



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


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the experiment tracking capability,
# and builds off the `nlp_example.py` script.
#
# 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 help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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     :							Any  							=				16
__lowerCamelCase     :							List[str]  							=				32

def    lowerCamelCase_(lowerCamelCase_    ,   lowerCamelCase_ = 16    ) ->       str:
						UpperCAmelCase         =     AutoTokenizer.from_pretrained("bert-base-cased"    )
						UpperCAmelCase         =     load_dataset("glue"    ,   "mrpc"    )

						def tokenize_function(lowerCamelCase_    ):
												# max_length=None => use the model max length (it's actually the default)
												UpperCAmelCase         =     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():
												UpperCAmelCase         =     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
						UpperCAmelCase         =     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.
												UpperCAmelCase         =     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         =     16
												elif accelerator.mixed_precision != "no":
																		UpperCAmelCase         =     8
												else:
																		UpperCAmelCase         =     None

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

						# Instantiate dataloaders.
						UpperCAmelCase         =     DataLoader(
						    tokenized_datasets["train"]    ,   shuffle=lowerCamelCase_    ,   collate_fn=lowerCamelCase_    ,   batch_size=lowerCamelCase_    )
						UpperCAmelCase         =     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

	__lowerCamelCase     :							str  							=				mocked_dataloaders  # noqa: F811

def    lowerCamelCase_(lowerCamelCase_    ,   lowerCamelCase_    ) ->       Tuple:
						# For testing only
						if os.environ.get("TESTING_MOCKED_DATALOADERS"    ,   lowerCamelCase_    ) == "1":
												UpperCAmelCase         =     2
						# Initialize Accelerator

						# New Code #
						# We pass in "all" to `log_with` to grab all available trackers in the environment
						# Note: If using a custom `Tracker` class, should be passed in here such as:
						# >>> log_with = ["all", MyCustomTrackerClassInstance()]
						if args.with_tracking:
												UpperCAmelCase         =     Accelerator(
												    cpu=args.cpu    ,   mixed_precision=args.mixed_precision    ,   log_with="all"    ,   project_dir=args.project_dir    )
						else:
												UpperCAmelCase         =     Accelerator(cpu=args.cpu    ,   mixed_precision=args.mixed_precision    )
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						UpperCAmelCase         =     config["lr"]
						UpperCAmelCase         =     int(config["num_epochs"]    )
						UpperCAmelCase         =     int(config["seed"]    )
						UpperCAmelCase         =     int(config["batch_size"]    )
						set_seed(lowerCamelCase_    )

						UpperCAmelCase       ,		UpperCAmelCase         =     get_dataloaders(lowerCamelCase_    ,   lowerCamelCase_    )
						UpperCAmelCase         =     evaluate.load("glue"    ,   "mrpc"    )

						# If the batch size is too big we use gradient accumulation
						UpperCAmelCase         =     1
						if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
												UpperCAmelCase         =     batch_size // MAX_GPU_BATCH_SIZE
												UpperCAmelCase         =     MAX_GPU_BATCH_SIZE

						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						UpperCAmelCase         =     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).
						UpperCAmelCase         =     model.to(accelerator.device    )

						# Instantiate optimizer
						UpperCAmelCase         =     AdamW(params=model.parameters()    ,   lr=lowerCamelCase_    )

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

						# New Code #
						# We need to initialize the trackers we use. Overall configurations can also be stored
						if args.with_tracking:
												UpperCAmelCase         =     os.path.split(lowerCamelCase_    )[-1].split("."    )[0]
												accelerator.init_trackers(lowerCamelCase_    ,   lowerCamelCase_    )

						# Now we train the model
						for epoch in range(lowerCamelCase_    ):
												model.train()
												# New Code #
												# For our tracking example, we will log the total loss of each epoch
												if args.with_tracking:
																		UpperCAmelCase         =     0
												for step, batch in enumerate(lowerCamelCase_    ):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		batch.to(accelerator.device    )
																		UpperCAmelCase         =     model(**lowerCamelCase_    )
																		UpperCAmelCase         =     outputs.loss
																		# New Code #
																		if args.with_tracking:
																								total_loss += loss.detach().float()
																		UpperCAmelCase         =     loss / gradient_accumulation_steps
																		accelerator.backward(lowerCamelCase_    )
																		if step % gradient_accumulation_steps == 0:
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()

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

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

												# New Code #
												# To actually log, we call `Accelerator.log`
												# The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
												if args.with_tracking:
																		accelerator.log(
																		    {
																		        "accuracy": eval_metric["accuracy"],
																		        "f1": eval_metric["f1"],
																		        "train_loss": total_loss.item() / len(lowerCamelCase_    ),
																		        "epoch": epoch,
																		    }    ,   step=lowerCamelCase_    ,   )

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
						if args.with_tracking:
												accelerator.end_training()

def    lowerCamelCase_() ->       List[str]:
						UpperCAmelCase         =     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."    ,   )
						parser.add_argument("--cpu"    ,   action="store_true"    ,   help="If passed, will train on the CPU."    )
						parser.add_argument(
						    "--with_tracking"    ,   action="store_true"    ,   help="Whether to load in all available experiment trackers from the environment and use them for logging."    ,   )
						parser.add_argument(
						    "--project_dir"    ,   type=lowerCamelCase_    ,   default="logs"    ,   help="Location on where to store experiment tracking logs` and relevent project information"    ,   )
						UpperCAmelCase         =     parser.parse_args()
						UpperCAmelCase         =     {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
						training_function(lowerCamelCase_    ,   lowerCamelCase_    )


if __name__ == "__main__":
	main()








import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


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





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






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[int]      )				->					Optional[Any]:
								'''simple docstring'''







								super().tearDown()
								gc.collect()






		def SCREAMING_SNAKE_CASE_			(						self      :     Tuple      )				->					Dict:
								'''simple docstring'''







								UpperCAmelCase       ,		UpperCAmelCase         =     FlaxControlNetModel.from_pretrained(
								    "lllyasviel/sd-controlnet-canny"				,		from_pt=UpperCamelCase__				,		dtype=jnp.bfloataa      )
								UpperCAmelCase       ,		UpperCAmelCase         =     FlaxStableDiffusionControlNetPipeline.from_pretrained(
								    "runwayml/stable-diffusion-v1-5"				,		controlnet=UpperCamelCase__				,		from_pt=UpperCamelCase__				,		dtype=jnp.bfloataa      )
								UpperCAmelCase         =     controlnet_params

								UpperCAmelCase         =     "bird"
								UpperCAmelCase         =     jax.device_count()
								UpperCAmelCase         =     pipe.prepare_text_inputs([prompts] * num_samples      )

								UpperCAmelCase         =     load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"      )
								UpperCAmelCase         =     pipe.prepare_image_inputs([canny_image] * num_samples      )

								UpperCAmelCase         =     jax.random.PRNGKey(0      )
								UpperCAmelCase         =     jax.random.split(UpperCamelCase__				,		jax.device_count()      )

								UpperCAmelCase         =     replicate(UpperCamelCase__      )
								UpperCAmelCase         =     shard(UpperCamelCase__      )
								UpperCAmelCase         =     shard(UpperCamelCase__      )

								UpperCAmelCase         =     pipe(
								    prompt_ids=UpperCamelCase__				,		image=UpperCamelCase__				,		params=UpperCamelCase__				,		prng_seed=UpperCamelCase__				,		num_inference_steps=50				,		jit=UpperCamelCase__				,		).images
								assert images.shape == (jax.device_count(), 1, 7_68, 5_12, 3)

								UpperCAmelCase         =     images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]      )
								UpperCAmelCase         =     images[0, 2_53:2_56, 2_53:2_56, -1]

								UpperCAmelCase         =     jnp.asarray(jax.device_get(image_slice.flatten()      )      )
								UpperCAmelCase         =     jnp.array(
								    [0.16_79_69, 0.11_66_99, 0.08_15_43, 0.15_42_97, 0.13_28_12, 0.10_88_87, 0.16_99_22, 0.16_99_22, 0.20_50_78]      )
								print(F'output_slice: {output_slice}'      )
								assert jnp.abs(output_slice - expected_slice      ).max() < 1e-2







		def SCREAMING_SNAKE_CASE_			(						self      :     Tuple      )				->					Tuple:
								'''simple docstring'''







								UpperCAmelCase       ,		UpperCAmelCase         =     FlaxControlNetModel.from_pretrained(
								    "lllyasviel/sd-controlnet-openpose"				,		from_pt=UpperCamelCase__				,		dtype=jnp.bfloataa      )
								UpperCAmelCase       ,		UpperCAmelCase         =     FlaxStableDiffusionControlNetPipeline.from_pretrained(
								    "runwayml/stable-diffusion-v1-5"				,		controlnet=UpperCamelCase__				,		from_pt=UpperCamelCase__				,		dtype=jnp.bfloataa      )
								UpperCAmelCase         =     controlnet_params

								UpperCAmelCase         =     "Chef in the kitchen"
								UpperCAmelCase         =     jax.device_count()
								UpperCAmelCase         =     pipe.prepare_text_inputs([prompts] * num_samples      )

								UpperCAmelCase         =     load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png"      )
								UpperCAmelCase         =     pipe.prepare_image_inputs([pose_image] * num_samples      )

								UpperCAmelCase         =     jax.random.PRNGKey(0      )
								UpperCAmelCase         =     jax.random.split(UpperCamelCase__				,		jax.device_count()      )

								UpperCAmelCase         =     replicate(UpperCamelCase__      )
								UpperCAmelCase         =     shard(UpperCamelCase__      )
								UpperCAmelCase         =     shard(UpperCamelCase__      )

								UpperCAmelCase         =     pipe(
								    prompt_ids=UpperCamelCase__				,		image=UpperCamelCase__				,		params=UpperCamelCase__				,		prng_seed=UpperCamelCase__				,		num_inference_steps=50				,		jit=UpperCamelCase__				,		).images
								assert images.shape == (jax.device_count(), 1, 7_68, 5_12, 3)

								UpperCAmelCase         =     images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]      )
								UpperCAmelCase         =     images[0, 2_53:2_56, 2_53:2_56, -1]

								UpperCAmelCase         =     jnp.asarray(jax.device_get(image_slice.flatten()      )      )
								UpperCAmelCase         =     jnp.array(
								    [[0.27_14_84, 0.26_17_19, 0.27_53_91, 0.27_73_44, 0.27_92_97, 0.29_10_16, 0.29_49_22, 0.30_27_34, 0.30_27_34]]      )
								print(F'output_slice: {output_slice}'      )
								assert jnp.abs(output_slice - expected_slice      ).max() < 1e-2



