
import logging
import os
import time
from argparse import Namespace
from pathlib import Path

import datasets
import torch
from accelerate import Accelerator, DistributedType
from accelerate.utils import ProjectConfiguration
from arguments import TrainingArguments
from datasets import load_dataset
from huggingface_hub import Repository
from torch.optim import AdamW
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from torch.utils.data.datapipes.iter.combinatorics import ShufflerIterDataPipe

import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, get_scheduler, set_seed


class 							lowerCamelCase       (		__snake_case					):



  """simple docstring"""





  def __init__(					self						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase=False						,    __UpperCamelCase=1024						,    __UpperCamelCase=1024						,    __UpperCamelCase=3.6						,    __UpperCamelCase=False						,    ):
      A_								=					tokenizer
      A_								=					tokenizer.bos_token_id
      A_								=					dataset
      A_								=					seq_length
      A_								=					0
      A_								=					infinite
      A_								=					0
      A_								=					tokenized

      if self.tokenized:
          A_								=					seq_length * num_of_sequences
          A_								=					"input_ids"
      else:
          A_								=					seq_length * chars_per_token * num_of_sequences
          A_								=					"content"

  def __iter__(					self					):
      A_								=					iter(self.dataset					)
      A_								=					True
      while more_examples:
          A_	,	A_								=					[], 0
          while True:
              if buffer_len >= self.max_buffer_size:
                  break
              try:
                  buffer.append(next(__UpperCamelCase					)[self.content_field]					)
                  buffer_len += len(buffer[-1]					)
              except StopIteration:
                  if self.infinite:
                      A_								=					iter(self.dataset					)
                      self.epoch += 1
                      logger.info(f'Dataset epoch: {self.epoch}'					)
                  else:
                      A_								=					False
                      break
          if self.tokenized:
              A_								=					buffer
          else:
              A_								=					self.tokenizer(__UpperCamelCase						,    truncation=__UpperCamelCase					)["input_ids"]
          A_								=					[]
          for tokenized_input in tokenized_inputs:
              all_token_ids.extend(tokenized_input + [self.concat_token_id]					)
          for i in range(0						,    len(__UpperCamelCase					)						,    self.seq_length					):
              A_								=					all_token_ids[i : i + self.seq_length]
              if len(__UpperCamelCase					) == self.seq_length:
                  self.current_size += 1
                  yield torch.tensor(__UpperCamelCase					)

  def 					lowercase_						(					self						,    __UpperCamelCase=1000					):
      return ShufflerIterDataPipe(self						,    buffer_size=__UpperCamelCase					)





def        lowerCAmelCase       (				snake_case__   :      Optional[int] )->			Dict:
    A_								=					args.model_ckpt.split("/" )[-1]
    A_								=					logging.getLogger(__name__ )
    A_								=					Path(args.save_dir ) / "log/"
    log_dir.mkdir(exist_ok=snake_case__ )
    A_								=					f'debug_{accelerator.process_index}.log'
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"				,					datefmt="%m/%d/%Y %H:%M:%S"				,					level=logging.INFO				,					handlers=[logging.FileHandler(log_dir / filename ), logging.StreamHandler()]				,					)
    if accelerator.is_main_process:  # we only want to setup logging once
        accelerator.init_trackers(snake_case__				,					vars(snake_case__ ) )
        A_								=					accelerator.trackers[0].run.name
        logger.setLevel(logging.INFO )
        datasets.utils.logging.set_verbosity_info()
        transformers.utils.logging.set_verbosity_info()
    else:
        A_								=					""
        logger.setLevel(logging.ERROR )
        datasets.utils.logging.set_verbosity_error()
        transformers.utils.logging.set_verbosity_error()
    return logger, run_name





def        lowerCAmelCase       (				snake_case__   :      Union[str, Any] )->			Tuple:
    A_								=					{"streaming": True}
    A_								=					load_dataset(args.dataset_name_train				,					split="train"				,					**snake_case__ )
    A_								=					train_data.shuffle(buffer_size=args.shuffle_buffer				,					seed=args.seed )
    A_								=					load_dataset(args.dataset_name_valid				,					split="train"				,					**snake_case__ )
    A_								=					ConstantLengthDataset(
        snake_case__				,					snake_case__				,					infinite=snake_case__				,					seq_length=args.seq_length				,					tokenized=args.tokenized )
    A_								=					ConstantLengthDataset(
        snake_case__				,					snake_case__				,					infinite=snake_case__				,					seq_length=args.seq_length				,					tokenized=args.tokenized )
    A_								=					train_dataset.shuffle(buffer_size=args.shuffle_buffer )
    A_								=					DataLoader(snake_case__				,					batch_size=args.train_batch_size				,					shuffle=snake_case__ )
    A_								=					DataLoader(snake_case__				,					batch_size=args.valid_batch_size )
    return train_dataloader, eval_dataloader





def        lowerCAmelCase       (				snake_case__   :      int				,					snake_case__   :      List[Any]				,					snake_case__   :      Optional[int]=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"] )->			int:
    A_	,	A_								=					[], []
    for n, p in model.named_parameters():
        if any(nd in n for nd in no_decay ):
            params_without_wd.append(snake_case__ )
        else:
            params_with_wd.append(snake_case__ )
    return [
        {"params": params_with_wd, "weight_decay": args.weight_decay},
        {"params": params_without_wd, "weight_decay": 0.0},
    ]





def        lowerCAmelCase       (				snake_case__   :      int				,					snake_case__   :      str )->			Any:
    logger.info(f'Step {step}: {metrics}' )
    if accelerator.is_main_process:
        accelerator.log(snake_case__				,					snake_case__ )





def        lowerCAmelCase       (				snake_case__   :      List[str]				,					snake_case__   :      List[str]				,					snake_case__   :      Any )->			Tuple:
    # TFLOPs formula (from Equation 3 in Section 5.1 of https://arxiv.org/pdf/2104.04473.pdf).
    A_								=					accelerator.unwrap_model(snake_case__ ).config
    A_								=					4 if args.gradient_checkpointing else 3
    A_								=					args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
    A_								=					24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
    A_								=					factor * (
        1.0
        + (args.seq_length / (6.0 * config_model.n_embd))
        + (tokenizer.vocab_size / (1_6.0 * config_model.n_layer * config_model.n_embd))
    )
    A_								=					flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
    return tflops





def        lowerCAmelCase       (				snake_case__   :      str )->			int:
    model.eval()
    A_								=					[]
    for step, batch in enumerate(snake_case__ ):
        with torch.no_grad():
            A_								=					model(snake_case__				,					labels=snake_case__ )
        A_								=					outputs.loss.repeat(args.valid_batch_size )
        losses.append(accelerator.gather(snake_case__ ) )
        if args.max_eval_steps > 0 and step >= args.max_eval_steps:
            break
    A_								=					torch.cat(snake_case__ )
    A_								=					losses[: eval_dataloader.dataset.current_size].mean()
    try:
        A_								=					torch.exp(snake_case__ )
    except OverflowError:
        A_								=					float("inf" )
    return loss.item(), perplexity.item()


# Settings
__magic_name__  :  Optional[int]     			=   HfArgumentParser(TrainingArguments)
__magic_name__  :  Union[str, Any]     			=   parser.parse_args()

# Accelerator
__magic_name__  :  Optional[Any]     			=   ProjectConfiguration(project_dir=args.save_dir, logging_dir='log')
__magic_name__  :  str     			=   Accelerator(log_with=['wandb', 'tensorboard'], project_config=config)
__magic_name__  :  List[Any]     			=   {str(k): str(v) for k, v in accelerator.state.__dict__.items()}

__magic_name__  :  int     			=   Namespace(**vars(args), **acc_state)
__magic_name__  :  Optional[int]     			=   accelerator.state.num_processes * args.train_batch_size
set_seed(args.seed)

# Clone model repository
if accelerator.is_main_process:
     __magic_name__  :  str     			=   Repository(args.save_dir, clone_from=args.model_ckpt)

# Logging
__magic_name__			,	__magic_name__  :  Dict     			=   setup_logging(args)
logger.info(accelerator.state)

# Checkout new branch on repo
if accelerator.is_main_process:
     hf_repo.git_checkout(run_name, create_branch_ok=True)

# Load model and tokenizer
__magic_name__  :  str     			=   AutoModelForCausalLM.from_pretrained(args.save_dir)
if args.gradient_checkpointing:
     model.gradient_checkpointing_enable()
__magic_name__  :  Any     			=   AutoTokenizer.from_pretrained(args.save_dir)

# Load dataset and dataloader
__magic_name__			,	__magic_name__  :  Optional[int]     			=   create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
__magic_name__  :  Tuple     			=   AdamW(get_grouped_params(model, args), lr=args.learning_rate)
__magic_name__  :  str     			=   get_scheduler(
    name=args.lr_scheduler_type,
    optimizer=optimizer,
    num_warmup_steps=args.num_warmup_steps,
    num_training_steps=args.max_train_steps,
)
accelerator.register_for_checkpointing(lr_scheduler)





def        lowerCAmelCase       (				)->			Optional[Any]:
    return optimizer.param_groups[0]["lr"]


# Prepare everything with our `accelerator`.
__magic_name__			,	__magic_name__			,	__magic_name__			,	__magic_name__  :  Optional[int]     			=   accelerator.prepare(
    model, optimizer, train_dataloader, eval_dataloader
)

# load in the weights and states from a previous save
if args.resume_from_checkpoint:
     if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
          accelerator.print(f"""Resumed from checkpoint: {args.resume_from_checkpoint}""")
          accelerator.load_state(args.resume_from_checkpoint)
          __magic_name__  :  str     			=   os.path.basename(args.resume_from_checkpoint)
     else:
          # Get the most recent checkpoint
          __magic_name__  :  List[str]     			=   [f.name for f in os.scandir(args.save_dir) if f.is_dir() and 'step' in str(f)]
          dirs.sort(key=os.path.getctime)
          __magic_name__  :  str     			=   dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
     # Extract the step of the checkpoint to continue from there
     __magic_name__  :  List[Any]     			=   os.path.splitext(path)[0]
     __magic_name__  :  Optional[Any]     			=   int(training_difference.replace('step_', ''))

# Train model
model.train()
__magic_name__  :  Any     			=   0
__magic_name__  :  Union[str, Any]     			=   time.time()
__magic_name__  :  Any     			=   0
for step, batch in enumerate(train_dataloader, start=1):
     if args.resume_from_checkpoint and step < resume_step:
          continue  # we need to skip steps until we reach the resumed step
     __magic_name__  :  Optional[int]     			=   model(batch, labels=batch, use_cache=False).loss
     __magic_name__  :  Any     			=   accelerator.gather(loss.repeat(args.train_batch_size)).mean()
     loss_tracking += avg_loss.item() / args.gradient_accumulation_steps
     log_metrics(step, {'samples': step * samples_per_step, 'loss_per_step/train': loss.item()})
     __magic_name__  :  Dict     			=   loss / args.gradient_accumulation_steps
     if step % args.gradient_accumulation_steps != 0:
          # Prevent backward from doing gradient all_reduce in every step
          if accelerator.distributed_type == DistributedType.MULTI_GPU:
               with model.no_sync():
                    accelerator.backward(loss)
          else:
               accelerator.backward(loss)
     else:
          __magic_name__  :  List[str]     			=   get_lr()
          accelerator.backward(loss)
          accelerator.clip_grad_norm_(model.parameters(), 1.0)
          optimizer.step()
          lr_scheduler.step()
          optimizer.zero_grad()
          __magic_name__  :  Optional[Any]     			=   time.time() - t_start
          __magic_name__  :  Dict     			=   compute_tflops(elapsed_time, accelerator, args)
          log_metrics(
              step,
              {
                  'steps': completed_steps,
                  'loss/train': loss_tracking,
                  'lr': lr,
                  'tflops': tflops,
                  'time_per_iteration': elapsed_time,
              },
          )
          __magic_name__  :  Optional[int]     			=   time.time()
          __magic_name__  :  int     			=   0
          completed_steps += 1
     if step % args.save_checkpoint_steps == 0:
          logger.info('Evaluating and saving model checkpoint')
          __magic_name__			,	__magic_name__  :  Any     			=   evaluate(args)
          log_metrics(step, {'loss/eval': eval_loss, 'perplexity': perplexity})
          accelerator.wait_for_everyone()
          __magic_name__  :  Union[str, Any]     			=   os.path.join(args.save_dir, f"""step_{step}""")
          accelerator.save_state(save_dir)
          if accelerator.is_main_process:
               hf_repo.push_to_hub(commit_message=f"""step {step}""")
          model.train()
     if completed_steps >= args.max_train_steps:
          break

# Evaluate and save the last checkpoint
logger.info('Evaluating and saving model after training')
__magic_name__			,	__magic_name__  :  Dict     			=   evaluate(args)
log_metrics(step, {'loss/eval': eval_loss, 'perplexity': perplexity})
accelerator.wait_for_everyone()
__magic_name__  :  Optional[int]     			=   accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(args.save_dir, save_function=accelerator.save)
__magic_name__  :  Optional[Any]     			=   os.path.join(args.save_dir, f"""step_{step}""")
accelerator.save_state(save_dir)
if accelerator.is_main_process:
     hf_repo.push_to_hub(commit_message='final model')








import math

import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute





def        lowerCAmelCase       (				snake_case__   :      int = 1				,					snake_case__   :      int = 1 )->			qiskit.result.counts.Counts:
    if isinstance(snake_case__				,					snake_case__ ) or isinstance(snake_case__				,					snake_case__ ):
        raise TypeError("inputs must be integers." )
    if (bit_a < 0) or (bit_a < 0):
        raise ValueError("inputs must be positive." )
    if (math.floor(snake_case__ ) != bit_a) or (math.floor(snake_case__ ) != bit_a):
        raise ValueError("inputs must be exact integers." )
    if (bit_a > 1) or (bit_a > 1):
        raise ValueError("inputs must be less or equal to 1." )

    # build registers
    A_								=					QuantumRegister(2				,					"qr" )
    A_								=					ClassicalRegister(2				,					"cr" )

    A_								=					QuantumCircuit(snake_case__				,					snake_case__ )

    # entanglement the qubits
    quantum_circuit.h(1 )
    quantum_circuit.cx(1				,					0 )

    # send the information
    A_								=					str(snake_case__ ) + str(snake_case__ )

    if c_information == "11":
        quantum_circuit.x(1 )
        quantum_circuit.z(1 )
    elif c_information == "10":
        quantum_circuit.z(1 )
    elif c_information == "01":
        quantum_circuit.x(1 )
    else:
        quantum_circuit.i(1 )

    # unentangled the circuit
    quantum_circuit.cx(1				,					0 )
    quantum_circuit.h(1 )

    # measure the circuit
    quantum_circuit.measure(snake_case__				,					snake_case__ )

    A_								=					Aer.get_backend("aer_simulator" )
    A_								=					execute(snake_case__				,					snake_case__				,					shots=1000 )

    return job.result().get_counts(snake_case__ )


if __name__ == "__main__":
     print(f"""Counts for classical state send: {superdense_coding(1,1)}""")






