




'''simple docstring'''




import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler






class     UpperCamelCase_     (unittest.TestCase     ):







						"""simple docstring"""


						_lowerCAmelCase    						=					(ScoreSdeVeScheduler,)
						_lowerCAmelCase    						=					()







						@property
						def       _a    (     self      :       Optional[Any]    ):





											"""simple docstring"""







											A_ :     int =							4
											A_ :     List[Any] =							3
											A_ :     Any =							8
											A_ :     Dict =							8

											A_ :     Union[str, Any] =							torch.rand((batch_size, num_channels, height, width)    )

											return sample







						@property
						def       _a    (     self      :       Any    ):





											"""simple docstring"""







											A_ :     Optional[int] =							4
											A_ :     Optional[int] =							3
											A_ :     int =							8
											A_ :     Optional[int] =							8

											A_ :     Any =							batch_size * num_channels * height * width
											A_ :     List[Any] =							torch.arange(_lowerCamelCase    )
											A_ :     str =							sample.reshape(_lowerCamelCase							, _lowerCamelCase							, _lowerCamelCase							, _lowerCamelCase    )
											A_ :     Dict =							sample / num_elems
											A_ :     int =							sample.permute(3							, 0							, 1							, 2    )

											return sample







						def       _a    (     self      :       Tuple    ):





											"""simple docstring"""







											def model(_lowerCamelCase      :       str							, _lowerCamelCase      :       List[Any]							, *_lowerCamelCase      :       str    ):
																return sample * t / (t + 1)

											return model







						def       _a    (     self      :       Union[str, Any]							, **_lowerCamelCase      :       Optional[Any]    ):





											"""simple docstring"""







											A_ :     Optional[int] =							{
											    '''num_train_timesteps''': 2000,
											    '''snr''': 0.15,
											    '''sigma_min''': 0.01,
											    '''sigma_max''': 1348,
											    '''sampling_eps''': 1E-5,
											}

											config.update(**_lowerCamelCase    )
											return config







						def       _a    (     self      :       List[Any]							, _lowerCamelCase      :       Dict=0							, **_lowerCamelCase      :       Union[str, Any]    ):





											"""simple docstring"""







											A_ :     Any =							dict(self.forward_default_kwargs    )

											for scheduler_class in self.scheduler_classes:
																A_ :     Dict =							self.dummy_sample
																A_ :     int =							0.1 * sample

																A_ :     List[Any] =							self.get_scheduler_config(**_lowerCamelCase    )
																A_ :     str =							scheduler_class(**_lowerCamelCase    )

																with tempfile.TemporaryDirectory() as tmpdirname:
																					scheduler.save_config(_lowerCamelCase    )
																					A_ :     List[str] =							scheduler_class.from_pretrained(_lowerCamelCase    )

																A_ :     Union[str, Any] =							scheduler.step_pred(
																    _lowerCamelCase							, _lowerCamelCase							, _lowerCamelCase							, generator=torch.manual_seed(0    )							, **_lowerCamelCase    ).prev_sample
																A_ :     Union[str, Any] =							new_scheduler.step_pred(
																    _lowerCamelCase							, _lowerCamelCase							, _lowerCamelCase							, generator=torch.manual_seed(0    )							, **_lowerCamelCase    ).prev_sample

																assert torch.sum(torch.abs(output - new_output    )    ) < 1E-5, "Scheduler outputs are not identical"

																A_ :     Any =							scheduler.step_correct(_lowerCamelCase							, _lowerCamelCase							, generator=torch.manual_seed(0    )							, **_lowerCamelCase    ).prev_sample
																A_ :     Dict =							new_scheduler.step_correct(
																    _lowerCamelCase							, _lowerCamelCase							, generator=torch.manual_seed(0    )							, **_lowerCamelCase    ).prev_sample

																assert torch.sum(torch.abs(output - new_output    )    ) < 1E-5, "Scheduler correction are not identical"







						def       _a    (     self      :       Optional[Any]							, _lowerCamelCase      :       Dict=0							, **_lowerCamelCase      :       int    ):





											"""simple docstring"""







											A_ :     Tuple =							dict(self.forward_default_kwargs    )
											kwargs.update(_lowerCamelCase    )

											for scheduler_class in self.scheduler_classes:
																A_ :     Optional[Any] =							self.dummy_sample
																A_ :     Optional[Any] =							0.1 * sample

																A_ :     str =							self.get_scheduler_config()
																A_ :     List[str] =							scheduler_class(**_lowerCamelCase    )

																with tempfile.TemporaryDirectory() as tmpdirname:
																					scheduler.save_config(_lowerCamelCase    )
																					A_ :     int =							scheduler_class.from_pretrained(_lowerCamelCase    )

																A_ :     Optional[int] =							scheduler.step_pred(
																    _lowerCamelCase							, _lowerCamelCase							, _lowerCamelCase							, generator=torch.manual_seed(0    )							, **_lowerCamelCase    ).prev_sample
																A_ :     List[Any] =							new_scheduler.step_pred(
																    _lowerCamelCase							, _lowerCamelCase							, _lowerCamelCase							, generator=torch.manual_seed(0    )							, **_lowerCamelCase    ).prev_sample

																assert torch.sum(torch.abs(output - new_output    )    ) < 1E-5, "Scheduler outputs are not identical"

																A_ :     List[str] =							scheduler.step_correct(_lowerCamelCase							, _lowerCamelCase							, generator=torch.manual_seed(0    )							, **_lowerCamelCase    ).prev_sample
																A_ :     str =							new_scheduler.step_correct(
																    _lowerCamelCase							, _lowerCamelCase							, generator=torch.manual_seed(0    )							, **_lowerCamelCase    ).prev_sample

																assert torch.sum(torch.abs(output - new_output    )    ) < 1E-5, "Scheduler correction are not identical"







						def       _a    (     self      :       Optional[int]    ):





											"""simple docstring"""







											for timesteps in [10, 100, 1000]:
																self.check_over_configs(num_train_timesteps=_lowerCamelCase    )







						def       _a    (     self      :       List[Any]    ):





											"""simple docstring"""







											for sigma_min, sigma_max in zip([0.00_01, 0.0_01, 0.01]							, [1, 100, 1000]    ):
																self.check_over_configs(sigma_min=_lowerCamelCase							, sigma_max=_lowerCamelCase    )







						def       _a    (     self      :       Optional[int]    ):





											"""simple docstring"""







											for t in [0.1, 0.5, 0.75]:
																self.check_over_forward(time_step=_lowerCamelCase    )







						def       _a    (     self      :       Optional[Any]    ):





											"""simple docstring"""







											A_ :     Tuple =							dict(self.forward_default_kwargs    )

											A_ :     Tuple =							self.scheduler_classes[0]
											A_ :     str =							self.get_scheduler_config()
											A_ :     Union[str, Any] =							scheduler_class(**_lowerCamelCase    )

											A_ :     Optional[Any] =							3

											A_ :     int =							self.dummy_model()
											A_ :     Optional[int] =							self.dummy_sample_deter

											scheduler.set_sigmas(_lowerCamelCase    )
											scheduler.set_timesteps(_lowerCamelCase    )
											A_ :     Union[str, Any] =							torch.manual_seed(0    )

											for i, t in enumerate(scheduler.timesteps    ):
																A_ :     List[Any] =							scheduler.sigmas[i]

																for _ in range(scheduler.config.correct_steps    ):
																					with torch.no_grad():
																										A_ :     int =							model(_lowerCamelCase							, _lowerCamelCase    )
																					A_ :     Optional[Any] =							scheduler.step_correct(_lowerCamelCase							, _lowerCamelCase							, generator=_lowerCamelCase							, **_lowerCamelCase    ).prev_sample

																with torch.no_grad():
																					A_ :     Tuple =							model(_lowerCamelCase							, _lowerCamelCase    )

																A_ :     List[Any] =							scheduler.step_pred(_lowerCamelCase							, _lowerCamelCase							, _lowerCamelCase							, generator=_lowerCamelCase							, **_lowerCamelCase    )
																A_ ,A_ :     Optional[Any] =							output.prev_sample, output.prev_sample_mean

											A_ :     List[Any] =							torch.sum(torch.abs(_lowerCamelCase    )    )
											A_ :     List[Any] =							torch.mean(torch.abs(_lowerCamelCase    )    )

											assert np.isclose(result_sum.item()							, 1_43_72_75_85_28.0    )
											assert np.isclose(result_mean.item()							, 18_71_45_30.0    )







						def       _a    (     self      :       List[str]    ):





											"""simple docstring"""







											A_ :     Optional[Any] =							dict(self.forward_default_kwargs    )

											A_ :     Optional[int] =							kwargs.pop('''num_inference_steps'''							, _lowerCamelCase    )

											for scheduler_class in self.scheduler_classes:
																A_ :     Tuple =							self.get_scheduler_config()
																A_ :     Union[str, Any] =							scheduler_class(**_lowerCamelCase    )

																A_ :     List[str] =							self.dummy_sample
																A_ :     List[Any] =							0.1 * sample

																if num_inference_steps is not None and hasattr(_lowerCamelCase							, '''set_timesteps'''    ):
																					scheduler.set_timesteps(_lowerCamelCase    )
																elif num_inference_steps is not None and not hasattr(_lowerCamelCase							, '''set_timesteps'''    ):
																					A_ :     Tuple =							num_inference_steps

																A_ :     int =							scheduler.step_pred(_lowerCamelCase							, 0							, _lowerCamelCase							, generator=torch.manual_seed(0    )							, **_lowerCamelCase    ).prev_sample
																A_ :     Dict =							scheduler.step_pred(_lowerCamelCase							, 1							, _lowerCamelCase							, generator=torch.manual_seed(0    )							, **_lowerCamelCase    ).prev_sample

																self.assertEqual(output_a.shape							, sample.shape    )
																self.assertEqual(output_a.shape							, output_a.shape    )









'''simple docstring'''




import argparse
import logging
import sys
import time

import tensorflow as tf
from datasets import load_dataset

from transformers import AutoTokenizer, TFAutoModelForSequenceClassification


if __name__ == "__main__":
							snake_case__          =						argparse.ArgumentParser()

							# Hyperparameters sent by the client are passed as command-line arguments to the script.
							parser.add_argument("""--epochs""", type=int, default=1)
							parser.add_argument("""--per_device_train_batch_size""", type=int, default=16)
							parser.add_argument("""--per_device_eval_batch_size""", type=int, default=8)
							parser.add_argument("""--model_name_or_path""", type=str)
							parser.add_argument("""--learning_rate""", type=str, default=5e-5)
							parser.add_argument("""--do_train""", type=bool, default=True)
							parser.add_argument("""--do_eval""", type=bool, default=True)
							parser.add_argument("""--output_dir""", type=str)

							snake_case__       ,						snake_case__          =						parser.parse_known_args()

							# overwrite batch size until we have tf_glue.py
							snake_case__          =						16
							snake_case__          =						16

							# Set up logging
							snake_case__          =						logging.getLogger(__name__)

							logging.basicConfig(
							    level=logging.getLevelName("""INFO"""),
							    handlers=[logging.StreamHandler(sys.stdout)],
							    format="""%(asctime)s - %(name)s - %(levelname)s - %(message)s""",
							)

							# Load model and tokenizer
							snake_case__          =						TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
							snake_case__          =						AutoTokenizer.from_pretrained(args.model_name_or_path)

							# Load dataset
							snake_case__       ,						snake_case__          =						load_dataset("""imdb""", split=["""train""", """test"""])
							snake_case__          =						train_dataset.shuffle().select(range(50_00))  # smaller the size for train dataset to 5k
							snake_case__          =						test_dataset.shuffle().select(range(5_00))  # smaller the size for test dataset to 500

							# Preprocess train dataset
							snake_case__          =						train_dataset.map(
							    lambda e: tokenizer(e["""text"""], truncation=True, padding="""max_length"""), batched=True
							)
							train_dataset.set_format(type="""tensorflow""", columns=["""input_ids""", """attention_mask""", """label"""])

							snake_case__          =						{
							    x: train_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
							    for x in ["""input_ids""", """attention_mask"""]
							}
							snake_case__          =						tf.data.Dataset.from_tensor_slices((train_features, train_dataset["""label"""])).batch(
							    args.per_device_train_batch_size
							)

							# Preprocess test dataset
							snake_case__          =						test_dataset.map(
							    lambda e: tokenizer(e["""text"""], truncation=True, padding="""max_length"""), batched=True
							)
							test_dataset.set_format(type="""tensorflow""", columns=["""input_ids""", """attention_mask""", """label"""])

							snake_case__          =						{
							    x: test_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
							    for x in ["""input_ids""", """attention_mask"""]
							}
							snake_case__          =						tf.data.Dataset.from_tensor_slices((test_features, test_dataset["""label"""])).batch(
							    args.per_device_eval_batch_size
							)

							# fine optimizer and loss
							snake_case__          =						tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
							snake_case__          =						tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
							snake_case__          =						[tf.keras.metrics.SparseCategoricalAccuracy()]
							model.compile(optimizer=optimizer, loss=loss, metrics=metrics)

							snake_case__          =						time.time()
							snake_case__          =						model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
							snake_case__          =						time.time() - start_train_time

							logger.info("""*** Train ***""")
							logger.info(F'train_runtime = {end_train_time}')
							for key, value in train_results.history.items():
														logger.info(F'  {key} = {value}')



