import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest



class  UpperCamelCase				( UpperCAmelCase_ ):

					'''simple docstring'''

					A_										=  (DPMSolverSinglestepScheduler,)
					A_										=  (('num_inference_steps', 25),)



					def 		UpperCamelCase_						(					self				,				**A_						) ->				Tuple:






											"""simple docstring"""
											_lowerCamelCase			    =				{
											    'num_train_timesteps': 10_00,
											    '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 		UpperCamelCase_						(					self				,				A_=0				,				**A_						) ->				Dict:






											"""simple docstring"""
											_lowerCamelCase			    =				dict(self.forward_default_kwargs						)
											_lowerCamelCase			    =				kwargs.pop('''num_inference_steps'''				,				_lowercase						)
											_lowerCamelCase			    =				self.dummy_sample
											_lowerCamelCase			    =				0.1 * sample
											_lowerCamelCase			    =				[residual + 0.2, residual + 0.15, residual + 0.10]

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

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

																	_lowerCamelCase			    =				sample, sample
																	for t in range(_lowercase				,				time_step + scheduler.config.solver_order + 1						):
																							_lowerCamelCase			    =				scheduler.step(_lowercase				,				_lowercase				,				_lowercase				,				**_lowercase						).prev_sample
																							_lowerCamelCase			    =				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 		UpperCamelCase_						(					self						) ->				List[str]:






											"""simple docstring"""
											pass



					def 		UpperCamelCase_						(					self				,				A_=0				,				**A_						) ->				List[str]:






											"""simple docstring"""
											_lowerCamelCase			    =				dict(self.forward_default_kwargs						)
											_lowerCamelCase			    =				kwargs.pop('''num_inference_steps'''				,				_lowercase						)
											_lowerCamelCase			    =				self.dummy_sample
											_lowerCamelCase			    =				0.1 * sample
											_lowerCamelCase			    =				[residual + 0.2, residual + 0.15, residual + 0.10]

											for scheduler_class in self.scheduler_classes:
																	_lowerCamelCase			    =				self.get_scheduler_config()
																	_lowerCamelCase			    =				scheduler_class(**_lowercase						)
																	scheduler.set_timesteps(_lowercase						)

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

																	with tempfile.TemporaryDirectory() as tmpdirname:
																							scheduler.save_config(_lowercase						)
																							_lowerCamelCase			    =				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			    =				dummy_past_residuals[: new_scheduler.config.solver_order]

																	_lowerCamelCase			    =				scheduler.step(_lowercase				,				_lowercase				,				_lowercase				,				**_lowercase						).prev_sample
																	_lowerCamelCase			    =				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 		UpperCamelCase_						(					self				,				A_=None				,				**A_						) ->				Optional[Any]:






											"""simple docstring"""
											if scheduler is None:
																	_lowerCamelCase			    =				self.scheduler_classes[0]
																	_lowerCamelCase			    =				self.get_scheduler_config(**_lowercase						)
																	_lowerCamelCase			    =				scheduler_class(**_lowercase						)

											_lowerCamelCase			    =				self.scheduler_classes[0]
											_lowerCamelCase			    =				self.get_scheduler_config(**_lowercase						)
											_lowerCamelCase			    =				scheduler_class(**_lowercase						)

											_lowerCamelCase			    =				10
											_lowerCamelCase			    =				self.dummy_model()
											_lowerCamelCase			    =				self.dummy_sample_deter
											scheduler.set_timesteps(_lowercase						)

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

											return sample



					def 		UpperCamelCase_						(					self						) ->				List[str]:






											"""simple docstring"""
											_lowerCamelCase			    =				DPMSolverSinglestepScheduler(**self.get_scheduler_config()						)
											_lowerCamelCase			    =				50
											_lowerCamelCase			    =				self.dummy_model()
											_lowerCamelCase			    =				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			    =				model(_lowercase				,				_lowercase						)
																	_lowerCamelCase			    =				scheduler.step(_lowercase				,				_lowercase				,				_lowercase						).prev_sample

											_lowerCamelCase			    =				torch.mean(torch.abs(_lowercase						)						)

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



					def 		UpperCamelCase_						(					self						) ->				Dict:






											"""simple docstring"""
											for timesteps in [25, 50, 1_00, 9_99, 10_00]:
																	self.check_over_configs(num_train_timesteps=_lowercase						)



					def 		UpperCamelCase_						(					self						) ->				List[str]:






											"""simple docstring"""
											# make sure that iterating over schedulers with same config names gives same results
											# for defaults
											_lowerCamelCase			    =				DPMSolverSinglestepScheduler(**self.get_scheduler_config()						)
											_lowerCamelCase			    =				self.full_loop(scheduler=_lowercase						)
											_lowerCamelCase			    =				torch.mean(torch.abs(_lowercase						)						)

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

											_lowerCamelCase			    =				DEISMultistepScheduler.from_config(scheduler.config						)
											_lowerCamelCase			    =				DPMSolverMultistepScheduler.from_config(scheduler.config						)
											_lowerCamelCase			    =				UniPCMultistepScheduler.from_config(scheduler.config						)
											_lowerCamelCase			    =				DPMSolverSinglestepScheduler.from_config(scheduler.config						)

											_lowerCamelCase			    =				self.full_loop(scheduler=_lowercase						)
											_lowerCamelCase			    =				torch.mean(torch.abs(_lowercase						)						)

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



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






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



					def 		UpperCamelCase_						(					self						) ->				Dict:






											"""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			    =				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 		UpperCamelCase_						(					self						) ->				int:






											"""simple docstring"""
											self.check_over_configs(lower_order_final=_lowercase						)
											self.check_over_configs(lower_order_final=_lowercase						)



					def 		UpperCamelCase_						(					self						) ->				Union[str, Any]:






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



					def 		UpperCamelCase_						(					self						) ->				Tuple:






											"""simple docstring"""
											self.check_over_configs(variance_type=_lowercase						)
											self.check_over_configs(variance_type='''learned_range'''						)



					def 		UpperCamelCase_						(					self						) ->				Dict:






											"""simple docstring"""
											for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
																	self.check_over_forward(num_inference_steps=_lowercase				,				time_step=0						)



					def 		UpperCamelCase_						(					self						) ->				int:






											"""simple docstring"""
											_lowerCamelCase			    =				self.full_loop()
											_lowerCamelCase			    =				torch.mean(torch.abs(_lowercase						)						)

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



					def 		UpperCamelCase_						(					self						) ->				Any:






											"""simple docstring"""
											_lowerCamelCase			    =				self.full_loop(use_karras_sigmas=_lowercase						)
											_lowerCamelCase			    =				torch.mean(torch.abs(_lowercase						)						)

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



					def 		UpperCamelCase_						(					self						) ->				Tuple:






											"""simple docstring"""
											_lowerCamelCase			    =				self.full_loop(prediction_type='''v_prediction'''						)
											_lowerCamelCase			    =				torch.mean(torch.abs(_lowercase						)						)

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



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






											"""simple docstring"""
											_lowerCamelCase			    =				self.full_loop(prediction_type='''v_prediction'''				,				use_karras_sigmas=_lowercase						)
											_lowerCamelCase			    =				torch.mean(torch.abs(_lowercase						)						)

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





					def 		UpperCamelCase_						(					self						) ->				Dict:






											"""simple docstring"""
											_lowerCamelCase			    =				self.scheduler_classes[0]
											_lowerCamelCase			    =				self.get_scheduler_config(thresholding=_lowercase				,				dynamic_thresholding_ratio=0						)
											_lowerCamelCase			    =				scheduler_class(**_lowercase						)

											_lowerCamelCase			    =				10
											_lowerCamelCase			    =				self.dummy_model()
											_lowerCamelCase			    =				self.dummy_sample_deter.half()
											scheduler.set_timesteps(_lowercase						)

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

											assert sample.dtype == torch.floataa
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging


logging.set_verbosity_info()
snake_case__				= logging.get_logger()



@dataclass
class  UpperCamelCase				:

     '''simple docstring'''

     A_										=  42
     A_										=  field(default_factory=__lowercase )
     A_										=  field(default_factory=__lowercase )



     def 		UpperCamelCase_						(					self				,				A_				,				A_				,				A_						) ->				Any:






           """simple docstring"""
           _lowerCamelCase			    =				len(list(m.modules()						)						) == 1 or isinstance(A_				,				nn.Convad						) or isinstance(A_				,				nn.BatchNormad						)
           if has_not_submodules:
                 self.traced.append(A_						)



     def __call__(					self				,				A_						) ->				Tuple:






           """simple docstring"""
           for m in self.module.modules():
                 self.handles.append(m.register_forward_hook(self._forward_hook						)						)
           self.module(A_						)
           [x.remove() for x in self.handles]
           return self





     @property
     def 		UpperCamelCase_						(					self						) ->				List[str]:






           """simple docstring"""
           # check the len of the state_dict keys to see if we have learnable params
           return list(filter(lambda A_						: len(list(x.state_dict().keys()						)						) > 0				,				self.traced						)						)





@dataclass
class  UpperCamelCase				:

     '''simple docstring'''

     A_										=  42
     A_										=  42
     A_										=  0
     A_										=  field(default_factory=__lowercase )
     A_										=  field(default_factory=__lowercase )



     def __call__(					self				,				A_						) ->				List[Any]:






           """simple docstring"""
           _lowerCamelCase			    =				Tracker(self.dest						)(A_						).parametrized
           _lowerCamelCase			    =				Tracker(self.src						)(A_						).parametrized

           _lowerCamelCase			    =				list(filter(lambda A_						: type(A_						) not in self.src_skip				,				A_						)						)
           _lowerCamelCase			    =				list(filter(lambda A_						: type(A_						) not in self.dest_skip				,				A_						)						)

           if len(A_						) != len(A_						):
                 raise Exception(
                     F'Numbers of operations are different. Source module has {len(A_						)} operations while'
                     F' destination module has {len(A_						)}.'						)

           for dest_m, src_m in zip(A_				,				A_						):
                 dest_m.load_state_dict(src_m.state_dict()						)
                 if self.verbose == 1:
                       print(F'Transfered from={src_m} to={dest_m}'						)





def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase = True		)						->    Optional[int]:



      '''simple docstring'''

      print(F'Converting {name}...'		)
      with torch.no_grad():
            _lowerCamelCase			    =				timm.create_model(__UpperCAmelCase ,						pretrained=__UpperCAmelCase		).eval()
            _lowerCamelCase			    =				ResNetForImageClassification(__UpperCAmelCase		).eval()
            _lowerCamelCase			    =				ModuleTransfer(src=__UpperCAmelCase ,						dest=__UpperCAmelCase		)
            _lowerCamelCase			    =				torch.randn((1, 3, 224, 224)		)
            module_transfer(__UpperCAmelCase		)

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

      _lowerCamelCase			    =				F'resnet{"-".join(name.split("resnet"		)		)}'
      print(__UpperCAmelCase		)

      if push_to_hub:
            our_model.push_to_hub(
                repo_path_or_name=save_directory / checkpoint_name ,						commit_message='''Add model''' ,						use_temp_dir=__UpperCAmelCase ,						)

            # we can use the convnext one
            _lowerCamelCase			    =				AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''		)
            image_processor.push_to_hub(
                repo_path_or_name=save_directory / checkpoint_name ,						commit_message='''Add image processor''' ,						use_temp_dir=__UpperCAmelCase ,						)

            print(F'Pushed {checkpoint_name}'		)
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase = None ,						__UpperCAmelCase = True		)						->    Optional[int]:



      '''simple docstring'''

      _lowerCamelCase			    =				'''imagenet-1k-id2label.json'''
      _lowerCamelCase			    =				1000
      _lowerCamelCase			    =				(1, num_labels)

      _lowerCamelCase			    =				'''huggingface/label-files'''
      _lowerCamelCase			    =				num_labels
      _lowerCamelCase			    =				json.load(open(hf_hub_download(__UpperCAmelCase ,						__UpperCAmelCase ,						repo_type='''dataset'''		) ,						'''r'''		)		)
      _lowerCamelCase			    =				{int(__UpperCAmelCase		): v for k, v in idalabel.items()}

      _lowerCamelCase			    =				idalabel
      _lowerCamelCase			    =				{v: k for k, v in idalabel.items()}

      _lowerCamelCase			    =				partial(__UpperCAmelCase ,						num_labels=__UpperCAmelCase ,						idalabel=__UpperCAmelCase ,						labelaid=__UpperCAmelCase		)

      _lowerCamelCase			    =				{
          '''resnet18''': ImageNetPreTrainedConfig(
              depths=[2, 2, 2, 2] ,						hidden_sizes=[64, 128, 256, 512] ,						layer_type='''basic'''		),
          '''resnet26''': ImageNetPreTrainedConfig(
              depths=[2, 2, 2, 2] ,						hidden_sizes=[256, 512, 1024, 2048] ,						layer_type='''bottleneck'''		),
          '''resnet34''': ImageNetPreTrainedConfig(
              depths=[3, 4, 6, 3] ,						hidden_sizes=[64, 128, 256, 512] ,						layer_type='''basic'''		),
          '''resnet50''': ImageNetPreTrainedConfig(
              depths=[3, 4, 6, 3] ,						hidden_sizes=[256, 512, 1024, 2048] ,						layer_type='''bottleneck'''		),
          '''resnet101''': ImageNetPreTrainedConfig(
              depths=[3, 4, 23, 3] ,						hidden_sizes=[256, 512, 1024, 2048] ,						layer_type='''bottleneck'''		),
          '''resnet152''': ImageNetPreTrainedConfig(
              depths=[3, 8, 36, 3] ,						hidden_sizes=[256, 512, 1024, 2048] ,						layer_type='''bottleneck'''		),
      }

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


if __name__ == "__main__":
     snake_case__				= 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 resnet* architecture,'
             ' currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted.'
         ),
     )
     parser.add_argument(
         '--pytorch_dump_folder_path',
         default=None,
         type=Path,
         required=True,
         help='Path to the output PyTorch model directory.',
     )
     parser.add_argument(
         '--push_to_hub',
         default=True,
         type=bool,
         required=False,
         help='If True, push model and image processor to the hub.',
     )

     snake_case__				= parser.parse_args()
     snake_case__				= 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)