

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 							__lowerCAmelCase     (							)		->				Optional[int]:
 '''simple docstring'''


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

 return image


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Dict						)		->				List[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.embeddings.layernorm.weight')						)
 rename_keys.append(('Qformer.bert.embeddings.LayerNorm.bias', 'qformer.embeddings.layernorm.bias')						)

 # fmt: on
 return rename_keys


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Any		,				_UpperCamelCase		:	List[Any]		,				_UpperCamelCase		:	int						)		->				List[str]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      dct.pop(_UpperCamelCase						)
 SCREAMING_SNAKE_CASE		    =      val


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Optional[Any]		,				_UpperCamelCase		:	List[str]						)		->				str:
 '''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		:	Optional[int]						)		->				Tuple:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      3_64 if 'coco' in model_name else 2_24
 SCREAMING_SNAKE_CASE		    =      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:
  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()
 elif "vicuna-7b" in model_name:
  SCREAMING_SNAKE_CASE		    =      LlamaConfig.from_pretrained('decapoda-research/llama-7b-hf'		,				vocab_size=3_20_01						).to_dict()
 elif "vicuna-13b" in model_name:
  SCREAMING_SNAKE_CASE		    =      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
 SCREAMING_SNAKE_CASE		    =      InstructBlipQFormerConfig(vocab_size=3_05_23						).to_dict()
 SCREAMING_SNAKE_CASE		    =      InstructBlipConfig(vision_config=_UpperCamelCase		,				text_config=_UpperCamelCase		,				qformer_config=_UpperCamelCase						)

 return config, image_size


@torch.no_grad()
def 							__lowerCAmelCase     (							_UpperCamelCase		:	Dict		,				_UpperCamelCase		:	List[Any]=None		,				_UpperCamelCase		:	int=False						)		->				Any:
 '''simple docstring'''


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

 if "t5" in model_name:
  SCREAMING_SNAKE_CASE		    =      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>"})
  SCREAMING_SNAKE_CASE		    =      LlamaTokenizerFast.from_pretrained(
      'huggyllama/llama-7b'		,				truncation_side='left'		,				bos_token='</s>'		,				unk_token='</s>'						)
  tokenizer.add_special_tokens({'pad_token': '[PAD]'}						)

 SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      get_blipa_config(_UpperCamelCase						)
 SCREAMING_SNAKE_CASE		    =      InstructBlipForConditionalGeneration(_UpperCamelCase						).eval()

 SCREAMING_SNAKE_CASE		    =      {
     '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'),
 }

 SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      model_name_to_original[model_name]

 # load original model
 print('Loading original model...'						)
 SCREAMING_SNAKE_CASE		    =      'cuda:1' if torch.cuda.is_available() else 'cpu'
 SCREAMING_SNAKE_CASE		    =      'cuda:2' 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 "llm_proj" in key:
   SCREAMING_SNAKE_CASE		    =      key.replace('llm_proj'		,				'language_projection'						)
  if "t5_proj" in key:
   SCREAMING_SNAKE_CASE		    =      key.replace('t5_proj'		,				'language_projection'						)
  if key.startswith('llm_model'						):
   SCREAMING_SNAKE_CASE		    =      key.replace('llm_model'		,				'language_model'						)
  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						)

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

 SCREAMING_SNAKE_CASE		    =      load_demo_image()
 SCREAMING_SNAKE_CASE		    =      'What is unusual about this image?'

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

 # make sure processor creates exact same pixel values
 SCREAMING_SNAKE_CASE		    =      vis_processors['eval'](_UpperCamelCase						).unsqueeze(0						).to(_UpperCamelCase						)
 SCREAMING_SNAKE_CASE		    =      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:
   SCREAMING_SNAKE_CASE		    =      original_model({'image': original_pixel_values, 'text_input': [prompt]}						).logits
   SCREAMING_SNAKE_CASE		    =      hf_model(**_UpperCamelCase						).logits
  else:
   SCREAMING_SNAKE_CASE		    =      original_model(
       {'image': original_pixel_values, 'text_input': [prompt], 'text_output': ['\n']}						).logits
   SCREAMING_SNAKE_CASE		    =      tokenizer('\n'		,				return_tensors='pt'						).input_ids.to(_UpperCamelCase						)
   SCREAMING_SNAKE_CASE		    =      label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id		,				-1_00						)
   SCREAMING_SNAKE_CASE		    =      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
 SCREAMING_SNAKE_CASE		    =      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...'						)
 SCREAMING_SNAKE_CASE		    =      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...'						)
 SCREAMING_SNAKE_CASE		    =      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?
  SCREAMING_SNAKE_CASE		    =      2
 print('Original generation:'		,				_UpperCamelCase						)
 SCREAMING_SNAKE_CASE		    =      processor.batch_decode(_UpperCamelCase		,				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"""Salesforce/{model_name}"""						)
  hf_model.push_to_hub(f"""Salesforce/{model_name}"""						)


if __name__ == "__main__":
      a_     :    Optional[Any]     		=   argparse.ArgumentParser()
      a_     :    Optional[Any]     		=   [
          "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_     :    Dict     		=   parser.parse_args()

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




import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding




class   UpperCamelCase			(  SCREAMING_SNAKE_CASE ):
    __UpperCamelCase    =["image_processor", "tokenizer"]
    __UpperCamelCase    ="CLIPImageProcessor"
    __UpperCamelCase    =("CLIPTokenizer", "CLIPTokenizerFast")

    def __init__(      self			:   int				,     snake_case__			:   List[Any]=None				,     snake_case__			:   Any=None				,     **snake_case__			:   Any     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      None
     if "feature_extractor" in kwargs:
      warnings.warn(
          'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
          ' instead.'				,     snake_case__				,     )
      SCREAMING_SNAKE_CASE		    =      kwargs.pop('feature_extractor'     )

     SCREAMING_SNAKE_CASE		    =      image_processor if image_processor is not None else feature_extractor
     if image_processor is None:
      raise ValueError('You need to specify an `image_processor`.'     )
     if tokenizer is None:
      raise ValueError('You need to specify a `tokenizer`.'     )

     super().__init__(snake_case__				,     snake_case__     )

    def __call__(      self			:   List[Any]				,     snake_case__			:   Tuple=None				,     snake_case__			:   Optional[int]=None				,     snake_case__			:   List[str]=None				,     **snake_case__			:   Dict     ):

     """simple docstring"""






     if text is None and images is None:
      raise ValueError('You have to specify either text or images. Both cannot be none.'     )

     if text is not None:
      SCREAMING_SNAKE_CASE		    =      self.tokenizer(snake_case__				,     return_tensors=snake_case__				,     **snake_case__     )

     if images is not None:
      SCREAMING_SNAKE_CASE		    =      self.image_processor(snake_case__				,     return_tensors=snake_case__				,     **snake_case__     )

     if text is not None and images is not None:
      SCREAMING_SNAKE_CASE		    =      image_features.pixel_values
      return encoding
     elif text is not None:
      return encoding
     else:
      return BatchEncoding(data=dict(**snake_case__     )				,     tensor_type=snake_case__     )

    def UpperCamelCase      (      self			:   Union[str, Any]				,     *snake_case__			:   Optional[Any]				,     **snake_case__			:   Optional[int]     ):

     """simple docstring"""






     return self.tokenizer.batch_decode(*snake_case__				,     **snake_case__     )

    def UpperCamelCase      (      self			:   Dict				,     *snake_case__			:   Optional[Any]				,     **snake_case__			:   Dict     ):

     """simple docstring"""






     return self.tokenizer.decode(*snake_case__				,     **snake_case__     )

    @property
    def UpperCamelCase      (      self			:   Any     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      self.tokenizer.model_input_names
     SCREAMING_SNAKE_CASE		    =      self.image_processor.model_input_names
     return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )

    @property
    def UpperCamelCase      (      self			:   int     ):

     """simple docstring"""






     warnings.warn(
         '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'				,     snake_case__				,     )
     return self.image_processor_class




    @property
    def UpperCamelCase      (      self			:   Tuple     ):

     """simple docstring"""






     warnings.warn(
         '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'				,     snake_case__				,     )
     return self.image_processor

