import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase__							= logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
lowerCAmelCase__							= []
for i in range(6):
    # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
    rename_keys.append(
        (F'transformer.encoder.layers.{i}.self_attn.out_proj.weight', F'encoder.layers.{i}.self_attn.out_proj.weight')
    )
    rename_keys.append(
        (F'transformer.encoder.layers.{i}.self_attn.out_proj.bias', F'encoder.layers.{i}.self_attn.out_proj.bias')
    )
    rename_keys.append((F'transformer.encoder.layers.{i}.linear1.weight', F'encoder.layers.{i}.fc1.weight'))
    rename_keys.append((F'transformer.encoder.layers.{i}.linear1.bias', F'encoder.layers.{i}.fc1.bias'))
    rename_keys.append((F'transformer.encoder.layers.{i}.linear2.weight', F'encoder.layers.{i}.fc2.weight'))
    rename_keys.append((F'transformer.encoder.layers.{i}.linear2.bias', F'encoder.layers.{i}.fc2.bias'))
    rename_keys.append(
        (F'transformer.encoder.layers.{i}.norm1.weight', F'encoder.layers.{i}.self_attn_layer_norm.weight')
    )
    rename_keys.append((F'transformer.encoder.layers.{i}.norm1.bias', F'encoder.layers.{i}.self_attn_layer_norm.bias'))
    rename_keys.append((F'transformer.encoder.layers.{i}.norm2.weight', F'encoder.layers.{i}.final_layer_norm.weight'))
    rename_keys.append((F'transformer.encoder.layers.{i}.norm2.bias', F'encoder.layers.{i}.final_layer_norm.bias'))
    # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
    rename_keys.append(
        (F'transformer.decoder.layers.{i}.self_attn.out_proj.weight', F'decoder.layers.{i}.self_attn.out_proj.weight')
    )
    rename_keys.append(
        (F'transformer.decoder.layers.{i}.self_attn.out_proj.bias', F'decoder.layers.{i}.self_attn.out_proj.bias')
    )
    rename_keys.append(
        (
            F'transformer.decoder.layers.{i}.multihead_attn.out_proj.weight',
            F'decoder.layers.{i}.encoder_attn.out_proj.weight',
        )
    )
    rename_keys.append(
        (
            F'transformer.decoder.layers.{i}.multihead_attn.out_proj.bias',
            F'decoder.layers.{i}.encoder_attn.out_proj.bias',
        )
    )
    rename_keys.append((F'transformer.decoder.layers.{i}.linear1.weight', F'decoder.layers.{i}.fc1.weight'))
    rename_keys.append((F'transformer.decoder.layers.{i}.linear1.bias', F'decoder.layers.{i}.fc1.bias'))
    rename_keys.append((F'transformer.decoder.layers.{i}.linear2.weight', F'decoder.layers.{i}.fc2.weight'))
    rename_keys.append((F'transformer.decoder.layers.{i}.linear2.bias', F'decoder.layers.{i}.fc2.bias'))
    rename_keys.append(
        (F'transformer.decoder.layers.{i}.norm1.weight', F'decoder.layers.{i}.self_attn_layer_norm.weight')
    )
    rename_keys.append((F'transformer.decoder.layers.{i}.norm1.bias', F'decoder.layers.{i}.self_attn_layer_norm.bias'))
    rename_keys.append(
        (F'transformer.decoder.layers.{i}.norm2.weight', F'decoder.layers.{i}.encoder_attn_layer_norm.weight')
    )
    rename_keys.append(
        (F'transformer.decoder.layers.{i}.norm2.bias', F'decoder.layers.{i}.encoder_attn_layer_norm.bias')
    )
    rename_keys.append((F'transformer.decoder.layers.{i}.norm3.weight', F'decoder.layers.{i}.final_layer_norm.weight'))
    rename_keys.append((F'transformer.decoder.layers.{i}.norm3.bias', F'decoder.layers.{i}.final_layer_norm.bias'))

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
rename_keys.extend(
    [
        ('input_proj.weight', 'input_projection.weight'),
        ('input_proj.bias', 'input_projection.bias'),
        ('query_embed.weight', 'query_position_embeddings.weight'),
        ('transformer.decoder.norm.weight', 'decoder.layernorm.weight'),
        ('transformer.decoder.norm.bias', 'decoder.layernorm.bias'),
        ('class_embed.weight', 'class_labels_classifier.weight'),
        ('class_embed.bias', 'class_labels_classifier.bias'),
        ('bbox_embed.layers.0.weight', 'bbox_predictor.layers.0.weight'),
        ('bbox_embed.layers.0.bias', 'bbox_predictor.layers.0.bias'),
        ('bbox_embed.layers.1.weight', 'bbox_predictor.layers.1.weight'),
        ('bbox_embed.layers.1.bias', 'bbox_predictor.layers.1.bias'),
        ('bbox_embed.layers.2.weight', 'bbox_predictor.layers.2.weight'),
        ('bbox_embed.layers.2.bias', 'bbox_predictor.layers.2.bias'),
    ]
)






def       __lowerCamelCase		(						lowerCAmelCase__	,  lowerCAmelCase__	,  lowerCAmelCase__	):
       lowerCAmelCase__     	=					state_dict.pop(lowerCAmelCase__	)
       lowerCAmelCase__     	=					val






def       __lowerCamelCase		(						lowerCAmelCase__	):
       lowerCAmelCase__     	=					OrderedDict()
       for key, value in state_dict.items():
              if "backbone.0.body" in key:
                     lowerCAmelCase__     	=					key.replace('backbone.0.body'	,  'backbone.conv_encoder.model'	)
                     lowerCAmelCase__     	=					value
              else:
                     lowerCAmelCase__     	=					value

       return new_state_dict






def       __lowerCamelCase		(						lowerCAmelCase__	,  lowerCAmelCase__=False	):
       lowerCAmelCase__     	=					''
       if is_panoptic:
              lowerCAmelCase__     	=					'detr.'

       # first: transformer encoder
       for i in range(6	):
              # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
              lowerCAmelCase__     	=					state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""	)
              lowerCAmelCase__     	=					state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias"""	)
              # next, add query, keys and values (in that order) to the state dict
              lowerCAmelCase__     	=					in_proj_weight[:2_5_6, :]
              lowerCAmelCase__     	=					in_proj_bias[:2_5_6]
              lowerCAmelCase__     	=					in_proj_weight[2_5_6:5_1_2, :]
              lowerCAmelCase__     	=					in_proj_bias[2_5_6:5_1_2]
              lowerCAmelCase__     	=					in_proj_weight[-2_5_6:, :]
              lowerCAmelCase__     	=					in_proj_bias[-2_5_6:]
       # next: transformer decoder (which is a bit more complex because it also includes cross-attention)
       for i in range(6	):
              # read in weights + bias of input projection layer of self-attention
              lowerCAmelCase__     	=					state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""	)
              lowerCAmelCase__     	=					state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias"""	)
              # next, add query, keys and values (in that order) to the state dict
              lowerCAmelCase__     	=					in_proj_weight[:2_5_6, :]
              lowerCAmelCase__     	=					in_proj_bias[:2_5_6]
              lowerCAmelCase__     	=					in_proj_weight[2_5_6:5_1_2, :]
              lowerCAmelCase__     	=					in_proj_bias[2_5_6:5_1_2]
              lowerCAmelCase__     	=					in_proj_weight[-2_5_6:, :]
              lowerCAmelCase__     	=					in_proj_bias[-2_5_6:]
              # read in weights + bias of input projection layer of cross-attention
              lowerCAmelCase__     	=					state_dict.pop(
                  F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""	)
              lowerCAmelCase__     	=					state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias"""	)
              # next, add query, keys and values (in that order) of cross-attention to the state dict
              lowerCAmelCase__     	=					in_proj_weight_cross_attn[:2_5_6, :]
              lowerCAmelCase__     	=					in_proj_bias_cross_attn[:2_5_6]
              lowerCAmelCase__     	=					in_proj_weight_cross_attn[2_5_6:5_1_2, :]
              lowerCAmelCase__     	=					in_proj_bias_cross_attn[2_5_6:5_1_2]
              lowerCAmelCase__     	=					in_proj_weight_cross_attn[-2_5_6:, :]
              lowerCAmelCase__     	=					in_proj_bias_cross_attn[-2_5_6:]






def       __lowerCamelCase		(						):
       lowerCAmelCase__     	=					'http://images.cocodataset.org/val2017/000000039769.jpg'
       lowerCAmelCase__     	=					Image.open(requests.get(lowerCAmelCase__	,  stream=lowerCAmelCase__	).raw	)

       return im






@torch.no_grad()
def       __lowerCamelCase		(						lowerCAmelCase__	,  lowerCAmelCase__	):
       lowerCAmelCase__     	=					DetrConfig()
       # set backbone and dilation attributes
       if "resnet101" in model_name:
              lowerCAmelCase__     	=					'resnet101'
       if "dc5" in model_name:
              lowerCAmelCase__     	=					True
       lowerCAmelCase__     	=					'panoptic' in model_name
       if is_panoptic:
              lowerCAmelCase__     	=					2_5_0
       else:
              lowerCAmelCase__     	=					9_1
              lowerCAmelCase__     	=					'huggingface/label-files'
              lowerCAmelCase__     	=					'coco-detection-id2label.json'
              lowerCAmelCase__     	=					json.load(open(hf_hub_download(lowerCAmelCase__	,  lowerCAmelCase__	,  repo_type='dataset'	)	,  'r'	)	)
              lowerCAmelCase__     	=					{int(lowerCAmelCase__	): v for k, v in idalabel.items()}
              lowerCAmelCase__     	=					idalabel
              lowerCAmelCase__     	=					{v: k for k, v in idalabel.items()}

       # load image processor
       lowerCAmelCase__     	=					'coco_panoptic' if is_panoptic else 'coco_detection'
       lowerCAmelCase__     	=					DetrImageProcessor(format=lowerCAmelCase__	)

       # prepare image
       lowerCAmelCase__     	=					prepare_img()
       lowerCAmelCase__     	=					image_processor(images=lowerCAmelCase__	,  return_tensors='pt'	)
       lowerCAmelCase__     	=					encoding['pixel_values']

       logger.info(F"""Converting model {model_name}..."""	)

       # load original model from torch hub
       lowerCAmelCase__     	=					torch.hub.load('facebookresearch/detr'	,  lowerCAmelCase__	,  pretrained=lowerCAmelCase__	).eval()
       lowerCAmelCase__     	=					detr.state_dict()
       # rename keys
       for src, dest in rename_keys:
              if is_panoptic:
                     lowerCAmelCase__     	=					'detr.' + src
              rename_key(lowerCAmelCase__	,  lowerCAmelCase__	,  lowerCAmelCase__	)
       lowerCAmelCase__     	=					rename_backbone_keys(lowerCAmelCase__	)
       # query, key and value matrices need special treatment
       read_in_q_k_v(lowerCAmelCase__	,  is_panoptic=lowerCAmelCase__	)
       # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
       lowerCAmelCase__     	=					'detr.model.' if is_panoptic else 'model.'
       for key in state_dict.copy().keys():
              if is_panoptic:
                     if (
                         key.startswith('detr'	)
                         and not key.startswith('class_labels_classifier'	)
                         and not key.startswith('bbox_predictor'	)
                     ):
                            lowerCAmelCase__     	=					state_dict.pop(lowerCAmelCase__	)
                            lowerCAmelCase__     	=					val
                     elif "class_labels_classifier" in key or "bbox_predictor" in key:
                            lowerCAmelCase__     	=					state_dict.pop(lowerCAmelCase__	)
                            lowerCAmelCase__     	=					val
                     elif key.startswith('bbox_attention'	) or key.startswith('mask_head'	):
                            continue
                     else:
                            lowerCAmelCase__     	=					state_dict.pop(lowerCAmelCase__	)
                            lowerCAmelCase__     	=					val
              else:
                     if not key.startswith('class_labels_classifier'	) and not key.startswith('bbox_predictor'	):
                            lowerCAmelCase__     	=					state_dict.pop(lowerCAmelCase__	)
                            lowerCAmelCase__     	=					val
    # finally, create HuggingFace model and load state dict
       lowerCAmelCase__     	=					DetrForSegmentation(lowerCAmelCase__	) if is_panoptic else DetrForObjectDetection(lowerCAmelCase__	)
       model.load_state_dict(lowerCAmelCase__	)
       model.eval()
       # verify our conversion
       lowerCAmelCase__     	=					detr(lowerCAmelCase__	)
       lowerCAmelCase__     	=					model(lowerCAmelCase__	)
       assert torch.allclose(outputs.logits	,  original_outputs['pred_logits']	,  atol=1e-4	)
       assert torch.allclose(outputs.pred_boxes	,  original_outputs['pred_boxes']	,  atol=1e-4	)
       if is_panoptic:
              assert torch.allclose(outputs.pred_masks	,  original_outputs['pred_masks']	,  atol=1e-4	)

       # Save model and image processor
       logger.info(F"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""	)
       Path(lowerCAmelCase__	).mkdir(exist_ok=lowerCAmelCase__	)
       model.save_pretrained(lowerCAmelCase__	)
       image_processor.save_pretrained(lowerCAmelCase__	)


if __name__ == "__main__":
    lowerCAmelCase__							= argparse.ArgumentParser()

    parser.add_argument(
        '--model_name', default='detr_resnet50', type=str, help='Name of the DETR model you\'d like to convert.'
    )
    parser.add_argument(
        '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
    )
    lowerCAmelCase__							= parser.parse_args()
    convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModelWithProjection

from diffusers import (
    AutoencoderKL,
    DPMSolverMultistepScheduler,
    PNDMScheduler,
    StableDiffusionImageVariationPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
class 				a_ (						SCREAMING_SNAKE_CASE       ,     SCREAMING_SNAKE_CASE       ,     SCREAMING_SNAKE_CASE       ,     unittest.TestCase						):






 '''simple docstring'''






 UpperCAmelCase_	 =					StableDiffusionImageVariationPipeline
 UpperCAmelCase_	 =					IMAGE_VARIATION_PARAMS
 UpperCAmelCase_	 =					IMAGE_VARIATION_BATCH_PARAMS
 UpperCAmelCase_	 =					frozenset([]						)
 # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
 UpperCAmelCase_	 =					frozenset([]						)




 def 		__snake_case						(			self					:  Optional[Any]):







        '''simple docstring'''



        torch.manual_seed(0)
        lowerCAmelCase__     	=					UNetaDConditionModel(
            block_out_channels=(32, 64)			,    layers_per_block=2			,    sample_size=32			,    in_channels=4			,    out_channels=4			,    down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')			,    up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')			,    cross_attention_dim=32			,    )
        lowerCAmelCase__     	=					PNDMScheduler(skip_prk_steps=lowercase__)
        torch.manual_seed(0)
        lowerCAmelCase__     	=					AutoencoderKL(
            block_out_channels=[32, 64]			,    in_channels=3			,    out_channels=3			,    down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']			,    up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']			,    latent_channels=4			,    )
        torch.manual_seed(0)
        lowerCAmelCase__     	=					CLIPVisionConfig(
            hidden_size=32			,    projection_dim=32			,    intermediate_size=37			,    layer_norm_eps=1e-05			,    num_attention_heads=4			,    num_hidden_layers=5			,    image_size=32			,    patch_size=4			,    )
        lowerCAmelCase__     	=					CLIPVisionModelWithProjection(lowercase__)
        lowerCAmelCase__     	=					CLIPImageProcessor(crop_size=32			,    size=32)

        lowerCAmelCase__     	=					{
            'unet': unet,
            'scheduler': scheduler,
            'vae': vae,
            'image_encoder': image_encoder,
            'feature_extractor': feature_extractor,
            'safety_checker': None,
        }
        return components




 def 		__snake_case						(			self					:  Optional[int]			,    lowercase__					:  Dict			,    lowercase__					:  Tuple=0):







        '''simple docstring'''



        lowerCAmelCase__     	=					floats_tensor((1, 3, 32, 32)			,    rng=random.Random(lowercase__))
        lowerCAmelCase__     	=					image.cpu().permute(0			,    2			,    3			,    1)[0]
        lowerCAmelCase__     	=					Image.fromarray(np.uinta(lowercase__)).convert('RGB').resize((32, 32))
        if str(lowercase__).startswith('mps'):
               lowerCAmelCase__     	=					torch.manual_seed(lowercase__)
        else:
               lowerCAmelCase__     	=					torch.Generator(device=lowercase__).manual_seed(lowercase__)
        lowerCAmelCase__     	=					{
            'image': image,
            'generator': generator,
            'num_inference_steps': 2,
            'guidance_scale': 6.0,
            'output_type': 'numpy',
        }
        return inputs




 def 		__snake_case						(			self					:  Union[str, Any]):







        '''simple docstring'''



        lowerCAmelCase__     	=					'cpu'  # ensure determinism for the device-dependent torch.Generator
        lowerCAmelCase__     	=					self.get_dummy_components()
        lowerCAmelCase__     	=					StableDiffusionImageVariationPipeline(**lowercase__)
        lowerCAmelCase__     	=					sd_pipe.to(lowercase__)
        sd_pipe.set_progress_bar_config(disable=lowercase__)

        lowerCAmelCase__     	=					self.get_dummy_inputs(lowercase__)
        lowerCAmelCase__     	=					sd_pipe(**lowercase__).images
        lowerCAmelCase__     	=					image[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)
        lowerCAmelCase__     	=					np.array([0.5_239, 0.5_723, 0.4_796, 0.5_049, 0.5_550, 0.4_685, 0.5_329, 0.4_891, 0.4_921])

        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3




 def 		__snake_case						(			self					:  Any):







        '''simple docstring'''



        lowerCAmelCase__     	=					'cpu'  # ensure determinism for the device-dependent torch.Generator
        lowerCAmelCase__     	=					self.get_dummy_components()
        lowerCAmelCase__     	=					StableDiffusionImageVariationPipeline(**lowercase__)
        lowerCAmelCase__     	=					sd_pipe.to(lowercase__)
        sd_pipe.set_progress_bar_config(disable=lowercase__)

        lowerCAmelCase__     	=					self.get_dummy_inputs(lowercase__)
        lowerCAmelCase__     	=					2 * [inputs['image']]
        lowerCAmelCase__     	=					sd_pipe(**lowercase__)

        lowerCAmelCase__     	=					output.images

        lowerCAmelCase__     	=					image[-1, -3:, -3:, -1]

        assert image.shape == (2, 64, 64, 3)
        lowerCAmelCase__     	=					np.array([0.6_892, 0.5_637, 0.5_836, 0.5_771, 0.6_254, 0.6_409, 0.5_580, 0.5_569, 0.5_289])

        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3




 def 		__snake_case						(			self					:  Optional[int]):







        '''simple docstring'''



        super().test_inference_batch_single_identical(expected_max_diff=3e-3)






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






 '''simple docstring'''






 def 		__snake_case						(			self					:  List[Any]):







        '''simple docstring'''



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




 def 		__snake_case						(			self					:  List[Any]			,    lowercase__					:  List[str]			,    lowercase__					:  Optional[int]="cpu"			,    lowercase__					:  List[str]=torch.floataa			,    lowercase__					:  Dict=0):







        '''simple docstring'''



        lowerCAmelCase__     	=					torch.Generator(device=lowercase__).manual_seed(lowercase__)
        lowerCAmelCase__     	=					load_image(
            'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
            '/stable_diffusion_imgvar/input_image_vermeer.png')
        lowerCAmelCase__     	=					np.random.RandomState(lowercase__).standard_normal((1, 4, 64, 64))
        lowerCAmelCase__     	=					torch.from_numpy(lowercase__).to(device=lowercase__			,    dtype=lowercase__)
        lowerCAmelCase__     	=					{
            'image': init_image,
            'latents': latents,
            'generator': generator,
            'num_inference_steps': 3,
            'guidance_scale': 7.5,
            'output_type': 'numpy',
        }
        return inputs




 def 		__snake_case						(			self					:  List[str]):







        '''simple docstring'''



        lowerCAmelCase__     	=					StableDiffusionImageVariationPipeline.from_pretrained(
            'lambdalabs/sd-image-variations-diffusers'			,    safety_checker=lowercase__)
        lowerCAmelCase__     	=					sd_pipe.to(lowercase__)
        sd_pipe.set_progress_bar_config(disable=lowercase__)

        lowerCAmelCase__     	=					self.get_inputs(lowercase__)
        lowerCAmelCase__     	=					sd_pipe(**lowercase__).images
        lowerCAmelCase__     	=					image[0, -3:, -3:, -1].flatten()

        assert image.shape == (1, 512, 512, 3)
        lowerCAmelCase__     	=					np.array([0.84_491, 0.90_789, 0.75_708, 0.78_734, 0.83_485, 0.70_099, 0.66_938, 0.68_727, 0.61_379])
        assert np.abs(image_slice - expected_slice).max() < 6e-3




 def 		__snake_case						(			self					:  Optional[Any]):







        '''simple docstring'''



        lowerCAmelCase__     	=					0

        def callback_fn(lowercase__					:  int			,    lowercase__					:  int			,    lowercase__					:  torch.FloatTensor) -> None:
               lowerCAmelCase__     	=					True
               nonlocal number_of_steps
               number_of_steps += 1
               if step == 1:
                      lowerCAmelCase__     	=					latents.detach().cpu().numpy()
                      assert latents.shape == (1, 4, 64, 64)
                      lowerCAmelCase__     	=					latents[0, -3:, -3:, -1]
                      lowerCAmelCase__     	=					np.array(
                          [-0.1_621, 0.2_837, -0.7_979, -0.1_221, -1.3_057, 0.7_681, -2.1_191, 0.0_464, 1.6_309])

                      assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2
               elif step == 2:
                      lowerCAmelCase__     	=					latents.detach().cpu().numpy()
                      assert latents.shape == (1, 4, 64, 64)
                      lowerCAmelCase__     	=					latents[0, -3:, -3:, -1]
                      lowerCAmelCase__     	=					np.array([0.6_299, 1.7_500, 1.1_992, -2.1_582, -1.8_994, 0.7_334, -0.7_090, 1.0_137, 1.5_273])

                      assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2

        lowerCAmelCase__     	=					False

        lowerCAmelCase__     	=					StableDiffusionImageVariationPipeline.from_pretrained(
            'fusing/sd-image-variations-diffusers'			,    safety_checker=lowercase__			,    torch_dtype=torch.floataa			,    )
        pipe.to(lowercase__)
        pipe.set_progress_bar_config(disable=lowercase__)
        pipe.enable_attention_slicing()

        lowerCAmelCase__     	=					self.get_inputs(lowercase__			,    dtype=torch.floataa)
        pipe(**lowercase__			,    callback=lowercase__			,    callback_steps=1)
        assert callback_fn.has_been_called
        assert number_of_steps == inputs["num_inference_steps"]




 def 		__snake_case						(			self					:  str):







        '''simple docstring'''



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

        lowerCAmelCase__     	=					'fusing/sd-image-variations-diffusers'
        lowerCAmelCase__     	=					StableDiffusionImageVariationPipeline.from_pretrained(
            lowercase__			,    safety_checker=lowercase__			,    torch_dtype=torch.floataa)
        lowerCAmelCase__     	=					pipe.to(lowercase__)
        pipe.set_progress_bar_config(disable=lowercase__)
        pipe.enable_attention_slicing(1)
        pipe.enable_sequential_cpu_offload()

        lowerCAmelCase__     	=					self.get_inputs(lowercase__			,    dtype=torch.floataa)
        lowerCAmelCase__     	=					pipe(**lowercase__)

        lowerCAmelCase__     	=					torch.cuda.max_memory_allocated()
        # make sure that less than 2.6 GB is allocated
        assert mem_bytes < 2.6 * 10**9






@nightly
@require_torch_gpu
class 				a_ (						unittest.TestCase						):






 '''simple docstring'''






 def 		__snake_case						(			self					:  str):







        '''simple docstring'''



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




 def 		__snake_case						(			self					:  int			,    lowercase__					:  List[str]			,    lowercase__					:  Dict="cpu"			,    lowercase__					:  Optional[int]=torch.floataa			,    lowercase__					:  Optional[Any]=0):







        '''simple docstring'''



        lowerCAmelCase__     	=					torch.Generator(device=lowercase__).manual_seed(lowercase__)
        lowerCAmelCase__     	=					load_image(
            'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
            '/stable_diffusion_imgvar/input_image_vermeer.png')
        lowerCAmelCase__     	=					np.random.RandomState(lowercase__).standard_normal((1, 4, 64, 64))
        lowerCAmelCase__     	=					torch.from_numpy(lowercase__).to(device=lowercase__			,    dtype=lowercase__)
        lowerCAmelCase__     	=					{
            'image': init_image,
            'latents': latents,
            'generator': generator,
            'num_inference_steps': 50,
            'guidance_scale': 7.5,
            'output_type': 'numpy',
        }
        return inputs




 def 		__snake_case						(			self					:  Optional[Any]):







        '''simple docstring'''



        lowerCAmelCase__     	=					StableDiffusionImageVariationPipeline.from_pretrained('fusing/sd-image-variations-diffusers')
        sd_pipe.to(lowercase__)
        sd_pipe.set_progress_bar_config(disable=lowercase__)

        lowerCAmelCase__     	=					self.get_inputs(lowercase__)
        lowerCAmelCase__     	=					sd_pipe(**lowercase__).images[0]

        lowerCAmelCase__     	=					load_numpy(
            'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
            '/stable_diffusion_imgvar/lambdalabs_variations_pndm.npy')
        lowerCAmelCase__     	=					np.abs(expected_image - image).max()
        assert max_diff < 1e-3




 def 		__snake_case						(			self					:  Tuple):







        '''simple docstring'''



        lowerCAmelCase__     	=					StableDiffusionImageVariationPipeline.from_pretrained('fusing/sd-image-variations-diffusers')
        lowerCAmelCase__     	=					DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config)
        sd_pipe.to(lowercase__)
        sd_pipe.set_progress_bar_config(disable=lowercase__)

        lowerCAmelCase__     	=					self.get_inputs(lowercase__)
        lowerCAmelCase__     	=					25
        lowerCAmelCase__     	=					sd_pipe(**lowercase__).images[0]

        lowerCAmelCase__     	=					load_numpy(
            'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
            '/stable_diffusion_imgvar/lambdalabs_variations_dpm_multi.npy')
        lowerCAmelCase__     	=					np.abs(expected_image - image).max()
        assert max_diff < 1e-3
