

"""simple docstring"""


import argparse
import os
import re

import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor

from accelerate import Accelerator






def    a__   (   __SCREAMING_SNAKE_CASE			)     ->     Union[str, Any]:
		__lowerCAmelCase:	int   				=      fname.split(os.path.sep			)[-1]
		return re.search(R"^(.*)_\d+\.jpg$" ,			__SCREAMING_SNAKE_CASE			).groups()[0]





class    snake_case     (    __snake_case  ):

				def __init__(     self							:    Optional[int]					,  UpperCamelCase__							:    Dict					,  UpperCamelCase__							:    List[str]=None					,  UpperCamelCase__							:    Dict=None)->       Dict:



						'''simple docstring'''


						__lowerCAmelCase:	Dict   				=      file_names
						__lowerCAmelCase:	List[Any]   				=      image_transform
						__lowerCAmelCase:	List[str]   				=      label_to_id

				def __len__(     self							:    Dict)->       Optional[int]:



						'''simple docstring'''


						return len(self.file_names)




				def __getitem__(     self							:    List[str]					,  UpperCamelCase__							:    Any)->       str:



						'''simple docstring'''


						__lowerCAmelCase:	Optional[int]   				=      self.file_names[idx]
						__lowerCAmelCase:	Any   				=      PIL.Image.open(UpperCamelCase__)
						__lowerCAmelCase:	Tuple   				=      raw_image.convert("RGB")
						if self.image_transform is not None:
								__lowerCAmelCase:	Union[str, Any]   				=      self.image_transform(UpperCamelCase__)
						__lowerCAmelCase:	Optional[int]   				=      extract_label(UpperCamelCase__)
						if self.label_to_id is not None:
								__lowerCAmelCase:	int   				=      self.label_to_id[label]
						return {"image": image, "label": label}






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     List[Any]:
		# Initialize accelerator
		if args.with_tracking:
				__lowerCAmelCase:	int   				=      Accelerator(
				    cpu=args.cpu ,			mixed_precision=args.mixed_precision ,			log_with="all" ,			project_dir=args.project_dir			)
		else:
				__lowerCAmelCase:	str   				=      Accelerator(cpu=args.cpu ,			mixed_precision=args.mixed_precision			)

		# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
		__lowerCAmelCase:	Optional[int]   				=      config["lr"]
		__lowerCAmelCase:	Optional[int]   				=      int(config["num_epochs"]			)
		__lowerCAmelCase:	str   				=      int(config["seed"]			)
		__lowerCAmelCase:	Optional[int]   				=      int(config["batch_size"]			)
		__lowerCAmelCase:	List[str]   				=      config["image_size"]
		if not isinstance(__SCREAMING_SNAKE_CASE ,			(list, tuple)			):
				__lowerCAmelCase:	int   				=      (image_size, image_size)

		# Parse out whether we are saving every epoch or after a certain number of batches
		if hasattr(args.checkpointing_steps ,			"isdigit"			):
				if args.checkpointing_steps == "epoch":
						__lowerCAmelCase:	List[Any]   				=      args.checkpointing_steps
				elif args.checkpointing_steps.isdigit():
						__lowerCAmelCase:	Any   				=      int(args.checkpointing_steps			)
				else:
						raise ValueError(
						    F"Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."			)
		else:
				__lowerCAmelCase:	List[Any]   				=      None

		# We need to initialize the trackers we use, and also store our configuration
		if args.with_tracking:
				__lowerCAmelCase:	List[Any]   				=      os.path.split(__SCREAMING_SNAKE_CASE			)[-1].split("."			)[0]
				accelerator.init_trackers(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)

		# Grab all the image filenames
		__lowerCAmelCase:	str   				=      [os.path.join(args.data_dir ,			__SCREAMING_SNAKE_CASE			) for fname in os.listdir(args.data_dir			) if fname.endswith(".jpg"			)]

		# Build the label correspondences
		__lowerCAmelCase:	List[str]   				=      [extract_label(__SCREAMING_SNAKE_CASE			) for fname in file_names]
		__lowerCAmelCase:	Tuple   				=      list(set(__SCREAMING_SNAKE_CASE			)			)
		id_to_label.sort()
		__lowerCAmelCase:	str   				=      {lbl: i for i, lbl in enumerate(__SCREAMING_SNAKE_CASE			)}

		# Set the seed before splitting the data.
		np.random.seed(__SCREAMING_SNAKE_CASE			)
		torch.manual_seed(__SCREAMING_SNAKE_CASE			)
		torch.cuda.manual_seed_all(__SCREAMING_SNAKE_CASE			)

		# Split our filenames between train and validation
		__lowerCAmelCase:	str   				=      np.random.permutation(len(__SCREAMING_SNAKE_CASE			)			)
		__lowerCAmelCase:	str   				=      int(0.8 * len(__SCREAMING_SNAKE_CASE			)			)
		__lowerCAmelCase:	Any   				=      random_perm[:cut]
		__lowerCAmelCase:	Optional[int]   				=      random_perm[cut:]

		# For training we use a simple RandomResizedCrop
		__lowerCAmelCase:	Union[str, Any]   				=      Compose([RandomResizedCrop(__SCREAMING_SNAKE_CASE ,			scale=(0.5, 1.0)			), ToTensor()]			)
		__lowerCAmelCase:	Tuple   				=      PetsDataset(
		    [file_names[i] for i in train_split] ,			image_transform=__SCREAMING_SNAKE_CASE ,			label_to_id=__SCREAMING_SNAKE_CASE			)

		# For evaluation, we use a deterministic Resize
		__lowerCAmelCase:	Optional[int]   				=      Compose([Resize(__SCREAMING_SNAKE_CASE			), ToTensor()]			)
		__lowerCAmelCase:	Optional[int]   				=      PetsDataset([file_names[i] for i in eval_split] ,			image_transform=__SCREAMING_SNAKE_CASE ,			label_to_id=__SCREAMING_SNAKE_CASE			)

		# Instantiate dataloaders.
		__lowerCAmelCase:	Optional[int]   				=      DataLoader(__SCREAMING_SNAKE_CASE ,			shuffle=__SCREAMING_SNAKE_CASE ,			batch_size=__SCREAMING_SNAKE_CASE ,			num_workers=4			)
		__lowerCAmelCase:	Tuple   				=      DataLoader(__SCREAMING_SNAKE_CASE ,			shuffle=__SCREAMING_SNAKE_CASE ,			batch_size=__SCREAMING_SNAKE_CASE ,			num_workers=4			)

		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		__lowerCAmelCase:	int   				=      create_model("resnet50d" ,			pretrained=__SCREAMING_SNAKE_CASE ,			num_classes=len(__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:	str   				=      model.to(accelerator.device			)

		# Freezing the base model
		for param in model.parameters():
				__lowerCAmelCase:	Optional[Any]   				=      False
		for param in model.get_classifier().parameters():
				__lowerCAmelCase:	Tuple   				=      True

		# We normalize the batches of images to be a bit faster.
		__lowerCAmelCase:	List[str]   				=      torch.tensor(model.default_cfg["mean"]			)[None, :, None, None].to(accelerator.device			)
		__lowerCAmelCase:	Union[str, Any]   				=      torch.tensor(model.default_cfg["std"]			)[None, :, None, None].to(accelerator.device			)

		# Instantiate optimizer
		__lowerCAmelCase:	List[Any]   				=      torch.optim.Adam(params=model.parameters() ,			lr=lr / 2_5			)

		# Instantiate learning rate scheduler
		__lowerCAmelCase:	List[str]   				=      OneCycleLR(optimizer=__SCREAMING_SNAKE_CASE ,			max_lr=__SCREAMING_SNAKE_CASE ,			epochs=__SCREAMING_SNAKE_CASE ,			steps_per_epoch=len(__SCREAMING_SNAKE_CASE			)			)

		# 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:	Any   				=      accelerator.prepare(
		    __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)
		# We need to keep track of how many total steps we have iterated over
		__lowerCAmelCase:	List[str]   				=      0
		# We also need to keep track of the starting epoch so files are named properly
		__lowerCAmelCase:	Tuple   				=      0

		# Potentially load in the weights and states from a previous save
		if args.resume_from_checkpoint:
				if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
						accelerator.print(F"Resumed from checkpoint: {args.resume_from_checkpoint}"			)
						accelerator.load_state(args.resume_from_checkpoint			)
						__lowerCAmelCase:	List[str]   				=      os.path.basename(args.resume_from_checkpoint			)
				else:
						# Get the most recent checkpoint
						__lowerCAmelCase:	List[str]   				=      [f.name for f in os.scandir(os.getcwd()			) if f.is_dir()]
						dirs.sort(key=os.path.getctime			)
						__lowerCAmelCase:	Tuple   				=      dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
				# Extract `epoch_{i}` or `step_{i}`
				__lowerCAmelCase:	Union[str, Any]   				=      os.path.splitext(__SCREAMING_SNAKE_CASE			)[0]

				if "epoch" in training_difference:
						__lowerCAmelCase:	Tuple   				=      int(training_difference.replace("epoch_" ,			""			)			) + 1
						__lowerCAmelCase:	List[str]   				=      None
				else:
						__lowerCAmelCase:	Tuple   				=      int(training_difference.replace("step_" ,			""			)			)
						__lowerCAmelCase:	int   				=      resume_step // len(__SCREAMING_SNAKE_CASE			)
						resume_step -= starting_epoch * len(__SCREAMING_SNAKE_CASE			)

    # Now we train the model
		for epoch in range(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			):
				model.train()
				if args.with_tracking:
						__lowerCAmelCase:	Optional[Any]   				=      0
				if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
						# We need to skip steps until we reach the resumed step
						__lowerCAmelCase:	int   				=      accelerator.skip_first_batches(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)
						overall_step += resume_step
				else:
						# After the first iteration though, we need to go back to the original dataloader
						__lowerCAmelCase:	Any   				=      train_dataloader
				for batch in active_dataloader:
						# We could avoid this line since we set the accelerator with `device_placement=True`.
						__lowerCAmelCase:	List[str]   				=      {k: v.to(accelerator.device			) for k, v in batch.items()}
						__lowerCAmelCase:	Union[str, Any]   				=      (batch["image"] - mean) / std
						__lowerCAmelCase:	Dict   				=      model(__SCREAMING_SNAKE_CASE			)
						__lowerCAmelCase:	List[str]   				=      torch.nn.functional.cross_entropy(__SCREAMING_SNAKE_CASE ,			batch["label"]			)
						# We keep track of the loss at each epoch
						if args.with_tracking:
								total_loss += loss.detach().float()
						accelerator.backward(__SCREAMING_SNAKE_CASE			)
						optimizer.step()
						lr_scheduler.step()
						optimizer.zero_grad()
						overall_step += 1
						if isinstance(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			):
								__lowerCAmelCase:	List[Any]   				=      F"step_{overall_step}"
								if overall_step % checkpointing_steps == 0:
										if args.output_dir is not None:
												__lowerCAmelCase:	int   				=      os.path.join(args.output_dir ,			__SCREAMING_SNAKE_CASE			)
										accelerator.save_state(__SCREAMING_SNAKE_CASE			)
				model.eval()
				__lowerCAmelCase:	str   				=      0
				__lowerCAmelCase:	List[Any]   				=      0
				for step, batch in enumerate(__SCREAMING_SNAKE_CASE			):
						# We could avoid this line since we set the accelerator with `device_placement=True`.
						__lowerCAmelCase:	Union[str, Any]   				=      {k: v.to(accelerator.device			) for k, v in batch.items()}
						__lowerCAmelCase:	Optional[int]   				=      (batch["image"] - mean) / std
						with torch.no_grad():
								__lowerCAmelCase:	Any   				=      model(__SCREAMING_SNAKE_CASE			)
						__lowerCAmelCase:	Union[str, Any]   				=      outputs.argmax(dim=-1			)
						__lowerCAmelCase				,			__lowerCAmelCase:	Dict   				=      accelerator.gather_for_metrics((predictions, batch["label"])			)
						__lowerCAmelCase:	Optional[Any]   				=      predictions == references
						num_elems += accurate_preds.shape[0]
						accurate += accurate_preds.long().sum()

				__lowerCAmelCase:	Optional[int]   				=      accurate.item() / num_elems
				# Use accelerator.print to print only on the main process.
				accelerator.print(F"epoch {epoch}: {1_0_0 * eval_metric:.2f}"			)
				if args.with_tracking:
						accelerator.log(
						    {
						        "accuracy": 1_0_0 * eval_metric,
						        "train_loss": total_loss.item() / len(__SCREAMING_SNAKE_CASE			),
						        "epoch": epoch,
						    } ,			step=__SCREAMING_SNAKE_CASE ,			)
				if checkpointing_steps == "epoch":
						__lowerCAmelCase:	Dict   				=      F"epoch_{epoch}"
						if args.output_dir is not None:
								__lowerCAmelCase:	List[Any]   				=      os.path.join(args.output_dir ,			__SCREAMING_SNAKE_CASE			)
						accelerator.save_state(__SCREAMING_SNAKE_CASE			)

		if args.with_tracking:
				accelerator.end_training()






def    a__   (   )     ->     List[str]:
		__lowerCAmelCase:	Optional[int]   				=      argparse.ArgumentParser(description="Simple example of training script."			)
		parser.add_argument("--data_dir" ,			required=__SCREAMING_SNAKE_CASE ,			help="The data folder on disk."			)
		parser.add_argument("--fp16" ,			action="store_true" ,			help="If passed, will use FP16 training."			)
		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." ,			)
		parser.add_argument("--cpu" ,			action="store_true" ,			help="If passed, will train on the CPU."			)
		parser.add_argument(
		    "--checkpointing_steps" ,			type=__SCREAMING_SNAKE_CASE ,			default=__SCREAMING_SNAKE_CASE ,			help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch." ,			)
		parser.add_argument(
		    "--output_dir" ,			type=__SCREAMING_SNAKE_CASE ,			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=__SCREAMING_SNAKE_CASE ,			default=__SCREAMING_SNAKE_CASE ,			help="If the training should continue from a checkpoint folder." ,			)
		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=__SCREAMING_SNAKE_CASE ,			default="logs" ,			help="Location on where to store experiment tracking logs` and relevent project information" ,			)
		__lowerCAmelCase:	Tuple   				=      parser.parse_args()
		__lowerCAmelCase:	Union[str, Any]   				=      {"lr": 3E-2, "num_epochs": 3, "seed": 4_2, "batch_size": 6_4, "image_size": 2_2_4}
		training_function(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)


if __name__ == "__main__":
			main()





"""simple docstring"""


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    snake_case     (    __snake_case,							unittest.TestCase  ):
				SCREAMING_SNAKE_CASE_       :				Tuple       				=  PriorTransformer
				SCREAMING_SNAKE_CASE_       :				List[str]       				=  """hidden_states"""

				@property
				def 							lowercase_					(     self							:    Dict)->       str:



						'''simple docstring'''


						__lowerCAmelCase:	str   				=      4
						__lowerCAmelCase:	int   				=      8
						__lowerCAmelCase:	int   				=      7

						__lowerCAmelCase:	str   				=      floats_tensor((batch_size, embedding_dim)).to(UpperCamelCase__)

						__lowerCAmelCase:	Optional[Any]   				=      floats_tensor((batch_size, embedding_dim)).to(UpperCamelCase__)
						__lowerCAmelCase:	Any   				=      floats_tensor((batch_size, num_embeddings, embedding_dim)).to(UpperCamelCase__)

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

				def 							lowercase_					(     self							:    Optional[int]					,  UpperCamelCase__							:    str=0)->       str:



						'''simple docstring'''


						torch.manual_seed(UpperCamelCase__)
						__lowerCAmelCase:	List[Any]   				=      4
						__lowerCAmelCase:	Dict   				=      8
						__lowerCAmelCase:	int   				=      7

						__lowerCAmelCase:	List[str]   				=      torch.randn((batch_size, embedding_dim)).to(UpperCamelCase__)

						__lowerCAmelCase:	Tuple   				=      torch.randn((batch_size, embedding_dim)).to(UpperCamelCase__)
						__lowerCAmelCase:	List[Any]   				=      torch.randn((batch_size, num_embeddings, embedding_dim)).to(UpperCamelCase__)

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

				@property
				def 							lowercase_					(     self							:    Dict)->       List[Any]:



						'''simple docstring'''


						return (4, 8)

				@property
				def 							lowercase_					(     self							:    Optional[int])->       int:



						'''simple docstring'''


						return (4, 8)

				def 							lowercase_					(     self							:    Optional[int])->       Tuple:



						'''simple docstring'''


						__lowerCAmelCase:	str   				=      {
						    "num_attention_heads": 2,
						    "attention_head_dim": 4,
						    "num_layers": 2,
						    "embedding_dim": 8,
						    "num_embeddings": 7,
						    "additional_embeddings": 4,
						}
						__lowerCAmelCase:	Any   				=      self.dummy_input
						return init_dict, inputs_dict

				def 							lowercase_					(     self							:    List[Any])->       int:



						'''simple docstring'''


						__lowerCAmelCase				,			__lowerCAmelCase:	Optional[int]   				=      PriorTransformer.from_pretrained(
						    "hf-internal-testing/prior-dummy"					,  output_loading_info=UpperCamelCase__)
						self.assertIsNotNone(UpperCamelCase__)
						self.assertEqual(len(loading_info["missing_keys"])					,  0)

						model.to(UpperCamelCase__)
						__lowerCAmelCase:	Dict   				=      model(**self.dummy_input)[0]

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

				def 							lowercase_					(     self							:    List[str])->       Tuple:



						'''simple docstring'''


						__lowerCAmelCase				,			__lowerCAmelCase:	Optional[Any]   				=      self.prepare_init_args_and_inputs_for_common()

						__lowerCAmelCase:	Tuple   				=      self.model_class(**UpperCamelCase__)
						__lowerCAmelCase:	List[str]   				=      inspect.signature(model.forward)
						# signature.parameters is an OrderedDict => so arg_names order is deterministic
						__lowerCAmelCase:	List[Any]   				=      [*signature.parameters.keys()]

						__lowerCAmelCase:	Any   				=      ["hidden_states", "timestep"]
						self.assertListEqual(arg_names[:2]					,  UpperCamelCase__)




				def 							lowercase_					(     self							:    Optional[int])->       List[str]:



						'''simple docstring'''


						__lowerCAmelCase:	int   				=      PriorTransformer.from_pretrained("hf-internal-testing/prior-dummy")
						__lowerCAmelCase:	Union[str, Any]   				=      model.to(UpperCamelCase__)

						if hasattr(UpperCamelCase__					,  "set_default_attn_processor"):
								model.set_default_attn_processor()

						__lowerCAmelCase:	str   				=      self.get_dummy_seed_input()

						with torch.no_grad():
								__lowerCAmelCase:	Dict   				=      model(**UpperCamelCase__)[0]

						__lowerCAmelCase:	Dict   				=      output[0, :5].flatten().cpu()
						print(UpperCamelCase__)

						# 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.
						__lowerCAmelCase:	List[str]   				=      torch.tensor([-1.3436, -0.2870, 0.7538, 0.4368, -0.0239])
						self.assertTrue(torch_all_close(UpperCamelCase__					,  UpperCamelCase__					,  rtol=1e-2))





@slow
class    snake_case     (    unittest.TestCase  ):

				def 							lowercase_					(     self							:    int					,  UpperCamelCase__							:    Dict=1					,  UpperCamelCase__							:    str=7_6_8					,  UpperCamelCase__							:    int=7_7					,  UpperCamelCase__							:    Any=0)->       Union[str, Any]:



						'''simple docstring'''


						torch.manual_seed(UpperCamelCase__)
						__lowerCAmelCase:	List[Any]   				=      batch_size
						__lowerCAmelCase:	Any   				=      embedding_dim
						__lowerCAmelCase:	Dict   				=      num_embeddings

						__lowerCAmelCase:	Dict   				=      torch.randn((batch_size, embedding_dim)).to(UpperCamelCase__)

						__lowerCAmelCase:	str   				=      torch.randn((batch_size, embedding_dim)).to(UpperCamelCase__)
						__lowerCAmelCase:	int   				=      torch.randn((batch_size, num_embeddings, embedding_dim)).to(UpperCamelCase__)

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

				def 							lowercase_					(     self							:    List[Any])->       Union[str, Any]:



						'''simple docstring'''


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




				@parameterized.expand(
				    [
				        # fmt: off
				        [1_3, [-0.5861,  0.1283, -0.0931,  0.0882,  0.4476,  0.1329, -0.0498,  0.0640]],
				        [3_7, [-0.4913,  0.0110, -0.0483,  0.0541,  0.4954, -0.0170,  0.0354,  0.1651]],
				        # fmt: on
				    ])
				def 							lowercase_					(     self							:    Optional[int]					,  UpperCamelCase__							:    List[str]					,  UpperCamelCase__							:    int)->       List[Any]:



						'''simple docstring'''


						__lowerCAmelCase:	List[str]   				=      PriorTransformer.from_pretrained("kandinsky-community/kandinsky-2-1-prior"					,  subfolder="prior")
						model.to(UpperCamelCase__)
						__lowerCAmelCase:	Dict   				=      self.get_dummy_seed_input(seed=UpperCamelCase__)

						with torch.no_grad():
								__lowerCAmelCase:	Optional[Any]   				=      model(**UpperCamelCase__)[0]

						assert list(sample.shape) == [1, 7_6_8]

						__lowerCAmelCase:	Dict   				=      sample[0, :8].flatten().cpu()
						print(UpperCamelCase__)
						__lowerCAmelCase:	Union[str, Any]   				=      torch.tensor(UpperCamelCase__)

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


