

import argparse
import json
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.utils.deepspeed import DummyOptim, DummyScheduler


A_			:					Optional[Any]    			=     16
A_			:					Dict    			=     32

def 		UpperCamelCase   (lowercase_:					Accelerator       , lowercase_:					int = 16       , lowercase_:					str = "bert-base-cased"   )	-> Union[str, Any]:
	A__				:       Optional[int]	  =						AutoTokenizer.from_pretrained(lowercase_   )
	A__				:       Any	  =						load_dataset("""glue"""       , """mrpc"""   )

	def tokenize_function(lowercase_:					Dict   ):
		# max_length=None => use the model max length (it's actually the default)
		A__				:       str	  =						tokenizer(examples["""sentence1"""]       , examples["""sentence2"""]       , truncation=lowercase_       , max_length=lowercase_   )
		return outputs

	# Apply the method we just defined to all the examples in all the splits of the dataset
	A__				:       Tuple	  =						datasets.map(
	    lowercase_       , batched=lowercase_       , remove_columns=["""idx""", """sentence1""", """sentence2"""]       , load_from_cache_file=lowercase_   )

	# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
	# transformers library
	A__				:       Optional[int]	  =						tokenized_datasets.rename_column("""label"""       , """labels"""   )

	def collate_fn(lowercase_:					List[str]   ):
		# On TPU it's best to pad everything to the same length or training will be very slow.
		if accelerator.distributed_type == DistributedType.TPU:
			return tokenizer.pad(lowercase_       , padding="""max_length"""       , max_length=128       , return_tensors="""pt"""   )
		return tokenizer.pad(lowercase_       , padding="""longest"""       , return_tensors="""pt"""   )

	# Instantiate dataloaders.
	A__				:       int	  =						DataLoader(
	    tokenized_datasets["""train"""]       , shuffle=lowercase_       , collate_fn=lowercase_       , batch_size=lowercase_   )
	A__				:       Optional[int]	  =						DataLoader(
	    tokenized_datasets["""validation"""]       , shuffle=lowercase_       , collate_fn=lowercase_       , batch_size=lowercase_   )

	return train_dataloader, eval_dataloader

def 		UpperCamelCase   (lowercase_:					Dict       , lowercase_:					List[Any]       , lowercase_:					Any       , lowercase_:					Optional[Any]   )	-> Any:
	model.eval()
	A__				:       Union[str, Any]	  =						0
	for step, batch in enumerate(lowercase_   ):
		# We could avoid this line since we set the accelerator with `device_placement=True`.
		batch.to(accelerator.device   )
		with torch.no_grad():
			A__				:       str	  =						model(**lowercase_   )
		A__				:       Dict	  =						outputs.logits.argmax(dim=-1   )
		# It is slightly faster to call this once, than multiple times
		A__  ,  A__				:       Dict	  =						accelerator.gather(
		    (predictions, batch["""labels"""])   )  # If we are in a multiprocess environment, the last batch has duplicates
		if accelerator.use_distributed:
			if step == len(lowercase_   ) - 1:
				A__				:       Dict	  =						predictions[: len(eval_dataloader.dataset   ) - samples_seen]
				A__				:       str	  =						references[: len(eval_dataloader.dataset   ) - samples_seen]
			else:
				samples_seen += references.shape[0]
		metric.add_batch(
		    predictions=lowercase_       , references=lowercase_       , )

	A__				:       Optional[int]	  =						metric.compute()
	return eval_metric["accuracy"]

def 		UpperCamelCase   (lowercase_:					Tuple       , lowercase_:					str   )	-> Tuple:
	# Initialize accelerator
	A__				:       Optional[Any]	  =						Accelerator()

	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
	A__				:       str	  =						config["""lr"""]
	A__				:       int	  =						int(config["""num_epochs"""]   )
	A__				:       List[Any]	  =						int(config["""seed"""]   )
	A__				:       Dict	  =						int(config["""batch_size"""]   )
	A__				:       Tuple	  =						args.model_name_or_path

	set_seed(lowercase_   )
	A__  ,  A__				:       Union[str, Any]	  =						get_dataloaders(lowercase_       , lowercase_       , lowercase_   )

	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	A__				:       int	  =						AutoModelForSequenceClassification.from_pretrained(lowercase_       , return_dict=lowercase_   )

	# Instantiate optimizer
	A__				:       Dict	  =						(
	    AdamW
	    if accelerator.state.deepspeed_plugin is None
	    or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
	    else DummyOptim
	)
	A__				:       List[str]	  =						optimizer_cls(params=model.parameters()       , lr=lowercase_   )

	if accelerator.state.deepspeed_plugin is not None:
		A__				:       Optional[Any]	  =						accelerator.state.deepspeed_plugin.deepspeed_config[
		    """gradient_accumulation_steps"""
		]
	else:
		A__				:       List[Any]	  =						1
	A__				:       Optional[Any]	  =						(len(lowercase_   ) * num_epochs) // gradient_accumulation_steps

	# Instantiate scheduler
	if (
	    accelerator.state.deepspeed_plugin is None
	    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
	):
		A__				:       str	  =						get_linear_schedule_with_warmup(
		    optimizer=lowercase_       , num_warmup_steps=0       , num_training_steps=lowercase_       , )
	else:
		A__				:       Optional[Any]	  =						DummyScheduler(lowercase_       , total_num_steps=lowercase_       , warmup_num_steps=0   )

	# 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__				:       Dict	  =						accelerator.prepare(
	    lowercase_       , lowercase_       , lowercase_       , lowercase_       , lowercase_   )

	# We need to keep track of how many total steps we have iterated over
	A__				:       str	  =						0
	# We also need to keep track of the stating epoch so files are named properly
	A__				:       int	  =						0
	A__				:       Tuple	  =						evaluate.load("""glue"""       , """mrpc"""   )
	A__				:       List[str]	  =						num_epochs

	if args.partial_train_epoch is not None:
		A__				:       Tuple	  =						args.partial_train_epoch

	if args.resume_from_checkpoint:
		accelerator.load_state(args.resume_from_checkpoint   )
		A__				:       Optional[Any]	  =						args.resume_from_checkpoint.split("""epoch_"""   )[1]
		A__				:       List[Any]	  =						""""""
		for char in epoch_string:
			if char.isdigit():
				state_epoch_num += char
			else:
				break
		A__				:       Dict	  =						int(lowercase_   ) + 1
		A__				:       Union[str, Any]	  =						evaluation_loop(lowercase_       , lowercase_       , lowercase_       , lowercase_   )
		accelerator.print("""resumed checkpoint performance:"""       , lowercase_   )
		accelerator.print("""resumed checkpoint's scheduler's lr:"""       , lr_scheduler.get_lr()[0]   )
		accelerator.print("""resumed optimizers's lr:"""       , optimizer.param_groups[0]["""lr"""]   )
		with open(os.path.join(args.output_dir       , f"""state_{starting_epoch-1}.json"""   )       , """r"""   ) as f:
			A__				:       Tuple	  =						json.load(lowercase_   )
			assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
			assert (
			    resumed_state["lr"] == lr_scheduler.get_lr()[0]
			), "Scheduler learning rate mismatch, loading from checkpoint failed"
			assert (
			    resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
			), "Optimizer learning rate mismatch, loading from checkpoint failed"
			assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
			return

    # Now we train the model
	A__				:       int	  =						{}
	for epoch in range(lowercase_       , lowercase_   ):
		model.train()
		for step, batch in enumerate(lowercase_   ):
			A__				:       str	  =						model(**lowercase_   )
			A__				:       List[Any]	  =						outputs.loss
			A__				:       List[str]	  =						loss / gradient_accumulation_steps
			accelerator.backward(lowercase_   )
			if step % gradient_accumulation_steps == 0:
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

			overall_step += 1
		A__				:       Dict	  =						f"""epoch_{epoch}"""
		A__				:       Optional[Any]	  =						os.path.join(args.output_dir       , lowercase_   )
		accelerator.save_state(lowercase_   )
		A__				:       Optional[int]	  =						evaluation_loop(lowercase_       , lowercase_       , lowercase_       , lowercase_   )
		A__				:       List[Any]	  =						accuracy
		A__				:       Any	  =						lr_scheduler.get_lr()[0]
		A__				:       Dict	  =						optimizer.param_groups[0]["""lr"""]
		A__				:       Optional[int]	  =						epoch
		A__				:       Union[str, Any]	  =						overall_step
		accelerator.print(f"""epoch {epoch}:"""       , lowercase_   )

		accelerator.wait_for_everyone()
		if accelerator.is_main_process:
			with open(os.path.join(args.output_dir       , f"""state_{epoch}.json"""   )       , """w"""   ) as f:
				json.dump(lowercase_       , lowercase_   )

def 		UpperCamelCase   ()	-> List[Any]:
	A__				:       Any	  =						argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage."""   )
	parser.add_argument(
	    """--model_name_or_path"""       , type=lowercase_       , default="""bert-base-cased"""       , help="""Path to pretrained model or model identifier from huggingface.co/models."""       , required=lowercase_       , )
	parser.add_argument(
	    """--output_dir"""       , type=lowercase_       , default="""."""       , help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""       , )
	parser.add_argument(
	    """--resume_from_checkpoint"""       , type=lowercase_       , default=lowercase_       , help="""If the training should continue from a checkpoint folder."""       , )
	parser.add_argument(
	    """--partial_train_epoch"""       , type=lowercase_       , default=lowercase_       , help="""If passed, the training will stop after this number of epochs."""       , )
	parser.add_argument(
	    """--num_epochs"""       , type=lowercase_       , default=2       , help="""Number of train epochs."""       , )
	A__				:       Union[str, Any]	  =						parser.parse_args()
	A__				:       Dict	  =						{"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}

	training_function(lowercase_       , lowercase_   )


if __name__ == "__main__":
	main()






import os
import tempfile
import unittest

import numpy as np

from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow


if is_flax_available():
	import jax
	import jax.numpy as jnp
	from flax.jax_utils import replicate
	from flax.training.common_utils import shard

	from diffusers import FlaxDDIMScheduler, FlaxDiffusionPipeline, FlaxStableDiffusionPipeline



@require_flax
class        _a		(unittest.TestCase							):


					'''simple docstring'''






					def     __A     (					self     ):
						with tempfile.TemporaryDirectory() as tmpdirname:
							# pipeline has Flax weights
							A__				:       Optional[int]	  =						FlaxDiffusionPipeline.from_pretrained(
							    """hf-internal-testing/tiny-stable-diffusion-pipe"""       ,			safety_checker=A__       ,			cache_dir=A__     )

							A__				:       int	  =						[t[-1] for t in os.walk(os.path.join(A__       ,			os.listdir(A__     )[0]       ,			"""snapshots"""     )     )]
							A__				:       str	  =						[item for sublist in all_root_files for item in sublist]

							# None of the downloaded files should be a PyTorch file even if we have some here:
							# https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_pytorch_model.bin
							assert not any(f.endswith(""".bin"""     ) for f in files     )



@slow
@require_flax
class        _a		(unittest.TestCase							):


					'''simple docstring'''






					def     __A     (					self     ):
						A__  ,  A__				:       Any	  =						FlaxStableDiffusionPipeline.from_pretrained(
						    """hf-internal-testing/tiny-stable-diffusion-pipe"""       ,			safety_checker=A__     )

						A__				:       Optional[int]	  =						(
						    """A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"""
						    """ field, close up, split lighting, cinematic"""
						)

						A__				:       Tuple	  =						jax.random.PRNGKey(0     )
						A__				:       int	  =						4

						A__				:       Optional[Any]	  =						jax.device_count()
						A__				:       Union[str, Any]	  =						num_samples * [prompt]
						A__				:       str	  =						pipeline.prepare_inputs(A__     )

						# shard inputs and rng
						A__				:       str	  =						replicate(A__     )
						A__				:       Any	  =						jax.random.split(A__       ,			A__     )
						A__				:       Optional[Any]	  =						shard(A__     )

						A__				:       int	  =						pipeline(A__       ,			A__       ,			A__       ,			A__       ,			jit=A__     ).images

						assert images.shape == (num_samples, 1, 64, 64, 3)
						if jax.device_count() == 8:
							assert np.abs(np.abs(images[0, 0, :2, :2, -2:]       ,			dtype=np.floataa     ).sum() - 4.1_5_1_4_7_4_5     ) < 1e-3
							assert np.abs(np.abs(A__       ,			dtype=np.floataa     ).sum() - 4_9_9_4_7.8_7_5     ) < 5e-1

						A__				:       str	  =						pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:]     )     )     )
						assert len(A__     ) == num_samples






					def     __A     (					self     ):
						A__  ,  A__				:       Any	  =						FlaxStableDiffusionPipeline.from_pretrained(
						    """CompVis/stable-diffusion-v1-4"""       ,			revision="""flax"""       ,			safety_checker=A__     )

						A__				:       Tuple	  =						(
						    """A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"""
						    """ field, close up, split lighting, cinematic"""
						)

						A__				:       Tuple	  =						jax.random.PRNGKey(0     )
						A__				:       Optional[Any]	  =						50

						A__				:       List[str]	  =						jax.device_count()
						A__				:       Dict	  =						num_samples * [prompt]
						A__				:       Union[str, Any]	  =						pipeline.prepare_inputs(A__     )

						# shard inputs and rng
						A__				:       int	  =						replicate(A__     )
						A__				:       List[str]	  =						jax.random.split(A__       ,			A__     )
						A__				:       Optional[Any]	  =						shard(A__     )

						A__				:       Optional[Any]	  =						pipeline(A__       ,			A__       ,			A__       ,			A__       ,			jit=A__     ).images

						assert images.shape == (num_samples, 1, 512, 512, 3)
						if jax.device_count() == 8:
							assert np.abs((np.abs(images[0, 0, :2, :2, -2:]       ,			dtype=np.floataa     ).sum() - 0.0_5_6_5_2_4_0_1)     ) < 1e-3
							assert np.abs((np.abs(A__       ,			dtype=np.floataa     ).sum() - 2_3_8_3_8_0_8.2)     ) < 5e-1






					def     __A     (					self     ):
						A__  ,  A__				:       int	  =						FlaxStableDiffusionPipeline.from_pretrained(
						    """CompVis/stable-diffusion-v1-4"""       ,			revision="""bf16"""       ,			dtype=jnp.bfloataa       ,			safety_checker=A__     )

						A__				:       Any	  =						(
						    """A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"""
						    """ field, close up, split lighting, cinematic"""
						)

						A__				:       Dict	  =						jax.random.PRNGKey(0     )
						A__				:       str	  =						50

						A__				:       Any	  =						jax.device_count()
						A__				:       List[Any]	  =						num_samples * [prompt]
						A__				:       Any	  =						pipeline.prepare_inputs(A__     )

						# shard inputs and rng
						A__				:       Any	  =						replicate(A__     )
						A__				:       str	  =						jax.random.split(A__       ,			A__     )
						A__				:       Optional[Any]	  =						shard(A__     )

						A__				:       Dict	  =						pipeline(A__       ,			A__       ,			A__       ,			A__       ,			jit=A__     ).images

						assert images.shape == (num_samples, 1, 512, 512, 3)
						if jax.device_count() == 8:
							assert np.abs((np.abs(images[0, 0, :2, :2, -2:]       ,			dtype=np.floataa     ).sum() - 0.0_4_0_0_3_9_0_6)     ) < 1e-3
							assert np.abs((np.abs(A__       ,			dtype=np.floataa     ).sum() - 2_3_7_3_5_1_6.7_5)     ) < 5e-1






					def     __A     (					self     ):
						A__  ,  A__				:       Tuple	  =						FlaxStableDiffusionPipeline.from_pretrained(
						    """CompVis/stable-diffusion-v1-4"""       ,			revision="""bf16"""       ,			dtype=jnp.bfloataa     )

						A__				:       List[str]	  =						(
						    """A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"""
						    """ field, close up, split lighting, cinematic"""
						)

						A__				:       int	  =						jax.random.PRNGKey(0     )
						A__				:       Optional[int]	  =						50

						A__				:       Optional[Any]	  =						jax.device_count()
						A__				:       Optional[int]	  =						num_samples * [prompt]
						A__				:       Optional[int]	  =						pipeline.prepare_inputs(A__     )

						# shard inputs and rng
						A__				:       Optional[int]	  =						replicate(A__     )
						A__				:       Tuple	  =						jax.random.split(A__       ,			A__     )
						A__				:       Optional[int]	  =						shard(A__     )

						A__				:       Optional[int]	  =						pipeline(A__       ,			A__       ,			A__       ,			A__       ,			jit=A__     ).images

						assert images.shape == (num_samples, 1, 512, 512, 3)
						if jax.device_count() == 8:
							assert np.abs((np.abs(images[0, 0, :2, :2, -2:]       ,			dtype=np.floataa     ).sum() - 0.0_4_0_0_3_9_0_6)     ) < 1e-3
							assert np.abs((np.abs(A__       ,			dtype=np.floataa     ).sum() - 2_3_7_3_5_1_6.7_5)     ) < 5e-1






					def     __A     (					self     ):
						A__				:       int	  =						FlaxDDIMScheduler(
						    beta_start=0.0_0_0_8_5       ,			beta_end=0.0_1_2       ,			beta_schedule="""scaled_linear"""       ,			set_alpha_to_one=A__       ,			steps_offset=1       ,			)

						A__  ,  A__				:       Tuple	  =						FlaxStableDiffusionPipeline.from_pretrained(
						    """CompVis/stable-diffusion-v1-4"""       ,			revision="""bf16"""       ,			dtype=jnp.bfloataa       ,			scheduler=A__       ,			safety_checker=A__       ,			)
						A__				:       Union[str, Any]	  =						scheduler.create_state()

						A__				:       Optional[Any]	  =						scheduler_state

						A__				:       Union[str, Any]	  =						(
						    """A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"""
						    """ field, close up, split lighting, cinematic"""
						)

						A__				:       Tuple	  =						jax.random.PRNGKey(0     )
						A__				:       List[Any]	  =						50

						A__				:       Optional[int]	  =						jax.device_count()
						A__				:       Dict	  =						num_samples * [prompt]
						A__				:       List[Any]	  =						pipeline.prepare_inputs(A__     )

						# shard inputs and rng
						A__				:       Tuple	  =						replicate(A__     )
						A__				:       Dict	  =						jax.random.split(A__       ,			A__     )
						A__				:       Dict	  =						shard(A__     )

						A__				:       int	  =						pipeline(A__       ,			A__       ,			A__       ,			A__       ,			jit=A__     ).images

						assert images.shape == (num_samples, 1, 512, 512, 3)
						if jax.device_count() == 8:
							assert np.abs((np.abs(images[0, 0, :2, :2, -2:]       ,			dtype=np.floataa     ).sum() - 0.0_4_5_0_4_3_9_4_5)     ) < 1e-3
							assert np.abs((np.abs(A__       ,			dtype=np.floataa     ).sum() - 2_3_4_7_6_9_3.5)     ) < 5e-1






					def     __A     (					self     ):
						A__				:       Optional[int]	  =						(
						    """A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"""
						    """ field, close up, split lighting, cinematic"""
						)

						A__				:       Optional[int]	  =						jax.device_count()
						A__				:       str	  =						num_samples * [prompt]
						A__				:       Any	  =						jax.random.split(jax.random.PRNGKey(0     )       ,			A__     )

						A__  ,  A__				:       Optional[Any]	  =						FlaxStableDiffusionPipeline.from_pretrained(
						    """CompVis/stable-diffusion-v1-4"""       ,			revision="""bf16"""       ,			dtype=jnp.bfloataa       ,			safety_checker=A__       ,			)

						A__				:       Tuple	  =						replicate(A__     )
						A__				:       Dict	  =						pipeline.prepare_inputs(A__     )
						A__				:       str	  =						shard(A__     )
						A__				:       Tuple	  =						pipeline(A__       ,			A__       ,			A__       ,			jit=A__     ).images
						assert images.shape == (num_samples, 1, 512, 512, 3)
						A__				:       Optional[Any]	  =						images[2, 0, 256, 10:17, 1]

						# With memory efficient attention
						A__  ,  A__				:       Any	  =						FlaxStableDiffusionPipeline.from_pretrained(
						    """CompVis/stable-diffusion-v1-4"""       ,			revision="""bf16"""       ,			dtype=jnp.bfloataa       ,			safety_checker=A__       ,			use_memory_efficient_attention=A__       ,			)

						A__				:       Any	  =						replicate(A__     )
						A__				:       Optional[int]	  =						pipeline.prepare_inputs(A__     )
						A__				:       Dict	  =						shard(A__     )
						A__				:       Tuple	  =						pipeline(A__       ,			A__       ,			A__       ,			jit=A__     ).images
						assert images_eff.shape == (num_samples, 1, 512, 512, 3)
						A__				:       Union[str, Any]	  =						images[2, 0, 256, 10:17, 1]

						# I checked the results visually and they are very similar. However, I saw that the max diff is `1` and the `sum`
						# over the 8 images is exactly `256`, which is very suspicious. Testing a random slice for now.
						assert abs(slice_eff - slice     ).max() < 1e-2



