

'''simple docstring'''





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


########################################################################
# This is a fully working simple example to use Accelerate
# and perform 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
#
########################################################################


UpperCamelCase_    						=       16
UpperCamelCase_    						=       32






def 				_lowerCAmelCase			(   __magic_name__						:     Accelerator   , __magic_name__						:     int = 16						)				->  Tuple:
						lowercase :							Tuple    		=AutoTokenizer.from_pretrained('''bert-base-cased'''						)
						lowercase :							Tuple    		=load_dataset('''glue'''   , '''mrpc'''						)

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

						def collate_fn(__magic_name__						:     Optional[int]						):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												lowercase :							Optional[int]    		=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":
																		lowercase :							Union[str, Any]    		=16
												elif accelerator.mixed_precision != "no":
																		lowercase :							Any    		=8
												else:
																		lowercase :							Union[str, Any]    		=None

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

						# Instantiate dataloaders.
						lowercase :							Union[str, Any]    		=DataLoader(
						    tokenized_datasets['''train''']   , shuffle=__magic_name__   , collate_fn=__magic_name__   , batch_size=__magic_name__						)
						lowercase :							Dict    		=DataLoader(
						    tokenized_datasets['''validation''']   , shuffle=__magic_name__   , collate_fn=__magic_name__   , batch_size=__magic_name__						)

						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_    						=       mocked_dataloaders  # noqa: F811






def 				_lowerCAmelCase			(   __magic_name__						:     int   , __magic_name__						:     str						)				->  int:
						# For testing only
						if os.environ.get('''TESTING_MOCKED_DATALOADERS'''   , __magic_name__						) == "1":
												lowercase :							List[Any]    		=2
						# New Code #
						lowercase :							List[Any]    		=int(args.gradient_accumulation_steps						)
						# Initialize accelerator
						lowercase :							Optional[int]    		=Accelerator(
						    cpu=args.cpu   , mixed_precision=args.mixed_precision   , gradient_accumulation_steps=__magic_name__						)
						if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
												raise NotImplementedError(
												    '''Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`'''						)
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						lowercase :							str    		=config['''lr''']
						lowercase :							Optional[int]    		=int(config['''num_epochs''']						)
						lowercase :							str    		=int(config['''seed''']						)
						lowercase :							Tuple    		=int(config['''batch_size''']						)

						lowercase :							Dict    		=evaluate.load('''glue'''   , '''mrpc'''						)

						set_seed(__magic_name__						)
						lowercase		,	lowercase :							List[str]    		=get_dataloaders(__magic_name__   , __magic_name__						)
						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						lowercase :							List[str]    		=AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''   , return_dict=__magic_name__						)

						# 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).
						lowercase :							str    		=model.to(accelerator.device						)

						# Instantiate optimizer
						lowercase :							Tuple    		=AdamW(params=model.parameters()   , lr=__magic_name__						)

						# Instantiate scheduler
						lowercase :							Tuple    		=get_linear_schedule_with_warmup(
						    optimizer=__magic_name__   , num_warmup_steps=100   , num_training_steps=(len(__magic_name__						) * 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.
						lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase :							Dict    		=accelerator.prepare(
						    __magic_name__   , __magic_name__   , __magic_name__   , __magic_name__   , __magic_name__						)

						# Now we train the model
						for epoch in range(__magic_name__						):
												model.train()
												for step, batch in enumerate(__magic_name__						):
																		# 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(__magic_name__						):
																								lowercase :							Dict    		=model(**__magic_name__						)
																								lowercase :							Optional[Any]    		=output.loss
																								accelerator.backward(__magic_name__						)
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()

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

												lowercase :							Optional[Any]    		=metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(f'''epoch {epoch}:'''   , __magic_name__						)






def 				_lowerCAmelCase			(   )				->  str:
						lowercase :							Union[str, Any]    		=argparse.ArgumentParser(description='''Simple example of training script.'''						)
						parser.add_argument(
						    '''--mixed_precision'''   , type=__magic_name__   , default=__magic_name__   , 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=__magic_name__   , default=1   , help='''The number of minibatches to be ran before gradients are accumulated.'''   , )
						parser.add_argument('''--cpu'''   , action='''store_true'''   , help='''If passed, will train on the CPU.'''						)
						lowercase :							List[Any]    		=parser.parse_args()
						lowercase :							Dict    		={'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
						training_function(__magic_name__   , __magic_name__						)


if __name__ == "__main__":
							main()











from collections import OrderedDict
from typing import Mapping

from packaging import version

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


SCREAMING_SNAKE_CASE__     :     Optional[int]		    =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__     :     str		    =       {
    "google/mobilenet_v2_1.4_224": "https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json",
    "google/mobilenet_v2_1.0_224": "https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json",
    "google/mobilenet_v2_0.75_160": "https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json",
    "google/mobilenet_v2_0.35_96": "https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json",
    # See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}



class       a_       (     SCREAMING_SNAKE_CASE__     ):
	A    							= '''mobilenet_v2'''





	def __init__(							self      ,     SCREAMING_SNAKE_CASE=3      ,     SCREAMING_SNAKE_CASE=224      ,     SCREAMING_SNAKE_CASE=1.0      ,     SCREAMING_SNAKE_CASE=8      ,     SCREAMING_SNAKE_CASE=8      ,     SCREAMING_SNAKE_CASE=6      ,     SCREAMING_SNAKE_CASE=32      ,     SCREAMING_SNAKE_CASE=True      ,     SCREAMING_SNAKE_CASE=True      ,     SCREAMING_SNAKE_CASE="relu6"      ,     SCREAMING_SNAKE_CASE=True      ,     SCREAMING_SNAKE_CASE=0.8      ,     SCREAMING_SNAKE_CASE=0.0_2      ,     SCREAMING_SNAKE_CASE=0.0_0_1      ,     SCREAMING_SNAKE_CASE=255      ,     **SCREAMING_SNAKE_CASE      ,     )  ->					Dict:




				"""simple docstring"""



				super().__init__(**SCREAMING_SNAKE_CASE      )

				if depth_multiplier <= 0:
							raise ValueError('depth_multiplier must be greater than zero.'      )

				SCREAMING_SNAKE_CASE_								=						num_channels
				SCREAMING_SNAKE_CASE_								=						image_size
				SCREAMING_SNAKE_CASE_								=						depth_multiplier
				SCREAMING_SNAKE_CASE_								=						depth_divisible_by
				SCREAMING_SNAKE_CASE_								=						min_depth
				SCREAMING_SNAKE_CASE_								=						expand_ratio
				SCREAMING_SNAKE_CASE_								=						output_stride
				SCREAMING_SNAKE_CASE_								=						first_layer_is_expansion
				SCREAMING_SNAKE_CASE_								=						finegrained_output
				SCREAMING_SNAKE_CASE_								=						hidden_act
				SCREAMING_SNAKE_CASE_								=						tf_padding
				SCREAMING_SNAKE_CASE_								=						classifier_dropout_prob
				SCREAMING_SNAKE_CASE_								=						initializer_range
				SCREAMING_SNAKE_CASE_								=						layer_norm_eps
				SCREAMING_SNAKE_CASE_								=						semantic_loss_ignore_index




class       a_       (     SCREAMING_SNAKE_CASE__     ):
	A    							= version.parse('''1.11'''     )





	@property
	def 						A_(							self      )  ->					Mapping[str, Mapping[int, str]]:




				"""simple docstring"""



				return OrderedDict([('pixel_values', {0: 'batch'})]      )





	@property
	def 						A_(							self      )  ->					Mapping[str, Mapping[int, str]]:




				"""simple docstring"""



				if self.task == "image-classification":
							return OrderedDict([('logits', {0: 'batch'})]      )
				else:
							return OrderedDict([('last_hidden_state', {0: 'batch'}), ('pooler_output', {0: 'batch'})]      )





	@property
	def 						A_(							self      )  ->					float:




				"""simple docstring"""



				return 1e-4
