


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,
)


__UpperCamelCase				:  str      					=							{
    '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_						( __lowercase       ):
   UpperCAmelCase_							:      Union[str, Any]						=   {}
   state_dict.pop('''pixel_mean'''			,       A_       )
   state_dict.pop('''pixel_std'''			,       A_       )

   UpperCAmelCase_							:      Tuple						=   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:
            UpperCAmelCase_							:      Tuple						=   key.replace(A_			,       A_       )

      if re.match(A_			,       A_       ):
         UpperCAmelCase_							:      List[str]						=   int(re.match(A_			,       A_       ).group(2       )       )
         if layer_nb == 0:
            UpperCAmelCase_							:      Union[str, Any]						=   key.replace('''layers.0'''			,       '''proj_in'''       )
         elif layer_nb == 1:
            UpperCAmelCase_							:      Union[str, Any]						=   key.replace('''layers.1'''			,       '''layers.0'''       )
         elif layer_nb == 2:
            UpperCAmelCase_							:      Optional[int]						=   key.replace('''layers.2'''			,       '''proj_out'''       )

      UpperCAmelCase_							:      int						=   value

   UpperCAmelCase_							:      Dict						=   model_state_dict[
       '''prompt_encoder.shared_embedding.positional_embedding'''
   ]

   return model_state_dict

def      snake_case_						( __lowercase			,       __lowercase			,       __lowercase			,       __lowercase="ybelkada/segment-anything"       ):
   UpperCAmelCase_							:      int						=   hf_hub_download(A_			,       F'''checkpoints/{model_name}.pth'''       )

   if "sam_vit_b" in model_name:
      UpperCAmelCase_							:      Optional[Any]						=   SamConfig()
   elif "sam_vit_l" in model_name:
      UpperCAmelCase_							:      Optional[Any]						=   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]			,       )

      UpperCAmelCase_							:      str						=   SamConfig(
          vision_config=A_			,       )
   elif "sam_vit_h" in model_name:
      UpperCAmelCase_							:      str						=   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]			,       )

      UpperCAmelCase_							:      Union[str, Any]						=   SamConfig(
          vision_config=A_			,       )

   UpperCAmelCase_							:      Optional[Any]						=   torch.load(A_			,       map_location='''cpu'''       )
   UpperCAmelCase_							:      Dict						=   replace_keys(A_       )

   UpperCAmelCase_							:      Optional[int]						=   SamImageProcessor()

   UpperCAmelCase_							:      List[str]						=   SamProcessor(image_processor=A_       )
   UpperCAmelCase_							:      Dict						=   SamModel(A_       )

   hf_model.load_state_dict(A_       )
   UpperCAmelCase_							:      Union[str, Any]						=   hf_model.to('''cuda'''       )

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

   UpperCAmelCase_							:      str						=   [[[4_0_0, 6_5_0]]]
   UpperCAmelCase_							:      List[str]						=   [[1]]

   UpperCAmelCase_							:      Union[str, Any]						=   processor(images=np.array(A_       )			,       return_tensors='''pt'''       ).to('''cuda'''       )

   with torch.no_grad():
      UpperCAmelCase_							:      Optional[Any]						=   hf_model(**A_       )
   UpperCAmelCase_							:      List[str]						=   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

      UpperCAmelCase_							:      Any						=   processor(
          images=np.array(A_       )			,       input_points=A_			,       input_labels=A_			,       return_tensors='''pt'''       ).to('''cuda'''       )

      with torch.no_grad():
         UpperCAmelCase_							:      Tuple						=   hf_model(**A_       )
      UpperCAmelCase_							:      Union[str, Any]						=   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

      UpperCAmelCase_							:      Optional[int]						=   ((7_5, 2_7_5, 1_7_2_5, 8_5_0),)

      UpperCAmelCase_							:      Union[str, Any]						=   processor(images=np.array(A_       )			,       input_boxes=A_			,       return_tensors='''pt'''       ).to('''cuda'''       )

      with torch.no_grad():
         UpperCAmelCase_							:      Optional[Any]						=   hf_model(**A_       )
      UpperCAmelCase_							:      Tuple						=   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.
      UpperCAmelCase_							:      Optional[int]						=   [[[4_0_0, 6_5_0], [8_0_0, 6_5_0]]]
      UpperCAmelCase_							:      str						=   [[1, 1]]

      UpperCAmelCase_							:      str						=   processor(
          images=np.array(A_       )			,       input_points=A_			,       input_labels=A_			,       return_tensors='''pt'''       ).to('''cuda'''       )

      with torch.no_grad():
         UpperCAmelCase_							:      List[str]						=   hf_model(**A_       )
      UpperCAmelCase_							:      List[Any]						=   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__":
       __UpperCamelCase				:  int      					=							argparse.ArgumentParser()
       __UpperCamelCase				:  Optional[Any]      					=							['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',
       )

       __UpperCamelCase				:  Dict      					=							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 requests
import torch

# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image

from transformers import (
    AutoTokenizer,
    BlipaConfig,
    BlipaForConditionalGeneration,
    BlipaProcessor,
    BlipaVisionConfig,
    BlipImageProcessor,
    OPTConfig,
    TaConfig,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
def      snake_case_						( ):
   UpperCAmelCase_							:      int						=   '''https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png'''
   UpperCAmelCase_							:      Dict						=   Image.open(requests.get(__lowercase			,       stream=__lowercase       ).raw       ).convert('''RGB'''       )

   return image
def      snake_case_						( __lowercase       ):
   UpperCAmelCase_							:      List[str]						=   []
   # fmt: off

   # vision encoder
   rename_keys.append(('''visual_encoder.cls_token''', '''vision_model.embeddings.class_embedding''')       )
   rename_keys.append(('''visual_encoder.pos_embed''', '''vision_model.embeddings.position_embedding''')       )
   rename_keys.append(('''visual_encoder.patch_embed.proj.weight''', '''vision_model.embeddings.patch_embedding.weight''')       )
   rename_keys.append(('''visual_encoder.patch_embed.proj.bias''', '''vision_model.embeddings.patch_embedding.bias''')       )
   rename_keys.append(('''ln_vision.weight''', '''vision_model.post_layernorm.weight''')       )
   rename_keys.append(('''ln_vision.bias''', '''vision_model.post_layernorm.bias''')       )

   for i in range(config.vision_config.num_hidden_layers       ):
      rename_keys.append((F'''visual_encoder.blocks.{i}.norm1.weight''', F'''vision_model.encoder.layers.{i}.layer_norm1.weight''')       )
      rename_keys.append((F'''visual_encoder.blocks.{i}.norm1.bias''', F'''vision_model.encoder.layers.{i}.layer_norm1.bias''')       )
      rename_keys.append((F'''visual_encoder.blocks.{i}.norm2.weight''', F'''vision_model.encoder.layers.{i}.layer_norm2.weight''')       )
      rename_keys.append((F'''visual_encoder.blocks.{i}.norm2.bias''', F'''vision_model.encoder.layers.{i}.layer_norm2.bias''')       )
      rename_keys.append((F'''visual_encoder.blocks.{i}.attn.qkv.weight''', F'''vision_model.encoder.layers.{i}.self_attn.qkv.weight''')       )
      rename_keys.append((F'''visual_encoder.blocks.{i}.attn.proj.weight''', F'''vision_model.encoder.layers.{i}.self_attn.projection.weight''',)       )
      rename_keys.append((F'''visual_encoder.blocks.{i}.attn.proj.bias''', F'''vision_model.encoder.layers.{i}.self_attn.projection.bias''')       )
      rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc1.weight''', F'''vision_model.encoder.layers.{i}.mlp.fc1.weight''')       )
      rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc1.bias''', F'''vision_model.encoder.layers.{i}.mlp.fc1.bias''')       )
      rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc2.weight''', F'''vision_model.encoder.layers.{i}.mlp.fc2.weight''')       )
      rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc2.bias''', F'''vision_model.encoder.layers.{i}.mlp.fc2.bias''')       )

   # QFormer
   rename_keys.append(('''Qformer.bert.embeddings.LayerNorm.weight''', '''qformer.layernorm.weight''')       )
   rename_keys.append(('''Qformer.bert.embeddings.LayerNorm.bias''', '''qformer.layernorm.bias''')       )

   # fmt: on
   return rename_keys
def      snake_case_						( __lowercase			,       __lowercase			,       __lowercase       ):
   UpperCAmelCase_							:      Any						=   dct.pop(__lowercase       )
   UpperCAmelCase_							:      Optional[Any]						=   val
def      snake_case_						( __lowercase			,       __lowercase       ):
   for i in range(config.vision_config.num_hidden_layers       ):
      # read in original q and v biases
      UpperCAmelCase_							:      Optional[int]						=   state_dict.pop(F'''visual_encoder.blocks.{i}.attn.q_bias'''       )
      UpperCAmelCase_							:      Any						=   state_dict.pop(F'''visual_encoder.blocks.{i}.attn.v_bias'''       )

      # next, set bias in the state dict
      UpperCAmelCase_							:      int						=   torch.cat((q_bias, torch.zeros_like(__lowercase			,       requires_grad=__lowercase       ), v_bias)       )
      UpperCAmelCase_							:      List[str]						=   qkv_bias
def      snake_case_						( __lowercase			,       __lowercase       ):
   UpperCAmelCase_							:      Optional[Any]						=   3_6_4 if '''coco''' in model_name else 2_2_4
   UpperCAmelCase_							:      Any						=   BlipaVisionConfig(image_size=__lowercase       ).to_dict()

   # make sure the models have proper bos_token_id and eos_token_id set (important for generation)
   # seems like flan-T5 models don't have bos_token_id properly set?
   if "opt-2.7b" in model_name:
      UpperCAmelCase_							:      Any						=   OPTConfig.from_pretrained('''facebook/opt-2.7b'''			,       eos_token_id=__lowercase       ).to_dict()
   elif "opt-6.7b" in model_name:
      UpperCAmelCase_							:      List[str]						=   OPTConfig.from_pretrained('''facebook/opt-6.7b'''			,       eos_token_id=__lowercase       ).to_dict()
   elif "t5-xl" in model_name:
      UpperCAmelCase_							:      List[str]						=   TaConfig.from_pretrained('''google/flan-t5-xl'''			,       dense_act_fn='''gelu'''			,       bos_token_id=1       ).to_dict()
   elif "t5-xxl" in model_name:
      UpperCAmelCase_							:      Any						=   TaConfig.from_pretrained('''google/flan-t5-xxl'''			,       dense_act_fn='''gelu'''			,       bos_token_id=1       ).to_dict()

   UpperCAmelCase_							:      List[Any]						=   BlipaConfig(vision_config=__lowercase			,       text_config=__lowercase       )

   return config, image_size
@torch.no_grad()
def      snake_case_						( __lowercase			,       __lowercase=None			,       __lowercase=False       ):
   UpperCAmelCase_							:      List[Any]						=   (
       AutoTokenizer.from_pretrained('''facebook/opt-2.7b'''       )
       if '''opt''' in model_name
       else AutoTokenizer.from_pretrained('''google/flan-t5-xl'''       )
   )
   UpperCAmelCase_							:      List[str]						=   tokenizer('''\n'''			,       add_special_tokens=__lowercase       ).input_ids[0]
   UpperCAmelCase_		,      UpperCAmelCase_							:      str						=   get_blipa_config(__lowercase			,       eos_token_id=__lowercase       )

   UpperCAmelCase_							:      List[Any]						=   BlipaForConditionalGeneration(__lowercase       ).eval()

   UpperCAmelCase_							:      Tuple						=   {
       '''blip2-opt-2.7b''': ('''blip2_opt''', '''pretrain_opt2.7b'''),
       '''blip2-opt-6.7b''': ('''blip2_opt''', '''pretrain_opt6.7b'''),
       '''blip2-opt-2.7b-coco''': ('''blip2_opt''', '''caption_coco_opt2.7b'''),
       '''blip2-opt-6.7b-coco''': ('''blip2_opt''', '''caption_coco_opt6.7b'''),
       '''blip2-flan-t5-xl''': ('''blip2_t5''', '''pretrain_flant5xl'''),
       '''blip2-flan-t5-xl-coco''': ('''blip2_t5''', '''caption_coco_flant5xl'''),
       '''blip2-flan-t5-xxl''': ('''blip2_t5''', '''pretrain_flant5xxl'''),
   }

   UpperCAmelCase_		,      UpperCAmelCase_							:      Optional[Any]						=   model_name_to_original[model_name]

   # load original model
   print('''Loading original model...'''       )
   UpperCAmelCase_							:      int						=   '''cuda''' if torch.cuda.is_available() else '''cpu'''
   UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_							:      Optional[Any]						=   load_model_and_preprocess(
       name=__lowercase			,       model_type=__lowercase			,       is_eval=__lowercase			,       device=__lowercase       )
   original_model.eval()
   print('''Done!'''       )

   # update state dict keys
   UpperCAmelCase_							:      Optional[Any]						=   original_model.state_dict()
   UpperCAmelCase_							:      List[Any]						=   create_rename_keys(__lowercase       )
   for src, dest in rename_keys:
      rename_key(__lowercase			,       __lowercase			,       __lowercase       )

   # some keys can be renamed efficiently
   for key, val in state_dict.copy().items():
      UpperCAmelCase_							:      Union[str, Any]						=   state_dict.pop(__lowercase       )
      if key.startswith('''Qformer.bert'''       ):
         UpperCAmelCase_							:      Tuple						=   key.replace('''Qformer.bert'''			,       '''qformer'''       )
      if "attention.self" in key:
         UpperCAmelCase_							:      Optional[Any]						=   key.replace('''self'''			,       '''attention'''       )
      if "opt_proj" in key:
         UpperCAmelCase_							:      Any						=   key.replace('''opt_proj'''			,       '''language_projection'''       )
      if "t5_proj" in key:
         UpperCAmelCase_							:      Tuple						=   key.replace('''t5_proj'''			,       '''language_projection'''       )
      if key.startswith('''opt'''       ):
         UpperCAmelCase_							:      Any						=   key.replace('''opt'''			,       '''language'''       )
      if key.startswith('''t5'''       ):
         UpperCAmelCase_							:      Optional[Any]						=   key.replace('''t5'''			,       '''language'''       )
      UpperCAmelCase_							:      List[str]						=   val

   # read in qv biases
   read_in_q_v_bias(__lowercase			,       __lowercase       )

   UpperCAmelCase_		,      UpperCAmelCase_							:      Optional[int]						=   hf_model.load_state_dict(__lowercase			,       strict=__lowercase       )
   assert len(__lowercase       ) == 0
   assert unexpected_keys == ["qformer.embeddings.position_ids"]

   UpperCAmelCase_							:      str						=   load_demo_image()
   UpperCAmelCase_							:      Any						=   vis_processors['''eval'''](__lowercase       ).unsqueeze(0       ).to(__lowercase       )
   UpperCAmelCase_							:      Optional[Any]						=   tokenizer(['''\n''']			,       return_tensors='''pt'''       ).input_ids.to(__lowercase       )

   # create processor
   UpperCAmelCase_							:      Optional[int]						=   BlipImageProcessor(
       size={'''height''': image_size, '''width''': image_size}			,       image_mean=__lowercase			,       image_std=__lowercase       )
   UpperCAmelCase_							:      Tuple						=   BlipaProcessor(image_processor=__lowercase			,       tokenizer=__lowercase       )
   UpperCAmelCase_							:      str						=   processor(images=__lowercase			,       return_tensors='''pt'''       ).pixel_values.to(__lowercase       )

   # make sure processor creates exact same pixel values
   assert torch.allclose(__lowercase			,       __lowercase       )

   original_model.to(__lowercase       )
   hf_model.to(__lowercase       )
   with torch.no_grad():
      if "opt" in model_name:
         UpperCAmelCase_							:      Tuple						=   original_model({'''image''': original_pixel_values, '''text_input''': ['''''']}       ).logits
         UpperCAmelCase_							:      Optional[int]						=   hf_model(__lowercase			,       __lowercase       ).logits
      else:
         UpperCAmelCase_							:      int						=   original_model(
             {'''image''': original_pixel_values, '''text_input''': ['''\n'''], '''text_output''': ['''\n''']}       ).logits
         UpperCAmelCase_							:      Optional[int]						=   input_ids.masked_fill(input_ids == tokenizer.pad_token_id			,       -1_0_0       )
         UpperCAmelCase_							:      int						=   hf_model(__lowercase			,       __lowercase			,       labels=__lowercase       ).logits

   assert original_logits.shape == logits.shape
   print('''First values of original logits:'''			,       original_logits[0, :3, :3]       )
   print('''First values of HF logits:'''			,       logits[0, :3, :3]       )

   # assert values
   if model_name == "blip2-flan-t5-xl":
      UpperCAmelCase_							:      Tuple						=   torch.tensor(
          [[-4_1.5_8_5_0, -4.4_4_4_0, -8.9_9_2_2], [-4_7.4_3_2_2, -5.9_1_4_3, -1.7_3_4_0]]			,       device=__lowercase       )
      assert torch.allclose(logits[0, :3, :3]			,       __lowercase			,       atol=1e-4       )
   elif model_name == "blip2-flan-t5-xl-coco":
      UpperCAmelCase_							:      Tuple						=   torch.tensor(
          [[-5_7.0_1_0_9, -9.8_9_6_7, -1_2.6_2_8_0], [-6_8.6_5_7_8, -1_2.7_1_9_1, -1_0.5_0_6_5]]			,       device=__lowercase       )
   else:
      # cast to same type
      UpperCAmelCase_							:      Optional[int]						=   logits.dtype
      assert torch.allclose(original_logits.to(__lowercase       )			,       __lowercase			,       atol=1e-2       )
   print('''Looks ok!'''       )

   print('''Generating a caption...'''       )
   UpperCAmelCase_							:      Union[str, Any]						=   ''''''
   UpperCAmelCase_							:      Optional[Any]						=   tokenizer(__lowercase			,       return_tensors='''pt'''       ).input_ids.to(__lowercase       )

   UpperCAmelCase_							:      int						=   original_model.generate({'''image''': original_pixel_values}       )
   UpperCAmelCase_							:      Optional[int]						=   hf_model.generate(
       __lowercase			,       __lowercase			,       do_sample=__lowercase			,       num_beams=5			,       max_length=3_0			,       min_length=1			,       top_p=0.9			,       repetition_penalty=1.0			,       length_penalty=1.0			,       temperature=1			,       )
   print('''Original generation:'''			,       __lowercase       )
   UpperCAmelCase_							:      Tuple						=   input_ids.shape[1]
   UpperCAmelCase_							:      str						=   processor.batch_decode(outputs[:, prompt_length:]			,       skip_special_tokens=__lowercase       )
   UpperCAmelCase_							:      Optional[int]						=   [text.strip() for text in output_text]
   print('''HF generation:'''			,       __lowercase       )

   if pytorch_dump_folder_path is not None:
      processor.save_pretrained(__lowercase       )
      hf_model.save_pretrained(__lowercase       )

   if push_to_hub:
      processor.push_to_hub(F'''nielsr/{model_name}'''       )
      hf_model.push_to_hub(F'''nielsr/{model_name}'''       )


if __name__ == "__main__":
       __UpperCamelCase				:  List[Any]      					=							argparse.ArgumentParser()
       __UpperCamelCase				:  Optional[Any]      					=							[
           'blip2-opt-2.7b',
           'blip2-opt-6.7b',
           'blip2-opt-2.7b-coco',
           'blip2-opt-6.7b-coco',
           'blip2-flan-t5-xl',
           'blip2-flan-t5-xl-coco',
           'blip2-flan-t5-xxl',
       ]
       parser.add_argument(
           '--model_name',
           default='blip2-opt-2.7b',
           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',
       )

       __UpperCamelCase				:  int      					=							parser.parse_args()

       convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)