


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()
lowerCamelCase											=       logging.get_logger()




def 	a_      (    SCREAMING_SNAKE_CASE__					:      int		,			SCREAMING_SNAKE_CASE__					:      str		,			SCREAMING_SNAKE_CASE__					:      LevitConfig		,			SCREAMING_SNAKE_CASE__					:      Path		,			SCREAMING_SNAKE_CASE__					:      bool = True     ):
       '''simple docstring'''





       print(F'''Converting {name}...'''     )

       with torch.no_grad():
              if hidden_sizes == 128:
                     if name[-1] == "S":
                            _lowerCamelCase       :		Optional[int]		=timm.create_model('levit_128s'		,			pretrained=SCREAMING_SNAKE_CASE__     )
                     else:
                            _lowerCamelCase       :		Any		=timm.create_model('levit_128'		,			pretrained=SCREAMING_SNAKE_CASE__     )
              if hidden_sizes == 192:
                     _lowerCamelCase       :		int		=timm.create_model('levit_192'		,			pretrained=SCREAMING_SNAKE_CASE__     )
              if hidden_sizes == 256:
                     _lowerCamelCase       :		Tuple		=timm.create_model('levit_256'		,			pretrained=SCREAMING_SNAKE_CASE__     )
              if hidden_sizes == 384:
                     _lowerCamelCase       :		Dict		=timm.create_model('levit_384'		,			pretrained=SCREAMING_SNAKE_CASE__     )

              from_model.eval()
              _lowerCamelCase       :		Optional[int]		=LevitForImageClassificationWithTeacher(SCREAMING_SNAKE_CASE__     ).eval()
              _lowerCamelCase       :		Tuple		=OrderedDict()

              _lowerCamelCase       :		Union[str, Any]		=from_model.state_dict()
              _lowerCamelCase       :		List[str]		=list(from_model.state_dict().keys()     )
              _lowerCamelCase       :		Optional[Any]		=list(our_model.state_dict().keys()     )
              print(len(SCREAMING_SNAKE_CASE__     )		,			len(SCREAMING_SNAKE_CASE__     )     )
              for i in range(len(SCREAMING_SNAKE_CASE__     )     ):
                     _lowerCamelCase       :		Optional[Any]		=weights[og_keys[i]]
              our_model.load_state_dict(SCREAMING_SNAKE_CASE__     )

              _lowerCamelCase       :		Any		=torch.randn((2, 3, 224, 224)     )
              _lowerCamelCase       :		Union[str, Any]		=from_model(SCREAMING_SNAKE_CASE__     )
              _lowerCamelCase       :		Any		=our_model(SCREAMING_SNAKE_CASE__     ).logits

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

       _lowerCamelCase       :		Tuple		=name
       print(SCREAMING_SNAKE_CASE__     )

       if push_to_hub:
              our_model.save_pretrained(save_directory / checkpoint_name     )
              _lowerCamelCase       :		Optional[int]		=LevitImageProcessor()
              image_processor.save_pretrained(save_directory / checkpoint_name     )

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




def 	a_      (    SCREAMING_SNAKE_CASE__					:      Path		,			SCREAMING_SNAKE_CASE__					:      str = None		,			SCREAMING_SNAKE_CASE__					:      bool = True     ):
       '''simple docstring'''





       _lowerCamelCase       :		Any		='imagenet-1k-id2label.json'
       _lowerCamelCase       :		Tuple		=1_000
       _lowerCamelCase       :		Optional[Any]		=(1, num_labels)

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

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

       _lowerCamelCase       :		Dict		=partial(SCREAMING_SNAKE_CASE__		,			num_labels=SCREAMING_SNAKE_CASE__		,			idalabel=SCREAMING_SNAKE_CASE__		,			labelaid=SCREAMING_SNAKE_CASE__     )

       _lowerCamelCase       :		Union[str, Any]		={
           'levit-128S': 128,
           'levit-128': 128,
           'levit-192': 192,
           'levit-256': 256,
           'levit-384': 384,
       }

       _lowerCamelCase       :		Tuple		={
           'levit-128S': ImageNetPreTrainedConfig(
               hidden_sizes=[128, 256, 384]		,			num_attention_heads=[4, 6, 8]		,			depths=[2, 3, 4]		,			key_dim=[16, 16, 16]		,			drop_path_rate=0		,			),
           'levit-128': ImageNetPreTrainedConfig(
               hidden_sizes=[128, 256, 384]		,			num_attention_heads=[4, 8, 12]		,			depths=[4, 4, 4]		,			key_dim=[16, 16, 16]		,			drop_path_rate=0		,			),
           'levit-192': ImageNetPreTrainedConfig(
               hidden_sizes=[192, 288, 384]		,			num_attention_heads=[3, 5, 6]		,			depths=[4, 4, 4]		,			key_dim=[32, 32, 32]		,			drop_path_rate=0		,			),
           'levit-256': ImageNetPreTrainedConfig(
               hidden_sizes=[256, 384, 512]		,			num_attention_heads=[4, 6, 8]		,			depths=[4, 4, 4]		,			key_dim=[32, 32, 32]		,			drop_path_rate=0		,			),
           'levit-384': ImageNetPreTrainedConfig(
               hidden_sizes=[384, 512, 768]		,			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]		,			SCREAMING_SNAKE_CASE__		,			names_to_config[model_name]		,			SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )
       else:
              for model_name, config in names_to_config.items():
                     convert_weight_and_push(names_to_hidden_sizes[model_name]		,			SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )
       return config, expected_shape


if __name__ == "__main__":
 lowerCamelCase											=       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',
 )

 lowerCamelCase											=       parser.parse_args()
 lowerCamelCase											=       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)






import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest




class A  (   UpperCamelCase_     ):
      UpperCamelCase__							:  Any      =(DPMSolverSinglestepScheduler,)
      UpperCamelCase__							:  Tuple      =(('num_inference_steps', 25),)






      def        lowerCamelCase       (					self						:     Optional[Any]			,   **lowercase_						:     List[Any] )					->						Optional[Any]:
             """simple docstring"""




             _lowerCamelCase       :		Union[str, Any]		={
                 'num_train_timesteps': 1000,
                 'beta_start': 0.0001,
                 'beta_end': 0.02,
                 'beta_schedule': 'linear',
                 'solver_order': 2,
                 'prediction_type': 'epsilon',
                 'thresholding': False,
                 'sample_max_value': 1.0,
                 'algorithm_type': 'dpmsolver++',
                 'solver_type': 'midpoint',
                 'lambda_min_clipped': -float('inf' ),
                 'variance_type': None,
             }

             config.update(**lowercase_ )
             return config






      def        lowerCamelCase       (					self						:     Optional[int]			,   lowercase_						:     Optional[Any]=0			,   **lowercase_						:     Any )					->						List[Any]:
             """simple docstring"""




             _lowerCamelCase       :		List[Any]		=dict(self.forward_default_kwargs )
             _lowerCamelCase       :		Optional[Any]		=kwargs.pop('num_inference_steps'			,   lowercase_ )
             _lowerCamelCase       :		Optional[int]		=self.dummy_sample
             _lowerCamelCase       :		Dict		=0.1 * sample
             _lowerCamelCase       :		Optional[int]		=[residual + 0.2, residual + 0.15, residual + 0.10]

             for scheduler_class in self.scheduler_classes:
                    _lowerCamelCase       :		int		=self.get_scheduler_config(**lowercase_ )
                    _lowerCamelCase       :		str		=scheduler_class(**lowercase_ )
                    scheduler.set_timesteps(lowercase_ )
                    # copy over dummy past residuals
                    _lowerCamelCase       :		int		=dummy_past_residuals[: scheduler.config.solver_order]

                    with tempfile.TemporaryDirectory() as tmpdirname:
                           scheduler.save_config(lowercase_ )
                           _lowerCamelCase       :		Dict		=scheduler_class.from_pretrained(lowercase_ )
                           new_scheduler.set_timesteps(lowercase_ )
                           # copy over dummy past residuals
                           _lowerCamelCase       :		str		=dummy_past_residuals[: new_scheduler.config.solver_order]

                    _lowerCamelCase	,     _lowerCamelCase       :		Any		=sample, sample
                    for t in range(lowercase_			,   time_step + scheduler.config.solver_order + 1 ):
                           _lowerCamelCase       :		List[str]		=scheduler.step(lowercase_			,   lowercase_			,   lowercase_			,   **lowercase_ ).prev_sample
                           _lowerCamelCase       :		Optional[Any]		=new_scheduler.step(lowercase_			,   lowercase_			,   lowercase_			,   **lowercase_ ).prev_sample

                           assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"






      def        lowerCamelCase       (					self						:     Tuple )					->						Optional[int]:
             """simple docstring"""




             pass






      def        lowerCamelCase       (					self						:     str			,   lowercase_						:     str=0			,   **lowercase_						:     List[Any] )					->						Dict:
             """simple docstring"""




             _lowerCamelCase       :		int		=dict(self.forward_default_kwargs )
             _lowerCamelCase       :		Dict		=kwargs.pop('num_inference_steps'			,   lowercase_ )
             _lowerCamelCase       :		Optional[int]		=self.dummy_sample
             _lowerCamelCase       :		Any		=0.1 * sample
             _lowerCamelCase       :		int		=[residual + 0.2, residual + 0.15, residual + 0.10]

             for scheduler_class in self.scheduler_classes:
                    _lowerCamelCase       :		Optional[int]		=self.get_scheduler_config()
                    _lowerCamelCase       :		Any		=scheduler_class(**lowercase_ )
                    scheduler.set_timesteps(lowercase_ )

                    # copy over dummy past residuals (must be after setting timesteps)
                    _lowerCamelCase       :		Dict		=dummy_past_residuals[: scheduler.config.solver_order]

                    with tempfile.TemporaryDirectory() as tmpdirname:
                           scheduler.save_config(lowercase_ )
                           _lowerCamelCase       :		Optional[int]		=scheduler_class.from_pretrained(lowercase_ )
                           # copy over dummy past residuals
                           new_scheduler.set_timesteps(lowercase_ )

                           # copy over dummy past residual (must be after setting timesteps)
                           _lowerCamelCase       :		str		=dummy_past_residuals[: new_scheduler.config.solver_order]

                    _lowerCamelCase       :		Dict		=scheduler.step(lowercase_			,   lowercase_			,   lowercase_			,   **lowercase_ ).prev_sample
                    _lowerCamelCase       :		Dict		=new_scheduler.step(lowercase_			,   lowercase_			,   lowercase_			,   **lowercase_ ).prev_sample

                    assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"






      def        lowerCamelCase       (					self						:     Any			,   lowercase_						:     Union[str, Any]=None			,   **lowercase_						:     List[str] )					->						str:
             """simple docstring"""




             if scheduler is None:
                    _lowerCamelCase       :		Tuple		=self.scheduler_classes[0]
                    _lowerCamelCase       :		Optional[int]		=self.get_scheduler_config(**lowercase_ )
                    _lowerCamelCase       :		Union[str, Any]		=scheduler_class(**lowercase_ )

             _lowerCamelCase       :		List[Any]		=self.scheduler_classes[0]
             _lowerCamelCase       :		Optional[Any]		=self.get_scheduler_config(**lowercase_ )
             _lowerCamelCase       :		List[Any]		=scheduler_class(**lowercase_ )

             _lowerCamelCase       :		str		=10
             _lowerCamelCase       :		Union[str, Any]		=self.dummy_model()
             _lowerCamelCase       :		Dict		=self.dummy_sample_deter
             scheduler.set_timesteps(lowercase_ )

             for i, t in enumerate(scheduler.timesteps ):
                    _lowerCamelCase       :		List[str]		=model(lowercase_			,   lowercase_ )
                    _lowerCamelCase       :		Optional[int]		=scheduler.step(lowercase_			,   lowercase_			,   lowercase_ ).prev_sample

             return sample






      def        lowerCamelCase       (					self						:     Dict )					->						int:
             """simple docstring"""




             _lowerCamelCase       :		Any		=DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
             _lowerCamelCase       :		int		=50
             _lowerCamelCase       :		Optional[int]		=self.dummy_model()
             _lowerCamelCase       :		Tuple		=self.dummy_sample_deter
             scheduler.set_timesteps(lowercase_ )

             # make sure that the first t is uneven
             for i, t in enumerate(scheduler.timesteps[3:] ):
                    _lowerCamelCase       :		Any		=model(lowercase_			,   lowercase_ )
                    _lowerCamelCase       :		List[str]		=scheduler.step(lowercase_			,   lowercase_			,   lowercase_ ).prev_sample

             _lowerCamelCase       :		int		=torch.mean(torch.abs(lowercase_ ) )

             assert abs(result_mean.item() - 0.2574 ) < 1E-3






      def        lowerCamelCase       (					self						:     Any )					->						List[str]:
             """simple docstring"""




             for timesteps in [25, 50, 100, 999, 1000]:
                    self.check_over_configs(num_train_timesteps=lowercase_ )






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




             _lowerCamelCase       :		Optional[int]		=DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
             _lowerCamelCase       :		Optional[Any]		=self.full_loop(scheduler=lowercase_ )
             _lowerCamelCase       :		Tuple		=torch.mean(torch.abs(lowercase_ ) )

             assert abs(result_mean.item() - 0.2791 ) < 1E-3

             _lowerCamelCase       :		Dict		=DEISMultistepScheduler.from_config(scheduler.config )
             _lowerCamelCase       :		Optional[int]		=DPMSolverMultistepScheduler.from_config(scheduler.config )
             _lowerCamelCase       :		Union[str, Any]		=UniPCMultistepScheduler.from_config(scheduler.config )
             _lowerCamelCase       :		List[str]		=DPMSolverSinglestepScheduler.from_config(scheduler.config )

             _lowerCamelCase       :		List[Any]		=self.full_loop(scheduler=lowercase_ )
             _lowerCamelCase       :		List[str]		=torch.mean(torch.abs(lowercase_ ) )

             assert abs(result_mean.item() - 0.2791 ) < 1E-3






      def        lowerCamelCase       (					self						:     str )					->						Dict:
             """simple docstring"""




             self.check_over_configs(thresholding=lowercase_ )
             for order in [1, 2, 3]:
                    for solver_type in ["midpoint", "heun"]:
                           for threshold in [0.5, 1.0, 2.0]:
                                  for prediction_type in ["epsilon", "sample"]:
                                         self.check_over_configs(
                                             thresholding=lowercase_			,   prediction_type=lowercase_			,   sample_max_value=lowercase_			,   algorithm_type='dpmsolver++'			,   solver_order=lowercase_			,   solver_type=lowercase_			,   )






      def        lowerCamelCase       (					self						:     Any )					->						Any:
             """simple docstring"""




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






      def        lowerCamelCase       (					self						:     List[str] )					->						Optional[int]:
             """simple docstring"""




             for algorithm_type in ["dpmsolver", "dpmsolver++"]:
                    for solver_type in ["midpoint", "heun"]:
                           for order in [1, 2, 3]:
                                  for prediction_type in ["epsilon", "sample"]:
                                         self.check_over_configs(
                                             solver_order=lowercase_			,   solver_type=lowercase_			,   prediction_type=lowercase_			,   algorithm_type=lowercase_			,   )
                                         _lowerCamelCase       :		Any		=self.full_loop(
                                             solver_order=lowercase_			,   solver_type=lowercase_			,   prediction_type=lowercase_			,   algorithm_type=lowercase_			,   )
                                         assert not torch.isnan(lowercase_ ).any(), "Samples have nan numbers"






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




             self.check_over_configs(lower_order_final=lowercase_ )
             self.check_over_configs(lower_order_final=lowercase_ )






      def        lowerCamelCase       (					self						:     Union[str, Any] )					->						int:
             """simple docstring"""




             self.check_over_configs(lambda_min_clipped=-float('inf' ) )
             self.check_over_configs(lambda_min_clipped=-5.1 )






      def        lowerCamelCase       (					self						:     Union[str, Any] )					->						Any:
             """simple docstring"""




             self.check_over_configs(variance_type=lowercase_ )
             self.check_over_configs(variance_type='learned_range' )






      def        lowerCamelCase       (					self						:     Union[str, Any] )					->						Any:
             """simple docstring"""




             for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
                    self.check_over_forward(num_inference_steps=lowercase_			,   time_step=0 )






      def        lowerCamelCase       (					self						:     str )					->						Tuple:
             """simple docstring"""




             _lowerCamelCase       :		Dict		=self.full_loop()
             _lowerCamelCase       :		Dict		=torch.mean(torch.abs(lowercase_ ) )

             assert abs(result_mean.item() - 0.2791 ) < 1E-3






      def        lowerCamelCase       (					self						:     Any )					->						Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase       :		str		=self.full_loop(use_karras_sigmas=lowercase_ )
             _lowerCamelCase       :		Optional[Any]		=torch.mean(torch.abs(lowercase_ ) )

             assert abs(result_mean.item() - 0.2248 ) < 1E-3






      def        lowerCamelCase       (					self						:     Dict )					->						Tuple:
             """simple docstring"""




             _lowerCamelCase       :		Any		=self.full_loop(prediction_type='v_prediction' )
             _lowerCamelCase       :		Dict		=torch.mean(torch.abs(lowercase_ ) )

             assert abs(result_mean.item() - 0.1453 ) < 1E-3






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




             _lowerCamelCase       :		str		=self.full_loop(prediction_type='v_prediction'			,   use_karras_sigmas=lowercase_ )
             _lowerCamelCase       :		List[str]		=torch.mean(torch.abs(lowercase_ ) )

             assert abs(result_mean.item() - 0.0649 ) < 1E-3






      def        lowerCamelCase       (					self						:     List[Any] )					->						str:
             """simple docstring"""




             _lowerCamelCase       :		List[str]		=self.scheduler_classes[0]
             _lowerCamelCase       :		List[str]		=self.get_scheduler_config(thresholding=lowercase_			,   dynamic_thresholding_ratio=0 )
             _lowerCamelCase       :		List[Any]		=scheduler_class(**lowercase_ )

             _lowerCamelCase       :		Optional[Any]		=10
             _lowerCamelCase       :		Optional[int]		=self.dummy_model()
             _lowerCamelCase       :		List[Any]		=self.dummy_sample_deter.half()
             scheduler.set_timesteps(lowercase_ )

             for i, t in enumerate(scheduler.timesteps ):
                    _lowerCamelCase       :		str		=model(lowercase_			,   lowercase_ )
                    _lowerCamelCase       :		Any		=scheduler.step(lowercase_			,   lowercase_			,   lowercase_ ).prev_sample

             assert sample.dtype == torch.floataa


