



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			,						__UpperCamelCase       )    ->   Optional[int]:
			A__	  =     'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'
			A__	  =     Image.open(requests.get(__UpperCamelCase			,						stream=__UpperCamelCase       ).raw       ).convert('RGB'       )

			A__	  =     transforms.Compose(
			    [
			        transforms.Resize((image_size, image_size)			,						interpolation=InterpolationMode.BICUBIC       ),
			        transforms.ToTensor(),
			        transforms.Normalize((0.4814_5466, 0.457_8275, 0.4082_1073)			,						(0.2686_2954, 0.2613_0258, 0.2757_7711)       ),
			    ]       )
			A__	  =     transform(__UpperCamelCase       ).unsqueeze(0       ).to(__UpperCamelCase       )
			return image


def A    (							__UpperCamelCase       )    ->   Optional[int]:
			if "visual_encoder" in key:
						A__	  =     re.sub('visual_encoder*'			,						'vision_model.encoder'			,						__UpperCamelCase       )
			if "blocks" in key:
						A__	  =     re.sub(r'blocks'			,						'layers'			,						__UpperCamelCase       )
			if "attn" in key:
						A__	  =     re.sub(r'attn'			,						'self_attn'			,						__UpperCamelCase       )
			if "norm1" in key:
						A__	  =     re.sub(r'norm1'			,						'layer_norm1'			,						__UpperCamelCase       )
			if "norm2" in key:
						A__	  =     re.sub(r'norm2'			,						'layer_norm2'			,						__UpperCamelCase       )
			if "encoder.norm" in key:
						A__	  =     re.sub(r'encoder.norm'			,						'post_layernorm'			,						__UpperCamelCase       )
			if "encoder.patch_embed.proj" in key:
						A__	  =     re.sub(r'encoder.patch_embed.proj'			,						'embeddings.patch_embedding'			,						__UpperCamelCase       )

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

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

			return key


@torch.no_grad()
def A    (							__UpperCamelCase			,						__UpperCamelCase=None       )    ->   List[Any]:
			if config_path is not None:
						A__	  =     BlipConfig.from_pretrained(__UpperCamelCase       )
			else:
						A__	  =     BlipConfig(projection_dim=512			,						text_config={}			,						vision_config={}       )

			A__	  =     BlipForConditionalGeneration(__UpperCamelCase       ).eval()

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

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

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

			hf_model.load_state_dict(__UpperCamelCase       )

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

			A__	  =     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]

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

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

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

			A__	  =     BlipForQuestionAnswering(__UpperCamelCase       )

			hf_vqa_model.load_state_dict(__UpperCamelCase       )

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

			A__	  =     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'       )

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

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

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

			A__	  =     BlipForImageTextRetrieval(__UpperCamelCase       )

			A__	  =     ['A picture of a woman with a dog sitting in a beach']
			A__	  =     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()

			A__	  =     hf_itm_model(__UpperCamelCase			,						__UpperCamelCase			,						use_itm_head=__UpperCamelCase       )
			A__	  =     hf_itm_model(__UpperCamelCase			,						__UpperCamelCase			,						use_itm_head=__UpperCamelCase       )

			assert out[0].item() == 0.2110_6874_9427_7954
			assert torch.nn.functional.softmax(out_itm[0]			,						dim=1       )[:, 1].item() == 0.4_5698_8453_8650_5127

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


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__   			=   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__   			=   parser.parse_args()

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










import warnings

from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor


lowerCamelCase__					:  Optional[int]      							=				logging.get_logger(__name__)





class 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE   ):


 '''simple docstring'''







 def __init__(				self    :						str							,							*_lowerCAmelCase    :						Tuple							,							**_lowerCAmelCase    :						int		):
       warnings.warn(
           'The class MobileViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
           ' Please use MobileViTImageProcessor instead.'							,							_lowerCAmelCase							,							)
       super().__init__(*_lowerCAmelCase							,							**_lowerCAmelCase		)