



"""simple docstring"""





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()
lowerCamelCase  			=		logging.get_logger(__name__)
def   a__      ( lowerCAmelCase__ ):
      UpperCAmelCase_     					=				"huggingface/label-files"
      UpperCAmelCase_     					=				"imagenet-1k-id2label.json"
      UpperCAmelCase_     					=				json.load(open(hf_hub_download(lowerCAmelCase__      ,     lowerCAmelCase__      ,     repo_type="dataset" )      ,     "r" ) )
      UpperCAmelCase_     					=				{int(lowerCAmelCase__ ): v for k, v in idalabel.items()}
      UpperCAmelCase_     					=				{v: k for k, v in idalabel.items()}

      UpperCAmelCase_     					=				"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"
      UpperCAmelCase_     					=				BitConfig(
          conv_layer=lowerCAmelCase__      ,     num_labels=1000      ,     idalabel=lowerCAmelCase__      ,     labelaid=lowerCAmelCase__      ,     )

      return config
def   a__      ( lowerCAmelCase__ ):
      if "stem.conv" in name:
            UpperCAmelCase_     					=				name.replace("stem.conv"      ,     "bit.embedder.convolution" )
      if "blocks" in name:
            UpperCAmelCase_     					=				name.replace("blocks"      ,     "layers" )
      if "head.fc" in name:
            UpperCAmelCase_     					=				name.replace("head.fc"      ,     "classifier.1" )
      if name.startswith("norm" ):
            UpperCAmelCase_     					=				"bit." + name
      if "bit" not in name and "classifier" not in name:
            UpperCAmelCase_     					=				"bit.encoder." + name

      return name
def   a__      ( ):
      UpperCAmelCase_     					=				"http://images.cocodataset.org/val2017/000000039769.jpg"
      UpperCAmelCase_     					=				Image.open(requests.get(lowerCAmelCase__      ,     stream=lowerCAmelCase__ ).raw )
      return im
@torch.no_grad()
def   a__      ( lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__=False ):
      UpperCAmelCase_     					=				get_config(lowerCAmelCase__ )

      # load original model from timm
      UpperCAmelCase_     					=				create_model(lowerCAmelCase__      ,     pretrained=lowerCAmelCase__ )
      timm_model.eval()

      # load state_dict of original model
      UpperCAmelCase_     					=				timm_model.state_dict()
      for key in state_dict.copy().keys():
            UpperCAmelCase_     					=				state_dict.pop(lowerCAmelCase__ )
            UpperCAmelCase_     					=				val.squeeze() if "head" in key else val

      # load HuggingFace model
      UpperCAmelCase_     					=				BitForImageClassification(lowerCAmelCase__ )
      model.eval()
      model.load_state_dict(lowerCAmelCase__ )

      # create image processor
      UpperCAmelCase_     					=				create_transform(**resolve_data_config({}      ,     model=lowerCAmelCase__ ) )
      UpperCAmelCase_     					=				transform.transforms

      UpperCAmelCase_     					=				{
          "bilinear": PILImageResampling.BILINEAR,
          "bicubic": PILImageResampling.BICUBIC,
          "nearest": PILImageResampling.NEAREST,
      }

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

      UpperCAmelCase_     					=				prepare_img()
      UpperCAmelCase_     					=				transform(lowerCAmelCase__ ).unsqueeze(0 )
      UpperCAmelCase_     					=				processor(lowerCAmelCase__      ,     return_tensors="pt" ).pixel_values

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

      # verify logits
      with torch.no_grad():
            UpperCAmelCase_     					=				model(lowerCAmelCase__ )
            UpperCAmelCase_     					=				outputs.logits

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

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

      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__":
  lowerCamelCase  			=		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.""",
  )

  lowerCamelCase  			=		parser.parse_args()
  convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)











'''simple docstring'''




from ..utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_scipy_available,
    is_torch_available,
    is_torchsde_available,
)


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 from ..utils.dummy_pt_objects import *  # noqa F403
else:
 from .scheduling_consistency_models import CMStochasticIterativeScheduler
 from .scheduling_ddim import DDIMScheduler
 from .scheduling_ddim_inverse import DDIMInverseScheduler
 from .scheduling_ddim_parallel import DDIMParallelScheduler
 from .scheduling_ddpm import DDPMScheduler
 from .scheduling_ddpm_parallel import DDPMParallelScheduler
 from .scheduling_deis_multistep import DEISMultistepScheduler
 from .scheduling_dpmsolver_multistep import DPMSolverMultistepScheduler
 from .scheduling_dpmsolver_multistep_inverse import DPMSolverMultistepInverseScheduler
 from .scheduling_dpmsolver_singlestep import DPMSolverSinglestepScheduler
 from .scheduling_euler_ancestral_discrete import EulerAncestralDiscreteScheduler
 from .scheduling_euler_discrete import EulerDiscreteScheduler
 from .scheduling_heun_discrete import HeunDiscreteScheduler
 from .scheduling_ipndm import IPNDMScheduler
 from .scheduling_k_dpm_2_ancestral_discrete import KDPMaAncestralDiscreteScheduler
 from .scheduling_k_dpm_2_discrete import KDPMaDiscreteScheduler
 from .scheduling_karras_ve import KarrasVeScheduler
 from .scheduling_pndm import PNDMScheduler
 from .scheduling_repaint import RePaintScheduler
 from .scheduling_sde_ve import ScoreSdeVeScheduler
 from .scheduling_sde_vp import ScoreSdeVpScheduler
 from .scheduling_unclip import UnCLIPScheduler
 from .scheduling_unipc_multistep import UniPCMultistepScheduler
 from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
 from .scheduling_vq_diffusion import VQDiffusionScheduler

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 from ..utils.dummy_flax_objects import *  # noqa F403
else:
 from .scheduling_ddim_flax import FlaxDDIMScheduler
 from .scheduling_ddpm_flax import FlaxDDPMScheduler
 from .scheduling_dpmsolver_multistep_flax import FlaxDPMSolverMultistepScheduler
 from .scheduling_karras_ve_flax import FlaxKarrasVeScheduler
 from .scheduling_lms_discrete_flax import FlaxLMSDiscreteScheduler
 from .scheduling_pndm_flax import FlaxPNDMScheduler
 from .scheduling_sde_ve_flax import FlaxScoreSdeVeScheduler
 from .scheduling_utils_flax import (
     FlaxKarrasDiffusionSchedulers,
     FlaxSchedulerMixin,
     FlaxSchedulerOutput,
     broadcast_to_shape_from_left,
 )


try:
 if not (is_torch_available() and is_scipy_available()):
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 from ..utils.dummy_torch_and_scipy_objects import *  # noqa F403
else:
 from .scheduling_lms_discrete import LMSDiscreteScheduler

try:
 if not (is_torch_available() and is_torchsde_available()):
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 from ..utils.dummy_torch_and_torchsde_objects import *  # noqa F403
else:
 from .scheduling_dpmsolver_sde import DPMSolverSDEScheduler

