




'''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 					UpperCamelCase_(     ):

							'''simple docstring'''






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

							return image


def 					UpperCamelCase_(     snake_case							:	List[str]					):

							'''simple docstring'''






							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 					UpperCamelCase_(     snake_case							:	Any							,    snake_case							:	Optional[int]							,    snake_case							:	List[Any]					):

							'''simple docstring'''






							snake_case_      =				dct.pop(snake_case					)
							snake_case_      =				val


def 					UpperCamelCase_(     snake_case							:	Optional[Any]							,    snake_case							:	Tuple					):

							'''simple docstring'''






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

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


def 					UpperCamelCase_(     snake_case							:	List[str]							,    snake_case							:	str					):

							'''simple docstring'''






							snake_case_      =				3_6_4 if "coco" in model_name else 2_2_4
							snake_case_      =				BlipaVisionConfig(image_size=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:
														snake_case_      =				OPTConfig.from_pretrained("facebook/opt-2.7b"							,    eos_token_id=snake_case					).to_dict()
							elif "opt-6.7b" in model_name:
														snake_case_      =				OPTConfig.from_pretrained("facebook/opt-6.7b"							,    eos_token_id=snake_case					).to_dict()
							elif "t5-xl" in model_name:
														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:
														snake_case_      =				TaConfig.from_pretrained("google/flan-t5-xxl"							,    dense_act_fn="gelu"							,    bos_token_id=1					).to_dict()

							snake_case_      =				BlipaConfig(vision_config=snake_case							,    text_config=snake_case					)

							return config, image_size


@torch.no_grad()
def 					UpperCamelCase_(     snake_case							:	Any							,    snake_case							:	Optional[Any]=None							,    snake_case							:	Dict=False					):

							'''simple docstring'''






							snake_case_      =				(
							    AutoTokenizer.from_pretrained("facebook/opt-2.7b"					)
							    if "opt" in model_name
							    else AutoTokenizer.from_pretrained("google/flan-t5-xl"					)
							)
							snake_case_      =				tokenizer("\n"							,    add_special_tokens=snake_case					).input_ids[0]
							snake_case_ ,				snake_case_      =				get_blipa_config(snake_case							,    eos_token_id=snake_case					)

							snake_case_      =				BlipaForConditionalGeneration(snake_case					).eval()

							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"),
							}

							snake_case_ ,				snake_case_      =				model_name_to_original[model_name]

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

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

							# some keys can be renamed efficiently
							for key, val in state_dict.copy().items():
														snake_case_      =				state_dict.pop(snake_case					)
														if key.startswith("Qformer.bert"					):
																					snake_case_      =				key.replace("Qformer.bert"							,    "qformer"					)
														if "attention.self" in key:
																					snake_case_      =				key.replace("self"							,    "attention"					)
														if "opt_proj" in key:
																					snake_case_      =				key.replace("opt_proj"							,    "language_projection"					)
														if "t5_proj" in key:
																					snake_case_      =				key.replace("t5_proj"							,    "language_projection"					)
														if key.startswith("opt"					):
																					snake_case_      =				key.replace("opt"							,    "language"					)
														if key.startswith("t5"					):
																					snake_case_      =				key.replace("t5"							,    "language"					)
														snake_case_      =				val

							# read in qv biases
							read_in_q_v_bias(snake_case							,    snake_case					)

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

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

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

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

							original_model.to(snake_case					)
							hf_model.to(snake_case					)
							with torch.no_grad():
														if "opt" in model_name:
																					snake_case_      =				original_model({"image": original_pixel_values, "text_input": [""]}					).logits
																					snake_case_      =				hf_model(snake_case							,    snake_case					).logits
														else:
																					snake_case_      =				original_model(
																					    {"image": original_pixel_values, "text_input": ["\n"], "text_output": ["\n"]}					).logits
																					snake_case_      =				input_ids.masked_fill(input_ids == tokenizer.pad_token_id							,    -1_0_0					)
																					snake_case_      =				hf_model(snake_case							,    snake_case							,    labels=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":
														snake_case_      =				torch.tensor(
														    [[-41.5850, -4.4440, -8.9922], [-47.4322, -5.9143, -1.7340]]							,    device=snake_case					)
														assert torch.allclose(logits[0, :3, :3]							,    snake_case							,    atol=1e-4					)
							elif model_name == "blip2-flan-t5-xl-coco":
														snake_case_      =				torch.tensor(
														    [[-57.0109, -9.8967, -12.6280], [-68.6578, -12.7191, -10.5065]]							,    device=snake_case					)
							else:
														# cast to same type
														snake_case_      =				logits.dtype
														assert torch.allclose(original_logits.to(snake_case					)							,    snake_case							,    atol=1e-2					)
							print("Looks ok!"					)

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

							snake_case_      =				original_model.generate({"image": original_pixel_values}					)
							snake_case_      =				hf_model.generate(
							    snake_case							,    snake_case							,    do_sample=snake_case							,    num_beams=5							,    max_length=3_0							,    min_length=1							,    top_p=0.9							,    repetition_penalty=1.0							,    length_penalty=1.0							,    temperature=1							,    )
							print("Original generation:"							,    snake_case					)
							snake_case_      =				input_ids.shape[1]
							snake_case_      =				processor.batch_decode(outputs[:, prompt_length:]							,    skip_special_tokens=snake_case					)
							snake_case_      =				[text.strip() for text in output_text]
							print("HF generation:"							,    snake_case					)

							if pytorch_dump_folder_path is not None:
														processor.save_pretrained(snake_case					)
														hf_model.save_pretrained(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__":
		_SCREAMING_SNAKE_CASE							:       str						=			argparse.ArgumentParser()
		_SCREAMING_SNAKE_CASE							:       Optional[Any]						=			[
		    "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",
		)

		_SCREAMING_SNAKE_CASE							:       List[Any]						=			parser.parse_args()

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










'''simple docstring'''





from sympy import diff, lambdify, symbols
from sympy.functions import *  # noqa: F403


def 					UpperCamelCase_(     snake_case							:	str							,    snake_case							:	complex							,    snake_case							:	str = "x"							,    snake_case							:	float = 1_0**-1_0							,    snake_case							:	int = 1							,    ):

							'''simple docstring'''






							snake_case_      =				symbols(snake_case					)
							snake_case_      =				lambdify(snake_case							,    snake_case					)
							snake_case_      =				lambdify(snake_case							,    diff(snake_case							,    snake_case					)					)

							snake_case_      =				starting_point

							while True:
														if diff_function(snake_case					) != 0:
																					snake_case_      =				prev_guess - multiplicity * func(snake_case					) / diff_function(
																					    snake_case					)
														else:
																					raise ZeroDivisionError("Could not find root"					) from None

														# Precision is checked by comparing the difference of consecutive guesses
														if abs(next_guess - prev_guess					) < precision:
																					return next_guess

														snake_case_      =				next_guess


# Let's Execute
if __name__ == "__main__":
		# Find root of trigonometric function
		# Find value of pi
		print(F"The root of sin(x) = 0 is {newton_raphson('sin(x)', 2)}")

		# Find root of polynomial
		# Find fourth Root of 5
		print(F"The root of x**4 - 5 = 0 is {newton_raphson('x**4 -5', 0.4 +5j)}")

		# Find value of e
		print(
		    "The root of log(y) - 1 = 0 is ",
		    F"{newton_raphson('log(y) - 1', 2, variable='y')}",
		)

		# Exponential Roots
		print(
		    "The root of exp(x) - 1 = 0 is",
		    F"{newton_raphson('exp(x) - 1', 10, precision=0.0_0_5)}",
		)

		# Find root of cos(x)
		print(F"The root of cos(x) = 0 is {newton_raphson('cos(x)', 0)}")




