





'''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       __snake_case		( SCREAMING_SNAKE_CASE_ :			List[str]			,	SCREAMING_SNAKE_CASE_ :			str )							->     List[Any]:

       """simple docstring"""


       UpperCAmelCase  =						'''https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'''
       UpperCAmelCase  =						Image.open(requests.get(SCREAMING_SNAKE_CASE_			,	stream=SCREAMING_SNAKE_CASE_ ).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(SCREAMING_SNAKE_CASE_ ).unsqueeze(0 ).to(SCREAMING_SNAKE_CASE_ )
       return image
def       __snake_case		( SCREAMING_SNAKE_CASE_ :			List[str] )							->     Dict:

       """simple docstring"""


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

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

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

       return key
@torch.no_grad()
def       __snake_case		( SCREAMING_SNAKE_CASE_ :			List[str]			,	SCREAMING_SNAKE_CASE_ :			Any=None )							->     int:

       """simple docstring"""


       if config_path is not None:
              UpperCAmelCase  =						BlipConfig.from_pretrained(SCREAMING_SNAKE_CASE_ )
       else:
              UpperCAmelCase  =						BlipConfig(projection_dim=512			,	text_config={}			,	vision_config={} )

       UpperCAmelCase  =						BlipForConditionalGeneration(SCREAMING_SNAKE_CASE_ ).eval()

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

       UpperCAmelCase  =						blip_decoder(pretrained=SCREAMING_SNAKE_CASE_			,	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(SCREAMING_SNAKE_CASE_ )
              UpperCAmelCase  =						rename_key(SCREAMING_SNAKE_CASE_ )
              UpperCAmelCase  =						value

       hf_model.load_state_dict(SCREAMING_SNAKE_CASE_ )

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

       UpperCAmelCase  =						hf_model.generate(SCREAMING_SNAKE_CASE_			,	SCREAMING_SNAKE_CASE_ )

       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(SCREAMING_SNAKE_CASE_ )

       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(SCREAMING_SNAKE_CASE_ )

       # 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=SCREAMING_SNAKE_CASE_			,	image_size=SCREAMING_SNAKE_CASE_			,	vit='''base''' )
       vqa_model.eval()

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

       UpperCAmelCase  =						BlipForQuestionAnswering(SCREAMING_SNAKE_CASE_ )

       hf_vqa_model.load_state_dict(SCREAMING_SNAKE_CASE_ )

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

       UpperCAmelCase  =						hf_vqa_model.generate(SCREAMING_SNAKE_CASE_			,	SCREAMING_SNAKE_CASE_ )
       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=SCREAMING_SNAKE_CASE_			,	image_size=SCREAMING_SNAKE_CASE_			,	vit='''base''' )
       itm_model.eval()

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

       UpperCAmelCase  =						BlipForImageTextRetrieval(SCREAMING_SNAKE_CASE_ )

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

       hf_itm_model.load_state_dict(SCREAMING_SNAKE_CASE_ )
       hf_itm_model.eval()

       UpperCAmelCase  =						hf_itm_model(SCREAMING_SNAKE_CASE_			,	SCREAMING_SNAKE_CASE_			,	use_itm_head=SCREAMING_SNAKE_CASE_ )
       UpperCAmelCase  =						hf_itm_model(SCREAMING_SNAKE_CASE_			,	SCREAMING_SNAKE_CASE_			,	use_itm_head=SCREAMING_SNAKE_CASE_ )

       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__":
    a__			:					Dict       						=						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__			:					int       						=						parser.parse_args()

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






import logging
import os
from typing import List, TextIO, Union

from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask


SCREAMING_SNAKE_CASE__     					=			logging.getLogger(__name__)



class 						_UpperCAmelCase      (     lowercase							):






		def __init__(						self				:     Optional[int]   ,  UpperCAmelCase				:     Any=-1):
				# in NER datasets, the last column is usually reserved for NER label
				SCREAMING_SNAKE_CASE_				:Tuple				     =						label_idx






		def _snake_case		(						self				:     Tuple   ,  UpperCAmelCase				:     Any   ,  UpperCAmelCase				:     Union[Split, str]):
				if isinstance(UpperCAmelCase   ,  UpperCAmelCase):
						SCREAMING_SNAKE_CASE_				:List[Any]				     =						mode.value
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						os.path.join(UpperCAmelCase   ,  F"{mode}.txt")
				SCREAMING_SNAKE_CASE_				:Tuple				     =						1
				SCREAMING_SNAKE_CASE_				:str				     =						[]
				with open(UpperCAmelCase   ,  encoding="utf-8") as f:
						SCREAMING_SNAKE_CASE_				:Tuple				     =						[]
						SCREAMING_SNAKE_CASE_				:int				     =						[]
						for line in f:
								if line.startswith("-DOCSTART-") or line == "" or line == "\n":
										if words:
												examples.append(InputExample(guid=F"{mode}-{guid_index}"   ,  words=UpperCAmelCase   ,  labels=UpperCAmelCase))
												guid_index += 1
												SCREAMING_SNAKE_CASE_				:Tuple				     =						[]
												SCREAMING_SNAKE_CASE_				:Any				     =						[]
								else:
										SCREAMING_SNAKE_CASE_				:int				     =						line.split(" ")
										words.append(splits[0])
										if len(UpperCAmelCase) > 1:
												labels.append(splits[self.label_idx].replace("\n"   ,  ""))
										else:
												# Examples could have no label for mode = "test"
												labels.append("O")
						if words:
								examples.append(InputExample(guid=F"{mode}-{guid_index}"   ,  words=UpperCAmelCase   ,  labels=UpperCAmelCase))
				return examples






		def _snake_case		(						self				:     List[Any]   ,  UpperCAmelCase				:     TextIO   ,  UpperCAmelCase				:     TextIO   ,  UpperCAmelCase				:     List):
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						0
				for line in test_input_reader:
						if line.startswith("-DOCSTART-") or line == "" or line == "\n":
								writer.write(UpperCAmelCase)
								if not preds_list[example_id]:
										example_id += 1
						elif preds_list[example_id]:
								SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						line.split()[0] + " " + preds_list[example_id].pop(0) + "\n"
								writer.write(UpperCAmelCase)
						else:
								logger.warning("Maximum sequence length exceeded: No prediction for '%s'."   ,  line.split()[0])







		def _snake_case		(						self				:     List[str]   ,  UpperCAmelCase				:     str):
				if path:
						with open(UpperCAmelCase   ,  "r") as f:
								SCREAMING_SNAKE_CASE_				:Any				     =						f.read().splitlines()
						if "O" not in labels:
								SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						["O"] + labels
						return labels
				else:
						return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]




class 						_UpperCAmelCase      (     lowercase							):






		def __init__(						self				:     Dict):
				# in CONLL2003 dataset chunk column is second-to-last
				super().__init__(label_idx=-2)







		def _snake_case		(						self				:     Union[str, Any]   ,  UpperCAmelCase				:     str):
				if path:
						with open(UpperCAmelCase   ,  "r") as f:
								SCREAMING_SNAKE_CASE_				:Optional[int]				     =						f.read().splitlines()
						if "O" not in labels:
								SCREAMING_SNAKE_CASE_				:Dict				     =						["O"] + labels
						return labels
				else:
						return [
						    "O",
						    "B-ADVP",
						    "B-INTJ",
						    "B-LST",
						    "B-PRT",
						    "B-NP",
						    "B-SBAR",
						    "B-VP",
						    "B-ADJP",
						    "B-CONJP",
						    "B-PP",
						    "I-ADVP",
						    "I-INTJ",
						    "I-LST",
						    "I-PRT",
						    "I-NP",
						    "I-SBAR",
						    "I-VP",
						    "I-ADJP",
						    "I-CONJP",
						    "I-PP",
						]




class 						_UpperCAmelCase      (     lowercase							):






		def _snake_case		(						self				:     int   ,  UpperCAmelCase				:     List[Any]   ,  UpperCAmelCase				:     Union[Split, str]):
				if isinstance(UpperCAmelCase   ,  UpperCAmelCase):
						SCREAMING_SNAKE_CASE_				:List[str]				     =						mode.value
				SCREAMING_SNAKE_CASE_				:List[str]				     =						os.path.join(UpperCAmelCase   ,  F"{mode}.txt")
				SCREAMING_SNAKE_CASE_				:Dict				     =						1
				SCREAMING_SNAKE_CASE_				:List[str]				     =						[]

				with open(UpperCAmelCase   ,  encoding="utf-8") as f:
						for sentence in parse_incr(UpperCAmelCase):
								SCREAMING_SNAKE_CASE_				:List[str]				     =						[]
								SCREAMING_SNAKE_CASE_				:int				     =						[]
								for token in sentence:
										words.append(token["form"])
										labels.append(token["upos"])
								assert len(UpperCAmelCase) == len(UpperCAmelCase)
								if words:
										examples.append(InputExample(guid=F"{mode}-{guid_index}"   ,  words=UpperCAmelCase   ,  labels=UpperCAmelCase))
										guid_index += 1
				return examples






		def _snake_case		(						self				:     List[Any]   ,  UpperCAmelCase				:     TextIO   ,  UpperCAmelCase				:     TextIO   ,  UpperCAmelCase				:     List):
				SCREAMING_SNAKE_CASE_				:List[str]				     =						0
				for sentence in parse_incr(UpperCAmelCase):
						SCREAMING_SNAKE_CASE_				:str				     =						preds_list[example_id]
						SCREAMING_SNAKE_CASE_				:List[Any]				     =						""
						for token in sentence:
								out += F"{token['form']} ({token['upos']}|{s_p.pop(0)}) "
						out += "\n"
						writer.write(UpperCAmelCase)
						example_id += 1







		def _snake_case		(						self				:     Tuple   ,  UpperCAmelCase				:     str):
				if path:
						with open(UpperCAmelCase   ,  "r") as f:
								return f.read().splitlines()
				else:
						return [
						    "ADJ",
						    "ADP",
						    "ADV",
						    "AUX",
						    "CCONJ",
						    "DET",
						    "INTJ",
						    "NOUN",
						    "NUM",
						    "PART",
						    "PRON",
						    "PROPN",
						    "PUNCT",
						    "SCONJ",
						    "SYM",
						    "VERB",
						    "X",
						]






