




import os
from argparse import ArgumentParser, Namespace

from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand


if not is_tf_available() and not is_torch_available():
			raise RuntimeError("""At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training""")

# TF training parameters
__SCREAMING_SNAKE_CASE			    =False
__SCREAMING_SNAKE_CASE			    =False

def       a (_lowerCAmelCase  ):
							return TrainCommand(SCREAMING_SNAKE_CASE_  )






class 			__magic_name__    (	__UpperCAmelCase):
				'''simple docstring'''







				@staticmethod
				def      _A				(				_lowerCamelCase: Optional[Any]		):
											SCREAMING_SNAKE_CASE_ 						=     parser.add_parser('''train'''		,       help='''CLI tool to train a model on a task.'''		)

											train_parser.add_argument(
											    '''--train_data'''		,       type=_lowercase		,       required=_lowercase		,       help='''path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences.'''		,       )
											train_parser.add_argument(
											    '''--column_label'''		,       type=_lowercase		,       default=0		,       help='''Column of the dataset csv file with example labels.'''		)
											train_parser.add_argument(
											    '''--column_text'''		,       type=_lowercase		,       default=1		,       help='''Column of the dataset csv file with example texts.'''		)
											train_parser.add_argument(
											    '''--column_id'''		,       type=_lowercase		,       default=2		,       help='''Column of the dataset csv file with example ids.'''		)
											train_parser.add_argument(
											    '''--skip_first_row'''		,       action='''store_true'''		,       help='''Skip the first row of the csv file (headers).'''		)

											train_parser.add_argument('''--validation_data'''		,       type=_lowercase		,       default=''''''		,       help='''path to validation dataset.'''		)
											train_parser.add_argument(
											    '''--validation_split'''		,       type=_lowercase		,       default=0.1		,       help='''if validation dataset is not provided, fraction of train dataset to use as validation dataset.'''		,       )

											train_parser.add_argument('''--output'''		,       type=_lowercase		,       default='''./'''		,       help='''path to saved the trained model.'''		)

											train_parser.add_argument(
											    '''--task'''		,       type=_lowercase		,       default='''text_classification'''		,       help='''Task to train the model on.'''		)
											train_parser.add_argument(
											    '''--model'''		,       type=_lowercase		,       default='''bert-base-uncased'''		,       help='''Model\'s name or path to stored model.'''		)
											train_parser.add_argument('''--train_batch_size'''		,       type=_lowercase		,       default=32		,       help='''Batch size for training.'''		)
											train_parser.add_argument('''--valid_batch_size'''		,       type=_lowercase		,       default=64		,       help='''Batch size for validation.'''		)
											train_parser.add_argument('''--learning_rate'''		,       type=_lowercase		,       default=3E-5		,       help='''Learning rate.'''		)
											train_parser.add_argument('''--adam_epsilon'''		,       type=_lowercase		,       default=1E-08		,       help='''Epsilon for Adam optimizer.'''		)
											train_parser.set_defaults(func=_lowercase		)


				def __init__(				self: List[Any]		,       _lowerCamelCase: Union[str, Any]		):
											SCREAMING_SNAKE_CASE_ 						=     logging.get_logger('''transformers-cli/training'''		)

											SCREAMING_SNAKE_CASE_ 						=     '''tf''' if is_tf_available() else '''torch'''

											os.makedirs(args.output		,       exist_ok=_lowercase		)
											SCREAMING_SNAKE_CASE_ 						=     args.output

											SCREAMING_SNAKE_CASE_ 						=     args.column_label
											SCREAMING_SNAKE_CASE_ 						=     args.column_text
											SCREAMING_SNAKE_CASE_ 						=     args.column_id

											self.logger.info(f"Loading {args.task} pipeline for {args.model}"		)
											if args.task == "text_classification":
																		SCREAMING_SNAKE_CASE_ 						=     TextClassificationPipeline.from_pretrained(args.model		)
											elif args.task == "token_classification":
																		raise NotImplementedError
											elif args.task == "question_answering":
																		raise NotImplementedError

											self.logger.info(f"Loading dataset from {args.train_data}"		)
											SCREAMING_SNAKE_CASE_ 						=     Processor.create_from_csv(
											    args.train_data		,       column_label=args.column_label		,       column_text=args.column_text		,       column_id=args.column_id		,       skip_first_row=args.skip_first_row		,       )
											SCREAMING_SNAKE_CASE_ 						=     None
											if args.validation_data:
																		self.logger.info(f"Loading validation dataset from {args.validation_data}"		)
																		SCREAMING_SNAKE_CASE_ 						=     Processor.create_from_csv(
																		    args.validation_data		,       column_label=args.column_label		,       column_text=args.column_text		,       column_id=args.column_id		,       skip_first_row=args.skip_first_row		,       )

											SCREAMING_SNAKE_CASE_ 						=     args.validation_split
											SCREAMING_SNAKE_CASE_ 						=     args.train_batch_size
											SCREAMING_SNAKE_CASE_ 						=     args.valid_batch_size
											SCREAMING_SNAKE_CASE_ 						=     args.learning_rate
											SCREAMING_SNAKE_CASE_ 						=     args.adam_epsilon


				def      _A				(				self: List[Any]		):
											if self.framework == "tf":
																		return self.run_tf()
											return self.run_torch()


				def      _A				(				self: Union[str, Any]		):
											raise NotImplementedError





				def      _A				(				self: Union[str, Any]		):
											self.pipeline.fit(
											    self.train_dataset		,       validation_data=self.valid_dataset		,       validation_split=self.validation_split		,       learning_rate=self.learning_rate		,       adam_epsilon=self.adam_epsilon		,       train_batch_size=self.train_batch_size		,       valid_batch_size=self.valid_batch_size		,       )

											# Save trained pipeline
											self.pipeline.save_pretrained(self.output		)





import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class 						__magic_name__					(							snake_case				):
     UpperCamelCase_	:Dict										=      (KDPMaDiscreteScheduler,)
     UpperCamelCase_	:str										=      1_0






     def 	UpperCAmelCase_				(				self		,						**_lowercase	)->     str:
         UpperCamelCase_			  =						{
             "num_train_timesteps": 1_100,
             "beta_start": 0.0_001,
             "beta_end": 0.02,
             "beta_schedule": "linear",
         }

         config.update(**_lowercase	)
         return config






     def 	UpperCAmelCase_				(				self	)->     Union[str, Any]:
         for timesteps in [10, 50, 100, 1_000]:
             self.check_over_configs(num_train_timesteps=_lowercase	)






     def 	UpperCAmelCase_				(				self	)->     int:
         for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]		,						[0.0_002, 0.002, 0.02]	):
             self.check_over_configs(beta_start=_lowercase		,						beta_end=_lowercase	)






     def 	UpperCAmelCase_				(				self	)->     str:
         for schedule in ["linear", "scaled_linear"]:
             self.check_over_configs(beta_schedule=_lowercase	)






     def 	UpperCAmelCase_				(				self	)->     Any:
         for prediction_type in ["epsilon", "v_prediction"]:
             self.check_over_configs(prediction_type=_lowercase	)






     def 	UpperCAmelCase_				(				self	)->     Optional[int]:
         UpperCamelCase_			  =						self.scheduler_classes[0]
         UpperCamelCase_			  =						self.get_scheduler_config(prediction_type="v_prediction"	)
         UpperCamelCase_			  =						scheduler_class(**_lowercase	)

         scheduler.set_timesteps(self.num_inference_steps	)

         UpperCamelCase_			  =						self.dummy_model()
         UpperCamelCase_			  =						self.dummy_sample_deter * scheduler.init_noise_sigma
         UpperCamelCase_			  =						sample.to(_lowercase	)

         for i, t in enumerate(scheduler.timesteps	):
             UpperCamelCase_			  =						scheduler.scale_model_input(_lowercase		,						_lowercase	)

             UpperCamelCase_			  =						model(_lowercase		,						_lowercase	)

             UpperCamelCase_			  =						scheduler.step(_lowercase		,						_lowercase		,						_lowercase	)
             UpperCamelCase_			  =						output.prev_sample

         UpperCamelCase_			  =						torch.sum(torch.abs(_lowercase	)	)
         UpperCamelCase_			  =						torch.mean(torch.abs(_lowercase	)	)

         if torch_device in ["cpu", "mps"]:
             assert abs(result_sum.item() - 4.6_9_3_4e-0_7	) < 1e-2
             assert abs(result_mean.item() - 6.1_1_1_2e-1_0	) < 1e-3
         else:
             # CUDA
             assert abs(result_sum.item() - 4.6_9_3_4_2_8_6_5_0_1_7_0_9_7_2e-0_7	) < 1e-2
             assert abs(result_mean.item() - 0.0_002	) < 1e-3






     def 	UpperCAmelCase_				(				self	)->     Dict:
         if torch_device == "mps":
             return
         UpperCamelCase_			  =						self.scheduler_classes[0]
         UpperCamelCase_			  =						self.get_scheduler_config()
         UpperCamelCase_			  =						scheduler_class(**_lowercase	)

         scheduler.set_timesteps(self.num_inference_steps	)

         UpperCamelCase_			  =						self.dummy_model()
         UpperCamelCase_			  =						self.dummy_sample_deter * scheduler.init_noise_sigma
         UpperCamelCase_			  =						sample.to(_lowercase	)

         for i, t in enumerate(scheduler.timesteps	):
             UpperCamelCase_			  =						scheduler.scale_model_input(_lowercase		,						_lowercase	)

             UpperCamelCase_			  =						model(_lowercase		,						_lowercase	)

             UpperCamelCase_			  =						scheduler.step(_lowercase		,						_lowercase		,						_lowercase	)
             UpperCamelCase_			  =						output.prev_sample

         UpperCamelCase_			  =						torch.sum(torch.abs(_lowercase	)	)
         UpperCamelCase_			  =						torch.mean(torch.abs(_lowercase	)	)

         if torch_device in ["cpu", "mps"]:
             assert abs(result_sum.item() - 20.4_125	) < 1e-2
             assert abs(result_mean.item() - 0.0_266	) < 1e-3
         else:
             # CUDA
             assert abs(result_sum.item() - 20.4_125	) < 1e-2
             assert abs(result_mean.item() - 0.0_266	) < 1e-3






     def 	UpperCAmelCase_				(				self	)->     Optional[int]:
         if torch_device == "mps":
             return
         UpperCamelCase_			  =						self.scheduler_classes[0]
         UpperCamelCase_			  =						self.get_scheduler_config()
         UpperCamelCase_			  =						scheduler_class(**_lowercase	)

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

         UpperCamelCase_			  =						self.dummy_model()
         UpperCamelCase_			  =						self.dummy_sample_deter.to(_lowercase	) * scheduler.init_noise_sigma

         for t in scheduler.timesteps:
             UpperCamelCase_			  =						scheduler.scale_model_input(_lowercase		,						_lowercase	)

             UpperCamelCase_			  =						model(_lowercase		,						_lowercase	)

             UpperCamelCase_			  =						scheduler.step(_lowercase		,						_lowercase		,						_lowercase	)
             UpperCamelCase_			  =						output.prev_sample

         UpperCamelCase_			  =						torch.sum(torch.abs(_lowercase	)	)
         UpperCamelCase_			  =						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() - 20.4_125	) < 1e-2
             assert abs(result_mean.item() - 0.0_266	) < 1e-3
         else:
             # CUDA
             assert abs(result_sum.item() - 20.4_125	) < 1e-2
             assert abs(result_mean.item() - 0.0_266	) < 1e-3
