



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_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, we had to include /home/niels/python_projects/checkpoints/FastChat/vicuna-7b in lavis/configs/models/blip2/blip2_instruct_vicuna7b.yaml
# same for Vicuna-13b
from lavis.models import load_model_and_preprocess
from PIL import Image

from transformers import (
    AutoTokenizer,
    BlipImageProcessor,
    InstructBlipConfig,
    InstructBlipForConditionalGeneration,
    InstructBlipProcessor,
    InstructBlipQFormerConfig,
    InstructBlipVisionConfig,
    LlamaConfig,
    LlamaTokenizerFast,
    TaConfig,
    TaTokenizerFast,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD


def 	UpperCAmelCase__      (      ):






 '''simple docstring'''

 a						=		"https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg"
 a						=		Image.open(requests.get(UpperCAmelCase__ ,					stream=UpperCAmelCase__					).raw					).convert("RGB"					)

 return image


def 	UpperCAmelCase__      (      UpperCAmelCase__ :Optional[int]					):






 '''simple docstring'''

 a						=		[]
 # 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.embeddings.layernorm.weight")					)
 rename_keys.append(("Qformer.bert.embeddings.LayerNorm.bias", "qformer.embeddings.layernorm.bias")					)

 # fmt: on
 return rename_keys


def 	UpperCAmelCase__      (      UpperCAmelCase__ :int ,					UpperCAmelCase__ :Any ,					UpperCAmelCase__ :Any					):






 '''simple docstring'''

 a						=		dct.pop(UpperCAmelCase__					)
 a						=		val


def 	UpperCAmelCase__      (      UpperCAmelCase__ :Optional[Any] ,					UpperCAmelCase__ :int					):






 '''simple docstring'''

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

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


def 	UpperCAmelCase__      (      UpperCAmelCase__ :List[Any]					):






 '''simple docstring'''

 a						=		3_64 if "coco" in model_name else 2_24
 a						=		InstructBlipVisionConfig(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 "t5-xl" in model_name:
  a						=		TaConfig.from_pretrained("google/flan-t5-xl" ,					dense_act_fn="gelu" ,					bos_token_id=1					).to_dict()
 elif "t5-xxl" in model_name:
  a						=		TaConfig.from_pretrained("google/flan-t5-xxl" ,					dense_act_fn="gelu" ,					bos_token_id=1					).to_dict()
 elif "vicuna-7b" in model_name:
  a						=		LlamaConfig.from_pretrained("decapoda-research/llama-7b-hf" ,					vocab_size=3_20_01					).to_dict()
 elif "vicuna-13b" in model_name:
  a						=		LlamaConfig.from_pretrained("decapoda-research/llama-13b-hf" ,					vocab_size=3_20_01					).to_dict()
 else:
  raise ValueError("Model name not supported"					)

 # the authors add one special "[DEC]" token to the vocab of Q-Former, hence vocab size = 30522 + 1
 a						=		InstructBlipQFormerConfig(vocab_size=3_05_23					).to_dict()
 a						=		InstructBlipConfig(vision_config=UpperCAmelCase__ ,					text_config=UpperCAmelCase__ ,					qformer_config=UpperCAmelCase__					)

 return config, image_size


@torch.no_grad()
def 	UpperCAmelCase__      (      UpperCAmelCase__ :List[str] ,					UpperCAmelCase__ :Union[str, Any]=None ,					UpperCAmelCase__ :List[str]=False					):






 '''simple docstring'''

 a						=		AutoTokenizer.from_pretrained("bert-base-uncased" ,					truncation_side="left"					)
 qformer_tokenizer.add_special_tokens({"bos_token": "[DEC]"}					)

 if "t5" in model_name:
  a						=		TaTokenizerFast.from_pretrained("google/flan-t5-xl" ,					truncation_side="left"					)
 elif "vicuna" in model_name:
  # the following was used in the original implementation:
  # tokenizer = LlamaTokenizer.from_pretrained("huggyllama/llama-7b", use_fast=False, truncation_side="left")
  # tokenizer.add_special_tokens({"pad_token": "[PAD]"})
  # tokenizer.add_special_tokens({"bos_token": "</s>"})
  # tokenizer.add_special_tokens({"eos_token": "</s>"})
  # tokenizer.add_special_tokens({"unk_token": "</s>"})
  a						=		LlamaTokenizerFast.from_pretrained(
      "huggyllama/llama-7b" ,					truncation_side="left" ,					bos_token="</s>" ,					unk_token="</s>"					)
  tokenizer.add_special_tokens({"pad_token": "[PAD]"}					)

 a		,  a						=		get_blipa_config(UpperCAmelCase__					)
 a						=		InstructBlipForConditionalGeneration(UpperCAmelCase__					).eval()

 a						=		{
     "instructblip-vicuna-7b": ("blip2_vicuna_instruct", "vicuna7b"),
     "instructblip-vicuna-13b": ("blip2_vicuna_instruct", "vicuna13b"),
     "instructblip-flan-t5-xl": ("blip2_t5_instruct", "flant5xl"),
     "instructblip-flan-t5-xxl": ("blip2_t5_instruct", "flant5xxl"),
 }

 a		,  a						=		model_name_to_original[model_name]

 # load original model
 print("Loading original model..."					)
 a						=		"cuda:1" if torch.cuda.is_available() else "cpu"
 a						=		"cuda:2" if torch.cuda.is_available() else "cpu"
 a		,  a		,  a						=		load_model_and_preprocess(
     name=UpperCAmelCase__ ,					model_type=UpperCAmelCase__ ,					is_eval=UpperCAmelCase__ ,					device=UpperCAmelCase__					)
 original_model.eval()
 print("Done!"					)

 # update state dict keys
 a						=		original_model.state_dict()
 a						=		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():
  a						=		state_dict.pop(UpperCAmelCase__					)
  if key.startswith("Qformer.bert"					):
   a						=		key.replace("Qformer.bert" ,					"qformer"					)
  if "attention.self" in key:
   a						=		key.replace("self" ,					"attention"					)
  if "llm_proj" in key:
   a						=		key.replace("llm_proj" ,					"language_projection"					)
  if "t5_proj" in key:
   a						=		key.replace("t5_proj" ,					"language_projection"					)
  if key.startswith("llm_model"					):
   a						=		key.replace("llm_model" ,					"language_model"					)
  if key.startswith("t5"					):
   a						=		key.replace("t5" ,					"language"					)
  a						=		val

 # read in qv biases
 read_in_q_v_bias(UpperCAmelCase__ ,					UpperCAmelCase__					)

 # note: weights get loaded in torch.float32 by default
 hf_model.load_state_dict(UpperCAmelCase__ ,					strict=UpperCAmelCase__					)

 a						=		load_demo_image()
 a						=		"What is unusual about this image?"

 # create processor
 a						=		BlipImageProcessor(
     size={"height": image_size, "width": image_size} ,					image_mean=UpperCAmelCase__ ,					image_std=UpperCAmelCase__					)
 a						=		InstructBlipProcessor(
     image_processor=UpperCAmelCase__ ,					tokenizer=UpperCAmelCase__ ,					qformer_tokenizer=UpperCAmelCase__ ,					)
 a						=		processor(images=UpperCAmelCase__ ,					text=UpperCAmelCase__ ,					return_tensors="pt"					).to(UpperCAmelCase__					)

 # make sure processor creates exact same pixel values
 a						=		vis_processors["eval"](UpperCAmelCase__					).unsqueeze(0					).to(UpperCAmelCase__					)
 a						=		inputs.pixel_values
 assert torch.allclose(original_pixel_values.to(pixel_values.device					) ,					UpperCAmelCase__					)

 original_model.to(UpperCAmelCase__					)
 hf_model.to(UpperCAmelCase__					)
 with torch.no_grad():
  if "vicuna" in model_name:
   a						=		original_model({"image": original_pixel_values, "text_input": [prompt]}					).logits
   a						=		hf_model(**UpperCAmelCase__					).logits
  else:
   a						=		original_model(
       {"image": original_pixel_values, "text_input": [prompt], "text_output": ["\n"]}					).logits
   a						=		tokenizer("\n" ,					return_tensors="pt"					).input_ids.to(UpperCAmelCase__					)
   a						=		label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id ,					-1_00					)
   a						=		hf_model(**UpperCAmelCase__ ,					labels=UpperCAmelCase__					).logits

 print("First values of original logits:" ,					original_logits[0, :3, :3]					)
 print("First values of HF logits:" ,					logits[0, :3, :3]					)

 # assert values
 assert original_logits.shape == logits.shape
 a						=		1E-4 if "vicuna" in model_name else 1E-5
 assert torch.allclose(original_logits.to(logits.device					) ,					UpperCAmelCase__ ,					atol=UpperCAmelCase__					)
 print("Looks ok!"					)

 print("Generating with original model..."					)
 a						=		original_model.generate({"image": original_pixel_values, "prompt": prompt} ,					num_beams=5					)

 # important: we need to cast the weights of the HF model to the appropriate type
 print("Generating with HF model..."					)
 a						=		hf_model.generate(
     **UpperCAmelCase__ ,					do_sample=UpperCAmelCase__ ,					num_beams=5 ,					max_length=2_56 ,					min_length=1 ,					top_p=0.9 ,					repetition_penalty=1.5 ,					length_penalty=1.0 ,					temperature=1 ,					)
 if "vicuna" in model_name:
  # convert output id 0 to 2 (eos_token_id)
  # TODO add this in the generate method?
  a						=		2
 print("Original generation:" ,					UpperCAmelCase__					)
 a						=		processor.batch_decode(UpperCAmelCase__ ,					skip_special_tokens=UpperCAmelCase__					)
 a						=		[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"""Salesforce/{model_name}"""					)
  hf_model.push_to_hub(F"""Salesforce/{model_name}"""					)


if __name__ == "__main__":
     A_		:     List[str]        =     argparse.ArgumentParser()
     A_		:     List[str]        =     [
         '''instructblip-vicuna-7b''',
         '''instructblip-vicuna-13b''',
         '''instructblip-flan-t5-xl''',
         '''instructblip-flan-t5-xxl''',
     ]
     parser.add_argument(
         '''--model_name''',
         default='''instructblip-flan-t5-xl''',
         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_		:     List[str]        =     parser.parse_args()

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






from ...configuration_utils import PretrainedConfig
from ...utils import logging


A_		:     Any        =     logging.get_logger(__name__)

A_		:     Optional[int]        =     {
    '''SCUT-DLVCLab/lilt-roberta-en-base''': (
        '''https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json'''
    ),
}



class 		_lowercase		(       UpperCAmelCase__	):
   _UpperCAmelCase     	=			'''lilt'''

   def __init__(      self			:       Union[str, Any]							,   __lowerCAmelCase			:       Optional[Any]=3_0522							,   __lowerCAmelCase			:       str=768							,   __lowerCAmelCase			:       Any=12							,   __lowerCAmelCase			:       Optional[Any]=12							,   __lowerCAmelCase			:       List[Any]=3072							,   __lowerCAmelCase			:       Dict="gelu"							,   __lowerCAmelCase			:       int=0.1							,   __lowerCAmelCase			:       Union[str, Any]=0.1							,   __lowerCAmelCase			:       List[str]=512							,   __lowerCAmelCase			:       Optional[int]=2							,   __lowerCAmelCase			:       int=0.0_2							,   __lowerCAmelCase			:       Union[str, Any]=1E-12							,   __lowerCAmelCase			:       Tuple=0							,   __lowerCAmelCase			:       List[Any]="absolute"							,   __lowerCAmelCase			:       int=None							,   __lowerCAmelCase			:       Dict=4							,   __lowerCAmelCase			:       Dict=1024							,   **__lowerCAmelCase			:       Dict							,   )       ->				int:


    """simple docstring"""






    super().__init__(pad_token_id=__lowerCAmelCase							,   **__lowerCAmelCase							)

    a						=		vocab_size
    a						=		hidden_size
    a						=		num_hidden_layers
    a						=		num_attention_heads
    a						=		hidden_act
    a						=		intermediate_size
    a						=		hidden_dropout_prob
    a						=		attention_probs_dropout_prob
    a						=		max_position_embeddings
    a						=		type_vocab_size
    a						=		initializer_range
    a						=		layer_norm_eps
    a						=		position_embedding_type
    a						=		classifier_dropout
    a						=		channel_shrink_ratio
    a						=		max_ad_position_embeddings

