
'''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
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 			_a		(	):
						_SCREAMING_SNAKE_CASE				= "https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png"
						_SCREAMING_SNAKE_CASE				= Image.open(requests.get(_SCREAMING_SNAKE_CASE	,			stream=_SCREAMING_SNAKE_CASE  ).raw  ).convert("RGB"  )

						return image



def 			_a		(	_SCREAMING_SNAKE_CASE		:   List[Any]  ):
						_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.layernorm.weight")  )
						rename_keys.append(("Qformer.bert.embeddings.LayerNorm.bias", "qformer.layernorm.bias")  )

						# fmt: on
						return rename_keys



def 			_a		(	_SCREAMING_SNAKE_CASE		:   List[str]	,			_SCREAMING_SNAKE_CASE		:   Dict	,			_SCREAMING_SNAKE_CASE		:   List[str]  ):
						_SCREAMING_SNAKE_CASE				= dct.pop(_SCREAMING_SNAKE_CASE  )
						_SCREAMING_SNAKE_CASE				= val



def 			_a		(	_SCREAMING_SNAKE_CASE		:   str	,			_SCREAMING_SNAKE_CASE		:   List[str]  ):
						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(_SCREAMING_SNAKE_CASE	,			requires_grad=_SCREAMING_SNAKE_CASE  ), v_bias)  )
												_SCREAMING_SNAKE_CASE				= qkv_bias



def 			_a		(	_SCREAMING_SNAKE_CASE		:   int	,			_SCREAMING_SNAKE_CASE		:   int  ):
						_SCREAMING_SNAKE_CASE				= 364 if "coco" in model_name else 224
						_SCREAMING_SNAKE_CASE				= BlipaVisionConfig(image_size=_SCREAMING_SNAKE_CASE  ).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:
												_SCREAMING_SNAKE_CASE				= OPTConfig.from_pretrained("facebook/opt-2.7b"	,			eos_token_id=_SCREAMING_SNAKE_CASE  ).to_dict()
						elif "opt-6.7b" in model_name:
												_SCREAMING_SNAKE_CASE				= OPTConfig.from_pretrained("facebook/opt-6.7b"	,			eos_token_id=_SCREAMING_SNAKE_CASE  ).to_dict()
						elif "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()

						_SCREAMING_SNAKE_CASE				= BlipaConfig(vision_config=_SCREAMING_SNAKE_CASE	,			text_config=_SCREAMING_SNAKE_CASE  )

						return config, image_size



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

						_SCREAMING_SNAKE_CASE				= BlipaForConditionalGeneration(_SCREAMING_SNAKE_CASE  ).eval()

						_SCREAMING_SNAKE_CASE				= {
						    "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"),
						}

						_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= model_name_to_original[model_name]

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

						# update state dict keys
						_SCREAMING_SNAKE_CASE				= original_model.state_dict()
						_SCREAMING_SNAKE_CASE				= create_rename_keys(_SCREAMING_SNAKE_CASE  )
						for src, dest in rename_keys:
												rename_key(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  )

						# some keys can be renamed efficiently
						for key, val in state_dict.copy().items():
												_SCREAMING_SNAKE_CASE				= state_dict.pop(_SCREAMING_SNAKE_CASE  )
												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 "opt_proj" in key:
																		_SCREAMING_SNAKE_CASE				= key.replace("opt_proj"	,			"language_projection"  )
												if "t5_proj" in key:
																		_SCREAMING_SNAKE_CASE				= key.replace("t5_proj"	,			"language_projection"  )
												if key.startswith("opt"  ):
																		_SCREAMING_SNAKE_CASE				= key.replace("opt"	,			"language"  )
												if key.startswith("t5"  ):
																		_SCREAMING_SNAKE_CASE				= key.replace("t5"	,			"language"  )
												_SCREAMING_SNAKE_CASE				= val

						# read in qv biases
						read_in_q_v_bias(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  )

						_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= hf_model.load_state_dict(_SCREAMING_SNAKE_CASE	,			strict=_SCREAMING_SNAKE_CASE  )
						assert len(_SCREAMING_SNAKE_CASE  ) == 0
						assert unexpected_keys == ["qformer.embeddings.position_ids"]

						_SCREAMING_SNAKE_CASE				= load_demo_image()
						_SCREAMING_SNAKE_CASE				= vis_processors["eval"](_SCREAMING_SNAKE_CASE  ).unsqueeze(0  ).to(_SCREAMING_SNAKE_CASE  )
						_SCREAMING_SNAKE_CASE				= tokenizer(["\n"]	,			return_tensors="pt"  ).input_ids.to(_SCREAMING_SNAKE_CASE  )

						# create processor
						_SCREAMING_SNAKE_CASE				= BlipImageProcessor(
						    size={"height": image_size, "width": image_size}	,			image_mean=_SCREAMING_SNAKE_CASE	,			image_std=_SCREAMING_SNAKE_CASE  )
						_SCREAMING_SNAKE_CASE				= BlipaProcessor(image_processor=_SCREAMING_SNAKE_CASE	,			tokenizer=_SCREAMING_SNAKE_CASE  )
						_SCREAMING_SNAKE_CASE				= processor(images=_SCREAMING_SNAKE_CASE	,			return_tensors="pt"  ).pixel_values.to(_SCREAMING_SNAKE_CASE  )

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

						original_model.to(_SCREAMING_SNAKE_CASE  )
						hf_model.to(_SCREAMING_SNAKE_CASE  )
						with torch.no_grad():
												if "opt" in model_name:
																		_SCREAMING_SNAKE_CASE				= original_model({"image": original_pixel_values, "text_input": [""]}  ).logits
																		_SCREAMING_SNAKE_CASE				= hf_model(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  ).logits
												else:
																		_SCREAMING_SNAKE_CASE				= original_model(
																		    {"image": original_pixel_values, "text_input": ["\n"], "text_output": ["\n"]}  ).logits
																		_SCREAMING_SNAKE_CASE				= input_ids.masked_fill(input_ids == tokenizer.pad_token_id	,			-100  )
																		_SCREAMING_SNAKE_CASE				= hf_model(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE	,			labels=_SCREAMING_SNAKE_CASE  ).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":
												_SCREAMING_SNAKE_CASE				= torch.tensor(
												    [[-41.5_850, -4.4_440, -8.9_922], [-47.4_322, -5.9_143, -1.7_340]]	,			device=_SCREAMING_SNAKE_CASE  )
												assert torch.allclose(logits[0, :3, :3]	,			_SCREAMING_SNAKE_CASE	,			atol=1e-4  )
						elif model_name == "blip2-flan-t5-xl-coco":
												_SCREAMING_SNAKE_CASE				= torch.tensor(
												    [[-57.0_109, -9.8_967, -12.6_280], [-68.6_578, -12.7_191, -10.5_065]]	,			device=_SCREAMING_SNAKE_CASE  )
						else:
												# cast to same type
												_SCREAMING_SNAKE_CASE				= logits.dtype
												assert torch.allclose(original_logits.to(_SCREAMING_SNAKE_CASE  )	,			_SCREAMING_SNAKE_CASE	,			atol=1e-2  )
						print("Looks ok!"  )

						print("Generating a caption..."  )
						_SCREAMING_SNAKE_CASE				= ""
						_SCREAMING_SNAKE_CASE				= tokenizer(_SCREAMING_SNAKE_CASE	,			return_tensors="pt"  ).input_ids.to(_SCREAMING_SNAKE_CASE  )

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

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

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


if __name__ == "__main__":
						_snake_case				:   Optional[Any]     =				argparse.ArgumentParser()
						_snake_case				:   int     =				[
						    """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""",
						)

						_snake_case				:   Dict     =				parser.parse_args()

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

'''simple docstring'''







def 			_a		(	_SCREAMING_SNAKE_CASE		:   list[int]	,			_SCREAMING_SNAKE_CASE		:   list[int]  ):

						# Check if the input is valid
						if not len(_SCREAMING_SNAKE_CASE  ) == len(_SCREAMING_SNAKE_CASE  ) == 3:
												raise ValueError("Please enter a valid equation."  )
						if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
												raise ValueError("Both a & b of two equations can't be zero."  )

						# Extract the coefficients
						_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= equationa
						_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= equationa

						# Calculate the determinants of the matrices
						_SCREAMING_SNAKE_CASE				= aa * ba - aa * ba
						_SCREAMING_SNAKE_CASE				= ca * ba - ca * ba
						_SCREAMING_SNAKE_CASE				= aa * ca - aa * ca

						# Check if the system of linear equations has a solution (using Cramer's rule)
						if determinant == 0:
												if determinant_x == determinant_y == 0:
																		raise ValueError("Infinite solutions. (Consistent system)"  )
												else:
																		raise ValueError("No solution. (Inconsistent system)"  )
						else:
												if determinant_x == determinant_y == 0:
																		# Trivial solution (Inconsistent system)
																		return (0.0, 0.0)
												else:
																		_SCREAMING_SNAKE_CASE				= determinant_x / determinant
																		_SCREAMING_SNAKE_CASE				= determinant_y / determinant
																		# Non-Trivial Solution (Consistent system)
																		return (x, y)