import argparse

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


########################################################################
# This is a fully working simple example to use Accelerate
#
# 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					:   List[str]							=		16
_lowerCamelCase					:   Any							=		32





def 	__lowerCamelCase							(UpperCAmelCase__     :							List[str]      , UpperCAmelCase__     :							Optional[Any] = 1_6  ):
					SCREAMING_SNAKE_CASE    			=       AutoTokenizer.from_pretrained("bert-base-cased"  )
					SCREAMING_SNAKE_CASE    			=       load_dataset("glue"      , "mrpc"  )

					def tokenize_function(UpperCAmelCase__     :							Any  ):
										# max_length=None => use the model max length (it's actually the default)
										SCREAMING_SNAKE_CASE    			=       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():
										SCREAMING_SNAKE_CASE    			=       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
					SCREAMING_SNAKE_CASE    			=       tokenized_datasets.rename_column("label"      , "labels"  )

					def collate_fn(UpperCAmelCase__     :							Any  ):
										# On TPU it's best to pad everything to the same length or training will be very slow.
										SCREAMING_SNAKE_CASE    			=       1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
										# When using mixed precision we want round multiples of 8/16
										if accelerator.mixed_precision == "fp8":
															SCREAMING_SNAKE_CASE    			=       1_6
										elif accelerator.mixed_precision != "no":
															SCREAMING_SNAKE_CASE    			=       8
										else:
															SCREAMING_SNAKE_CASE    			=       None

										return tokenizer.pad(
										    lowerCAmelCase_      , padding="longest"      , max_length=lowerCAmelCase_      , pad_to_multiple_of=lowerCAmelCase_      , return_tensors="pt"      , )

					# Instantiate dataloaders.
					SCREAMING_SNAKE_CASE    			=       DataLoader(
					    tokenized_datasets["train"]      , shuffle=lowerCAmelCase_      , collate_fn=lowerCAmelCase_      , batch_size=lowerCAmelCase_      , drop_last=lowerCAmelCase_  )
					SCREAMING_SNAKE_CASE    			=       DataLoader(
					    tokenized_datasets["validation"]      , shuffle=lowerCAmelCase_      , collate_fn=lowerCAmelCase_      , batch_size=lowerCAmelCase_      , drop_last=(accelerator.mixed_precision == "fp8")      , )

					return train_dataloader, eval_dataloader





def 	__lowerCamelCase							(UpperCAmelCase__     :							Optional[Any]      , UpperCAmelCase__     :							str  ):
					SCREAMING_SNAKE_CASE    			=       Accelerator(cpu=args.cpu      , mixed_precision=args.mixed_precision  )
					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					SCREAMING_SNAKE_CASE    			=       config["lr"]
					SCREAMING_SNAKE_CASE    			=       int(config["num_epochs"]  )
					SCREAMING_SNAKE_CASE    			=       int(config["seed"]  )
					SCREAMING_SNAKE_CASE    			=       int(config["batch_size"]  )

					SCREAMING_SNAKE_CASE    			=       evaluate.load("glue"      , "mrpc"  )

					# If the batch size is too big we use gradient accumulation
					SCREAMING_SNAKE_CASE    			=       1
					if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
										SCREAMING_SNAKE_CASE    			=       batch_size // MAX_GPU_BATCH_SIZE
										SCREAMING_SNAKE_CASE    			=       MAX_GPU_BATCH_SIZE

					set_seed(lowerCAmelCase_  )
					SCREAMING_SNAKE_CASE    			=       get_dataloaders(lowerCAmelCase_      , lowerCAmelCase_  )
					# Instantiate the model (we build the model here so that the seed also control new weights initialization)
					SCREAMING_SNAKE_CASE    			=       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).
					SCREAMING_SNAKE_CASE    			=       model.to(accelerator.device  )
					# Instantiate optimizer
					SCREAMING_SNAKE_CASE    			=       AdamW(params=model.parameters()      , lr=lowerCAmelCase_  )

					# Instantiate scheduler
					SCREAMING_SNAKE_CASE    			=       get_linear_schedule_with_warmup(
					    optimizer=lowerCAmelCase_      , num_warmup_steps=1_0_0      , num_training_steps=(len(lowerCAmelCase_  ) * num_epochs) // gradient_accumulation_steps      , )

					# 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.

					SCREAMING_SNAKE_CASE    			=       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  )
															SCREAMING_SNAKE_CASE    			=       model(**lowerCAmelCase_  )
															SCREAMING_SNAKE_CASE    			=       outputs.loss
															SCREAMING_SNAKE_CASE    			=       loss / gradient_accumulation_steps
															accelerator.backward(lowerCAmelCase_  )
															if step % gradient_accumulation_steps == 0:
																				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():
																				SCREAMING_SNAKE_CASE    			=       model(**lowerCAmelCase_  )
															SCREAMING_SNAKE_CASE    			=       outputs.logits.argmax(dim=-1  )
															SCREAMING_SNAKE_CASE    			=       accelerator.gather_for_metrics((predictions, batch["labels"])  )
															metric.add_batch(
															    predictions=lowerCAmelCase_      , references=lowerCAmelCase_      , )

										SCREAMING_SNAKE_CASE    			=       metric.compute()
										# Use accelerator.print to print only on the main process.
										accelerator.print(F"epoch {epoch}:"      , lowerCAmelCase_  )





def 	__lowerCamelCase							():
					SCREAMING_SNAKE_CASE    			=       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."  )
					SCREAMING_SNAKE_CASE    			=       parser.parse_args()
					SCREAMING_SNAKE_CASE    			=       {"lr": 2e-5, "num_epochs": 3, "seed": 4_2, "batch_size": 1_6}
					training_function(lowerCAmelCase_      , lowerCAmelCase_  )


if __name__ == "__main__":
							main()





from typing import TYPE_CHECKING

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


_lowerCamelCase					:   Optional[int]							=		{
    '''configuration_blenderbot''': [
        '''BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BlenderbotConfig''',
        '''BlenderbotOnnxConfig''',
    ],
    '''tokenization_blenderbot''': ['''BlenderbotTokenizer'''],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowerCamelCase					:   List[Any]							=		['''BlenderbotTokenizerFast''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowerCamelCase					:   Tuple							=		[
							    '''BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''BlenderbotForCausalLM''',
							    '''BlenderbotForConditionalGeneration''',
							    '''BlenderbotModel''',
							    '''BlenderbotPreTrainedModel''',
							]


try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowerCamelCase					:   List[str]							=		[
							    '''TFBlenderbotForConditionalGeneration''',
							    '''TFBlenderbotModel''',
							    '''TFBlenderbotPreTrainedModel''',
							]


try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowerCamelCase					:   Optional[int]							=		[
							    '''FlaxBlenderbotForConditionalGeneration''',
							    '''FlaxBlenderbotModel''',
							    '''FlaxBlenderbotPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_blenderbot import (
							    BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    BlenderbotConfig,
							    BlenderbotOnnxConfig,
							)
							from .tokenization_blenderbot import BlenderbotTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_blenderbot import (
														    BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    BlenderbotForCausalLM,
														    BlenderbotForConditionalGeneration,
														    BlenderbotModel,
														    BlenderbotPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_blenderbot import (
														    TFBlenderbotForConditionalGeneration,
														    TFBlenderbotModel,
														    TFBlenderbotPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_blenderbot import (
														    FlaxBlenderbotForConditionalGeneration,
														    FlaxBlenderbotModel,
														    FlaxBlenderbotPreTrainedModel,
														)

else:
							import sys

							_lowerCamelCase					:   Dict							=		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




