






'''simple docstring'''






import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest




@require_torchsde
class a_    (    UpperCamelCase_							):
   _snake_case        =							(DPMSolverSDEScheduler,)
   _snake_case        =							10







   def     SCREAMING_SNAKE_CASE__					(self					,   **__a)			->      int:
      """simple docstring"""






      __snake_case				:      Optional[int]     							= {
          'num_train_timesteps': 1_1_0_0,
          'beta_start': 0.0_001,
          'beta_end': 0.02,
          'beta_schedule': 'linear',
          'noise_sampler_seed': 0,
      }

      config.update(**__a)
      return config







   def     SCREAMING_SNAKE_CASE__					(self)			->      int:
      """simple docstring"""






      for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
         self.check_over_configs(num_train_timesteps=__a)







   def     SCREAMING_SNAKE_CASE__					(self)			->      Dict:
      """simple docstring"""






      for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]					,   [0.0_002, 0.002, 0.02]):
         self.check_over_configs(beta_start=__a					,   beta_end=__a)







   def     SCREAMING_SNAKE_CASE__					(self)			->      Optional[Any]:
      """simple docstring"""






      for schedule in ["linear", "scaled_linear"]:
         self.check_over_configs(beta_schedule=__a)







   def     SCREAMING_SNAKE_CASE__					(self)			->      Optional[Any]:
      """simple docstring"""






      for prediction_type in ["epsilon", "v_prediction"]:
         self.check_over_configs(prediction_type=__a)







   def     SCREAMING_SNAKE_CASE__					(self)			->      str:
      """simple docstring"""






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

      scheduler.set_timesteps(self.num_inference_steps)

      __snake_case				:      Dict     							= self.dummy_model()
      __snake_case				:      Tuple     							= self.dummy_sample_deter * scheduler.init_noise_sigma
      __snake_case				:      int     							= sample.to(__a)

      for i, t in enumerate(scheduler.timesteps):
         __snake_case				:      List[str]     							= scheduler.scale_model_input(__a					,   __a)

         __snake_case				:      Optional[int]     							= model(__a					,   __a)

         __snake_case				:      List[Any]     							= scheduler.step(__a					,   __a					,   __a)
         __snake_case				:      str     							= output.prev_sample

      __snake_case				:      Any     							= torch.sum(torch.abs(__a))
      __snake_case				:      int     							= torch.mean(torch.abs(__a))

      if torch_device in ["mps"]:
         assert abs(result_sum.item() - 167.47_821_044_921_875) < 1E-2
         assert abs(result_mean.item() - 0.2_178_705_964_565_277) < 1E-3
      elif torch_device in ["cuda"]:
         assert abs(result_sum.item() - 171.59_352_111_816_406) < 1E-2
         assert abs(result_mean.item() - 0.22_342_906_892_299_652) < 1E-3
      else:
         assert abs(result_sum.item() - 162.52_383_422_851_562) < 1E-2
         assert abs(result_mean.item() - 0.211_619_570_851_326) < 1E-3







   def     SCREAMING_SNAKE_CASE__					(self)			->      Tuple:
      """simple docstring"""






      __snake_case				:      Tuple     							= self.scheduler_classes[0]
      __snake_case				:      int     							= self.get_scheduler_config(prediction_type='v_prediction')
      __snake_case				:      Dict     							= scheduler_class(**__a)

      scheduler.set_timesteps(self.num_inference_steps)

      __snake_case				:      Union[str, Any]     							= self.dummy_model()
      __snake_case				:      Any     							= self.dummy_sample_deter * scheduler.init_noise_sigma
      __snake_case				:      Dict     							= sample.to(__a)

      for i, t in enumerate(scheduler.timesteps):
         __snake_case				:      int     							= scheduler.scale_model_input(__a					,   __a)

         __snake_case				:      Optional[Any]     							= model(__a					,   __a)

         __snake_case				:      Tuple     							= scheduler.step(__a					,   __a					,   __a)
         __snake_case				:      Optional[int]     							= output.prev_sample

      __snake_case				:      Optional[int]     							= torch.sum(torch.abs(__a))
      __snake_case				:      Tuple     							= torch.mean(torch.abs(__a))

      if torch_device in ["mps"]:
         assert abs(result_sum.item() - 124.77_149_200_439_453) < 1E-2
         assert abs(result_mean.item() - 0.16_226_289_014_816_284) < 1E-3
      elif torch_device in ["cuda"]:
         assert abs(result_sum.item() - 128.1_663_360_595_703) < 1E-2
         assert abs(result_mean.item() - 0.16_688_326_001_167_297) < 1E-3
      else:
         assert abs(result_sum.item() - 119.8_487_548_828_125) < 1E-2
         assert abs(result_mean.item() - 0.1_560_530_662_536_621) < 1E-3







   def     SCREAMING_SNAKE_CASE__					(self)			->      int:
      """simple docstring"""






      __snake_case				:      Dict     							= self.scheduler_classes[0]
      __snake_case				:      Dict     							= self.get_scheduler_config()
      __snake_case				:      List[str]     							= scheduler_class(**__a)

      scheduler.set_timesteps(self.num_inference_steps					,   device=__a)

      __snake_case				:      Dict     							= self.dummy_model()
      __snake_case				:      str     							= self.dummy_sample_deter.to(__a) * scheduler.init_noise_sigma

      for t in scheduler.timesteps:
         __snake_case				:      Optional[int]     							= scheduler.scale_model_input(__a					,   __a)

         __snake_case				:      Tuple     							= model(__a					,   __a)

         __snake_case				:      Dict     							= scheduler.step(__a					,   __a					,   __a)
         __snake_case				:      Union[str, Any]     							= output.prev_sample

      __snake_case				:      Tuple     							= torch.sum(torch.abs(__a))
      __snake_case				:      Optional[int]     							= torch.mean(torch.abs(__a))

      if torch_device in ["mps"]:
         assert abs(result_sum.item() - 167.46_957_397_460_938) < 1E-2
         assert abs(result_mean.item() - 0.21_805_934_607_982_635) < 1E-3
      elif torch_device in ["cuda"]:
         assert abs(result_sum.item() - 171.59_353_637_695_312) < 1E-2
         assert abs(result_mean.item() - 0.22_342_908_382_415_771) < 1E-3
      else:
         assert abs(result_sum.item() - 162.52_383_422_851_562) < 1E-2
         assert abs(result_mean.item() - 0.211_619_570_851_326) < 1E-3







   def     SCREAMING_SNAKE_CASE__					(self)			->      Dict:
      """simple docstring"""






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

      scheduler.set_timesteps(self.num_inference_steps					,   device=__a)

      __snake_case				:      int     							= self.dummy_model()
      __snake_case				:      List[Any]     							= self.dummy_sample_deter.to(__a) * scheduler.init_noise_sigma
      __snake_case				:      Any     							= sample.to(__a)

      for t in scheduler.timesteps:
         __snake_case				:      List[Any]     							= scheduler.scale_model_input(__a					,   __a)

         __snake_case				:      Optional[Any]     							= model(__a					,   __a)

         __snake_case				:      Union[str, Any]     							= scheduler.step(__a					,   __a					,   __a)
         __snake_case				:      List[Any]     							= output.prev_sample

      __snake_case				:      Optional[Any]     							= torch.sum(torch.abs(__a))
      __snake_case				:      Tuple     							= torch.mean(torch.abs(__a))

      if torch_device in ["mps"]:
         assert abs(result_sum.item() - 176.66_974_135_742_188) < 1E-2
         assert abs(result_mean.item() - 0.23_003_872_730_981_811) < 1E-2
      elif torch_device in ["cuda"]:
         assert abs(result_sum.item() - 177.63_653_564_453_125) < 1E-2
         assert abs(result_mean.item() - 0.23_003_872_730_981_811) < 1E-2
      else:
         assert abs(result_sum.item() - 170.3_135_223_388_672) < 1E-2
         assert abs(result_mean.item() - 0.23_003_872_730_981_811) < 1E-2







'''simple docstring'''






import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path

import timm
import torch
from huggingface_hub import hf_hub_download

from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__A    	=   logging.get_logger()




def        _SCREAMING_SNAKE_CASE  (				A  :       int  ,		A  :       str  ,		A  :       LevitConfig  ,		A  :       Path  ,		A  :       bool = True  )    -> Dict:







   """simple docstring"""
   print(F"""Converting {name}..."""  )

   with torch.no_grad():
      if hidden_sizes == 1_28:
         if name[-1] == "S":
            __snake_case				:      Optional[int]     							= timm.create_model('levit_128s'  ,		pretrained=A  )
         else:
            __snake_case				:      Tuple     							= timm.create_model('levit_128'  ,		pretrained=A  )
      if hidden_sizes == 1_92:
         __snake_case				:      int     							= timm.create_model('levit_192'  ,		pretrained=A  )
      if hidden_sizes == 2_56:
         __snake_case				:      List[Any]     							= timm.create_model('levit_256'  ,		pretrained=A  )
      if hidden_sizes == 3_84:
         __snake_case				:      int     							= timm.create_model('levit_384'  ,		pretrained=A  )

      from_model.eval()
      __snake_case				:      str     							= LevitForImageClassificationWithTeacher(A  ).eval()
      __snake_case				:      int     							= OrderedDict()

      __snake_case				:      Optional[Any]     							= from_model.state_dict()
      __snake_case				:      Tuple     							= list(from_model.state_dict().keys()  )
      __snake_case				:      List[str]     							= list(our_model.state_dict().keys()  )
      print(len(A  )  ,		len(A  )  )
      for i in range(len(A  )  ):
         __snake_case				:      Optional[int]     							= weights[og_keys[i]]
      our_model.load_state_dict(A  )

      __snake_case				:      Tuple     							= torch.randn((2, 3, 2_24, 2_24)  )
      __snake_case				:      Union[str, Any]     							= from_model(A  )
      __snake_case				:      List[str]     							= our_model(A  ).logits

   assert torch.allclose(A  ,		A  ), "The model logits don't match the original one."

   __snake_case				:      int     							= name
   print(A  )

   if push_to_hub:
      our_model.save_pretrained(save_directory / checkpoint_name  )
      __snake_case				:      int     							= LevitImageProcessor()
      image_processor.save_pretrained(save_directory / checkpoint_name  )

      print(F"""Pushed {checkpoint_name}"""  )




def        _SCREAMING_SNAKE_CASE  (				A  :       Path  ,		A  :       str = None  ,		A  :       bool = True  )    -> List[Any]:







   """simple docstring"""
   __snake_case				:      Optional[Any]     							= 'imagenet-1k-id2label.json'
   __snake_case				:      Tuple     							= 10_00
   __snake_case				:      Dict     							= (1, num_labels)

   __snake_case				:      List[str]     							= 'huggingface/label-files'
   __snake_case				:      Any     							= num_labels
   __snake_case				:      str     							= json.load(open(hf_hub_download(A  ,		A  ,		repo_type='dataset'  )  ,		'r'  )  )
   __snake_case				:      Any     							= {int(A  ): v for k, v in idalabel.items()}

   __snake_case				:      int     							= idalabel
   __snake_case				:      Union[str, Any]     							= {v: k for k, v in idalabel.items()}

   __snake_case				:      Optional[int]     							= partial(A  ,		num_labels=A  ,		idalabel=A  ,		labelaid=A  )

   __snake_case				:      Dict     							= {
       'levit-128S': 1_28,
       'levit-128': 1_28,
       'levit-192': 1_92,
       'levit-256': 2_56,
       'levit-384': 3_84,
   }

   __snake_case				:      Union[str, Any]     							= {
       'levit-128S': ImageNetPreTrainedConfig(
           hidden_sizes=[1_28, 2_56, 3_84]  ,		num_attention_heads=[4, 6, 8]  ,		depths=[2, 3, 4]  ,		key_dim=[16, 16, 16]  ,		drop_path_rate=0  ,		),
       'levit-128': ImageNetPreTrainedConfig(
           hidden_sizes=[1_28, 2_56, 3_84]  ,		num_attention_heads=[4, 8, 12]  ,		depths=[4, 4, 4]  ,		key_dim=[16, 16, 16]  ,		drop_path_rate=0  ,		),
       'levit-192': ImageNetPreTrainedConfig(
           hidden_sizes=[1_92, 2_88, 3_84]  ,		num_attention_heads=[3, 5, 6]  ,		depths=[4, 4, 4]  ,		key_dim=[32, 32, 32]  ,		drop_path_rate=0  ,		),
       'levit-256': ImageNetPreTrainedConfig(
           hidden_sizes=[2_56, 3_84, 5_12]  ,		num_attention_heads=[4, 6, 8]  ,		depths=[4, 4, 4]  ,		key_dim=[32, 32, 32]  ,		drop_path_rate=0  ,		),
       'levit-384': ImageNetPreTrainedConfig(
           hidden_sizes=[3_84, 5_12, 7_68]  ,		num_attention_heads=[6, 9, 12]  ,		depths=[4, 4, 4]  ,		key_dim=[32, 32, 32]  ,		drop_path_rate=0.1  ,		),
   }

   if model_name:
      convert_weight_and_push(
          names_to_hidden_sizes[model_name]  ,		A  ,		names_to_config[model_name]  ,		A  ,		A  )
   else:
      for model_name, config in names_to_config.items():
         convert_weight_and_push(names_to_hidden_sizes[model_name]  ,		A  ,		A  ,		A  ,		A  )
   return config, expected_shape


if __name__ == "__main__":
      __A    	=   argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '''--model_name''',
          default=None,
          type=str,
          help='''The name of the model you wish to convert, it must be one of the supported Levit* architecture,''',
      )
      parser.add_argument(
          '''--pytorch_dump_folder_path''',
          default='''levit-dump-folder/''',
          type=Path,
          required=False,
          help='''Path to the output PyTorch model directory.''',
      )
      parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Push model and image processor to the hub''')
      parser.add_argument(
          '''--no-push_to_hub''',
          dest='''push_to_hub''',
          action='''store_false''',
          help='''Do not push model and image processor to the hub''',
      )

      __A    	=   parser.parse_args()
      __A    	=   args.pytorch_dump_folder_path
      pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
      convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)