




"""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				:     Dict					=				[]
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        a__					(							snake_case__			,					snake_case__			,					snake_case__							)		->   Optional[Any]:
			lowerCamelCase      =   state_dict.pop(__UpperCamelCase							)
			lowerCamelCase      =   val



def        a__					(							snake_case__							)		->   int:
			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        a__					(							snake_case__			,					snake_case__=False							)		->   Optional[Any]:
			lowerCamelCase      =   """"""
			if is_panoptic:
						lowerCamelCase      =   """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)
						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_56, :]
						lowerCamelCase      =   in_proj_bias[:2_56]
						lowerCamelCase      =   in_proj_weight[2_56:5_12, :]
						lowerCamelCase      =   in_proj_bias[2_56:5_12]
						lowerCamelCase      =   in_proj_weight[-2_56:, :]
						lowerCamelCase      =   in_proj_bias[-2_56:]



def        a__					(							)		->   List[Any]:
			lowerCamelCase      =   """http://images.cocodataset.org/val2017/000000039769.jpg"""
			lowerCamelCase      =   Image.open(requests.get(__UpperCamelCase			,					stream=__UpperCamelCase							).raw							)

			return im



@torch.no_grad()
def        a__					(							snake_case__			,					snake_case__							)		->   Tuple:
			lowerCamelCase      =   ConditionalDetrConfig()
			# 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_50
			else:
						lowerCamelCase      =   91
						lowerCamelCase      =   """huggingface/label-files"""
						lowerCamelCase      =   """coco-detection-id2label.json"""
						lowerCamelCase      =   json.load(open(hf_hub_download(__UpperCamelCase			,					__UpperCamelCase			,					repo_type="""dataset"""							)			,					"""r"""							)							)
						lowerCamelCase      =   {int(__UpperCamelCase							): 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      =   ConditionalDetrImageProcessor(format=__UpperCamelCase							)

			# prepare image
			lowerCamelCase      =   prepare_img()
			lowerCamelCase      =   image_processor(images=__UpperCamelCase			,					return_tensors="""pt"""							)
			lowerCamelCase      =   encoding["""pixel_values"""]

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

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


if __name__ == "__main__":
					lowerCAmelCase				:     Optional[Any]					=				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				:     List[str]					=				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 CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
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, skip_mps

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class        lowerCAmelCase_  (     UpperCAmelCase      ,							UpperCAmelCase      ,							unittest.TestCase	):
       __UpperCAmelCase					=CycleDiffusionPipeline
       __UpperCAmelCase					=TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
           'negative_prompt',
           'height',
           'width',
           'negative_prompt_embeds',
       }
       __UpperCAmelCase					=PipelineTesterMixin.required_optional_params - {'latents'}
       __UpperCAmelCase					=TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'source_prompt'}	)
       __UpperCAmelCase					=IMAGE_TO_IMAGE_IMAGE_PARAMS
       __UpperCAmelCase					=IMAGE_TO_IMAGE_IMAGE_PARAMS






       def   UpperCamelCase						( self   )->      Dict:
            torch.manual_seed(0   )
            _A				=							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			,     )
            _A				=							DDIMScheduler(
                beta_start=0.0_0085			,     beta_end=0.012			,     beta_schedule='scaled_linear'			,     num_train_timesteps=1000			,     clip_sample=_UpperCamelCase			,     set_alpha_to_one=_UpperCamelCase			,     )
            torch.manual_seed(0   )
            _A				=							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   )
            _A				=							CLIPTextConfig(
                bos_token_id=0			,     eos_token_id=2			,     hidden_size=32			,     intermediate_size=37			,     layer_norm_eps=1e-05			,     num_attention_heads=4			,     num_hidden_layers=5			,     pad_token_id=1			,     vocab_size=1000			,     )
            _A				=							CLIPTextModel(_UpperCamelCase   )
            _A				=							CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'   )

            _A				=							{
                'unet': unet,
                'scheduler': scheduler,
                'vae': vae,
                'text_encoder': text_encoder,
                'tokenizer': tokenizer,
                'safety_checker': None,
                'feature_extractor': None,
            }
            return components






       def   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase=0   )->      List[Any]:
            _A				=							floats_tensor((1, 3, 32, 32)			,     rng=random.Random(_UpperCamelCase   )   ).to(_UpperCamelCase   )
            _A				=							image / 2 + 0.5
            if str(_UpperCamelCase   ).startswith('mps'   ):
                 _A				=							torch.manual_seed(_UpperCamelCase   )
            else:
                 _A				=							torch.Generator(device=_UpperCamelCase   ).manual_seed(_UpperCamelCase   )
            _A				=							{
                'prompt': 'An astronaut riding an elephant',
                'source_prompt': 'An astronaut riding a horse',
                'image': image,
                'generator': generator,
                'num_inference_steps': 2,
                'eta': 0.1,
                'strength': 0.8,
                'guidance_scale': 3,
                'source_guidance_scale': 1,
                'output_type': 'numpy',
            }
            return inputs






       def   UpperCamelCase						( self   )->      Any:
            _A				=							'cpu'  # ensure determinism for the device-dependent torch.Generator

            _A				=							self.get_dummy_components()
            _A				=							CycleDiffusionPipeline(**_UpperCamelCase   )
            _A				=							pipe.to(_UpperCamelCase   )
            pipe.set_progress_bar_config(disable=_UpperCamelCase   )

            _A				=							self.get_dummy_inputs(_UpperCamelCase   )
            _A				=							pipe(**_UpperCamelCase   )
            _A				=							output.images

            _A				=							images[0, -3:, -3:, -1]

            assert images.shape == (1, 32, 32, 3)
            _A				=							np.array([0.4459, 0.4943, 0.4544, 0.6643, 0.5474, 0.4327, 0.5701, 0.5959, 0.5179]   )

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






       @unittest.skipIf(torch_device != 'cuda'			,     'This test requires a GPU'   )
       def   UpperCamelCase						( self   )->      List[Any]:
            _A				=							self.get_dummy_components()
            for name, module in components.items():
                 if hasattr(_UpperCamelCase			,     'half'   ):
                      _A				=							module.half()
            _A				=							CycleDiffusionPipeline(**_UpperCamelCase   )
            _A				=							pipe.to(_UpperCamelCase   )
            pipe.set_progress_bar_config(disable=_UpperCamelCase   )

            _A				=							self.get_dummy_inputs(_UpperCamelCase   )
            _A				=							pipe(**_UpperCamelCase   )
            _A				=							output.images

            _A				=							images[0, -3:, -3:, -1]

            assert images.shape == (1, 32, 32, 3)
            _A				=							np.array([0.3506, 0.4543, 0.446, 0.4575, 0.5195, 0.4155, 0.5273, 0.518, 0.4116]   )

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






       @skip_mps
       def   UpperCamelCase						( self   )->      Any:
            return super().test_save_load_local()






       @unittest.skip('non-deterministic pipeline'   )
       def   UpperCamelCase						( self   )->      List[Any]:
            return super().test_inference_batch_single_identical()






       @skip_mps
       def   UpperCamelCase						( self   )->      int:
            return super().test_dict_tuple_outputs_equivalent()






       @skip_mps
       def   UpperCamelCase						( self   )->      Tuple:
            return super().test_save_load_optional_components()






       @skip_mps
       def   UpperCamelCase						( self   )->      Optional[Any]:
            return super().test_attention_slicing_forward_pass()






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






       def   UpperCamelCase						( self   )->      Any:
            # clean up the VRAM after each test
            super().tearDown()
            gc.collect()
            torch.cuda.empty_cache()






       def   UpperCamelCase						( self   )->      int:
            _A				=							load_image(
                'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
                '/cycle-diffusion/black_colored_car.png'   )
            _A				=							load_numpy(
                'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy'   )
            _A				=							init_image.resize((512, 512)   )

            _A				=							'CompVis/stable-diffusion-v1-4'
            _A				=							DDIMScheduler.from_pretrained(_UpperCamelCase			,     subfolder='scheduler'   )
            _A				=							CycleDiffusionPipeline.from_pretrained(
                _UpperCamelCase			,     scheduler=_UpperCamelCase			,     safety_checker=_UpperCamelCase			,     torch_dtype=torch.floataa			,     revision='fp16'   )

            pipe.to(_UpperCamelCase   )
            pipe.set_progress_bar_config(disable=_UpperCamelCase   )
            pipe.enable_attention_slicing()

            _A				=							'A black colored car'
            _A				=							'A blue colored car'

            _A				=							torch.manual_seed(0   )
            _A				=							pipe(
                prompt=_UpperCamelCase			,     source_prompt=_UpperCamelCase			,     image=_UpperCamelCase			,     num_inference_steps=100			,     eta=0.1			,     strength=0.85			,     guidance_scale=3			,     source_guidance_scale=1			,     generator=_UpperCamelCase			,     output_type='np'			,     )
            _A				=							output.images

            # the values aren't exactly equal, but the images look the same visually
            assert np.abs(image - expected_image   ).max() < 5e-1






       def   UpperCamelCase						( self   )->      Union[str, Any]:
            _A				=							load_image(
                'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
                '/cycle-diffusion/black_colored_car.png'   )
            _A				=							load_numpy(
                'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy'   )
            _A				=							init_image.resize((512, 512)   )

            _A				=							'CompVis/stable-diffusion-v1-4'
            _A				=							DDIMScheduler.from_pretrained(_UpperCamelCase			,     subfolder='scheduler'   )
            _A				=							CycleDiffusionPipeline.from_pretrained(_UpperCamelCase			,     scheduler=_UpperCamelCase			,     safety_checker=_UpperCamelCase   )

            pipe.to(_UpperCamelCase   )
            pipe.set_progress_bar_config(disable=_UpperCamelCase   )
            pipe.enable_attention_slicing()

            _A				=							'A black colored car'
            _A				=							'A blue colored car'

            _A				=							torch.manual_seed(0   )
            _A				=							pipe(
                prompt=_UpperCamelCase			,     source_prompt=_UpperCamelCase			,     image=_UpperCamelCase			,     num_inference_steps=100			,     eta=0.1			,     strength=0.85			,     guidance_scale=3			,     source_guidance_scale=1			,     generator=_UpperCamelCase			,     output_type='np'			,     )
            _A				=							output.images

            assert np.abs(image - expected_image   ).max() < 2e-2





