





from typing import List, Optional, Union

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


UpperCAmelCase_  							=				logging.get_logger(__name__)  # pylint: disable=invalid-name

UpperCAmelCase_  							=				'\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-prior")\n        >>> pipe_prior.to("cuda")\n        >>> prompt = "red cat, 4k photo"\n        >>> out = pipe_prior(prompt)\n        >>> image_emb = out.image_embeds\n        >>> zero_image_emb = out.negative_image_embeds\n        >>> pipe = KandinskyV22Pipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder")\n        >>> pipe.to("cuda")\n        >>> image = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=50,\n        ... ).images\n        >>> image[0].save("cat.png")\n        ```\n'




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Union[str, Any]	,     __UpperCAmelCase:    Any	,     __UpperCAmelCase:    Optional[Any]=8   )   ->			int:
    UpperCamelCase__							:       Optional[int]      =   height // scale_factor**2
    if height % scale_factor**2 != 0:
        new_height += 1
    UpperCamelCase__							:       Optional[Any]      =   width // scale_factor**2
    if width % scale_factor**2 != 0:
        new_width += 1
    return new_height * scale_factor, new_width * scale_factor






class 					lowercase__ (			__lowerCamelCase       ):

   '''simple docstring'''


   def __init__(					self,	__magic_name__,	__magic_name__,	__magic_name__,	)				->   List[Any]:






       """simple docstring"""
       super().__init__()

       self.register_modules(
           unet=__magic_name__,	scheduler=__magic_name__,	movq=__magic_name__,	)
       UpperCamelCase__							:       Optional[int]      =   2 ** (len(self.movq.config.block_out_channels							) - 1)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__							)				->   int:






       """simple docstring"""
       if latents is None:
           UpperCamelCase__							:       List[str]      =   randn_tensor(__magic_name__,	generator=__magic_name__,	device=__magic_name__,	dtype=__magic_name__							)
       else:
           if latents.shape != shape:
               raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}"							)
           UpperCamelCase__							:       List[Any]      =   latents.to(__magic_name__							)

       UpperCamelCase__							:       str      =   latents * scheduler.init_noise_sigma
       return latents




   def    UpperCamelCase__						(					self,	__magic_name__=0							)				->   Optional[int]:






       """simple docstring"""
       if is_accelerate_available():
           from accelerate import cpu_offload
       else:
           raise ImportError('''Please install accelerate via `pip install accelerate`'''							)

       UpperCamelCase__							:       List[Any]      =   torch.device(f"cuda:{gpu_id}"							)

       UpperCamelCase__							:       Optional[Any]      =   [
           self.unet,
           self.movq,
       ]
       for cpu_offloaded_model in models:
           if cpu_offloaded_model is not None:
               cpu_offload(__magic_name__,	__magic_name__							)




   def    UpperCamelCase__						(					self,	__magic_name__=0							)				->   Optional[int]:






       """simple docstring"""
       if is_accelerate_available() and is_accelerate_version('''>=''',	'''0.17.0.dev0'''							):
           from accelerate import cpu_offload_with_hook
       else:
           raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'''							)

       UpperCamelCase__							:       List[str]      =   torch.device(f"cuda:{gpu_id}"							)

       if self.device.type != "cpu":
           self.to('''cpu''',	silence_dtype_warnings=__magic_name__							)
           torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

       UpperCamelCase__							:       Any      =   None
       for cpu_offloaded_model in [self.unet, self.movq]:
           UpperCamelCase__     ,UpperCamelCase__							:       Any      =   cpu_offload_with_hook(__magic_name__,	__magic_name__,	prev_module_hook=__magic_name__							)

       # We'll offload the last model manually.
       UpperCamelCase__							:       Optional[Any]      =   hook




   @property
   # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
   def    UpperCamelCase__						(					self							)				->   List[str]:






       """simple docstring"""
       if not hasattr(self.unet,	'''_hf_hook'''							):
           return self.device
       for module in self.unet.modules():
           if (
               hasattr(__magic_name__,	'''_hf_hook'''							)
               and hasattr(module._hf_hook,	'''execution_device'''							)
               and module._hf_hook.execution_device is not None
           ):
               return torch.device(module._hf_hook.execution_device							)
       return self.device




   @torch.no_grad()
   @replace_example_docstring(__magic_name__							)
   def __call__(					self,	__magic_name__,	__magic_name__,	__magic_name__ = 512,	__magic_name__ = 512,	__magic_name__ = 100,	__magic_name__ = 4.0,	__magic_name__ = 1,	__magic_name__ = None,	__magic_name__ = None,	__magic_name__ = "pil",	__magic_name__ = True,	)				->   str:






       """simple docstring"""
       UpperCamelCase__							:       List[str]      =   self._execution_device

       UpperCamelCase__							:       List[str]      =   guidance_scale > 1.0

       if isinstance(__magic_name__,	__magic_name__							):
           UpperCamelCase__							:       List[Any]      =   torch.cat(__magic_name__,	dim=0							)
       UpperCamelCase__							:       int      =   image_embeds.shape[0] * num_images_per_prompt
       if isinstance(__magic_name__,	__magic_name__							):
           UpperCamelCase__							:       int      =   torch.cat(__magic_name__,	dim=0							)

       if do_classifier_free_guidance:
           UpperCamelCase__							:       Optional[Any]      =   image_embeds.repeat_interleave(__magic_name__,	dim=0							)
           UpperCamelCase__							:       Union[str, Any]      =   negative_image_embeds.repeat_interleave(__magic_name__,	dim=0							)

       UpperCamelCase__							:       Optional[Any]      =   torch.cat([negative_image_embeds, image_embeds],	dim=0							).to(dtype=self.unet.dtype,	device=__magic_name__							)

       self.scheduler.set_timesteps(__magic_name__,	device=__magic_name__							)
       UpperCamelCase__							:       List[str]      =   self.scheduler.timesteps

       UpperCamelCase__							:       List[str]      =   self.unet.config.in_channels

       UpperCamelCase__     ,UpperCamelCase__							:       int      =   downscale_height_and_width(__magic_name__,	__magic_name__,	self.movq_scale_factor							)

       # create initial latent
       UpperCamelCase__							:       Union[str, Any]      =   self.prepare_latents(
           (batch_size, num_channels_latents, height, width),	image_embeds.dtype,	__magic_name__,	__magic_name__,	__magic_name__,	self.scheduler,	)

       for i, t in enumerate(self.progress_bar(__magic_name__							)							):
           # expand the latents if we are doing classifier free guidance
           UpperCamelCase__							:       Tuple      =   torch.cat([latents] * 2							) if do_classifier_free_guidance else latents

           UpperCamelCase__							:       Optional[Any]      =   {'''image_embeds''': image_embeds}
           UpperCamelCase__							:       Tuple      =   self.unet(
               sample=__magic_name__,	timestep=__magic_name__,	encoder_hidden_states=__magic_name__,	added_cond_kwargs=__magic_name__,	return_dict=__magic_name__,	)[0]

           if do_classifier_free_guidance:
               UpperCamelCase__     ,UpperCamelCase__							:       str      =   noise_pred.split(latents.shape[1],	dim=1							)
               UpperCamelCase__     ,UpperCamelCase__							:       Any      =   noise_pred.chunk(2							)
               UpperCamelCase__     ,UpperCamelCase__							:       Tuple      =   variance_pred.chunk(2							)
               UpperCamelCase__							:       Optional[int]      =   noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
               UpperCamelCase__							:       Dict      =   torch.cat([noise_pred, variance_pred_text],	dim=1							)

           if not (
               hasattr(self.scheduler.config,	'''variance_type'''							)
               and self.scheduler.config.variance_type in ["learned", "learned_range"]
           ):
               UpperCamelCase__     ,UpperCamelCase__							:       List[str]      =   noise_pred.split(latents.shape[1],	dim=1							)

           # compute the previous noisy sample x_t -> x_t-1
           UpperCamelCase__							:       Dict      =   self.scheduler.step(
               __magic_name__,	__magic_name__,	__magic_name__,	generator=__magic_name__,	)[0]
       # post-processing
       UpperCamelCase__							:       Dict      =   self.movq.decode(__magic_name__,	force_not_quantize=__magic_name__							)['''sample''']

       if output_type not in ["pt", "np", "pil"]:
           raise ValueError(f"Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"							)

       if output_type in ["np", "pil"]:
           UpperCamelCase__							:       Union[str, Any]      =   image * 0.5 + 0.5
           UpperCamelCase__							:       str      =   image.clamp(0,	1							)
           UpperCamelCase__							:       int      =   image.cpu().permute(0,	2,	3,	1							).float().numpy()

       if output_type == "pil":
           UpperCamelCase__							:       List[Any]      =   self.numpy_to_pil(__magic_name__							)

       if not return_dict:
           return (image,)

       return ImagePipelineOutput(images=__magic_name__							)








import argparse
import json
from pathlib import Path

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

from transformers import YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging


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




def 	lowerCAmelCase_					(						__UpperCAmelCase:    str   )   ->			YolosConfig:
    UpperCamelCase__							:       str      =   YolosConfig()

    # size of the architecture
    if "yolos_ti" in yolos_name:
        UpperCamelCase__							:       Dict      =   192
        UpperCamelCase__							:       Dict      =   768
        UpperCamelCase__							:       Optional[Any]      =   12
        UpperCamelCase__							:       List[Any]      =   3
        UpperCamelCase__							:       Optional[int]      =   [800, 1333]
        UpperCamelCase__							:       Tuple      =   False
    elif yolos_name == "yolos_s_dWr":
        UpperCamelCase__							:       int      =   330
        UpperCamelCase__							:       Tuple      =   14
        UpperCamelCase__							:       str      =   6
        UpperCamelCase__							:       Optional[int]      =   1320
    elif "yolos_s" in yolos_name:
        UpperCamelCase__							:       Optional[int]      =   384
        UpperCamelCase__							:       Any      =   1536
        UpperCamelCase__							:       Union[str, Any]      =   12
        UpperCamelCase__							:       int      =   6
    elif "yolos_b" in yolos_name:
        UpperCamelCase__							:       Dict      =   [800, 1344]

    UpperCamelCase__							:       List[Any]      =   91
    UpperCamelCase__							:       str      =   '''huggingface/label-files'''
    UpperCamelCase__							:       Dict      =   '''coco-detection-id2label.json'''
    UpperCamelCase__							:       List[str]      =   json.load(open(hf_hub_download(__UpperCAmelCase	,     __UpperCAmelCase	,     repo_type='''dataset'''   )	,     '''r'''   )   )
    UpperCamelCase__							:       str      =   {int(__UpperCAmelCase   ): v for k, v in idalabel.items()}
    UpperCamelCase__							:       str      =   idalabel
    UpperCamelCase__							:       Optional[int]      =   {v: k for k, v in idalabel.items()}

    return config




def 	lowerCAmelCase_					(						__UpperCAmelCase:    dict	,     __UpperCAmelCase:    YolosConfig	,     __UpperCAmelCase:    bool = False   )   ->			str:
    for i in range(config.num_hidden_layers   ):
        # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
        UpperCamelCase__							:       str      =   state_dict.pop(f"blocks.{i}.attn.qkv.weight"   )
        UpperCamelCase__							:       Dict      =   state_dict.pop(f"blocks.{i}.attn.qkv.bias"   )
        # next, add query, keys and values (in that order) to the state dict
        UpperCamelCase__							:       List[str]      =   in_proj_weight[: config.hidden_size, :]
        UpperCamelCase__							:       Any      =   in_proj_bias[: config.hidden_size]
        UpperCamelCase__							:       Tuple      =   in_proj_weight[
            config.hidden_size : config.hidden_size * 2, :
        ]
        UpperCamelCase__							:       List[str]      =   in_proj_bias[
            config.hidden_size : config.hidden_size * 2
        ]
        UpperCamelCase__							:       Optional[int]      =   in_proj_weight[-config.hidden_size :, :]
        UpperCamelCase__							:       Optional[int]      =   in_proj_bias[-config.hidden_size :]




def 	lowerCAmelCase_					(						__UpperCAmelCase:    str   )   ->			str:
    if "backbone" in name:
        UpperCamelCase__							:       str      =   name.replace('''backbone'''	,     '''vit'''   )
    if "cls_token" in name:
        UpperCamelCase__							:       str      =   name.replace('''cls_token'''	,     '''embeddings.cls_token'''   )
    if "det_token" in name:
        UpperCamelCase__							:       str      =   name.replace('''det_token'''	,     '''embeddings.detection_tokens'''   )
    if "mid_pos_embed" in name:
        UpperCamelCase__							:       List[str]      =   name.replace('''mid_pos_embed'''	,     '''encoder.mid_position_embeddings'''   )
    if "pos_embed" in name:
        UpperCamelCase__							:       List[Any]      =   name.replace('''pos_embed'''	,     '''embeddings.position_embeddings'''   )
    if "patch_embed.proj" in name:
        UpperCamelCase__							:       Any      =   name.replace('''patch_embed.proj'''	,     '''embeddings.patch_embeddings.projection'''   )
    if "blocks" in name:
        UpperCamelCase__							:       Union[str, Any]      =   name.replace('''blocks'''	,     '''encoder.layer'''   )
    if "attn.proj" in name:
        UpperCamelCase__							:       Dict      =   name.replace('''attn.proj'''	,     '''attention.output.dense'''   )
    if "attn" in name:
        UpperCamelCase__							:       Optional[int]      =   name.replace('''attn'''	,     '''attention.self'''   )
    if "norm1" in name:
        UpperCamelCase__							:       int      =   name.replace('''norm1'''	,     '''layernorm_before'''   )
    if "norm2" in name:
        UpperCamelCase__							:       str      =   name.replace('''norm2'''	,     '''layernorm_after'''   )
    if "mlp.fc1" in name:
        UpperCamelCase__							:       Optional[Any]      =   name.replace('''mlp.fc1'''	,     '''intermediate.dense'''   )
    if "mlp.fc2" in name:
        UpperCamelCase__							:       List[str]      =   name.replace('''mlp.fc2'''	,     '''output.dense'''   )
    if "class_embed" in name:
        UpperCamelCase__							:       List[Any]      =   name.replace('''class_embed'''	,     '''class_labels_classifier'''   )
    if "bbox_embed" in name:
        UpperCamelCase__							:       Optional[int]      =   name.replace('''bbox_embed'''	,     '''bbox_predictor'''   )
    if "vit.norm" in name:
        UpperCamelCase__							:       Tuple      =   name.replace('''vit.norm'''	,     '''vit.layernorm'''   )

    return name




def 	lowerCAmelCase_					(						__UpperCAmelCase:    dict	,     __UpperCAmelCase:    YolosForObjectDetection   )   ->			dict:
    for key in orig_state_dict.copy().keys():
        UpperCamelCase__							:       Union[str, Any]      =   orig_state_dict.pop(__UpperCAmelCase   )

        if "qkv" in key:
            UpperCamelCase__							:       Optional[int]      =   key.split('''.'''   )
            UpperCamelCase__							:       Union[str, Any]      =   int(key_split[2]   )
            UpperCamelCase__							:       Tuple      =   model.vit.encoder.layer[layer_num].attention.attention.all_head_size
            if "weight" in key:
                UpperCamelCase__							:       Dict      =   val[:dim, :]
                UpperCamelCase__							:       int      =   val[
                    dim : dim * 2, :
                ]
                UpperCamelCase__							:       Dict      =   val[-dim:, :]
            else:
                UpperCamelCase__							:       List[Any]      =   val[:dim]
                UpperCamelCase__							:       List[str]      =   val[dim : dim * 2]
                UpperCamelCase__							:       int      =   val[-dim:]
        else:
            UpperCamelCase__							:       Union[str, Any]      =   val

    return orig_state_dict




def 	lowerCAmelCase_					(						)   ->			torch.Tensor:
    UpperCamelCase__							:       str      =   '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    UpperCamelCase__							:       Dict      =   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]:
    UpperCamelCase__							:       List[str]      =   get_yolos_config(__UpperCAmelCase   )

    # load original state_dict
    UpperCamelCase__							:       List[Any]      =   torch.load(__UpperCAmelCase	,     map_location='''cpu'''   )['''model''']

    # load 🤗 model
    UpperCamelCase__							:       List[Any]      =   YolosForObjectDetection(__UpperCAmelCase   )
    model.eval()
    UpperCamelCase__							:       Dict      =   convert_state_dict(__UpperCAmelCase	,     __UpperCAmelCase   )
    model.load_state_dict(__UpperCAmelCase   )

    # Check outputs on an image, prepared by YolosImageProcessor
    UpperCamelCase__							:       List[Any]      =   800 if yolos_name != '''yolos_ti''' else 512
    UpperCamelCase__							:       int      =   YolosImageProcessor(format='''coco_detection'''	,     size=__UpperCAmelCase   )
    UpperCamelCase__							:       Union[str, Any]      =   image_processor(images=prepare_img()	,     return_tensors='''pt'''   )
    UpperCamelCase__							:       Dict      =   model(**__UpperCAmelCase   )
    UpperCamelCase__     ,UpperCamelCase__							:       Optional[Any]      =   outputs.logits, outputs.pred_boxes

    UpperCamelCase__     ,UpperCamelCase__							:       int      =   None, None
    if yolos_name == "yolos_ti":
        UpperCamelCase__							:       List[str]      =   torch.tensor(
            [[-39.5022, -11.9820, -17.6888], [-29.9574, -9.9769, -17.7691], [-42.3281, -20.7200, -30.6294]]   )
        UpperCamelCase__							:       Dict      =   torch.tensor(
            [[0.4021, 0.0836, 0.7979], [0.0184, 0.2609, 0.0364], [0.1781, 0.2004, 0.2095]]   )
    elif yolos_name == "yolos_s_200_pre":
        UpperCamelCase__							:       Optional[int]      =   torch.tensor(
            [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]]   )
        UpperCamelCase__							:       Union[str, Any]      =   torch.tensor(
            [[0.2559, 0.5455, 0.4706], [0.2989, 0.7279, 0.1875], [0.7732, 0.4017, 0.4462]]   )
    elif yolos_name == "yolos_s_300_pre":
        UpperCamelCase__							:       Optional[Any]      =   torch.tensor(
            [[-36.2220, -14.4385, -23.5457], [-35.6970, -14.7583, -21.3935], [-31.5939, -13.6042, -16.8049]]   )
        UpperCamelCase__							:       List[Any]      =   torch.tensor(
            [[0.7614, 0.2316, 0.4728], [0.7168, 0.4495, 0.3855], [0.4996, 0.1466, 0.9996]]   )
    elif yolos_name == "yolos_s_dWr":
        UpperCamelCase__							:       Any      =   torch.tensor(
            [[-42.8668, -24.1049, -41.1690], [-34.7456, -14.1274, -24.9194], [-33.7898, -12.1946, -25.6495]]   )
        UpperCamelCase__							:       List[Any]      =   torch.tensor(
            [[0.5587, 0.2773, 0.0605], [0.5004, 0.3014, 0.9994], [0.4999, 0.1548, 0.9994]]   )
    elif yolos_name == "yolos_base":
        UpperCamelCase__							:       Optional[Any]      =   torch.tensor(
            [[-40.6064, -24.3084, -32.6447], [-55.1990, -30.7719, -35.5877], [-51.4311, -33.3507, -35.6462]]   )
        UpperCamelCase__							:       List[Any]      =   torch.tensor(
            [[0.5555, 0.2794, 0.0655], [0.9049, 0.2664, 0.1894], [0.9183, 0.1984, 0.1635]]   )
    else:
        raise ValueError(f"Unknown yolos_name: {yolos_name}"   )

    assert torch.allclose(logits[0, :3, :3]	,     __UpperCAmelCase	,     atol=1e-4   )
    assert torch.allclose(pred_boxes[0, :3, :3]	,     __UpperCAmelCase	,     atol=1e-4   )

    Path(__UpperCAmelCase   ).mkdir(exist_ok=__UpperCAmelCase   )
    print(f"Saving model {yolos_name} to {pytorch_dump_folder_path}"   )
    model.save_pretrained(__UpperCAmelCase   )
    print(f"Saving image processor to {pytorch_dump_folder_path}"   )
    image_processor.save_pretrained(__UpperCAmelCase   )

    if push_to_hub:
        UpperCamelCase__							:       Any      =   {
            '''yolos_ti''': '''yolos-tiny''',
            '''yolos_s_200_pre''': '''yolos-small''',
            '''yolos_s_300_pre''': '''yolos-small-300''',
            '''yolos_s_dWr''': '''yolos-small-dwr''',
            '''yolos_base''': '''yolos-base''',
        }

        print('''Pushing to the hub...'''   )
        UpperCamelCase__							:       Optional[int]      =   model_mapping[yolos_name]
        image_processor.push_to_hub(__UpperCAmelCase	,     organization='''hustvl'''   )
        model.push_to_hub(__UpperCAmelCase	,     organization='''hustvl'''   )


if __name__ == "__main__":
  UpperCAmelCase_  							=				argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--yolos_name',
      default='yolos_s_200_pre',
      type=str,
      help=(
          'Name of the YOLOS model you\'d like to convert. Should be one of \'yolos_ti\', \'yolos_s_200_pre\','
          ' \'yolos_s_300_pre\', \'yolos_s_dWr\', \'yolos_base\'.'
      ),
  )
  parser.add_argument(
      '--checkpoint_path', default=None, 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.'
  )

  UpperCAmelCase_  							=				parser.parse_args()
  convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)

