





import argparse
import logging
import os
import sys
import time

import tensorflow as tf
from datasets import load_dataset
from tqdm import tqdm

from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
from transformers.utils import is_sagemaker_dp_enabled


if os.environ.get("SDP_ENABLED") or is_sagemaker_dp_enabled():
						A_        =  True
						A_        =  "p3dn.24xlarge"
						import smdistributed.dataparallel.tensorflow as sdp
else:
						A_        =  False





def  __UpperCamelCase   (     a,  a,  a,  a,  a,  a,  a=None)			->int:
					lowerCamelCase__      =							tqdm(a)
					for i, batch in enumerate(a):
										with tf.GradientTape() as tape:
															lowerCamelCase__     ,		lowerCamelCase__      =							batch
															lowerCamelCase__      =							model(a)
															lowerCamelCase__      =							loss(a,  outputs.logits)

										if SDP_ENABLED:
															lowerCamelCase__      =							sdp.DistributedGradientTape(a,  sparse_as_dense=a)

										lowerCamelCase__      =							tape.gradient(a,  model.trainable_variables)
										opt.apply_gradients(zip(a,  model.trainable_variables))

										pbar.set_description(f"Loss: {loss_value:.4f}")

										if SDP_ENABLED and i == 0:
															sdp.broadcast_variables(model.variables,  root_rank=0)
															sdp.broadcast_variables(opt.variables(),  root_rank=0)

										if max_steps and i >= max_steps:
															break

					lowerCamelCase__      =							{"loss": loss_value.numpy()}
					return train_results





def  __UpperCamelCase   (     a,  a,  a)			->Tuple:
					# Load dataset
					lowerCamelCase__     ,		lowerCamelCase__      =							load_dataset("imdb",  split=["train", "test"])

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

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

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

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

					if SDP_ENABLED:
										lowerCamelCase__      =							tf_train_dataset.shard(sdp.size(),  sdp.rank())
										lowerCamelCase__      =							tf_test_dataset.shard(sdp.size(),  sdp.rank())
					lowerCamelCase__      =							tf_train_dataset.batch(a,  drop_remainder=a)
					lowerCamelCase__      =							tf_test_dataset.batch(a,  drop_remainder=a)

					return tf_train_dataset, tf_test_dataset


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

						# Hyperparameters sent by the client are passed as command-line arguments to the script.
						parser.add_argument("--epochs", type=int, default=3)
						parser.add_argument("--per_device_train_batch_size", type=int, default=1_6)
						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)
						parser.add_argument("--max_steps", type=int, default=None)

						# Data, model, and output directories
						parser.add_argument("--output_data_dir", type=str, default=os.environ["SM_OUTPUT_DATA_DIR"])
						parser.add_argument("--model_dir", type=str, default=os.environ["SM_MODEL_DIR"])
						parser.add_argument("--n_gpus", type=str, default=os.environ["SM_NUM_GPUS"])

						A_    ,						A_        =  parser.parse_known_args()

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

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

						if SDP_ENABLED:
												sdp.init()

												A_        =  tf.config.experimental.list_physical_devices("GPU")
												for gpu in gpus:
																		tf.config.experimental.set_memory_growth(gpu, True)
												if gpus:
																		tf.config.experimental.set_visible_devices(gpus[sdp.local_rank()], "GPU")

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

						# get datasets
						A_    ,						A_        =  get_datasets(
						    tokenizer=tokenizer,
						    train_batch_size=args.per_device_train_batch_size,
						    eval_batch_size=args.per_device_eval_batch_size,
						)

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

						# Training
						if args.do_train:
												# train_results = model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.train_batch_size)
												A_        =  time.time()
												A_        =  fit(
												    model,
												    loss,
												    optimizer,
												    tf_train_dataset,
												    args.epochs,
												    args.per_device_train_batch_size,
												    max_steps=args.max_steps,
												)
												A_        =  time.time() - start_train_time
												logger.info("*** Train ***")
												logger.info(f"""train_runtime = {end_train_time}""")

												A_        =  os.path.join(args.output_dir, "train_results.txt")

												if not SDP_ENABLED or sdp.rank() == 0:
																		with open(output_eval_file, "w") as writer:
																								logger.info("***** Train results *****")
																								logger.info(train_results)
																								for key, value in train_results.items():
																														logger.info(f"""  {key} = {value}""")
																														writer.write(f"""{key} = {value}\n""")

    # Evaluation
						if args.do_eval and (not SDP_ENABLED or sdp.rank() == 0):
												A_        =  model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
												logger.info("*** Evaluate ***")

												A_        =  os.path.join(args.output_dir, "eval_results.txt")

												with open(output_eval_file, "w") as writer:
																		logger.info("***** Eval results *****")
																		logger.info(result)
																		for key, value in result.items():
																								logger.info(f"""  {key} = {value}""")
																								writer.write(f"""{key} = {value}\n""")

    # Save result
						if SDP_ENABLED:
												if sdp.rank() == 0:
																		model.save_pretrained(args.output_dir)
																		tokenizer.save_pretrained(args.output_dir)
						else:
												model.save_pretrained(args.output_dir)
												tokenizer.save_pretrained(args.output_dir)








import unittest

import numpy as np
import torch

from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device


enable_full_determinism()

class SCREAMING_SNAKE_CASE_			(       unittest.TestCase ):





					"""simple docstring"""


					@property
					def        __magic_name__			(			self						):
										torch.manual_seed(0						)
										lowerCamelCase__      =							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        __magic_name__			(			self						):
										lowerCamelCase__      =							"cpu"
										lowerCamelCase__      =							self.dummy_uncond_unet
										lowerCamelCase__      =							DDPMScheduler()

										lowerCamelCase__      =							DDPMPipeline(unet=_lowerCAmelCase						,	scheduler=_lowerCAmelCase						)
										ddpm.to(_lowerCAmelCase						)
										ddpm.set_progress_bar_config(disable=_lowerCAmelCase						)

										lowerCamelCase__      =							torch.Generator(device=_lowerCAmelCase						).manual_seed(0						)
										lowerCamelCase__      =							ddpm(generator=_lowerCAmelCase						,	num_inference_steps=2						,	output_type="numpy"						).images

										lowerCamelCase__      =							torch.Generator(device=_lowerCAmelCase						).manual_seed(0						)
										lowerCamelCase__      =							ddpm(generator=_lowerCAmelCase						,	num_inference_steps=2						,	output_type="numpy"						,	return_dict=_lowerCAmelCase						)[0]

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

										assert image.shape == (1, 32, 32, 3)
										lowerCamelCase__      =							np.array(
										    [9.9_56E-01, 5.7_85E-01, 4.6_75E-01, 9.9_30E-01, 0.0, 1.000, 1.1_99E-03, 2.6_48E-04, 5.1_01E-04]						)

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






					def        __magic_name__			(			self						):
										lowerCamelCase__      =							self.dummy_uncond_unet
										lowerCamelCase__      =							DDPMScheduler(prediction_type="sample"						)

										lowerCamelCase__      =							DDPMPipeline(unet=_lowerCAmelCase						,	scheduler=_lowerCAmelCase						)
										ddpm.to(_lowerCAmelCase						)
										ddpm.set_progress_bar_config(disable=_lowerCAmelCase						)

										lowerCamelCase__      =							torch.manual_seed(0						)
										lowerCamelCase__      =							ddpm(generator=_lowerCAmelCase						,	num_inference_steps=2						,	output_type="numpy"						).images

										lowerCamelCase__      =							torch.manual_seed(0						)
										lowerCamelCase__      =							ddpm(generator=_lowerCAmelCase						,	num_inference_steps=2						,	output_type="numpy"						)[0]

										lowerCamelCase__      =							image[0, -3:, -3:, -1]
										lowerCamelCase__      =							image_eps[0, -3:, -3:, -1]

										assert image.shape == (1, 32, 32, 3)
										lowerCamelCase__      =							1E-2 if torch_device != "mps" else 3E-2
										assert np.abs(image_slice.flatten() - image_eps_slice.flatten()						).max() < tolerance

@slow
@require_torch_gpu
class SCREAMING_SNAKE_CASE_			(       unittest.TestCase ):





					"""simple docstring"""


					def        __magic_name__			(			self						):
										lowerCamelCase__      =							"google/ddpm-cifar10-32"

										lowerCamelCase__      =							UNetaDModel.from_pretrained(_lowerCAmelCase						)
										lowerCamelCase__      =							DDPMScheduler.from_pretrained(_lowerCAmelCase						)

										lowerCamelCase__      =							DDPMPipeline(unet=_lowerCAmelCase						,	scheduler=_lowerCAmelCase						)
										ddpm.to(_lowerCAmelCase						)
										ddpm.set_progress_bar_config(disable=_lowerCAmelCase						)

										lowerCamelCase__      =							torch.manual_seed(0						)
										lowerCamelCase__      =							ddpm(generator=_lowerCAmelCase						,	output_type="numpy"						).images

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

										assert image.shape == (1, 32, 32, 3)
										lowerCamelCase__      =							np.array([0.4_200, 0.3_588, 0.1_939, 0.3_847, 0.3_382, 0.2_647, 0.4_155, 0.3_582, 0.3_385]						)
										assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2

