



"""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_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       _snake_case						(						):
 UpperCAmelCase	:					Union[str, Any]	=	"""https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg"""
 UpperCAmelCase	:					Optional[int]	=	Image.open(requests.get(__lowercase			,		stream=__lowercase						).raw						).convert("""RGB"""						)

 return image

def       _snake_case						(						UpperCamelCase						:		Optional[Any]						):
 UpperCAmelCase	:					List[Any]	=	[]
 # 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       _snake_case						(						UpperCamelCase						:		List[Any]			,		UpperCamelCase						:		Any			,		UpperCamelCase						:		Any						):
 UpperCAmelCase	:					Any	=	dct.pop(__lowercase						)
 UpperCAmelCase	:					Dict	=	val

def       _snake_case						(						UpperCamelCase						:		str			,		UpperCamelCase						:		Tuple						):






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

  # next, set bias in the state dict
  UpperCAmelCase	:					Optional[Any]	=	torch.cat((q_bias, torch.zeros_like(__lowercase			,		requires_grad=__lowercase						), v_bias)						)
  UpperCAmelCase	:					List[str]	=	qkv_bias

def       _snake_case						(						UpperCamelCase						:		int						):
 UpperCAmelCase	:					List[str]	=	364 if """coco""" in model_name else 224
 UpperCAmelCase	:					Optional[Any]	=	InstructBlipVisionConfig(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 "t5-xl" in model_name:
  UpperCAmelCase	:					Optional[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	:					Union[str, Any]	=	TaConfig.from_pretrained("""google/flan-t5-xxl"""			,		dense_act_fn="""gelu"""			,		bos_token_id=1						).to_dict()
 elif "vicuna-7b" in model_name:
  UpperCAmelCase	:					List[Any]	=	LlamaConfig.from_pretrained("""decapoda-research/llama-7b-hf"""			,		vocab_size=32001						).to_dict()
 elif "vicuna-13b" in model_name:
  UpperCAmelCase	:					int	=	LlamaConfig.from_pretrained("""decapoda-research/llama-13b-hf"""			,		vocab_size=32001						).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
 UpperCAmelCase	:					Union[str, Any]	=	InstructBlipQFormerConfig(vocab_size=30523						).to_dict()
 UpperCAmelCase	:					Tuple	=	InstructBlipConfig(vision_config=__lowercase			,		text_config=__lowercase			,		qformer_config=__lowercase						)

 return config, image_size

@torch.no_grad()
def       _snake_case						(						UpperCamelCase						:		Union[str, Any]			,		UpperCamelCase						:		Dict=None			,		UpperCamelCase						:		Dict=False						):
 UpperCAmelCase	:					Optional[int]	=	AutoTokenizer.from_pretrained("""bert-base-uncased"""			,		truncation_side="""left"""						)
 qformer_tokenizer.add_special_tokens({"""bos_token""": """[DEC]"""}						)

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

 UpperCAmelCase	,		UpperCAmelCase	:					Tuple	=	get_blipa_config(__lowercase						)
 UpperCAmelCase	:					str	=	InstructBlipForConditionalGeneration(__lowercase						).eval()

 UpperCAmelCase	:					List[Any]	=	{
     """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"""),
 }

 UpperCAmelCase	,		UpperCAmelCase	:					int	=	model_name_to_original[model_name]

 # load original model
 print("""Loading original model..."""						)
 UpperCAmelCase	:					Union[str, Any]	=	"""cuda:1""" if torch.cuda.is_available() else """cpu"""
 UpperCAmelCase	:					Any	=	"""cuda:2""" if torch.cuda.is_available() else """cpu"""
 UpperCAmelCase	,		UpperCAmelCase	,		UpperCAmelCase	:					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	:					str	=	original_model.state_dict()
 UpperCAmelCase	:					int	=	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	:					Any	=	state_dict.pop(__lowercase						)
  if key.startswith("""Qformer.bert"""						):
   UpperCAmelCase	:					List[str]	=	key.replace("""Qformer.bert"""			,		"""qformer"""						)
  if "attention.self" in key:
   UpperCAmelCase	:					List[Any]	=	key.replace("""self"""			,		"""attention"""						)
  if "llm_proj" in key:
   UpperCAmelCase	:					Optional[int]	=	key.replace("""llm_proj"""			,		"""language_projection"""						)
  if "t5_proj" in key:
   UpperCAmelCase	:					Optional[Any]	=	key.replace("""t5_proj"""			,		"""language_projection"""						)
  if key.startswith("""llm_model"""						):
   UpperCAmelCase	:					Optional[int]	=	key.replace("""llm_model"""			,		"""language_model"""						)
  if key.startswith("""t5"""						):
   UpperCAmelCase	:					int	=	key.replace("""t5"""			,		"""language"""						)
  UpperCAmelCase	:					Optional[int]	=	val

 # read in qv biases
 read_in_q_v_bias(__lowercase			,		__lowercase						)

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

 UpperCAmelCase	:					int	=	load_demo_image()
 UpperCAmelCase	:					Dict	=	"""What is unusual about this image?"""

 # create processor
 UpperCAmelCase	:					Union[str, Any]	=	BlipImageProcessor(
     size={"""height""": image_size, """width""": image_size}			,		image_mean=__lowercase			,		image_std=__lowercase						)
 UpperCAmelCase	:					Union[str, Any]	=	InstructBlipProcessor(
     image_processor=__lowercase			,		tokenizer=__lowercase			,		qformer_tokenizer=__lowercase			,		)
 UpperCAmelCase	:					Tuple	=	processor(images=__lowercase			,		text=__lowercase			,		return_tensors="""pt"""						).to(__lowercase						)

 # make sure processor creates exact same pixel values
 UpperCAmelCase	:					Dict	=	vis_processors["""eval"""](__lowercase						).unsqueeze(0						).to(__lowercase						)
 UpperCAmelCase	:					Optional[int]	=	inputs.pixel_values
 assert torch.allclose(original_pixel_values.to(pixel_values.device						)			,		__lowercase						)

 original_model.to(__lowercase						)
 hf_model.to(__lowercase						)
 with torch.no_grad():
  if "vicuna" in model_name:
   UpperCAmelCase	:					List[str]	=	original_model({"""image""": original_pixel_values, """text_input""": [prompt]}						).logits
   UpperCAmelCase	:					Any	=	hf_model(**__lowercase						).logits
  else:
   UpperCAmelCase	:					List[str]	=	original_model(
       {"""image""": original_pixel_values, """text_input""": [prompt], """text_output""": ["""\n"""]}						).logits
   UpperCAmelCase	:					Any	=	tokenizer("""\n"""			,		return_tensors="""pt"""						).input_ids.to(__lowercase						)
   UpperCAmelCase	:					Optional[Any]	=	label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id			,		-100						)
   UpperCAmelCase	:					Optional[int]	=	hf_model(**__lowercase			,		labels=__lowercase						).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
 UpperCAmelCase	:					List[str]	=	1e-4 if """vicuna""" in model_name else 1e-5
 assert torch.allclose(original_logits.to(logits.device						)			,		__lowercase			,		atol=__lowercase						)
 print("""Looks ok!"""						)

 print("""Generating with original model..."""						)
 UpperCAmelCase	:					Optional[int]	=	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..."""						)
 UpperCAmelCase	:					Any	=	hf_model.generate(
     **__lowercase			,		do_sample=__lowercase			,		num_beams=5			,		max_length=256			,		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?
  UpperCAmelCase	:					List[str]	=	2
 print("""Original generation:"""			,		__lowercase						)
 UpperCAmelCase	:					Any	=	processor.batch_decode(__lowercase			,		skip_special_tokens=__lowercase						)
 UpperCAmelCase	:					Optional[Any]	=	[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"Salesforce/{model_name}"						)
  hf_model.push_to_hub(F"Salesforce/{model_name}"						)


if __name__ == "__main__":
      A:   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:   Any								=		parser.parse_args()

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







'''simple docstring'''
from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					Any					=["image_processor", "tokenizer"]
 SCREAMING_SNAKE_CASE_	:					List[Any]					="BlipImageProcessor"
 SCREAMING_SNAKE_CASE_	:					Optional[int]					=("BertTokenizer", "BertTokenizerFast")






 def __init__(       self       :							Dict							,	__A       :							Optional[int]							,	__A       :							List[Any]	):
        __UpperCamelCase       =  False
        super().__init__(__A							,	__A	)
        __UpperCamelCase       =  self.image_processor






 def __call__(       self       :							List[Any]							,	__A       :							ImageInput = None							,	__A       :							Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None							,	__A       :							bool = True							,	__A       :							Union[bool, str, PaddingStrategy] = False							,	__A       :							Union[bool, str, TruncationStrategy] = None							,	__A       :							Optional[int] = None							,	__A       :							int = 0							,	__A       :							Optional[int] = None							,	__A       :							Optional[bool] = None							,	__A       :							bool = False							,	__A       :							bool = False							,	__A       :							bool = False							,	__A       :							bool = False							,	__A       :							bool = False							,	__A       :							bool = True							,	__A       :							Optional[Union[str, TensorType]] = None							,	**__A       :							List[Any]							,	):
        if images is None and text is None:
               raise ValueError('You have to specify either images or text.'	)

        # Get only text
        if images is None:
               __UpperCamelCase       =  self.tokenizer
               __UpperCamelCase       =  self.tokenizer(
                   text=__A							,	add_special_tokens=__A							,	padding=__A							,	truncation=__A							,	max_length=__A							,	stride=__A							,	pad_to_multiple_of=__A							,	return_attention_mask=__A							,	return_overflowing_tokens=__A							,	return_special_tokens_mask=__A							,	return_offsets_mapping=__A							,	return_token_type_ids=__A							,	return_length=__A							,	verbose=__A							,	return_tensors=__A							,	**__A							,	)
               return text_encoding

        # add pixel_values
        __UpperCamelCase       =  self.image_processor(__A							,	return_tensors=__A	)

        if text is not None:
               __UpperCamelCase       =  self.tokenizer(
                   text=__A							,	add_special_tokens=__A							,	padding=__A							,	truncation=__A							,	max_length=__A							,	stride=__A							,	pad_to_multiple_of=__A							,	return_attention_mask=__A							,	return_overflowing_tokens=__A							,	return_special_tokens_mask=__A							,	return_offsets_mapping=__A							,	return_token_type_ids=__A							,	return_length=__A							,	verbose=__A							,	return_tensors=__A							,	**__A							,	)
        else:
               __UpperCamelCase       =  None

        if text_encoding is not None:
               encoding_image_processor.update(__A	)

        return encoding_image_processor






 def        _lowerCamelCase					(       self       :							List[Any]							,	*__A       :							Dict							,	**__A       :							Optional[int]	):
        return self.tokenizer.batch_decode(*__A							,	**__A	)






 def        _lowerCamelCase					(       self       :							List[Any]							,	*__A       :							List[str]							,	**__A       :							Dict	):
        return self.tokenizer.decode(*__A							,	**__A	)






 @property
 def        _lowerCamelCase					(       self       :							Tuple	):
        __UpperCamelCase       =  self.tokenizer.model_input_names
        __UpperCamelCase       =  self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names	)	)
