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():
			_SCREAMING_SNAKE_CASE:    Tuple     						=							True
			_SCREAMING_SNAKE_CASE:    Tuple     						=							'''p3dn.24xlarge'''
			import smdistributed.dataparallel.tensorflow as sdp
else:
			_SCREAMING_SNAKE_CASE:    str     						=							False






def  UpperCAmelCase__(    lowerCamelCase_				,						lowerCamelCase_				,						lowerCamelCase_				,						lowerCamelCase_				,						lowerCamelCase_				,						lowerCamelCase_				,						lowerCamelCase_=None	):



			"""simple docstring"""





			_snake_case														=   tqdm(lowerCamelCase_	)
			for i, batch in enumerate(lowerCamelCase_	):
						with tf.GradientTape() as tape:
									_snake_case							,			_snake_case														=   batch
									_snake_case														=   model(lowerCamelCase_	)
									_snake_case														=   loss(lowerCamelCase_				,						outputs.logits	)

						if SDP_ENABLED:
									_snake_case														=   sdp.DistributedGradientTape(lowerCamelCase_				,						sparse_as_dense=lowerCamelCase_	)

						_snake_case														=   tape.gradient(lowerCamelCase_				,						model.trainable_variables	)
						opt.apply_gradients(zip(lowerCamelCase_				,						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

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






def  UpperCAmelCase__(    lowerCamelCase_				,						lowerCamelCase_				,						lowerCamelCase_	):



			"""simple docstring"""





			_snake_case							,			_snake_case														=   load_dataset("imdb"				,						split=["train", "test"]	)

			# Preprocess train dataset
			_snake_case														=   train_dataset.map(
			    lambda lowerCamelCase_	: tokenizer(e["text"]				,						truncation=lowerCamelCase_				,						padding="max_length"	)				,						batched=lowerCamelCase_	)
			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"])	)

			# Preprocess test dataset
			_snake_case														=   test_dataset.map(
			    lambda lowerCamelCase_	: tokenizer(e["text"]				,						truncation=lowerCamelCase_				,						padding="max_length"	)				,						batched=lowerCamelCase_	)
			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"])	)

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

			return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
			_SCREAMING_SNAKE_CASE:    Any     						=							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'''])

			_SCREAMING_SNAKE_CASE						,					_SCREAMING_SNAKE_CASE:    Optional[int]     						=							parser.parse_known_args()

			# Set up logging
			_SCREAMING_SNAKE_CASE:    Any     						=							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()

						_SCREAMING_SNAKE_CASE:    int     						=							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
			_SCREAMING_SNAKE_CASE:    List[Any]     						=							TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
			_SCREAMING_SNAKE_CASE:    List[Any]     						=							AutoTokenizer.from_pretrained(args.model_name_or_path)

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

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

						_SCREAMING_SNAKE_CASE:    Optional[Any]     						=							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 normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_SCREAMING_SNAKE_CASE:    List[Any]     						=							logging.get_logger(__name__)

class 						_SCREAMING_SNAKE_CASE				(  _lowercase				):






		"""simple docstring"""







		A_    			=   ['''pixel_values''']



		def __init__(      self     :					Tuple	,       __SCREAMING_SNAKE_CASE     :					bool = True	,       __SCREAMING_SNAKE_CASE     :					Optional[Dict[str, int]] = None	,       __SCREAMING_SNAKE_CASE     :					PILImageResampling = PILImageResampling.BILINEAR	,       __SCREAMING_SNAKE_CASE     :					bool = True	,       __SCREAMING_SNAKE_CASE     :					Union[int, float] = 1 / 255	,       __SCREAMING_SNAKE_CASE     :					bool = True	,       __SCREAMING_SNAKE_CASE     :					Optional[Union[float, List[float]]] = None	,       __SCREAMING_SNAKE_CASE     :					Optional[Union[float, List[float]]] = None	,       **__SCREAMING_SNAKE_CASE     :					str	,       ):






					"""simple docstring"""
					super().__init__(**__SCREAMING_SNAKE_CASE )
					_snake_case														=   size if size is not None else {"height": 224, "width": 224}
					_snake_case														=   get_size_dict(__SCREAMING_SNAKE_CASE )
					_snake_case														=   do_resize
					_snake_case														=   do_rescale
					_snake_case														=   do_normalize
					_snake_case														=   size
					_snake_case														=   resample
					_snake_case														=   rescale_factor
					_snake_case														=   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
					_snake_case														=   image_std if image_std is not None else IMAGENET_STANDARD_STD



		def 					_a					(      self     :					int	,       __SCREAMING_SNAKE_CASE     :					np.ndarray	,       __SCREAMING_SNAKE_CASE     :					Dict[str, int]	,       __SCREAMING_SNAKE_CASE     :					PILImageResampling = PILImageResampling.BILINEAR	,       __SCREAMING_SNAKE_CASE     :					Optional[Union[str, ChannelDimension]] = None	,       **__SCREAMING_SNAKE_CASE     :					Tuple	,       ):






					"""simple docstring"""
					_snake_case														=   get_size_dict(__SCREAMING_SNAKE_CASE )
					if "height" not in size or "width" not in size:
								raise ValueError(F'The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}' )
					return resize(
					    __SCREAMING_SNAKE_CASE	,       size=(size["height"], size["width"])	,       resample=__SCREAMING_SNAKE_CASE	,       data_format=__SCREAMING_SNAKE_CASE	,       **__SCREAMING_SNAKE_CASE )



		def 					_a					(      self     :					Tuple	,       __SCREAMING_SNAKE_CASE     :					np.ndarray	,       __SCREAMING_SNAKE_CASE     :					float	,       __SCREAMING_SNAKE_CASE     :					Optional[Union[str, ChannelDimension]] = None	,       **__SCREAMING_SNAKE_CASE     :					List[str] ):






					"""simple docstring"""
					return rescale(__SCREAMING_SNAKE_CASE	,       scale=__SCREAMING_SNAKE_CASE	,       data_format=__SCREAMING_SNAKE_CASE	,       **__SCREAMING_SNAKE_CASE )



		def 					_a					(      self     :					Optional[Any]	,       __SCREAMING_SNAKE_CASE     :					np.ndarray	,       __SCREAMING_SNAKE_CASE     :					Union[float, List[float]]	,       __SCREAMING_SNAKE_CASE     :					Union[float, List[float]]	,       __SCREAMING_SNAKE_CASE     :					Optional[Union[str, ChannelDimension]] = None	,       **__SCREAMING_SNAKE_CASE     :					Optional[Any]	,       ):






					"""simple docstring"""
					return normalize(__SCREAMING_SNAKE_CASE	,       mean=__SCREAMING_SNAKE_CASE	,       std=__SCREAMING_SNAKE_CASE	,       data_format=__SCREAMING_SNAKE_CASE	,       **__SCREAMING_SNAKE_CASE )







		def 					_a					(      self     :					int	,       __SCREAMING_SNAKE_CASE     :					ImageInput	,       __SCREAMING_SNAKE_CASE     :					Optional[bool] = None	,       __SCREAMING_SNAKE_CASE     :					Dict[str, int] = None	,       __SCREAMING_SNAKE_CASE     :					PILImageResampling = None	,       __SCREAMING_SNAKE_CASE     :					Optional[bool] = None	,       __SCREAMING_SNAKE_CASE     :					Optional[float] = None	,       __SCREAMING_SNAKE_CASE     :					Optional[bool] = None	,       __SCREAMING_SNAKE_CASE     :					Optional[Union[float, List[float]]] = None	,       __SCREAMING_SNAKE_CASE     :					Optional[Union[float, List[float]]] = None	,       __SCREAMING_SNAKE_CASE     :					Optional[Union[str, TensorType]] = None	,       __SCREAMING_SNAKE_CASE     :					Union[str, ChannelDimension] = ChannelDimension.FIRST	,       **__SCREAMING_SNAKE_CASE     :					List[Any]	,       ):






					"""simple docstring"""
					_snake_case														=   do_resize if do_resize is not None else self.do_resize
					_snake_case														=   do_rescale if do_rescale is not None else self.do_rescale
					_snake_case														=   do_normalize if do_normalize is not None else self.do_normalize
					_snake_case														=   resample if resample is not None else self.resample
					_snake_case														=   rescale_factor if rescale_factor is not None else self.rescale_factor
					_snake_case														=   image_mean if image_mean is not None else self.image_mean
					_snake_case														=   image_std if image_std is not None else self.image_std

					_snake_case														=   size if size is not None else self.size
					_snake_case														=   get_size_dict(__SCREAMING_SNAKE_CASE )

					_snake_case														=   make_list_of_images(__SCREAMING_SNAKE_CASE )

					if not valid_images(__SCREAMING_SNAKE_CASE ):
								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_rescale and rescale_factor is None:
								raise ValueError("Rescale factor must be specified if do_rescale is True." )

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

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

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

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

					_snake_case														=   [to_channel_dimension_format(__SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE ) for image in images]

					_snake_case														=   {"pixel_values": images}
					return BatchFeature(data=__SCREAMING_SNAKE_CASE	,       tensor_type=__SCREAMING_SNAKE_CASE )

