


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    __lowerCamelCase   (      A__						: Optional[int]					)     ->    Optional[Any]:
   if hor == 128:
      lowerCamelCase_						:   Dict		= ("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
      lowerCamelCase_						:   List[str]		= (32, 128, 256)
      lowerCamelCase_						:   Optional[int]		= ("""UpResnetBlock1D""", """UpResnetBlock1D""")

   elif hor == 32:
      lowerCamelCase_						:   int		= ("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
      lowerCamelCase_						:   Any		= (32, 64, 128, 256)
      lowerCamelCase_						:   Any		= ("""UpResnetBlock1D""", """UpResnetBlock1D""", """UpResnetBlock1D""")
   lowerCamelCase_						:   Tuple		= torch.load(f'''/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch'''					)
   lowerCamelCase_						:   Union[str, Any]		= model.state_dict()
   lowerCamelCase_						:   List[str]		= {
       """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_						:   Tuple		= UNetaDModel(**A__					)
   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		= dict(zip(model.state_dict().keys()						,						hf_value_function.state_dict().keys()					)					)
   for k, v in mapping.items():
      lowerCamelCase_						:   Dict		= state_dict.pop(A__					)
   hf_value_function.load_state_dict(A__					)

   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(A__						,						A__					)


def    __lowerCamelCase   (      )     ->    Union[str, Any]:
   lowerCamelCase_						:   List[str]		= {
       """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_						:   Optional[int]		= torch.load("""/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"""					)
   lowerCamelCase_						:   Any		= model
   lowerCamelCase_						:   Union[str, Any]		= UNetaDModel(**A__					)
   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_						:   Union[str, Any]		= dict(zip(state_dict.keys()						,						hf_value_function.state_dict().keys()					)					)
   for k, v in mapping.items():
      lowerCamelCase_						:   str		= state_dict.pop(A__					)

   hf_value_function.load_state_dict(A__					)

   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(A__						,						A__					)


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









import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
snake_case__				:	Dict					       =					logging.get_logger(__name__)


def    __lowerCamelCase   (      A__						: Optional[Any]					)     ->    List[str]:
   lowerCamelCase_						:   int		= """huggingface/label-files"""
   lowerCamelCase_						:   Dict		= """imagenet-1k-id2label.json"""
   lowerCamelCase_						:   Optional[Any]		= json.load(open(hf_hub_download(A__						,						A__						,						repo_type="""dataset"""					)						,						"""r"""					)					)
   lowerCamelCase_						:   str		= {int(A__					): v for k, v in idalabel.items()}
   lowerCamelCase_						:   List[Any]		= {v: k for k, v in idalabel.items()}

   lowerCamelCase_						:   str		= """std_conv""" if """bit""" in model_name else False

   # note that when using BiT as backbone for ViT-hybrid checkpoints,
   # one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
   # config.conv_layer = "std_conv_same"
   lowerCamelCase_						:   Optional[Any]		= BitConfig(
       conv_layer=A__						,						num_labels=1000						,						idalabel=A__						,						labelaid=A__						,						)

   return config


def    __lowerCamelCase   (      A__						: str					)     ->    Any:
   if "stem.conv" in name:
      lowerCamelCase_						:   Any		= name.replace("""stem.conv"""						,						"""bit.embedder.convolution"""					)
   if "blocks" in name:
      lowerCamelCase_						:   Dict		= name.replace("""blocks"""						,						"""layers"""					)
   if "head.fc" in name:
      lowerCamelCase_						:   Optional[Any]		= name.replace("""head.fc"""						,						"""classifier.1"""					)
   if name.startswith("""norm"""					):
      lowerCamelCase_						:   int		= """bit.""" + name
   if "bit" not in name and "classifier" not in name:
      lowerCamelCase_						:   str		= """bit.encoder.""" + name

   return name


def    __lowerCamelCase   (      )     ->    List[Any]:
   lowerCamelCase_						:   List[Any]		= """http://images.cocodataset.org/val2017/000000039769.jpg"""
   lowerCamelCase_						:   Optional[Any]		= Image.open(requests.get(A__						,						stream=A__					).raw					)
   return im


@torch.no_grad()
def    __lowerCamelCase   (      A__						: List[Any]						,						A__						: List[str]						,						A__						: Tuple=False					)     ->    List[str]:
   lowerCamelCase_						:   Optional[Any]		= get_config(A__					)

   # load original model from timm
   lowerCamelCase_						:   Optional[Any]		= create_model(A__						,						pretrained=A__					)
   timm_model.eval()

   # load state_dict of original model
   lowerCamelCase_						:   Optional[int]		= timm_model.state_dict()
   for key in state_dict.copy().keys():
      lowerCamelCase_						:   int		= state_dict.pop(A__					)
      lowerCamelCase_						:   Union[str, Any]		= val.squeeze() if """head""" in key else val

   # load HuggingFace model
   lowerCamelCase_						:   Tuple		= BitForImageClassification(A__					)
   model.eval()
   model.load_state_dict(A__					)

   # create image processor
   lowerCamelCase_						:   Optional[Any]		= create_transform(**resolve_data_config({}						,						model=A__					)					)
   lowerCamelCase_						:   List[Any]		= transform.transforms

   lowerCamelCase_						:   List[Any]		= {
       """bilinear""": PILImageResampling.BILINEAR,
       """bicubic""": PILImageResampling.BICUBIC,
       """nearest""": PILImageResampling.NEAREST,
   }

   lowerCamelCase_						:   List[str]		= BitImageProcessor(
       do_resize=A__						,						size={"""shortest_edge""": timm_transforms[0].size}						,						resample=pillow_resamplings[timm_transforms[0].interpolation.value]						,						do_center_crop=A__						,						crop_size={"""height""": timm_transforms[1].size[0], """width""": timm_transforms[1].size[1]}						,						do_normalize=A__						,						image_mean=timm_transforms[-1].mean.tolist()						,						image_std=timm_transforms[-1].std.tolist()						,						)

   lowerCamelCase_						:   int		= prepare_img()
   lowerCamelCase_						:   int		= transform(A__					).unsqueeze(0					)
   lowerCamelCase_						:   List[str]		= processor(A__						,						return_tensors="""pt"""					).pixel_values

   # verify pixel values
   assert torch.allclose(A__						,						A__					)

   # verify logits
   with torch.no_grad():
      lowerCamelCase_						:   str		= model(A__					)
      lowerCamelCase_						:   int		= outputs.logits

   print("""Logits:"""						,						logits[0, :3]					)
   print("""Predicted class:"""						,						model.config.idalabel[logits.argmax(-1					).item()]					)
   lowerCamelCase_						:   List[Any]		= timm_model(A__					)
   assert timm_logits.shape == outputs.logits.shape
   assert torch.allclose(A__						,						outputs.logits						,						atol=1e-3					)
   print("""Looks ok!"""					)

   if pytorch_dump_folder_path is not None:
      Path(A__					).mkdir(exist_ok=A__					)
      print(f'''Saving model {model_name} and processor to {pytorch_dump_folder_path}'''					)
      model.save_pretrained(A__					)
      processor.save_pretrained(A__					)

   if push_to_hub:
      print(f'''Pushing model {model_name} and processor to the hub'''					)
      model.push_to_hub(f'''ybelkada/{model_name}'''					)
      processor.push_to_hub(f'''ybelkada/{model_name}'''					)


if __name__ == "__main__":
      snake_case__				:	Dict					       =					argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '--model_name',
          default='resnetv2_50x1_bitm',
          type=str,
          help='Name of the BiT timm model you\'d like to convert.',
      )
      parser.add_argument(
          '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
      )
      parser.add_argument(
          '--push_to_hub',
          action='store_true',
          help='Whether to push the model to the hub.',
      )

      snake_case__				:	int					       =					parser.parse_args()
      convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





