




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		:						Union[str, Any]   ,		__UpperCAmelCase		:						List[str]      ) ->					str:
							"""simple docstring"""




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

							lowerCamelCase_			:    Union[str, Any]   						=      transforms.Compose(
							    [
							        transforms.Resize((image_size, image_size)   ,		interpolation=InterpolationMode.BICUBIC      ),
							        transforms.ToTensor(),
							        transforms.Normalize((0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3)   ,		(0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1)      ),
							    ]      )
							lowerCamelCase_			:    Optional[Any]   						=      transform(_snake_case      ).unsqueeze(0      ).to(_snake_case      )
							return image



def        __a							(  __UpperCAmelCase		:						List[str]      ) ->					Dict:
							"""simple docstring"""




							if "visual_encoder" in key:
														lowerCamelCase_			:    List[str]   						=      re.sub("visual_encoder*"   ,		"vision_model.encoder"   ,		_snake_case      )
							if "blocks" in key:
														lowerCamelCase_			:    Union[str, Any]   						=      re.sub(R"blocks"   ,		"layers"   ,		_snake_case      )
							if "attn" in key:
														lowerCamelCase_			:    Union[str, Any]   						=      re.sub(R"attn"   ,		"self_attn"   ,		_snake_case      )
							if "norm1" in key:
														lowerCamelCase_			:    Optional[int]   						=      re.sub(R"norm1"   ,		"layer_norm1"   ,		_snake_case      )
							if "norm2" in key:
														lowerCamelCase_			:    Tuple   						=      re.sub(R"norm2"   ,		"layer_norm2"   ,		_snake_case      )
							if "encoder.norm" in key:
														lowerCamelCase_			:    Dict   						=      re.sub(R"encoder.norm"   ,		"post_layernorm"   ,		_snake_case      )
							if "encoder.patch_embed.proj" in key:
														lowerCamelCase_			:    Optional[int]   						=      re.sub(R"encoder.patch_embed.proj"   ,		"embeddings.patch_embedding"   ,		_snake_case      )

							if "encoder.pos_embed" in key:
														lowerCamelCase_			:    List[Any]   						=      re.sub(R"encoder.pos_embed"   ,		"embeddings.position_embedding"   ,		_snake_case      )
							if "encoder.cls_token" in key:
														lowerCamelCase_			:    int   						=      re.sub(R"encoder.cls_token"   ,		"embeddings.class_embedding"   ,		_snake_case      )

							if "self_attn" in key:
														lowerCamelCase_			:    Dict   						=      re.sub(R"self_attn.proj"   ,		"self_attn.projection"   ,		_snake_case      )

							return key



@torch.no_grad()
def        __a							(  __UpperCAmelCase		:						Optional[int]   ,		__UpperCAmelCase		:						List[str]=None      ) ->					Any:
							"""simple docstring"""




							if config_path is not None:
														lowerCamelCase_			:    Optional[int]   						=      BlipConfig.from_pretrained(_snake_case      )
							else:
														lowerCamelCase_			:    Any   						=      BlipConfig(projection_dim=512   ,		text_config={}   ,		vision_config={}      )

							lowerCamelCase_			:    Union[str, Any]   						=      BlipForConditionalGeneration(_snake_case      ).eval()

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

							lowerCamelCase_			:    int   						=      blip_decoder(pretrained=_snake_case   ,		image_size=384   ,		vit="base"      )
							lowerCamelCase_			:    int   						=      pt_model.eval()

							lowerCamelCase_			:    Any   						=      pt_model.state_dict()
							for key in modified_state_dict.copy():
														lowerCamelCase_			:    Tuple   						=      modified_state_dict.pop(_snake_case      )
														lowerCamelCase_			:    Optional[int]   						=      rename_key(_snake_case      )
														lowerCamelCase_			:    Union[str, Any]   						=      value

							hf_model.load_state_dict(_snake_case      )

							lowerCamelCase_			:    Dict   						=      384
							lowerCamelCase_			:    Union[str, Any]   						=      load_demo_image(image_size=_snake_case   ,		device="cpu"      )
							lowerCamelCase_			:    Dict   						=      BertTokenizer.from_pretrained("bert-base-uncased"      )
							lowerCamelCase_			:    List[Any]   						=      tokenizer(["a picture of"]      ).input_ids

							lowerCamelCase_			:    Dict   						=      hf_model.generate(_snake_case   ,		_snake_case      )

							assert out[0].tolist() == [30522, 1037, 3861, 1997, 1037, 2450, 3564, 2006, 1996, 3509, 2007, 2014, 3899, 102]

							lowerCamelCase_			:    Any   						=      hf_model.generate(_snake_case      )

							assert out[0].tolist() == [30522, 1037, 2450, 3564, 2006, 1996, 3509, 2007, 2014, 3899, 102]

							if pytorch_dump_folder_path is not None:
														hf_model.save_pretrained(_snake_case      )

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

							lowerCamelCase_			:    Tuple   						=      blip_vqa(pretrained=_snake_case   ,		image_size=_snake_case   ,		vit="base"      )
							vqa_model.eval()

							lowerCamelCase_			:    List[Any]   						=      vqa_model.state_dict()
							for key in modified_state_dict.copy():
														lowerCamelCase_			:    Optional[int]   						=      modified_state_dict.pop(_snake_case      )
														lowerCamelCase_			:    Any   						=      rename_key(_snake_case      )
														lowerCamelCase_			:    Tuple   						=      value

							lowerCamelCase_			:    Tuple   						=      BlipForQuestionAnswering(_snake_case      )

							hf_vqa_model.load_state_dict(_snake_case      )

							lowerCamelCase_			:    List[str]   						=      ["How many dogs are in this image?"]
							lowerCamelCase_			:    Optional[Any]   						=      tokenizer(_snake_case   ,		return_tensors="pt"      ).input_ids

							lowerCamelCase_			:    int   						=      hf_vqa_model.generate(_snake_case   ,		_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"      )

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

							lowerCamelCase_			:    Tuple   						=      blip_itm(pretrained=_snake_case   ,		image_size=_snake_case   ,		vit="base"      )
							itm_model.eval()

							lowerCamelCase_			:    Union[str, Any]   						=      itm_model.state_dict()
							for key in modified_state_dict.copy():
														lowerCamelCase_			:    Optional[Any]   						=      modified_state_dict.pop(_snake_case      )
														lowerCamelCase_			:    Optional[Any]   						=      rename_key(_snake_case      )
														lowerCamelCase_			:    Tuple   						=      value

							lowerCamelCase_			:    Optional[Any]   						=      BlipForImageTextRetrieval(_snake_case      )

							lowerCamelCase_			:    Union[str, Any]   						=      ["A picture of a woman with a dog sitting in a beach"]
							lowerCamelCase_			:    Dict   						=      tokenizer(
							    _snake_case   ,		return_tensors="pt"   ,		padding="max_length"   ,		truncation=_snake_case   ,		max_length=35   ,		).input_ids

							hf_itm_model.load_state_dict(_snake_case      )
							hf_itm_model.eval()

							lowerCamelCase_			:    Any   						=      hf_itm_model(_snake_case   ,		_snake_case   ,		use_itm_head=_snake_case      )
							lowerCamelCase_			:    int   						=      hf_itm_model(_snake_case   ,		_snake_case   ,		use_itm_head=_snake_case      )

							assert out[0].item() == 0.2_1_1_0_6_8_7_4_9_4_2_7_7_9_5_4
							assert torch.nn.functional.softmax(out_itm[0]   ,		dim=1      )[:, 1].item() == 0.4_5_6_9_8_8_4_5_3_8_6_5_0_5_1_2_7

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


if __name__ == "__main__":
	snake_case_  :      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")
	snake_case_  :      Dict		=		parser.parse_args()

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







"""simple docstring"""

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a							      =     logging.get_logger(__name__)

a							      =     {
    '''bigcode/gpt_bigcode-santacoder''': '''https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json''',
}





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''
 UpperCAmelCase       :	int     =      '''gpt_bigcode'''
 UpperCAmelCase       :	str     =      ['''past_key_values''']
 UpperCAmelCase       :	Dict     =      {
     '''hidden_size''': '''n_embd''',
     '''max_position_embeddings''': '''n_positions''',
     '''num_attention_heads''': '''n_head''',
     '''num_hidden_layers''': '''n_layer''',
 }

 def __init__(					self			:			Tuple       ,				_UpperCAmelCase			:			Dict=50_257       ,				_UpperCAmelCase			:			List[Any]=1_024       ,				_UpperCAmelCase			:			Any=768       ,				_UpperCAmelCase			:			int=12       ,				_UpperCAmelCase			:			Any=12       ,				_UpperCAmelCase			:			Tuple=None       ,				_UpperCAmelCase			:			str="gelu_pytorch_tanh"       ,				_UpperCAmelCase			:			str=0.1       ,				_UpperCAmelCase			:			Optional[Any]=0.1       ,				_UpperCAmelCase			:			Tuple=0.1       ,				_UpperCAmelCase			:			List[Any]=1E-5       ,				_UpperCAmelCase			:			List[Any]=0.02       ,				_UpperCAmelCase			:			Optional[Any]=True       ,				_UpperCAmelCase			:			Dict=True       ,				_UpperCAmelCase			:			List[Any]=50_256       ,				_UpperCAmelCase			:			Dict=50_256       ,				_UpperCAmelCase			:			int=True       ,				_UpperCAmelCase			:			Union[str, Any]=True       ,				_UpperCAmelCase			:			Any=True       ,				**_UpperCAmelCase			:			Any       ,				):
       _A     =  vocab_size
       _A     =  n_positions
       _A     =  n_embd
       _A     =  n_layer
       _A     =  n_head
       _A     =  n_inner
       _A     =  activation_function
       _A     =  resid_pdrop
       _A     =  embd_pdrop
       _A     =  attn_pdrop
       _A     =  layer_norm_epsilon
       _A     =  initializer_range
       _A     =  scale_attn_weights
       _A     =  use_cache
       _A     =  attention_softmax_in_fpaa
       _A     =  scale_attention_softmax_in_fpaa
       _A     =  multi_query

       _A     =  bos_token_id
       _A     =  eos_token_id

       super().__init__(bos_token_id=_UpperCAmelCase       ,				eos_token_id=_UpperCAmelCase       ,				**_UpperCAmelCase						)

