




"""simple docstring"""







import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetImgaImgPipeline,
    KandinskyVaaPriorEmbaEmbPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class       SCREAMING_SNAKE_CASE__					(					_a						,				unittest.TestCase					):
						_a    						=						KandinskyVaaControlnetImgaImgPipeline
						_a    						=						['image_embeds', 'negative_image_embeds', 'image', 'hint']
						_a    						=						['image_embeds', 'negative_image_embeds', 'image', 'hint']
						_a    						=						[
						    'generator',
						    'height',
						    'width',
						    'strength',
						    'guidance_scale',
						    'num_inference_steps',
						    'return_dict',
						    'guidance_scale',
						    'num_images_per_prompt',
						    'output_type',
						    'return_dict',
						]
						_a    						=						False


						@property
						def   __lowercase (							self				:			str					):
										return 32


						@property
						def   __lowercase (							self				:			Optional[int]					):
										return 32


						@property
						def   __lowercase (							self				:			Dict					):
										return self.time_input_dim


						@property
						def   __lowercase (							self				:			Union[str, Any]					):
										return self.time_input_dim * 4


						@property
						def   __lowercase (							self				:			Any					):
										return 100


						@property
						def   __lowercase (							self				:			Any					):
										torch.manual_seed(0					)

										lowerCAmelCase  =				{
										    """in_channels""": 8,
										    # Out channels is double in channels because predicts mean and variance
										    """out_channels""": 8,
										    """addition_embed_type""": """image_hint""",
										    """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
										    """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
										    """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
										    """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
										    """layers_per_block""": 1,
										    """encoder_hid_dim""": self.text_embedder_hidden_size,
										    """encoder_hid_dim_type""": """image_proj""",
										    """cross_attention_dim""": self.cross_attention_dim,
										    """attention_head_dim""": 4,
										    """resnet_time_scale_shift""": """scale_shift""",
										    """class_embed_type""": None,
										}

										lowerCAmelCase  =				UNetaDConditionModel(**lowerCAmelCase					)
										return model


						@property
						def   __lowercase (							self				:			Tuple					):
										return {
										    "block_out_channels": [32, 32, 64, 64],
										    "down_block_types": [
										        "DownEncoderBlock2D",
										        "DownEncoderBlock2D",
										        "DownEncoderBlock2D",
										        "AttnDownEncoderBlock2D",
										    ],
										    "in_channels": 3,
										    "latent_channels": 4,
										    "layers_per_block": 1,
										    "norm_num_groups": 8,
										    "norm_type": "spatial",
										    "num_vq_embeddings": 12,
										    "out_channels": 3,
										    "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
										    "vq_embed_dim": 4,
										}


						@property
						def   __lowercase (							self				:			Union[str, Any]					):
										torch.manual_seed(0					)
										lowerCAmelCase  =				VQModel(**self.dummy_movq_kwargs					)
										return model


						def   __lowercase (							self				:			Tuple					):
										lowerCAmelCase  =				self.dummy_unet
										lowerCAmelCase  =				self.dummy_movq

										lowerCAmelCase  =				{
										    """num_train_timesteps""": 1000,
										    """beta_schedule""": """linear""",
										    """beta_start""": 0.0_0085,
										    """beta_end""": 0.012,
										    """clip_sample""": False,
										    """set_alpha_to_one""": False,
										    """steps_offset""": 0,
										    """prediction_type""": """epsilon""",
										    """thresholding""": False,
										}

										lowerCAmelCase  =				DDIMScheduler(**lowerCAmelCase					)

										lowerCAmelCase  =				{
										    """unet""": unet,
										    """scheduler""": scheduler,
										    """movq""": movq,
										}

										return components


						def   __lowercase (							self				:			Tuple						,						lowerCAmelCase				:			List[str]						,						lowerCAmelCase				:			Union[str, Any]=0					):
										lowerCAmelCase  =				floats_tensor((1, self.text_embedder_hidden_size)						,						rng=random.Random(lowerCAmelCase					)					).to(lowerCAmelCase					)
										lowerCAmelCase  =				floats_tensor((1, self.text_embedder_hidden_size)						,						rng=random.Random(seed + 1					)					).to(
										    lowerCAmelCase					)
										# create init_image
										lowerCAmelCase  =				floats_tensor((1, 3, 64, 64)						,						rng=random.Random(lowerCAmelCase					)					).to(lowerCAmelCase					)
										lowerCAmelCase  =				image.cpu().permute(0						,						2						,						3						,						1					)[0]
										lowerCAmelCase  =				Image.fromarray(np.uinta(lowerCAmelCase					)					).convert("""RGB"""					).resize((256, 256)					)
										# create hint
										lowerCAmelCase  =				floats_tensor((1, 3, 64, 64)						,						rng=random.Random(lowerCAmelCase					)					).to(lowerCAmelCase					)

										if str(lowerCAmelCase					).startswith("""mps"""					):
														lowerCAmelCase  =				torch.manual_seed(lowerCAmelCase					)
										else:
														lowerCAmelCase  =				torch.Generator(device=lowerCAmelCase					).manual_seed(lowerCAmelCase					)
										lowerCAmelCase  =				{
										    """image""": init_image,
										    """image_embeds""": image_embeds,
										    """negative_image_embeds""": negative_image_embeds,
										    """hint""": hint,
										    """generator""": generator,
										    """height""": 64,
										    """width""": 64,
										    """num_inference_steps""": 10,
										    """guidance_scale""": 7.0,
										    """strength""": 0.2,
										    """output_type""": """np""",
										}
										return inputs




						def   __lowercase (							self				:			Optional[Any]					):
										lowerCAmelCase  =				"""cpu"""

										lowerCAmelCase  =				self.get_dummy_components()

										lowerCAmelCase  =				self.pipeline_class(**lowerCAmelCase					)
										lowerCAmelCase  =				pipe.to(lowerCAmelCase					)

										pipe.set_progress_bar_config(disable=lowerCAmelCase					)

										lowerCAmelCase  =				pipe(**self.get_dummy_inputs(lowerCAmelCase					)					)
										lowerCAmelCase  =				output.images

										lowerCAmelCase  =				pipe(
										    **self.get_dummy_inputs(lowerCAmelCase					)						,						return_dict=lowerCAmelCase						,						)[0]

										lowerCAmelCase  =				image[0, -3:, -3:, -1]
										lowerCAmelCase  =				image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 64, 64, 3)

										lowerCAmelCase  =				np.array(
										    [0.5498_5034, 0.5550_9365, 0.5256_1504, 0.557_0494, 0.559_3818, 0.526_3979, 0.5028_5643, 0.506_9846, 0.5119_6736]					)
										assert (
										    np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2
										), f''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
										assert (
										    np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() < 1e-2
										), f''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''






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


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




						def   __lowercase (							self				:			Any					):
										lowerCAmelCase  =				load_numpy(
										    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
										    """/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy"""					)

										lowerCAmelCase  =				load_image(
										    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""					)
										lowerCAmelCase  =				init_image.resize((512, 512)					)

										lowerCAmelCase  =				load_image(
										    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
										    """/kandinskyv22/hint_image_cat.png"""					)
										lowerCAmelCase  =				torch.from_numpy(np.array(lowerCAmelCase					)					).float() / 255.0
										lowerCAmelCase  =				hint.permute(2						,						0						,						1					).unsqueeze(0					)

										lowerCAmelCase  =				"""A robot, 4k photo"""

										lowerCAmelCase  =				KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
										    """kandinsky-community/kandinsky-2-2-prior"""						,						torch_dtype=torch.floataa					)
										pipe_prior.to(lowerCAmelCase					)

										lowerCAmelCase  =				KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
										    """kandinsky-community/kandinsky-2-2-controlnet-depth"""						,						torch_dtype=torch.floataa					)
										lowerCAmelCase  =				pipeline.to(lowerCAmelCase					)

										pipeline.set_progress_bar_config(disable=lowerCAmelCase					)

										lowerCAmelCase  =				torch.Generator(device="""cpu"""					).manual_seed(0					)

										lowerCAmelCase ,						lowerCAmelCase  =				pipe_prior(
										    lowerCAmelCase						,						image=lowerCAmelCase						,						strength=0.85						,						generator=lowerCAmelCase						,						negative_prompt=""""""						,						).to_tuple()

										lowerCAmelCase  =				pipeline(
										    image=lowerCAmelCase						,						image_embeds=lowerCAmelCase						,						negative_image_embeds=lowerCAmelCase						,						hint=lowerCAmelCase						,						generator=lowerCAmelCase						,						num_inference_steps=100						,						height=512						,						width=512						,						strength=0.5						,						output_type="""np"""						,						)

										lowerCAmelCase  =				output.images[0]

										assert image.shape == (512, 512, 3)

										assert_mean_pixel_difference(lowerCAmelCase						,						lowerCAmelCase					)











"""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


########################################################################
# 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
#
########################################################################

a      =							1_6
a      =							3_2





def 							lowercase					(snake_case__   : Accelerator      ,       snake_case__   : int = 16 )     ->  Dict:


				'''simple docstring'''






				lowerCAmelCase  =				AutoTokenizer.from_pretrained("""bert-base-cased""" )
				lowerCAmelCase  =				load_dataset("""glue"""      ,       """mrpc""" )

				def tokenize_function(snake_case__   : List[Any] ):
								# max_length=None => use the model max length (it's actually the default)
								lowerCAmelCase  =				tokenizer(examples["""sentence1"""]      ,       examples["""sentence2"""]      ,       truncation=snake_case__      ,       max_length=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(
								    snake_case__      ,       batched=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(snake_case__   : Union[str, Any] ):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								lowerCAmelCase  =				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":
												lowerCAmelCase  =				16
								elif accelerator.mixed_precision != "no":
												lowerCAmelCase  =				8
								else:
												lowerCAmelCase  =				None

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

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

		a      =							mocked_dataloaders  # noqa: F811





def 							lowercase					(snake_case__   : int      ,       snake_case__   : Tuple )     ->  int:


				'''simple docstring'''






				if os.environ.get("""TESTING_MOCKED_DATALOADERS"""      ,       snake_case__ ) == "1":
								lowerCAmelCase  =				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:
								lowerCAmelCase  =				Accelerator(
								    cpu=args.cpu      ,       mixed_precision=args.mixed_precision      ,       log_with="""all"""      ,       project_dir=args.project_dir )
				else:
								lowerCAmelCase  =				Accelerator(cpu=args.cpu      ,       mixed_precision=args.mixed_precision )
				# 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"""] )
				set_seed(snake_case__ )

				lowerCAmelCase ,						lowerCAmelCase  =				get_dataloaders(snake_case__      ,       snake_case__ )
				lowerCAmelCase  =				evaluate.load("""glue"""      ,       """mrpc""" )

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

				# 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=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=snake_case__ )

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

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

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

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

								lowerCAmelCase  =				metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(f'''epoch {epoch}:'''      ,       snake_case__ )

								# 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(snake_case__ ),
												        """epoch""": epoch,
												    }      ,       step=snake_case__      ,       )

    # 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 							lowercase					()     ->  str:


				'''simple docstring'''






				lowerCAmelCase  =				argparse.ArgumentParser(description="""Simple example of training script.""" )
				parser.add_argument(
				    """--mixed_precision"""      ,       type=snake_case__      ,       default=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."""      ,       )
				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=snake_case__      ,       default="""logs"""      ,       help="""Location on where to store experiment tracking logs` and relevent project information"""      ,       )
				lowerCAmelCase  =				parser.parse_args()
				lowerCAmelCase  =				{"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
				training_function(snake_case__      ,       snake_case__ )


if __name__ == "__main__":
		main()





