



import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor




def 	__UpperCamelCase	(  _lowerCAmelCase		)       ->  Tuple:



    """simple docstring"""






    A :       str          =   SwinConfig(image_size=192		)

    if "base" in model_name:
        A :       List[str]          =   6
        A :       Optional[int]          =   128
        A :       Tuple          =   (2, 2, 18, 2)
        A :       List[Any]          =   (4, 8, 16, 32)
    elif "large" in model_name:
        A :       Any          =   12
        A :       Optional[Any]          =   192
        A :       Any          =   (2, 2, 18, 2)
        A :       Optional[Any]          =   (6, 12, 24, 48)
    else:
        raise ValueError("""Model not supported, only supports base and large variants"""		)

    A :       str          =   window_size
    A :       str          =   embed_dim
    A :       Optional[int]          =   depths
    A :       Optional[Any]          =   num_heads

    return config




def 	__UpperCamelCase	(  _lowerCAmelCase		)       ->  Any:



    """simple docstring"""






    if "encoder.mask_token" in name:
        A :       Optional[int]          =   name.replace("""encoder.mask_token"""      ,     """embeddings.mask_token"""		)
    if "encoder.patch_embed.proj" in name:
        A :       int          =   name.replace("""encoder.patch_embed.proj"""      ,     """embeddings.patch_embeddings.projection"""		)
    if "encoder.patch_embed.norm" in name:
        A :       Union[str, Any]          =   name.replace("""encoder.patch_embed.norm"""      ,     """embeddings.norm"""		)
    if "attn.proj" in name:
        A :       List[Any]          =   name.replace("""attn.proj"""      ,     """attention.output.dense"""		)
    if "attn" in name:
        A :       List[str]          =   name.replace("""attn"""      ,     """attention.self"""		)
    if "norm1" in name:
        A :       str          =   name.replace("""norm1"""      ,     """layernorm_before"""		)
    if "norm2" in name:
        A :       Any          =   name.replace("""norm2"""      ,     """layernorm_after"""		)
    if "mlp.fc1" in name:
        A :       List[Any]          =   name.replace("""mlp.fc1"""      ,     """intermediate.dense"""		)
    if "mlp.fc2" in name:
        A :       Optional[Any]          =   name.replace("""mlp.fc2"""      ,     """output.dense"""		)

    if name == "encoder.norm.weight":
        A :       Dict          =   """layernorm.weight"""
    if name == "encoder.norm.bias":
        A :       List[str]          =   """layernorm.bias"""

    if "decoder" in name:
        pass
    else:
        A :       List[Any]          =   """swin.""" + name

    return name




def 	__UpperCamelCase	(  _lowerCAmelCase      ,     _lowerCAmelCase		)       ->  str:



    """simple docstring"""






    for key in orig_state_dict.copy().keys():
        A :       Optional[int]          =   orig_state_dict.pop(_lowerCAmelCase		)

        if "attn_mask" in key:
            pass
        elif "qkv" in key:
            A :       Optional[Any]          =   key.split("""."""		)
            A :       Optional[int]          =   int(key_split[2]		)
            A :       Optional[int]          =   int(key_split[4]		)
            A :       str          =   model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

            if "weight" in key:
                A :       int          =   val[:dim, :]
                A :       List[str]          =   val[
                    dim : dim * 2, :
                ]
                A :       Optional[Any]          =   val[-dim:, :]
            else:
                A :       Union[str, Any]          =   val[
                    :dim
                ]
                A :       int          =   val[
                    dim : dim * 2
                ]
                A :       Optional[int]          =   val[
                    -dim:
                ]
        else:
            A :       int          =   val

    return orig_state_dict




def 	__UpperCamelCase	(  _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase		)       ->  int:



    """simple docstring"""






    A :       List[Any]          =   torch.load(_lowerCAmelCase      ,     map_location="""cpu"""		)["""model"""]

    A :       str          =   get_swin_config(_lowerCAmelCase		)
    A :       str          =   SwinForMaskedImageModeling(_lowerCAmelCase		)
    model.eval()

    A :       Optional[Any]          =   convert_state_dict(_lowerCAmelCase      ,     _lowerCAmelCase		)
    model.load_state_dict(_lowerCAmelCase		)

    A :       Any          =   """http://images.cocodataset.org/val2017/000000039769.jpg"""

    A :       Dict          =   ViTImageProcessor(size={"""height""": 192, """width""": 192}		)
    A :       int          =   Image.open(requests.get(_lowerCAmelCase      ,     stream=_lowerCAmelCase		).raw		)
    A :       Dict          =   image_processor(images=_lowerCAmelCase      ,     return_tensors="""pt"""		)

    with torch.no_grad():
        A :       Optional[int]          =   model(**_lowerCAmelCase		).logits

    print(outputs.keys()		)
    print("""Looks ok!"""		)

    if pytorch_dump_folder_path is not None:
        print(f'''Saving model {model_name} to {pytorch_dump_folder_path}'''		)
        model.save_pretrained(_lowerCAmelCase		)

        print(f'''Saving image processor to {pytorch_dump_folder_path}'''		)
        image_processor.save_pretrained(_lowerCAmelCase		)

    if push_to_hub:
        print(f'''Pushing model and image processor for {model_name} to hub'''		)
        model.push_to_hub(f'''microsoft/{model_name}'''		)
        image_processor.push_to_hub(f'''microsoft/{model_name}'''		)


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE_:Union[str, Any]             =						argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         """--model_name""",
         default="""swin-base-simmim-window6-192""",
         type=str,
         choices=["""swin-base-simmim-window6-192""", """swin-large-simmim-window12-192"""],
         help="""Name of the Swin SimMIM model you'd like to convert.""",
     )
     parser.add_argument(
         """--checkpoint_path""",
         default="""/Users/nielsrogge/Documents/SwinSimMIM/simmim_pretrain__swin_base__img192_window6__100ep.pth""",
         type=str,
         help="""Path to the original PyTorch checkpoint (.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."""
     )

     SCREAMING_SNAKE_CASE_:Dict             =						parser.parse_args()
     convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)







import gc
import random
import unittest

import numpy as np
import torch

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
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

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__ ,						unittest.TestCase    ):




    '''simple docstring'''






    __lowerCamelCase			: Union[str, Any]					=      KandinskyVaaControlnetPipeline
    __lowerCamelCase			: Optional[int]					=      ["image_embeds", "negative_image_embeds", "hint"]
    __lowerCamelCase			: Dict					=      ["image_embeds", "negative_image_embeds", "hint"]
    __lowerCamelCase			: List[str]					=      [
        "generator",
        "height",
        "width",
        "latents",
        "guidance_scale",
        "num_inference_steps",
        "return_dict",
        "guidance_scale",
        "num_images_per_prompt",
        "output_type",
        "return_dict",
    ]
    __lowerCamelCase			: Dict					=      False






    @property
    def 			_lowerCAmelCase					(       self		):
        return 32






    @property
    def 			_lowerCAmelCase					(       self		):
        return 32






    @property
    def 			_lowerCAmelCase					(       self		):
        return self.time_input_dim






    @property
    def 			_lowerCAmelCase					(       self		):
        return self.time_input_dim * 4






    @property
    def 			_lowerCAmelCase					(       self		):
        return 100






    @property
    def 			_lowerCAmelCase					(       self		):
        torch.manual_seed(0		)

        A :       Any          =   {
            """in_channels""": 8,
            # Out channels is double in channels because predicts mean and variance
            """out_channels""": 8,
            """addition_embed_type""": """image_hint""",
            """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
            """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
            """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
            """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
            """layers_per_block""": 1,
            """encoder_hid_dim""": self.text_embedder_hidden_size,
            """encoder_hid_dim_type""": """image_proj""",
            """cross_attention_dim""": self.cross_attention_dim,
            """attention_head_dim""": 4,
            """resnet_time_scale_shift""": """scale_shift""",
            """class_embed_type""": None,
        }

        A :       List[str]          =   UNetaDConditionModel(**lowerCamelCase__		)
        return model






    @property
    def 			_lowerCAmelCase					(       self		):
        return {
            "block_out_channels": [32, 32, 64, 64],
            "down_block_types": [
                "DownEncoderBlock2D",
                "DownEncoderBlock2D",
                "DownEncoderBlock2D",
                "AttnDownEncoderBlock2D",
            ],
            "in_channels": 3,
            "latent_channels": 4,
            "layers_per_block": 1,
            "norm_num_groups": 8,
            "norm_type": "spatial",
            "num_vq_embeddings": 12,
            "out_channels": 3,
            "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
            "vq_embed_dim": 4,
        }






    @property
    def 			_lowerCAmelCase					(       self		):
        torch.manual_seed(0		)
        A :       List[Any]          =   VQModel(**self.dummy_movq_kwargs		)
        return model






    def 			_lowerCAmelCase					(       self		):
        A :       Optional[Any]          =   self.dummy_unet
        A :       Tuple          =   self.dummy_movq

        A :       List[Any]          =   DDIMScheduler(
            num_train_timesteps=1000,		beta_schedule="""linear""",		beta_start=0.0_0085,		beta_end=0.012,		clip_sample=lowerCamelCase__,		set_alpha_to_one=lowerCamelCase__,		steps_offset=1,		prediction_type="""epsilon""",		thresholding=lowerCamelCase__,		)

        A :       int          =   {
            """unet""": unet,
            """scheduler""": scheduler,
            """movq""": movq,
        }
        return components






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__=0		):
        A :       Optional[Any]          =   floats_tensor((1, self.text_embedder_hidden_size),		rng=random.Random(lowerCamelCase__		)		).to(lowerCamelCase__		)
        A :       List[Any]          =   floats_tensor((1, self.text_embedder_hidden_size),		rng=random.Random(seed + 1		)		).to(
            lowerCamelCase__		)

        # create hint
        A :       int          =   floats_tensor((1, 3, 64, 64),		rng=random.Random(lowerCamelCase__		)		).to(lowerCamelCase__		)

        if str(lowerCamelCase__		).startswith("""mps"""		):
            A :       Optional[Any]          =   torch.manual_seed(lowerCamelCase__		)
        else:
            A :       str          =   torch.Generator(device=lowerCamelCase__		).manual_seed(lowerCamelCase__		)
        A :       List[str]          =   {
            """image_embeds""": image_embeds,
            """negative_image_embeds""": negative_image_embeds,
            """hint""": hint,
            """generator""": generator,
            """height""": 64,
            """width""": 64,
            """guidance_scale""": 4.0,
            """num_inference_steps""": 2,
            """output_type""": """np""",
        }
        return inputs






    def 			_lowerCAmelCase					(       self		):
        A :       Dict          =   """cpu"""

        A :       List[str]          =   self.get_dummy_components()

        A :       Dict          =   self.pipeline_class(**lowerCamelCase__		)
        A :       Optional[Any]          =   pipe.to(lowerCamelCase__		)

        pipe.set_progress_bar_config(disable=lowerCamelCase__		)

        A :       int          =   pipe(**self.get_dummy_inputs(lowerCamelCase__		)		)
        A :       Union[str, Any]          =   output.images

        A :       str          =   pipe(
            **self.get_dummy_inputs(lowerCamelCase__		),		return_dict=lowerCamelCase__,		)[0]

        A :       Optional[int]          =   image[0, -3:, -3:, -1]
        A :       Any          =   image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)

        A :       Dict          =   np.array(
            [0.695_9826, 0.86_8279, 0.755_8092, 0.6876_9467, 0.8580_5804, 0.6597_7496, 0.4488_5302, 0.595_9111, 0.425_1595]		)

        assert (
            np.abs(image_slice.flatten() - expected_slice		).max() < 1e-2
        ), f''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''

        assert (
            np.abs(image_from_tuple_slice.flatten() - expected_slice		).max() < 1e-2
        ), f''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''






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




    '''simple docstring'''






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






    def 			_lowerCAmelCase					(       self		):
        A :       Tuple          =   load_numpy(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy"""		)

        A :       Any          =   load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/kandinskyv22/hint_image_cat.png"""		)
        A :       Optional[Any]          =   torch.from_numpy(np.array(lowerCamelCase__		)		).float() / 255.0
        A :       List[str]          =   hint.permute(2,		0,		1		).unsqueeze(0		)

        A :       Union[str, Any]          =   KandinskyVaaPriorPipeline.from_pretrained(
            """kandinsky-community/kandinsky-2-2-prior""",		torch_dtype=torch.floataa		)
        pipe_prior.to(lowerCamelCase__		)

        A :       Tuple          =   KandinskyVaaControlnetPipeline.from_pretrained(
            """kandinsky-community/kandinsky-2-2-controlnet-depth""",		torch_dtype=torch.floataa		)
        A :       Union[str, Any]          =   pipeline.to(lowerCamelCase__		)
        pipeline.set_progress_bar_config(disable=lowerCamelCase__		)

        A :       Optional[Any]          =   """A robot, 4k photo"""

        A :       Union[str, Any]          =   torch.Generator(device="""cuda"""		).manual_seed(0		)
        A     ,							A :       int          =   pipe_prior(
            lowerCamelCase__,		generator=lowerCamelCase__,		num_inference_steps=5,		negative_prompt="""""",		).to_tuple()

        A :       Union[str, Any]          =   torch.Generator(device="""cuda"""		).manual_seed(0		)
        A :       int          =   pipeline(
            image_embeds=lowerCamelCase__,		negative_image_embeds=lowerCamelCase__,		hint=lowerCamelCase__,		generator=lowerCamelCase__,		num_inference_steps=100,		output_type="""np""",		)

        A :       Optional[int]          =   output.images[0]

        assert image.shape == (512, 512, 3)

        assert_mean_pixel_difference(lowerCamelCase__,		lowerCamelCase__		)


