





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


UpperCAmelCase						:   Union[str, Any]			  =     16
UpperCAmelCase						:   Optional[Any]			  =     32







def  _SCREAMING_SNAKE_CASE				(   a					,						a = 16  )      ->      Tuple:
					__A	:						int       =				AutoTokenizer.from_pretrained('bert-base-cased'  )
					__A	:						Optional[Any]       =				load_dataset('glue'					,						'mrpc'  )

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

					def collate_fn(a  ):
										# On TPU it's best to pad everything to the same length or training will be very slow.
										__A	:						Optional[Any]       =				1_28 if accelerator.distributed_type == DistributedType.TPU else None
										# When using mixed precision we want round multiples of 8/16
										if accelerator.mixed_precision == "fp8":
															__A	:						List[str]       =				16
										elif accelerator.mixed_precision != "no":
															__A	:						List[Any]       =				8
										else:
															__A	:						List[Any]       =				None

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

					# Instantiate dataloaders.
					__A	:						Optional[int]       =				DataLoader(
					    tokenized_datasets['train']					,						shuffle=a					,						collate_fn=a					,						batch_size=a					,						drop_last=a  )
					__A	:						Optional[Any]       =				DataLoader(
					    tokenized_datasets['validation']					,						shuffle=a					,						collate_fn=a					,						batch_size=a					,						drop_last=(accelerator.mixed_precision == 'fp8')					,						)

					return train_dataloader, eval_dataloader







def  _SCREAMING_SNAKE_CASE				(   a					,						a  )      ->      int:
					# Initialize accelerator
					__A	:						int       =				Accelerator(cpu=args.cpu					,						mixed_precision=args.mixed_precision  )
					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					__A	:						Union[str, Any]       =				config['lr']
					__A	:						Optional[Any]       =				int(config['num_epochs']  )
					__A	:						Union[str, Any]       =				int(config['seed']  )
					__A	:						List[Any]       =				int(config['batch_size']  )

					__A	:						int       =				evaluate.load('glue'					,						'mrpc'  )

					# If the batch size is too big we use gradient accumulation
					__A	:						List[Any]       =				1
					if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
										__A	:						List[Any]       =				batch_size // MAX_GPU_BATCH_SIZE
										__A	:						int       =				MAX_GPU_BATCH_SIZE

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

					# 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).
					__A	:						Dict       =				model.to(accelerator.device  )
					# Instantiate optimizer
					__A	:						Union[str, Any]       =				AdamW(params=model.parameters()					,						lr=a  )

					# Instantiate scheduler
					__A	:						Tuple       =				get_linear_schedule_with_warmup(
					    optimizer=a					,						num_warmup_steps=1_00					,						num_training_steps=(len(a  ) * 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.

					__A    ,				__A    ,				__A    ,				__A    ,				__A	:						Optional[int]       =				accelerator.prepare(
					    a					,						a					,						a					,						a					,						a  )

					# Now we train the model
					for epoch in range(a  ):
										model.train()
										for step, batch in enumerate(a  ):
															# We could avoid this line since we set the accelerator with `device_placement=True`.
															batch.to(accelerator.device  )
															__A	:						Tuple       =				model(**a  )
															__A	:						Union[str, Any]       =				outputs.loss
															__A	:						str       =				loss / gradient_accumulation_steps
															accelerator.backward(a  )
															if step % gradient_accumulation_steps == 0:
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()

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

										__A	:						Optional[Any]       =				metric.compute()
										# Use accelerator.print to print only on the main process.
										accelerator.print(F"""epoch {epoch}:"""					,						a  )







def  _SCREAMING_SNAKE_CASE				(   )      ->      Dict:
					__A	:						Union[str, Any]       =				argparse.ArgumentParser(description='Simple example of training script.'  )
					parser.add_argument(
					    '--mixed_precision'					,						type=a					,						default=a					,						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.'  )
					__A	:						Tuple       =				parser.parse_args()
					__A	:						int       =				{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
					training_function(a					,						a  )


if __name__ == "__main__":
				main()











import hashlib
import unittest
from typing import Dict

import numpy as np

from transformers import (
    MODEL_FOR_MASK_GENERATION_MAPPING,
    TF_MODEL_FOR_MASK_GENERATION_MAPPING,
    is_vision_available,
    pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)


if is_vision_available():
				from PIL import Image
else:





				class 						_A:


								"""simple docstring"""


								@staticmethod
								def 					UpperCAmelCase_		(			*_A     ,					**_A     ):
													pass







def  _SCREAMING_SNAKE_CASE				(   a  )      ->      str:
					__A	:						str       =				hashlib.mda(image.tobytes()  )
					return m.hexdigest()[:10]







def  _SCREAMING_SNAKE_CASE				(   a  )      ->      Dict:
					__A	:						Dict       =				np.array(a  )
					__A	:						List[Any]       =				npimg.shape
					return {"hash": hashimage(a  ), "shape": shape}





@is_pipeline_test
@require_vision
@require_torch
class 						_A(					unittest.TestCase   ):


				"""simple docstring"""


				UpperCamelCase    :  str					    =	dict(
				    (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()   ) if MODEL_FOR_MASK_GENERATION_MAPPING else [])   )
				UpperCamelCase    :  int					    =	dict(
				    (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()   ) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])   )



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ,					_A     ):
									__A	:						Dict       =				MaskGenerationPipeline(model=_A     ,					image_processor=_A     )
									return image_segmenter, [
									    "./tests/fixtures/tests_samples/COCO/000000039769.png",
									    "./tests/fixtures/tests_samples/COCO/000000039769.png",
									]



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ):
									pass



				@require_tf
				@unittest.skip('Image segmentation not implemented in TF'     )
				def 					UpperCAmelCase_		(			self     ):
									pass



				@slow
				@require_torch
				def 					UpperCAmelCase_		(			self     ):
									__A	:						Union[str, Any]       =				pipeline('mask-generation'     ,					model='facebook/sam-vit-huge'     )

									__A	:						List[str]       =				image_segmenter('http://images.cocodataset.org/val2017/000000039769.jpg'     ,					points_per_batch=256     )

									# Shortening by hashing
									__A	:						List[Any]       =				[]
									for i, o in enumerate(outputs['masks']     ):
														new_outupt += [{"mask": mask_to_test_readable(_A     ), "scores": outputs["scores"][i]}]

									# fmt: off
									self.assertEqual(
									    nested_simplify(_A     ,					decimals=4     )     ,					[
									        {'mask': {'hash': '115ad19f5f', 'shape': (480, 640)}, 'scores': 1.0_4_4_4},
									        {'mask': {'hash': '6affa964c6', 'shape': (480, 640)}, 'scores': 1.0_2_1},
									        {'mask': {'hash': 'dfe28a0388', 'shape': (480, 640)}, 'scores': 1.0_1_6_7},
									        {'mask': {'hash': 'c0a5f4a318', 'shape': (480, 640)}, 'scores': 1.0_1_3_2},
									        {'mask': {'hash': 'fe8065c197', 'shape': (480, 640)}, 'scores': 1.0_0_5_3},
									        {'mask': {'hash': 'e2d0b7a0b7', 'shape': (480, 640)}, 'scores': 0.9_9_6_7},
									        {'mask': {'hash': '453c7844bd', 'shape': (480, 640)}, 'scores': 0.9_9_3},
									        {'mask': {'hash': '3d44f2926d', 'shape': (480, 640)}, 'scores': 0.9_9_0_9},
									        {'mask': {'hash': '64033ddc3f', 'shape': (480, 640)}, 'scores': 0.9_8_7_9},
									        {'mask': {'hash': '801064ff79', 'shape': (480, 640)}, 'scores': 0.9_8_3_4},
									        {'mask': {'hash': '6172f276ef', 'shape': (480, 640)}, 'scores': 0.9_7_1_6},
									        {'mask': {'hash': 'b49e60e084', 'shape': (480, 640)}, 'scores': 0.9_6_1_2},
									        {'mask': {'hash': 'a811e775fd', 'shape': (480, 640)}, 'scores': 0.9_5_9_9},
									        {'mask': {'hash': 'a6a8ebcf4b', 'shape': (480, 640)}, 'scores': 0.9_5_5_2},
									        {'mask': {'hash': '9d8257e080', 'shape': (480, 640)}, 'scores': 0.9_5_3_2},
									        {'mask': {'hash': '32de6454a8', 'shape': (480, 640)}, 'scores': 0.9_5_1_6},
									        {'mask': {'hash': 'af3d4af2c8', 'shape': (480, 640)}, 'scores': 0.9_4_9_9},
									        {'mask': {'hash': '3c6db475fb', 'shape': (480, 640)}, 'scores': 0.9_4_8_3},
									        {'mask': {'hash': 'c290813fb9', 'shape': (480, 640)}, 'scores': 0.9_4_6_4},
									        {'mask': {'hash': 'b6f0b8f606', 'shape': (480, 640)}, 'scores': 0.9_4_3},
									        {'mask': {'hash': '92ce16bfdf', 'shape': (480, 640)}, 'scores': 0.9_4_3},
									        {'mask': {'hash': 'c749b25868', 'shape': (480, 640)}, 'scores': 0.9_4_0_8},
									        {'mask': {'hash': 'efb6cab859', 'shape': (480, 640)}, 'scores': 0.9_3_3_5},
									        {'mask': {'hash': '1ff2eafb30', 'shape': (480, 640)}, 'scores': 0.9_3_2_6},
									        {'mask': {'hash': '788b798e24', 'shape': (480, 640)}, 'scores': 0.9_2_6_2},
									        {'mask': {'hash': 'abea804f0e', 'shape': (480, 640)}, 'scores': 0.8_9_9_9},
									        {'mask': {'hash': '7b9e8ddb73', 'shape': (480, 640)}, 'scores': 0.8_9_8_6},
									        {'mask': {'hash': 'cd24047c8a', 'shape': (480, 640)}, 'scores': 0.8_9_8_4},
									        {'mask': {'hash': '6943e6bcbd', 'shape': (480, 640)}, 'scores': 0.8_8_7_3},
									        {'mask': {'hash': 'b5f47c9191', 'shape': (480, 640)}, 'scores': 0.8_8_7_1}
									    ]     ,					)
									# fmt: on





				@require_torch
				@slow
				def 					UpperCAmelCase_		(			self     ):
									__A	:						Optional[Any]       =				'facebook/sam-vit-huge'
									__A	:						List[str]       =				pipeline('mask-generation'     ,					model=_A     )

									__A	:						Tuple       =				image_segmenter(
									    'http://images.cocodataset.org/val2017/000000039769.jpg'     ,					pred_iou_thresh=1     ,					points_per_batch=256     )

									# Shortening by hashing
									__A	:						List[str]       =				[]
									for i, o in enumerate(outputs['masks']     ):
														new_outupt += [{"mask": mask_to_test_readable(_A     ), "scores": outputs["scores"][i]}]

									self.assertEqual(
									    nested_simplify(_A     ,					decimals=4     )     ,					[
									        {'mask': {'hash': '115ad19f5f', 'shape': (480, 640)}, 'scores': 1.0_4_4_4},
									        {'mask': {'hash': '6affa964c6', 'shape': (480, 640)}, 'scores': 1.0_2_1_0},
									        {'mask': {'hash': 'dfe28a0388', 'shape': (480, 640)}, 'scores': 1.0_1_6_7},
									        {'mask': {'hash': 'c0a5f4a318', 'shape': (480, 640)}, 'scores': 1.0_1_3_2},
									        {'mask': {'hash': 'fe8065c197', 'shape': (480, 640)}, 'scores': 1.0_0_5_3},
									    ]     ,					)




