






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

			SCREAMING_SNAKE_CASE							=			transforms.Compose(
			    [
			        transforms.Resize((image_size, image_size)				,							interpolation=InterpolationMode.BICUBIC),
			        transforms.ToTensor(),
			        transforms.Normalize((0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73)				,							(0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11)),
			    ])
			SCREAMING_SNAKE_CASE							=			transform(_UpperCAmelCase).unsqueeze(0).to(_UpperCAmelCase)
			return image






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

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

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

			return key






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

			SCREAMING_SNAKE_CASE							=			BlipForConditionalGeneration(_UpperCAmelCase).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=_UpperCAmelCase				,							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(_UpperCAmelCase)
						SCREAMING_SNAKE_CASE							=			rename_key(_UpperCAmelCase)
						SCREAMING_SNAKE_CASE							=			value

			hf_model.load_state_dict(_UpperCAmelCase)

			SCREAMING_SNAKE_CASE							=			384
			SCREAMING_SNAKE_CASE							=			load_demo_image(image_size=_UpperCAmelCase				,							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(_UpperCAmelCase				,							_UpperCAmelCase)

			assert out[0].tolist() == [3_0522, 1037, 3861, 1997, 1037, 2450, 3564, 2006, 1996, 3509, 2007, 2014, 3899, 102]

			SCREAMING_SNAKE_CASE							=			hf_model.generate(_UpperCAmelCase)

			assert out[0].tolist() == [3_0522, 1037, 2450, 3564, 2006, 1996, 3509, 2007, 2014, 3899, 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'
			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=_UpperCAmelCase				,							image_size=_UpperCAmelCase				,							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(_UpperCAmelCase)
						SCREAMING_SNAKE_CASE							=			rename_key(_UpperCAmelCase)
						SCREAMING_SNAKE_CASE							=			value

			SCREAMING_SNAKE_CASE							=			BlipForQuestionAnswering(_UpperCAmelCase)

			hf_vqa_model.load_state_dict(_UpperCAmelCase)

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

			SCREAMING_SNAKE_CASE							=			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')

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

			SCREAMING_SNAKE_CASE							=			blip_itm(pretrained=_UpperCAmelCase				,							image_size=_UpperCAmelCase				,							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(_UpperCAmelCase)
						SCREAMING_SNAKE_CASE							=			rename_key(_UpperCAmelCase)
						SCREAMING_SNAKE_CASE							=			value

			SCREAMING_SNAKE_CASE							=			BlipForImageTextRetrieval(_UpperCAmelCase)

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

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

			assert out[0].item() == 0.21_10_68_74_94_27_79_54
			assert torch.nn.functional.softmax(out_itm[0]				,							dim=1)[:, 1].item() == 0.4_56_98_84_53_86_50_51_27

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


if __name__ == "__main__":
				a_		:    List[Any]   							=     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')
				a_		:    Union[str, Any]   							=     parser.parse_args()

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





'''simple docstring'''






import logging
import random

import ray

from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex


_UpperCAmelCase    :     int         =       logging.getLogger(__name__)







class lowercase_       :







   """simple docstring"""

   def __init__(      self      :			Tuple     )  ->			int:
        _A    		=  False
   def 	__UpperCAmelCase   (      self      :			Optional[int], UpperCamelCase__      :			str, UpperCamelCase__      :			Optional[Any], UpperCamelCase__      :			int, UpperCamelCase__      :			Optional[Any]     )  ->			Any:
        if not self.initialized:
             _A    		=  RagRetriever(
                 UpperCamelCase__, question_encoder_tokenizer=UpperCamelCase__, generator_tokenizer=UpperCamelCase__, index=UpperCamelCase__, init_retrieval=UpperCamelCase__, )
             _A    		=  True
   def 	__UpperCAmelCase   (      self      :			Any     )  ->			List[Any]:
        self.retriever.index.init_index()

   def 	__UpperCAmelCase   (      self      :			int, UpperCamelCase__      :			Optional[Any], UpperCamelCase__      :			Dict     )  ->			Optional[Any]:
        _A		,       _A    		=  self.retriever._main_retrieve(UpperCamelCase__, UpperCamelCase__     )
        return doc_ids, retrieved_doc_embeds







class lowercase_       (		_UpperCamelCase			):







   """simple docstring"""

   def __init__(      self      :			Optional[Any], UpperCamelCase__      :			Optional[Any], UpperCamelCase__      :			Optional[int], UpperCamelCase__      :			Dict, UpperCamelCase__      :			Union[str, Any], UpperCamelCase__      :			int=None     )  ->			Optional[int]:
        if index is not None and index.is_initialized() and len(UpperCamelCase__     ) > 0:
             raise ValueError(
                 'When using Ray for distributed fine-tuning, '
                 'you\'ll need to provide the paths instead, '
                 'as the dataset and the index are loaded '
                 'separately. More info in examples/rag/use_own_knowledge_dataset.py '     )
        super().__init__(
            UpperCamelCase__, question_encoder_tokenizer=UpperCamelCase__, generator_tokenizer=UpperCamelCase__, index=UpperCamelCase__, init_retrieval=UpperCamelCase__, )
        _A    		=  retrieval_workers
        if len(self.retrieval_workers     ) > 0:
             ray.get(
                 [
                     worker.create_rag_retriever.remote(UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__     )
                     for worker in self.retrieval_workers
                 ]     )
   def 	__UpperCAmelCase   (      self      :			Dict     )  ->			int:
        logger.info('initializing retrieval'     )

        if len(self.retrieval_workers     ) > 0:
             ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers]     )
        else:
             # Non-distributed training. Load index into this same process.
             self.index.init_index()
   def 	__UpperCAmelCase   (      self      :			Optional[Any], UpperCamelCase__      :			Dict, UpperCamelCase__      :			Tuple     )  ->			Dict:
        if len(self.retrieval_workers     ) > 0:
             # Select a random retrieval actor.
             _A    		=  self.retrieval_workers[random.randint(0, len(self.retrieval_workers     ) - 1     )]
             _A		,       _A    		=  ray.get(random_worker.retrieve.remote(UpperCamelCase__, UpperCamelCase__     )     )
        else:
             _A		,       _A    		=  self._main_retrieve(UpperCamelCase__, UpperCamelCase__     )
        return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(UpperCamelCase__     )
   @classmethod
   def 	__UpperCAmelCase   (      cls      :			List[str], UpperCamelCase__      :			List[Any], UpperCamelCase__      :			List[Any]=None, **UpperCamelCase__      :			Optional[int]     )  ->			Optional[Any]:
        return super(UpperCamelCase__, cls     ).get_tokenizers(UpperCamelCase__, UpperCamelCase__, **UpperCamelCase__     )

   @classmethod
   def 	__UpperCAmelCase   (      cls      :			Any, UpperCamelCase__      :			Tuple, UpperCamelCase__      :			int, UpperCamelCase__      :			List[Any]=None, **UpperCamelCase__      :			List[str]     )  ->			Tuple:
        _A    		=  kwargs.pop('config', UpperCamelCase__     ) or RagConfig.from_pretrained(UpperCamelCase__, **UpperCamelCase__     )
        _A    		=  RagTokenizer.from_pretrained(UpperCamelCase__, config=UpperCamelCase__     )
        _A    		=  rag_tokenizer.question_encoder
        _A    		=  rag_tokenizer.generator
        if indexed_dataset is not None:
             _A    		=  'custom'
             _A    		=  CustomHFIndex(config.retrieval_vector_size, UpperCamelCase__     )
        else:
             _A    		=  cls._build_index(UpperCamelCase__     )
        return cls(
            UpperCamelCase__, question_encoder_tokenizer=UpperCamelCase__, generator_tokenizer=UpperCamelCase__, retrieval_workers=UpperCamelCase__, index=UpperCamelCase__, )



