


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

							return image







def        lowerCAmelCase_  (      __lowerCamelCase			):
							__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_  (      __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase			):
							__snake_case	:    Optional[int]											=     dct.pop(__lowerCamelCase			)
							__snake_case	:    int											=     val







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase			):
							for i in range(config.vision_config.num_hidden_layers			):
														# read in original q and v biases
														__snake_case	:    str											=     state_dict.pop(F'visual_encoder.blocks.{i}.attn.q_bias'			)
														__snake_case	:    Union[str, Any]											=     state_dict.pop(F'visual_encoder.blocks.{i}.attn.v_bias'			)

														# next, set bias in the state dict
														__snake_case	:    Any											=     torch.cat((q_bias, torch.zeros_like(__lowerCamelCase							,			requires_grad=__lowerCamelCase			), v_bias)			)
														__snake_case	:    Tuple											=     qkv_bias







def        lowerCAmelCase_  (      __lowerCamelCase			):
							__snake_case	:    Optional[int]											=     3_6_4 if "coco" in model_name else 2_2_4
							__snake_case	:    List[Any]											=     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:
														__snake_case	:    int											=     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	:    Union[str, Any]											=     TaConfig.from_pretrained("google/flan-t5-xxl"							,			dense_act_fn="gelu"							,			bos_token_id=1			).to_dict()
							elif "vicuna-7b" in model_name:
														__snake_case	:    Tuple											=     LlamaConfig.from_pretrained("decapoda-research/llama-7b-hf"							,			vocab_size=3_2_0_0_1			).to_dict()
							elif "vicuna-13b" in model_name:
														__snake_case	:    Any											=     LlamaConfig.from_pretrained("decapoda-research/llama-13b-hf"							,			vocab_size=3_2_0_0_1			).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
							__snake_case	:    Any											=     InstructBlipQFormerConfig(vocab_size=3_0_5_2_3			).to_dict()
							__snake_case	:    Dict											=     InstructBlipConfig(vision_config=__lowerCamelCase							,			text_config=__lowerCamelCase							,			qformer_config=__lowerCamelCase			)

							return config, image_size







@torch.no_grad()
def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase=None							,			__lowerCamelCase=False			):
							__snake_case	:    Dict											=     AutoTokenizer.from_pretrained("bert-base-uncased"							,			truncation_side="left"			)
							qformer_tokenizer.add_special_tokens({"bos_token": "[DEC]"}			)

							if "t5" in model_name:
														__snake_case	:    Optional[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>"})
														__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]"}			)

							__snake_case	:    str											=     get_blipa_config(__lowerCamelCase			)
							__snake_case	:    Optional[int]											=     InstructBlipForConditionalGeneration(__lowerCamelCase			).eval()

							__snake_case	:    int											=     {
							    "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"),
							}

							__snake_case	:    List[Any]											=     model_name_to_original[model_name]

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

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

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

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

							# make sure processor creates exact same pixel values
							__snake_case	:    Tuple											=     vis_processors["eval"](__lowerCamelCase			).unsqueeze(0			).to(__lowerCamelCase			)
							__snake_case	:    Any											=     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:
																					__snake_case	:    Dict											=     original_model({"image": original_pixel_values, "text_input": [prompt]}			).logits
																					__snake_case	:    str											=     hf_model(**__lowerCamelCase			).logits
														else:
																					__snake_case	:    Tuple											=     original_model(
																					    {"image": original_pixel_values, "text_input": [prompt], "text_output": ["\n"]}			).logits
																					__snake_case	:    Optional[int]											=     tokenizer("\n"							,			return_tensors="pt"			).input_ids.to(__lowerCamelCase			)
																					__snake_case	:    Any											=     label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id							,			-1_0_0			)
																					__snake_case	:    Optional[int]											=     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
							__snake_case	:    Any											=     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..."			)
							__snake_case	:    Tuple											=     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..."			)
							__snake_case	:    Union[str, Any]											=     hf_model.generate(
							    **__lowerCamelCase							,			do_sample=__lowerCamelCase							,			num_beams=5							,			max_length=2_5_6							,			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?
														__snake_case	:    Union[str, Any]											=     2
							print("Original generation:"							,			__lowerCamelCase			)
							__snake_case	:    Any											=     processor.batch_decode(__lowerCamelCase							,			skip_special_tokens=__lowerCamelCase			)
							__snake_case	:    List[Any]											=     [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__":
			_snake_case  :	Dict			      =       argparse.ArgumentParser()
			_snake_case  :	Dict			      =       [
			    "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",
			)

			_snake_case  :	Any			      =       parser.parse_args()

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





from __future__ import annotations

_snake_case  :	Union[str, Any]			      =       {
    "A": ["B", "C", "E"],
    "B": ["A", "D", "E"],
    "C": ["A", "F", "G"],
    "D": ["B"],
    "E": ["A", "B", "D"],
    "F": ["C"],
    "G": ["C"],
}

class        a	:




							"""simple docstring"""


							def __init__(							self :						Tuple					,			lowerCamelCase :						dict[str, list[str]]					,			lowerCamelCase :						str       )			->					None:
														__snake_case	:    Tuple											=     graph
														# mapping node to its parent in resulting breadth first tree
														__snake_case	:    dict[str, str | None]											=     {}
														__snake_case	:    Dict											=     source_vertex

							def 					__snake_case      (							self :						Optional[int]       )			->					None:
														__snake_case	:    Dict											=     {self.source_vertex}
														__snake_case	:    List[str]											=     None
														__snake_case	:    Optional[Any]											=     [self.source_vertex]  # first in first out queue

														while queue:
																					__snake_case	:    List[Any]											=     queue.pop(0       )
																					for adjacent_vertex in self.graph[vertex]:
																												if adjacent_vertex not in visited:
																																			visited.add(lowerCamelCase       )
																																			__snake_case	:    Any											=     vertex
																																			queue.append(lowerCamelCase       )






							def 					__snake_case      (							self :						Optional[Any]					,			lowerCamelCase :						str       )			->					str:
														if target_vertex == self.source_vertex:
																					return self.source_vertex

														__snake_case	:    Optional[Any]											=     self.parent.get(lowerCamelCase       )
														if target_vertex_parent is None:
																					__snake_case	:    Optional[Any]											=     (
																					    F'No path from vertex: {self.source_vertex} to vertex: {target_vertex}'
																					)
																					raise ValueError(lowerCamelCase       )

														return self.shortest_path(lowerCamelCase       ) + F'->{target_vertex}'


if __name__ == "__main__":
			_snake_case  :	Optional[Any]			      =       Graph(graph, "G")
			g.breath_first_search()
			print(g.shortest_path("D"))
			print(g.shortest_path("G"))
			print(g.shortest_path("Foo"))

