

'''simple docstring'''
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 	_lowerCAmelCase (						)				->      int:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   ='https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png'
     _SCREAMING_SNAKE_CASE							   =Image.open(requests.get(_UpperCamelCase							,			stream=_UpperCamelCase    ).raw    ).convert('RGB'    )

     return image


def 	_lowerCAmelCase (						_UpperCamelCase						:					Union[str, Any]    )				->      Union[str, Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =[]
     # 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 	_lowerCAmelCase (						_UpperCamelCase						:					Tuple							,			_UpperCamelCase						:					str							,			_UpperCamelCase						:					List[str]    )				->      List[str]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =dct.pop(_UpperCamelCase    )
     _SCREAMING_SNAKE_CASE							   =val


def 	_lowerCAmelCase (						_UpperCamelCase						:					int							,			_UpperCamelCase						:					int    )				->      Any:
     """simple docstring"""




     for i in range(config.vision_config.num_hidden_layers    ):
          # read in original q and v biases
          _SCREAMING_SNAKE_CASE							   =state_dict.pop(f"visual_encoder.blocks.{i}.attn.q_bias"    )
          _SCREAMING_SNAKE_CASE							   =state_dict.pop(f"visual_encoder.blocks.{i}.attn.v_bias"    )

          # next, set bias in the state dict
          _SCREAMING_SNAKE_CASE							   =torch.cat((q_bias, torch.zeros_like(_UpperCamelCase							,			requires_grad=_UpperCamelCase    ), v_bias)    )
          _SCREAMING_SNAKE_CASE							   =qkv_bias


def 	_lowerCAmelCase (						_UpperCamelCase						:					Union[str, Any]							,			_UpperCamelCase						:					Tuple    )				->      List[str]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =3_64 if 'coco' in model_name else 2_24
     _SCREAMING_SNAKE_CASE							   =BlipaVisionConfig(image_size=_UpperCamelCase    ).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:
          _SCREAMING_SNAKE_CASE							   =OPTConfig.from_pretrained('facebook/opt-2.7b'							,			eos_token_id=_UpperCamelCase    ).to_dict()
     elif "opt-6.7b" in model_name:
          _SCREAMING_SNAKE_CASE							   =OPTConfig.from_pretrained('facebook/opt-6.7b'							,			eos_token_id=_UpperCamelCase    ).to_dict()
     elif "t5-xl" in model_name:
          _SCREAMING_SNAKE_CASE							   =TaConfig.from_pretrained('google/flan-t5-xl'							,			dense_act_fn='gelu'							,			bos_token_id=1    ).to_dict()
     elif "t5-xxl" in model_name:
          _SCREAMING_SNAKE_CASE							   =TaConfig.from_pretrained('google/flan-t5-xxl'							,			dense_act_fn='gelu'							,			bos_token_id=1    ).to_dict()

     _SCREAMING_SNAKE_CASE							   =BlipaConfig(vision_config=_UpperCamelCase							,			text_config=_UpperCamelCase    )

     return config, image_size


@torch.no_grad()
def 	_lowerCAmelCase (						_UpperCamelCase						:					List[Any]							,			_UpperCamelCase						:					Union[str, Any]=None							,			_UpperCamelCase						:					List[str]=False    )				->      Dict:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =(
         AutoTokenizer.from_pretrained('facebook/opt-2.7b'    )
         if 'opt' in model_name
         else AutoTokenizer.from_pretrained('google/flan-t5-xl'    )
     )
     _SCREAMING_SNAKE_CASE							   =tokenizer('\n'							,			add_special_tokens=_UpperCamelCase    ).input_ids[0]
     _SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE							   =get_blipa_config(_UpperCamelCase							,			eos_token_id=_UpperCamelCase    )

     _SCREAMING_SNAKE_CASE							   =BlipaForConditionalGeneration(_UpperCamelCase    ).eval()

     _SCREAMING_SNAKE_CASE							   ={
         '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'),
     }

     _SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE							   =model_name_to_original[model_name]

     # load original model
     print('Loading original model...'    )
     _SCREAMING_SNAKE_CASE							   ='cuda' if torch.cuda.is_available() else 'cpu'
     _SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE							   =load_model_and_preprocess(
         name=_UpperCamelCase							,			model_type=_UpperCamelCase							,			is_eval=_UpperCamelCase							,			device=_UpperCamelCase    )
     original_model.eval()
     print('Done!'    )

     # update state dict keys
     _SCREAMING_SNAKE_CASE							   =original_model.state_dict()
     _SCREAMING_SNAKE_CASE							   =create_rename_keys(_UpperCamelCase    )
     for src, dest in rename_keys:
          rename_key(_UpperCamelCase							,			_UpperCamelCase							,			_UpperCamelCase    )

     # some keys can be renamed efficiently
     for key, val in state_dict.copy().items():
          _SCREAMING_SNAKE_CASE							   =state_dict.pop(_UpperCamelCase    )
          if key.startswith('Qformer.bert'    ):
               _SCREAMING_SNAKE_CASE							   =key.replace('Qformer.bert'							,			'qformer'    )
          if "attention.self" in key:
               _SCREAMING_SNAKE_CASE							   =key.replace('self'							,			'attention'    )
          if "opt_proj" in key:
               _SCREAMING_SNAKE_CASE							   =key.replace('opt_proj'							,			'language_projection'    )
          if "t5_proj" in key:
               _SCREAMING_SNAKE_CASE							   =key.replace('t5_proj'							,			'language_projection'    )
          if key.startswith('opt'    ):
               _SCREAMING_SNAKE_CASE							   =key.replace('opt'							,			'language'    )
          if key.startswith('t5'    ):
               _SCREAMING_SNAKE_CASE							   =key.replace('t5'							,			'language'    )
          _SCREAMING_SNAKE_CASE							   =val

     # read in qv biases
     read_in_q_v_bias(_UpperCamelCase							,			_UpperCamelCase    )

     _SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE							   =hf_model.load_state_dict(_UpperCamelCase							,			strict=_UpperCamelCase    )
     assert len(_UpperCamelCase    ) == 0
     assert unexpected_keys == ["qformer.embeddings.position_ids"]

     _SCREAMING_SNAKE_CASE							   =load_demo_image()
     _SCREAMING_SNAKE_CASE							   =vis_processors['eval'](_UpperCamelCase    ).unsqueeze(0    ).to(_UpperCamelCase    )
     _SCREAMING_SNAKE_CASE							   =tokenizer(['\n']							,			return_tensors='pt'    ).input_ids.to(_UpperCamelCase    )

     # create processor
     _SCREAMING_SNAKE_CASE							   =BlipImageProcessor(
         size={'height': image_size, 'width': image_size}							,			image_mean=_UpperCamelCase							,			image_std=_UpperCamelCase    )
     _SCREAMING_SNAKE_CASE							   =BlipaProcessor(image_processor=_UpperCamelCase							,			tokenizer=_UpperCamelCase    )
     _SCREAMING_SNAKE_CASE							   =processor(images=_UpperCamelCase							,			return_tensors='pt'    ).pixel_values.to(_UpperCamelCase    )

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

     original_model.to(_UpperCamelCase    )
     hf_model.to(_UpperCamelCase    )
     with torch.no_grad():
          if "opt" in model_name:
               _SCREAMING_SNAKE_CASE							   =original_model({'image': original_pixel_values, 'text_input': ['']}    ).logits
               _SCREAMING_SNAKE_CASE							   =hf_model(_UpperCamelCase							,			_UpperCamelCase    ).logits
          else:
               _SCREAMING_SNAKE_CASE							   =original_model(
                   {'image': original_pixel_values, 'text_input': ['\n'], 'text_output': ['\n']}    ).logits
               _SCREAMING_SNAKE_CASE							   =input_ids.masked_fill(input_ids == tokenizer.pad_token_id							,			-1_00    )
               _SCREAMING_SNAKE_CASE							   =hf_model(_UpperCamelCase							,			_UpperCamelCase							,			labels=_UpperCamelCase    ).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":
          _SCREAMING_SNAKE_CASE							   =torch.tensor(
              [[-41.58_50, -4.44_40, -8.99_22], [-47.43_22, -5.91_43, -1.73_40]]							,			device=_UpperCamelCase    )
          assert torch.allclose(logits[0, :3, :3]							,			_UpperCamelCase							,			atol=1E-4    )
     elif model_name == "blip2-flan-t5-xl-coco":
          _SCREAMING_SNAKE_CASE							   =torch.tensor(
              [[-57.01_09, -9.89_67, -12.62_80], [-68.65_78, -12.71_91, -10.50_65]]							,			device=_UpperCamelCase    )
     else:
          # cast to same type
          _SCREAMING_SNAKE_CASE							   =logits.dtype
          assert torch.allclose(original_logits.to(_UpperCamelCase    )							,			_UpperCamelCase							,			atol=1E-2    )
     print('Looks ok!'    )

     print('Generating a caption...'    )
     _SCREAMING_SNAKE_CASE							   =''
     _SCREAMING_SNAKE_CASE							   =tokenizer(_UpperCamelCase							,			return_tensors='pt'    ).input_ids.to(_UpperCamelCase    )

     _SCREAMING_SNAKE_CASE							   =original_model.generate({'image': original_pixel_values}    )
     _SCREAMING_SNAKE_CASE							   =hf_model.generate(
         _UpperCamelCase							,			_UpperCamelCase							,			do_sample=_UpperCamelCase							,			num_beams=5							,			max_length=30							,			min_length=1							,			top_p=0.9							,			repetition_penalty=1.0							,			length_penalty=1.0							,			temperature=1							,			)
     print('Original generation:'							,			_UpperCamelCase    )
     _SCREAMING_SNAKE_CASE							   =input_ids.shape[1]
     _SCREAMING_SNAKE_CASE							   =processor.batch_decode(outputs[:, prompt_length:]							,			skip_special_tokens=_UpperCamelCase    )
     _SCREAMING_SNAKE_CASE							   =[text.strip() for text in output_text]
     print('HF generation:'							,			_UpperCamelCase    )

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

     if push_to_hub:
          processor.push_to_hub(f"nielsr/{model_name}"    )
          hf_model.push_to_hub(f"nielsr/{model_name}"    )


if __name__ == "__main__":
     lowerCamelCase			:				List[Any] 			=					argparse.ArgumentParser()
     lowerCamelCase			:				int 			=					[
         "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",
     )

     lowerCamelCase			:				Union[str, Any] 			=					parser.parse_args()

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



'''simple docstring'''
import warnings


warnings.warn(
    "memory_utils has been reorganized to utils.memory. Import `find_executable_batchsize` from the main `__init__`: "
    "`from accelerate import find_executable_batch_size` to avoid this warning.",
    FutureWarning,
)
