


'''simple docstring'''
import argparse
import json
import pickle
from pathlib import Path

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

from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging


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

def 					lowerCamelCase_ ( __UpperCamelCase					:       str		)					->	List[Any]:







     """simple docstring"""
     _A				=							SwinConfig.from_pretrained(
         'microsoft/swin-tiny-patch4-window7-224'							,    out_features=['stage1', 'stage2', 'stage3', 'stage4']		)
     _A				=							MaskFormerConfig(backbone_config=__UpperCamelCase		)

     _A				=							'huggingface/label-files'
     if "ade20k-full" in model_name:
          # this should be ok
          _A				=							8_4_7
          _A				=							'maskformer-ade20k-full-id2label.json'
     elif "ade" in model_name:
          # this should be ok
          _A				=							1_5_0
          _A				=							'ade20k-id2label.json'
     elif "coco-stuff" in model_name:
          # this should be ok
          _A				=							1_7_1
          _A				=							'maskformer-coco-stuff-id2label.json'
     elif "coco" in model_name:
          # TODO
          _A				=							1_3_3
          _A				=							'coco-panoptic-id2label.json'
     elif "cityscapes" in model_name:
          # this should be ok
          _A				=							1_9
          _A				=							'cityscapes-id2label.json'
     elif "vistas" in model_name:
          # this should be ok
          _A				=							6_5
          _A				=							'mapillary-vistas-id2label.json'

     _A				=							json.load(open(hf_hub_download(__UpperCamelCase							,    __UpperCamelCase							,    repo_type='dataset'		)							,    'r'		)		)
     _A				=							{int(__UpperCamelCase		): v for k, v in idalabel.items()}

     return config

def 					lowerCamelCase_ ( __UpperCamelCase					:       Union[str, Any]		)					->	Tuple:







     """simple docstring"""
     _A				=							[]
     # stem
     # fmt: off
     rename_keys.append(('backbone.patch_embed.proj.weight', 'model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight')		)
     rename_keys.append(('backbone.patch_embed.proj.bias', 'model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias')		)
     rename_keys.append(('backbone.patch_embed.norm.weight', 'model.pixel_level_module.encoder.model.embeddings.norm.weight')		)
     rename_keys.append(('backbone.patch_embed.norm.bias', 'model.pixel_level_module.encoder.model.embeddings.norm.bias')		)
     # stages
     for i in range(len(config.backbone_config.depths		)		):
          for j in range(config.backbone_config.depths[i]		):
               rename_keys.append((F'backbone.layers.{i}.blocks.{j}.norm1.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight')		)
               rename_keys.append((F'backbone.layers.{i}.blocks.{j}.norm1.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias')		)
               rename_keys.append((F'backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table')		)
               rename_keys.append((F'backbone.layers.{i}.blocks.{j}.attn.relative_position_index', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index')		)
               rename_keys.append((F'backbone.layers.{i}.blocks.{j}.attn.proj.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight')		)
               rename_keys.append((F'backbone.layers.{i}.blocks.{j}.attn.proj.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias')		)
               rename_keys.append((F'backbone.layers.{i}.blocks.{j}.norm2.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight')		)
               rename_keys.append((F'backbone.layers.{i}.blocks.{j}.norm2.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias')		)
               rename_keys.append((F'backbone.layers.{i}.blocks.{j}.mlp.fc1.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight')		)
               rename_keys.append((F'backbone.layers.{i}.blocks.{j}.mlp.fc1.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias')		)
               rename_keys.append((F'backbone.layers.{i}.blocks.{j}.mlp.fc2.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight')		)
               rename_keys.append((F'backbone.layers.{i}.blocks.{j}.mlp.fc2.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias')		)

          if i < 3:
               rename_keys.append((F'backbone.layers.{i}.downsample.reduction.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight')		)
               rename_keys.append((F'backbone.layers.{i}.downsample.norm.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight')		)
               rename_keys.append((F'backbone.layers.{i}.downsample.norm.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias')		)
          rename_keys.append((F'backbone.norm{i}.weight', F'model.pixel_level_module.encoder.hidden_states_norms.{i}.weight')		)
          rename_keys.append((F'backbone.norm{i}.bias', F'model.pixel_level_module.encoder.hidden_states_norms.{i}.bias')		)

     # FPN
     rename_keys.append(('sem_seg_head.layer_4.weight', 'model.pixel_level_module.decoder.fpn.stem.0.weight')		)
     rename_keys.append(('sem_seg_head.layer_4.norm.weight', 'model.pixel_level_module.decoder.fpn.stem.1.weight')		)
     rename_keys.append(('sem_seg_head.layer_4.norm.bias', 'model.pixel_level_module.decoder.fpn.stem.1.bias')		)
     for source_index, target_index in zip(range(3							,    0							,    -1		)							,    range(0							,    3		)		):
          rename_keys.append((F'sem_seg_head.adapter_{source_index}.weight', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight')		)
          rename_keys.append((F'sem_seg_head.adapter_{source_index}.norm.weight', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight')		)
          rename_keys.append((F'sem_seg_head.adapter_{source_index}.norm.bias', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias')		)
          rename_keys.append((F'sem_seg_head.layer_{source_index}.weight', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight')		)
          rename_keys.append((F'sem_seg_head.layer_{source_index}.norm.weight', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight')		)
          rename_keys.append((F'sem_seg_head.layer_{source_index}.norm.bias', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias')		)
     rename_keys.append(('sem_seg_head.mask_features.weight', 'model.pixel_level_module.decoder.mask_projection.weight')		)
     rename_keys.append(('sem_seg_head.mask_features.bias', 'model.pixel_level_module.decoder.mask_projection.bias')		)

     # Transformer decoder
     for idx in range(config.decoder_config.decoder_layers		):
          # self-attention out projection
          rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight', F'model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight')		)
          rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias', F'model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias')		)
          # cross-attention out projection
          rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight', F'model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight')		)
          rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias', F'model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias')		)
          # MLP 1
          rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight', F'model.transformer_module.decoder.layers.{idx}.fc1.weight')		)
          rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias', F'model.transformer_module.decoder.layers.{idx}.fc1.bias')		)
          # MLP 2
          rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight', F'model.transformer_module.decoder.layers.{idx}.fc2.weight')		)
          rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias', F'model.transformer_module.decoder.layers.{idx}.fc2.bias')		)
          # layernorm 1 (self-attention layernorm)
          rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight', F'model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight')		)
          rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias', F'model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias')		)
          # layernorm 2 (cross-attention layernorm)
          rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight', F'model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight')		)
          rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias', F'model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias')		)
          # layernorm 3 (final layernorm)
          rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight', F'model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight')		)
          rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias', F'model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias')		)

     rename_keys.append(('sem_seg_head.predictor.transformer.decoder.norm.weight', 'model.transformer_module.decoder.layernorm.weight')		)
     rename_keys.append(('sem_seg_head.predictor.transformer.decoder.norm.bias', 'model.transformer_module.decoder.layernorm.bias')		)

     # heads on top
     rename_keys.append(('sem_seg_head.predictor.query_embed.weight', 'model.transformer_module.queries_embedder.weight')		)

     rename_keys.append(('sem_seg_head.predictor.input_proj.weight', 'model.transformer_module.input_projection.weight')		)
     rename_keys.append(('sem_seg_head.predictor.input_proj.bias', 'model.transformer_module.input_projection.bias')		)

     rename_keys.append(('sem_seg_head.predictor.class_embed.weight', 'class_predictor.weight')		)
     rename_keys.append(('sem_seg_head.predictor.class_embed.bias', 'class_predictor.bias')		)

     for i in range(3		):
          rename_keys.append((F'sem_seg_head.predictor.mask_embed.layers.{i}.weight', F'mask_embedder.{i}.0.weight')		)
          rename_keys.append((F'sem_seg_head.predictor.mask_embed.layers.{i}.bias', F'mask_embedder.{i}.0.bias')		)
     # fmt: on

     return rename_keys

def 					lowerCamelCase_ ( __UpperCamelCase					:       int							,    __UpperCamelCase					:       List[Any]							,    __UpperCamelCase					:       str		)					->	Any:







     """simple docstring"""
     _A				=							dct.pop(__UpperCamelCase		)
     _A				=							val

def 					lowerCamelCase_ ( __UpperCamelCase					:       Any							,    __UpperCamelCase					:       str		)					->	Dict:







     """simple docstring"""
     _A				=							[int(backbone_config.embed_dim * 2**i		) for i in range(len(backbone_config.depths		)		)]
     for i in range(len(backbone_config.depths		)		):
          _A				=							num_features[i]
          for j in range(backbone_config.depths[i]		):
               # fmt: off
               # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
               _A				=							state_dict.pop(F'backbone.layers.{i}.blocks.{j}.attn.qkv.weight'		)
               _A				=							state_dict.pop(F'backbone.layers.{i}.blocks.{j}.attn.qkv.bias'		)
               # next, add query, keys and values (in that order) to the state dict
               _A				=							in_proj_weight[:dim, :]
               _A				=							in_proj_bias[: dim]
               _A				=							in_proj_weight[
                   dim : dim * 2, :
               ]
               _A				=							in_proj_bias[
                   dim : dim * 2
               ]
               _A				=							in_proj_weight[
                   -dim :, :
               ]
               _A				=							in_proj_bias[-dim :]
               # fmt: on

def 					lowerCamelCase_ ( __UpperCamelCase					:       Optional[int]							,    __UpperCamelCase					:       Any		)					->	Any:







     """simple docstring"""
     # fmt: off
     _A				=							config.decoder_config.hidden_size
     for idx in range(config.decoder_config.decoder_layers		):
          # read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
          _A				=							state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight'		)
          _A				=							state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias'		)
          # next, add query, keys and values (in that order) to the state dict
          _A				=							in_proj_weight[: hidden_size, :]
          _A				=							in_proj_bias[:config.hidden_size]
          _A				=							in_proj_weight[hidden_size : hidden_size * 2, :]
          _A				=							in_proj_bias[hidden_size : hidden_size * 2]
          _A				=							in_proj_weight[-hidden_size :, :]
          _A				=							in_proj_bias[-hidden_size :]
          # read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
          _A				=							state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight'		)
          _A				=							state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias'		)
          # next, add query, keys and values (in that order) to the state dict
          _A				=							in_proj_weight[: hidden_size, :]
          _A				=							in_proj_bias[:config.hidden_size]
          _A				=							in_proj_weight[hidden_size : hidden_size * 2, :]
          _A				=							in_proj_bias[hidden_size : hidden_size * 2]
          _A				=							in_proj_weight[-hidden_size :, :]
          _A				=							in_proj_bias[-hidden_size :]
     # fmt: on

def 					lowerCamelCase_ ( )					->	torch.Tensor:







     """simple docstring"""
     _A				=							'http://images.cocodataset.org/val2017/000000039769.jpg'
     _A				=							Image.open(requests.get(__UpperCamelCase							,    stream=__UpperCamelCase		).raw		)
     return im

@torch.no_grad()
def 					lowerCamelCase_ ( __UpperCamelCase					:       str							,    __UpperCamelCase					:       str							,    __UpperCamelCase					:       str							,    __UpperCamelCase					:       bool = False		)					->	Optional[int]:







     """simple docstring"""
     _A				=							get_maskformer_config(__UpperCamelCase		)

     # load original state_dict
     with open(__UpperCamelCase							,    'rb'		) as f:
          _A				=							pickle.load(__UpperCamelCase		)
     _A				=							data['model']

     # for name, param in state_dict.items():
     #     print(name, param.shape)

     # rename keys
     _A				=							create_rename_keys(__UpperCamelCase		)
     for src, dest in rename_keys:
          rename_key(__UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase		)
     read_in_swin_q_k_v(__UpperCamelCase							,    config.backbone_config		)
     read_in_decoder_q_k_v(__UpperCamelCase							,    __UpperCamelCase		)

     # update to torch tensors
     for key, value in state_dict.items():
          _A				=							torch.from_numpy(__UpperCamelCase		)

     # load 🤗 model
     _A				=							MaskFormerForInstanceSegmentation(__UpperCamelCase		)
     model.eval()

     for name, param in model.named_parameters():
          print(__UpperCamelCase							,    param.shape		)

     _A		,				_A				=							model.load_state_dict(__UpperCamelCase							,    strict=__UpperCamelCase		)
     assert missing_keys == [
         "model.pixel_level_module.encoder.model.layernorm.weight",
         "model.pixel_level_module.encoder.model.layernorm.bias",
     ]
     assert len(__UpperCamelCase		) == 0, F'Unexpected keys: {unexpected_keys}'

     # verify results
     _A				=							prepare_img()
     if "vistas" in model_name:
          _A				=							6_5
     elif "cityscapes" in model_name:
          _A				=							6_5_5_3_5
     else:
          _A				=							2_5_5
     _A				=							True if 'ade' in model_name else False
     _A				=							MaskFormerImageProcessor(ignore_index=__UpperCamelCase							,    reduce_labels=__UpperCamelCase		)

     _A				=							image_processor(__UpperCamelCase							,    return_tensors='pt'		)

     _A				=							model(**__UpperCamelCase		)

     print('Logits:'							,    outputs.class_queries_logits[0, :3, :3]		)

     if model_name == "maskformer-swin-tiny-ade":
          _A				=							torch.tensor(
              [[3.63_53, -4.47_70, -2.60_65], [0.50_81, -4.23_94, -3.53_43], [2.19_09, -5.03_53, -1.93_23]]		)
     assert torch.allclose(outputs.class_queries_logits[0, :3, :3]							,    __UpperCamelCase							,    atol=1e-4		)
     print('Looks ok!'		)

     if pytorch_dump_folder_path is not None:
          print(F'Saving 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 push_to_hub:
          print('Pushing model and image processor to the hub...'		)
          model.push_to_hub(F'nielsr/{model_name}'		)
          image_processor.push_to_hub(F'nielsr/{model_name}'		)


if __name__ == "__main__":
    lowerCAmelCase						=					argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        """--model_name""",
        default="""maskformer-swin-tiny-ade""",
        type=str,
        help=("""Name of the MaskFormer model you'd like to convert""",),
    )
    parser.add_argument(
        """--checkpoint_path""",
        default="""/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl""",
        type=str,
        help="""Path to the original state dict (.pth file).""",
    )
    parser.add_argument(
        """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
    )
    parser.add_argument(
        """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
    )

    lowerCAmelCase						=					parser.parse_args()
    convert_maskformer_checkpoint(
        args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
    )









'''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





