



'''simple docstring'''

import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest

class __snake_case( lowercase__   ):


						'''simple docstring'''



						UpperCAmelCase				:       Optional[Any] 	=						(PNDMScheduler,)
						UpperCAmelCase				:       Optional[int] 	=						(("num_inference_steps", 50),)


						def 							__snake_case       (   self      ,			**A_ )      ->      Optional[int]:
							lowerCAmelCase       =      {
							    """num_train_timesteps""": 1000,
							    """beta_start""": 0.0_0_0_1,
							    """beta_end""": 0.0_2,
							    """beta_schedule""": """linear""",
							}

							config.update(**lowercase_ )
							return config


						def 							__snake_case       (   self      ,			A_=0      ,			**A_ )      ->      List[Any]:
							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 + 0.1_5, residual + 0.1, residual + 0.0_5]

							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[:]

								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[:]

								lowerCAmelCase       =      scheduler.step_prk(lowercase_      ,			lowercase_      ,			lowercase_      ,			**lowercase_ ).prev_sample
								lowerCAmelCase       =      new_scheduler.step_prk(lowercase_      ,			lowercase_      ,			lowercase_      ,			**lowercase_ ).prev_sample

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

								lowerCAmelCase       =      scheduler.step_plms(lowercase_      ,			lowercase_      ,			lowercase_      ,			**lowercase_ ).prev_sample
								lowerCAmelCase       =      new_scheduler.step_plms(lowercase_      ,			lowercase_      ,			lowercase_      ,			**lowercase_ ).prev_sample

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


						def 							__snake_case       (   self )      ->      Tuple:
							pass


						def 							__snake_case       (   self      ,			A_=0      ,			**A_ )      ->      str:
							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 + 0.1_5, residual + 0.1, residual + 0.0_5]

							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[:]

								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[:]

								lowerCAmelCase       =      scheduler.step_prk(lowercase_      ,			lowercase_      ,			lowercase_      ,			**lowercase_ ).prev_sample
								lowerCAmelCase       =      new_scheduler.step_prk(lowercase_      ,			lowercase_      ,			lowercase_      ,			**lowercase_ ).prev_sample

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

								lowerCAmelCase       =      scheduler.step_plms(lowercase_      ,			lowercase_      ,			lowercase_      ,			**lowercase_ ).prev_sample
								lowerCAmelCase       =      new_scheduler.step_plms(lowercase_      ,			lowercase_      ,			lowercase_      ,			**lowercase_ ).prev_sample

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


						def 							__snake_case       (   self      ,			**A_ )      ->      int:
							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.prk_timesteps ):
								lowerCAmelCase       =      model(lowercase_      ,			lowercase_ )
								lowerCAmelCase       =      scheduler.step_prk(lowercase_      ,			lowercase_      ,			lowercase_ ).prev_sample

							for i, t in enumerate(scheduler.plms_timesteps ):
								lowerCAmelCase       =      model(lowercase_      ,			lowercase_ )
								lowerCAmelCase       =      scheduler.step_plms(lowercase_      ,			lowercase_      ,			lowercase_ ).prev_sample

							return sample


						def 							__snake_case       (   self )      ->      Dict:
							lowerCAmelCase       =      dict(self.forward_default_kwargs )

							lowerCAmelCase       =      kwargs.pop("""num_inference_steps"""      ,			lowercase_ )

							for scheduler_class in self.scheduler_classes:
								lowerCAmelCase       =      self.get_scheduler_config()
								lowerCAmelCase       =      scheduler_class(**lowercase_ )

								lowerCAmelCase       =      self.dummy_sample
								lowerCAmelCase       =      0.1 * sample

								if num_inference_steps is not None and hasattr(lowercase_      ,			"""set_timesteps""" ):
									scheduler.set_timesteps(lowercase_ )
								elif num_inference_steps is not None and not hasattr(lowercase_      ,			"""set_timesteps""" ):
									lowerCAmelCase       =      num_inference_steps

								# copy over dummy past residuals (must be done after set_timesteps)
								lowerCAmelCase       =      [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
								lowerCAmelCase       =      dummy_past_residuals[:]

								lowerCAmelCase       =      scheduler.step_prk(lowercase_      ,			0      ,			lowercase_      ,			**lowercase_ ).prev_sample
								lowerCAmelCase       =      scheduler.step_prk(lowercase_      ,			1      ,			lowercase_      ,			**lowercase_ ).prev_sample

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

								lowerCAmelCase       =      scheduler.step_plms(lowercase_      ,			0      ,			lowercase_      ,			**lowercase_ ).prev_sample
								lowerCAmelCase       =      scheduler.step_plms(lowercase_      ,			1      ,			lowercase_      ,			**lowercase_ ).prev_sample

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


						def 							__snake_case       (   self )      ->      str:
							for timesteps in [100, 1000]:
								self.check_over_configs(num_train_timesteps=lowercase_ )


						def 							__snake_case       (   self )      ->      Any:
							for steps_offset in [0, 1]:
								self.check_over_configs(steps_offset=lowercase_ )

							lowerCAmelCase       =      self.scheduler_classes[0]
							lowerCAmelCase       =      self.get_scheduler_config(steps_offset=1 )
							lowerCAmelCase       =      scheduler_class(**lowercase_ )
							scheduler.set_timesteps(10 )
							assert torch.equal(
							    scheduler.timesteps      ,			torch.LongTensor(
							        [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1] )      ,			)


						def 							__snake_case       (   self )      ->      List[str]:
							for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1]      ,			[0.0_0_2, 0.0_2] ):
								self.check_over_configs(beta_start=lowercase_      ,			beta_end=lowercase_ )


						def 							__snake_case       (   self )      ->      Tuple:
							for schedule in ["linear", "squaredcos_cap_v2"]:
								self.check_over_configs(beta_schedule=lowercase_ )


						def 							__snake_case       (   self )      ->      Dict:
							for prediction_type in ["epsilon", "v_prediction"]:
								self.check_over_configs(prediction_type=lowercase_ )


						def 							__snake_case       (   self )      ->      Dict:
							for t in [1, 5, 10]:
								self.check_over_forward(time_step=lowercase_ )


						def 							__snake_case       (   self )      ->      Optional[Any]:
							for t, num_inference_steps in zip([1, 5, 10]      ,			[10, 50, 100] ):
								self.check_over_forward(num_inference_steps=lowercase_ )


						def 							__snake_case       (   self )      ->      Union[str, Any]:
							# earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
							lowerCAmelCase       =      27

							for scheduler_class in self.scheduler_classes:
								lowerCAmelCase       =      self.dummy_sample
								lowerCAmelCase       =      0.1 * sample

								lowerCAmelCase       =      self.get_scheduler_config()
								lowerCAmelCase       =      scheduler_class(**lowercase_ )

								scheduler.set_timesteps(lowercase_ )

								# before power of 3 fix, would error on first step, so we only need to do two
								for i, t in enumerate(scheduler.prk_timesteps[:2] ):
									lowerCAmelCase       =      scheduler.step_prk(lowercase_      ,			lowercase_      ,			lowercase_ ).prev_sample


						def 							__snake_case       (   self )      ->      Dict:
							with self.assertRaises(lowercase_ ):
								lowerCAmelCase       =      self.scheduler_classes[0]
								lowerCAmelCase       =      self.get_scheduler_config()
								lowerCAmelCase       =      scheduler_class(**lowercase_ )

								scheduler.step_plms(self.dummy_sample      ,			1      ,			self.dummy_sample ).prev_sample


						def 							__snake_case       (   self )      ->      int:
							lowerCAmelCase       =      self.full_loop()
							lowerCAmelCase       =      torch.sum(torch.abs(lowercase_ ) )
							lowerCAmelCase       =      torch.mean(torch.abs(lowercase_ ) )

							assert abs(result_sum.item() - 198.1318 ) < 1e-2
							assert abs(result_mean.item() - 0.2_5_8_0 ) < 1e-3


						def 							__snake_case       (   self )      ->      Any:
							lowerCAmelCase       =      self.full_loop(prediction_type="""v_prediction""" )
							lowerCAmelCase       =      torch.sum(torch.abs(lowercase_ ) )
							lowerCAmelCase       =      torch.mean(torch.abs(lowercase_ ) )

							assert abs(result_sum.item() - 6_7.3_9_8_6 ) < 1e-2
							assert abs(result_mean.item() - 0.0_8_7_8 ) < 1e-3


						def 							__snake_case       (   self )      ->      Tuple:
							# We specify different beta, so that the first alpha is 0.99
							lowerCAmelCase       =      self.full_loop(set_alpha_to_one=lowercase_      ,			beta_start=0.0_1 )
							lowerCAmelCase       =      torch.sum(torch.abs(lowercase_ ) )
							lowerCAmelCase       =      torch.mean(torch.abs(lowercase_ ) )

							assert abs(result_sum.item() - 230.0399 ) < 1e-2
							assert abs(result_mean.item() - 0.2_9_9_5 ) < 1e-3




						def 							__snake_case       (   self )      ->      List[str]:
							# We specify different beta, so that the first alpha is 0.99
							lowerCAmelCase       =      self.full_loop(set_alpha_to_one=lowercase_      ,			beta_start=0.0_1 )
							lowerCAmelCase       =      torch.sum(torch.abs(lowercase_ ) )
							lowerCAmelCase       =      torch.mean(torch.abs(lowercase_ ) )

							assert abs(result_sum.item() - 186.9482 ) < 1e-2
							assert abs(result_mean.item() - 0.2_4_3_4 ) < 1e-3



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:						List[str]   				=       logging.get_logger()






def   _lowercase(					__a				:						int    ,     __a				:						str    ,     __a				:						LevitConfig    ,     __a				:						Path    ,     __a				:						bool = True     ):
							print(f"""Converting {name}..."""     )

							with torch.no_grad():
														if hidden_sizes == 128:
																					if name[-1] == "S":
																												a__      =timm.create_model('levit_128s'    ,     pretrained=__a     )
																					else:
																												a__      =timm.create_model('levit_128'    ,     pretrained=__a     )
														if hidden_sizes == 192:
																					a__      =timm.create_model('levit_192'    ,     pretrained=__a     )
														if hidden_sizes == 256:
																					a__      =timm.create_model('levit_256'    ,     pretrained=__a     )
														if hidden_sizes == 384:
																					a__      =timm.create_model('levit_384'    ,     pretrained=__a     )

														from_model.eval()
														a__      =LevitForImageClassificationWithTeacher(__a     ).eval()
														a__      =OrderedDict()

														a__      =from_model.state_dict()
														a__      =list(from_model.state_dict().keys()     )
														a__      =list(our_model.state_dict().keys()     )
														print(len(__a     )    ,     len(__a     )     )
														for i in range(len(__a     )     ):
																					a__      =weights[og_keys[i]]
														our_model.load_state_dict(__a     )

														a__      =torch.randn((2, 3, 224, 224)     )
														a__      =from_model(__a     )
														a__      =our_model(__a     ).logits

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

							a__      =name
							print(__a     )

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

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






def   _lowercase(					__a				:						Path    ,     __a				:						str = None    ,     __a				:						bool = True     ):
							a__      ='imagenet-1k-id2label.json'
							a__      =1000
							a__      =(1, num_labels)

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

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

							a__      =partial(__a    ,     num_labels=__a    ,     idalabel=__a    ,     labelaid=__a     )

							a__      ={
							    'levit-128S': 128,
							    'levit-128': 128,
							    'levit-192': 192,
							    'levit-256': 256,
							    'levit-384': 384,
							}

							a__      ={
							    '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]    ,     __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__":
	_lowerCAmelCase:						Any   				=       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:						Union[str, Any]   				=       parser.parse_args()
	_lowerCAmelCase:						Path   				=       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)

