"""simple docstring"""






import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode

import transformers
from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    ViTImageProcessor,
    ViTMAEConfig,
    ViTMAEForPreTraining,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
lowerCAmelCase__  :  List[str]						     =	logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')

require_version('datasets>=1.8.0', 'To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt')
@dataclass
class 				snake_case       :




					"""simple docstring"""

					snake_case__				    = field(
					    default="cifar10"      ,    metadata={"help": "Name of a dataset from the datasets package"}				)
					snake_case__				    = field(
					    default=__UpperCAmelCase      ,    metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}				)
					snake_case__				    = field(
					    default=__UpperCAmelCase      ,    metadata={"help": "The column name of the images in the files."}				)
					snake_case__				    = field(default=__UpperCAmelCase      ,    metadata={"help": "A folder containing the training data."}				)
					snake_case__				    = field(default=__UpperCAmelCase      ,    metadata={"help": "A folder containing the validation data."}				)
					snake_case__				    = field(
					    default=0.15      ,    metadata={"help": "Percent to split off of train for validation."}				)
					snake_case__				    = field(
					    default=__UpperCAmelCase      ,    metadata={
					        "help": (
					            "For debugging purposes or quicker training, truncate the number of training examples to this "
					            "value if set."
					        )
					    }      ,    )
					snake_case__				    = field(
					    default=__UpperCAmelCase      ,    metadata={
					        "help": (
					            "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
					            "value if set."
					        )
					    }      ,    )







					def 		__lowerCAmelCase						(				self   :	Any			):
									UpperCAmelCase__             =							{}
									if self.train_dir is not None:
													UpperCAmelCase__             =							self.train_dir
									if self.validation_dir is not None:
													UpperCAmelCase__             =							self.validation_dir
									UpperCAmelCase__             =							data_files if data_files else None






@dataclass
class 				snake_case       :




					"""simple docstring"""

					snake_case__				    = field(
					    default=__UpperCAmelCase      ,    metadata={
					        "help": (
					            "The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."
					        )
					    }      ,    )
					snake_case__				    = field(
					    default=__UpperCAmelCase      ,    metadata={"help": "Pretrained config name or path if not the same as model_name_or_path"}				)
					snake_case__				    = field(
					    default=__UpperCAmelCase      ,    metadata={
					        "help": (
					            "Override some existing default config settings when a model is trained from scratch. Example: "
					            "n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"
					        )
					    }      ,    )
					snake_case__				    = field(
					    default=__UpperCAmelCase      ,    metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}				)
					snake_case__				    = field(
					    default="main"      ,    metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}      ,    )
					snake_case__				    = field(default=__UpperCAmelCase      ,    metadata={"help": "Name or path of preprocessor config."}				)
					snake_case__				    = field(
					    default=__UpperCAmelCase      ,    metadata={
					        "help": (
					            "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
					            "with private models)."
					        )
					    }      ,    )
					snake_case__				    = field(
					    default=0.75      ,    metadata={"help": "The ratio of the number of masked tokens in the input sequence."}				)
					snake_case__				    = field(
					    default=__UpperCAmelCase      ,    metadata={"help": "Whether or not to train with normalized pixel values as target."}				)






@dataclass
class 				snake_case       ( __UpperCAmelCase				):




					"""simple docstring"""

					snake_case__				    = field(
					    default=1E-3      ,    metadata={"help": "Base learning rate: absolute_lr = base_lr * total_batch_size / 256."}				)






def   a_						(					lowerCamelCase       ):
				UpperCAmelCase__             =							torch.stack([example['pixel_values'] for example in examples]       )
				return {"pixel_values": pixel_values}



def   a_						(					):
				# See all possible arguments in src/transformers/training_args.py
				# or by passing the --help flag to this script.
				# We now keep distinct sets of args, for a cleaner separation of concerns.

				UpperCAmelCase__             =							HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments)       )
				if len(sys.argv       ) == 2 and sys.argv[1].endswith('.json'       ):
								# If we pass only one argument to the script and it's the path to a json file,
								# let's parse it to get our arguments.
								UpperCAmelCase__      ,	UpperCAmelCase__      ,	UpperCAmelCase__             =							parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]       )       )
				else:
								UpperCAmelCase__      ,	UpperCAmelCase__      ,	UpperCAmelCase__             =							parser.parse_args_into_dataclasses()

				# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
				# information sent is the one passed as arguments along with your Python/PyTorch versions.
				send_example_telemetry('run_mae'					,			lowerCamelCase					,			lowerCamelCase       )

				# Setup logging
				logging.basicConfig(
				    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'					,			datefmt='%m/%d/%Y %H:%M:%S'					,			handlers=[logging.StreamHandler(sys.stdout       )]					,			)

				if training_args.should_log:
								# The default of training_args.log_level is passive, so we set log level at info here to have that default.
								transformers.utils.logging.set_verbosity_info()

				UpperCAmelCase__             =							training_args.get_process_log_level()
				logger.setLevel(lowerCamelCase       )
				transformers.utils.logging.set_verbosity(lowerCamelCase       )
				transformers.utils.logging.enable_default_handler()
				transformers.utils.logging.enable_explicit_format()

				# Log on each process the small summary:
				logger.warning(
				    f'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
				    + f'''distributed training: {bool(training_args.local_rank != -1       )}, 16-bits training: {training_args.fpaa}'''       )
				logger.info(f'''Training/evaluation parameters {training_args}'''       )

				# Detecting last checkpoint.
				UpperCAmelCase__             =							None
				if os.path.isdir(training_args.output_dir       ) and training_args.do_train and not training_args.overwrite_output_dir:
								UpperCAmelCase__             =							get_last_checkpoint(training_args.output_dir       )
								if last_checkpoint is None and len(os.listdir(training_args.output_dir       )       ) > 0:
												raise ValueError(
												    f'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
												    'Use --overwrite_output_dir to overcome.'       )
								elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
												logger.info(
												    f'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
												    'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'       )

    # Initialize our dataset.
				UpperCAmelCase__             =							load_dataset(
				    data_args.dataset_name					,			data_args.dataset_config_name					,			data_files=data_args.data_files					,			cache_dir=model_args.cache_dir					,			use_auth_token=True if model_args.use_auth_token else None					,			)

				# If we don't have a validation split, split off a percentage of train as validation.
				UpperCAmelCase__             =							None if 'validation' in ds.keys() else data_args.train_val_split
				if isinstance(data_args.train_val_split					,			lowerCamelCase       ) and data_args.train_val_split > 0.0:
								UpperCAmelCase__             =							ds['train'].train_test_split(data_args.train_val_split       )
								UpperCAmelCase__             =							split['train']
								UpperCAmelCase__             =							split['test']

				# Load pretrained model and image processor
				#
				# Distributed training:
				# The .from_pretrained methods guarantee that only one local process can concurrently
				# download model & vocab.
				UpperCAmelCase__             =							{
				    'cache_dir': model_args.cache_dir,
				    'revision': model_args.model_revision,
				    'use_auth_token': True if model_args.use_auth_token else None,
				}
				if model_args.config_name:
								UpperCAmelCase__             =							ViTMAEConfig.from_pretrained(model_args.config_name					,			**lowerCamelCase       )
				elif model_args.model_name_or_path:
								UpperCAmelCase__             =							ViTMAEConfig.from_pretrained(model_args.model_name_or_path					,			**lowerCamelCase       )
				else:
								UpperCAmelCase__             =							ViTMAEConfig()
								logger.warning('You are instantiating a new config instance from scratch.'       )
								if model_args.config_overrides is not None:
												logger.info(f'''Overriding config: {model_args.config_overrides}'''       )
												config.update_from_string(model_args.config_overrides       )
												logger.info(f'''New config: {config}'''       )

    # adapt config
				config.update(
				    {
				        'mask_ratio': model_args.mask_ratio,
				        'norm_pix_loss': model_args.norm_pix_loss,
				    }       )

				# create image processor
				if model_args.image_processor_name:
								UpperCAmelCase__             =							ViTImageProcessor.from_pretrained(model_args.image_processor_name					,			**lowerCamelCase       )
				elif model_args.model_name_or_path:
								UpperCAmelCase__             =							ViTImageProcessor.from_pretrained(model_args.model_name_or_path					,			**lowerCamelCase       )
				else:
								UpperCAmelCase__             =							ViTImageProcessor()

				# create model
				if model_args.model_name_or_path:
								UpperCAmelCase__             =							ViTMAEForPreTraining.from_pretrained(
								    model_args.model_name_or_path					,			from_tf=bool('.ckpt' in model_args.model_name_or_path       )					,			config=lowerCamelCase					,			cache_dir=model_args.cache_dir					,			revision=model_args.model_revision					,			use_auth_token=True if model_args.use_auth_token else None					,			)
				else:
								logger.info('Training new model from scratch'       )
								UpperCAmelCase__             =							ViTMAEForPreTraining(lowerCamelCase       )

				if training_args.do_train:
								UpperCAmelCase__             =							ds['train'].column_names
				else:
								UpperCAmelCase__             =							ds['validation'].column_names

				if data_args.image_column_name is not None:
								UpperCAmelCase__             =							data_args.image_column_name
				elif "image" in column_names:
								UpperCAmelCase__             =							'image'
				elif "img" in column_names:
								UpperCAmelCase__             =							'img'
				else:
								UpperCAmelCase__             =							column_names[0]

				# transformations as done in original MAE paper
				# source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
				if "shortest_edge" in image_processor.size:
								UpperCAmelCase__             =							image_processor.size['shortest_edge']
				else:
								UpperCAmelCase__             =							(image_processor.size['height'], image_processor.size['width'])
				UpperCAmelCase__             =							Compose(
				    [
				        Lambda(lambda lowerCamelCase       : img.convert('RGB'       ) if img.mode != "RGB" else img       ),
				        RandomResizedCrop(lowerCamelCase					,			scale=(0.2, 1.0)					,			interpolation=InterpolationMode.BICUBIC       ),
				        RandomHorizontalFlip(),
				        ToTensor(),
				        Normalize(mean=image_processor.image_mean					,			std=image_processor.image_std       ),
				    ]       )

				def preprocess_images(lowerCamelCase       ):
								UpperCAmelCase__             =							[transforms(lowerCamelCase       ) for image in examples[image_column_name]]
								return examples

				if training_args.do_train:
								if "train" not in ds:
												raise ValueError('--do_train requires a train dataset'       )
								if data_args.max_train_samples is not None:
												UpperCAmelCase__             =							ds['train'].shuffle(seed=training_args.seed       ).select(range(data_args.max_train_samples       )       )
								# Set the training transforms
								ds["train"].set_transform(lowerCamelCase       )

				if training_args.do_eval:
								if "validation" not in ds:
												raise ValueError('--do_eval requires a validation dataset'       )
								if data_args.max_eval_samples is not None:
												UpperCAmelCase__             =							(
												    ds['validation'].shuffle(seed=training_args.seed       ).select(range(data_args.max_eval_samples       )       )
												)
								# Set the validation transforms
								ds["validation"].set_transform(lowerCamelCase       )

				# Compute absolute learning rate
				UpperCAmelCase__             =							(
				    training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
				)
				if training_args.base_learning_rate is not None:
								UpperCAmelCase__             =							training_args.base_learning_rate * total_train_batch_size / 2_5_6

				# Initialize our trainer
				UpperCAmelCase__             =							Trainer(
				    model=lowerCamelCase					,			args=lowerCamelCase					,			train_dataset=ds['train'] if training_args.do_train else None					,			eval_dataset=ds['validation'] if training_args.do_eval else None					,			tokenizer=lowerCamelCase					,			data_collator=lowerCamelCase					,			)

				# Training
				if training_args.do_train:
								UpperCAmelCase__             =							None
								if training_args.resume_from_checkpoint is not None:
												UpperCAmelCase__             =							training_args.resume_from_checkpoint
								elif last_checkpoint is not None:
												UpperCAmelCase__             =							last_checkpoint
								UpperCAmelCase__             =							trainer.train(resume_from_checkpoint=lowerCamelCase       )
								trainer.save_model()
								trainer.log_metrics('train'					,			train_result.metrics       )
								trainer.save_metrics('train'					,			train_result.metrics       )
								trainer.save_state()

				# Evaluation
				if training_args.do_eval:
								UpperCAmelCase__             =							trainer.evaluate()
								trainer.log_metrics('eval'					,			lowerCamelCase       )
								trainer.save_metrics('eval'					,			lowerCamelCase       )

				# Write model card and (optionally) push to hub
				UpperCAmelCase__             =							{
				    'tasks': 'masked-auto-encoding',
				    'dataset': data_args.dataset_name,
				    'tags': ['masked-auto-encoding'],
				}
				if training_args.push_to_hub:
								trainer.push_to_hub(**lowerCamelCase       )
				else:
								trainer.create_model_card(**lowerCamelCase       )



def   a_						(					lowerCamelCase       ):
				# For xla_spawn (TPUs)
				main()


if __name__ == "__main__":
						main()




"""simple docstring"""






import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
class 				snake_case       ( __UpperCAmelCase      ,    unittest.TestCase				):




					"""simple docstring"""

					snake_case__				    = ShapEImgaImgPipeline
					snake_case__				    = ["image"]
					snake_case__				    = ["image"]
					snake_case__				    = [
					    "num_images_per_prompt",
					    "num_inference_steps",
					    "generator",
					    "latents",
					    "guidance_scale",
					    "frame_size",
					    "output_type",
					    "return_dict",
					]
					snake_case__				    = False







					@property
					def 		__lowerCAmelCase						(				self   :	List[str]			):
									return 32







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







					@property
					def 		__lowerCAmelCase						(				self   :	int			):
									return self.time_input_dim * 4







					@property
					def 		__lowerCAmelCase						(				self   :	List[Any]			):
									return 8







					@property
					def 		__lowerCAmelCase						(				self   :	Optional[int]			):
									torch.manual_seed(0			)
									UpperCAmelCase__             =							CLIPVisionConfig(
									    hidden_size=self.text_embedder_hidden_size    ,image_size=64    ,projection_dim=self.text_embedder_hidden_size    ,intermediate_size=37    ,num_attention_heads=4    ,num_channels=3    ,num_hidden_layers=5    ,patch_size=1    ,)

									UpperCAmelCase__             =							CLIPVisionModel(lowerCamelCase__			)
									return model







					@property
					def 		__lowerCAmelCase						(				self   :	Optional[Any]			):
									UpperCAmelCase__             =							CLIPImageProcessor(
									    crop_size=224    ,do_center_crop=lowerCamelCase__    ,do_normalize=lowerCamelCase__    ,do_resize=lowerCamelCase__    ,image_mean=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]    ,image_std=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]    ,resample=3    ,size=224    ,)

									return image_processor







					@property
					def 		__lowerCAmelCase						(				self   :	str			):
									torch.manual_seed(0			)

									UpperCAmelCase__             =							{
									    'num_attention_heads': 2,
									    'attention_head_dim': 16,
									    'embedding_dim': self.time_input_dim,
									    'num_embeddings': 32,
									    'embedding_proj_dim': self.text_embedder_hidden_size,
									    'time_embed_dim': self.time_embed_dim,
									    'num_layers': 1,
									    'clip_embed_dim': self.time_input_dim * 2,
									    'additional_embeddings': 0,
									    'time_embed_act_fn': 'gelu',
									    'norm_in_type': 'layer',
									    'embedding_proj_norm_type': 'layer',
									    'encoder_hid_proj_type': None,
									    'added_emb_type': None,
									}

									UpperCAmelCase__             =							PriorTransformer(**lowerCamelCase__			)
									return model







					@property
					def 		__lowerCAmelCase						(				self   :	Tuple			):
									torch.manual_seed(0			)

									UpperCAmelCase__             =							{
									    'param_shapes': (
									        (self.renderer_dim, 93),
									        (self.renderer_dim, 8),
									        (self.renderer_dim, 8),
									        (self.renderer_dim, 8),
									    ),
									    'd_latent': self.time_input_dim,
									    'd_hidden': self.renderer_dim,
									    'n_output': 12,
									    'background': (
									        0.1,
									        0.1,
									        0.1,
									    ),
									}
									UpperCAmelCase__             =							ShapERenderer(**lowerCamelCase__			)
									return model







					def 		__lowerCAmelCase						(				self   :	Any			):
									UpperCAmelCase__             =							self.dummy_prior
									UpperCAmelCase__             =							self.dummy_image_encoder
									UpperCAmelCase__             =							self.dummy_image_processor
									UpperCAmelCase__             =							self.dummy_renderer

									UpperCAmelCase__             =							HeunDiscreteScheduler(
									    beta_schedule='exp'    ,num_train_timesteps=1_024    ,prediction_type='sample'    ,use_karras_sigmas=lowerCamelCase__    ,clip_sample=lowerCamelCase__    ,clip_sample_range=1.0    ,)
									UpperCAmelCase__             =							{
									    'prior': prior,
									    'image_encoder': image_encoder,
									    'image_processor': image_processor,
									    'renderer': renderer,
									    'scheduler': scheduler,
									}

									return components







					def 		__lowerCAmelCase						(				self   :	Optional[int]    ,lowerCamelCase__   :	Any    ,lowerCamelCase__   :	str=0			):
									UpperCAmelCase__             =							floats_tensor((1, 3, 64, 64)    ,rng=random.Random(lowerCamelCase__			)			).to(lowerCamelCase__			)

									if str(lowerCamelCase__			).startswith('mps'			):
													UpperCAmelCase__             =							torch.manual_seed(lowerCamelCase__			)
									else:
													UpperCAmelCase__             =							torch.Generator(device=lowerCamelCase__			).manual_seed(lowerCamelCase__			)
									UpperCAmelCase__             =							{
									    'image': input_image,
									    'generator': generator,
									    'num_inference_steps': 1,
									    'frame_size': 32,
									    'output_type': 'np',
									}
									return inputs







					def 		__lowerCAmelCase						(				self   :	Optional[int]			):
									UpperCAmelCase__             =							'cpu'

									UpperCAmelCase__             =							self.get_dummy_components()

									UpperCAmelCase__             =							self.pipeline_class(**lowerCamelCase__			)
									UpperCAmelCase__             =							pipe.to(lowerCamelCase__			)

									pipe.set_progress_bar_config(disable=lowerCamelCase__			)

									UpperCAmelCase__             =							pipe(**self.get_dummy_inputs(lowerCamelCase__			)			)
									UpperCAmelCase__             =							output.images[0]
									UpperCAmelCase__             =							image[0, -3:, -3:, -1]

									assert image.shape == (20, 32, 32, 3)

									UpperCAmelCase__             =							np.array(
									    [
									        0.0_0_0_3_9_2_1_6,
									        0.0_0_0_3_9_2_1_6,
									        0.0_0_0_3_9_2_1_6,
									        0.0_0_0_3_9_2_1_6,
									        0.0_0_0_3_9_2_1_6,
									        0.0_0_0_3_9_2_1_6,
									        0.0_0_0_3_9_2_1_6,
									        0.0_0_0_3_9_2_1_6,
									        0.0_0_0_3_9_2_1_6,
									    ]			)

									assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2







					def 		__lowerCAmelCase						(				self   :	Tuple			):
									# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
									self._test_inference_batch_consistent(batch_sizes=[1, 2]			)







					def 		__lowerCAmelCase						(				self   :	Tuple			):
									UpperCAmelCase__             =							torch_device == 'cpu'
									UpperCAmelCase__             =							True
									self._test_inference_batch_single_identical(
									    batch_size=2    ,test_max_difference=lowerCamelCase__    ,relax_max_difference=lowerCamelCase__    ,)







					def 		__lowerCAmelCase						(				self   :	List[Any]			):
									UpperCAmelCase__             =							self.get_dummy_components()
									UpperCAmelCase__             =							self.pipeline_class(**lowerCamelCase__			)
									UpperCAmelCase__             =							pipe.to(lowerCamelCase__			)
									pipe.set_progress_bar_config(disable=lowerCamelCase__			)

									UpperCAmelCase__             =							1
									UpperCAmelCase__             =							2

									UpperCAmelCase__             =							self.get_dummy_inputs(lowerCamelCase__			)

									for key in inputs.keys():
													if key in self.batch_params:
																	UpperCAmelCase__             =							batch_size * [inputs[key]]

									UpperCAmelCase__             =							pipe(**lowerCamelCase__    ,num_images_per_prompt=lowerCamelCase__			)[0]

									assert images.shape[0] == batch_size * num_images_per_prompt






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




					"""simple docstring"""

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







					def 		__lowerCAmelCase						(				self   :	Any			):
									UpperCAmelCase__             =							load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/shap_e/corgi.png'			)
									UpperCAmelCase__             =							load_numpy(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
									    '/shap_e/test_shap_e_img2img_out.npy'			)
									UpperCAmelCase__             =							ShapEImgaImgPipeline.from_pretrained('openai/shap-e-img2img'			)
									UpperCAmelCase__             =							pipe.to(lowerCamelCase__			)
									pipe.set_progress_bar_config(disable=lowerCamelCase__			)

									UpperCAmelCase__             =							torch.Generator(device=lowerCamelCase__			).manual_seed(0			)

									UpperCAmelCase__             =							pipe(
									    lowerCamelCase__    ,generator=lowerCamelCase__    ,guidance_scale=3.0    ,num_inference_steps=64    ,frame_size=64    ,output_type='np'    ,).images[0]

									assert images.shape == (20, 64, 64, 3)

									assert_mean_pixel_difference(lowerCamelCase__    ,lowerCamelCase__			)



