




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
# and perform 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
#
########################################################################


UpperCamelCase__    			=			16
UpperCamelCase__    			=			32







def 	_a      (						SCREAMING_SNAKE_CASE_							:				Accelerator      ,      SCREAMING_SNAKE_CASE_							:				int = 16     ):
					__lowerCAmelCase				   =       AutoTokenizer.from_pretrained("bert-base-cased"     )
					__lowerCAmelCase				   =       load_dataset("glue"      ,      "mrpc"     )

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

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

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

					# Instantiate dataloaders.
					__lowerCAmelCase				   =       DataLoader(
					    tokenized_datasets["train"]      ,      shuffle=SCREAMING_SNAKE_CASE_      ,      collate_fn=SCREAMING_SNAKE_CASE_      ,      batch_size=SCREAMING_SNAKE_CASE_     )
					__lowerCAmelCase				   =       DataLoader(
					    tokenized_datasets["validation"]      ,      shuffle=SCREAMING_SNAKE_CASE_      ,      collate_fn=SCREAMING_SNAKE_CASE_      ,      batch_size=SCREAMING_SNAKE_CASE_     )

					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

					UpperCamelCase__    			=			mocked_dataloaders  # noqa: F811







def 	_a      (						SCREAMING_SNAKE_CASE_							:				Tuple      ,      SCREAMING_SNAKE_CASE_							:				Tuple     ):
					# For testing only
					if os.environ.get("TESTING_MOCKED_DATALOADERS"      ,      SCREAMING_SNAKE_CASE_     ) == "1":
										__lowerCAmelCase				   =       2
					# New Code #
					__lowerCAmelCase				   =       int(args.gradient_accumulation_steps     )
					# Initialize accelerator
					__lowerCAmelCase				   =       Accelerator(
					    cpu=args.cpu      ,      mixed_precision=args.mixed_precision      ,      gradient_accumulation_steps=SCREAMING_SNAKE_CASE_     )
					if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
										raise NotImplementedError(
										    "Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`"     )
					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					__lowerCAmelCase				   =       config["lr"]
					__lowerCAmelCase				   =       int(config["num_epochs"]     )
					__lowerCAmelCase				   =       int(config["seed"]     )
					__lowerCAmelCase				   =       int(config["batch_size"]     )

					__lowerCAmelCase				   =       evaluate.load("glue"      ,      "mrpc"     )

					set_seed(SCREAMING_SNAKE_CASE_     )
					__lowerCAmelCase   ,							__lowerCAmelCase				   =       get_dataloaders(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )
					# Instantiate the model (we build the model here so that the seed also control new weights initialization)
					__lowerCAmelCase				   =       AutoModelForSequenceClassification.from_pretrained("bert-base-cased"      ,      return_dict=SCREAMING_SNAKE_CASE_     )

					# 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).
					__lowerCAmelCase				   =       model.to(accelerator.device     )

					# Instantiate optimizer
					__lowerCAmelCase				   =       AdamW(params=model.parameters()      ,      lr=SCREAMING_SNAKE_CASE_     )

					# Instantiate scheduler
					__lowerCAmelCase				   =       get_linear_schedule_with_warmup(
					    optimizer=SCREAMING_SNAKE_CASE_      ,      num_warmup_steps=1_00      ,      num_training_steps=(len(SCREAMING_SNAKE_CASE_     ) * 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.
					__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase				   =       accelerator.prepare(
					    SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )

					# Now we train the model
					for epoch in range(SCREAMING_SNAKE_CASE_     ):
										model.train()
										for step, batch in enumerate(SCREAMING_SNAKE_CASE_     ):
															# 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(SCREAMING_SNAKE_CASE_     ):
																				__lowerCAmelCase				   =       model(**SCREAMING_SNAKE_CASE_     )
																				__lowerCAmelCase				   =       output.loss
																				accelerator.backward(SCREAMING_SNAKE_CASE_     )
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()

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

										__lowerCAmelCase				   =       metric.compute()
										# Use accelerator.print to print only on the main process.
										accelerator.print(F"""epoch {epoch}:"""      ,      SCREAMING_SNAKE_CASE_     )







def 	_a      (						):
					__lowerCAmelCase				   =       argparse.ArgumentParser(description="Simple example of training script."     )
					parser.add_argument(
					    "--mixed_precision"      ,      type=SCREAMING_SNAKE_CASE_      ,      default=SCREAMING_SNAKE_CASE_      ,      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=SCREAMING_SNAKE_CASE_      ,      default=1      ,      help="The number of minibatches to be ran before gradients are accumulated."      ,      )
					parser.add_argument("--cpu"      ,      action="store_true"      ,      help="If passed, will train on the CPU."     )
					__lowerCAmelCase				   =       parser.parse_args()
					__lowerCAmelCase				   =       {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
					training_function(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )


if __name__ == "__main__":
					main()










import gc
import unittest

import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    PriorTransformer,
    StableUnCLIPPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()
class 			a__    (					snake_case__ ,				snake_case__ ,				snake_case__ ,				unittest.TestCase      ):
						_a							:    str					=    StableUnCLIPPipeline
						_a							:    Union[str, Any]					=    TEXT_TO_IMAGE_PARAMS
						_a							:    Dict					=    TEXT_TO_IMAGE_BATCH_PARAMS
						_a							:    Optional[int]					=    TEXT_TO_IMAGE_IMAGE_PARAMS
						_a							:    Dict					=    TEXT_TO_IMAGE_IMAGE_PARAMS

						# TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
						_a							:    Optional[Any]					=    False






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       3_2
											__lowerCAmelCase				   =       embedder_hidden_size

											# prior components

											torch.manual_seed(0			)
											__lowerCAmelCase				   =       CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"			)

											torch.manual_seed(0			)
											__lowerCAmelCase				   =       CLIPTextModelWithProjection(
											    CLIPTextConfig(
											        bos_token_id=0		,			eos_token_id=2		,			hidden_size=_A		,			projection_dim=_A		,			intermediate_size=3_7		,			layer_norm_eps=1E-0_5		,			num_attention_heads=4		,			num_hidden_layers=5		,			pad_token_id=1		,			vocab_size=1_0_0_0		,			)			)

											torch.manual_seed(0			)
											__lowerCAmelCase				   =       PriorTransformer(
											    num_attention_heads=2		,			attention_head_dim=1_2		,			embedding_dim=_A		,			num_layers=1		,			)

											torch.manual_seed(0			)
											__lowerCAmelCase				   =       DDPMScheduler(
											    variance_type="fixed_small_log"		,			prediction_type="sample"		,			num_train_timesteps=1_0_0_0		,			clip_sample=_A		,			clip_sample_range=5.0		,			beta_schedule="squaredcos_cap_v2"		,			)

											# regular denoising components

											torch.manual_seed(0			)
											__lowerCAmelCase				   =       StableUnCLIPImageNormalizer(embedding_dim=_A			)
											__lowerCAmelCase				   =       DDPMScheduler(beta_schedule="squaredcos_cap_v2"			)

											torch.manual_seed(0			)
											__lowerCAmelCase				   =       CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"			)

											torch.manual_seed(0			)
											__lowerCAmelCase				   =       CLIPTextModel(
											    CLIPTextConfig(
											        bos_token_id=0		,			eos_token_id=2		,			hidden_size=_A		,			projection_dim=3_2		,			intermediate_size=3_7		,			layer_norm_eps=1E-0_5		,			num_attention_heads=4		,			num_hidden_layers=5		,			pad_token_id=1		,			vocab_size=1_0_0_0		,			)			)

											torch.manual_seed(0			)
											__lowerCAmelCase				   =       UNetaDConditionModel(
											    sample_size=3_2		,			in_channels=4		,			out_channels=4		,			down_block_types=("CrossAttnDownBlock2D", "DownBlock2D")		,			up_block_types=("UpBlock2D", "CrossAttnUpBlock2D")		,			block_out_channels=(3_2, 6_4)		,			attention_head_dim=(2, 4)		,			class_embed_type="projection"		,			projection_class_embeddings_input_dim=embedder_projection_dim * 2		,			cross_attention_dim=_A		,			layers_per_block=1		,			upcast_attention=_A		,			use_linear_projection=_A		,			)

											torch.manual_seed(0			)
											__lowerCAmelCase				   =       DDIMScheduler(
											    beta_schedule="scaled_linear"		,			beta_start=0.0_00_85		,			beta_end=0.0_12		,			prediction_type="v_prediction"		,			set_alpha_to_one=_A		,			steps_offset=1		,			)

											torch.manual_seed(0			)
											__lowerCAmelCase				   =       AutoencoderKL()

											__lowerCAmelCase				   =       {
											    # prior components
											    "prior_tokenizer": prior_tokenizer,
											    "prior_text_encoder": prior_text_encoder,
											    "prior": prior,
											    "prior_scheduler": prior_scheduler,
											    # image noising components
											    "image_normalizer": image_normalizer,
											    "image_noising_scheduler": image_noising_scheduler,
											    # regular denoising components
											    "tokenizer": tokenizer,
											    "text_encoder": text_encoder,
											    "unet": unet,
											    "scheduler": scheduler,
											    "vae": vae,
											}

											return components






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A=0			):




											"""simple docstring"""



											if str(_A			).startswith("mps"			):
																__lowerCAmelCase				   =       torch.manual_seed(_A			)
											else:
																__lowerCAmelCase				   =       torch.Generator(device=_A			).manual_seed(_A			)
											__lowerCAmelCase				   =       {
											    "prompt": "A painting of a squirrel eating a burger",
											    "generator": generator,
											    "num_inference_steps": 2,
											    "prior_num_inference_steps": 2,
											    "output_type": "numpy",
											}
											return inputs






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       torch_device == "cpu"

											self._test_attention_slicing_forward_pass(test_max_difference=_A			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       torch_device in ["cpu", "mps"]

											self._test_inference_batch_single_identical(test_max_difference=_A			)






@slow
@require_torch_gpu
class 			a__    (					unittest.TestCase      ):






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       load_numpy(
											    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy"			)

											__lowerCAmelCase				   =       StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l"		,			torch_dtype=torch.floataa			)
											pipe.to(_A			)
											pipe.set_progress_bar_config(disable=_A			)
											# stable unclip will oom when integration tests are run on a V100,
											# so turn on memory savings
											pipe.enable_attention_slicing()
											pipe.enable_sequential_cpu_offload()

											__lowerCAmelCase				   =       torch.Generator(device="cpu"			).manual_seed(0			)
											__lowerCAmelCase				   =       pipe("anime turle"		,			generator=_A		,			output_type="np"			)

											__lowerCAmelCase				   =       output.images[0]

											assert image.shape == (7_6_8, 7_6_8, 3)

											assert_mean_pixel_difference(_A		,			_A			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											torch.cuda.empty_cache()
											torch.cuda.reset_max_memory_allocated()
											torch.cuda.reset_peak_memory_stats()

											__lowerCAmelCase				   =       StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l"		,			torch_dtype=torch.floataa			)
											__lowerCAmelCase				   =       pipe.to(_A			)
											pipe.set_progress_bar_config(disable=_A			)
											pipe.enable_attention_slicing()
											pipe.enable_sequential_cpu_offload()

											__lowerCAmelCase				   =       pipe(
											    "anime turtle"		,			prior_num_inference_steps=2		,			num_inference_steps=2		,			output_type="np"		,			)

											__lowerCAmelCase				   =       torch.cuda.max_memory_allocated()
											# make sure that less than 7 GB is allocated
											assert mem_bytes < 7 * 1_0**9




