





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      _UpperCamelCase  (a__ :str ,      a__ :str							):






							"""simple docstring"""





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

							UpperCamelCase__					  =      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)							),
							    ]							)
							UpperCamelCase__					  =      transform(a__							).unsqueeze(0							).to(a__							)
							return image





def      _UpperCamelCase  (a__ :Optional[Any]							):






							"""simple docstring"""





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

							if "encoder.pos_embed" in key:
														UpperCamelCase__					  =      re.sub(r"""encoder.pos_embed""" ,      """embeddings.position_embedding""" ,      a__							)
							if "encoder.cls_token" in key:
														UpperCamelCase__					  =      re.sub(r"""encoder.cls_token""" ,      """embeddings.class_embedding""" ,      a__							)

							if "self_attn" in key:
														UpperCamelCase__					  =      re.sub(r"""self_attn.proj""" ,      """self_attn.projection""" ,      a__							)

							return key





@torch.no_grad()
def      _UpperCamelCase  (a__ :Tuple ,      a__ :Union[str, Any]=None							):






							"""simple docstring"""





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

							UpperCamelCase__					  =      BlipForConditionalGeneration(a__							).eval()

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

							UpperCamelCase__					  =      blip_decoder(pretrained=a__ ,      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(a__							)
														UpperCamelCase__					  =      rename_key(a__							)
														UpperCamelCase__					  =      value

							hf_model.load_state_dict(a__							)

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

							UpperCamelCase__					  =      hf_model.generate(a__ ,      a__							)

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

							UpperCamelCase__					  =      hf_model.generate(a__							)

							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(a__							)

							# 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=a__ ,      image_size=a__ ,      vit="""base"""							)
							vqa_model.eval()

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

							UpperCamelCase__					  =      BlipForQuestionAnswering(a__							)

							hf_vqa_model.load_state_dict(a__							)

							UpperCamelCase__					  =      ["""How many dogs are in this image?"""]
							UpperCamelCase__					  =      tokenizer(a__ ,      return_tensors="""pt"""							).input_ids

							UpperCamelCase__					  =      hf_vqa_model.generate(a__ ,      a__							)
							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=a__ ,      image_size=a__ ,      vit="""base"""							)
							itm_model.eval()

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

							UpperCamelCase__					  =      BlipForImageTextRetrieval(a__							)

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

							hf_itm_model.load_state_dict(a__							)
							hf_itm_model.eval()

							UpperCamelCase__					  =      hf_itm_model(a__ ,      a__ ,      use_itm_head=a__							)
							UpperCamelCase__					  =      hf_itm_model(a__ ,      a__ ,      use_itm_head=a__							)

							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__":
					UpperCamelCase__           =			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__           =			parser.parse_args()

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











import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


UpperCamelCase__           =			False

class 							__SCREAMING_SNAKE_CASE       (  unittest.TestCase							):
							pass






@nightly
@require_torch_gpu
class 							__SCREAMING_SNAKE_CASE       (  unittest.TestCase							):






							def 					_lowerCamelCase		(    self	):
														# clean up the VRAM after each test
														super().tearDown()
														gc.collect()
														torch.cuda.empty_cache()






							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      VersatileDiffusionTextToImagePipeline.from_pretrained("""shi-labs/versatile-diffusion"""	)
														# remove text_unet
														pipe.remove_unused_weights()
														pipe.to(__lowerCAmelCase	)
														pipe.set_progress_bar_config(disable=__lowerCAmelCase	)

														UpperCamelCase__					  =      """A painting of a squirrel eating a burger """
														UpperCamelCase__					  =      torch.manual_seed(0	)
														UpperCamelCase__					  =      pipe(
														    prompt=__lowerCAmelCase	,			generator=__lowerCAmelCase	,			guidance_scale=7.5	,			num_inference_steps=2	,			output_type="""numpy"""	).images

														with tempfile.TemporaryDirectory() as tmpdirname:
																					pipe.save_pretrained(__lowerCAmelCase	)
																					UpperCamelCase__					  =      VersatileDiffusionTextToImagePipeline.from_pretrained(__lowerCAmelCase	)
														pipe.to(__lowerCAmelCase	)
														pipe.set_progress_bar_config(disable=__lowerCAmelCase	)

														UpperCamelCase__					  =      generator.manual_seed(0	)
														UpperCamelCase__					  =      pipe(
														    prompt=__lowerCAmelCase	,			generator=__lowerCAmelCase	,			guidance_scale=7.5	,			num_inference_steps=2	,			output_type="""numpy"""	).images

														assert np.abs(image - new_image	).sum() < 1E-5, "Models don't have the same forward pass"







							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      VersatileDiffusionTextToImagePipeline.from_pretrained(
														    """shi-labs/versatile-diffusion"""	,			torch_dtype=torch.floataa	)
														pipe.to(__lowerCAmelCase	)
														pipe.set_progress_bar_config(disable=__lowerCAmelCase	)

														UpperCamelCase__					  =      """A painting of a squirrel eating a burger """
														UpperCamelCase__					  =      torch.manual_seed(0	)
														UpperCamelCase__					  =      pipe(
														    prompt=__lowerCAmelCase	,			generator=__lowerCAmelCase	,			guidance_scale=7.5	,			num_inference_steps=50	,			output_type="""numpy"""	).images

														UpperCamelCase__					  =      image[0, 253:256, 253:256, -1]

														assert image.shape == (1, 512, 512, 3)
														UpperCamelCase__					  =      np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778]	)

														assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2




