
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
#
########################################################################


lowercase__		:Dict			  =   16
lowercase__		:str			  =   32



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__ = 16		):
					'''simple docstring'''
					lowercase						     =						AutoTokenizer.from_pretrained('''bert-base-cased'''		)
					lowercase						     =						load_dataset('''glue'''			,      '''mrpc'''		)

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

					def collate_fn(lowerCAmelCase__		):
										# On TPU it's best to pad everything to the same length or training will be very slow.
										lowercase						     =						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						     =						16
										elif accelerator.mixed_precision != "no":
															lowercase						     =						8
										else:
															lowercase						     =						None

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

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

					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

							lowercase__		:Tuple			  =   mocked_dataloaders  # noqa: F811



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''
					# For testing only
					if os.environ.get('''TESTING_MOCKED_DATALOADERS'''			,      lowerCAmelCase__		) == "1":
										lowercase						     =						2
					# New Code #
					lowercase						     =						int(args.gradient_accumulation_steps		)
					lowercase						     =						int(args.local_sgd_steps		)
					# Initialize accelerator
					lowercase						     =						Accelerator(
					    cpu=args.cpu			,      mixed_precision=args.mixed_precision			,      gradient_accumulation_steps=lowerCAmelCase__		)
					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
					lowercase						     =						config['''lr''']
					lowercase						     =						int(config['''num_epochs''']		)
					lowercase						     =						int(config['''seed''']		)
					lowercase						     =						int(config['''batch_size''']		)

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

					set_seed(lowerCAmelCase__		)
					lowercase     ,						lowercase						     =						get_dataloaders(lowerCAmelCase__			,      lowerCAmelCase__		)
					# Instantiate the model (we build the model here so that the seed also control new weights initialization)
					lowercase						     =						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).
					lowercase						     =						model.to(accelerator.device		)

					# Instantiate optimizer
					lowercase						     =						AdamW(params=model.parameters()			,      lr=lowerCAmelCase__		)

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

					# Now we train the model
					for epoch in range(lowerCAmelCase__		):
										model.train()
										with LocalSGD(
										    accelerator=lowerCAmelCase__			,      model=lowerCAmelCase__			,      local_sgd_steps=lowerCAmelCase__			,      enabled=local_sgd_steps is not None		) as local_sgd:
															for step, batch in enumerate(lowerCAmelCase__		):
																				# 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(lowerCAmelCase__		):
																									lowercase						     =						model(**lowerCAmelCase__		)
																									lowercase						     =						output.loss
																									accelerator.backward(lowerCAmelCase__		)
																									optimizer.step()
																									lr_scheduler.step()
																									optimizer.zero_grad()
																									# LocalSGD-specific line
																									local_sgd.step()

										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():
																				lowercase						     =						model(**lowerCAmelCase__		)
															lowercase						     =						outputs.logits.argmax(dim=-1		)
															lowercase     ,						lowercase						     =						accelerator.gather_for_metrics((predictions, batch['''labels'''])		)
															metric.add_batch(
															    predictions=lowerCAmelCase__			,      references=lowerCAmelCase__			,      )

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



def 	UpperCamelCase   (							):
					'''simple docstring'''
					lowercase						     =						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.'''			,      )
					# New Code #
					parser.add_argument(
					    '''--gradient_accumulation_steps'''			,      type=lowerCAmelCase__			,      default=1			,      help='''The number of minibatches to be ran before gradients are accumulated.'''			,      )
					parser.add_argument(
					    '''--local_sgd_steps'''			,      type=lowerCAmelCase__			,      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.'''		)
					lowercase						     =						parser.parse_args()
					lowercase						     =						{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
					training_function(lowerCAmelCase__			,      lowerCAmelCase__		)


if __name__ == "__main__":
							main()






import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import ConditionalDetrImageProcessor







class     lowercase		( unittest.TestCase		):




							def __init__(    self       ,A__       ,A__=7       ,A__=3       ,A__=3_0       ,A__=4_0_0       ,A__=True       ,A__=None       ,A__=True       ,A__=[0.5, 0.5, 0.5]       ,A__=[0.5, 0.5, 0.5]       ,A__=True       ,A__=1 / 2_5_5       ,A__=True       ,):
												# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
												lowercase						     =						size if size is not None else {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}
												lowercase						     =						parent
												lowercase						     =						batch_size
												lowercase						     =						num_channels
												lowercase						     =						min_resolution
												lowercase						     =						max_resolution
												lowercase						     =						do_resize
												lowercase						     =						size
												lowercase						     =						do_normalize
												lowercase						     =						image_mean
												lowercase						     =						image_std
												lowercase						     =						do_rescale
												lowercase						     =						rescale_factor
												lowercase						     =						do_pad




							def 			A__				(    self):
												return {
												    "do_resize": self.do_resize,
												    "size": self.size,
												    "do_normalize": self.do_normalize,
												    "image_mean": self.image_mean,
												    "image_std": self.image_std,
												    "do_rescale": self.do_rescale,
												    "rescale_factor": self.rescale_factor,
												    "do_pad": self.do_pad,
												}





							def 			A__				(    self       ,A__       ,A__=False):
												if not batched:
																	lowercase						     =						image_inputs[0]
																	if isinstance(A__       ,Image.Image):
																						lowercase     ,						lowercase						     =						image.size
																	else:
																						lowercase     ,						lowercase						     =						image.shape[1], image.shape[2]
																	if w < h:
																						lowercase						     =						int(self.size['''shortest_edge'''] * h / w)
																						lowercase						     =						self.size['''shortest_edge''']
																	elif w > h:
																						lowercase						     =						self.size['''shortest_edge''']
																						lowercase						     =						int(self.size['''shortest_edge'''] * w / h)
																	else:
																						lowercase						     =						self.size['''shortest_edge''']
																						lowercase						     =						self.size['''shortest_edge''']

												else:
																	lowercase						     =						[]
																	for image in image_inputs:
																						lowercase     ,						lowercase						     =						self.get_expected_values([image])
																						expected_values.append((expected_height, expected_width))
																	lowercase						     =						max(A__       ,key=lambda A__: item[0])[0]
																	lowercase						     =						max(A__       ,key=lambda A__: item[1])[1]

												return expected_height, expected_width







@require_torch
@require_vision
class     lowercase		( SCREAMING_SNAKE_CASE__	,  unittest.TestCase		):
							lowercase_	:	Dict	 =ConditionalDetrImageProcessor if is_vision_available() else None




							def 			A__				(    self):
												lowercase						     =						ConditionalDetrImageProcessingTester(self)




							@property
							def 			A__				(    self):
												return self.image_processor_tester.prepare_image_processor_dict()




							def 			A__				(    self):
												lowercase						     =						self.image_processing_class(**self.image_processor_dict)
												self.assertTrue(hasattr(A__       ,'''image_mean'''))
												self.assertTrue(hasattr(A__       ,'''image_std'''))
												self.assertTrue(hasattr(A__       ,'''do_normalize'''))
												self.assertTrue(hasattr(A__       ,'''do_resize'''))
												self.assertTrue(hasattr(A__       ,'''size'''))




							def 			A__				(    self):
												lowercase						     =						self.image_processing_class.from_dict(self.image_processor_dict)
												self.assertEqual(image_processor.size       ,{'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3})
												self.assertEqual(image_processor.do_pad       ,A__)

												lowercase						     =						self.image_processing_class.from_dict(
												    self.image_processor_dict       ,size=4_2       ,max_size=8_4       ,pad_and_return_pixel_mask=A__)
												self.assertEqual(image_processor.size       ,{'''shortest_edge''': 4_2, '''longest_edge''': 8_4})
												self.assertEqual(image_processor.do_pad       ,A__)




							def 			A__				(    self):
												pass




							def 			A__				(    self):
												# Initialize image_processing
												lowercase						     =						self.image_processing_class(**self.image_processor_dict)
												# create random PIL images
												lowercase						     =						prepare_image_inputs(self.image_processor_tester       ,equal_resolution=A__)
												for image in image_inputs:
																	self.assertIsInstance(A__       ,Image.Image)

												# Test not batched input
												lowercase						     =						image_processing(image_inputs[0]       ,return_tensors='''pt''').pixel_values

												lowercase     ,						lowercase						     =						self.image_processor_tester.get_expected_values(A__)

												self.assertEqual(
												    encoded_images.shape       ,(1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,)

												# Test batched
												lowercase     ,						lowercase						     =						self.image_processor_tester.get_expected_values(A__       ,batched=A__)

												lowercase						     =						image_processing(A__       ,return_tensors='''pt''').pixel_values
												self.assertEqual(
												    encoded_images.shape       ,(
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.num_channels,
												        expected_height,
												        expected_width,
												    )       ,)




							def 			A__				(    self):
												# Initialize image_processing
												lowercase						     =						self.image_processing_class(**self.image_processor_dict)
												# create random numpy tensors
												lowercase						     =						prepare_image_inputs(self.image_processor_tester       ,equal_resolution=A__       ,numpify=A__)
												for image in image_inputs:
																	self.assertIsInstance(A__       ,np.ndarray)

												# Test not batched input
												lowercase						     =						image_processing(image_inputs[0]       ,return_tensors='''pt''').pixel_values

												lowercase     ,						lowercase						     =						self.image_processor_tester.get_expected_values(A__)

												self.assertEqual(
												    encoded_images.shape       ,(1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,)

												# Test batched
												lowercase						     =						image_processing(A__       ,return_tensors='''pt''').pixel_values

												lowercase     ,						lowercase						     =						self.image_processor_tester.get_expected_values(A__       ,batched=A__)

												self.assertEqual(
												    encoded_images.shape       ,(
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.num_channels,
												        expected_height,
												        expected_width,
												    )       ,)




							def 			A__				(    self):
												# Initialize image_processing
												lowercase						     =						self.image_processing_class(**self.image_processor_dict)
												# create random PyTorch tensors
												lowercase						     =						prepare_image_inputs(self.image_processor_tester       ,equal_resolution=A__       ,torchify=A__)
												for image in image_inputs:
																	self.assertIsInstance(A__       ,torch.Tensor)

												# Test not batched input
												lowercase						     =						image_processing(image_inputs[0]       ,return_tensors='''pt''').pixel_values

												lowercase     ,						lowercase						     =						self.image_processor_tester.get_expected_values(A__)

												self.assertEqual(
												    encoded_images.shape       ,(1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,)

												# Test batched
												lowercase						     =						image_processing(A__       ,return_tensors='''pt''').pixel_values

												lowercase     ,						lowercase						     =						self.image_processor_tester.get_expected_values(A__       ,batched=A__)

												self.assertEqual(
												    encoded_images.shape       ,(
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.num_channels,
												        expected_height,
												        expected_width,
												    )       ,)




							@slow
							def 			A__				(    self):
												# prepare image and target
												lowercase						     =						Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
												with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''       ,'''r''') as f:
																	lowercase						     =						json.loads(f.read())

												lowercase						     =						{'''image_id''': 3_9_7_6_9, '''annotations''': target}

												# encode them
												lowercase						     =						ConditionalDetrImageProcessor.from_pretrained('''microsoft/conditional-detr-resnet-50''')
												lowercase						     =						image_processing(images=A__       ,annotations=A__       ,return_tensors='''pt''')

												# verify pixel values
												lowercase						     =						torch.Size([1, 3, 8_0_0, 1_0_6_6])
												self.assertEqual(encoding['''pixel_values'''].shape       ,A__)

												lowercase						     =						torch.tensor([0.2796, 0.3138, 0.3481])
												self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]       ,A__       ,atol=1E-4))

												# verify area
												lowercase						     =						torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438])
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']       ,A__))
												# verify boxes
												lowercase						     =						torch.Size([6, 4])
												self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape       ,A__)
												lowercase						     =						torch.tensor([0.5503, 0.2765, 0.0604, 0.2215])
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]       ,A__       ,atol=1E-3))
												# verify image_id
												lowercase						     =						torch.tensor([3_9_7_6_9])
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']       ,A__))
												# verify is_crowd
												lowercase						     =						torch.tensor([0, 0, 0, 0, 0, 0])
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']       ,A__))
												# verify class_labels
												lowercase						     =						torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7])
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']       ,A__))
												# verify orig_size
												lowercase						     =						torch.tensor([4_8_0, 6_4_0])
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']       ,A__))
												# verify size
												lowercase						     =						torch.tensor([8_0_0, 1_0_6_6])
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']       ,A__))





							@slow
							def 			A__				(    self):
												# prepare image, target and masks_path
												lowercase						     =						Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
												with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''       ,'''r''') as f:
																	lowercase						     =						json.loads(f.read())

												lowercase						     =						{'''file_name''': '''000000039769.png''', '''image_id''': 3_9_7_6_9, '''segments_info''': target}

												lowercase						     =						pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic''')

												# encode them
												lowercase						     =						ConditionalDetrImageProcessor(format='''coco_panoptic''')
												lowercase						     =						image_processing(images=A__       ,annotations=A__       ,masks_path=A__       ,return_tensors='''pt''')

												# verify pixel values
												lowercase						     =						torch.Size([1, 3, 8_0_0, 1_0_6_6])
												self.assertEqual(encoding['''pixel_values'''].shape       ,A__)

												lowercase						     =						torch.tensor([0.2796, 0.3138, 0.3481])
												self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]       ,A__       ,atol=1E-4))

												# verify area
												lowercase						     =						torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147])
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']       ,A__))
												# verify boxes
												lowercase						     =						torch.Size([6, 4])
												self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape       ,A__)
												lowercase						     =						torch.tensor([0.2625, 0.5437, 0.4688, 0.8625])
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]       ,A__       ,atol=1E-3))
												# verify image_id
												lowercase						     =						torch.tensor([3_9_7_6_9])
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']       ,A__))
												# verify is_crowd
												lowercase						     =						torch.tensor([0, 0, 0, 0, 0, 0])
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']       ,A__))
												# verify class_labels
												lowercase						     =						torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3])
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']       ,A__))
												# verify masks
												lowercase						     =						8_2_2_8_7_3
												self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()       ,A__)
												# verify orig_size
												lowercase						     =						torch.tensor([4_8_0, 6_4_0])
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']       ,A__))
												# verify size
												lowercase						     =						torch.tensor([8_0_0, 1_0_6_6])
												self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']       ,A__))




