'''simple docstring'''





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
_lowerCAmelCase  :List[str]  				=    False
_lowerCAmelCase  :Optional[Any]  				=    False


def     __lowerCAmelCase						(       a_		)      ->	Any:





     '''simple docstring'''





     return TrainCommand(a_		)






class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    @staticmethod
    def       _UpperCamelCase    (       lowercase__ )							-> Optional[Any]:
         SCREAMING_SNAKE_CASE					:			Tuple    	=       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    ,							lowercase__ )							-> List[Any]:
         SCREAMING_SNAKE_CASE					:			Tuple    	=       logging.get_logger('transformers-cli/training' )

         SCREAMING_SNAKE_CASE					:			Any    	=       'tf' if is_tf_available() else 'torch'

         os.makedirs(args.output    ,							exist_ok=lowercase__ )
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       args.output

         SCREAMING_SNAKE_CASE					:			Any    	=       args.column_label
         SCREAMING_SNAKE_CASE					:			int    	=       args.column_text
         SCREAMING_SNAKE_CASE					:			List[str]    	=       args.column_id

         self.logger.info(F"""Loading {args.task} pipeline for {args.model}""" )
         if args.task == "text_classification":
              SCREAMING_SNAKE_CASE					:			List[Any]    	=       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					:			Union[str, Any]    	=       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					:			List[str]    	=       None
         if args.validation_data:
              self.logger.info(F"""Loading validation dataset from {args.validation_data}""" )
              SCREAMING_SNAKE_CASE					:			List[Any]    	=       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					:			List[str]    	=       args.validation_split
         SCREAMING_SNAKE_CASE					:			str    	=       args.train_batch_size
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       args.valid_batch_size
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       args.learning_rate
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       args.adam_epsilon






    def       _UpperCamelCase    (       self )							-> Tuple:
         if self.framework == "tf":
              return self.run_tf()
         return self.run_torch()






    def       _UpperCamelCase    (       self )							-> List[Any]:
         raise NotImplementedError






    def       _UpperCamelCase    (       self )							-> Tuple:
         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 )


'''simple docstring'''





import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest






class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    snake_case__							:   List[str]							=						(DDPMParallelScheduler,)






    def       _UpperCamelCase    (       self    ,							**lowercase__ )							-> Tuple:
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       {
             'num_train_timesteps': 1_000,
             'beta_start': 0.0_0_0_1,
             'beta_end': 0.0_2,
             'beta_schedule': 'linear',
             'variance_type': 'fixed_small',
             'clip_sample': True,
         }

         config.update(**lowercase__ )
         return config






    def       _UpperCamelCase    (       self )							-> int:
         for timesteps in [1, 5, 100, 1_000]:
              self.check_over_configs(num_train_timesteps=lowercase__ )






    def       _UpperCamelCase    (       self )							-> Optional[Any]:
         for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]    ,							[0.0_0_2, 0.0_2, 0.2, 2] ):
              self.check_over_configs(beta_start=lowercase__    ,							beta_end=lowercase__ )






    def       _UpperCamelCase    (       self )							-> List[Any]:
         for schedule in ["linear", "squaredcos_cap_v2"]:
              self.check_over_configs(beta_schedule=lowercase__ )






    def       _UpperCamelCase    (       self )							-> str:
         for variance in ["fixed_small", "fixed_large", "other"]:
              self.check_over_configs(variance_type=lowercase__ )






    def       _UpperCamelCase    (       self )							-> Optional[Any]:
         for clip_sample in [True, False]:
              self.check_over_configs(clip_sample=lowercase__ )






    def       _UpperCamelCase    (       self )							-> List[str]:
         self.check_over_configs(thresholding=lowercase__ )
         for threshold in [0.5, 1.0, 2.0]:
              for prediction_type in ["epsilon", "sample", "v_prediction"]:
                   self.check_over_configs(
                       thresholding=lowercase__    ,							prediction_type=lowercase__    ,							sample_max_value=lowercase__    ,							)






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






    def       _UpperCamelCase    (       self )							-> Optional[Any]:
         for t in [0, 500, 999]:
              self.check_over_forward(time_step=lowercase__ )






    def       _UpperCamelCase    (       self )							-> Any:
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       self.scheduler_classes[0]
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       self.get_scheduler_config()
         SCREAMING_SNAKE_CASE					:			Tuple    	=       scheduler_class(**lowercase__ )

         assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.0_0_9_7_9 ) ) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.0_2 ) ) < 1E-5






    def       _UpperCamelCase    (       self )							-> int:
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       self.scheduler_classes[0]
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       self.get_scheduler_config()
         SCREAMING_SNAKE_CASE					:			Tuple    	=       scheduler_class(**lowercase__ )

         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       len(lowercase__ )

         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self.dummy_model()
         SCREAMING_SNAKE_CASE					:			str    	=       self.dummy_sample_deter
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       self.dummy_sample_deter + 0.1
         SCREAMING_SNAKE_CASE					:			int    	=       self.dummy_sample_deter - 0.1

         SCREAMING_SNAKE_CASE					:			List[Any]    	=       samplea.shape[0]
         SCREAMING_SNAKE_CASE					:			str    	=       torch.stack([samplea, samplea, samplea]    ,							dim=0 )
         SCREAMING_SNAKE_CASE					:			List[str]    	=       torch.arange(lowercase__ )[0:3, None].repeat(1    ,							lowercase__ )

         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       model(samples.flatten(0    ,							1 )    ,							timesteps.flatten(0    ,							1 ) )
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       scheduler.batch_step_no_noise(lowercase__    ,							timesteps.flatten(0    ,							1 )    ,							samples.flatten(0    ,							1 ) )

         SCREAMING_SNAKE_CASE					:			List[Any]    	=       torch.sum(torch.abs(lowercase__ ) )
         SCREAMING_SNAKE_CASE					:			Any    	=       torch.mean(torch.abs(lowercase__ ) )

         assert abs(result_sum.item() - 1_1_5_3.1_8_3_3 ) < 1E-2
         assert abs(result_mean.item() - 0.5_0_0_5 ) < 1E-3






    def       _UpperCamelCase    (       self )							-> Tuple:
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self.scheduler_classes[0]
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self.get_scheduler_config()
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       scheduler_class(**lowercase__ )

         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       len(lowercase__ )

         SCREAMING_SNAKE_CASE					:			Any    	=       self.dummy_model()
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self.dummy_sample_deter
         SCREAMING_SNAKE_CASE					:			Any    	=       torch.manual_seed(0 )

         for t in reversed(range(lowercase__ ) ):
              # 1. predict noise residual
              SCREAMING_SNAKE_CASE					:			int    	=       model(lowercase__    ,							lowercase__ )

              # 2. predict previous mean of sample x_t-1
              SCREAMING_SNAKE_CASE					:			Dict    	=       scheduler.step(lowercase__    ,							lowercase__    ,							lowercase__    ,							generator=lowercase__ ).prev_sample

              SCREAMING_SNAKE_CASE					:			List[str]    	=       pred_prev_sample

         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       torch.sum(torch.abs(lowercase__ ) )
         SCREAMING_SNAKE_CASE					:			Dict    	=       torch.mean(torch.abs(lowercase__ ) )

         assert abs(result_sum.item() - 2_5_8.9_6_0_6 ) < 1E-2
         assert abs(result_mean.item() - 0.3_3_7_2 ) < 1E-3






    def       _UpperCamelCase    (       self )							-> List[str]:
         SCREAMING_SNAKE_CASE					:			Dict    	=       self.scheduler_classes[0]
         SCREAMING_SNAKE_CASE					:			Any    	=       self.get_scheduler_config(prediction_type='v_prediction' )
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       scheduler_class(**lowercase__ )

         SCREAMING_SNAKE_CASE					:			str    	=       len(lowercase__ )

         SCREAMING_SNAKE_CASE					:			Tuple    	=       self.dummy_model()
         SCREAMING_SNAKE_CASE					:			List[str]    	=       self.dummy_sample_deter
         SCREAMING_SNAKE_CASE					:			int    	=       torch.manual_seed(0 )

         for t in reversed(range(lowercase__ ) ):
              # 1. predict noise residual
              SCREAMING_SNAKE_CASE					:			Tuple    	=       model(lowercase__    ,							lowercase__ )

              # 2. predict previous mean of sample x_t-1
              SCREAMING_SNAKE_CASE					:			Optional[int]    	=       scheduler.step(lowercase__    ,							lowercase__    ,							lowercase__    ,							generator=lowercase__ ).prev_sample

              SCREAMING_SNAKE_CASE					:			List[Any]    	=       pred_prev_sample

         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       torch.sum(torch.abs(lowercase__ ) )
         SCREAMING_SNAKE_CASE					:			str    	=       torch.mean(torch.abs(lowercase__ ) )

         assert abs(result_sum.item() - 2_0_2.0_2_9_6 ) < 1E-2
         assert abs(result_mean.item() - 0.2_6_3_1 ) < 1E-3






    def       _UpperCamelCase    (       self )							-> List[str]:
         SCREAMING_SNAKE_CASE					:			List[str]    	=       self.scheduler_classes[0]
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       self.get_scheduler_config()
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       scheduler_class(**lowercase__ )

         SCREAMING_SNAKE_CASE					:			Tuple    	=       [100, 87, 50, 1, 0]

         scheduler.set_timesteps(timesteps=lowercase__ )

         SCREAMING_SNAKE_CASE					:			str    	=       scheduler.timesteps

         for i, timestep in enumerate(lowercase__ ):
              if i == len(lowercase__ ) - 1:
                   SCREAMING_SNAKE_CASE					:			List[str]    	=       -1
              else:
                   SCREAMING_SNAKE_CASE					:			Any    	=       timesteps[i + 1]

              SCREAMING_SNAKE_CASE					:			str    	=       scheduler.previous_timestep(lowercase__ )
              SCREAMING_SNAKE_CASE					:			str    	=       prev_t.item()

              self.assertEqual(lowercase__    ,							lowercase__ )






    def       _UpperCamelCase    (       self )							-> Optional[int]:
         SCREAMING_SNAKE_CASE					:			Tuple    	=       self.scheduler_classes[0]
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       self.get_scheduler_config()
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       scheduler_class(**lowercase__ )

         SCREAMING_SNAKE_CASE					:			int    	=       [100, 87, 50, 51, 0]

         with self.assertRaises(lowercase__    ,							msg='`custom_timesteps` must be in descending order.' ):
              scheduler.set_timesteps(timesteps=lowercase__ )






    def       _UpperCamelCase    (       self )							-> Union[str, Any]:
         SCREAMING_SNAKE_CASE					:			Tuple    	=       self.scheduler_classes[0]
         SCREAMING_SNAKE_CASE					:			List[str]    	=       self.get_scheduler_config()
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       scheduler_class(**lowercase__ )

         SCREAMING_SNAKE_CASE					:			Tuple    	=       [100, 87, 50, 1, 0]
         SCREAMING_SNAKE_CASE					:			List[str]    	=       len(lowercase__ )

         with self.assertRaises(lowercase__    ,							msg='Can only pass one of `num_inference_steps` or `custom_timesteps`.' ):
              scheduler.set_timesteps(num_inference_steps=lowercase__    ,							timesteps=lowercase__ )






    def       _UpperCamelCase    (       self )							-> List[str]:
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self.scheduler_classes[0]
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self.get_scheduler_config()
         SCREAMING_SNAKE_CASE					:			Tuple    	=       scheduler_class(**lowercase__ )

         SCREAMING_SNAKE_CASE					:			Any    	=       [scheduler.config.num_train_timesteps]

         with self.assertRaises(
             lowercase__    ,							msg='`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'    ,							):
              scheduler.set_timesteps(timesteps=lowercase__ )



