

'''simple docstring'''





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 ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


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






def 							_lowerCAmelCase (      __magic_name__	:    List[Any]      ,   __magic_name__	:    List[Any]=False	)							->     Any:
      lowercase   :       Union[str, Any]								=[]
      for i in range(config.num_hidden_layers	):
            # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
            rename_keys.append((f'''blocks.{i}.norm1.weight''', f'''vit.encoder.layer.{i}.layernorm_before.weight''')	)
            rename_keys.append((f'''blocks.{i}.norm1.bias''', f'''vit.encoder.layer.{i}.layernorm_before.bias''')	)
            rename_keys.append((f'''blocks.{i}.attn.proj.weight''', f'''vit.encoder.layer.{i}.attention.output.dense.weight''')	)
            rename_keys.append((f'''blocks.{i}.attn.proj.bias''', f'''vit.encoder.layer.{i}.attention.output.dense.bias''')	)
            rename_keys.append((f'''blocks.{i}.norm2.weight''', f'''vit.encoder.layer.{i}.layernorm_after.weight''')	)
            rename_keys.append((f'''blocks.{i}.norm2.bias''', f'''vit.encoder.layer.{i}.layernorm_after.bias''')	)
            rename_keys.append((f'''blocks.{i}.mlp.fc1.weight''', f'''vit.encoder.layer.{i}.intermediate.dense.weight''')	)
            rename_keys.append((f'''blocks.{i}.mlp.fc1.bias''', f'''vit.encoder.layer.{i}.intermediate.dense.bias''')	)
            rename_keys.append((f'''blocks.{i}.mlp.fc2.weight''', f'''vit.encoder.layer.{i}.output.dense.weight''')	)
            rename_keys.append((f'''blocks.{i}.mlp.fc2.bias''', f'''vit.encoder.layer.{i}.output.dense.bias''')	)

      # projection layer + position embeddings
      rename_keys.extend(
          [
              ('''cls_token''', '''vit.embeddings.cls_token'''),
              ('''patch_embed.proj.weight''', '''vit.embeddings.patch_embeddings.projection.weight'''),
              ('''patch_embed.proj.bias''', '''vit.embeddings.patch_embeddings.projection.bias'''),
              ('''pos_embed''', '''vit.embeddings.position_embeddings'''),
          ]	)

      if base_model:
            # layernorm + pooler
            rename_keys.extend(
                [
                    ('''norm.weight''', '''layernorm.weight'''),
                    ('''norm.bias''', '''layernorm.bias'''),
                ]	)

            # if just the base model, we should remove "vit" from all keys that start with "vit"
            lowercase   :       Tuple								=[(pair[0], pair[1][4:]) if pair[1].startswith('''vit'''	) else pair for pair in rename_keys]
      else:
            # layernorm + classification head
            rename_keys.extend(
                [
                    ('''norm.weight''', '''vit.layernorm.weight'''),
                    ('''norm.bias''', '''vit.layernorm.bias'''),
                    ('''head.weight''', '''classifier.weight'''),
                    ('''head.bias''', '''classifier.bias'''),
                ]	)

      return rename_keys






def 							_lowerCAmelCase (      __magic_name__	:    Union[str, Any]      ,   __magic_name__	:    Optional[int]      ,   __magic_name__	:    List[Any]=False	)							->     str:


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






def 							_lowerCAmelCase (      __magic_name__	:    int	)							->     Optional[Any]:
      lowercase   :       List[str]								=["head.weight", "head.bias"]
      for k in ignore_keys:
            state_dict.pop(lowerCamelCase__      ,   lowerCamelCase__	)






def 							_lowerCAmelCase (      __magic_name__	:    Optional[int]      ,   __magic_name__	:    List[str]      ,   __magic_name__	:    List[Any]	)							->     Dict:
      lowercase   :       str								=dct.pop(lowerCamelCase__	)
      lowercase   :       Optional[Any]								=val






def 							_lowerCAmelCase (      )							->     Tuple:
      lowercase   :       Dict								="http://images.cocodataset.org/val2017/000000039769.jpg"
      lowercase   :       int								=Image.open(requests.get(lowerCamelCase__      ,   stream=lowerCamelCase__	).raw	)
      return im






@torch.no_grad()
def 							_lowerCAmelCase (      __magic_name__	:    Any      ,   __magic_name__	:    List[Any]      ,   __magic_name__	:    List[str]=True	)							->     str:
      lowercase   :       Dict								=ViTConfig()
      # patch_size
      if model_name[-1] == "8":
            lowercase   :       Union[str, Any]								=8
      # set labels if required
      if not base_model:
            lowercase   :       str								=1000
            lowercase   :       str								="huggingface/label-files"
            lowercase   :       str								="imagenet-1k-id2label.json"
            lowercase   :       Optional[int]								=json.load(open(hf_hub_download(lowerCamelCase__      ,   lowerCamelCase__      ,   repo_type='''dataset'''	)      ,   '''r'''	)	)
            lowercase   :       Tuple								={int(lowerCamelCase__	): v for k, v in idalabel.items()}
            lowercase   :       str								=idalabel
            lowercase   :       Tuple								={v: k for k, v in idalabel.items()}
      # size of the architecture
      if model_name in ["dino_vits8", "dino_vits16"]:
            lowercase   :       str								=384
            lowercase   :       Optional[int]								=1536
            lowercase   :       Union[str, Any]								=12
            lowercase   :       int								=6

      # load original model from torch hub
      lowercase   :       List[str]								=torch.hub.load('''facebookresearch/dino:main'''      ,   lowerCamelCase__	)
      original_model.eval()

      # load state_dict of original model, remove and rename some keys
      lowercase   :       Tuple								=original_model.state_dict()
      if base_model:
            remove_classification_head_(lowerCamelCase__	)
      lowercase   :       Union[str, Any]								=create_rename_keys(lowerCamelCase__      ,   base_model=lowerCamelCase__	)
      for src, dest in rename_keys:
            rename_key(lowerCamelCase__      ,   lowerCamelCase__      ,   lowerCamelCase__	)
      read_in_q_k_v(lowerCamelCase__      ,   lowerCamelCase__      ,   lowerCamelCase__	)

      # load HuggingFace model
      if base_model:
            lowercase   :       List[str]								=ViTModel(lowerCamelCase__      ,   add_pooling_layer=lowerCamelCase__	).eval()
      else:
            lowercase   :       str								=ViTForImageClassification(lowerCamelCase__	).eval()
      model.load_state_dict(lowerCamelCase__	)

      # Check outputs on an image, prepared by ViTImageProcessor
      lowercase   :       List[str]								=ViTImageProcessor()
      lowercase   :       Dict								=image_processor(images=prepare_img()      ,   return_tensors='''pt'''	)
      lowercase   :       Dict								=encoding["pixel_values"]
      lowercase   :       str								=model(lowerCamelCase__	)

      if base_model:
            lowercase   :       List[Any]								=original_model(lowerCamelCase__	)
            assert torch.allclose(lowerCamelCase__      ,   outputs.last_hidden_state[:, 0, :]      ,   atol=1E-1	)
      else:
            lowercase   :       int								=original_model(lowerCamelCase__	)
            assert logits.shape == outputs.logits.shape
            assert torch.allclose(lowerCamelCase__      ,   outputs.logits      ,   atol=1E-3	)

      Path(lowerCamelCase__	).mkdir(exist_ok=lowerCamelCase__	)
      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 __name__ == "__main__":
       UpperCamelCase_							=					argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           """--model_name""",
           default="""dino_vitb16""",
           type=str,
           help="""Name of the model trained with DINO you'd like to convert.""",
       )
       parser.add_argument(
           """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
       )
       parser.add_argument(
           """--base_model""",
           action="""store_true""",
           help="""Whether to only convert the base model (no projection head weights).""",
       )

       parser.set_defaults(base_model=True)
       UpperCamelCase_							=					parser.parse_args()
       convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)








'''simple docstring'''





from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class 		__SCREAMING_SNAKE_CASE       ( lowercase__			):







				def __init__(					self    :		Optional[Any]		,				UpperCAmelCase__    :		Optional[int]		,				UpperCAmelCase__    :		int      ):
										'''simple docstring'''
										super().__init__()

										# make sure scheduler can always be converted to DDIM
										lowercase   :       Any								=DDIMScheduler.from_config(scheduler.config      )

										self.register_modules(unet=UpperCAmelCase__		,				scheduler=UpperCAmelCase__      )







				@torch.no_grad()
				def __call__(					self    :		List[Any]		,				UpperCAmelCase__    :		int = 1		,				UpperCAmelCase__    :		Optional[Union[torch.Generator, List[torch.Generator]]] = None		,				UpperCAmelCase__    :		float = 0.0		,				UpperCAmelCase__    :		int = 50		,				UpperCAmelCase__    :		Optional[bool] = None		,				UpperCAmelCase__    :		Optional[str] = "pil"		,				UpperCAmelCase__    :		bool = True		,				):
										'''simple docstring'''

										# Sample gaussian noise to begin loop
										if isinstance(self.unet.config.sample_size		,				UpperCAmelCase__      ):
																lowercase   :       Optional[int]								=(
																    batch_size,
																    self.unet.config.in_channels,
																    self.unet.config.sample_size,
																    self.unet.config.sample_size,
																)
										else:
																lowercase   :       Optional[int]								=(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

										if isinstance(UpperCAmelCase__		,				UpperCAmelCase__      ) and len(UpperCAmelCase__      ) != batch_size:
																raise ValueError(
																    F'''You have passed a list of generators of length {len(UpperCAmelCase__      )}, but requested an effective batch'''
																    F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''      )

										lowercase   :       str								=randn_tensor(UpperCAmelCase__		,				generator=UpperCAmelCase__		,				device=self.device		,				dtype=self.unet.dtype      )

										# set step values
										self.scheduler.set_timesteps(UpperCAmelCase__      )

										for t in self.progress_bar(self.scheduler.timesteps      ):
																# 1. predict noise model_output
																lowercase   :       Dict								=self.unet(UpperCAmelCase__		,				UpperCAmelCase__      ).sample

																# 2. predict previous mean of image x_t-1 and add variance depending on eta
																# eta corresponds to η in paper and should be between [0, 1]
																# do x_t -> x_t-1
																lowercase   :       Dict								=self.scheduler.step(
																    UpperCAmelCase__		,				UpperCAmelCase__		,				UpperCAmelCase__		,				eta=UpperCAmelCase__		,				use_clipped_model_output=UpperCAmelCase__		,				generator=UpperCAmelCase__      ).prev_sample

										lowercase   :       Optional[Any]								=(image / 2 + 0.5).clamp(0		,				1      )
										lowercase   :       Optional[Any]								=image.cpu().permute(0		,				2		,				3		,				1      ).numpy()
										if output_type == "pil":
																lowercase   :       List[str]								=self.numpy_to_pil(UpperCAmelCase__      )

										if not return_dict:
																return (image,)

										return ImagePipelineOutput(images=UpperCAmelCase__      )





