

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__ (					):
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       "https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg"
	SCREAMING_SNAKE_CASE   :      Tuple				     =       Image.open(requests.get(_a							,			stream=_a).raw).convert("RGB")

	return image

def  lowerCamelCase__ (					_a):
	SCREAMING_SNAKE_CASE   :      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  lowerCamelCase__ (					_a							,			_a							,			_a):
	SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       dct.pop(_a)
	SCREAMING_SNAKE_CASE   :      Dict				     =       val

def  lowerCamelCase__ (					_a							,			_a):
	for i in range(config.vision_config.num_hidden_layers):
		# read in original q and v biases
		SCREAMING_SNAKE_CASE   :      List[Any]				     =       state_dict.pop(f"visual_encoder.blocks.{i}.attn.q_bias")
		SCREAMING_SNAKE_CASE   :      List[Any]				     =       state_dict.pop(f"visual_encoder.blocks.{i}.attn.v_bias")

		# next, set bias in the state dict
		SCREAMING_SNAKE_CASE   :      Optional[int]				     =       torch.cat((q_bias, torch.zeros_like(_a							,			requires_grad=_a), v_bias))
		SCREAMING_SNAKE_CASE   :      str				     =       qkv_bias

def  lowerCamelCase__ (					_a):
	SCREAMING_SNAKE_CASE   :      List[str]				     =       364 if "coco" in model_name else 224
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       InstructBlipVisionConfig(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 "t5-xl" in model_name:
		SCREAMING_SNAKE_CASE   :      str				     =       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   :      List[str]				     =       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   :      Dict				     =       LlamaConfig.from_pretrained("decapoda-research/llama-7b-hf"							,			vocab_size=32001).to_dict()
	elif "vicuna-13b" in model_name:
		SCREAMING_SNAKE_CASE   :      Optional[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
	SCREAMING_SNAKE_CASE   :      int				     =       InstructBlipQFormerConfig(vocab_size=30523).to_dict()
	SCREAMING_SNAKE_CASE   :      List[str]				     =       InstructBlipConfig(vision_config=_a							,			text_config=_a							,			qformer_config=_a)

	return config, image_size

@torch.no_grad()
def  lowerCamelCase__ (					_a							,			_a=None							,			_a=False):
	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       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   :      Union[str, Any]				     =       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   :      Optional[int]				     =       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   :      Any				     =       get_blipa_config(_a)
	SCREAMING_SNAKE_CASE   :      int				     =       InstructBlipForConditionalGeneration(_a).eval()

	SCREAMING_SNAKE_CASE   :      Dict				     =       {
	    "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   :      List[Any]				     =       model_name_to_original[model_name]

	# load original model
	print("Loading original model...")
	SCREAMING_SNAKE_CASE   :      Tuple				     =       "cuda:1" if torch.cuda.is_available() else "cpu"
	SCREAMING_SNAKE_CASE   :      Dict				     =       "cuda:2" if torch.cuda.is_available() else "cpu"
	SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      List[str]				     =       load_model_and_preprocess(
	    name=_a							,			model_type=_a							,			is_eval=_a							,			device=_a)
	original_model.eval()
	print("Done!")

	# update state dict keys
	SCREAMING_SNAKE_CASE   :      str				     =       original_model.state_dict()
	SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       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():
		SCREAMING_SNAKE_CASE   :      List[str]				     =       state_dict.pop(_a)
		if key.startswith("Qformer.bert"):
			SCREAMING_SNAKE_CASE   :      List[Any]				     =       key.replace("Qformer.bert"							,			"qformer")
		if "attention.self" in key:
			SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       key.replace("self"							,			"attention")
		if "llm_proj" in key:
			SCREAMING_SNAKE_CASE   :      int				     =       key.replace("llm_proj"							,			"language_projection")
		if "t5_proj" in key:
			SCREAMING_SNAKE_CASE   :      Any				     =       key.replace("t5_proj"							,			"language_projection")
		if key.startswith("llm_model"):
			SCREAMING_SNAKE_CASE   :      List[str]				     =       key.replace("llm_model"							,			"language_model")
		if key.startswith("t5"):
			SCREAMING_SNAKE_CASE   :      Tuple				     =       key.replace("t5"							,			"language")
		SCREAMING_SNAKE_CASE   :      Tuple				     =       val

	# read in qv biases
	read_in_q_v_bias(_a							,			_a)

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

	SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       load_demo_image()
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       "What is unusual about this image?"

	# create processor
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       BlipImageProcessor(
	    size={"height": image_size, "width": image_size}							,			image_mean=_a							,			image_std=_a)
	SCREAMING_SNAKE_CASE   :      int				     =       InstructBlipProcessor(
	    image_processor=_a							,			tokenizer=_a							,			qformer_tokenizer=_a							,			)
	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       processor(images=_a							,			text=_a							,			return_tensors="pt").to(_a)

	# make sure processor creates exact same pixel values
	SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       vis_processors["eval"](_a).unsqueeze(0).to(_a)
	SCREAMING_SNAKE_CASE   :      List[str]				     =       inputs.pixel_values
	assert torch.allclose(original_pixel_values.to(pixel_values.device)							,			_a)

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

	print("Generating with original model...")
	SCREAMING_SNAKE_CASE   :      Any				     =       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   :      Optional[int]				     =       hf_model.generate(
	    **_a							,			do_sample=_a							,			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?
		SCREAMING_SNAKE_CASE   :      int				     =       2
	print("Original generation:"							,			_a)
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       processor.batch_decode(_a							,			skip_special_tokens=_a)
	SCREAMING_SNAKE_CASE   :      str				     =       [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"Salesforce/{model_name}")
		hf_model.push_to_hub(f"Salesforce/{model_name}")


if __name__ == "__main__":
			a_						     =    argparse.ArgumentParser()
			a_						     =    [
			    '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_						     =    parser.parse_args()

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


from __future__ import annotations

def  lowerCamelCase__ (					_a							,			_a):
	if b == 0:
		return (1, 0)
	((SCREAMING_SNAKE_CASE)     ,(SCREAMING_SNAKE_CASE))   :      Tuple				     =       extended_euclid(_a							,			a % b)
	SCREAMING_SNAKE_CASE   :      Dict				     =       a // b
	return (y, x - k * y)

def  lowerCamelCase__ (					_a							,			_a							,			_a							,			_a):
	((SCREAMING_SNAKE_CASE)     ,(SCREAMING_SNAKE_CASE))   :      str				     =       extended_euclid(_a							,			_a)
	SCREAMING_SNAKE_CASE   :      str				     =       na * na
	SCREAMING_SNAKE_CASE   :      int				     =       ra * x * na + ra * y * na
	return (n % m + m) % m

def  lowerCamelCase__ (					_a							,			_a):
	((SCREAMING_SNAKE_CASE)     ,(SCREAMING_SNAKE_CASE))   :      List[str]				     =       extended_euclid(_a							,			_a)
	if b < 0:
		SCREAMING_SNAKE_CASE   :      int				     =       (b % n + n) % n
	return b

def  lowerCamelCase__ (					_a							,			_a							,			_a							,			_a):
	SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Dict				     =       invert_modulo(_a							,			_a), invert_modulo(_a							,			_a)
	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       na * na
	SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       ra * x * na + ra * y * na
	return (n % m + m) % m


if __name__ == "__main__":
			from doctest import testmod

			testmod(name='chinese_remainder_theorem', verbose=True)
			testmod(name='chinese_remainder_theorem2', verbose=True)
			testmod(name='invert_modulo', verbose=True)
			testmod(name='extended_euclid', verbose=True)