





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





def    _lowercase    (  _UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase=None )  ->     Dict:
				lowerCamelCase		       =tqdm(_UpperCAmelCase )
				for i, batch in enumerate(_UpperCAmelCase ):
								with tf.GradientTape() as tape:
												lowerCamelCase       ,     lowerCamelCase		       =batch
												lowerCamelCase		       =model(_UpperCAmelCase )
												lowerCamelCase		       =loss(_UpperCAmelCase	,					outputs.logits )

								if SDP_ENABLED:
												lowerCamelCase		       =sdp.DistributedGradientTape(_UpperCAmelCase	,					sparse_as_dense=_UpperCAmelCase )

								lowerCamelCase		       =tape.gradient(_UpperCAmelCase	,					model.trainable_variables )
								opt.apply_gradients(zip(_UpperCAmelCase	,					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    _lowercase    (  _UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase )  ->     Dict:
				# Load dataset
				lowerCamelCase       ,     lowerCamelCase		       =load_dataset("""imdb"""	,					split=["""train""", """test"""] )

				# Preprocess train dataset
				lowerCamelCase		       =train_dataset.map(
				    lambda _UpperCAmelCase : tokenizer(e["""text"""]	,					truncation=_UpperCAmelCase	,					padding="""max_length""" )	,					batched=_UpperCAmelCase )
				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 _UpperCAmelCase : tokenizer(e["""text"""]	,					truncation=_UpperCAmelCase	,					padding="""max_length""" )	,					batched=_UpperCAmelCase )
				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(_UpperCAmelCase	,					drop_remainder=_UpperCAmelCase )
				lowerCamelCase		       =tf_test_dataset.batch(_UpperCAmelCase	,					drop_remainder=_UpperCAmelCase )

				return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
							UpperCAmelCase__  :	Tuple     =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'''])

							UpperCAmelCase__ ,UpperCAmelCase__  :	List[Any]     =parser.parse_known_args()

							# Set up logging
							UpperCAmelCase__  :	Dict     =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()

														UpperCAmelCase__  :	Any     =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
							UpperCAmelCase__  :	Any     =TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
							UpperCAmelCase__  :	Optional[Any]     =AutoTokenizer.from_pretrained(args.model_name_or_path)

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

														UpperCAmelCase__  :	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):
														UpperCAmelCase__  :	Any     =model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
														logger.info('''*** Evaluate ***''')

														UpperCAmelCase__  :	List[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)












from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


UpperCAmelCase__  :	Dict     =logging.get_logger(__name__)


if is_vision_available():
							import PIL

class 					__A   (  a			):
				__A            = ["""pixel_values"""]


				def __init__(						self      ,       UpperCAmelCase_ = True      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = PILImageResampling.BICUBIC      ,       UpperCAmelCase_ = True      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = True      ,       UpperCAmelCase_ = 1 / 255      ,       UpperCAmelCase_ = True      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = True      ,       **UpperCAmelCase_      ,       ):
								super().__init__(**UpperCAmelCase_							)
								lowerCamelCase		       =size if size is not None else {"""shortest_edge""": 224}
								lowerCamelCase		       =get_size_dict(UpperCAmelCase_      ,       default_to_square=UpperCAmelCase_							)
								lowerCamelCase		       =crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
								lowerCamelCase		       =get_size_dict(UpperCAmelCase_							)

								lowerCamelCase		       =do_resize
								lowerCamelCase		       =size
								lowerCamelCase		       =resample
								lowerCamelCase		       =do_center_crop
								lowerCamelCase		       =crop_size
								lowerCamelCase		       =do_rescale
								lowerCamelCase		       =rescale_factor
								lowerCamelCase		       =do_normalize
								lowerCamelCase		       =image_mean if image_mean is not None else OPENAI_CLIP_MEAN
								lowerCamelCase		       =image_std if image_std is not None else OPENAI_CLIP_STD
								lowerCamelCase		       =do_convert_rgb


				def _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_ = PILImageResampling.BICUBIC      ,       UpperCAmelCase_ = None      ,       **UpperCAmelCase_      ,       ):
								lowerCamelCase		       =get_size_dict(UpperCAmelCase_      ,       default_to_square=UpperCAmelCase_							)
								lowerCamelCase		       =get_resize_output_image_size(
								    UpperCAmelCase_      ,       size=(size["""height"""], size["""width"""])      ,       default_to_square=UpperCAmelCase_							)
								return resize(UpperCAmelCase_      ,       size=UpperCAmelCase_      ,       resample=UpperCAmelCase_      ,       data_format=UpperCAmelCase_      ,       **UpperCAmelCase_							)


				def _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_ = None      ,       **UpperCAmelCase_      ,       ):
								lowerCamelCase		       =get_size_dict(UpperCAmelCase_							)
								return center_crop(UpperCAmelCase_      ,       size=(size["""height"""], size["""width"""])      ,       data_format=UpperCAmelCase_      ,       **UpperCAmelCase_							)


				def _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_ = None      ,       **UpperCAmelCase_      ,       ):
								return rescale(UpperCAmelCase_      ,       scale=UpperCAmelCase_      ,       data_format=UpperCAmelCase_      ,       **UpperCAmelCase_							)


				def _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_ = None      ,       **UpperCAmelCase_      ,       ):
								return normalize(UpperCAmelCase_      ,       mean=UpperCAmelCase_      ,       std=UpperCAmelCase_      ,       data_format=UpperCAmelCase_      ,       **UpperCAmelCase_							)


				def _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = ChannelDimension.FIRST      ,       **UpperCAmelCase_      ,       ):
								lowerCamelCase		       =do_resize if do_resize is not None else self.do_resize
								lowerCamelCase		       =size if size is not None else self.size
								lowerCamelCase		       =get_size_dict(UpperCAmelCase_      ,       default_to_square=UpperCAmelCase_							)
								lowerCamelCase		       =resample if resample is not None else self.resample
								lowerCamelCase		       =do_center_crop if do_center_crop is not None else self.do_center_crop
								lowerCamelCase		       =crop_size if crop_size is not None else self.crop_size
								lowerCamelCase		       =get_size_dict(UpperCAmelCase_							)
								lowerCamelCase		       =do_rescale if do_rescale is not None else self.do_rescale
								lowerCamelCase		       =rescale_factor if rescale_factor is not None else self.rescale_factor
								lowerCamelCase		       =do_normalize if do_normalize is not None else self.do_normalize
								lowerCamelCase		       =image_mean if image_mean is not None else self.image_mean
								lowerCamelCase		       =image_std if image_std is not None else self.image_std
								lowerCamelCase		       =do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

								lowerCamelCase		       =make_list_of_images(UpperCAmelCase_							)

								if not valid_images(UpperCAmelCase_							):
												raise ValueError(
												    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
												    """torch.Tensor, tf.Tensor or jax.ndarray."""							)

								if do_resize and size is None:
												raise ValueError("""Size must be specified if do_resize is True."""							)

								if do_center_crop and crop_size is None:
												raise ValueError("""Crop size must be specified if do_center_crop is True."""							)

								if do_rescale and rescale_factor is None:
												raise ValueError("""Rescale factor must be specified if do_rescale is True."""							)

								if do_normalize and (image_mean is None or image_std is None):
												raise ValueError("""Image mean and std must be specified if do_normalize is True."""							)

								# PIL RGBA images are converted to RGB
								if do_convert_rgb:
												lowerCamelCase		       =[convert_to_rgb(UpperCAmelCase_							) for image in images]

								# All transformations expect numpy arrays.
								lowerCamelCase		       =[to_numpy_array(UpperCAmelCase_							) for image in images]

								if do_resize:
												lowerCamelCase		       =[self.resize(image=UpperCAmelCase_      ,       size=UpperCAmelCase_      ,       resample=UpperCAmelCase_							) for image in images]

								if do_center_crop:
												lowerCamelCase		       =[self.center_crop(image=UpperCAmelCase_      ,       size=UpperCAmelCase_							) for image in images]

								if do_rescale:
												lowerCamelCase		       =[self.rescale(image=UpperCAmelCase_      ,       scale=UpperCAmelCase_							) for image in images]

								if do_normalize:
												lowerCamelCase		       =[self.normalize(image=UpperCAmelCase_      ,       mean=UpperCAmelCase_      ,       std=UpperCAmelCase_							) for image in images]

								lowerCamelCase		       =[to_channel_dimension_format(UpperCAmelCase_      ,       UpperCAmelCase_							) for image in images]

								lowerCamelCase		       ={"""pixel_values""": images}
								return BatchFeature(data=UpperCAmelCase_      ,       tensor_type=UpperCAmelCase_							)





