


'''simple docstring'''
import inspect
import unittest

from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch
					from torch import nn

					from transformers import ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
					from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image




class        _UpperCamelCase	:


				'''simple docstring'''


				def __init__(     self							,      a_							,      a_=1_3							,      a_=6_4							,      a_=2							,      a_=3							,      a_=True							,      a_=True							,      a_=3_2							,      a_=5							,      a_=4							,      a_=3_7							,      a_="gelu"							,      a_=0.1							,      a_=0.1							,      a_=1_0							,      a_=0.02							,      a_=[1, 1_6, 4, 4]							,      a_=None							,      )    ->  List[Any]:
					lowercase						:		int   					=		parent
					lowercase						:		Union[str, Any]   					=		batch_size
					lowercase						:		Optional[int]   					=		image_size
					lowercase						:		Optional[Any]   					=		patch_size
					lowercase						:		Tuple   					=		num_channels
					lowercase						:		Optional[Any]   					=		is_training
					lowercase						:		Any   					=		use_labels
					lowercase						:		Any   					=		hidden_size
					lowercase						:		str   					=		num_hidden_layers
					lowercase						:		Optional[Any]   					=		num_attention_heads
					lowercase						:		Optional[Any]   					=		intermediate_size
					lowercase						:		Optional[Any]   					=		hidden_act
					lowercase						:		List[str]   					=		hidden_dropout_prob
					lowercase						:		Tuple   					=		attention_probs_dropout_prob
					lowercase						:		Dict   					=		type_sequence_label_size
					lowercase						:		Optional[Any]   					=		initializer_range
					lowercase						:		Union[str, Any]   					=		scope
					lowercase						:		Tuple   					=		backbone_featmap_shape

					# in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
					# the number of patches is based on the feature map of the backbone, which by default uses an output stride
					# of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
					lowercase						:		int   					=		(self.image_size // 3_2) ** 2
					lowercase						:		Dict   					=		num_patches + 1

				def 	a__		(     self							)    ->  Union[str, Any]:
					lowercase						:		Optional[Any]   					=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

					lowercase						:		Union[str, Any]   					=		None
					if self.use_labels:
						lowercase						:		Tuple   					=		ids_tensor([self.batch_size]							,      self.type_sequence_label_size							)

					lowercase						:		Union[str, Any]   					=		self.get_config()

					return config, pixel_values, labels

				def 	a__		(     self							)    ->  List[Any]:
					lowercase						:		Any   					=		{
					    "global_padding": "same",
					    "layer_type": "bottleneck",
					    "depths": [3, 4, 9],
					    "out_features": ["stage1", "stage2", "stage3"],
					    "embedding_dynamic_padding": True,
					    "hidden_sizes": [4, 8, 1_6, 3_2],
					    "num_groups": 2,
					}

					return ViTHybridConfig(
					    image_size=self.image_size							,      patch_size=self.patch_size							,      num_channels=self.num_channels							,      hidden_size=self.hidden_size							,      num_hidden_layers=self.num_hidden_layers							,      num_attention_heads=self.num_attention_heads							,      intermediate_size=self.intermediate_size							,      hidden_act=self.hidden_act							,      hidden_dropout_prob=self.hidden_dropout_prob							,      attention_probs_dropout_prob=self.attention_probs_dropout_prob							,      is_decoder=a_							,      initializer_range=self.initializer_range							,      backbone_featmap_shape=self.backbone_featmap_shape							,      backbone_config=a_							,      )

				def 	a__		(     self							,      a_							,      a_							,      a_							)    ->  List[Any]:
					lowercase						:		Any   					=		ViTHybridModel(config=a_							)
					model.to(a_							)
					model.eval()
					lowercase						:		List[str]   					=		model(a_							)
					self.parent.assertEqual(result.last_hidden_state.shape							,      (self.batch_size, self.seq_length, self.hidden_size)							)

				def 	a__		(     self							,      a_							,      a_							,      a_							)    ->  Optional[int]:
					lowercase						:		int   					=		self.type_sequence_label_size
					lowercase						:		Dict   					=		ViTHybridForImageClassification(a_							)
					model.to(a_							)
					model.eval()
					lowercase						:		str   					=		model(a_							,      labels=a_							)
					self.parent.assertEqual(result.logits.shape							,      (self.batch_size, self.type_sequence_label_size)							)


				def 	a__		(     self							)    ->  Optional[int]:
					lowercase						:		str   					=		self.prepare_config_and_inputs()
					lowercase					,		lowercase					,		lowercase						:		List[str]   					=		config_and_inputs
					lowercase						:		str   					=		{"pixel_values": pixel_values}
					return config, inputs_dict




@require_torch
class        _UpperCamelCase	(    SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			unittest.TestCase):


				'''simple docstring'''


				_snake_case						       =				(ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
				_snake_case						       =				(
				    {'''feature-extraction''': ViTHybridModel, '''image-classification''': ViTHybridForImageClassification}
				    if is_torch_available()
				    else {}
				)
				_snake_case						       =				False
				_snake_case						       =				False
				_snake_case						       =				False

				def 	a__		(     self							)    ->  Tuple:
					lowercase						:		Dict   					=		ViTHybridModelTester(self							)
					lowercase						:		List[Any]   					=		ConfigTester(self							,      config_class=a_							,      has_text_modality=a_							,      hidden_size=3_7							)

				def 	a__		(     self							)    ->  Union[str, Any]:
					self.config_tester.run_common_tests()

				@unittest.skip(reason="ViT does not use inputs_embeds"							)
				def 	a__		(     self							)    ->  Union[str, Any]:
					pass

				def 	a__		(     self							)    ->  int:
					lowercase					,		lowercase						:		str   					=		self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
						lowercase						:		int   					=		model_class(a_							)
						self.assertIsInstance(model.get_input_embeddings()							,      (nn.Module)							)
						lowercase						:		Optional[Any]   					=		model.get_output_embeddings()
						self.assertTrue(x is None or isinstance(a_							,      nn.Linear							)							)

				def 	a__		(     self							)    ->  Optional[Any]:
					lowercase					,		lowercase						:		Any   					=		self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
						lowercase						:		str   					=		model_class(a_							)
						lowercase						:		Tuple   					=		inspect.signature(model.forward							)
						# signature.parameters is an OrderedDict => so arg_names order is deterministic
						lowercase						:		Optional[Any]   					=		[*signature.parameters.keys()]

						lowercase						:		str   					=		["pixel_values"]
						self.assertListEqual(arg_names[:1]							,      a_							)

				def 	a__		(     self							)    ->  Any:
					lowercase						:		Optional[Any]   					=		self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*a_							)

				def 	a__		(     self							)    ->  Optional[Any]:
					lowercase						:		Any   					=		self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_image_classification(*a_							)

				def 	a__		(     self							)    ->  Union[str, Any]:
					lowercase					,		lowercase						:		Tuple   					=		self.model_tester.prepare_config_and_inputs_for_common()

					lowercase						:		Any   					=		_config_zero_init(a_							)
					for model_class in self.all_model_classes:
						lowercase						:		Union[str, Any]   					=		model_class(config=a_							)
						# Skip the check for the backbone
						for name, module in model.named_modules():
							if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
								lowercase						:		List[Any]   					=		[F'''{name}.{key}''' for key in module.state_dict().keys()]
								break

						for name, param in model.named_parameters():
							if param.requires_grad:
								if name in backbone_params:
									continue
								self.assertIn(
								    ((param.data.mean() * 1e9).round() / 1e9).item()							,      [0.0, 1.0]							,      msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''							,      )


				@slow
				def 	a__		(     self							)    ->  List[str]:
					for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						lowercase						:		int   					=		ViTHybridModel.from_pretrained(a_							)
						self.assertIsNotNone(a_							)




def 	_A  (       )  ->				str:
	lowercase						:		List[str]   					=		Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"							)
	return image






@require_torch
@require_vision
class        _UpperCamelCase	(    unittest.TestCase):


				'''simple docstring'''


				@cached_property
				def 	a__		(     self							)    ->  Tuple:
					return (
					    ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]							)
					    if is_vision_available()
					    else None
					)

				@slow
				def 	a__		(     self							)    ->  Tuple:
					lowercase						:		str   					=		ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]							).to(
					    a_							)

					lowercase						:		Dict   					=		self.default_image_processor
					lowercase						:		Dict   					=		prepare_img()
					lowercase						:		Tuple   					=		image_processor(images=a_							,      return_tensors="pt"							).to(a_							)

					# forward pass
					with torch.no_grad():
						lowercase						:		Optional[int]   					=		model(**a_							)

					# verify the logits
					lowercase						:		str   					=		torch.Size((1, 1_0_0_0)							)
					self.assertEqual(outputs.logits.shape							,      a_							)

					lowercase						:		Any   					=		torch.tensor([-1.90_90, -0.49_93, -0.23_89]							).to(a_							)

					self.assertTrue(torch.allclose(outputs.logits[0, :3]							,      a_							,      atol=1e-4							)							)


				@slow
				@require_accelerate
				def 	a__		(     self							)    ->  int:
					lowercase						:		Optional[Any]   					=		ViTHybridImageProcessor.from_pretrained("google/vit-hybrid-base-bit-384"							)
					lowercase						:		Tuple   					=		ViTHybridForImageClassification.from_pretrained("google/vit-hybrid-base-bit-384"							,      device_map="auto"							)

					lowercase						:		List[str]   					=		prepare_img()

					lowercase						:		List[str]   					=		image_processor(images=a_							,      return_tensors="pt"							)
					lowercase						:		Union[str, Any]   					=		model(**a_							)
					lowercase						:		Any   					=		outputs.logits
					# model predicts one of the 1000 ImageNet classes
					lowercase						:		List[Any]   					=		logits.argmax(-1							).item()

					self.assertTrue(model.config.idalabel[predicted_class_idx]							,      "tabby, tabby cat"							)






'''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
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
#
########################################################################


lowerCAmelCase   : Tuple             =				1_6
lowerCAmelCase   : Optional[int]             =				3_2




def 	_A  (       A							,A = 1_6							)  ->				Union[str, Any]:
	lowercase						:		List[Any]   					=		AutoTokenizer.from_pretrained("bert-base-cased"							)
	lowercase						:		Any   					=		load_dataset("glue"							,"mrpc"							)

	def tokenize_function(A							):
		# max_length=None => use the model max length (it's actually the default)
		lowercase						:		List[str]   					=		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():
		lowercase						:		List[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
	lowercase						:		Optional[int]   					=		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.
		lowercase						:		Tuple   					=		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":
			lowercase						:		Any   					=		1_6
		elif accelerator.mixed_precision != "no":
			lowercase						:		List[Any]   					=		8
		else:
			lowercase						:		List[Any]   					=		None

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

	# Instantiate dataloaders.
	lowercase						:		Any   					=		DataLoader(
	    tokenized_datasets["train"]							,shuffle=A							,collate_fn=A							,batch_size=A							)
	lowercase						:		List[str]   					=		DataLoader(
	    tokenized_datasets["validation"]							,shuffle=A							,collate_fn=A							,batch_size=A							)

	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

					lowerCAmelCase   : List[Any]             =				mocked_dataloaders  # noqa: F811




def 	_A  (       A							,A							)  ->				str:
	# For testing only
	if os.environ.get("TESTING_MOCKED_DATALOADERS"							,A							) == "1":
		lowercase						:		Optional[Any]   					=		2
	# New Code #
	lowercase						:		List[str]   					=		int(args.gradient_accumulation_steps							)
	lowercase						:		Union[str, Any]   					=		int(args.local_sgd_steps							)
	# Initialize accelerator
	lowercase						:		Union[str, Any]   					=		Accelerator(
	    cpu=args.cpu							,mixed_precision=args.mixed_precision							,gradient_accumulation_steps=A							)
	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						:		Dict   					=		config["lr"]
	lowercase						:		List[Any]   					=		int(config["num_epochs"]							)
	lowercase						:		int   					=		int(config["seed"]							)
	lowercase						:		Dict   					=		int(config["batch_size"]							)

	lowercase						:		List[str]   					=		evaluate.load("glue"							,"mrpc"							)

	set_seed(A							)
	lowercase					,		lowercase						:		Any   					=		get_dataloaders(A							,A							)
	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	lowercase						:		Optional[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).
	lowercase						:		Optional[int]   					=		model.to(accelerator.device							)

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

	# Instantiate scheduler
	lowercase						:		Union[str, Any]   					=		get_linear_schedule_with_warmup(
	    optimizer=A							,num_warmup_steps=1_0_0							,num_training_steps=(len(A							) * 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						:		Tuple   					=		accelerator.prepare(
	    A							,A							,A							,A							,A							)

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

		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():
				lowercase						:		Dict   					=		model(**A							)
			lowercase						:		Tuple   					=		outputs.logits.argmax(dim=-1							)
			lowercase					,		lowercase						:		str   					=		accelerator.gather_for_metrics((predictions, batch["labels"])							)
			metric.add_batch(
			    predictions=A							,references=A							,)

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




def 	_A  (       )  ->				Dict:
	lowercase						:		Optional[int]   					=		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."							,)
	# New Code #
	parser.add_argument(
	    "--gradient_accumulation_steps"							,type=A							,default=1							,help="The number of minibatches to be ran before gradients are accumulated."							,)
	parser.add_argument(
	    "--local_sgd_steps"							,type=A							,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						:		Optional[Any]   					=		parser.parse_args()
	lowercase						:		Union[str, Any]   					=		{"lr": 2e-5, "num_epochs": 3, "seed": 4_2, "batch_size": 1_6}
	training_function(A							,A							)


if __name__ == "__main__":
					main()


