
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__							(							):




			"""simple docstring"""


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

			return image






def   lowerCamelCase__							(							__lowerCAmelCase    :    Optional[int]							):




			"""simple docstring"""


			lowerCAmelCase_         =      []
			# 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__							(							__lowerCAmelCase    :    Optional[Any]       ,				__lowerCAmelCase    :    str       ,				__lowerCAmelCase    :    Union[str, Any]							):




			"""simple docstring"""


			lowerCAmelCase_         =      dct.pop(__lowerCAmelCase							)
			lowerCAmelCase_         =      val






def   lowerCamelCase__							(							__lowerCAmelCase    :    Any       ,				__lowerCAmelCase    :    Union[str, Any]							):




			"""simple docstring"""


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

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






def   lowerCamelCase__							(							__lowerCAmelCase    :    Optional[int]							):




			"""simple docstring"""


			lowerCAmelCase_         =      364 if "coco" in model_name else 224
			lowerCAmelCase_         =      InstructBlipVisionConfig(image_size=__lowerCAmelCase							).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:
						lowerCAmelCase_         =      TaConfig.from_pretrained("google/flan-t5-xl"       ,				dense_act_fn="gelu"       ,				bos_token_id=1							).to_dict()
			elif "t5-xxl" in model_name:
						lowerCAmelCase_         =      TaConfig.from_pretrained("google/flan-t5-xxl"       ,				dense_act_fn="gelu"       ,				bos_token_id=1							).to_dict()
			elif "vicuna-7b" in model_name:
						lowerCAmelCase_         =      LlamaConfig.from_pretrained("decapoda-research/llama-7b-hf"       ,				vocab_size=32001							).to_dict()
			elif "vicuna-13b" in model_name:
						lowerCAmelCase_         =      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
			lowerCAmelCase_         =      InstructBlipQFormerConfig(vocab_size=30523							).to_dict()
			lowerCAmelCase_         =      InstructBlipConfig(vision_config=__lowerCAmelCase       ,				text_config=__lowerCAmelCase       ,				qformer_config=__lowerCAmelCase							)

			return config, image_size






@torch.no_grad()
def   lowerCamelCase__							(							__lowerCAmelCase    :    str       ,				__lowerCAmelCase    :    int=None       ,				__lowerCAmelCase    :    Dict=False							):




			"""simple docstring"""


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

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

			lowerCAmelCase_      ,    lowerCAmelCase_         =      get_blipa_config(__lowerCAmelCase							)
			lowerCAmelCase_         =      InstructBlipForConditionalGeneration(__lowerCAmelCase							).eval()

			lowerCAmelCase_         =      {
			    "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"),
			}

			lowerCAmelCase_      ,    lowerCAmelCase_         =      model_name_to_original[model_name]

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

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

			# some keys can be renamed efficiently
			for key, val in state_dict.copy().items():
						lowerCAmelCase_         =      state_dict.pop(__lowerCAmelCase							)
						if key.startswith("Qformer.bert"							):
									lowerCAmelCase_         =      key.replace("Qformer.bert"       ,				"qformer"							)
						if "attention.self" in key:
									lowerCAmelCase_         =      key.replace("self"       ,				"attention"							)
						if "llm_proj" in key:
									lowerCAmelCase_         =      key.replace("llm_proj"       ,				"language_projection"							)
						if "t5_proj" in key:
									lowerCAmelCase_         =      key.replace("t5_proj"       ,				"language_projection"							)
						if key.startswith("llm_model"							):
									lowerCAmelCase_         =      key.replace("llm_model"       ,				"language_model"							)
						if key.startswith("t5"							):
									lowerCAmelCase_         =      key.replace("t5"       ,				"language"							)
						lowerCAmelCase_         =      val

			# read in qv biases
			read_in_q_v_bias(__lowerCAmelCase       ,				__lowerCAmelCase							)

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

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

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

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

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

			print("Generating with original model..."							)
			lowerCAmelCase_         =      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..."							)
			lowerCAmelCase_         =      hf_model.generate(
			    **__lowerCAmelCase       ,				do_sample=__lowerCAmelCase       ,				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?
						lowerCAmelCase_         =      2
			print("Original generation:"       ,				__lowerCAmelCase							)
			lowerCAmelCase_         =      processor.batch_decode(__lowerCAmelCase       ,				skip_special_tokens=__lowerCAmelCase							)
			lowerCAmelCase_         =      [text.strip() for text in output_text]
			print("HF generation:"       ,				__lowerCAmelCase							)

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

			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 collections.abc import Callable






def   lowerCamelCase__							(							__lowerCAmelCase    :    Callable[[float], float]       ,				__lowerCAmelCase    :    float       ,				__lowerCAmelCase    :    float							):




			"""simple docstring"""


			lowerCAmelCase_         =      a
			lowerCAmelCase_         =      b
			if function(__lowerCAmelCase							) == 0:  # one of the a or b is a root for the function
						return a
			elif function(__lowerCAmelCase							) == 0:
						return b
			elif (
			    function(__lowerCAmelCase							) * function(__lowerCAmelCase							) > 0
			):  # if none of these are root and they are both positive or negative,
						# then this algorithm can't find the root
						raise ValueError("could not find root in given interval."							)
			else:
						lowerCAmelCase_         =      start + (end - start) / 2.0
						while abs(start - mid							) > 10**-7:  # until precisely equals to 10^-7
									if function(__lowerCAmelCase							) == 0:
												return mid
									elif function(__lowerCAmelCase							) * function(__lowerCAmelCase							) < 0:
												lowerCAmelCase_         =      mid
									else:
												lowerCAmelCase_         =      mid
									lowerCAmelCase_         =      start + (end - start) / 2.0
						return mid






def   lowerCamelCase__							(							__lowerCAmelCase    :    float							):




			"""simple docstring"""


			return x**3 - 2 * x - 5


if __name__ == "__main__":
			print(bisection(f, 1, 10_00))

			import doctest

			doctest.testmod()



