
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 						UpperCAmelCase      (     a_		,       a_						)		->     Optional[int]:







				"""simple docstring"""



				__A             =			"https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg"
				__A             =			Image.open(requests.get(a_		,       stream=a_						).raw						).convert("RGB"						)

				__A             =			transforms.Compose(
				    [
				        transforms.Resize((image_size, image_size)		,       interpolation=InterpolationMode.BICUBIC						),
				        transforms.ToTensor(),
				        transforms.Normalize((0.48_145_466, 0.4_578_275, 0.40_821_073)		,       (0.26_862_954, 0.26_130_258, 0.27_577_711)						),
				    ]						)
				__A             =			transform(a_						).unsqueeze(0						).to(a_						)
				return image


def 						UpperCAmelCase      (     a_						)		->     Optional[int]:







				"""simple docstring"""



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

				if "encoder.pos_embed" in key:
								__A             =			re.sub(r"encoder.pos_embed"		,       "embeddings.position_embedding"		,       a_						)
				if "encoder.cls_token" in key:
								__A             =			re.sub(r"encoder.cls_token"		,       "embeddings.class_embedding"		,       a_						)

				if "self_attn" in key:
								__A             =			re.sub(r"self_attn.proj"		,       "self_attn.projection"		,       a_						)

				return key


@torch.no_grad()
def 						UpperCAmelCase      (     a_		,       a_=None						)		->     Dict:







				"""simple docstring"""



				if config_path is not None:
								__A             =			BlipConfig.from_pretrained(a_						)
				else:
								__A             =			BlipConfig(projection_dim=5_1_2		,       text_config={}		,       vision_config={}						)

				__A             =			BlipForConditionalGeneration(a_						).eval()

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

				__A             =			blip_decoder(pretrained=a_		,       image_size=3_8_4		,       vit="base"						)
				__A             =			pt_model.eval()

				__A             =			pt_model.state_dict()
				for key in modified_state_dict.copy():
								__A             =			modified_state_dict.pop(a_						)
								__A             =			rename_key(a_						)
								__A             =			value

				hf_model.load_state_dict(a_						)

				__A             =			3_8_4
				__A             =			load_demo_image(image_size=a_		,       device="cpu"						)
				__A             =			BertTokenizer.from_pretrained("bert-base-uncased"						)
				__A             =			tokenizer(["a picture of"]						).input_ids

				__A             =			hf_model.generate(a_		,       a_						)

				assert out[0].tolist() == [3_0_5_2_2, 1_0_3_7, 3_8_6_1, 1_9_9_7, 1_0_3_7, 2_4_5_0, 3_5_6_4, 2_0_0_6, 1_9_9_6, 3_5_0_9, 2_0_0_7, 2_0_1_4, 3_8_9_9, 1_0_2]

				__A             =			hf_model.generate(a_						)

				assert out[0].tolist() == [3_0_5_2_2, 1_0_3_7, 2_4_5_0, 3_5_6_4, 2_0_0_6, 1_9_9_6, 3_5_0_9, 2_0_0_7, 2_0_1_4, 3_8_9_9, 1_0_2]

				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'
				__A             =			(
				    "https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth"
				)

				__A             =			blip_vqa(pretrained=a_		,       image_size=a_		,       vit="base"						)
				vqa_model.eval()

				__A             =			vqa_model.state_dict()
				for key in modified_state_dict.copy():
								__A             =			modified_state_dict.pop(a_						)
								__A             =			rename_key(a_						)
								__A             =			value

				__A             =			BlipForQuestionAnswering(a_						)

				hf_vqa_model.load_state_dict(a_						)

				__A             =			["How many dogs are in this image?"]
				__A             =			tokenizer(a_		,       return_tensors="pt"						).input_ids

				__A             =			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"						)

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

				__A             =			blip_itm(pretrained=a_		,       image_size=a_		,       vit="base"						)
				itm_model.eval()

				__A             =			itm_model.state_dict()
				for key in modified_state_dict.copy():
								__A             =			modified_state_dict.pop(a_						)
								__A             =			rename_key(a_						)
								__A             =			value

				__A             =			BlipForImageTextRetrieval(a_						)

				__A             =			["A picture of a woman with a dog sitting in a beach"]
				__A             =			tokenizer(
				    a_		,       return_tensors="pt"		,       padding="max_length"		,       truncation=a_		,       max_length=3_5		,       ).input_ids

				hf_itm_model.load_state_dict(a_						)
				hf_itm_model.eval()

				__A             =			hf_itm_model(a_		,       a_		,       use_itm_head=a_						)
				__A             =			hf_itm_model(a_		,       a_		,       use_itm_head=a_						)

				assert out[0].item() == 0.2_110_687_494_277_954
				assert torch.nn.functional.softmax(out_itm[0]		,       dim=1						)[:, 1].item() == 0.45_698_845_386_505_127

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


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:List[str]										=    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')
					SCREAMING_SNAKE_CASE							:str										=    parser.parse_args()

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


import argparse
from collections import defaultdict

import yaml


SCREAMING_SNAKE_CASE							:str										=    'docs/source/en/_toctree.yml'


def 						UpperCAmelCase      (     a_						)		->     int:







				"""simple docstring"""



				__A             =			defaultdict(a_						)
				for doc in model_doc:
								counts[doc["local"]] += 1
				__A             =			[key for key, value in counts.items() if value > 1]

				__A             =			[]
				for duplicate_key in duplicates:
								__A             =			list({doc["title"] for doc in model_doc if doc["local"] == duplicate_key}						)
								if len(a_						) > 1:
												raise ValueError(
												    F'''{duplicate_key} is present several times in the documentation table of content at '''
												    "`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the "
												    "others."						)
								# Only add this once
								new_doc.append({"local": duplicate_key, "title": titles[0]}						)

				# Add none duplicate-keys
				new_doc.extend([doc for doc in model_doc if counts[doc["local"]] == 1]						)

				# Sort
				return sorted(a_		,       key=lambda a_						: s["title"].lower()						)


def 						UpperCAmelCase      (     a_=False						)		->     List[Any]:







				"""simple docstring"""



				with open(a_		,       encoding="utf-8"						) as f:
								__A             =			yaml.safe_load(f.read()						)

				# Get to the API doc
				__A             =			0
				while content[api_idx]["title"] != "API":
								api_idx += 1
				__A             =			content[api_idx]["sections"]

				# Then to the model doc
				__A             =			0
				while api_doc[model_idx]["title"] != "Models":
								model_idx += 1

				__A             =			api_doc[model_idx]["sections"]

				__A             =			[(idx, section) for idx, section in enumerate(a_						) if "sections" in section]
				__A             =			False
				for idx, modality_doc in modalities_docs:
								__A             =			modality_doc["sections"]
								__A             =			clean_model_doc_toc(a_						)

								if old_modality_doc != new_modality_doc:
												__A             =			True
												if overwrite:
																__A             =			new_modality_doc

				if diff:
								if overwrite:
												__A             =			model_doc
												__A             =			api_doc
												with open(a_		,       "w"		,       encoding="utf-8"						) as f:
																f.write(yaml.dump(a_		,       allow_unicode=a_						)						)
								else:
												raise ValueError(
												    "The model doc part of the table of content is not properly sorted, run `make style` to fix this."						)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:Tuple										=    argparse.ArgumentParser()
					parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
					SCREAMING_SNAKE_CASE							:List[str]										=    parser.parse_args()

					check_model_doc(args.fix_and_overwrite)
