






'''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 						A__			(				__lowerCAmelCase		:			List[Any]   ,    __lowerCAmelCase		:			List[str]						):
      lowerCamelCase__         =  """https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg"""
      lowerCamelCase__         =  Image.open(requests.get(__lowerCAmelCase   ,    stream=__lowerCAmelCase						).raw						).convert("""RGB"""						)

      lowerCamelCase__         =  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)						),
          ]						)
      lowerCamelCase__         =  transform(__lowerCAmelCase						).unsqueeze(0						).to(__lowerCAmelCase						)
      return image
def 						A__			(				__lowerCAmelCase		:			Tuple						):
      if "visual_encoder" in key:
            lowerCamelCase__         =  re.sub("""visual_encoder*"""   ,    """vision_model.encoder"""   ,    __lowerCAmelCase						)
      if "blocks" in key:
            lowerCamelCase__         =  re.sub(R"""blocks"""   ,    """layers"""   ,    __lowerCAmelCase						)
      if "attn" in key:
            lowerCamelCase__         =  re.sub(R"""attn"""   ,    """self_attn"""   ,    __lowerCAmelCase						)
      if "norm1" in key:
            lowerCamelCase__         =  re.sub(R"""norm1"""   ,    """layer_norm1"""   ,    __lowerCAmelCase						)
      if "norm2" in key:
            lowerCamelCase__         =  re.sub(R"""norm2"""   ,    """layer_norm2"""   ,    __lowerCAmelCase						)
      if "encoder.norm" in key:
            lowerCamelCase__         =  re.sub(R"""encoder.norm"""   ,    """post_layernorm"""   ,    __lowerCAmelCase						)
      if "encoder.patch_embed.proj" in key:
            lowerCamelCase__         =  re.sub(R"""encoder.patch_embed.proj"""   ,    """embeddings.patch_embedding"""   ,    __lowerCAmelCase						)

      if "encoder.pos_embed" in key:
            lowerCamelCase__         =  re.sub(R"""encoder.pos_embed"""   ,    """embeddings.position_embedding"""   ,    __lowerCAmelCase						)
      if "encoder.cls_token" in key:
            lowerCamelCase__         =  re.sub(R"""encoder.cls_token"""   ,    """embeddings.class_embedding"""   ,    __lowerCAmelCase						)

      if "self_attn" in key:
            lowerCamelCase__         =  re.sub(R"""self_attn.proj"""   ,    """self_attn.projection"""   ,    __lowerCAmelCase						)

      return key
@torch.no_grad()
def 						A__			(				__lowerCAmelCase		:			int   ,    __lowerCAmelCase		:			Optional[int]=None						):
      if config_path is not None:
            lowerCamelCase__         =  BlipConfig.from_pretrained(__lowerCAmelCase						)
      else:
            lowerCamelCase__         =  BlipConfig(projection_dim=512   ,    text_config={}   ,    vision_config={}						)

      lowerCamelCase__         =  BlipForConditionalGeneration(__lowerCAmelCase						).eval()

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

      lowerCamelCase__         =  blip_decoder(pretrained=__lowerCAmelCase   ,    image_size=384   ,    vit="""base"""						)
      lowerCamelCase__         =  pt_model.eval()

      lowerCamelCase__         =  pt_model.state_dict()
      for key in modified_state_dict.copy():
            lowerCamelCase__         =  modified_state_dict.pop(__lowerCAmelCase						)
            lowerCamelCase__         =  rename_key(__lowerCAmelCase						)
            lowerCamelCase__         =  value

      hf_model.load_state_dict(__lowerCAmelCase						)

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

      lowerCamelCase__         =  hf_model.generate(__lowerCAmelCase   ,    __lowerCAmelCase						)

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

      lowerCamelCase__         =  hf_model.generate(__lowerCAmelCase						)

      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(__lowerCAmelCase						)

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

      lowerCamelCase__         =  blip_vqa(pretrained=__lowerCAmelCase   ,    image_size=__lowerCAmelCase   ,    vit="""base"""						)
      vqa_model.eval()

      lowerCamelCase__         =  vqa_model.state_dict()
      for key in modified_state_dict.copy():
            lowerCamelCase__         =  modified_state_dict.pop(__lowerCAmelCase						)
            lowerCamelCase__         =  rename_key(__lowerCAmelCase						)
            lowerCamelCase__         =  value

      lowerCamelCase__         =  BlipForQuestionAnswering(__lowerCAmelCase						)

      hf_vqa_model.load_state_dict(__lowerCAmelCase						)

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

      lowerCamelCase__         =  hf_vqa_model.generate(__lowerCAmelCase   ,    __lowerCAmelCase						)
      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__         =  """https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth"""

      lowerCamelCase__         =  blip_itm(pretrained=__lowerCAmelCase   ,    image_size=__lowerCAmelCase   ,    vit="""base"""						)
      itm_model.eval()

      lowerCamelCase__         =  itm_model.state_dict()
      for key in modified_state_dict.copy():
            lowerCamelCase__         =  modified_state_dict.pop(__lowerCAmelCase						)
            lowerCamelCase__         =  rename_key(__lowerCAmelCase						)
            lowerCamelCase__         =  value

      lowerCamelCase__         =  BlipForImageTextRetrieval(__lowerCAmelCase						)

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

      hf_itm_model.load_state_dict(__lowerCAmelCase						)
      hf_itm_model.eval()

      lowerCamelCase__         =  hf_itm_model(__lowerCAmelCase   ,    __lowerCAmelCase   ,    use_itm_head=__lowerCAmelCase						)
      lowerCamelCase__         =  hf_itm_model(__lowerCAmelCase   ,    __lowerCAmelCase   ,    use_itm_head=__lowerCAmelCase						)

      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    :      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')
       UpperCamelCase    :      int  			=		parser.parse_args()

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









'''simple docstring'''







import json
import os

import torch

from diffusers import UNetaDModel


os.makedirs('hub/hopper-medium-v2/unet/hor32', exist_ok=True)
os.makedirs('hub/hopper-medium-v2/unet/hor128', exist_ok=True)

os.makedirs('hub/hopper-medium-v2/value_function', exist_ok=True)
def 						A__			(				__lowerCAmelCase		:			Union[str, Any]						):
						if hor == 128:
												lowerCamelCase__         =  ("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
												lowerCamelCase__         =  (32, 128, 256)
												lowerCamelCase__         =  ("""UpResnetBlock1D""", """UpResnetBlock1D""")

						elif hor == 32:
												lowerCamelCase__         =  ("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
												lowerCamelCase__         =  (32, 64, 128, 256)
												lowerCamelCase__         =  ("""UpResnetBlock1D""", """UpResnetBlock1D""", """UpResnetBlock1D""")
						lowerCamelCase__         =  torch.load(F'''/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch'''						)
						lowerCamelCase__         =  model.state_dict()
						lowerCamelCase__         =  {
						    """down_block_types""": down_block_types,
						    """block_out_channels""": block_out_channels,
						    """up_block_types""": up_block_types,
						    """layers_per_block""": 1,
						    """use_timestep_embedding""": True,
						    """out_block_type""": """OutConv1DBlock""",
						    """norm_num_groups""": 8,
						    """downsample_each_block""": False,
						    """in_channels""": 14,
						    """out_channels""": 14,
						    """extra_in_channels""": 0,
						    """time_embedding_type""": """positional""",
						    """flip_sin_to_cos""": False,
						    """freq_shift""": 1,
						    """sample_size""": 6_5536,
						    """mid_block_type""": """MidResTemporalBlock1D""",
						    """act_fn""": """mish""",
						}
						lowerCamelCase__         =  UNetaDModel(**__lowerCAmelCase						)
						print(F'''length of state dict: {len(state_dict.keys()						)}'''						)
						print(F'''length of value function dict: {len(hf_value_function.state_dict().keys()						)}'''						)
						lowerCamelCase__         =  dict(zip(model.state_dict().keys()   ,    hf_value_function.state_dict().keys()						)						)
						for k, v in mapping.items():
												lowerCamelCase__         =  state_dict.pop(__lowerCAmelCase						)
						hf_value_function.load_state_dict(__lowerCAmelCase						)

						torch.save(hf_value_function.state_dict()   ,    F'''hub/hopper-medium-v2/unet/hor{hor}/diffusion_pytorch_model.bin'''						)
						with open(F'''hub/hopper-medium-v2/unet/hor{hor}/config.json'''   ,    """w"""						) as f:
												json.dump(__lowerCAmelCase   ,    __lowerCAmelCase						)
def 						A__			(				):
						lowerCamelCase__         =  {
						    """in_channels""": 14,
						    """down_block_types""": ("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D"""),
						    """up_block_types""": (),
						    """out_block_type""": """ValueFunction""",
						    """mid_block_type""": """ValueFunctionMidBlock1D""",
						    """block_out_channels""": (32, 64, 128, 256),
						    """layers_per_block""": 1,
						    """downsample_each_block""": True,
						    """sample_size""": 6_5536,
						    """out_channels""": 14,
						    """extra_in_channels""": 0,
						    """time_embedding_type""": """positional""",
						    """use_timestep_embedding""": True,
						    """flip_sin_to_cos""": False,
						    """freq_shift""": 1,
						    """norm_num_groups""": 8,
						    """act_fn""": """mish""",
						}

						lowerCamelCase__         =  torch.load("""/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"""						)
						lowerCamelCase__         =  model
						lowerCamelCase__         =  UNetaDModel(**__lowerCAmelCase						)
						print(F'''length of state dict: {len(state_dict.keys()						)}'''						)
						print(F'''length of value function dict: {len(hf_value_function.state_dict().keys()						)}'''						)

						lowerCamelCase__         =  dict(zip(state_dict.keys()   ,    hf_value_function.state_dict().keys()						)						)
						for k, v in mapping.items():
												lowerCamelCase__         =  state_dict.pop(__lowerCAmelCase						)

						hf_value_function.load_state_dict(__lowerCAmelCase						)

						torch.save(hf_value_function.state_dict()   ,    """hub/hopper-medium-v2/value_function/diffusion_pytorch_model.bin"""						)
						with open("""hub/hopper-medium-v2/value_function/config.json"""   ,    """w"""						) as f:
												json.dump(__lowerCAmelCase   ,    __lowerCAmelCase						)


if __name__ == "__main__":
							unet(32)
							# unet(128)
							value_function()

