
"""simple docstring"""




import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase__				=					logging.get_logger(__name__)



def 							_lowerCamelCase			(			__a,				__a=False     ):
		SCREAMING_SNAKE_CASE_    			=			[]
		for i in range(config.num_hidden_layers     ):
				# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
				rename_keys.append((F'blocks.{i}.norm1.weight', F'vit.encoder.layer.{i}.layernorm_before.weight')     )
				rename_keys.append((F'blocks.{i}.norm1.bias', F'vit.encoder.layer.{i}.layernorm_before.bias')     )
				rename_keys.append((F'blocks.{i}.attn.proj.weight', F'vit.encoder.layer.{i}.attention.output.dense.weight')     )
				rename_keys.append((F'blocks.{i}.attn.proj.bias', F'vit.encoder.layer.{i}.attention.output.dense.bias')     )
				rename_keys.append((F'blocks.{i}.norm2.weight', F'vit.encoder.layer.{i}.layernorm_after.weight')     )
				rename_keys.append((F'blocks.{i}.norm2.bias', F'vit.encoder.layer.{i}.layernorm_after.bias')     )
				rename_keys.append((F'blocks.{i}.mlp.fc1.weight', F'vit.encoder.layer.{i}.intermediate.dense.weight')     )
				rename_keys.append((F'blocks.{i}.mlp.fc1.bias', F'vit.encoder.layer.{i}.intermediate.dense.bias')     )
				rename_keys.append((F'blocks.{i}.mlp.fc2.weight', F'vit.encoder.layer.{i}.output.dense.weight')     )
				rename_keys.append((F'blocks.{i}.mlp.fc2.bias', F'vit.encoder.layer.{i}.output.dense.bias')     )

		# projection layer + position embeddings
		rename_keys.extend(
		    [
		        ('''cls_token''', '''vit.embeddings.cls_token'''),
		        ('''patch_embed.proj.weight''', '''vit.embeddings.patch_embeddings.projection.weight'''),
		        ('''patch_embed.proj.bias''', '''vit.embeddings.patch_embeddings.projection.bias'''),
		        ('''pos_embed''', '''vit.embeddings.position_embeddings'''),
		    ]     )

		if base_model:
				# layernorm + pooler
				rename_keys.extend(
				    [
				        ('''norm.weight''', '''layernorm.weight'''),
				        ('''norm.bias''', '''layernorm.bias'''),
				    ]     )

				# if just the base model, we should remove "vit" from all keys that start with "vit"
				SCREAMING_SNAKE_CASE_    			=			[(pair[0], pair[1][4:]) if pair[1].startswith('''vit'''     ) else pair for pair in rename_keys]
		else:
				# layernorm + classification head
				rename_keys.extend(
				    [
				        ('''norm.weight''', '''vit.layernorm.weight'''),
				        ('''norm.bias''', '''vit.layernorm.bias'''),
				        ('''head.weight''', '''classifier.weight'''),
				        ('''head.bias''', '''classifier.bias'''),
				    ]     )

		return rename_keys



def 							_lowerCamelCase			(			__a,				__a,				__a=False     ):
		for i in range(config.num_hidden_layers     ):
				if base_model:
						SCREAMING_SNAKE_CASE_    			=			''''''
				else:
						SCREAMING_SNAKE_CASE_    			=			'''vit.'''
				# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
				SCREAMING_SNAKE_CASE_    			=			state_dict.pop(F'blocks.{i}.attn.qkv.weight'     )
				SCREAMING_SNAKE_CASE_    			=			state_dict.pop(F'blocks.{i}.attn.qkv.bias'     )
				# next, add query, keys and values (in that order) to the state dict
				SCREAMING_SNAKE_CASE_    			=			in_proj_weight[
				    : config.hidden_size, :
				]
				SCREAMING_SNAKE_CASE_    			=			in_proj_bias[: config.hidden_size]
				SCREAMING_SNAKE_CASE_    			=			in_proj_weight[
				    config.hidden_size : config.hidden_size * 2, :
				]
				SCREAMING_SNAKE_CASE_    			=			in_proj_bias[
				    config.hidden_size : config.hidden_size * 2
				]
				SCREAMING_SNAKE_CASE_    			=			in_proj_weight[
				    -config.hidden_size :, :
				]
				SCREAMING_SNAKE_CASE_    			=			in_proj_bias[-config.hidden_size :]



def 							_lowerCamelCase			(			__a     ):
		SCREAMING_SNAKE_CASE_    			=			['''head.weight''', '''head.bias''']
		for k in ignore_keys:
				state_dict.pop(__a,				__a     )



def 							_lowerCamelCase			(			__a,				__a,				__a     ):
		SCREAMING_SNAKE_CASE_    			=			dct.pop(__a     )
		SCREAMING_SNAKE_CASE_    			=			val



def 							_lowerCamelCase			(			):
		SCREAMING_SNAKE_CASE_    			=			'''http://images.cocodataset.org/val2017/000000039769.jpg'''
		SCREAMING_SNAKE_CASE_    			=			Image.open(requests.get(__a,				stream=__a     ).raw     )
		return im



@torch.no_grad()
def 							_lowerCamelCase			(			__a,				__a,				__a=True     ):
		SCREAMING_SNAKE_CASE_    			=			ViTConfig()
		# patch_size
		if model_name[-1] == "8":
				SCREAMING_SNAKE_CASE_    			=			8
		# set labels if required
		if not base_model:
				SCREAMING_SNAKE_CASE_    			=			1_000
				SCREAMING_SNAKE_CASE_    			=			'''huggingface/label-files'''
				SCREAMING_SNAKE_CASE_    			=			'''imagenet-1k-id2label.json'''
				SCREAMING_SNAKE_CASE_    			=			json.load(open(hf_hub_download(__a,				__a,				repo_type='''dataset'''     ),				'''r'''     )     )
				SCREAMING_SNAKE_CASE_    			=			{int(__a     ): v for k, v in idalabel.items()}
				SCREAMING_SNAKE_CASE_    			=			idalabel
				SCREAMING_SNAKE_CASE_    			=			{v: k for k, v in idalabel.items()}
		# size of the architecture
		if model_name in ["dino_vits8", "dino_vits16"]:
				SCREAMING_SNAKE_CASE_    			=			384
				SCREAMING_SNAKE_CASE_    			=			1_536
				SCREAMING_SNAKE_CASE_    			=			12
				SCREAMING_SNAKE_CASE_    			=			6

		# load original model from torch hub
		SCREAMING_SNAKE_CASE_    			=			torch.hub.load('''facebookresearch/dino:main''',				__a     )
		original_model.eval()

		# load state_dict of original model, remove and rename some keys
		SCREAMING_SNAKE_CASE_    			=			original_model.state_dict()
		if base_model:
				remove_classification_head_(__a     )
		SCREAMING_SNAKE_CASE_    			=			create_rename_keys(__a,				base_model=__a     )
		for src, dest in rename_keys:
				rename_key(__a,				__a,				__a     )
		read_in_q_k_v(__a,				__a,				__a     )

		# load HuggingFace model
		if base_model:
				SCREAMING_SNAKE_CASE_    			=			ViTModel(__a,				add_pooling_layer=__a     ).eval()
		else:
				SCREAMING_SNAKE_CASE_    			=			ViTForImageClassification(__a     ).eval()
		model.load_state_dict(__a     )

		# Check outputs on an image, prepared by ViTImageProcessor
		SCREAMING_SNAKE_CASE_    			=			ViTImageProcessor()
		SCREAMING_SNAKE_CASE_    			=			image_processor(images=prepare_img(),				return_tensors='''pt'''     )
		SCREAMING_SNAKE_CASE_    			=			encoding['''pixel_values''']
		SCREAMING_SNAKE_CASE_    			=			model(__a     )

		if base_model:
				SCREAMING_SNAKE_CASE_    			=			original_model(__a     )
				assert torch.allclose(__a,				outputs.last_hidden_state[:, 0, :],				atol=1E-1     )
		else:
				SCREAMING_SNAKE_CASE_    			=			original_model(__a     )
				assert logits.shape == outputs.logits.shape
				assert torch.allclose(__a,				outputs.logits,				atol=1E-3     )

		Path(__a     ).mkdir(exist_ok=__a     )
		print(F'Saving model {model_name} to {pytorch_dump_folder_path}'     )
		model.save_pretrained(__a     )
		print(F'Saving image processor to {pytorch_dump_folder_path}'     )
		image_processor.save_pretrained(__a     )


if __name__ == "__main__":
						lowerCAmelCase__				=					argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '--model_name',
						    default='dino_vitb16',
						    type=str,
						    help='Name of the model trained with DINO you\'d like to convert.',
						)
						parser.add_argument(
						    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
						)
						parser.add_argument(
						    '--base_model',
						    action='store_true',
						    help='Whether to only convert the base model (no projection head weights).',
						)

						parser.set_defaults(base_model=True)
						lowerCAmelCase__				=					parser.parse_args()
						convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)

"""simple docstring"""




import argparse
import re

import requests
import torch

# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode

from transformers import (
    BertTokenizer,
    BlipConfig,
    BlipForConditionalGeneration,
    BlipForImageTextRetrieval,
    BlipForQuestionAnswering,
)



def 							_lowerCamelCase			(			__a,				__a     ):
		SCREAMING_SNAKE_CASE_    			=			'''https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'''
		SCREAMING_SNAKE_CASE_    			=			Image.open(requests.get(__a,				stream=__a     ).raw     ).convert('''RGB'''     )

		SCREAMING_SNAKE_CASE_    			=			transforms.Compose(
		    [
		        transforms.Resize((image_size, image_size),				interpolation=InterpolationMode.BICUBIC     ),
		        transforms.ToTensor(),
		        transforms.Normalize((0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3),				(0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1)     ),
		    ]     )
		SCREAMING_SNAKE_CASE_    			=			transform(__a     ).unsqueeze(0     ).to(__a     )
		return image



def 							_lowerCamelCase			(			__a     ):
		if "visual_encoder" in key:
				SCREAMING_SNAKE_CASE_    			=			re.sub('''visual_encoder*''',				'''vision_model.encoder''',				__a     )
		if "blocks" in key:
				SCREAMING_SNAKE_CASE_    			=			re.sub(r'''blocks''',				'''layers''',				__a     )
		if "attn" in key:
				SCREAMING_SNAKE_CASE_    			=			re.sub(r'''attn''',				'''self_attn''',				__a     )
		if "norm1" in key:
				SCREAMING_SNAKE_CASE_    			=			re.sub(r'''norm1''',				'''layer_norm1''',				__a     )
		if "norm2" in key:
				SCREAMING_SNAKE_CASE_    			=			re.sub(r'''norm2''',				'''layer_norm2''',				__a     )
		if "encoder.norm" in key:
				SCREAMING_SNAKE_CASE_    			=			re.sub(r'''encoder.norm''',				'''post_layernorm''',				__a     )
		if "encoder.patch_embed.proj" in key:
				SCREAMING_SNAKE_CASE_    			=			re.sub(r'''encoder.patch_embed.proj''',				'''embeddings.patch_embedding''',				__a     )

		if "encoder.pos_embed" in key:
				SCREAMING_SNAKE_CASE_    			=			re.sub(r'''encoder.pos_embed''',				'''embeddings.position_embedding''',				__a     )
		if "encoder.cls_token" in key:
				SCREAMING_SNAKE_CASE_    			=			re.sub(r'''encoder.cls_token''',				'''embeddings.class_embedding''',				__a     )

		if "self_attn" in key:
				SCREAMING_SNAKE_CASE_    			=			re.sub(r'''self_attn.proj''',				'''self_attn.projection''',				__a     )

		return key



@torch.no_grad()
def 							_lowerCamelCase			(			__a,				__a=None     ):
		if config_path is not None:
				SCREAMING_SNAKE_CASE_    			=			BlipConfig.from_pretrained(__a     )
		else:
				SCREAMING_SNAKE_CASE_    			=			BlipConfig(projection_dim=512,				text_config={},				vision_config={}     )

		SCREAMING_SNAKE_CASE_    			=			BlipForConditionalGeneration(__a     ).eval()

		SCREAMING_SNAKE_CASE_    			=			'''https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth'''

		SCREAMING_SNAKE_CASE_    			=			blip_decoder(pretrained=__a,				image_size=384,				vit='''base'''     )
		SCREAMING_SNAKE_CASE_    			=			pt_model.eval()

		SCREAMING_SNAKE_CASE_    			=			pt_model.state_dict()
		for key in modified_state_dict.copy():
				SCREAMING_SNAKE_CASE_    			=			modified_state_dict.pop(__a     )
				SCREAMING_SNAKE_CASE_    			=			rename_key(__a     )
				SCREAMING_SNAKE_CASE_    			=			value

		hf_model.load_state_dict(__a     )

		SCREAMING_SNAKE_CASE_    			=			384
		SCREAMING_SNAKE_CASE_    			=			load_demo_image(image_size=__a,				device='''cpu'''     )
		SCREAMING_SNAKE_CASE_    			=			BertTokenizer.from_pretrained('''bert-base-uncased'''     )
		SCREAMING_SNAKE_CASE_    			=			tokenizer(['''a picture of''']     ).input_ids

		SCREAMING_SNAKE_CASE_    			=			hf_model.generate(__a,				__a     )

		assert out[0].tolist() == [30_522, 1_037, 3_861, 1_997, 1_037, 2_450, 3_564, 2_006, 1_996, 3_509, 2_007, 2_014, 3_899, 102]

		SCREAMING_SNAKE_CASE_    			=			hf_model.generate(__a     )

		assert out[0].tolist() == [30_522, 1_037, 2_450, 3_564, 2_006, 1_996, 3_509, 2_007, 2_014, 3_899, 102]

		if pytorch_dump_folder_path is not None:
				hf_model.save_pretrained(__a     )

		# model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_vqa.pth'
		SCREAMING_SNAKE_CASE_    			=			(
		    '''https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth'''
		)

		SCREAMING_SNAKE_CASE_    			=			blip_vqa(pretrained=__a,				image_size=__a,				vit='''base'''     )
		vqa_model.eval()

		SCREAMING_SNAKE_CASE_    			=			vqa_model.state_dict()
		for key in modified_state_dict.copy():
				SCREAMING_SNAKE_CASE_    			=			modified_state_dict.pop(__a     )
				SCREAMING_SNAKE_CASE_    			=			rename_key(__a     )
				SCREAMING_SNAKE_CASE_    			=			value

		SCREAMING_SNAKE_CASE_    			=			BlipForQuestionAnswering(__a     )

		hf_vqa_model.load_state_dict(__a     )

		SCREAMING_SNAKE_CASE_    			=			['''How many dogs are in this image?''']
		SCREAMING_SNAKE_CASE_    			=			tokenizer(__a,				return_tensors='''pt'''     ).input_ids

		SCREAMING_SNAKE_CASE_    			=			hf_vqa_model.generate(__a,				__a     )
		print(tokenizer.decode(answer[0]     )     )

		assert tokenizer.decode(answer[0]     ) == "[UNK] 1 [SEP]"
		if pytorch_dump_folder_path is not None:
				hf_vqa_model.save_pretrained(pytorch_dump_folder_path + '''_vqa'''     )

		SCREAMING_SNAKE_CASE_    			=			'''https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth'''

		SCREAMING_SNAKE_CASE_    			=			blip_itm(pretrained=__a,				image_size=__a,				vit='''base'''     )
		itm_model.eval()

		SCREAMING_SNAKE_CASE_    			=			itm_model.state_dict()
		for key in modified_state_dict.copy():
				SCREAMING_SNAKE_CASE_    			=			modified_state_dict.pop(__a     )
				SCREAMING_SNAKE_CASE_    			=			rename_key(__a     )
				SCREAMING_SNAKE_CASE_    			=			value

		SCREAMING_SNAKE_CASE_    			=			BlipForImageTextRetrieval(__a     )

		SCREAMING_SNAKE_CASE_    			=			['''A picture of a woman with a dog sitting in a beach''']
		SCREAMING_SNAKE_CASE_    			=			tokenizer(
		    __a,				return_tensors='''pt''',				padding='''max_length''',				truncation=__a,				max_length=35,				).input_ids

		hf_itm_model.load_state_dict(__a     )
		hf_itm_model.eval()

		SCREAMING_SNAKE_CASE_    			=			hf_itm_model(__a,				__a,				use_itm_head=__a     )
		SCREAMING_SNAKE_CASE_    			=			hf_itm_model(__a,				__a,				use_itm_head=__a     )

		assert out[0].item() == 0.2_1_1_0_6_8_7_4_9_4_2_7_7_9_5_4
		assert torch.nn.functional.softmax(out_itm[0],				dim=1     )[:, 1].item() == 0.4_5_6_9_8_8_4_5_3_8_6_5_0_5_1_2_7

		if pytorch_dump_folder_path is not None:
				hf_itm_model.save_pretrained(pytorch_dump_folder_path + '''_itm'''     )


if __name__ == "__main__":
						lowerCAmelCase__				=					argparse.ArgumentParser()
						parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
						parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
						lowerCAmelCase__				=					parser.parse_args()

						convert_blip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)