



import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class      __lowerCAmelCase       (     A		):
			UpperCamelCase     =	(EulerAncestralDiscreteScheduler,)
			UpperCamelCase     =	1_0



			def        _lowerCamelCase			(		self  :    Optional[int]					,						**A  :    Tuple)  ->       List[str]:



						"""simple docstring"""






						_UpperCAmelCase									=  {
						    'num_train_timesteps': 11_00,
						    'beta_start': 0.0_0_0_1,
						    'beta_end': 0.0_2,
						    'beta_schedule': 'linear',
						}

						config.update(**A)
						return config



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



						"""simple docstring"""






						for timesteps in [10, 50, 1_00, 10_00]:
									self.check_over_configs(num_train_timesteps=A)



			def        _lowerCamelCase			(		self  :    Optional[int])  ->       Tuple:



						"""simple docstring"""






						for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]					,						[0.0_0_0_2, 0.0_0_2, 0.0_2]):
									self.check_over_configs(beta_start=A					,						beta_end=A)



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



						"""simple docstring"""






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



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



						"""simple docstring"""






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



			def        _lowerCamelCase			(		self  :    str)  ->       Optional[Any]:



						"""simple docstring"""






						_UpperCAmelCase									=  self.scheduler_classes[0]
						_UpperCAmelCase									=  self.get_scheduler_config()
						_UpperCAmelCase									=  scheduler_class(**A)

						scheduler.set_timesteps(self.num_inference_steps)

						_UpperCAmelCase									=  torch.manual_seed(0)

						_UpperCAmelCase									=  self.dummy_model()
						_UpperCAmelCase									=  self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
						_UpperCAmelCase									=  sample.to(A)

						for i, t in enumerate(scheduler.timesteps):
									_UpperCAmelCase									=  scheduler.scale_model_input(A					,						A)

									_UpperCAmelCase									=  model(A					,						A)

									_UpperCAmelCase									=  scheduler.step(A					,						A					,						A					,						generator=A)
									_UpperCAmelCase									=  output.prev_sample

						_UpperCAmelCase									=  torch.sum(torch.abs(A))
						_UpperCAmelCase									=  torch.mean(torch.abs(A))

						assert abs(result_sum.item() - 1_5_2.3_1_9_2) < 1E-2
						assert abs(result_mean.item() - 0.1_9_8_3) < 1E-3



			def        _lowerCamelCase			(		self  :    Union[str, Any])  ->       Optional[int]:



						"""simple docstring"""






						_UpperCAmelCase									=  self.scheduler_classes[0]
						_UpperCAmelCase									=  self.get_scheduler_config(prediction_type='v_prediction')
						_UpperCAmelCase									=  scheduler_class(**A)

						scheduler.set_timesteps(self.num_inference_steps)

						_UpperCAmelCase									=  torch.manual_seed(0)

						_UpperCAmelCase									=  self.dummy_model()
						_UpperCAmelCase									=  self.dummy_sample_deter * scheduler.init_noise_sigma
						_UpperCAmelCase									=  sample.to(A)

						for i, t in enumerate(scheduler.timesteps):
									_UpperCAmelCase									=  scheduler.scale_model_input(A					,						A)

									_UpperCAmelCase									=  model(A					,						A)

									_UpperCAmelCase									=  scheduler.step(A					,						A					,						A					,						generator=A)
									_UpperCAmelCase									=  output.prev_sample

						_UpperCAmelCase									=  torch.sum(torch.abs(A))
						_UpperCAmelCase									=  torch.mean(torch.abs(A))

						assert abs(result_sum.item() - 1_0_8.4_4_3_9) < 1E-2
						assert abs(result_mean.item() - 0.1_4_1_2) < 1E-3



			def        _lowerCamelCase			(		self  :    List[Any])  ->       int:



						"""simple docstring"""






						_UpperCAmelCase									=  self.scheduler_classes[0]
						_UpperCAmelCase									=  self.get_scheduler_config()
						_UpperCAmelCase									=  scheduler_class(**A)

						scheduler.set_timesteps(self.num_inference_steps					,						device=A)
						_UpperCAmelCase									=  torch.manual_seed(0)

						_UpperCAmelCase									=  self.dummy_model()
						_UpperCAmelCase									=  self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
						_UpperCAmelCase									=  sample.to(A)

						for t in scheduler.timesteps:
									_UpperCAmelCase									=  scheduler.scale_model_input(A					,						A)

									_UpperCAmelCase									=  model(A					,						A)

									_UpperCAmelCase									=  scheduler.step(A					,						A					,						A					,						generator=A)
									_UpperCAmelCase									=  output.prev_sample

						_UpperCAmelCase									=  torch.sum(torch.abs(A))
						_UpperCAmelCase									=  torch.mean(torch.abs(A))

						assert abs(result_sum.item() - 1_5_2.3_1_9_2) < 1E-2
						assert abs(result_mean.item() - 0.1_9_8_3) < 1E-3





import argparse

import torch

from transformers import ImageGPTConfig, ImageGPTForCausalLM, load_tf_weights_in_imagegpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()





def 				A      (	_UpperCAmelCase							:  Tuple				,  _UpperCAmelCase							:  Any				,  _UpperCAmelCase							:  str       )							->					Union[str, Any]:







			'''simple docstring'''
			# Construct configuration depending on size
			_UpperCAmelCase									=  {'small': (512, 8, 24), 'medium': (1_024, 8, 36), 'large': (1_536, 16, 48)}
			_UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase									=  MODELS[model_size]  # set model hyperparameters
			_UpperCAmelCase									=  ImageGPTConfig(n_embd=_UpperCAmelCase				,  n_layer=_UpperCAmelCase				,  n_head=_UpperCAmelCase       )
			_UpperCAmelCase									=  ImageGPTForCausalLM(_UpperCAmelCase       )

			# Load weights from numpy
			load_tf_weights_in_imagegpt(_UpperCAmelCase				,  _UpperCAmelCase				,  _UpperCAmelCase       )

			# Save pytorch-model
			_UpperCAmelCase									=  pytorch_dump_folder_path + '/' + WEIGHTS_NAME
			_UpperCAmelCase									=  pytorch_dump_folder_path + '/' + CONFIG_NAME
			print(F"Save PyTorch model to {pytorch_weights_dump_path}"       )
			torch.save(model.state_dict()				,  _UpperCAmelCase       )
			print(F"Save configuration file to {pytorch_config_dump_path}"       )
			with open(_UpperCAmelCase				,  'w'				,  encoding='utf-8'       ) as f:
						f.write(config.to_json_string()       )


if __name__ == "__main__":
							UpperCAmelCase__  =			argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    "--imagegpt_checkpoint_path",
							    default=None,
							    type=str,
							    required=True,
							    help="Path to the TensorFlow checkpoint path.",
							)
							parser.add_argument(
							    "--model_size",
							    default=None,
							    type=str,
							    required=True,
							    help="Size of the model (can be either 'small', 'medium' or 'large').",
							)
							parser.add_argument(
							    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
							)
							UpperCAmelCase__  =			parser.parse_args()
							convert_imagegpt_checkpoint_to_pytorch(
							    args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
							)
