


"""simple docstring"""




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():
							snake_case_  :     Tuple							  =							True
							snake_case_  :     Optional[Any]							  =							'''p3dn.24xlarge'''
							import smdistributed.dataparallel.tensorflow as sdp
else:
							snake_case_  :     Optional[int]							  =							False







def   lowerCamelCase(				a__		,a__		,a__		,a__		,a__		,a__		,a__=None):
				_SCREAMING_SNAKE_CASE     					=tqdm(lowercase__)
				for i, batch in enumerate(lowercase__):
								with tf.GradientTape() as tape:
												_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE     					=batch
												_SCREAMING_SNAKE_CASE     					=model(lowercase__)
												_SCREAMING_SNAKE_CASE     					=loss(lowercase__		,outputs.logits)

								if SDP_ENABLED:
												_SCREAMING_SNAKE_CASE     					=sdp.DistributedGradientTape(lowercase__		,sparse_as_dense=lowercase__)

								_SCREAMING_SNAKE_CASE     					=tape.gradient(lowercase__		,model.trainable_variables)
								opt.apply_gradients(zip(lowercase__		,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

				_SCREAMING_SNAKE_CASE     					={'''loss''': loss_value.numpy()}
				return train_results







def   lowerCamelCase(				a__		,a__		,a__):
				# Load dataset
				_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE     					=load_dataset('''imdb'''		,split=['''train''', '''test'''])

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

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

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

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

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

				return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
							snake_case_  :     int							  =							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=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)
							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'''])

							snake_case_  ,			snake_case_  :     Any							  =							parser.parse_known_args()

							# Set up logging
							snake_case_  :     str							  =							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()

														snake_case_  :     str							  =							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
							snake_case_  :     Optional[Any]							  =							TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
							snake_case_  :     int							  =							AutoTokenizer.from_pretrained(args.model_name_or_path)

							# get datasets
							snake_case_  ,			snake_case_  :     Tuple							  =							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
							snake_case_  :     List[Any]							  =							tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
							snake_case_  :     int							  =							tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
							snake_case_  :     Any							  =							[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)
														snake_case_  :     List[Any]							  =							time.time()
														snake_case_  :     int							  =							fit(
														    model,
														    loss,
														    optimizer,
														    tf_train_dataset,
														    args.epochs,
														    args.per_device_train_batch_size,
														    max_steps=args.max_steps,
														)
														snake_case_  :     Optional[Any]							  =							time.time() - start_train_time
														logger.info('''*** Train ***''')
														logger.info(f"""train_runtime = {end_train_time}""")

														snake_case_  :     Dict							  =							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):
														snake_case_  :     Optional[int]							  =							model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
														logger.info('''*** Evaluate ***''')

														snake_case_  :     str							  =							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 sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize

class   A__							:




						def __init__(    self   : List[Any]			,      _a   : List[str]			,      _a   : Optional[Any]=sys.maxsize					)							->       Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					='''bilinear'''
										_SCREAMING_SNAKE_CASE     					=max_size
										_SCREAMING_SNAKE_CASE     					=short_edge_length




						def __call__(    self   : Any			,      _a   : Tuple					)							->       List[str]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=[]
										for img in imgs:
														_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE     					=img.shape[:2]
														# later: provide list and randomly choose index for resize
														_SCREAMING_SNAKE_CASE     					=np.random.randint(self.short_edge_length[0]			,      self.short_edge_length[1] + 1					)
														if size == 0:
																		return img
														_SCREAMING_SNAKE_CASE     					=size * 1.0 / min(_a			,      _a					)
														if h < w:
																		_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE     					=size, scale * w
														else:
																		_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE     					=scale * h, size
														if max(_a			,      _a					) > self.max_size:
																		_SCREAMING_SNAKE_CASE     					=self.max_size * 1.0 / max(_a			,      _a					)
																		_SCREAMING_SNAKE_CASE     					=newh * scale
																		_SCREAMING_SNAKE_CASE     					=neww * scale
														_SCREAMING_SNAKE_CASE     					=int(neww + 0.5					)
														_SCREAMING_SNAKE_CASE     					=int(newh + 0.5					)

														if img.dtype == np.uinta:
																		_SCREAMING_SNAKE_CASE     					=Image.fromarray(_a					)
																		_SCREAMING_SNAKE_CASE     					=pil_image.resize((neww, newh)			,      PILImageResampling.BILINEAR					)
																		_SCREAMING_SNAKE_CASE     					=np.asarray(_a					)
														else:
																		_SCREAMING_SNAKE_CASE     					=img.permute(2			,      0			,      1					).unsqueeze(0					)  # 3, 0, 1)  # hw(c) -> nchw
																		_SCREAMING_SNAKE_CASE     					=nn.functional.interpolate(
																		    _a			,      (newh, neww)			,      mode=self.interp_method			,      align_corners=_a					).squeeze(0					)
														img_augs.append(_a					)

										return img_augs

class   A__							:




						def __init__(    self   : Any			,      _a   : Tuple					)							->       Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]			,      cfg.INPUT.MAX_SIZE_TEST					)
										_SCREAMING_SNAKE_CASE     					=cfg.INPUT.FORMAT
										_SCREAMING_SNAKE_CASE     					=cfg.SIZE_DIVISIBILITY
										_SCREAMING_SNAKE_CASE     					=cfg.PAD_VALUE
										_SCREAMING_SNAKE_CASE     					=cfg.INPUT.MAX_SIZE_TEST
										_SCREAMING_SNAKE_CASE     					=cfg.MODEL.DEVICE
										_SCREAMING_SNAKE_CASE     					=torch.tensor(cfg.MODEL.PIXEL_STD					).to(self.device					).view(len(cfg.MODEL.PIXEL_STD					)			,      1			,      1					)
										_SCREAMING_SNAKE_CASE     					=torch.tensor(cfg.MODEL.PIXEL_MEAN					).to(self.device					).view(len(cfg.MODEL.PIXEL_STD					)			,      1			,      1					)
										_SCREAMING_SNAKE_CASE     					=lambda _a					: (x - self.pixel_mean) / self.pixel_std




						def 							__UpperCamelCase			(    self   : int			,      _a   : Optional[Any]					)							->       Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=tuple(max(_a					) for s in zip(*[img.shape for img in images]					)					)
										_SCREAMING_SNAKE_CASE     					=[im.shape[-2:] for im in images]
										_SCREAMING_SNAKE_CASE     					=[
										    nn.functional.pad(
										        _a			,      [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]			,      value=self.pad_value			,      )
										    for size, im in zip(_a			,      _a					)
										]

										return torch.stack(_a					), torch.tensor(_a					)




						def __call__(    self   : str			,      _a   : Union[str, Any]			,      _a   : Any=False					)							->       Tuple:


										"""simple docstring"""





										with torch.no_grad():
														if not isinstance(_a			,      _a					):
																		_SCREAMING_SNAKE_CASE     					=[images]
														if single_image:
																		assert len(_a					) == 1
														for i in range(len(_a					)					):
																		if isinstance(images[i]			,      torch.Tensor					):
																						images.insert(_a			,      images.pop(_a					).to(self.device					).float()					)
																		elif not isinstance(images[i]			,      torch.Tensor					):
																						images.insert(
																						    _a			,      torch.as_tensor(img_tensorize(images.pop(_a					)			,      input_format=self.input_format					)					)
																						    .to(self.device					)
																						    .float()			,      )
            # resize smallest edge
														_SCREAMING_SNAKE_CASE     					=torch.tensor([im.shape[:2] for im in images]					)
														_SCREAMING_SNAKE_CASE     					=self.aug(_a					)
														# transpose images and convert to torch tensors
														# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
														# now normalize before pad to avoid useless arithmetic
														_SCREAMING_SNAKE_CASE     					=[self.normalizer(_a					) for x in images]
														# now pad them to do the following operations
														_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE     					=self.pad(_a					)
														# Normalize

														if self.size_divisibility > 0:
																		raise NotImplementedError()
														# pad
														_SCREAMING_SNAKE_CASE     					=torch.true_divide(_a			,      _a					)
														if single_image:
																		return images[0], sizes[0], scales_yx[0]
														else:
																		return images, sizes, scales_yx







def   lowerCamelCase(				a__		,a__):
				boxes[:, 0::2] *= scale_yx[:, 1]
				boxes[:, 1::2] *= scale_yx[:, 0]
				return boxes







def   lowerCamelCase(				a__		,a__):
				assert torch.isfinite(a__).all(), "Box tensor contains infinite or NaN!"
				_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE     					=box_size
				tensor[:, 0].clamp_(min=0		,max=a__)
				tensor[:, 1].clamp_(min=0		,max=a__)
				tensor[:, 2].clamp_(min=0		,max=a__)
				tensor[:, 3].clamp_(min=0		,max=a__)