






'''simple docstring'''




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 (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCamelCase   :					Union[str, Any]   							=					logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__lowerCamelCase   :					Optional[Any]   							=					[]
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}.cross_attn.out_proj.weight''',
		        f'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
		    )
		)
		rename_keys.append(
		    (
		        f'''transformer.decoder.layers.{i}.cross_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'''))

		# q, k, v projections in self/cross-attention in decoder for conditional DETR
		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.sa_qcontent_proj.weight''', f'''decoder.layers.{i}.sa_qcontent_proj.weight''')
		)
		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.sa_kcontent_proj.weight''', f'''decoder.layers.{i}.sa_kcontent_proj.weight''')
		)
		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.sa_qpos_proj.weight''', f'''decoder.layers.{i}.sa_qpos_proj.weight''')
		)
		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.sa_kpos_proj.weight''', f'''decoder.layers.{i}.sa_kpos_proj.weight''')
		)
		rename_keys.append((f'''transformer.decoder.layers.{i}.sa_v_proj.weight''', f'''decoder.layers.{i}.sa_v_proj.weight'''))
		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.ca_qcontent_proj.weight''', f'''decoder.layers.{i}.ca_qcontent_proj.weight''')
		)
		# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.ca_kcontent_proj.weight''', f'''decoder.layers.{i}.ca_kcontent_proj.weight''')
		)
		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.ca_kpos_proj.weight''', f'''decoder.layers.{i}.ca_kpos_proj.weight''')
		)
		rename_keys.append((f'''transformer.decoder.layers.{i}.ca_v_proj.weight''', f'''decoder.layers.{i}.ca_v_proj.weight'''))
		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight''', f'''decoder.layers.{i}.ca_qpos_sine_proj.weight''')
		)

		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.sa_qcontent_proj.bias''', f'''decoder.layers.{i}.sa_qcontent_proj.bias''')
		)
		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.sa_kcontent_proj.bias''', f'''decoder.layers.{i}.sa_kcontent_proj.bias''')
		)
		rename_keys.append((f'''transformer.decoder.layers.{i}.sa_qpos_proj.bias''', f'''decoder.layers.{i}.sa_qpos_proj.bias'''))
		rename_keys.append((f'''transformer.decoder.layers.{i}.sa_kpos_proj.bias''', f'''decoder.layers.{i}.sa_kpos_proj.bias'''))
		rename_keys.append((f'''transformer.decoder.layers.{i}.sa_v_proj.bias''', f'''decoder.layers.{i}.sa_v_proj.bias'''))
		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.ca_qcontent_proj.bias''', f'''decoder.layers.{i}.ca_qcontent_proj.bias''')
		)
		# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.ca_kcontent_proj.bias''', f'''decoder.layers.{i}.ca_kcontent_proj.bias''')
		)
		rename_keys.append((f'''transformer.decoder.layers.{i}.ca_kpos_proj.bias''', f'''decoder.layers.{i}.ca_kpos_proj.bias'''))
		rename_keys.append((f'''transformer.decoder.layers.{i}.ca_v_proj.bias''', f'''decoder.layers.{i}.ca_v_proj.bias'''))
		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias''', f'''decoder.layers.{i}.ca_qpos_sine_proj.bias''')
		)

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
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'''),
        ('''transformer.decoder.ref_point_head.layers.0.weight''', '''decoder.ref_point_head.layers.0.weight'''),
        ('''transformer.decoder.ref_point_head.layers.0.bias''', '''decoder.ref_point_head.layers.0.bias'''),
        ('''transformer.decoder.ref_point_head.layers.1.weight''', '''decoder.ref_point_head.layers.1.weight'''),
        ('''transformer.decoder.ref_point_head.layers.1.bias''', '''decoder.ref_point_head.layers.1.bias'''),
        ('''transformer.decoder.query_scale.layers.0.weight''', '''decoder.query_scale.layers.0.weight'''),
        ('''transformer.decoder.query_scale.layers.0.bias''', '''decoder.query_scale.layers.0.bias'''),
        ('''transformer.decoder.query_scale.layers.1.weight''', '''decoder.query_scale.layers.1.weight'''),
        ('''transformer.decoder.query_scale.layers.1.bias''', '''decoder.query_scale.layers.1.bias'''),
        ('''transformer.decoder.layers.0.ca_qpos_proj.weight''', '''decoder.layers.0.ca_qpos_proj.weight'''),
        ('''transformer.decoder.layers.0.ca_qpos_proj.bias''', '''decoder.layers.0.ca_qpos_proj.bias'''),
    ]
)

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__     ,__magic_name__						)->    int:



			"""simple docstring"""



			snake_case_				:					Optional[Any]   =       state_dict.pop(__magic_name__						)
			snake_case_				:					Any   =       val

def 	__UpperCAmelCase       ( __magic_name__						)->    Optional[Any]:



			"""simple docstring"""



			snake_case_				:					Any   =       OrderedDict()
			for key, value in state_dict.items():
						if "backbone.0.body" in key:
									snake_case_				:					Optional[Any]   =       key.replace("backbone.0.body"     ,"backbone.conv_encoder.model"						)
									snake_case_				:					int   =       value
						else:
									snake_case_				:					int   =       value

			return new_state_dict

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__=False						)->    Optional[int]:



			"""simple docstring"""



			snake_case_				:					str   =       ""
			if is_panoptic:
						snake_case_				:					Dict   =       "conditional_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)
						snake_case_				:					Any   =       state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''						)
						snake_case_				:					Optional[int]   =       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
						snake_case_				:					Tuple   =       in_proj_weight[:256, :]
						snake_case_				:					List[Any]   =       in_proj_bias[:256]
						snake_case_				:					Optional[Any]   =       in_proj_weight[256:512, :]
						snake_case_				:					Optional[int]   =       in_proj_bias[256:512]
						snake_case_				:					Optional[int]   =       in_proj_weight[-256:, :]
						snake_case_				:					str   =       in_proj_bias[-256:]

def 	__UpperCAmelCase       ( )->    Optional[Any]:



			"""simple docstring"""



			snake_case_				:					Optional[int]   =       "http://images.cocodataset.org/val2017/000000039769.jpg"
			snake_case_				:					Optional[Any]   =       Image.open(requests.get(__magic_name__     ,stream=__magic_name__						).raw						)

			return im

@torch.no_grad()
def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__						)->    List[str]:



			"""simple docstring"""



			snake_case_				:					Optional[Any]   =       ConditionalDetrConfig()
			# set backbone and dilation attributes
			if "resnet101" in model_name:
						snake_case_				:					Optional[Any]   =       "resnet101"
			if "dc5" in model_name:
						snake_case_				:					List[str]   =       True
			snake_case_				:					Tuple   =       "panoptic" in model_name
			if is_panoptic:
						snake_case_				:					List[Any]   =       250
			else:
						snake_case_				:					Optional[Any]   =       91
						snake_case_				:					Optional[int]   =       "huggingface/label-files"
						snake_case_				:					Dict   =       "coco-detection-id2label.json"
						snake_case_				:					List[Any]   =       json.load(open(hf_hub_download(__magic_name__     ,__magic_name__     ,repo_type="dataset"						)     ,"r"						)						)
						snake_case_				:					Optional[int]   =       {int(__magic_name__						): v for k, v in idalabel.items()}
						snake_case_				:					int   =       idalabel
						snake_case_				:					Dict   =       {v: k for k, v in idalabel.items()}

			# load image processor
			snake_case_				:					Optional[int]   =       "coco_panoptic" if is_panoptic else "coco_detection"
			snake_case_				:					str   =       ConditionalDetrImageProcessor(format=__magic_name__						)

			# prepare image
			snake_case_				:					str   =       prepare_img()
			snake_case_				:					int   =       image_processor(images=__magic_name__     ,return_tensors="pt"						)
			snake_case_				:					Union[str, Any]   =       encoding["pixel_values"]

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

			# load original model from torch hub
			snake_case_				:					Union[str, Any]   =       torch.hub.load("DeppMeng/ConditionalDETR"     ,__magic_name__     ,pretrained=__magic_name__						).eval()
			snake_case_				:					Any   =       conditional_detr.state_dict()
			# rename keys
			for src, dest in rename_keys:
						if is_panoptic:
									snake_case_				:					Any   =       "conditional_detr." + src
						rename_key(__magic_name__     ,__magic_name__     ,__magic_name__						)
			snake_case_				:					Tuple   =       rename_backbone_keys(__magic_name__						)
			# query, key and value matrices need special treatment
			read_in_q_k_v(__magic_name__     ,is_panoptic=__magic_name__						)
			# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
			snake_case_				:					int   =       "conditional_detr.model." if is_panoptic else "model."
			for key in state_dict.copy().keys():
						if is_panoptic:
									if (
									    key.startswith("conditional_detr"						)
									    and not key.startswith("class_labels_classifier"						)
									    and not key.startswith("bbox_predictor"						)
									):
												snake_case_				:					Any   =       state_dict.pop(__magic_name__						)
												snake_case_				:					Optional[int]   =       val
									elif "class_labels_classifier" in key or "bbox_predictor" in key:
												snake_case_				:					Tuple   =       state_dict.pop(__magic_name__						)
												snake_case_				:					Any   =       val
									elif key.startswith("bbox_attention"						) or key.startswith("mask_head"						):
												continue
									else:
												snake_case_				:					Union[str, Any]   =       state_dict.pop(__magic_name__						)
												snake_case_				:					List[Any]   =       val
						else:
									if not key.startswith("class_labels_classifier"						) and not key.startswith("bbox_predictor"						):
												snake_case_				:					Any   =       state_dict.pop(__magic_name__						)
												snake_case_				:					List[Any]   =       val
    # finally, create HuggingFace model and load state dict
			snake_case_				:					Optional[int]   =       ConditionalDetrForSegmentation(__magic_name__						) if is_panoptic else ConditionalDetrForObjectDetection(__magic_name__						)
			model.load_state_dict(__magic_name__						)
			model.eval()
			model.push_to_hub(repo_id=__magic_name__     ,organization="DepuMeng"     ,commit_message="Add model"						)
			# verify our conversion
			snake_case_				:					Dict   =       conditional_detr(__magic_name__						)
			snake_case_				:					Union[str, Any]   =       model(__magic_name__						)
			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(__magic_name__						).mkdir(exist_ok=__magic_name__						)
			model.save_pretrained(__magic_name__						)
			image_processor.save_pretrained(__magic_name__						)


if __name__ == "__main__":
		__lowerCamelCase   :					Tuple   							=					argparse.ArgumentParser()

		parser.add_argument(
		    '''--model_name''',
		    default='''conditional_detr_resnet50''',
		    type=str,
		    help='''Name of the CONDITIONAL_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   :					int   							=					parser.parse_args()
		convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)












'''simple docstring'''




import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizer

from diffusers import (
    AltDiffusionImgaImgPipeline,
    AutoencoderKL,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()







class    A_	(unittest.TestCase							):
							"""simple docstring"""




							def 							_A	(		self			:Any				)				->  str:


										'''simple docstring'''




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

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


										'''simple docstring'''




										snake_case_				:					Any   =       1
										snake_case_				:					Dict   =       3
										snake_case_				:					Union[str, Any]   =       (32, 32)

										snake_case_				:					Optional[int]   =       floats_tensor((batch_size, num_channels) + sizes       ,							rng=random.Random(0				)				).to(lowerCAmelCase__				)
										return image

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


										'''simple docstring'''




										torch.manual_seed(0				)
										snake_case_				:					List[str]   =       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       ,							)
										return model

							@property
							def 							_A	(		self			:Dict				)				->  Any:


										'''simple docstring'''




										torch.manual_seed(0				)
										snake_case_				:					Optional[Any]   =       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       ,							)
										return model

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


										'''simple docstring'''




										torch.manual_seed(0				)
										snake_case_				:					str   =       RobertaSeriesConfig(
										    hidden_size=32       ,							project_dim=32       ,							intermediate_size=37       ,							layer_norm_eps=1E-0_5       ,							num_attention_heads=4       ,							num_hidden_layers=5       ,							pad_token_id=1       ,							vocab_size=5_006       ,							)
										return RobertaSeriesModelWithTransformation(lowerCAmelCase__				)

							@property
							def 							_A	(		self			:Any				)				->  str:


										'''simple docstring'''




										def extract(*lowerCAmelCase__			:Any       ,							**lowerCAmelCase__			:List[str]				):







													class    A_	:
																				"""simple docstring"""




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


																							'''simple docstring'''




																							snake_case_				:					str   =       torch.ones([0]				)






																				def 							_A	(		self			:int       ,							lowerCAmelCase__			:List[Any]				)				->  Tuple:


																							'''simple docstring'''




																							self.pixel_values.to(lowerCAmelCase__				)
																							return self
													return Out()

										return extract

							def 							_A	(		self			:int				)				->  Dict:


										'''simple docstring'''




										snake_case_				:					str   =       "cpu"  # ensure determinism for the device-dependent torch.Generator
										snake_case_				:					str   =       self.dummy_cond_unet
										snake_case_				:					Optional[int]   =       PNDMScheduler(skip_prk_steps=lowerCAmelCase__				)
										snake_case_				:					Dict   =       self.dummy_vae
										snake_case_				:					Dict   =       self.dummy_text_encoder
										snake_case_				:					Optional[int]   =       XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta"				)
										snake_case_				:					str   =       77

										snake_case_				:					Any   =       self.dummy_image.to(lowerCAmelCase__				)
										snake_case_				:					Tuple   =       init_image / 2 + 0.5

										# make sure here that pndm scheduler skips prk
										snake_case_				:					Optional[Any]   =       AltDiffusionImgaImgPipeline(
										    unet=lowerCAmelCase__       ,							scheduler=lowerCAmelCase__       ,							vae=lowerCAmelCase__       ,							text_encoder=lowerCAmelCase__       ,							tokenizer=lowerCAmelCase__       ,							safety_checker=lowerCAmelCase__       ,							feature_extractor=self.dummy_extractor       ,							)
										snake_case_				:					Union[str, Any]   =       VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor       ,							do_normalize=lowerCAmelCase__				)
										snake_case_				:					Optional[Any]   =       alt_pipe.to(lowerCAmelCase__				)
										alt_pipe.set_progress_bar_config(disable=lowerCAmelCase__				)

										snake_case_				:					Dict   =       "A painting of a squirrel eating a burger"
										snake_case_				:					List[str]   =       torch.Generator(device=lowerCAmelCase__				).manual_seed(0				)
										snake_case_				:					Dict   =       alt_pipe(
										    [prompt]       ,							generator=lowerCAmelCase__       ,							guidance_scale=6.0       ,							num_inference_steps=2       ,							output_type="np"       ,							image=lowerCAmelCase__       ,							)

										snake_case_				:					Any   =       output.images

										snake_case_				:					List[str]   =       torch.Generator(device=lowerCAmelCase__				).manual_seed(0				)
										snake_case_				:					Optional[Any]   =       alt_pipe(
										    [prompt]       ,							generator=lowerCAmelCase__       ,							guidance_scale=6.0       ,							num_inference_steps=2       ,							output_type="np"       ,							image=lowerCAmelCase__       ,							return_dict=lowerCAmelCase__       ,							)[0]

										snake_case_				:					Tuple   =       image[0, -3:, -3:, -1]
										snake_case_				:					Dict   =       image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 32, 32, 3)
										snake_case_				:					int   =       np.array([0.4_4_2_7, 0.3_7_3_1, 0.4_2_4_9, 0.4_9_4_1, 0.4_5_4_6, 0.4_1_4_8, 0.4_1_9_3, 0.4_6_6_6, 0.4_4_9_9]				)

										assert np.abs(image_slice.flatten() - expected_slice				).max() < 5E-3
										assert np.abs(image_from_tuple_slice.flatten() - expected_slice				).max() < 5E-3

							@unittest.skipIf(torch_device != "cuda"       ,							"This test requires a GPU"				)
							def 							_A	(		self			:int				)				->  List[str]:


										'''simple docstring'''




										snake_case_				:					Union[str, Any]   =       self.dummy_cond_unet
										snake_case_				:					Union[str, Any]   =       PNDMScheduler(skip_prk_steps=lowerCAmelCase__				)
										snake_case_				:					int   =       self.dummy_vae
										snake_case_				:					List[Any]   =       self.dummy_text_encoder
										snake_case_				:					int   =       XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta"				)
										snake_case_				:					int   =       77

										snake_case_				:					Dict   =       self.dummy_image.to(lowerCAmelCase__				)

										# put models in fp16
										snake_case_				:					Optional[Any]   =       unet.half()
										snake_case_				:					Tuple   =       vae.half()
										snake_case_				:					List[str]   =       bert.half()

										# make sure here that pndm scheduler skips prk
										snake_case_				:					Optional[int]   =       AltDiffusionImgaImgPipeline(
										    unet=lowerCAmelCase__       ,							scheduler=lowerCAmelCase__       ,							vae=lowerCAmelCase__       ,							text_encoder=lowerCAmelCase__       ,							tokenizer=lowerCAmelCase__       ,							safety_checker=lowerCAmelCase__       ,							feature_extractor=self.dummy_extractor       ,							)
										snake_case_				:					List[str]   =       VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor       ,							do_normalize=lowerCAmelCase__				)
										snake_case_				:					Optional[Any]   =       alt_pipe.to(lowerCAmelCase__				)
										alt_pipe.set_progress_bar_config(disable=lowerCAmelCase__				)

										snake_case_				:					List[Any]   =       "A painting of a squirrel eating a burger"
										snake_case_				:					str   =       torch.manual_seed(0				)
										snake_case_				:					Any   =       alt_pipe(
										    [prompt]       ,							generator=lowerCAmelCase__       ,							num_inference_steps=2       ,							output_type="np"       ,							image=lowerCAmelCase__       ,							).images

										assert image.shape == (1, 32, 32, 3)






							@unittest.skipIf(torch_device != "cuda"       ,							"This test requires a GPU"				)
							def 							_A	(		self			:Optional[int]				)				->  Any:


										'''simple docstring'''




										snake_case_				:					Union[str, Any]   =       load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
										    "/img2img/sketch-mountains-input.jpg"				)
										# resize to resolution that is divisible by 8 but not 16 or 32
										snake_case_				:					str   =       init_image.resize((760, 504)				)

										snake_case_				:					Optional[Any]   =       "BAAI/AltDiffusion"
										snake_case_				:					int   =       AltDiffusionImgaImgPipeline.from_pretrained(
										    lowerCAmelCase__       ,							safety_checker=lowerCAmelCase__       ,							)
										pipe.to(lowerCAmelCase__				)
										pipe.set_progress_bar_config(disable=lowerCAmelCase__				)
										pipe.enable_attention_slicing()

										snake_case_				:					Tuple   =       "A fantasy landscape, trending on artstation"

										snake_case_				:					int   =       torch.manual_seed(0				)
										snake_case_				:					List[str]   =       pipe(
										    prompt=lowerCAmelCase__       ,							image=lowerCAmelCase__       ,							strength=0.7_5       ,							guidance_scale=7.5       ,							generator=lowerCAmelCase__       ,							output_type="np"       ,							)
										snake_case_				:					str   =       output.images[0]

										snake_case_				:					List[Any]   =       image[255:258, 383:386, -1]

										assert image.shape == (504, 760, 3)
										snake_case_				:					Tuple   =       np.array([0.9_3_5_8, 0.9_3_9_7, 0.9_5_9_9, 0.9_9_0_1, 1.0_0_0_0, 1.0_0_0_0, 0.9_8_8_2, 1.0_0_0_0, 1.0_0_0_0]				)

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







@slow
@require_torch_gpu
class    A_	(unittest.TestCase							):
							"""simple docstring"""




							def 							_A	(		self			:Optional[Any]				)				->  Optional[int]:


										'''simple docstring'''




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






							def 							_A	(		self			:str				)				->  Any:


										'''simple docstring'''




										snake_case_				:					Optional[Any]   =       load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
										    "/img2img/sketch-mountains-input.jpg"				)
										snake_case_				:					List[Any]   =       init_image.resize((768, 512)				)
										snake_case_				:					Tuple   =       load_numpy(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy"				)

										snake_case_				:					Any   =       "BAAI/AltDiffusion"
										snake_case_				:					List[str]   =       AltDiffusionImgaImgPipeline.from_pretrained(
										    lowerCAmelCase__       ,							safety_checker=lowerCAmelCase__       ,							)
										pipe.to(lowerCAmelCase__				)
										pipe.set_progress_bar_config(disable=lowerCAmelCase__				)
										pipe.enable_attention_slicing()

										snake_case_				:					Tuple   =       "A fantasy landscape, trending on artstation"

										snake_case_				:					Tuple   =       torch.manual_seed(0				)
										snake_case_				:					List[Any]   =       pipe(
										    prompt=lowerCAmelCase__       ,							image=lowerCAmelCase__       ,							strength=0.7_5       ,							guidance_scale=7.5       ,							generator=lowerCAmelCase__       ,							output_type="np"       ,							)
										snake_case_				:					Optional[int]   =       output.images[0]

										assert image.shape == (512, 768, 3)
										# img2img is flaky across GPUs even in fp32, so using MAE here
										assert np.abs(expected_image - image				).max() < 1E-2




