


"""simple docstring"""



import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest







class _lowerCAmelCase							( a__      ):
   __UpperCAmelCase					:			Dict				    =    (CMStochasticIterativeScheduler,)
   __UpperCAmelCase					:			Optional[Any]				    =    10







   def 					lowerCamelCase						(      self		,      **UpperCamelCase__		)      ->						int:

       '''simple docstring'''




       snake_case       : List[str]    =       {
           """num_train_timesteps""": 201,
           """sigma_min""": 0.002,
           """sigma_max""": 80.0,
       }

       config.update(**__a		)
       return config







   def 					lowerCamelCase						(      self		)      ->						Optional[int]:

       '''simple docstring'''




       snake_case       : Union[str, Any]    =       10

       snake_case       : List[str]    =       self.get_scheduler_config()
       snake_case       : Tuple    =       self.scheduler_classes[0](**__a		)

       scheduler.set_timesteps(__a		)

       snake_case       : Tuple    =       scheduler.timesteps[0]
       snake_case       : Optional[int]    =       scheduler.timesteps[1]

       snake_case       : Optional[Any]    =       self.dummy_sample
       snake_case       : List[str]    =       0.1 * sample

       snake_case       : Union[str, Any]    =       scheduler.step(__a		,      __a		,      __a		).prev_sample
       snake_case       : str    =       scheduler.step(__a		,      __a		,      __a		).prev_sample

       self.assertEqual(output_a.shape		,      sample.shape		)
       self.assertEqual(output_a.shape		,      output_a.shape		)







   def 					lowerCamelCase						(      self		)      ->						int:

       '''simple docstring'''




       for timesteps in [10, 50, 100, 1000]:
           self.check_over_configs(num_train_timesteps=__a		)







   def 					lowerCamelCase						(      self		)      ->						List[Any]:

       '''simple docstring'''




       for clip_denoised in [True, False]:
           self.check_over_configs(clip_denoised=__a		)







   def 					lowerCamelCase						(      self		)      ->						str:

       '''simple docstring'''




       snake_case       : Tuple    =       self.scheduler_classes[0]
       snake_case       : Optional[Any]    =       self.get_scheduler_config()
       snake_case       : Tuple    =       scheduler_class(**__a		)

       snake_case       : Union[str, Any]    =       1
       scheduler.set_timesteps(__a		)
       snake_case       : Union[str, Any]    =       scheduler.timesteps

       snake_case       : Any    =       torch.manual_seed(0		)

       snake_case       : str    =       self.dummy_model()
       snake_case       : Optional[Any]    =       self.dummy_sample_deter * scheduler.init_noise_sigma

       for i, t in enumerate(__a		):
           # 1. scale model input
           snake_case       : List[str]    =       scheduler.scale_model_input(__a		,      __a		)

           # 2. predict noise residual
           snake_case       : Tuple    =       model(__a		,      __a		)

           # 3. predict previous sample x_t-1
           snake_case       : List[str]    =       scheduler.step(__a		,      __a		,      __a		,      generator=__a		).prev_sample

           snake_case       : Union[str, Any]    =       pred_prev_sample

       snake_case       : int    =       torch.sum(torch.abs(__a		)		)
       snake_case       : Optional[Any]    =       torch.mean(torch.abs(__a		)		)

       assert abs(result_sum.item() - 192.7614		) < 1e-2
       assert abs(result_mean.item() - 0.2510		) < 1e-3







   def 					lowerCamelCase						(      self		)      ->						int:

       '''simple docstring'''




       snake_case       : Union[str, Any]    =       self.scheduler_classes[0]
       snake_case       : Any    =       self.get_scheduler_config()
       snake_case       : Any    =       scheduler_class(**__a		)

       snake_case       : str    =       [106, 0]
       scheduler.set_timesteps(timesteps=__a		)
       snake_case       : Union[str, Any]    =       scheduler.timesteps

       snake_case       : Optional[Any]    =       torch.manual_seed(0		)

       snake_case       : int    =       self.dummy_model()
       snake_case       : Optional[Any]    =       self.dummy_sample_deter * scheduler.init_noise_sigma

       for t in timesteps:
           # 1. scale model input
           snake_case       : str    =       scheduler.scale_model_input(__a		,      __a		)

           # 2. predict noise residual
           snake_case       : Optional[int]    =       model(__a		,      __a		)

           # 3. predict previous sample x_t-1
           snake_case       : int    =       scheduler.step(__a		,      __a		,      __a		,      generator=__a		).prev_sample

           snake_case       : Optional[int]    =       pred_prev_sample

       snake_case       : List[str]    =       torch.sum(torch.abs(__a		)		)
       snake_case       : Dict    =       torch.mean(torch.abs(__a		)		)

       assert abs(result_sum.item() - 347.6357		) < 1e-2
       assert abs(result_mean.item() - 0.4527		) < 1e-3







   def 					lowerCamelCase						(      self		)      ->						Optional[Any]:

       '''simple docstring'''




       snake_case       : Any    =       self.scheduler_classes[0]
       snake_case       : int    =       self.get_scheduler_config()
       snake_case       : Union[str, Any]    =       scheduler_class(**__a		)

       snake_case       : Any    =       [39, 30, 12, 15, 0]

       with self.assertRaises(__a		,      msg="`timesteps` must be in descending order."		):
           scheduler.set_timesteps(timesteps=__a		)







   def 					lowerCamelCase						(      self		)      ->						Tuple:

       '''simple docstring'''




       snake_case       : Union[str, Any]    =       self.scheduler_classes[0]
       snake_case       : int    =       self.get_scheduler_config()
       snake_case       : Dict    =       scheduler_class(**__a		)

       snake_case       : List[Any]    =       [39, 30, 12, 1, 0]
       snake_case       : Optional[int]    =       len(__a		)

       with self.assertRaises(__a		,      msg="Can only pass one of `num_inference_steps` or `timesteps`."		):
           scheduler.set_timesteps(num_inference_steps=__a		,      timesteps=__a		)







   def 					lowerCamelCase						(      self		)      ->						Optional[int]:

       '''simple docstring'''




       snake_case       : int    =       self.scheduler_classes[0]
       snake_case       : Dict    =       self.get_scheduler_config()
       snake_case       : Tuple    =       scheduler_class(**__a		)

       snake_case       : Dict    =       [scheduler.config.num_train_timesteps]

       with self.assertRaises(
           __a		,      msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"		,      ):
           scheduler.set_timesteps(timesteps=__a		)









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)





