
"""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__							=				False
lowerCAmelCase__							=				False



def        _lowerCamelCase ( __a    ):
		return TrainCommand(__a    )





class  snake_case     (							__lowercase   ):

							@staticmethod
							def _lowercase					(SCREAMING_SNAKE_CASE_    ):

									"""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-0_8       ,       help='''Epsilon for Adam optimizer.'''    )
									train_parser.set_defaults(func=SCREAMING_SNAKE_CASE_    )

							def __init__(self       ,       SCREAMING_SNAKE_CASE_    ):

									"""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 _lowercase					(self    ):

									"""simple docstring"""







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

							def _lowercase					(self    ):

									"""simple docstring"""







									raise NotImplementedError







							def _lowercase					(self    ):

									"""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    )

"""simple docstring"""




import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


lowerCAmelCase__							=				False





class  snake_case     (							unittest.TestCase   ):
							pass







@nightly
@require_torch_gpu
class  snake_case     (							unittest.TestCase   ):

							def _lowercase					(self    ):

									"""simple docstring"""







									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''       ,       torch_dtype=torch.floataa    )
									pipe.to(SCREAMING_SNAKE_CASE_    )
									pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = load_image(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''    )

									SCREAMING_SNAKE_CASE_						 = torch.manual_seed(0    )
									SCREAMING_SNAKE_CASE_						 = pipe.dual_guided(
									    prompt='''first prompt'''       ,       image=SCREAMING_SNAKE_CASE_       ,       text_to_image_strength=0.75       ,       generator=SCREAMING_SNAKE_CASE_       ,       guidance_scale=7.5       ,       num_inference_steps=2       ,       output_type='''numpy'''       ,       ).images

									with tempfile.TemporaryDirectory() as tmpdirname:
											pipe.save_pretrained(SCREAMING_SNAKE_CASE_    )
											SCREAMING_SNAKE_CASE_						 = VersatileDiffusionPipeline.from_pretrained(SCREAMING_SNAKE_CASE_       ,       torch_dtype=torch.floataa    )
									pipe.to(SCREAMING_SNAKE_CASE_    )
									pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = generator.manual_seed(0    )
									SCREAMING_SNAKE_CASE_						 = pipe.dual_guided(
									    prompt='''first prompt'''       ,       image=SCREAMING_SNAKE_CASE_       ,       text_to_image_strength=0.75       ,       generator=SCREAMING_SNAKE_CASE_       ,       guidance_scale=7.5       ,       num_inference_steps=2       ,       output_type='''numpy'''       ,       ).images

									assert np.abs(image - new_image    ).sum() < 1e-5, "Models don't have the same forward pass"







							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''       ,       torch_dtype=torch.floataa    )
									pipe.to(SCREAMING_SNAKE_CASE_    )
									pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = '''cyberpunk 2077'''
									SCREAMING_SNAKE_CASE_						 = load_image(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''    )
									SCREAMING_SNAKE_CASE_						 = torch.manual_seed(0    )
									SCREAMING_SNAKE_CASE_						 = pipe.dual_guided(
									    prompt=SCREAMING_SNAKE_CASE_       ,       image=SCREAMING_SNAKE_CASE_       ,       text_to_image_strength=0.75       ,       generator=SCREAMING_SNAKE_CASE_       ,       guidance_scale=7.5       ,       num_inference_steps=50       ,       output_type='''numpy'''       ,       ).images

									SCREAMING_SNAKE_CASE_						 = image[0, 2_53:2_56, 2_53:2_56, -1]

									assert image.shape == (1, 5_12, 5_12, 3)
									SCREAMING_SNAKE_CASE_						 = np.array([0.14_48, 0.16_19, 0.17_41, 0.10_86, 0.11_47, 0.11_28, 0.11_99, 0.11_65, 0.10_01]    )

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

									SCREAMING_SNAKE_CASE_						 = '''A painting of a squirrel eating a burger '''
									SCREAMING_SNAKE_CASE_						 = torch.manual_seed(0    )
									SCREAMING_SNAKE_CASE_						 = pipe.text_to_image(
									    prompt=SCREAMING_SNAKE_CASE_       ,       generator=SCREAMING_SNAKE_CASE_       ,       guidance_scale=7.5       ,       num_inference_steps=50       ,       output_type='''numpy'''    ).images

									SCREAMING_SNAKE_CASE_						 = image[0, 2_53:2_56, 2_53:2_56, -1]

									assert image.shape == (1, 5_12, 5_12, 3)
									SCREAMING_SNAKE_CASE_						 = np.array([0.33_67, 0.31_69, 0.26_56, 0.38_70, 0.47_90, 0.37_96, 0.40_09, 0.48_78, 0.47_78]    )

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

									SCREAMING_SNAKE_CASE_						 = pipe.image_variation(SCREAMING_SNAKE_CASE_       ,       generator=SCREAMING_SNAKE_CASE_       ,       output_type='''numpy'''    ).images

									SCREAMING_SNAKE_CASE_						 = image[0, 2_53:2_56, 2_53:2_56, -1]

									assert image.shape == (1, 5_12, 5_12, 3)
									SCREAMING_SNAKE_CASE_						 = np.array([0.30_76, 0.31_23, 0.32_84, 0.37_82, 0.37_70, 0.38_94, 0.42_97, 0.43_31, 0.44_56]    )

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