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







def 		SCREAMING_SNAKE_CASE__					(							_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE=None  ):
		lowerCAmelCase_   :       Optional[int]    							=tqdm(A__  )
		for i, batch in enumerate(A__  ):
				with tf.GradientTape() as tape:
						lowerCAmelCase_							,		lowerCAmelCase_   :       Union[str, Any]    							=batch
						lowerCAmelCase_   :       Any    							=model(A__  )
						lowerCAmelCase_   :       Any    							=loss(A__  ,					outputs.logits  )

				if SDP_ENABLED:
						lowerCAmelCase_   :       Tuple    							=sdp.DistributedGradientTape(A__  ,					sparse_as_dense=A__  )

				lowerCAmelCase_   :       Optional[Any]    							=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_   :       int    							={'''loss''': loss_value.numpy()}
		return train_results







def 		SCREAMING_SNAKE_CASE__					(							_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ):
		lowerCAmelCase_							,		lowerCAmelCase_   :       Optional[Any]    							=load_dataset('''imdb'''  ,					split=['''train''', '''test''']  )

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

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

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

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

		if SDP_ENABLED:
				lowerCAmelCase_   :       int    							=tf_train_dataset.shard(sdp.size()  ,					sdp.rank()  )
				lowerCAmelCase_   :       List[Any]    							=tf_test_dataset.shard(sdp.size()  ,					sdp.rank()  )
		lowerCAmelCase_   :       List[str]    							=tf_train_dataset.batch(A__  ,					drop_remainder=A__  )
		lowerCAmelCase_   :       str    							=tf_test_dataset.batch(A__  ,					drop_remainder=A__  )

		return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
							__lowercase	=    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'''])

							__lowercase	=    parser.parse_known_args()

							# Set up logging
							__lowercase	=    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()

														__lowercase	=    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
							__lowercase	=    TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
							__lowercase	=    AutoTokenizer.from_pretrained(args.model_name_or_path)

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

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

														__lowercase	=    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)









"""simple docstring"""




# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


SCREAMING_SNAKE_CASE_  :      Tuple							=      {
    '''configuration_convnextv2''': [
        '''CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''ConvNextV2Config''',
    ]
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE_  :      int							=      [
         '''CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''ConvNextV2ForImageClassification''',
         '''ConvNextV2Model''',
         '''ConvNextV2PreTrainedModel''',
         '''ConvNextV2Backbone''',
     ]


if TYPE_CHECKING:
     from .configuration_convnextva import (
         CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
         ConvNextVaConfig,
     )

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_convnextva import (
              CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST,
              ConvNextVaBackbone,
              ConvNextVaForImageClassification,
              ConvNextVaModel,
              ConvNextVaPreTrainedModel,
          )

else:
     import sys

     SCREAMING_SNAKE_CASE_  :      Dict							=      _LazyModule(__name__, globals()['''__file__'''], _import_structure)




