


"""simple docstring"""


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,
)


_snake_case          =							{
    "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   snake_case						(				_a:     int							)->		Optional[int]:
      '''simple docstring'''


      lowerCamelCase__           =		{}
      state_dict.pop('pixel_mean'		,   _a							)
      state_dict.pop('pixel_std'		,   _a							)

      lowerCamelCase__           =		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:
                        lowerCamelCase__           =		key.replace(_a		,   _a							)

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

            lowerCamelCase__           =		value

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

      return model_state_dict



def   snake_case						(				_a:     Dict		,   _a:     Optional[Any]		,   _a:     Union[str, Any]		,   _a:     Optional[Any]="ybelkada/segment-anything"							)->		Tuple:
      '''simple docstring'''


      lowerCamelCase__           =		hf_hub_download(_a		,   F'checkpoints/{model_name}.pth'							)

      if "sam_vit_b" in model_name:
            lowerCamelCase__           =		SamConfig()
      elif "sam_vit_l" in model_name:
            lowerCamelCase__           =		SamVisionConfig(
                hidden_size=1024		,   num_hidden_layers=24		,   num_attention_heads=16		,   global_attn_indexes=[5, 11, 17, 23]		,   )

            lowerCamelCase__           =		SamConfig(
                vision_config=_a		,   )
      elif "sam_vit_h" in model_name:
            lowerCamelCase__           =		SamVisionConfig(
                hidden_size=1280		,   num_hidden_layers=32		,   num_attention_heads=16		,   global_attn_indexes=[7, 15, 23, 31]		,   )

            lowerCamelCase__           =		SamConfig(
                vision_config=_a		,   )

      lowerCamelCase__           =		torch.load(_a		,   map_location='cpu'							)
      lowerCamelCase__           =		replace_keys(_a							)

      lowerCamelCase__           =		SamImageProcessor()

      lowerCamelCase__           =		SamProcessor(image_processor=_a							)
      lowerCamelCase__           =		SamModel(_a							)

      hf_model.load_state_dict(_a							)
      lowerCamelCase__           =		hf_model.to('cuda'							)

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

      lowerCamelCase__           =		[[[400, 650]]]
      lowerCamelCase__           =		[[1]]

      lowerCamelCase__           =		processor(images=np.array(_a							)		,   return_tensors='pt'							).to('cuda'							)

      with torch.no_grad():
            lowerCamelCase__           =		hf_model(**_a							)
      lowerCamelCase__           =		output.iou_scores.squeeze()

      if model_name == "sam_vit_h_4b8939":
            assert scores[-1].item() == 0.579_8902_5115_9668

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

            with torch.no_grad():
                  lowerCamelCase__           =		hf_model(**_a							)
            lowerCamelCase__           =		output.iou_scores.squeeze()

            assert scores[-1].item() == 0.9712_6030_9219_3604

            lowerCamelCase__           =		((75, 275, 1725, 850),)

            lowerCamelCase__           =		processor(images=np.array(_a							)		,   input_boxes=_a		,   return_tensors='pt'							).to('cuda'							)

            with torch.no_grad():
                  lowerCamelCase__           =		hf_model(**_a							)
            lowerCamelCase__           =		output.iou_scores.squeeze()

            assert scores[-1].item() == 0.8686_0156_0592_6514

            # Test with 2 points and 1 image.
            lowerCamelCase__           =		[[[400, 650], [800, 650]]]
            lowerCamelCase__           =		[[1, 1]]

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

            with torch.no_grad():
                  lowerCamelCase__           =		hf_model(**_a							)
            lowerCamelCase__           =		output.iou_scores.squeeze()

            assert scores[-1].item() == 0.9936_0477_9243_4692


if __name__ == "__main__":
   _snake_case          =							argparse.ArgumentParser()
   _snake_case          =							["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",
   )

   _snake_case          =							parser.parse_args()

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







"""simple docstring"""


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


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

_snake_case          =							"\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   _a      (      SCREAMING_SNAKE_CASE_ ):
    a_    :				Union[PIL.Image.Image, np.ndarray]





class   _a      (      SCREAMING_SNAKE_CASE_ ):
    def __init__( self							:					Tuple							,					SCREAMING_SNAKE_CASE__							:					PriorTransformer							,					SCREAMING_SNAKE_CASE__							:					CLIPVisionModel							,					SCREAMING_SNAKE_CASE__							:					CLIPImageProcessor							,					SCREAMING_SNAKE_CASE__							:					HeunDiscreteScheduler							,					SCREAMING_SNAKE_CASE__							:					ShapERenderer							,					):
          super().__init__()

          self.register_modules(
              prior=SCREAMING_SNAKE_CASE__							,					image_encoder=SCREAMING_SNAKE_CASE__							,					image_processor=SCREAMING_SNAKE_CASE__							,					scheduler=SCREAMING_SNAKE_CASE__							,					renderer=SCREAMING_SNAKE_CASE__							,					)
    def      _UpperCamelCase       ( self							:					str							,					SCREAMING_SNAKE_CASE__							:					List[str]							,					SCREAMING_SNAKE_CASE__							:					Optional[int]							,					SCREAMING_SNAKE_CASE__							:					int							,					SCREAMING_SNAKE_CASE__							:					str							,					SCREAMING_SNAKE_CASE__							:					List[str]							,					SCREAMING_SNAKE_CASE__							:					List[str]				):
          if latents is None:
                lowerCamelCase__           =		randn_tensor(SCREAMING_SNAKE_CASE__							,					generator=SCREAMING_SNAKE_CASE__							,					device=SCREAMING_SNAKE_CASE__							,					dtype=SCREAMING_SNAKE_CASE__				)
          else:
                if latents.shape != shape:
                      raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {shape}'				)
                lowerCamelCase__           =		latents.to(SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		latents * scheduler.init_noise_sigma
          return latents
    def      _UpperCamelCase       ( self							:					List[Any]							,					SCREAMING_SNAKE_CASE__							:					List[str]=0				):
          if is_accelerate_available():
                from accelerate import cpu_offload
          else:
                raise ImportError('Please install accelerate via `pip install accelerate`'				)

          lowerCamelCase__           =		torch.device(F'cuda:{gpu_id}'				)

          lowerCamelCase__           =		[self.image_encoder, self.prior]
          for cpu_offloaded_model in models:
                if cpu_offloaded_model is not None:
                      cpu_offload(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)
    @property
    def      _UpperCamelCase       ( self							:					Dict				):
          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(SCREAMING_SNAKE_CASE__							,					'_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      _UpperCamelCase       ( self							:					Optional[int]							,					SCREAMING_SNAKE_CASE__							:					List[str]							,					SCREAMING_SNAKE_CASE__							:					Dict							,					SCREAMING_SNAKE_CASE__							:					Optional[Any]							,					SCREAMING_SNAKE_CASE__							:					int							,					):
          if isinstance(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				) and isinstance(image[0]							,					torch.Tensor				):
                lowerCamelCase__           =		torch.cat(SCREAMING_SNAKE_CASE__							,					axis=0				) if image[0].ndim == 4 else torch.stack(SCREAMING_SNAKE_CASE__							,					axis=0				)

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

          lowerCamelCase__           =		image.to(dtype=self.image_encoder.dtype							,					device=SCREAMING_SNAKE_CASE__				)

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

          lowerCamelCase__           =		image_embeds.repeat_interleave(SCREAMING_SNAKE_CASE__							,					dim=0				)

          if do_classifier_free_guidance:
                lowerCamelCase__           =		torch.zeros_like(SCREAMING_SNAKE_CASE__				)

                # 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
                lowerCamelCase__           =		torch.cat([negative_image_embeds, image_embeds]				)

          return image_embeds


    @torch.no_grad()
    @replace_example_docstring(SCREAMING_SNAKE_CASE__				)
    def __call__( self							:					List[Any]							,					SCREAMING_SNAKE_CASE__							:					Union[PIL.Image.Image, List[PIL.Image.Image]]							,					SCREAMING_SNAKE_CASE__							:					int = 1							,					SCREAMING_SNAKE_CASE__							:					int = 25							,					SCREAMING_SNAKE_CASE__							:					Optional[Union[torch.Generator, List[torch.Generator]]] = None							,					SCREAMING_SNAKE_CASE__							:					Optional[torch.FloatTensor] = None							,					SCREAMING_SNAKE_CASE__							:					float = 4.0							,					SCREAMING_SNAKE_CASE__							:					int = 64							,					SCREAMING_SNAKE_CASE__							:					Optional[str] = "pil"							,					SCREAMING_SNAKE_CASE__							:					bool = True							,					):

          if isinstance(SCREAMING_SNAKE_CASE__							,					PIL.Image.Image				):
                lowerCamelCase__           =		1
          elif isinstance(SCREAMING_SNAKE_CASE__							,					torch.Tensor				):
                lowerCamelCase__           =		image.shape[0]
          elif isinstance(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				) and isinstance(image[0]							,					(torch.Tensor, PIL.Image.Image)				):
                lowerCamelCase__           =		len(SCREAMING_SNAKE_CASE__				)
          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(SCREAMING_SNAKE_CASE__				)}'				)

          lowerCamelCase__           =		self._execution_device

          lowerCamelCase__           =		batch_size * num_images_per_prompt

          lowerCamelCase__           =		guidance_scale > 1.0
          lowerCamelCase__           =		self._encode_image(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)

          # prior

          self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE__							,					device=SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		self.scheduler.timesteps

          lowerCamelCase__           =		self.prior.config.num_embeddings
          lowerCamelCase__           =		self.prior.config.embedding_dim

          lowerCamelCase__           =		self.prepare_latents(
              (batch_size, num_embeddings * embedding_dim)							,					image_embeds.dtype							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					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
          lowerCamelCase__           =		latents.reshape(latents.shape[0]							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)

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

                lowerCamelCase__           =		self.prior(
                    SCREAMING_SNAKE_CASE__							,					timestep=SCREAMING_SNAKE_CASE__							,					proj_embedding=SCREAMING_SNAKE_CASE__							,					).predicted_image_embedding

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

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

                lowerCamelCase__           =		self.scheduler.step(
                    SCREAMING_SNAKE_CASE__							,					timestep=SCREAMING_SNAKE_CASE__							,					sample=SCREAMING_SNAKE_CASE__							,					).prev_sample

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

          lowerCamelCase__           =		[]
          for i, latent in enumerate(SCREAMING_SNAKE_CASE__				):
                print()
                lowerCamelCase__           =		self.renderer.decode(
                    latent[None, :]							,					SCREAMING_SNAKE_CASE__							,					size=SCREAMING_SNAKE_CASE__							,					ray_batch_size=40_96							,					n_coarse_samples=64							,					n_fine_samples=1_28							,					)

                images.append(SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		torch.stack(SCREAMING_SNAKE_CASE__				)

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

          lowerCamelCase__           =		images.cpu().numpy()

          if output_type == "pil":
                lowerCamelCase__           =		[self.numpy_to_pil(SCREAMING_SNAKE_CASE__				) 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=SCREAMING_SNAKE_CASE__				)



