




import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()



class       a_       (     unittest.TestCase     ):





	@property
	def 						A_(							self      )  ->					Tuple:




				"""simple docstring"""



				torch.manual_seed(0      )
				SCREAMING_SNAKE_CASE_								=						UNetaDModel(
				    block_out_channels=(32, 64)      ,     layers_per_block=2      ,     sample_size=32      ,     in_channels=3      ,     out_channels=3      ,     down_block_types=('DownBlock2D', 'AttnDownBlock2D')      ,     up_block_types=('AttnUpBlock2D', 'UpBlock2D')      ,     )
				return model





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




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						self.dummy_uncond_unet
				SCREAMING_SNAKE_CASE_								=						ScoreSdeVeScheduler()

				SCREAMING_SNAKE_CASE_								=						ScoreSdeVePipeline(unet=SCREAMING_SNAKE_CASE      ,     scheduler=SCREAMING_SNAKE_CASE      )
				sde_ve.to(SCREAMING_SNAKE_CASE      )
				sde_ve.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE      )

				SCREAMING_SNAKE_CASE_								=						torch.manual_seed(0      )
				SCREAMING_SNAKE_CASE_								=						sde_ve(num_inference_steps=2      ,     output_type='numpy'      ,     generator=SCREAMING_SNAKE_CASE      ).images

				SCREAMING_SNAKE_CASE_								=						torch.manual_seed(0      )
				SCREAMING_SNAKE_CASE_								=						sde_ve(num_inference_steps=2      ,     output_type='numpy'      ,     generator=SCREAMING_SNAKE_CASE      ,     return_dict=SCREAMING_SNAKE_CASE      )[
				    0
				]

				SCREAMING_SNAKE_CASE_								=						image[0, -3:, -3:, -1]
				SCREAMING_SNAKE_CASE_								=						image_from_tuple[0, -3:, -3:, -1]

				assert image.shape == (1, 32, 32, 3)
				SCREAMING_SNAKE_CASE_								=						np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]      )

				assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2
				assert np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1e-2




@slow
@require_torch
class       a_       (     unittest.TestCase     ):





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




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						'google/ncsnpp-church-256'
				SCREAMING_SNAKE_CASE_								=						UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE      )

				SCREAMING_SNAKE_CASE_								=						ScoreSdeVeScheduler.from_pretrained(SCREAMING_SNAKE_CASE      )

				SCREAMING_SNAKE_CASE_								=						ScoreSdeVePipeline(unet=SCREAMING_SNAKE_CASE      ,     scheduler=SCREAMING_SNAKE_CASE      )
				sde_ve.to(SCREAMING_SNAKE_CASE      )
				sde_ve.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE      )

				SCREAMING_SNAKE_CASE_								=						torch.manual_seed(0      )
				SCREAMING_SNAKE_CASE_								=						sde_ve(num_inference_steps=10      ,     output_type='numpy'      ,     generator=SCREAMING_SNAKE_CASE      ).images

				SCREAMING_SNAKE_CASE_								=						image[0, -3:, -3:, -1]

				assert image.shape == (1, 256, 256, 3)

				SCREAMING_SNAKE_CASE_								=						np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]      )

				assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2






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__     :     Union[str, Any]		    =       False
SCREAMING_SNAKE_CASE__     :     Optional[int]		    =       False

def lowercase    (  SCREAMING_SNAKE_CASE )			->							List[Any]:


			'''simple docstring'''







			return TrainCommand(SCREAMING_SNAKE_CASE )



class       a_       (     SCREAMING_SNAKE_CASE__     ):





	@staticmethod
	def 						A_(							SCREAMING_SNAKE_CASE      )  ->					Optional[int]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						parser.add_parser('train'      ,     help='CLI tool to train a model on a task.'      )

				train_parser.add_argument(
				    '--train_data'      ,     type=SCREAMING_SNAKE_CASE      ,     required=SCREAMING_SNAKE_CASE      ,     help='path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences.'      ,     )
				train_parser.add_argument(
				    '--column_label'      ,     type=SCREAMING_SNAKE_CASE      ,     default=0      ,     help='Column of the dataset csv file with example labels.'      )
				train_parser.add_argument(
				    '--column_text'      ,     type=SCREAMING_SNAKE_CASE      ,     default=1      ,     help='Column of the dataset csv file with example texts.'      )
				train_parser.add_argument(
				    '--column_id'      ,     type=SCREAMING_SNAKE_CASE      ,     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=SCREAMING_SNAKE_CASE      ,     default=''      ,     help='path to validation dataset.'      )
				train_parser.add_argument(
				    '--validation_split'      ,     type=SCREAMING_SNAKE_CASE      ,     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=SCREAMING_SNAKE_CASE      ,     default='./'      ,     help='path to saved the trained model.'      )

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





	def __init__(							self      ,     SCREAMING_SNAKE_CASE      )  ->					List[Any]:




				"""simple docstring"""



				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=SCREAMING_SNAKE_CASE      )
				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      )  ->					Dict:




				"""simple docstring"""



				if self.framework == "tf":
							return self.run_tf()
				return self.run_torch()





	def 						A_(							self      )  ->					Optional[int]:




				"""simple docstring"""



				raise NotImplementedError





	def 						A_(							self      )  ->					Dict:




				"""simple docstring"""



				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      )
