



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 							A			(  _UpperCAmelCase       :     Any							,     _UpperCAmelCase       :     str					)       ->				List[str]:







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

			_UpperCAmelCase	 =     transforms.Compose(
			    [
			        transforms.Resize((image_size, image_size)							,     interpolation=InterpolationMode.BICUBIC					),
			        transforms.ToTensor(),
			        transforms.Normalize((0.48145466, 0.4578275, 0.40821073)							,     (0.26862954, 0.26130258, 0.27577711)					),
			    ]					)
			_UpperCAmelCase	 =     transform(_UpperCAmelCase					).unsqueeze(0					).to(_UpperCAmelCase					)
			return image





def 							A			(  _UpperCAmelCase       :     Any					)       ->				List[Any]:







			'''simple docstring'''
			if "visual_encoder" in key:
						_UpperCAmelCase	 =     re.sub('visual_encoder*'							,     'vision_model.encoder'							,     _UpperCAmelCase					)
			if "blocks" in key:
						_UpperCAmelCase	 =     re.sub(R'blocks'							,     'layers'							,     _UpperCAmelCase					)
			if "attn" in key:
						_UpperCAmelCase	 =     re.sub(R'attn'							,     'self_attn'							,     _UpperCAmelCase					)
			if "norm1" in key:
						_UpperCAmelCase	 =     re.sub(R'norm1'							,     'layer_norm1'							,     _UpperCAmelCase					)
			if "norm2" in key:
						_UpperCAmelCase	 =     re.sub(R'norm2'							,     'layer_norm2'							,     _UpperCAmelCase					)
			if "encoder.norm" in key:
						_UpperCAmelCase	 =     re.sub(R'encoder.norm'							,     'post_layernorm'							,     _UpperCAmelCase					)
			if "encoder.patch_embed.proj" in key:
						_UpperCAmelCase	 =     re.sub(R'encoder.patch_embed.proj'							,     'embeddings.patch_embedding'							,     _UpperCAmelCase					)

			if "encoder.pos_embed" in key:
						_UpperCAmelCase	 =     re.sub(R'encoder.pos_embed'							,     'embeddings.position_embedding'							,     _UpperCAmelCase					)
			if "encoder.cls_token" in key:
						_UpperCAmelCase	 =     re.sub(R'encoder.cls_token'							,     'embeddings.class_embedding'							,     _UpperCAmelCase					)

			if "self_attn" in key:
						_UpperCAmelCase	 =     re.sub(R'self_attn.proj'							,     'self_attn.projection'							,     _UpperCAmelCase					)

			return key





@torch.no_grad()
def 							A			(  _UpperCAmelCase       :     Dict							,     _UpperCAmelCase       :     List[str]=None					)       ->				Tuple:







			'''simple docstring'''
			if config_path is not None:
						_UpperCAmelCase	 =     BlipConfig.from_pretrained(_UpperCAmelCase					)
			else:
						_UpperCAmelCase	 =     BlipConfig(projection_dim=512							,     text_config={}							,     vision_config={}					)

			_UpperCAmelCase	 =     BlipForConditionalGeneration(_UpperCAmelCase					).eval()

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

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

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

			hf_model.load_state_dict(_UpperCAmelCase					)

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

			_UpperCAmelCase	 =     hf_model.generate(_UpperCAmelCase							,     _UpperCAmelCase					)

			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]

			_UpperCAmelCase	 =     hf_model.generate(_UpperCAmelCase					)

			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(_UpperCAmelCase					)

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

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

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

			_UpperCAmelCase	 =     BlipForQuestionAnswering(_UpperCAmelCase					)

			hf_vqa_model.load_state_dict(_UpperCAmelCase					)

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

			_UpperCAmelCase	 =     hf_vqa_model.generate(_UpperCAmelCase							,     _UpperCAmelCase					)
			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'					)

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

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

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

			_UpperCAmelCase	 =     BlipForImageTextRetrieval(_UpperCAmelCase					)

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

			hf_itm_model.load_state_dict(_UpperCAmelCase					)
			hf_itm_model.eval()

			_UpperCAmelCase	 =     hf_itm_model(_UpperCAmelCase							,     _UpperCAmelCase							,     use_itm_head=_UpperCAmelCase					)
			_UpperCAmelCase	 =     hf_itm_model(_UpperCAmelCase							,     _UpperCAmelCase							,     use_itm_head=_UpperCAmelCase					)

			assert out[0].item() == 0.2110687494277954
			assert torch.nn.functional.softmax(out_itm[0]							,     dim=1					)[:, 1].item() == 0.45698845386505127

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


if __name__ == "__main__":
							UpperCAmelCase__     :   int     = 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")
							UpperCAmelCase__     :   Tuple     = parser.parse_args()

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





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


UpperCAmelCase__     = {
    "configuration_ctrl": ["CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP", "CTRLConfig"],
    "tokenization_ctrl": ["CTRLTokenizer"],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__     = [
							    "CTRL_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "CTRLForSequenceClassification",
							    "CTRLLMHeadModel",
							    "CTRLModel",
							    "CTRLPreTrainedModel",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__     = [
							    "TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "TFCTRLForSequenceClassification",
							    "TFCTRLLMHeadModel",
							    "TFCTRLModel",
							    "TFCTRLPreTrainedModel",
							]


if TYPE_CHECKING:
							from .configuration_ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig
							from .tokenization_ctrl import CTRLTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_ctrl import (
														    CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
														    CTRLForSequenceClassification,
														    CTRLLMHeadModel,
														    CTRLModel,
														    CTRLPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_ctrl import (
														    TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFCTRLForSequenceClassification,
														    TFCTRLLMHeadModel,
														    TFCTRLModel,
														    TFCTRLPreTrainedModel,
														)

else:
							import sys

							UpperCAmelCase__     = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
