




"""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										=    16
__lowerCamelCase										=    32



def 					a			(    __snake_case  :					Accelerator,     __snake_case  :					int = 16	):



				'''simple docstring'''





				UpperCAmelCase_       :str         =   AutoTokenizer.from_pretrained('''bert-base-cased'''	)
				UpperCAmelCase_       :Optional[int]         =   load_dataset('''glue''',     '''mrpc'''	)

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

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

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

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

				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										=    mocked_dataloaders  # noqa: F811



def 					a			(    __snake_case  :					Optional[Any],     __snake_case  :					Dict	):



				'''simple docstring'''





				if os.environ.get('''TESTING_MOCKED_DATALOADERS''',     UpperCAmelCase_	) == "1":
								UpperCAmelCase_       :Optional[int]         =   2
				# New Code #
				UpperCAmelCase_       :Tuple         =   int(args.gradient_accumulation_steps	)
				UpperCAmelCase_       :List[Any]         =   int(args.local_sgd_steps	)
				# Initialize accelerator
				UpperCAmelCase_       :List[Any]         =   Accelerator(
				    cpu=args.cpu,     mixed_precision=args.mixed_precision,     gradient_accumulation_steps=UpperCAmelCase_	)
				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
				UpperCAmelCase_       :List[Any]         =   config['''lr''']
				UpperCAmelCase_       :str         =   int(config['''num_epochs''']	)
				UpperCAmelCase_       :Dict         =   int(config['''seed''']	)
				UpperCAmelCase_       :int         =   int(config['''batch_size''']	)

				UpperCAmelCase_       :Union[str, Any]         =   evaluate.load('''glue''',     '''mrpc'''	)

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

				# 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).
				UpperCAmelCase_       :Optional[int]         =   model.to(accelerator.device	)

				# Instantiate optimizer
				UpperCAmelCase_       :str         =   AdamW(params=model.parameters(),     lr=UpperCAmelCase_	)

				# Instantiate scheduler
				UpperCAmelCase_       :str         =   get_linear_schedule_with_warmup(
				    optimizer=UpperCAmelCase_,     num_warmup_steps=100,     num_training_steps=(len(UpperCAmelCase_	) * 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.
				UpperCAmelCase_       :Optional[Any]         =   accelerator.prepare(
				    UpperCAmelCase_,     UpperCAmelCase_,     UpperCAmelCase_,     UpperCAmelCase_,     UpperCAmelCase_	)

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

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

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



def 					a			(    ):



				'''simple docstring'''





				UpperCAmelCase_       :Union[str, Any]         =   argparse.ArgumentParser(description='''Simple example of training script.'''	)
				parser.add_argument(
				    '''--mixed_precision''',     type=UpperCAmelCase_,     default=UpperCAmelCase_,     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=UpperCAmelCase_,     default=1,     help='''The number of minibatches to be ran before gradients are accumulated.''',     )
				parser.add_argument(
				    '''--local_sgd_steps''',     type=UpperCAmelCase_,     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.'''	)
				UpperCAmelCase_       :Dict         =   parser.parse_args()
				UpperCAmelCase_       :int         =   {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
				training_function(UpperCAmelCase_,     UpperCAmelCase_	)


if __name__ == "__main__":
						main()




import gc
import inspect
import unittest

import torch
from parameterized import parameterized

from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin


enable_full_determinism()


class 			lowercase_		(      A  ,      unittest.TestCase       ):
       __lowerCamelCase				       =						PriorTransformer
       __lowerCamelCase				       =						"hidden_states"


       @property
       def 	_snake_case				(    self				)						->    List[str]:
              SCREAMING_SNAKE_CASE_							:				List[str]		=4
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=8
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=7

              SCREAMING_SNAKE_CASE_							:				Dict		=floats_tensor((batch_size, embedding_dim)				).to(__A				)

              SCREAMING_SNAKE_CASE_							:				Dict		=floats_tensor((batch_size, embedding_dim)				).to(__A				)
              SCREAMING_SNAKE_CASE_							:				Dict		=floats_tensor((batch_size, num_embeddings, embedding_dim)				).to(__A				)

              return {
                  "hidden_states": hidden_states,
                  "timestep": 2,
                  "proj_embedding": proj_embedding,
                  "encoder_hidden_states": encoder_hidden_states,
              }


       def 	_snake_case				(    self						,       __A=0				)						->    int:
              torch.manual_seed(__A				)
              SCREAMING_SNAKE_CASE_							:				str		=4
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=8
              SCREAMING_SNAKE_CASE_							:				List[Any]		=7

              SCREAMING_SNAKE_CASE_							:				Tuple		=torch.randn((batch_size, embedding_dim)				).to(__A				)

              SCREAMING_SNAKE_CASE_							:				int		=torch.randn((batch_size, embedding_dim)				).to(__A				)
              SCREAMING_SNAKE_CASE_							:				List[Any]		=torch.randn((batch_size, num_embeddings, embedding_dim)				).to(__A				)

              return {
                  "hidden_states": hidden_states,
                  "timestep": 2,
                  "proj_embedding": proj_embedding,
                  "encoder_hidden_states": encoder_hidden_states,
              }


       @property
       def 	_snake_case				(    self				)						->    Union[str, Any]:
              return (4, 8)


       @property
       def 	_snake_case				(    self				)						->    int:
              return (4, 8)


       def 	_snake_case				(    self				)						->    List[Any]:
              SCREAMING_SNAKE_CASE_							:				List[Any]		={
                  '''num_attention_heads''': 2,
                  '''attention_head_dim''': 4,
                  '''num_layers''': 2,
                  '''embedding_dim''': 8,
                  '''num_embeddings''': 7,
                  '''additional_embeddings''': 4,
              }
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=self.dummy_input
              return init_dict, inputs_dict


       def 	_snake_case				(    self				)						->    Optional[int]:
              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Any		=PriorTransformer.from_pretrained(
                  '''hf-internal-testing/prior-dummy'''						,       output_loading_info=__A				)
              self.assertIsNotNone(__A				)
              self.assertEqual(len(loading_info['''missing_keys''']				)						,       0				)

              model.to(__A				)
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=model(**self.dummy_input				)[0]

              assert hidden_states is not None, "Make sure output is not None"


       def 	_snake_case				(    self				)						->    str:
              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				List[Any]		=self.prepare_init_args_and_inputs_for_common()

              SCREAMING_SNAKE_CASE_							:				Optional[int]		=self.model_class(**__A				)
              SCREAMING_SNAKE_CASE_							:				List[Any]		=inspect.signature(model.forward				)
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=[*signature.parameters.keys()]

              SCREAMING_SNAKE_CASE_							:				Optional[int]		=['''hidden_states''', '''timestep''']
              self.assertListEqual(arg_names[:2]						,       __A				)





       def 	_snake_case				(    self				)						->    Dict:
              SCREAMING_SNAKE_CASE_							:				Dict		=PriorTransformer.from_pretrained('''hf-internal-testing/prior-dummy'''				)
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=model.to(__A				)

              if hasattr(__A						,       '''set_default_attn_processor'''				):
                     model.set_default_attn_processor()

              SCREAMING_SNAKE_CASE_							:				List[Any]		=self.get_dummy_seed_input()

              with torch.no_grad():
                     SCREAMING_SNAKE_CASE_							:				str		=model(**__A				)[0]

              SCREAMING_SNAKE_CASE_							:				Any		=output[0, :5].flatten().cpu()
              print(__A				)

              # Since the VAE Gaussian prior's generator is seeded on the appropriate device,
              # the expected output slices are not the same for CPU and GPU.
              SCREAMING_SNAKE_CASE_							:				int		=torch.tensor([-1.3_436, -0.2_870, 0.7_538, 0.4_368, -0.0_239]				)
              self.assertTrue(torch_all_close(__A						,       __A						,       rtol=1e-2				)				)






@slow
class 			lowercase_		(      unittest.TestCase       ):


       def 	_snake_case				(    self						,       __A=1						,       __A=768						,       __A=77						,       __A=0				)						->    str:
              torch.manual_seed(__A				)
              SCREAMING_SNAKE_CASE_							:				Dict		=batch_size
              SCREAMING_SNAKE_CASE_							:				List[str]		=embedding_dim
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=num_embeddings

              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=torch.randn((batch_size, embedding_dim)				).to(__A				)

              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=torch.randn((batch_size, embedding_dim)				).to(__A				)
              SCREAMING_SNAKE_CASE_							:				List[str]		=torch.randn((batch_size, num_embeddings, embedding_dim)				).to(__A				)

              return {
                  "hidden_states": hidden_states,
                  "timestep": 2,
                  "proj_embedding": proj_embedding,
                  "encoder_hidden_states": encoder_hidden_states,
              }


       def 	_snake_case				(    self				)						->    Dict:
              # clean up the VRAM after each test
              super().tearDown()
              gc.collect()
              torch.cuda.empty_cache()





       @parameterized.expand(
           [
               # fmt: off
               [13, [-0.5_861,  0.1_283, -0.0_931,  0.0_882,  0.4_476,  0.1_329, -0.0_498,  0.0_640]],
               [37, [-0.4_913,  0.0_110, -0.0_483,  0.0_541,  0.4_954, -0.0_170,  0.0_354,  0.1_651]],
               # fmt: on
           ]				)
       def 	_snake_case				(    self						,       __A						,       __A				)						->    Optional[int]:
              SCREAMING_SNAKE_CASE_							:				Dict		=PriorTransformer.from_pretrained('''kandinsky-community/kandinsky-2-1-prior'''						,       subfolder='''prior'''				)
              model.to(__A				)
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=self.get_dummy_seed_input(seed=__A				)

              with torch.no_grad():
                     SCREAMING_SNAKE_CASE_							:				Dict		=model(**__A				)[0]

              assert list(sample.shape				) == [1, 768]

              SCREAMING_SNAKE_CASE_							:				Dict		=sample[0, :8].flatten().cpu()
              print(__A				)
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=torch.tensor(__A				)

              assert torch_all_close(__A						,       __A						,       atol=1e-3				)
