_UpperCamelCase   :		Tuple			=						attention_probs_dropout_prob
			_UpperCamelCase   :		Optional[int]			=						max_position_embeddings
			_UpperCamelCase   :		str			=						type_vocab_size
			_UpperCamelCase   :		Optional[Any]			=						initializer_range
			_UpperCamelCase   :		List[str]			=						layer_norm_eps
			_UpperCamelCase   :		Any			=						position_embedding_type
			_UpperCamelCase   :		Any			=						use_cache
			_UpperCamelCase   :		Any			=						classifier_dropout







class 	lowercase    (				_lowercase							):


		"""simple docstring"""




		@property
		def 		A__					(				self):
			if self.task == "multiple-choice":
				_UpperCamelCase   :		Union[str, Any]			=						{0: 'batch', 1: 'choice', 2: 'sequence'}
			else:
				_UpperCamelCase   :		Optional[Any]			=						{0: 'batch', 1: 'sequence'}
			return OrderedDict(
			    [
			        ('input_ids', dynamic_axis),
			        ('attention_mask', dynamic_axis),
			        ('token_type_ids', dynamic_axis),
			    ])




