



from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel

from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ...utils import (
    BaseOutput,
    is_accelerate_available,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .renderer import ShapERenderer


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

_lowercase    	=		"\n    Examples:\n        ```py\n        >>> from PIL import Image\n        >>> import torch\n        >>> from diffusers import DiffusionPipeline\n        >>> from diffusers.utils import export_to_gif, load_image\n\n        >>> device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\n        >>> repo = \"openai/shap-e-img2img\"\n        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)\n        >>> pipe = pipe.to(device)\n\n        >>> guidance_scale = 3.0\n        >>> image_url = \"https://hf.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi.png\"\n        >>> image = load_image(image_url).convert(\"RGB\")\n\n        >>> images = pipe(\n        ...     image,\n        ...     guidance_scale=guidance_scale,\n        ...     num_inference_steps=64,\n        ...     frame_size=256,\n        ... ).images\n\n        >>> gif_path = export_to_gif(images[0], \"corgi_3d.gif\")\n        ```\n"



@dataclass
class      _UpperCAmelCase      (							A__  ):
     UpperCamelCase__		     =					42







class      _UpperCAmelCase      (							A__  ):




     def __init__(					self			,    a__			,    a__			,    a__			,    a__			,    a__			,    ):
         super().__init__()

         self.register_modules(
             prior=a__			,    image_encoder=a__			,    image_processor=a__			,    scheduler=a__			,    renderer=a__			,    )




     def 		snake_case_		(					self			,    a__			,    a__			,    a__			,    a__			,    a__			,    a__):
         if latents is None:
             A__					=       randn_tensor(a__			,    generator=a__			,    device=a__			,    dtype=a__)
         else:
             if latents.shape != shape:
                 raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {shape}")
             A__					=       latents.to(a__)

         A__					=       latents * scheduler.init_noise_sigma
         return latents




     def 		snake_case_		(					self			,    a__=0):
         if is_accelerate_available():
             from accelerate import cpu_offload
         else:
             raise ImportError('''Please install accelerate via `pip install accelerate`''')

         A__					=       torch.device(F"cuda:{gpu_id}")

         A__					=       [self.image_encoder, self.prior]
         for cpu_offloaded_model in models:
             if cpu_offloaded_model is not None:
                 cpu_offload(a__			,    a__)




     @property
     def 		snake_case_		(					self):
         if self.device != torch.device('''meta''') or not hasattr(self.image_encoder			,    '''_hf_hook'''):
             return self.device
         for module in self.image_encoder.modules():
             if (
                 hasattr(a__			,    '''_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




     def 		snake_case_		(					self			,    a__			,    a__			,    a__			,    a__			,    ):
         if isinstance(a__			,    a__) and isinstance(image[0]			,    torch.Tensor):
             A__					=       torch.cat(a__			,    axis=0) if image[0].ndim == 4 else torch.stack(a__			,    axis=0)

         if not isinstance(a__			,    torch.Tensor):
             A__					=       self.image_processor(a__			,    return_tensors='''pt''').pixel_values[0].unsqueeze(0)

         A__					=       image.to(dtype=self.image_encoder.dtype			,    device=a__)

         A__					=       self.image_encoder(a__)['''last_hidden_state''']
         A__					=       image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

         A__					=       image_embeds.repeat_interleave(a__			,    dim=0)

         if do_classifier_free_guidance:
             A__					=       torch.zeros_like(a__)

             # For classifier free guidance, we need to do two forward passes.
             # Here we concatenate the unconditional and text embeddings into a single batch
             # to avoid doing two forward passes
             A__					=       torch.cat([negative_image_embeds, image_embeds])

         return image_embeds




     @torch.no_grad()
     @replace_example_docstring(a__)
     def __call__(					self			,    a__			,    a__ = 1			,    a__ = 2_5			,    a__ = None			,    a__ = None			,    a__ = 4.0			,    a__ = 6_4			,    a__ = "pil"			,    a__ = True			,    ):

         if isinstance(a__			,    PIL.Image.Image):
             A__					=       1
         elif isinstance(a__			,    torch.Tensor):
             A__					=       image.shape[0]
         elif isinstance(a__			,    a__) and isinstance(image[0]			,    (torch.Tensor, PIL.Image.Image)):
             A__					=       len(a__)
         else:
             raise ValueError(
                 F"`image` has to be of type `PIL.Image.Image`, `torch.Tensor`, `List[PIL.Image.Image]` or `List[torch.Tensor]` but is {type(a__)}")

         A__					=       self._execution_device

         A__					=       batch_size * num_images_per_prompt

         A__					=       guidance_scale > 1.0
         A__					=       self._encode_image(a__			,    a__			,    a__			,    a__)

         # prior

         self.scheduler.set_timesteps(a__			,    device=a__)
         A__					=       self.scheduler.timesteps

         A__					=       self.prior.config.num_embeddings
         A__					=       self.prior.config.embedding_dim

         A__					=       self.prepare_latents(
             (batch_size, num_embeddings * embedding_dim)			,    image_embeds.dtype			,    a__			,    a__			,    a__			,    self.scheduler			,    )

         # YiYi notes: for testing only to match ldm, we can directly create a latents with desired shape: batch_size, num_embeddings, embedding_dim
         A__					=       latents.reshape(latents.shape[0]			,    a__			,    a__)

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

             A__					=       self.prior(
                 a__			,    timestep=a__			,    proj_embedding=a__			,    ).predicted_image_embedding

             # remove the variance
             A__			,						A__					=       noise_pred.split(
                 scaled_model_input.shape[2]			,    dim=2)  # batch_size, num_embeddings, embedding_dim

             if do_classifier_free_guidance is not None:
                 A__			,						A__					=       noise_pred.chunk(2)
                 A__					=       noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

             A__					=       self.scheduler.step(
                 a__			,    timestep=a__			,    sample=a__			,    ).prev_sample

         if output_type == "latent":
             return ShapEPipelineOutput(images=a__)

         A__					=       []
         for i, latent in enumerate(a__):
             print()
             A__					=       self.renderer.decode(
                 latent[None, :]			,    a__			,    size=a__			,    ray_batch_size=4_0_9_6			,    n_coarse_samples=6_4			,    n_fine_samples=1_2_8			,    )

             images.append(a__)

         A__					=       torch.stack(a__)

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

         A__					=       images.cpu().numpy()

         if output_type == "pil":
             A__					=       [self.numpy_to_pil(a__) for image in images]

         # Offload last model to CPU
         if hasattr(self			,    '''final_offload_hook''') and self.final_offload_hook is not None:
             self.final_offload_hook.offload()

         if not return_dict:
             return (images,)

         return ShapEPipelineOutput(images=a__)





import argparse
import re

import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SamConfig,
    SamImageProcessor,
    SamModel,
    SamProcessor,
    SamVisionConfig,
)


_lowercase    	=		{
    "iou_prediction_head.layers.0": "iou_prediction_head.proj_in",
    "iou_prediction_head.layers.1": "iou_prediction_head.layers.0",
    "iou_prediction_head.layers.2": "iou_prediction_head.proj_out",
    "mask_decoder.output_upscaling.0": "mask_decoder.upscale_conv1",
    "mask_decoder.output_upscaling.1": "mask_decoder.upscale_layer_norm",
    "mask_decoder.output_upscaling.3": "mask_decoder.upscale_conv2",
    "mask_downscaling.0": "mask_embed.conv1",
    "mask_downscaling.1": "mask_embed.layer_norm1",
    "mask_downscaling.3": "mask_embed.conv2",
    "mask_downscaling.4": "mask_embed.layer_norm2",
    "mask_downscaling.6": "mask_embed.conv3",
    "point_embeddings": "point_embed",
    "pe_layer.positional_encoding_gaussian_matrix": "shared_embedding.positional_embedding",
    "image_encoder": "vision_encoder",
    "neck.0": "neck.conv1",
    "neck.1": "neck.layer_norm1",
    "neck.2": "neck.conv2",
    "neck.3": "neck.layer_norm2",
    "patch_embed.proj": "patch_embed.projection",
    ".norm": ".layer_norm",
    "blocks": "layers",
}



def 		lowerCAmelCase__     (       UpperCamelCase_					:							Any						)->	int:
    A__					=       {}
    state_dict.pop('''pixel_mean'''			,							UpperCamelCase_						)
    state_dict.pop('''pixel_std'''			,							UpperCamelCase_						)

    A__					=       r'''.*.output_hypernetworks_mlps.(\d+).layers.(\d+).*'''

    for key, value in state_dict.items():
        for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
            if key_to_modify in key:
                A__					=       key.replace(UpperCamelCase_			,							UpperCamelCase_						)

        if re.match(UpperCamelCase_			,							UpperCamelCase_						):
            A__					=       int(re.match(UpperCamelCase_			,							UpperCamelCase_						).group(2						)						)
            if layer_nb == 0:
                A__					=       key.replace('''layers.0'''			,							'''proj_in'''						)
            elif layer_nb == 1:
                A__					=       key.replace('''layers.1'''			,							'''layers.0'''						)
            elif layer_nb == 2:
                A__					=       key.replace('''layers.2'''			,							'''proj_out'''						)

        A__					=       value

    A__					=       model_state_dict[
        '''prompt_encoder.shared_embedding.positional_embedding'''
    ]

    return model_state_dict



def 		lowerCAmelCase__     (       UpperCamelCase_					:							Union[str, Any]			,							UpperCamelCase_					:							List[str]			,							UpperCamelCase_					:							Any			,							UpperCamelCase_					:							Union[str, Any]="ybelkada/segment-anything"						)->	Optional[int]:
    A__					=       hf_hub_download(UpperCamelCase_			,							f"checkpoints/{model_name}.pth"						)

    if "sam_vit_b" in model_name:
        A__					=       SamConfig()
    elif "sam_vit_l" in model_name:
        A__					=       SamVisionConfig(
            hidden_size=1_0_2_4			,							num_hidden_layers=2_4			,							num_attention_heads=1_6			,							global_attn_indexes=[5, 1_1, 1_7, 2_3]			,							)

        A__					=       SamConfig(
            vision_config=UpperCamelCase_			,							)
    elif "sam_vit_h" in model_name:
        A__					=       SamVisionConfig(
            hidden_size=1_2_8_0			,							num_hidden_layers=3_2			,							num_attention_heads=1_6			,							global_attn_indexes=[7, 1_5, 2_3, 3_1]			,							)

        A__					=       SamConfig(
            vision_config=UpperCamelCase_			,							)

    A__					=       torch.load(UpperCamelCase_			,							map_location='''cpu'''						)
    A__					=       replace_keys(UpperCamelCase_						)

    A__					=       SamImageProcessor()

    A__					=       SamProcessor(image_processor=UpperCamelCase_						)
    A__					=       SamModel(UpperCamelCase_						)

    hf_model.load_state_dict(UpperCamelCase_						)
    A__					=       hf_model.to('''cuda'''						)

    A__					=       '''https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png'''
    A__					=       Image.open(requests.get(UpperCamelCase_			,							stream=UpperCamelCase_						).raw						).convert('''RGB'''						)

    A__					=       [[[4_0_0, 6_5_0]]]
    A__					=       [[1]]

    A__					=       processor(images=np.array(UpperCamelCase_						)			,							return_tensors='''pt'''						).to('''cuda'''						)

    with torch.no_grad():
        A__					=       hf_model(**UpperCamelCase_						)
    A__					=       output.iou_scores.squeeze()

    if model_name == "sam_vit_h_4b8939":
        assert scores[-1].item() == 0.579890251159668

        A__					=       processor(
            images=np.array(UpperCamelCase_						)			,							input_points=UpperCamelCase_			,							input_labels=UpperCamelCase_			,							return_tensors='''pt'''						).to('''cuda'''						)

        with torch.no_grad():
            A__					=       hf_model(**UpperCamelCase_						)
        A__					=       output.iou_scores.squeeze()

        assert scores[-1].item() == 0.9712603092193604

        A__					=       ((7_5, 2_7_5, 1_7_2_5, 8_5_0),)

        A__					=       processor(images=np.array(UpperCamelCase_						)			,							input_boxes=UpperCamelCase_			,							return_tensors='''pt'''						).to('''cuda'''						)

        with torch.no_grad():
            A__					=       hf_model(**UpperCamelCase_						)
        A__					=       output.iou_scores.squeeze()

        assert scores[-1].item() == 0.8686015605926514

        # Test with 2 points and 1 image.
        A__					=       [[[4_0_0, 6_5_0], [8_0_0, 6_5_0]]]
        A__					=       [[1, 1]]

        A__					=       processor(
            images=np.array(UpperCamelCase_						)			,							input_points=UpperCamelCase_			,							input_labels=UpperCamelCase_			,							return_tensors='''pt'''						).to('''cuda'''						)

        with torch.no_grad():
            A__					=       hf_model(**UpperCamelCase_						)
        A__					=       output.iou_scores.squeeze()

        assert scores[-1].item() == 0.9936047792434692


if __name__ == "__main__":
   _lowercase    	=		argparse.ArgumentParser()
   _lowercase    	=		["sam_vit_b_01ec64", "sam_vit_h_4b8939", "sam_vit_l_0b3195"]
   parser.add_argument(
       "--model_name",
       default="sam_vit_h_4b8939",
       choices=choices,
       type=str,
       help="Path to hf config.json of model to convert",
   )
   parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
   parser.add_argument(
       "--push_to_hub",
       action="store_true",
       help="Whether to push the model and processor to the hub after converting",
   )
   parser.add_argument(
       "--model_hub_id",
       default="ybelkada/segment-anything",
       choices=choices,
       type=str,
       help="Path to hf config.json of model to convert",
   )

   _lowercase    	=		parser.parse_args()

   convert_sam_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub, args.model_hub_id)
