


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 :					int			=	True
   _SCREAMING_SNAKE_CASE :					Optional[Any]			=	"""p3dn.24xlarge"""
   import smdistributed.dataparallel.tensorflow as sdp
else:
   _SCREAMING_SNAKE_CASE :					int			=	False







def 						_lowercase       (     __lowerCamelCase			:					str		,__lowerCamelCase			:					Optional[Any]		,__lowerCamelCase			:					str		,__lowerCamelCase			:					str		,__lowerCamelCase			:					Optional[Any]		,__lowerCamelCase			:					int		,__lowerCamelCase			:					Dict=None			)  ->   Any:





     '''simple docstring'''





     UpperCamelCase__							:  List[Any]								=     tqdm(__lowerCamelCase			)
     for i, batch in enumerate(__lowerCamelCase			):
          with tf.GradientTape() as tape:
               UpperCamelCase__	,UpperCamelCase__							:  Tuple								=     batch
               UpperCamelCase__							:  Optional[Any]								=     model(__lowerCamelCase			)
               UpperCamelCase__							:  Dict								=     loss(__lowerCamelCase		,outputs.logits			)

          if SDP_ENABLED:
               UpperCamelCase__							:  int								=     sdp.DistributedGradientTape(__lowerCamelCase		,sparse_as_dense=__lowerCamelCase			)

          UpperCamelCase__							:  Optional[int]								=     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

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







def 						_lowercase       (     __lowerCamelCase			:					List[str]		,__lowerCamelCase			:					Tuple		,__lowerCamelCase			:					Dict			)  ->   Tuple:





     '''simple docstring'''





     UpperCamelCase__	,UpperCamelCase__							:  Tuple								=     load_dataset('''imdb'''		,split=['''train''', '''test''']			)

     # Preprocess train dataset
     UpperCamelCase__							:  Tuple								=     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''']			)

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

     # Preprocess test dataset
     UpperCamelCase__							:  Any								=     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''']			)

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

     if SDP_ENABLED:
          UpperCamelCase__							:  Optional[Any]								=     tf_train_dataset.shard(sdp.size()		,sdp.rank()			)
          UpperCamelCase__							:  Dict								=     tf_test_dataset.shard(sdp.size()		,sdp.rank()			)
     UpperCamelCase__							:  Optional[int]								=     tf_train_dataset.batch(__lowerCamelCase		,drop_remainder=__lowerCamelCase			)
     UpperCamelCase__							:  Tuple								=     tf_test_dataset.batch(__lowerCamelCase		,drop_remainder=__lowerCamelCase			)

     return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
   _SCREAMING_SNAKE_CASE :					Optional[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 :					Dict			=	parser.parse_known_args()

   # Set up logging
   _SCREAMING_SNAKE_CASE :					Optional[int]			=	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 :					Optional[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
   _SCREAMING_SNAKE_CASE :					Any			=	TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
   _SCREAMING_SNAKE_CASE :					Tuple			=	AutoTokenizer.from_pretrained(args.model_name_or_path)

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

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

      _SCREAMING_SNAKE_CASE :					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 __future__ import annotations

from math import pi, pow







def 						_lowercase       (     __lowerCamelCase			:					int | float			)  ->   float:





     '''simple docstring'''





     if side_length < 0:
          raise ValueError('''vol_cube() only accepts non-negative values'''			)
     return pow(__lowerCamelCase		,3			)







def 						_lowercase       (     __lowerCamelCase			:					float		,__lowerCamelCase			:					float			)  ->   float:





     '''simple docstring'''





     if height < 0 or radius < 0:
          raise ValueError('''vol_spherical_cap() only accepts non-negative values'''			)
     # Volume is 1/3 pi * height squared * (3 * radius - height)
     return 1 / 3 * pi * pow(__lowerCamelCase		,2			) * (3 * radius - height)







def 						_lowercase       (     __lowerCamelCase			:					float		,__lowerCamelCase			:					float		,__lowerCamelCase			:					float			)  ->   float:





     '''simple docstring'''





     if radius_a < 0 or radius_a < 0 or centers_distance < 0:
          raise ValueError('''vol_spheres_intersect() only accepts non-negative values'''			)
     if centers_distance == 0:
          return vol_sphere(min(__lowerCamelCase		,__lowerCamelCase			)			)

     UpperCamelCase__							:  Dict								=     (
         (radius_a - radius_a + centers_distance)
         * (radius_a + radius_a - centers_distance)
         / (2 * centers_distance)
     )
     UpperCamelCase__							:  Tuple								=     (
         (radius_a - radius_a + centers_distance)
         * (radius_a + radius_a - centers_distance)
         / (2 * centers_distance)
     )

     return vol_spherical_cap(__lowerCamelCase		,__lowerCamelCase			) + vol_spherical_cap(__lowerCamelCase		,__lowerCamelCase			)







def 						_lowercase       (     __lowerCamelCase			:					float		,__lowerCamelCase			:					float		,__lowerCamelCase			:					float			)  ->   float:





     '''simple docstring'''





     if radius_a <= 0 or radius_a <= 0 or centers_distance < 0:
          raise ValueError(
              '''vol_spheres_union() only accepts non-negative values, non-zero radius'''			)

     if centers_distance == 0:
          return vol_sphere(max(__lowerCamelCase		,__lowerCamelCase			)			)

     return (
         vol_sphere(__lowerCamelCase			)
         + vol_sphere(__lowerCamelCase			)
         - vol_spheres_intersect(__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase			)
     )







def 						_lowercase       (     __lowerCamelCase			:					float		,__lowerCamelCase			:					float		,__lowerCamelCase			:					float			)  ->   float:





     '''simple docstring'''





     if width < 0 or height < 0 or length < 0:
          raise ValueError('''vol_cuboid() only accepts non-negative values'''			)
     return float(width * height * length			)







def 						_lowercase       (     __lowerCamelCase			:					float		,__lowerCamelCase			:					float			)  ->   float:





     '''simple docstring'''





     if height < 0 or area_of_base < 0:
          raise ValueError('''vol_cone() only accepts non-negative values'''			)
     return area_of_base * height / 3.0







def 						_lowercase       (     __lowerCamelCase			:					float		,__lowerCamelCase			:					float			)  ->   float:





     '''simple docstring'''





     if height < 0 or radius < 0:
          raise ValueError('''vol_right_circ_cone() only accepts non-negative values'''			)
     return pi * pow(__lowerCamelCase		,2			) * height / 3.0







def 						_lowercase       (     __lowerCamelCase			:					float		,__lowerCamelCase			:					float			)  ->   float:





     '''simple docstring'''





     if height < 0 or area_of_base < 0:
          raise ValueError('''vol_prism() only accepts non-negative values'''			)
     return float(area_of_base * height			)







def 						_lowercase       (     __lowerCamelCase			:					float		,__lowerCamelCase			:					float			)  ->   float:





     '''simple docstring'''





     if height < 0 or area_of_base < 0:
          raise ValueError('''vol_pyramid() only accepts non-negative values'''			)
     return area_of_base * height / 3.0







def 						_lowercase       (     __lowerCamelCase			:					float			)  ->   float:





     '''simple docstring'''





     if radius < 0:
          raise ValueError('''vol_sphere() only accepts non-negative values'''			)
     # Volume is 4/3 * pi * radius cubed
     return 4 / 3 * pi * pow(__lowerCamelCase		,3			)







def 						_lowercase       (     __lowerCamelCase			:					float			)  ->   float:





     '''simple docstring'''





     if radius < 0:
          raise ValueError('''vol_hemisphere() only accepts non-negative values'''			)
     # Volume is radius cubed * pi * 2/3
     return pow(__lowerCamelCase		,3			) * pi * 2 / 3







def 						_lowercase       (     __lowerCamelCase			:					float		,__lowerCamelCase			:					float			)  ->   float:





     '''simple docstring'''





     if height < 0 or radius < 0:
          raise ValueError('''vol_circular_cylinder() only accepts non-negative values'''			)
     # Volume is radius squared * height * pi
     return pow(__lowerCamelCase		,2			) * height * pi







def 						_lowercase       (     __lowerCamelCase			:					float		,__lowerCamelCase			:					float		,__lowerCamelCase			:					float			)  ->   float:





     '''simple docstring'''





     if inner_radius < 0 or outer_radius < 0 or height < 0:
          raise ValueError(
              '''vol_hollow_circular_cylinder() only accepts non-negative values'''			)
     if outer_radius <= inner_radius:
          raise ValueError('''outer_radius must be greater than inner_radius'''			)
     return pi * (pow(__lowerCamelCase		,2			) - pow(__lowerCamelCase		,2			)) * height







def 						_lowercase       (     __lowerCamelCase			:					float		,__lowerCamelCase			:					float		,__lowerCamelCase			:					float			)  ->   float:





     '''simple docstring'''





     if radius_a < 0 or radius_a < 0 or height < 0:
          raise ValueError('''vol_conical_frustum() only accepts non-negative values'''			)
     return (
         1
         / 3
         * pi
         * height
         * (pow(__lowerCamelCase		,2			) + pow(__lowerCamelCase		,2			) + radius_a * radius_a)
     )







def 						_lowercase       (     __lowerCamelCase			:					float		,__lowerCamelCase			:					float			)  ->   float:





     '''simple docstring'''





     if torus_radius < 0 or tube_radius < 0:
          raise ValueError('''vol_torus() only accepts non-negative values'''			)
     return 2 * pow(__lowerCamelCase		,2			) * torus_radius * pow(__lowerCamelCase		,2			)







def 						_lowercase       (     )  ->   Tuple:





     '''simple docstring'''





     print('''Volumes:'''			)
     print(F'Cube: {vol_cube(2			) = }'			)  # = 8
     print(F'Cuboid: {vol_cuboid(2		,2		,2			) = }'			)  # = 8
     print(F'Cone: {vol_cone(2		,2			) = }'			)  # ~= 1.33
     print(F'Right Circular Cone: {vol_right_circ_cone(2		,2			) = }'			)  # ~= 8.38
     print(F'Prism: {vol_prism(2		,2			) = }'			)  # = 4
     print(F'Pyramid: {vol_pyramid(2		,2			) = }'			)  # ~= 1.33
     print(F'Sphere: {vol_sphere(2			) = }'			)  # ~= 33.5
     print(F'Hemisphere: {vol_hemisphere(2			) = }'			)  # ~= 16.75
     print(F'Circular Cylinder: {vol_circular_cylinder(2		,2			) = }'			)  # ~= 25.1
     print(F'Torus: {vol_torus(2		,2			) = }'			)  # ~= 157.9
     print(F'Conical Frustum: {vol_conical_frustum(2		,2		,4			) = }'			)  # ~= 58.6
     print(F'Spherical cap: {vol_spherical_cap(1		,2			) = }'			)  # ~= 5.24
     print(F'Spheres intersetion: {vol_spheres_intersect(2		,2		,1			) = }'			)  # ~= 21.21
     print(F'Spheres union: {vol_spheres_union(2		,2		,1			) = }'			)  # ~= 45.81
     print(
         F'Hollow Circular Cylinder: {vol_hollow_circular_cylinder(1		,2		,3			) = }'			)  # ~= 28.3


if __name__ == "__main__":
   main()




