



"""simple docstring"""






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]   		=		1_6
_lowercase				:				List[Any]   		=		3_2






def   snake_case__     (	__lowerCamelCase       :					Accelerator							,	__lowerCamelCase       :					int = 16							,	__lowerCamelCase       :					str = "bert-base-cased"			):
			"""simple docstring"""
			lowerCamelCase__	:	List[Any]				=AutoTokenizer.from_pretrained(__lowerCamelCase			)
			lowerCamelCase__	:	Optional[Any]				=load_dataset('''glue'''							,	'''mrpc'''			)

			def tokenize_function(__lowerCamelCase       :					str			):
						# max_length=None => use the model max length (it's actually the default)
						lowerCamelCase__	:	Union[str, Any]				=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
			lowerCamelCase__	:	Optional[Any]				=datasets.map(
			    __lowerCamelCase							,	batched=__lowerCamelCase							,	remove_columns=['''idx''', '''sentence1''', '''sentence2''']							,	load_from_cache_file=__lowerCamelCase			)

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

			def collate_fn(__lowerCamelCase       :					Optional[int]			):
						# 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(__lowerCamelCase							,	padding='''max_length'''							,	max_length=128							,	return_tensors='''pt'''			)
						return tokenizer.pad(__lowerCamelCase							,	padding='''longest'''							,	return_tensors='''pt'''			)

			# Instantiate dataloaders.
			lowerCamelCase__	:	Optional[Any]				=DataLoader(
			    tokenized_datasets['''train''']							,	shuffle=__lowerCamelCase							,	collate_fn=__lowerCamelCase							,	batch_size=__lowerCamelCase			)
			lowerCamelCase__	:	Dict				=DataLoader(
			    tokenized_datasets['''validation''']							,	shuffle=__lowerCamelCase							,	collate_fn=__lowerCamelCase							,	batch_size=__lowerCamelCase			)

			return train_dataloader, eval_dataloader






def   snake_case__     (	__lowerCamelCase       :					List[Any]							,	__lowerCamelCase       :					Tuple			):
			"""simple docstring"""
			# Initialize accelerator
			lowerCamelCase__	:	int				=Accelerator()

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

			set_seed(__lowerCamelCase			)
			lowerCamelCase__			,     lowerCamelCase__	:	Optional[int]				=get_dataloaders(__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase			)

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

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

			if accelerator.state.deepspeed_plugin is not None:
						lowerCamelCase__	:	Optional[int]				=accelerator.state.deepspeed_plugin.deepspeed_config[
						    '''gradient_accumulation_steps'''
						]
			else:
						lowerCamelCase__	:	Dict				=1
			lowerCamelCase__	:	str				=(len(__lowerCamelCase			) * num_epochs) // gradient_accumulation_steps

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

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

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

									overall_step += 1

						model.eval()
						lowerCamelCase__	:	Optional[Any]				=0
						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():
												lowerCamelCase__	:	List[Any]				=model(**__lowerCamelCase			)
									lowerCamelCase__	:	Dict				=outputs.logits.argmax(dim=-1			)
									# It is slightly faster to call this once, than multiple times
									lowerCamelCase__			,     lowerCamelCase__	:	Union[str, Any]				=accelerator.gather(
									    (predictions, batch['''labels'''])			)  # If we are in a multiprocess environment, the last batch has duplicates
									if accelerator.use_distributed:
												if step == len(__lowerCamelCase			) - 1:
															lowerCamelCase__	:	str				=predictions[: len(eval_dataloader.dataset			) - samples_seen]
															lowerCamelCase__	:	Dict				=references[: len(eval_dataloader.dataset			) - samples_seen]
												else:
															samples_seen += references.shape[0]
									metric.add_batch(
									    predictions=__lowerCamelCase							,	references=__lowerCamelCase							,	)

						lowerCamelCase__	:	Tuple				=metric.compute()
						# Use accelerator.print to print only on the main process.
						accelerator.print(f'''epoch {epoch}:'''							,	__lowerCamelCase			)
						lowerCamelCase__	:	Any				=eval_metric['''accuracy''']

						if best_performance < eval_metric["accuracy"]:
									lowerCamelCase__	:	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(__lowerCamelCase							,	__lowerCamelCase			)






def   snake_case__     (	):
			"""simple docstring"""
			lowerCamelCase__	:	str				=argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''			)
			parser.add_argument(
			    '''--model_name_or_path'''							,	type=__lowerCamelCase							,	default='''bert-base-cased'''							,	help='''Path to pretrained model or model identifier from huggingface.co/models.'''							,	required=__lowerCamelCase							,	)
			parser.add_argument(
			    '''--output_dir'''							,	type=__lowerCamelCase							,	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=__lowerCamelCase							,	default=__lowerCamelCase							,	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=__lowerCamelCase							,	default=3							,	help='''Number of train epochs.'''							,	)
			lowerCamelCase__	:	int				=parser.parse_args()
			lowerCamelCase__	:	Union[str, Any]				={'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}
			training_function(__lowerCamelCase							,	__lowerCamelCase			)


if __name__ == "__main__":
		main()









"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


_lowercase				:				Optional[Any]   		=		{
    "configuration_clip": [
        "CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "CLIPConfig",
        "CLIPOnnxConfig",
        "CLIPTextConfig",
        "CLIPVisionConfig",
    ],
    "processing_clip": ["CLIPProcessor"],
    "tokenization_clip": ["CLIPTokenizer"],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase				:				str   		=		["CLIPTokenizerFast"]

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase				:				Any   		=		["CLIPFeatureExtractor"]
		_lowercase				:				int   		=		["CLIPImageProcessor"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase				:				Optional[Any]   		=		[
		    "CLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "CLIPModel",
		    "CLIPPreTrainedModel",
		    "CLIPTextModel",
		    "CLIPTextModelWithProjection",
		    "CLIPVisionModel",
		    "CLIPVisionModelWithProjection",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase				:				Dict   		=		[
		    "TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "TFCLIPModel",
		    "TFCLIPPreTrainedModel",
		    "TFCLIPTextModel",
		    "TFCLIPVisionModel",
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase				:				Union[str, Any]   		=		[
		    "FlaxCLIPModel",
		    "FlaxCLIPPreTrainedModel",
		    "FlaxCLIPTextModel",
		    "FlaxCLIPTextPreTrainedModel",
		    "FlaxCLIPVisionModel",
		    "FlaxCLIPVisionPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_clip import (
		    CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    CLIPConfig,
		    CLIPOnnxConfig,
		    CLIPTextConfig,
		    CLIPVisionConfig,
		)
		from .processing_clip import CLIPProcessor
		from .tokenization_clip import CLIPTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_clip_fast import CLIPTokenizerFast

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_clip import CLIPFeatureExtractor
				from .image_processing_clip import CLIPImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_clip import (
				    CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    CLIPModel,
				    CLIPPreTrainedModel,
				    CLIPTextModel,
				    CLIPTextModelWithProjection,
				    CLIPVisionModel,
				    CLIPVisionModelWithProjection,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_clip import (
				    TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFCLIPModel,
				    TFCLIPPreTrainedModel,
				    TFCLIPTextModel,
				    TFCLIPVisionModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_clip import (
				    FlaxCLIPModel,
				    FlaxCLIPPreTrainedModel,
				    FlaxCLIPTextModel,
				    FlaxCLIPTextPreTrainedModel,
				    FlaxCLIPVisionModel,
				    FlaxCLIPVisionPreTrainedModel,
				)


else:
		import sys

		_lowercase				:				Union[str, Any]   		=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




