



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

import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForMaskedImageModeling,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
)
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
UpperCAmelCase__       =	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")

UpperCAmelCase__       =	list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
UpperCAmelCase__       =	tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)







@dataclass
class       __lowerCAmelCase   :
			UpperCamelCase        =					field(
			    default='''cifar10'''    ,						metadata={'''help''': '''Name of a dataset from the datasets package'''}						)
			UpperCamelCase        =					field(
			    default=A    ,						metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}						)
			UpperCamelCase        =					field(
			    default=A    ,						metadata={'''help''': '''The column name of the images in the files. If not set, will try to use \'image\' or \'img\'.'''}    ,						)
			UpperCamelCase        =					field(default=A    ,						metadata={'''help''': '''A folder containing the training data.'''}						)
			UpperCamelCase        =					field(default=A    ,						metadata={'''help''': '''A folder containing the validation data.'''}						)
			UpperCamelCase        =					field(
			    default=0.15    ,						metadata={'''help''': '''Percent to split off of train for validation.'''}						)
			UpperCamelCase        =					field(default=3_2    ,						metadata={'''help''': '''The size of the square patches to use for masking.'''}						)
			UpperCamelCase        =					field(
			    default=0.6    ,						metadata={'''help''': '''Percentage of patches to mask.'''}    ,						)
			UpperCamelCase        =					field(
			    default=A    ,						metadata={
			        '''help''': (
			            '''For debugging purposes or quicker training, truncate the number of training examples to this '''
			            '''value if set.'''
			        )
			    }    ,						)
			UpperCamelCase        =					field(
			    default=A    ,						metadata={
			        '''help''': (
			            '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
			            '''value if set.'''
			        )
			    }    ,						)



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



						"""simple docstring"""






						_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       __lowerCAmelCase   :
			UpperCamelCase        =					field(
			    default=A    ,						metadata={
			        '''help''': (
			            '''The model checkpoint for weights initialization. Can be a local path to a pytorch_model.bin or a '''
			            '''checkpoint identifier on the hub. '''
			            '''Don\'t set if you want to train a model from scratch.'''
			        )
			    }    ,						)
			UpperCamelCase        =					field(
			    default=A    ,						metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(A						)}    ,						)
			UpperCamelCase        =					field(
			    default=A    ,						metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}						)
			UpperCamelCase        =					field(
			    default=A    ,						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'''
			        )
			    }    ,						)
			UpperCamelCase        =					field(
			    default=A    ,						metadata={'''help''': '''Where do you want to store (cache) the pretrained models/datasets downloaded from the hub'''}    ,						)
			UpperCamelCase        =					field(
			    default='''main'''    ,						metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''}    ,						)
			UpperCamelCase        =					field(default=A    ,						metadata={'''help''': '''Name or path of preprocessor config.'''}						)
			UpperCamelCase        =					field(
			    default=A    ,						metadata={
			        '''help''': (
			            '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
			            '''with private models).'''
			        )
			    }    ,						)
			UpperCamelCase        =					field(
			    default=A    ,						metadata={
			        '''help''': (
			            '''The size (resolution) of each image. If not specified, will use `image_size` of the configuration.'''
			        )
			    }    ,						)
			UpperCamelCase        =					field(
			    default=A    ,						metadata={
			        '''help''': (
			            '''The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration.'''
			        )
			    }    ,						)
			UpperCamelCase        =					field(
			    default=A    ,						metadata={'''help''': '''Stride to use for the encoder.'''}    ,						)







class       __lowerCAmelCase   :



			def __init__(						self       :  Any			,		A       :  int=1_92			,		A       :  Tuple=32			,		A       :  List[str]=4			,		A       :  str=0.6)       ->   Optional[int]:



						"""simple docstring"""






						_UpperCAmelCase			=       input_size
						_UpperCAmelCase			=       mask_patch_size
						_UpperCAmelCase			=       model_patch_size
						_UpperCAmelCase			=       mask_ratio

						if self.input_size % self.mask_patch_size != 0:
									raise ValueError('Input size must be divisible by mask patch size')
						if self.mask_patch_size % self.model_patch_size != 0:
									raise ValueError('Mask patch size must be divisible by model patch size')

						_UpperCAmelCase			=       self.input_size // self.mask_patch_size
						_UpperCAmelCase			=       self.mask_patch_size // self.model_patch_size

						_UpperCAmelCase			=       self.rand_size**2
						_UpperCAmelCase			=       int(np.ceil(self.token_count * self.mask_ratio))



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



						"""simple docstring"""






						_UpperCAmelCase			=       np.random.permutation(self.token_count)[: self.mask_count]
						_UpperCAmelCase			=       np.zeros(self.token_count			,		dtype=A)
						_UpperCAmelCase			=       1

						_UpperCAmelCase			=       mask.reshape((self.rand_size, self.rand_size))
						_UpperCAmelCase			=       mask.repeat(self.scale			,		axis=0).repeat(self.scale			,		axis=1)

						return torch.tensor(mask.flatten())





def 							A     (					_UpperCAmelCase    :       Union[str, Any]   )    ->			int:







			'''simple docstring'''
			_UpperCAmelCase			=       torch.stack([example['pixel_values'] for example in examples]   )
			_UpperCAmelCase			=       torch.stack([example['mask'] for example in examples]   )
			return {"pixel_values": pixel_values, "bool_masked_pos": mask}





def 							A     (					)    ->			Dict:







			'''simple docstring'''
			# 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, TrainingArguments)   )
			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_mim'							,       _UpperCAmelCase							,       _UpperCAmelCase   )

			# 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(_UpperCAmelCase   )
			transformers.utils.logging.set_verbosity(_UpperCAmelCase   )
			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							,       _UpperCAmelCase   ) 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']

			# Create config
			# 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_or_path:
						_UpperCAmelCase			=       AutoConfig.from_pretrained(model_args.config_name_or_path							,       **_UpperCAmelCase   )
			elif model_args.model_name_or_path:
						_UpperCAmelCase			=       AutoConfig.from_pretrained(model_args.model_name_or_path							,       **_UpperCAmelCase   )
			else:
						_UpperCAmelCase			=       CONFIG_MAPPING[model_args.model_type]()
						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}"   )

    # make sure the decoder_type is "simmim" (only relevant for BEiT)
			if hasattr(_UpperCAmelCase							,       'decoder_type'   ):
						_UpperCAmelCase			=       'simmim'

			# adapt config
			_UpperCAmelCase			=       model_args.image_size if model_args.image_size is not None else config.image_size
			_UpperCAmelCase			=       model_args.patch_size if model_args.patch_size is not None else config.patch_size
			_UpperCAmelCase			=       (
			    model_args.encoder_stride if model_args.encoder_stride is not None else config.encoder_stride
			)

			config.update(
			    {
			        'image_size': model_args.image_size,
			        'patch_size': model_args.patch_size,
			        'encoder_stride': model_args.encoder_stride,
			    }   )

			# create image processor
			if model_args.image_processor_name:
						_UpperCAmelCase			=       AutoImageProcessor.from_pretrained(model_args.image_processor_name							,       **_UpperCAmelCase   )
			elif model_args.model_name_or_path:
						_UpperCAmelCase			=       AutoImageProcessor.from_pretrained(model_args.model_name_or_path							,       **_UpperCAmelCase   )
			else:
						_UpperCAmelCase			=       {
						    conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
						}
						_UpperCAmelCase			=       IMAGE_PROCESSOR_TYPES[model_args.model_type]()

			# create model
			if model_args.model_name_or_path:
						_UpperCAmelCase			=       AutoModelForMaskedImageModeling.from_pretrained(
						    model_args.model_name_or_path							,       from_tf=bool('.ckpt' in model_args.model_name_or_path   )							,       config=_UpperCAmelCase							,       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			=       AutoModelForMaskedImageModeling.from_config(_UpperCAmelCase   )

			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 SimMIM paper
			# source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
			_UpperCAmelCase			=       Compose(
			    [
			        Lambda(lambda _UpperCAmelCase   : img.convert('RGB'   ) if img.mode != "RGB" else img   ),
			        RandomResizedCrop(model_args.image_size							,       scale=(0.67, 1.0)							,       ratio=(3.0 / 4.0, 4.0 / 3.0)   ),
			        RandomHorizontalFlip(),
			        ToTensor(),
			        Normalize(mean=image_processor.image_mean							,       std=image_processor.image_std   ),
			    ]   )

			# create mask generator
			_UpperCAmelCase			=       MaskGenerator(
			    input_size=model_args.image_size							,       mask_patch_size=data_args.mask_patch_size							,       model_patch_size=model_args.patch_size							,       mask_ratio=data_args.mask_ratio							,       )

			def preprocess_images(_UpperCAmelCase    :       List[Any]   ):
						_UpperCAmelCase			=       [transforms(_UpperCAmelCase   ) for image in examples[image_column_name]]
						_UpperCAmelCase			=       [mask_generator() for i in range(len(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(_UpperCAmelCase   )

			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(_UpperCAmelCase   )

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

			# 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=_UpperCAmelCase   )
						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'							,       _UpperCAmelCase   )
						trainer.save_metrics('eval'							,       _UpperCAmelCase   )

			# Write model card and (optionally) push to hub
			_UpperCAmelCase			=       {
			    'finetuned_from': model_args.model_name_or_path,
			    'tasks': 'masked-image-modeling',
			    'dataset': data_args.dataset_name,
			    'tags': ['masked-image-modeling'],
			}
			if training_args.push_to_hub:
						trainer.push_to_hub(**_UpperCAmelCase   )
			else:
						trainer.create_model_card(**_UpperCAmelCase   )


if __name__ == "__main__":
							main()





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       __lowerCAmelCase   (      A    ,						unittest.TestCase						):
			UpperCamelCase        =					ShapEImgaImgPipeline
			UpperCamelCase        =					['''image''']
			UpperCamelCase        =					['''image''']
			UpperCamelCase        =					[
			    '''num_images_per_prompt''',
			    '''num_inference_steps''',
			    '''generator''',
			    '''latents''',
			    '''guidance_scale''',
			    '''frame_size''',
			    '''output_type''',
			    '''return_dict''',
			]
			UpperCamelCase        =					False



			@property
			def    _lowerCamelCase	(						self       :  List[Any])       ->   List[str]:



						"""simple docstring"""






						return 32



			@property
			def    _lowerCamelCase	(						self       :  List[Any])       ->   int:



						"""simple docstring"""






						return 32



			@property
			def    _lowerCamelCase	(						self       :  str)       ->   List[str]:



						"""simple docstring"""






						return self.time_input_dim * 4



			@property
			def    _lowerCamelCase	(						self       :  List[str])       ->   List[Any]:



						"""simple docstring"""






						return 8



			@property
			def    _lowerCamelCase	(						self       :  Union[str, Any])       ->   Dict:



						"""simple docstring"""






						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(A)
						return model



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



						"""simple docstring"""






						_UpperCAmelCase			=       CLIPImageProcessor(
						    crop_size=2_24			,		do_center_crop=A			,		do_normalize=A			,		do_resize=A			,		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=2_24			,		)

						return image_processor



			@property
			def    _lowerCamelCase	(						self       :  int)       ->   Tuple:



						"""simple docstring"""






						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(**A)
						return model



			@property
			def    _lowerCamelCase	(						self       :  Union[str, Any])       ->   List[str]:



						"""simple docstring"""






						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(**A)
						return model



			def    _lowerCamelCase	(						self       :  Any)       ->   Any:



						"""simple docstring"""






						_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=10_24			,		prediction_type='sample'			,		use_karras_sigmas=A			,		clip_sample=A			,		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       :  List[str]			,		A       :  Optional[Any]			,		A       :  Tuple=0)       ->   Dict:



						"""simple docstring"""






						_UpperCAmelCase			=       floats_tensor((1, 3, 64, 64)			,		rng=random.Random(A)).to(A)

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



			def    _lowerCamelCase	(						self       :  List[Any])       ->   Tuple:



						"""simple docstring"""






						_UpperCAmelCase			=       'cpu'

						_UpperCAmelCase			=       self.get_dummy_components()

						_UpperCAmelCase			=       self.pipeline_class(**A)
						_UpperCAmelCase			=       pipe.to(A)

						pipe.set_progress_bar_config(disable=A)

						_UpperCAmelCase			=       pipe(**self.get_dummy_inputs(A))
						_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       :  int)       ->   Tuple:



						"""simple docstring"""






						self._test_inference_batch_consistent(batch_sizes=[1, 2])



			def    _lowerCamelCase	(						self       :  Tuple)       ->   Union[str, Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       torch_device == 'cpu'
						_UpperCAmelCase			=       True
						self._test_inference_batch_single_identical(
						    batch_size=2			,		test_max_difference=A			,		relax_max_difference=A			,		)



			def    _lowerCamelCase	(						self       :  Tuple)       ->   Dict:



						"""simple docstring"""






						_UpperCAmelCase			=       self.get_dummy_components()
						_UpperCAmelCase			=       self.pipeline_class(**A)
						_UpperCAmelCase			=       pipe.to(A)
						pipe.set_progress_bar_config(disable=A)

						_UpperCAmelCase			=       1
						_UpperCAmelCase			=       2

						_UpperCAmelCase			=       self.get_dummy_inputs(A)

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

						_UpperCAmelCase			=       pipe(**A			,		num_images_per_prompt=A)[0]

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







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



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



						"""simple docstring"""






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



			def    _lowerCamelCase	(						self       :  Dict)       ->   List[str]:



						"""simple docstring"""






						_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(A)
						pipe.set_progress_bar_config(disable=A)

						_UpperCAmelCase			=       torch.Generator(device=A).manual_seed(0)

						_UpperCAmelCase			=       pipe(
						    A			,		generator=A			,		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(A			,		A)
