
'''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            = True
						UpperCAmelCase            = "p3dn.24xlarge"
						import smdistributed.dataparallel.tensorflow as sdp
else:
						UpperCAmelCase            = False

def   A			(							A_				:       Optional[int]     ,				A_				:       Optional[int]     ,				A_				:       Union[str, Any]     ,				A_				:       List[Any]     ,				A_				:       Union[str, Any]     ,				A_				:       List[str]     ,				A_				:       Dict=None							):
						snake_case							:  Dict			=     tqdm(_UpperCAmelCase							)
						for i, batch in enumerate(_UpperCAmelCase							):
												with tf.GradientTape() as tape:
																		snake_case							:  Tuple			=     batch
																		snake_case							:  Optional[int]			=     model(_UpperCAmelCase							)
																		snake_case							:  Any			=     loss(_UpperCAmelCase     ,				outputs.logits							)

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

												snake_case							:  int			=     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

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

def   A			(							A_				:       int     ,				A_				:       int     ,				A_				:       str							):
						snake_case							:  Union[str, Any]			=     load_dataset('''imdb'''     ,				split=['''train''', '''test''']							)

						# Preprocess train dataset
						snake_case							:  Any			=     train_dataset.map(
						    lambda A_							: tokenizer(e['''text''']     ,				truncation=_UpperCAmelCase     ,				padding='''max_length'''							)     ,				batched=_UpperCAmelCase							)
						train_dataset.set_format(type='''tensorflow'''     ,				columns=['''input_ids''', '''attention_mask''', '''label''']							)

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

						# Preprocess test dataset
						snake_case							:  List[str]			=     test_dataset.map(
						    lambda A_							: tokenizer(e['''text''']     ,				truncation=_UpperCAmelCase     ,				padding='''max_length'''							)     ,				batched=_UpperCAmelCase							)
						test_dataset.set_format(type='''tensorflow'''     ,				columns=['''input_ids''', '''attention_mask''', '''label''']							)

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

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

						return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
						UpperCAmelCase            = 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            = parser.parse_known_args()

						# Set up logging
						UpperCAmelCase            = 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            = 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            = TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
						UpperCAmelCase            = AutoTokenizer.from_pretrained(args.model_name_or_path)

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

												UpperCAmelCase            = 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            = model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
												logger.info("*** Evaluate ***")

												UpperCAmelCase            = 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'''


import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute




def       A							(      _UpperCAmelCase   :				float = np.pi / 2						,_UpperCAmelCase   :				float = np.pi / 2						,_UpperCAmelCase   :				float = np.pi / 2					)  ->				qiskit.result.counts.Counts:


  '''simple docstring'''




  __lowerCAmelCase		:    Optional[int]			     =  QuantumRegister(3						,'qr'					)  # Define the number of quantum bits
  __lowerCAmelCase		:    Optional[int]			     =  ClassicalRegister(1						,'cr'					)  # Define the number of classical bits

  __lowerCAmelCase		:    List[Any]			     =  QuantumCircuit(_UpperCAmelCase						,_UpperCAmelCase					)  # Define the quantum circuit.

  # Build the circuit
  quantum_circuit.u(_UpperCAmelCase						,_UpperCAmelCase						,_UpperCAmelCase						,0					)  # Quantum State to teleport
  quantum_circuit.h(1					)  # add hadamard gate
  quantum_circuit.cx(
      1						,2					)  # add control gate with qubit 1 as control and 2 as target.
  quantum_circuit.cx(0						,1					)
  quantum_circuit.h(0					)
  quantum_circuit.cz(0						,2					)  # add control z gate.
  quantum_circuit.cx(1						,2					)

  quantum_circuit.measure([2]						,[0]					)  # measure the qubit.

  # Simulate the circuit using qasm simulator
  __lowerCAmelCase		:    List[str]			     =  Aer.get_backend('aer_simulator'					)
  __lowerCAmelCase		:    Optional[Any]			     =  execute(_UpperCAmelCase						,_UpperCAmelCase						,shots=1_0_0_0					)

  return job.result().get_counts(_UpperCAmelCase					)


if __name__ == "__main__":
    print(
        "Total count for teleported state is: "
        F'''{quantum_teleportation(np.pi/2, np.pi/2, np.pi/2)}'''
    )





