






"""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  _snake_case				(     )				->	Any:
							lowerCamelCase_       :							Union[str, Any]									="https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png"
							lowerCamelCase_       :							Union[str, Any]									=Image.open(requests.get(UpperCamelCase__				,						stream=UpperCamelCase__  ).raw  ).convert("RGB"  )

							return image

def  _snake_case				(     lowerCamelCase__	:    Dict  )				->	Optional[int]:
							lowerCamelCase_       :							List[str]									=[]
							# 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  _snake_case				(     lowerCamelCase__	:    int				,						lowerCamelCase__	:    List[Any]				,						lowerCamelCase__	:    Any  )				->	Any:
							lowerCamelCase_       :							Dict									=dct.pop(UpperCamelCase__  )
							lowerCamelCase_       :							Union[str, Any]									=val

def  _snake_case				(     lowerCamelCase__	:    Optional[Any]				,						lowerCamelCase__	:    List[str]  )				->	List[str]:






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

														# next, set bias in the state dict
														lowerCamelCase_       :							Optional[Any]									=torch.cat((q_bias, torch.zeros_like(UpperCamelCase__				,						requires_grad=UpperCamelCase__  ), v_bias)  )
														lowerCamelCase_       :							str									=qkv_bias

def  _snake_case				(     lowerCamelCase__	:    Optional[int]				,						lowerCamelCase__	:    Optional[Any]  )				->	Optional[int]:
							lowerCamelCase_       :							Tuple									=364 if "coco" in model_name else 224
							lowerCamelCase_       :							List[str]									=BlipaVisionConfig(image_size=UpperCamelCase__  ).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:
														lowerCamelCase_       :							Tuple									=OPTConfig.from_pretrained("facebook/opt-2.7b"				,						eos_token_id=UpperCamelCase__  ).to_dict()
							elif "opt-6.7b" in model_name:
														lowerCamelCase_       :							List[str]									=OPTConfig.from_pretrained("facebook/opt-6.7b"				,						eos_token_id=UpperCamelCase__  ).to_dict()
							elif "t5-xl" in model_name:
														lowerCamelCase_       :							Optional[Any]									=TaConfig.from_pretrained("google/flan-t5-xl"				,						dense_act_fn="gelu"				,						bos_token_id=1  ).to_dict()
							elif "t5-xxl" in model_name:
														lowerCamelCase_       :							Dict									=TaConfig.from_pretrained("google/flan-t5-xxl"				,						dense_act_fn="gelu"				,						bos_token_id=1  ).to_dict()

							lowerCamelCase_       :							List[Any]									=BlipaConfig(vision_config=UpperCamelCase__				,						text_config=UpperCamelCase__  )

							return config, image_size

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

							lowerCamelCase_       :							Optional[Any]									=BlipaForConditionalGeneration(UpperCamelCase__  ).eval()

							lowerCamelCase_       :							Tuple									={
							    "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"),
							}

							lowerCamelCase_							,						lowerCamelCase_       :							List[Any]									=model_name_to_original[model_name]

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

							# update state dict keys
							lowerCamelCase_       :							str									=original_model.state_dict()
							lowerCamelCase_       :							Optional[int]									=create_rename_keys(UpperCamelCase__  )
							for src, dest in rename_keys:
														rename_key(UpperCamelCase__				,						UpperCamelCase__				,						UpperCamelCase__  )

							# some keys can be renamed efficiently
							for key, val in state_dict.copy().items():
														lowerCamelCase_       :							Dict									=state_dict.pop(UpperCamelCase__  )
														if key.startswith("Qformer.bert"  ):
																					lowerCamelCase_       :							Union[str, Any]									=key.replace("Qformer.bert"				,						"qformer"  )
														if "attention.self" in key:
																					lowerCamelCase_       :							int									=key.replace("self"				,						"attention"  )
														if "opt_proj" in key:
																					lowerCamelCase_       :							List[Any]									=key.replace("opt_proj"				,						"language_projection"  )
														if "t5_proj" in key:
																					lowerCamelCase_       :							int									=key.replace("t5_proj"				,						"language_projection"  )
														if key.startswith("opt"  ):
																					lowerCamelCase_       :							List[Any]									=key.replace("opt"				,						"language"  )
														if key.startswith("t5"  ):
																					lowerCamelCase_       :							Optional[Any]									=key.replace("t5"				,						"language"  )
														lowerCamelCase_       :							str									=val

							# read in qv biases
							read_in_q_v_bias(UpperCamelCase__				,						UpperCamelCase__  )

							lowerCamelCase_							,						lowerCamelCase_       :							int									=hf_model.load_state_dict(UpperCamelCase__				,						strict=UpperCamelCase__  )
							assert len(UpperCamelCase__  ) == 0
							assert unexpected_keys == ["qformer.embeddings.position_ids"]

							lowerCamelCase_       :							str									=load_demo_image()
							lowerCamelCase_       :							Optional[int]									=vis_processors["eval"](UpperCamelCase__  ).unsqueeze(0  ).to(UpperCamelCase__  )
							lowerCamelCase_       :							Optional[Any]									=tokenizer(["\n"]				,						return_tensors="pt"  ).input_ids.to(UpperCamelCase__  )

							# create processor
							lowerCamelCase_       :							Optional[int]									=BlipImageProcessor(
							    size={"height": image_size, "width": image_size}				,						image_mean=UpperCamelCase__				,						image_std=UpperCamelCase__  )
							lowerCamelCase_       :							str									=BlipaProcessor(image_processor=UpperCamelCase__				,						tokenizer=UpperCamelCase__  )
							lowerCamelCase_       :							int									=processor(images=UpperCamelCase__				,						return_tensors="pt"  ).pixel_values.to(UpperCamelCase__  )

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

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

							print("Generating a caption..."  )
							lowerCamelCase_       :							Dict									=""
							lowerCamelCase_       :							Union[str, Any]									=tokenizer(UpperCamelCase__				,						return_tensors="pt"  ).input_ids.to(UpperCamelCase__  )

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

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

							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__       :				Optional[Any] 					=					argparse.ArgumentParser()
						A__       :				List[str] 					=					[
						    """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__       :				Union[str, Any] 					=					parser.parse_args()

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






import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging


logging.set_verbosity_info()
_UpperCAmelCase		:   List[Any]										=       logging.get_logger()







@dataclass
class  lowercase				:
				__SCREAMING_SNAKE_CASE					: nn.Module
				__SCREAMING_SNAKE_CASE					: List[nn.Module]       			=			field(default_factory=lowercase_			)
				__SCREAMING_SNAKE_CASE					: list       			=			field(default_factory=lowercase_			)




				def     a							(					self      ,   snake_case      ,   snake_case      ,   snake_case       ):
											snake_case_							=    len(list(m.modules()       )       ) == 1 or isinstance(snake_case      ,   nn.Convad       ) or isinstance(snake_case      ,   nn.BatchNormad       )
											if has_not_submodules:
																		self.traced.append(snake_case       )




				def __call__(					self      ,   snake_case       ):
											for m in self.module.modules():
																		self.handles.append(m.register_forward_hook(self._forward_hook       )       )
											self.module(snake_case       )
											[x.remove() for x in self.handles]
											return self




				@property
				def     a							(					self       ):
											# check the len of the state_dict keys to see if we have learnable params
											return list(filter(lambda snake_case       : len(list(x.state_dict().keys()       )       ) > 0      ,   self.traced       )       )







@dataclass
class  lowercase				:
				__SCREAMING_SNAKE_CASE					: nn.Module
				__SCREAMING_SNAKE_CASE					: nn.Module
				__SCREAMING_SNAKE_CASE					: int       			=			0
				__SCREAMING_SNAKE_CASE					: List       			=			field(default_factory=lowercase_			)
				__SCREAMING_SNAKE_CASE					: List       			=			field(default_factory=lowercase_			)




				def __call__(					self      ,   snake_case       ):
											snake_case_							=    Tracker(self.dest       )(snake_case       ).parametrized
											snake_case_							=    Tracker(self.src       )(snake_case       ).parametrized

											snake_case_							=    list(filter(lambda snake_case       : type(snake_case       ) not in self.src_skip      ,   snake_case       )       )
											snake_case_							=    list(filter(lambda snake_case       : type(snake_case       ) not in self.dest_skip      ,   snake_case       )       )

											if len(snake_case       ) != len(snake_case       ):
																		raise Exception(
																		    F'''Numbers of operations are different. Source module has {len(snake_case       )} operations while'''
																		    F''' destination module has {len(snake_case       )}.'''       )

											for dest_m, src_m in zip(snake_case      ,   snake_case       ):
																		dest_m.load_state_dict(src_m.state_dict()       )
																		if self.verbose == 1:
																									print(F'''Transfered from={src_m} to={dest_m}'''       )





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__ = True      ):







							'''simple docstring'''






							print(F'''Converting {name}...'''      )
							with torch.no_grad():
														snake_case_							=    timm.create_model(UpperCamelCase__   ,							pretrained=UpperCamelCase__      ).eval()
														snake_case_							=    ResNetForImageClassification(UpperCamelCase__      ).eval()
														snake_case_							=    ModuleTransfer(src=UpperCamelCase__   ,							dest=UpperCamelCase__      )
														snake_case_							=    torch.randn((1, 3, 224, 224)      )
														module_transfer(UpperCamelCase__      )

							assert torch.allclose(from_model(UpperCamelCase__      )   ,							our_model(UpperCamelCase__      ).logits      ), "The model logits don't match the original one."

							snake_case_							=    F'''resnet{"-".join(name.split("resnet"      )      )}'''
							print(UpperCamelCase__      )

							if push_to_hub:
														our_model.push_to_hub(
														    repo_path_or_name=save_directory / checkpoint_name   ,							commit_message='Add model'   ,							use_temp_dir=UpperCamelCase__   ,							)

														# we can use the convnext one
														snake_case_							=    AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'      )
														image_processor.push_to_hub(
														    repo_path_or_name=save_directory / checkpoint_name   ,							commit_message='Add image processor'   ,							use_temp_dir=UpperCamelCase__   ,							)

														print(F'''Pushed {checkpoint_name}'''      )





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__ = None   ,							UpperCamelCase__ = True      ):







							'''simple docstring'''






							snake_case_							=    'imagenet-1k-id2label.json'
							snake_case_							=    1000
							snake_case_							=    (1, num_labels)

							snake_case_							=    'huggingface/label-files'
							snake_case_							=    num_labels
							snake_case_							=    json.load(open(hf_hub_download(UpperCamelCase__   ,							UpperCamelCase__   ,							repo_type='dataset'      )   ,							'r'      )      )
							snake_case_							=    {int(UpperCamelCase__      ): v for k, v in idalabel.items()}

							snake_case_							=    idalabel
							snake_case_							=    {v: k for k, v in idalabel.items()}

							snake_case_							=    partial(UpperCamelCase__   ,							num_labels=UpperCamelCase__   ,							idalabel=UpperCamelCase__   ,							labelaid=UpperCamelCase__      )

							snake_case_							=    {
							    'resnet18': ImageNetPreTrainedConfig(
							        depths=[2, 2, 2, 2]   ,							hidden_sizes=[64, 128, 256, 512]   ,							layer_type='basic'      ),
							    'resnet26': ImageNetPreTrainedConfig(
							        depths=[2, 2, 2, 2]   ,							hidden_sizes=[256, 512, 1024, 2048]   ,							layer_type='bottleneck'      ),
							    'resnet34': ImageNetPreTrainedConfig(
							        depths=[3, 4, 6, 3]   ,							hidden_sizes=[64, 128, 256, 512]   ,							layer_type='basic'      ),
							    'resnet50': ImageNetPreTrainedConfig(
							        depths=[3, 4, 6, 3]   ,							hidden_sizes=[256, 512, 1024, 2048]   ,							layer_type='bottleneck'      ),
							    'resnet101': ImageNetPreTrainedConfig(
							        depths=[3, 4, 23, 3]   ,							hidden_sizes=[256, 512, 1024, 2048]   ,							layer_type='bottleneck'      ),
							    'resnet152': ImageNetPreTrainedConfig(
							        depths=[3, 8, 36, 3]   ,							hidden_sizes=[256, 512, 1024, 2048]   ,							layer_type='bottleneck'      ),
							}

							if model_name:
														convert_weight_and_push(UpperCamelCase__   ,							names_to_config[model_name]   ,							UpperCamelCase__   ,							UpperCamelCase__      )
							else:
														for model_name, config in names_to_config.items():
																					convert_weight_and_push(UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      )
							return config, expected_shape


if __name__ == "__main__":
					_UpperCAmelCase		:   List[str]										=       argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--model_name""",
					    default=None,
					    type=str,
					    help=(
					        """The name of the model you wish to convert, it must be one of the supported resnet* architecture,"""
					        """ currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted."""
					    ),
					)
					parser.add_argument(
					    """--pytorch_dump_folder_path""",
					    default=None,
					    type=Path,
					    required=True,
					    help="""Path to the output PyTorch model directory.""",
					)
					parser.add_argument(
					    """--push_to_hub""",
					    default=True,
					    type=bool,
					    required=False,
					    help="""If True, push model and image processor to the hub.""",
					)

					_UpperCAmelCase		:   Optional[Any]										=       parser.parse_args()
					_UpperCAmelCase		:   Path										=       args.pytorch_dump_folder_path
					pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
					convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)




