

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

	return image



def       __lowerCAmelCase      (						A    ):
	UpperCAmelCase_       				=			[]
	# 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       __lowerCAmelCase      (						A       ,     A       ,     A    ):
	UpperCAmelCase_       				=			dct.pop(A    )
	UpperCAmelCase_       				=			val



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

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



def       __lowerCAmelCase      (						A       ,     A    ):
	UpperCAmelCase_       				=			364 if "coco" in model_name else 224
	UpperCAmelCase_       				=			BlipaVisionConfig(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 "opt-2.7b" in model_name:
		UpperCAmelCase_       				=			OPTConfig.from_pretrained("facebook/opt-2.7b"       ,     eos_token_id=A    ).to_dict()
	elif "opt-6.7b" in model_name:
		UpperCAmelCase_       				=			OPTConfig.from_pretrained("facebook/opt-6.7b"       ,     eos_token_id=A    ).to_dict()
	elif "t5-xl" in model_name:
		UpperCAmelCase_       				=			TaConfig.from_pretrained("google/flan-t5-xl"       ,     dense_act_fn="gelu"       ,     bos_token_id=1    ).to_dict()
	elif "t5-xxl" in model_name:
		UpperCAmelCase_       				=			TaConfig.from_pretrained("google/flan-t5-xxl"       ,     dense_act_fn="gelu"       ,     bos_token_id=1    ).to_dict()

	UpperCAmelCase_       				=			BlipaConfig(vision_config=A       ,     text_config=A    )

	return config, image_size



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

	UpperCAmelCase_       				=			BlipaForConditionalGeneration(A    ).eval()

	UpperCAmelCase_       				=			{
	    "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"),
	}

	UpperCAmelCase_				,   UpperCAmelCase_       				=			model_name_to_original[model_name]

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

	# update state dict keys
	UpperCAmelCase_       				=			original_model.state_dict()
	UpperCAmelCase_       				=			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():
		UpperCAmelCase_       				=			state_dict.pop(A    )
		if key.startswith("Qformer.bert"    ):
			UpperCAmelCase_       				=			key.replace("Qformer.bert"       ,     "qformer"    )
		if "attention.self" in key:
			UpperCAmelCase_       				=			key.replace("self"       ,     "attention"    )
		if "opt_proj" in key:
			UpperCAmelCase_       				=			key.replace("opt_proj"       ,     "language_projection"    )
		if "t5_proj" in key:
			UpperCAmelCase_       				=			key.replace("t5_proj"       ,     "language_projection"    )
		if key.startswith("opt"    ):
			UpperCAmelCase_       				=			key.replace("opt"       ,     "language"    )
		if key.startswith("t5"    ):
			UpperCAmelCase_       				=			key.replace("t5"       ,     "language"    )
		UpperCAmelCase_       				=			val

	# read in qv biases
	read_in_q_v_bias(A       ,     A    )

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

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

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

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

	original_model.to(A    )
	hf_model.to(A    )
	with torch.no_grad():
		if "opt" in model_name:
			UpperCAmelCase_       				=			original_model({"image": original_pixel_values, "text_input": [""]}    ).logits
			UpperCAmelCase_       				=			hf_model(A       ,     A    ).logits
		else:
			UpperCAmelCase_       				=			original_model(
			    {"image": original_pixel_values, "text_input": ["\n"], "text_output": ["\n"]}    ).logits
			UpperCAmelCase_       				=			input_ids.masked_fill(input_ids == tokenizer.pad_token_id       ,     -100    )
			UpperCAmelCase_       				=			hf_model(A       ,     A       ,     labels=A    ).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":
		UpperCAmelCase_       				=			torch.tensor(
		    [[-41.5850, -4.4440, -8.9922], [-47.4322, -5.9143, -1.7340]]       ,     device=A    )
		assert torch.allclose(logits[0, :3, :3]       ,     A       ,     atol=1E-4    )
	elif model_name == "blip2-flan-t5-xl-coco":
		UpperCAmelCase_       				=			torch.tensor(
		    [[-57.0109, -9.8967, -12.6280], [-68.6578, -12.7191, -10.5065]]       ,     device=A    )
	else:
		# cast to same type
		UpperCAmelCase_       				=			logits.dtype
		assert torch.allclose(original_logits.to(A    )       ,     A       ,     atol=1E-2    )
	print("Looks ok!"    )

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

	UpperCAmelCase_       				=			original_model.generate({"image": original_pixel_values}    )
	UpperCAmelCase_       				=			hf_model.generate(
	    A       ,     A       ,     do_sample=A       ,     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:"       ,     A    )
	UpperCAmelCase_       				=			input_ids.shape[1]
	UpperCAmelCase_       				=			processor.batch_decode(outputs[:, prompt_length:]       ,     skip_special_tokens=A    )
	UpperCAmelCase_       				=			[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"nielsr/{model_name}"    )
		hf_model.push_to_hub(F"nielsr/{model_name}"    )


if __name__ == "__main__":
				_a:    List[Any] 							=			argparse.ArgumentParser()
				_a:    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""",
				)

				_a:    Optional[int] 							=			parser.parse_args()

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


from __future__ import annotations

from collections import Counter
from random import random

class 						__UpperCamelCase    :




	def __init__(		self	:						Any							):


		'''simple docstring'''







		UpperCAmelCase_       				=			{}




	def       __A					(		self	:						List[str]		,	lowerCAmelCase	:						str							):


		'''simple docstring'''







		UpperCAmelCase_       				=			{}




	def       __A					(		self	:						Union[str, Any]		,	lowerCAmelCase	:						str		,	lowerCAmelCase	:						str		,	lowerCAmelCase	:						float							):


		'''simple docstring'''







		if nodea not in self.connections:
			self.add_node(lowerCAmelCase							)
		if nodea not in self.connections:
			self.add_node(lowerCAmelCase							)
		UpperCAmelCase_       				=			probability




	def       __A					(		self	:						Tuple							):


		'''simple docstring'''







		return list(self.connections							)




	def       __A					(		self	:						str		,	lowerCAmelCase	:						str							):


		'''simple docstring'''







		UpperCAmelCase_       				=			0
		UpperCAmelCase_       				=			random()

		for dest in self.connections[node]:
			current_probability += self.connections[node][dest]
			if current_probability > random_value:
				return dest
		return ""



def       __lowerCAmelCase      (						A       ,     A       ,     A    ):
	UpperCAmelCase_       				=			MarkovChainGraphUndirectedUnweighted()

	for nodea, nodea, probability in transitions:
		graph.add_transition_probability(A       ,     A       ,     A    )

	UpperCAmelCase_       				=			Counter(graph.get_nodes()    )
	UpperCAmelCase_       				=			start

	for _ in range(A    ):
		UpperCAmelCase_       				=			graph.transition(A    )
		visited[node] += 1

	return visited


if __name__ == "__main__":
				import doctest

				doctest.testmod()