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       __magic_name__(  __UpperCAmelCase		)						->    Any:



      '''simple docstring'''

      _lowerCamelCase			    =				{}
      state_dict.pop('''pixel_mean''' ,						__UpperCAmelCase		)
      state_dict.pop('''pixel_std''' ,						__UpperCAmelCase		)

      _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(__UpperCAmelCase ,						__UpperCAmelCase		)

            if re.match(__UpperCAmelCase ,						__UpperCAmelCase		):
                  _lowerCamelCase			    =				int(re.match(__UpperCAmelCase ,						__UpperCAmelCase		).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       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase="ybelkada/segment-anything"		)						->    Any:



      '''simple docstring'''

      _lowerCamelCase			    =				hf_hub_download(__UpperCAmelCase ,						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=__UpperCAmelCase ,						)
      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=__UpperCAmelCase ,						)

      _lowerCamelCase			    =				torch.load(__UpperCAmelCase ,						map_location='''cpu'''		)
      _lowerCamelCase			    =				replace_keys(__UpperCAmelCase		)

      _lowerCamelCase			    =				SamImageProcessor()

      _lowerCamelCase			    =				SamProcessor(image_processor=__UpperCAmelCase		)
      _lowerCamelCase			    =				SamModel(__UpperCAmelCase		)

      hf_model.load_state_dict(__UpperCAmelCase		)
      _lowerCamelCase			    =				hf_model.to('''cuda'''		)

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

      _lowerCamelCase			    =				[[[400, 650]]]
      _lowerCamelCase			    =				[[1]]

      _lowerCamelCase			    =				processor(images=np.array(__UpperCAmelCase		) ,						return_tensors='''pt'''		).to('''cuda'''		)

      with torch.no_grad():
            _lowerCamelCase			    =				hf_model(**__UpperCAmelCase		)
      _lowerCamelCase			    =				output.iou_scores.squeeze()

      if model_name == "sam_vit_h_4b8939":
            assert scores[-1].item() == 0.5_7_9_8_9_0_2_5_1_1_5_9_6_6_8

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

            with torch.no_grad():
                  _lowerCamelCase			    =				hf_model(**__UpperCAmelCase		)
            _lowerCamelCase			    =				output.iou_scores.squeeze()

            assert scores[-1].item() == 0.9_7_1_2_6_0_3_0_9_2_1_9_3_6_0_4

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

            _lowerCamelCase			    =				processor(images=np.array(__UpperCAmelCase		) ,						input_boxes=__UpperCAmelCase ,						return_tensors='''pt'''		).to('''cuda'''		)

            with torch.no_grad():
                  _lowerCamelCase			    =				hf_model(**__UpperCAmelCase		)
            _lowerCamelCase			    =				output.iou_scores.squeeze()

            assert scores[-1].item() == 0.8_6_8_6_0_1_5_6_0_5_9_2_6_5_1_4

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

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

            with torch.no_grad():
                  _lowerCamelCase			    =				hf_model(**__UpperCAmelCase		)
            _lowerCamelCase			    =				output.iou_scores.squeeze()

            assert scores[-1].item() == 0.9_9_3_6_0_4_7_7_9_2_4_3_4_6_9_2


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)



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()
snake_case__				= logging.get_logger(__name__)
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase=False		)						->    List[Any]:



      '''simple docstring'''

      _lowerCamelCase			    =				[]
      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"
            _lowerCamelCase			    =				[(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       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase=False		)						->    str:



      '''simple docstring'''

      for i in range(config.num_hidden_layers		):
            if base_model:
                  _lowerCamelCase			    =				''''''
            else:
                  _lowerCamelCase			    =				'''vit.'''
            # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
            _lowerCamelCase			    =				state_dict.pop(F'blocks.{i}.attn.qkv.weight'		)
            _lowerCamelCase			    =				state_dict.pop(F'blocks.{i}.attn.qkv.bias'		)
            # next, add query, keys and values (in that order) to the state dict
            _lowerCamelCase			    =				in_proj_weight[
                : config.hidden_size, :
            ]
            _lowerCamelCase			    =				in_proj_bias[: config.hidden_size]
            _lowerCamelCase			    =				in_proj_weight[
                config.hidden_size : config.hidden_size * 2, :
            ]
            _lowerCamelCase			    =				in_proj_bias[
                config.hidden_size : config.hidden_size * 2
            ]
            _lowerCamelCase			    =				in_proj_weight[
                -config.hidden_size :, :
            ]
            _lowerCamelCase			    =				in_proj_bias[-config.hidden_size :]
def       __magic_name__(  __UpperCAmelCase		)						->    Dict:



      '''simple docstring'''

      _lowerCamelCase			    =				['''head.weight''', '''head.bias''']
      for k in ignore_keys:
            state_dict.pop(__UpperCAmelCase ,						__UpperCAmelCase		)
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)						->    List[Any]:



      '''simple docstring'''

      _lowerCamelCase			    =				dct.pop(__UpperCAmelCase		)
      _lowerCamelCase			    =				val
def       __magic_name__(  )						->    List[str]:



      '''simple docstring'''

      _lowerCamelCase			    =				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
      _lowerCamelCase			    =				Image.open(requests.get(__UpperCAmelCase ,						stream=__UpperCAmelCase		).raw		)
      return im
@torch.no_grad()
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase=True		)						->    str:



      '''simple docstring'''

      _lowerCamelCase			    =				ViTConfig()
      # patch_size
      if model_name[-1] == "8":
            _lowerCamelCase			    =				8
      # set labels if required
      if not base_model:
            _lowerCamelCase			    =				1000
            _lowerCamelCase			    =				'''huggingface/label-files'''
            _lowerCamelCase			    =				'''imagenet-1k-id2label.json'''
            _lowerCamelCase			    =				json.load(open(hf_hub_download(__UpperCAmelCase ,						__UpperCAmelCase ,						repo_type='''dataset'''		) ,						'''r'''		)		)
            _lowerCamelCase			    =				{int(__UpperCAmelCase		): v for k, v in idalabel.items()}
            _lowerCamelCase			    =				idalabel
            _lowerCamelCase			    =				{v: k for k, v in idalabel.items()}
      # size of the architecture
      if model_name in ["dino_vits8", "dino_vits16"]:
            _lowerCamelCase			    =				384
            _lowerCamelCase			    =				1536
            _lowerCamelCase			    =				12
            _lowerCamelCase			    =				6

      # load original model from torch hub
      _lowerCamelCase			    =				torch.hub.load('''facebookresearch/dino:main''' ,						__UpperCAmelCase		)
      original_model.eval()

      # load state_dict of original model, remove and rename some keys
      _lowerCamelCase			    =				original_model.state_dict()
      if base_model:
            remove_classification_head_(__UpperCAmelCase		)
      _lowerCamelCase			    =				create_rename_keys(__UpperCAmelCase ,						base_model=__UpperCAmelCase		)
      for src, dest in rename_keys:
            rename_key(__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)
      read_in_q_k_v(__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)

      # load HuggingFace model
      if base_model:
            _lowerCamelCase			    =				ViTModel(__UpperCAmelCase ,						add_pooling_layer=__UpperCAmelCase		).eval()
      else:
            _lowerCamelCase			    =				ViTForImageClassification(__UpperCAmelCase		).eval()
      model.load_state_dict(__UpperCAmelCase		)

      # Check outputs on an image, prepared by ViTImageProcessor
      _lowerCamelCase			    =				ViTImageProcessor()
      _lowerCamelCase			    =				image_processor(images=prepare_img() ,						return_tensors='''pt'''		)
      _lowerCamelCase			    =				encoding['''pixel_values''']
      _lowerCamelCase			    =				model(__UpperCAmelCase		)

      if base_model:
            _lowerCamelCase			    =				original_model(__UpperCAmelCase		)
            assert torch.allclose(__UpperCAmelCase ,						outputs.last_hidden_state[:, 0, :] ,						atol=1E-1		)
      else:
            _lowerCamelCase			    =				original_model(__UpperCAmelCase		)
            assert logits.shape == outputs.logits.shape
            assert torch.allclose(__UpperCAmelCase ,						outputs.logits ,						atol=1E-3		)

      Path(__UpperCAmelCase		).mkdir(exist_ok=__UpperCAmelCase		)
      print(F'Saving model {model_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 __name__ == "__main__":
     snake_case__				= 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)
     snake_case__				= parser.parse_args()
     convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)