





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_ (							):
    UpperCamelCase__:	Union[str, Any]					=     "https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png"
    UpperCamelCase__:	Optional[Any]					=     Image.open(requests.get(A_    ,stream=A_).raw).convert("RGB")

    return image



def 				lowerCAmelCase_ (							A_):
    UpperCamelCase__:	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 				lowerCAmelCase_ (							A_    ,A_    ,A_):
    UpperCamelCase__:	Optional[Any]					=     dct.pop(A_)
    UpperCamelCase__:	Tuple					=     val



def 				lowerCAmelCase_ (							A_    ,A_):
    for i in range(config.vision_config.num_hidden_layers):
        # read in original q and v biases
        UpperCamelCase__:	List[str]					=     state_dict.pop(F"visual_encoder.blocks.{i}.attn.q_bias")
        UpperCamelCase__:	Optional[int]					=     state_dict.pop(F"visual_encoder.blocks.{i}.attn.v_bias")

        # next, set bias in the state dict
        UpperCamelCase__:	Union[str, Any]					=     torch.cat((q_bias, torch.zeros_like(A_    ,requires_grad=A_), v_bias))
        UpperCamelCase__:	Dict					=     qkv_bias



def 				lowerCAmelCase_ (							A_    ,A_):
    UpperCamelCase__:	List[str]					=     3_64 if "coco" in model_name else 2_24
    UpperCamelCase__:	str					=     BlipaVisionConfig(image_size=A_).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__:	Union[str, Any]					=     OPTConfig.from_pretrained("facebook/opt-2.7b"    ,eos_token_id=A_).to_dict()
    elif "opt-6.7b" in model_name:
        UpperCamelCase__:	str					=     OPTConfig.from_pretrained("facebook/opt-6.7b"    ,eos_token_id=A_).to_dict()
    elif "t5-xl" in model_name:
        UpperCamelCase__:	int					=     TaConfig.from_pretrained("google/flan-t5-xl"    ,dense_act_fn="gelu"    ,bos_token_id=1).to_dict()
    elif "t5-xxl" in model_name:
        UpperCamelCase__:	str					=     TaConfig.from_pretrained("google/flan-t5-xxl"    ,dense_act_fn="gelu"    ,bos_token_id=1).to_dict()

    UpperCamelCase__:	List[str]					=     BlipaConfig(vision_config=A_    ,text_config=A_)

    return config, image_size



@torch.no_grad()
def 				lowerCAmelCase_ (							A_    ,A_=None    ,A_=False):
    UpperCamelCase__:	List[str]					=     (
        AutoTokenizer.from_pretrained("facebook/opt-2.7b")
        if "opt" in model_name
        else AutoTokenizer.from_pretrained("google/flan-t5-xl")
    )
    UpperCamelCase__:	Dict					=     tokenizer("\n"    ,add_special_tokens=A_).input_ids[0]
    UpperCamelCase__				, UpperCamelCase__:	Optional[Any]					=     get_blipa_config(A_    ,eos_token_id=A_)

    UpperCamelCase__:	int					=     BlipaForConditionalGeneration(A_).eval()

    UpperCamelCase__:	Optional[Any]					=     {
        "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[int]					=     model_name_to_original[model_name]

    # load original model
    print("Loading original model...")
    UpperCamelCase__:	Optional[int]					=     "cuda" if torch.cuda.is_available() else "cpu"
    UpperCamelCase__				, UpperCamelCase__				, UpperCamelCase__:	str					=     load_model_and_preprocess(
        name=A_    ,model_type=A_    ,is_eval=A_    ,device=A_)
    original_model.eval()
    print("Done!")

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

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

    # read in qv biases
    read_in_q_v_bias(A_    ,A_)

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

    UpperCamelCase__:	List[str]					=     load_demo_image()
    UpperCamelCase__:	int					=     vis_processors["eval"](A_).unsqueeze(0).to(A_)
    UpperCamelCase__:	Dict					=     tokenizer(["\n"]    ,return_tensors="pt").input_ids.to(A_)

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

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

    original_model.to(A_)
    hf_model.to(A_)
    with torch.no_grad():
        if "opt" in model_name:
            UpperCamelCase__:	List[str]					=     original_model({"image": original_pixel_values, "text_input": [""]}).logits
            UpperCamelCase__:	Optional[int]					=     hf_model(A_    ,A_).logits
        else:
            UpperCamelCase__:	Dict					=     original_model(
                {"image": original_pixel_values, "text_input": ["\n"], "text_output": ["\n"]}).logits
            UpperCamelCase__:	Union[str, Any]					=     input_ids.masked_fill(input_ids == tokenizer.pad_token_id    ,-1_00)
            UpperCamelCase__:	Union[str, Any]					=     hf_model(A_    ,A_    ,labels=A_).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__:	Optional[int]					=     torch.tensor(
            [[-41.5850, -4.4440, -8.9922], [-47.4322, -5.9143, -1.7340]]    ,device=A_)
        assert torch.allclose(logits[0, :3, :3]    ,A_    ,atol=1e-4)
    elif model_name == "blip2-flan-t5-xl-coco":
        UpperCamelCase__:	List[Any]					=     torch.tensor(
            [[-57.0109, -9.8967, -12.6280], [-68.6578, -12.7191, -10.5065]]    ,device=A_)
    else:
        # cast to same type
        UpperCamelCase__:	List[str]					=     logits.dtype
        assert torch.allclose(original_logits.to(A_)    ,A_    ,atol=1e-2)
    print("Looks ok!")

    print("Generating a caption...")
    UpperCamelCase__:	List[Any]					=     ""
    UpperCamelCase__:	str					=     tokenizer(A_    ,return_tensors="pt").input_ids.to(A_)

    UpperCamelCase__:	List[str]					=     original_model.generate({"image": original_pixel_values})
    UpperCamelCase__:	Union[str, Any]					=     hf_model.generate(
        A_    ,A_    ,do_sample=A_    ,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:"    ,A_)
    UpperCamelCase__:	str					=     input_ids.shape[1]
    UpperCamelCase__:	Any					=     processor.batch_decode(outputs[:, prompt_length:]    ,skip_special_tokens=A_)
    UpperCamelCase__:	List[Any]					=     [text.strip() for text in output_text]
    print("HF generation:"    ,A_)

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

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


if __name__ == "__main__":
   A__:    int  	=	argparse.ArgumentParser()
   A__:    Tuple  	=	[
       '''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''',
   )

   A__:    str  	=	parser.parse_args()

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








import logging

from transformers import PretrainedConfig


A__:    Dict  	=	logging.getLogger(__name__)


A__:    List[Any]  	=	{
    '''bertabs-finetuned-cnndm''': '''https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json''',
}




class        _a    (    UpperCamelCase__):

      """simple docstring"""


      UpperCamelCase__  		=       """bertabs"""






      def __init__(		self:			Any	,    __lowerCamelCase:			Optional[int]=3_0522	,    __lowerCamelCase:			List[Any]=512	,    __lowerCamelCase:			Optional[Any]=6	,    __lowerCamelCase:			Optional[Any]=512	,    __lowerCamelCase:			List[str]=8	,    __lowerCamelCase:			List[Any]=512	,    __lowerCamelCase:			Optional[int]=0.2	,    __lowerCamelCase:			Optional[int]=6	,    __lowerCamelCase:			Optional[Any]=768	,    __lowerCamelCase:			int=8	,    __lowerCamelCase:			Union[str, Any]=2048	,    __lowerCamelCase:			Dict=0.2	,    **__lowerCamelCase:			Dict	,    ):
          '''simple docstring'''


          super().__init__(**__lowerCamelCase					)

          UpperCamelCase__:	str					=     vocab_size
          UpperCamelCase__:	Tuple					=     max_pos

          UpperCamelCase__:	Union[str, Any]					=     enc_layers
          UpperCamelCase__:	Optional[Any]					=     enc_hidden_size
          UpperCamelCase__:	Optional[Any]					=     enc_heads
          UpperCamelCase__:	Dict					=     enc_ff_size
          UpperCamelCase__:	Tuple					=     enc_dropout

          UpperCamelCase__:	Tuple					=     dec_layers
          UpperCamelCase__:	int					=     dec_hidden_size
          UpperCamelCase__:	int					=     dec_heads
          UpperCamelCase__:	str					=     dec_ff_size
          UpperCamelCase__:	Union[str, Any]					=     dec_dropout

