





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

def 						_SCREAMING_SNAKE_CASE       (_UpperCAmelCase :  Any   ,			_UpperCAmelCase :  Optional[Any]   ,			_UpperCAmelCase :  int   ,			_UpperCAmelCase :  Union[str, Any]   ,			_UpperCAmelCase :  Optional[int]   ,			_UpperCAmelCase :  Dict   ,			_UpperCAmelCase :  Optional[int]=None       ):
 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 						_SCREAMING_SNAKE_CASE       (_UpperCAmelCase :  List[Any]   ,			_UpperCAmelCase :  Union[str, Any]   ,			_UpperCAmelCase :  List[str]       ):
 # 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			:		List[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'''])

       __UpperCamelCase    ,__UpperCamelCase			:		str		    =   parser.parse_known_args()

       # Set up logging
       __UpperCamelCase			:		Union[str, 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()

              __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			:		List[str]		    =   TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
       __UpperCamelCase			:		Union[str, Any]		    =   AutoTokenizer.from_pretrained(args.model_name_or_path)

       # get datasets
       __UpperCamelCase    ,__UpperCamelCase			:		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
       __UpperCamelCase			:		Tuple		    =   tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
       __UpperCamelCase			:		Union[str, Any]		    =   tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
       __UpperCamelCase			:		Dict		    =   [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			:		Any		    =   time.time()
              __UpperCamelCase			:		List[Any]		    =   fit(
                  model,
                  loss,
                  optimizer,
                  tf_train_dataset,
                  args.epochs,
                  args.per_device_train_batch_size,
                  max_steps=args.max_steps,
              )
              __UpperCamelCase			:		Tuple		    =   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			:		Tuple		    =   model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
              logger.info('''*** Evaluate ***''')

              __UpperCamelCase			:		Union[str, 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)






import random


class   lowercase			:




							def __init__(  self      :    List[Any]		,							_UpperCamelCase      :    list[list[float]]		,							_UpperCamelCase      :    list[int]		,							_UpperCamelCase      :    float = 0.0_1		,							_UpperCamelCase      :    int = 1_000		,							_UpperCamelCase      :    float = -1		,							)    -> None:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				sample
												if len(self.sample					) == 0:
																	raise ValueError("Sample data can not be empty"					)
												SCREAMING_SNAKE_CASE				=				target
												if len(self.target					) == 0:
																	raise ValueError("Target data can not be empty"					)
												if len(self.sample					) != len(self.target					):
																	raise ValueError("Sample data and Target data do not have matching lengths"					)
												SCREAMING_SNAKE_CASE				=				learning_rate
												SCREAMING_SNAKE_CASE				=				epoch_number
												SCREAMING_SNAKE_CASE				=				bias
												SCREAMING_SNAKE_CASE				=				len(_UpperCamelCase					)
												SCREAMING_SNAKE_CASE				=				len(sample[0]					)  # number of columns in dataset
												SCREAMING_SNAKE_CASE				=				[]




							def 		__snake_case(  self      :    Any					)    -> None:



												'''simple docstring'''

												for sample in self.sample:
																	sample.insert(0		,							self.bias					)

												for _ in range(self.col_sample					):
																	self.weight.append(random.random()					)

												self.weight.insert(0		,							self.bias					)

												SCREAMING_SNAKE_CASE				=				0

												while True:
																	SCREAMING_SNAKE_CASE				=				False
																	for i in range(self.number_sample					):
																						SCREAMING_SNAKE_CASE				=				0
																						for j in range(self.col_sample + 1					):
																											SCREAMING_SNAKE_CASE				=				u + self.weight[j] * self.sample[i][j]
																						SCREAMING_SNAKE_CASE				=				self.sign(_UpperCamelCase					)
																						if y != self.target[i]:
																											for j in range(self.col_sample + 1					):
																																SCREAMING_SNAKE_CASE				=				(
																																    self.weight[j]
																																    + self.learning_rate
																																    * (self.target[i] - y)
																																    * self.sample[i][j]
																																)
																											SCREAMING_SNAKE_CASE				=				True
            # print('Epoch: \n',epoch_count)
																	SCREAMING_SNAKE_CASE				=				epoch_count + 1
																	# if you want control the epoch or just by error
																	if not has_misclassified:
																						print(("\nEpoch:\n", epoch_count)					)
																						print("------------------------\n"					)
																						# if epoch_count > self.epoch_number or not error:
																						break




							def 		__snake_case(  self      :    Union[str, Any]		,							_UpperCamelCase      :    list[float]					)    -> None:



												'''simple docstring'''

												if len(self.sample					) == 0:
																	raise ValueError("Sample data can not be empty"					)
												sample.insert(0		,							self.bias					)
												SCREAMING_SNAKE_CASE				=				0
												for i in range(self.col_sample + 1					):
																	SCREAMING_SNAKE_CASE				=				u + self.weight[i] * sample[i]

												SCREAMING_SNAKE_CASE				=				self.sign(_UpperCamelCase					)

												if y == -1:
																	print(("Sample: ", sample)					)
																	print("classification: P1"					)
												else:
																	print(("Sample: ", sample)					)
																	print("classification: P2"					)





							def 		__snake_case(  self      :    Dict		,							_UpperCamelCase      :    float					)    -> int:



												'''simple docstring'''

												return 1 if u >= 0 else -1



_lowerCamelCase    :      Union[str, Any]	 =						[
    [-0.6_508, 0.1_097, 4.0_009],
    [-1.4_492, 0.8_896, 4.4_005],
    [2.0_850, 0.6_876, 12.0_710],
    [0.2_626, 1.1_476, 7.7_985],
    [0.6_418, 1.0_234, 7.0_427],
    [0.2_569, 0.6_730, 8.3_265],
    [1.1_155, 0.6_043, 7.4_446],
    [0.0_914, 0.3_399, 7.0_677],
    [0.0_121, 0.5_256, 4.6_316],
    [-0.0_429, 0.4_660, 5.4_323],
    [0.4_340, 0.6_870, 8.2_287],
    [0.2_735, 1.0_287, 7.1_934],
    [0.4_839, 0.4_851, 7.4_850],
    [0.4_089, -0.1_267, 5.5_019],
    [1.4_391, 0.1_614, 8.5_843],
    [-0.9_115, -0.1_973, 2.1_962],
    [0.3_654, 1.0_475, 7.4_858],
    [0.2_144, 0.7_515, 7.1_699],
    [0.2_013, 1.0_014, 6.5_489],
    [0.6_483, 0.2_183, 5.8_991],
    [-0.1_147, 0.2_242, 7.2_435],
    [-0.7_970, 0.8_795, 3.8_762],
    [-1.0_625, 0.6_366, 2.4_707],
    [0.5_307, 0.1_285, 5.6_883],
    [-1.2_200, 0.7_777, 1.7_252],
    [0.3_957, 0.1_076, 5.6_623],
    [-0.1_013, 0.5_989, 7.1_812],
    [2.4_482, 0.9_455, 11.2_095],
    [2.0_149, 0.6_192, 10.9_263],
    [0.2_012, 0.2_611, 5.4_631],
]

_lowerCamelCase    :      Optional[Any]	 =						[
    -1,
    -1,
    -1,
    1,
    1,
    -1,
    1,
    -1,
    1,
    1,
    -1,
    1,
    -1,
    -1,
    -1,
    -1,
    1,
    1,
    1,
    1,
    -1,
    1,
    1,
    1,
    1,
    -1,
    -1,
    1,
    -1,
    1,
]


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							_lowerCamelCase    :      Tuple	 =						Perceptron(
							    sample=samples, target=target, learning_rate=0.01, epoch_number=10_00, bias=-1
							)
							network.training()
							print('''Finished training perceptron''')
							print('''Enter values to predict or q to exit''')
							while True:
														_lowerCamelCase    :      list	 =						[]
														for i in range(len(samples[0])):
																					_lowerCamelCase    :      Dict	 =						input('''value: ''').strip()
																					if user_input == "q":
																												break
																					_lowerCamelCase    :      Any	 =						float(user_input)
																					sample.insert(i, observation)
														network.sort(sample)




