




import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class 							__SCREAMING_SNAKE_CASE				(						lowerCamelCase    ):
					_lowerCamelCase						    =					(HeunDiscreteScheduler,)
					_lowerCamelCase						    =					10

					def       lowercase_				(							self					,			**__lowercase   ):







						'''simple docstring'''




						lowerCamelCase_				:						Union[str, Any]				  =				{
						    'num_train_timesteps': 1_100,
						    'beta_start': 0.00_01,
						    'beta_end': 0.02,
						    'beta_schedule': 'linear',
						}

						config.update(**__lowercase   )
						return config

					def       lowercase_				(							self   ):







						'''simple docstring'''




						for timesteps in [10, 50, 100, 1_000]:
							self.check_over_configs(num_train_timesteps=__lowercase   )

					def       lowercase_				(							self   ):







						'''simple docstring'''




						for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]					,			[0.00_02, 0.0_02, 0.02]   ):
							self.check_over_configs(beta_start=__lowercase					,			beta_end=__lowercase   )

					def       lowercase_				(							self   ):







						'''simple docstring'''




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

					def       lowercase_				(							self   ):







						'''simple docstring'''




						for clip_sample_range in [1.0, 2.0, 3.0]:
							self.check_over_configs(clip_sample_range=__lowercase					,			clip_sample=__lowercase   )

					def       lowercase_				(							self   ):







						'''simple docstring'''




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

					def       lowercase_				(							self   ):







						'''simple docstring'''




						lowerCamelCase_				:						Tuple				  =				self.scheduler_classes[0]
						lowerCamelCase_				:						Optional[int]				  =				self.get_scheduler_config()
						lowerCamelCase_				:						Optional[Any]				  =				scheduler_class(**__lowercase   )

						scheduler.set_timesteps(self.num_inference_steps   )

						lowerCamelCase_				:						List[Any]				  =				self.dummy_model()
						lowerCamelCase_				:						Optional[Any]				  =				self.dummy_sample_deter * scheduler.init_noise_sigma
						lowerCamelCase_				:						Optional[Any]				  =				sample.to(__lowercase   )

						for i, t in enumerate(scheduler.timesteps   ):
							lowerCamelCase_				:						Dict				  =				scheduler.scale_model_input(__lowercase					,			__lowercase   )

							lowerCamelCase_				:						Optional[int]				  =				model(__lowercase					,			__lowercase   )

							lowerCamelCase_				:						int				  =				scheduler.step(__lowercase					,			__lowercase					,			__lowercase   )
							lowerCamelCase_				:						List[str]				  =				output.prev_sample

						lowerCamelCase_				:						Optional[int]				  =				torch.sum(torch.abs(__lowercase   )   )
						lowerCamelCase_				:						Optional[int]				  =				torch.mean(torch.abs(__lowercase   )   )

						if torch_device in ["cpu", "mps"]:
							assert abs(result_sum.item() - 0.12_33   ) < 1E-2
							assert abs(result_mean.item() - 0.00_02   ) < 1E-3
						else:
							# CUDA
							assert abs(result_sum.item() - 0.12_33   ) < 1E-2
							assert abs(result_mean.item() - 0.00_02   ) < 1E-3

					def       lowercase_				(							self   ):







						'''simple docstring'''




						lowerCamelCase_				:						Any				  =				self.scheduler_classes[0]
						lowerCamelCase_				:						Optional[int]				  =				self.get_scheduler_config(prediction_type='v_prediction'   )
						lowerCamelCase_				:						str				  =				scheduler_class(**__lowercase   )

						scheduler.set_timesteps(self.num_inference_steps   )

						lowerCamelCase_				:						Any				  =				self.dummy_model()
						lowerCamelCase_				:						Any				  =				self.dummy_sample_deter * scheduler.init_noise_sigma
						lowerCamelCase_				:						Union[str, Any]				  =				sample.to(__lowercase   )

						for i, t in enumerate(scheduler.timesteps   ):
							lowerCamelCase_				:						List[Any]				  =				scheduler.scale_model_input(__lowercase					,			__lowercase   )

							lowerCamelCase_				:						Optional[Any]				  =				model(__lowercase					,			__lowercase   )

							lowerCamelCase_				:						Tuple				  =				scheduler.step(__lowercase					,			__lowercase					,			__lowercase   )
							lowerCamelCase_				:						str				  =				output.prev_sample

						lowerCamelCase_				:						Optional[Any]				  =				torch.sum(torch.abs(__lowercase   )   )
						lowerCamelCase_				:						str				  =				torch.mean(torch.abs(__lowercase   )   )

						if torch_device in ["cpu", "mps"]:
							assert abs(result_sum.item() - 4.6934E-07   ) < 1E-2
							assert abs(result_mean.item() - 6.1112E-10   ) < 1E-3
						else:
							# CUDA
							assert abs(result_sum.item() - 4.693428650170972E-07   ) < 1E-2
							assert abs(result_mean.item() - 0.00_02   ) < 1E-3

					def       lowercase_				(							self   ):







						'''simple docstring'''




						lowerCamelCase_				:						List[Any]				  =				self.scheduler_classes[0]
						lowerCamelCase_				:						Union[str, Any]				  =				self.get_scheduler_config()
						lowerCamelCase_				:						Optional[Any]				  =				scheduler_class(**__lowercase   )

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

						lowerCamelCase_				:						Dict				  =				self.dummy_model()
						lowerCamelCase_				:						List[str]				  =				self.dummy_sample_deter.to(__lowercase   ) * scheduler.init_noise_sigma

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

							lowerCamelCase_				:						Optional[Any]				  =				model(__lowercase					,			__lowercase   )

							lowerCamelCase_				:						List[Any]				  =				scheduler.step(__lowercase					,			__lowercase					,			__lowercase   )
							lowerCamelCase_				:						Optional[Any]				  =				output.prev_sample

						lowerCamelCase_				:						Any				  =				torch.sum(torch.abs(__lowercase   )   )
						lowerCamelCase_				:						List[Any]				  =				torch.mean(torch.abs(__lowercase   )   )

						if str(__lowercase   ).startswith('cpu'   ):
							# The following sum varies between 148 and 156 on mps. Why?
							assert abs(result_sum.item() - 0.12_33   ) < 1E-2
							assert abs(result_mean.item() - 0.00_02   ) < 1E-3
						elif str(__lowercase   ).startswith('mps'   ):
							# Larger tolerance on mps
							assert abs(result_mean.item() - 0.00_02   ) < 1E-2
						else:
							# CUDA
							assert abs(result_sum.item() - 0.12_33   ) < 1E-2
							assert abs(result_mean.item() - 0.00_02   ) < 1E-3





					def       lowercase_				(							self   ):







						'''simple docstring'''




						lowerCamelCase_				:						Dict				  =				self.scheduler_classes[0]
						lowerCamelCase_				:						str				  =				self.get_scheduler_config()
						lowerCamelCase_				:						Tuple				  =				scheduler_class(**__lowercase					,			use_karras_sigmas=__lowercase   )

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

						lowerCamelCase_				:						Optional[Any]				  =				self.dummy_model()
						lowerCamelCase_				:						Any				  =				self.dummy_sample_deter.to(__lowercase   ) * scheduler.init_noise_sigma
						lowerCamelCase_				:						str				  =				sample.to(__lowercase   )

						for t in scheduler.timesteps:
							lowerCamelCase_				:						Any				  =				scheduler.scale_model_input(__lowercase					,			__lowercase   )

							lowerCamelCase_				:						Dict				  =				model(__lowercase					,			__lowercase   )

							lowerCamelCase_				:						int				  =				scheduler.step(__lowercase					,			__lowercase					,			__lowercase   )
							lowerCamelCase_				:						Tuple				  =				output.prev_sample

						lowerCamelCase_				:						int				  =				torch.sum(torch.abs(__lowercase   )   )
						lowerCamelCase_				:						str				  =				torch.mean(torch.abs(__lowercase   )   )

						assert abs(result_sum.item() - 0.0_00_15   ) < 1E-2
						assert abs(result_mean.item() - 1.9869554535034695E-07   ) < 1E-2




import argparse

from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
from transformers.utils import logging


logging.set_verbosity_info()



def      __lowerCAmelCase    (				snake_case						:     Union[str, Any]    ,   snake_case						:     Optional[int]			)							->				List[Any]:
   # Initialize PyTorch model
   __lowerCamelCase:					str	 =						CanineConfig()
   __lowerCamelCase:					Union[str, Any]	 =						CanineModel(snake_case			)
   model.eval()

   print(f'Building PyTorch model from configuration: {config}'			)

   # Load weights from tf checkpoint
   load_tf_weights_in_canine(snake_case    ,   snake_case    ,   snake_case			)

   # Save pytorch-model (weights and configuration)
   print(f'Save PyTorch model to {pytorch_dump_path}'			)
   model.save_pretrained(snake_case			)

   # Save tokenizer files
   __lowerCamelCase:					Optional[int]	 =						CanineTokenizer()
   print(f'Save tokenizer files to {pytorch_dump_path}'			)
   tokenizer.save_pretrained(snake_case			)


if __name__ == "__main__":
   _A		:					int          =							argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--tf_checkpoint_path''',
       default=None,
       type=str,
       required=True,
       help='''Path to the TensorFlow checkpoint. Should end with model.ckpt''',
   )
   parser.add_argument(
       '''--pytorch_dump_path''',
       default=None,
       type=str,
       required=True,
       help='''Path to a folder where the PyTorch model will be placed.''',
   )
   _A		:					int          =							parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)


