


import logging

import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader

from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set_seed






class   __lowercase				(  __snake_case  ):





      def __init__(		self						:					Optional[int]					,       __lowerCamelCase						:					int					,       __lowerCamelCase						:					List[str]					,       __lowerCamelCase						:					Tuple=1_0_2_4					,       __lowerCamelCase						:					Optional[int]=1_0_2_4					,       __lowerCamelCase						:					Optional[Any]=3.6       )    ->   Optional[Any]:






         """simple docstring"""







         UpperCAmelCase							=     tokenizer
         UpperCAmelCase							=     tokenizer.bos_token_id
         UpperCAmelCase							=     dataset
         UpperCAmelCase							=     seq_length
         UpperCAmelCase							=     seq_length * chars_per_token * num_of_sequences





      def __iter__(		self						:					Union[str, Any]       )    ->   Any:






         """simple docstring"""







         UpperCAmelCase							=     iter(self.dataset       )
         UpperCAmelCase							=     True
         while more_examples:
            UpperCAmelCase				,  UpperCAmelCase							=     [], 0
            while True:
               if buffer_len >= self.input_characters:
                  break
               try:
                  buffer.append(next(__lowerCamelCase       )["""content"""]       )
                  buffer_len += len(buffer[-1]       )
               except StopIteration:
                  UpperCAmelCase							=     False
                  break
            UpperCAmelCase							=     tokenizer(__lowerCamelCase					,       truncation=__lowerCamelCase       )["""input_ids"""]
            UpperCAmelCase							=     []
            for tokenized_input in tokenized_inputs:
               all_token_ids.extend(tokenized_input + [self.concat_token_id]       )
            for i in range(0					,       len(__lowerCamelCase       )					,       self.seq_length       ):
               UpperCAmelCase							=     all_token_ids[i : i + self.seq_length]
               if len(__lowerCamelCase       ) == self.seq_length:
                  yield torch.tensor(__lowerCamelCase       )

def   _UpperCamelCase				(						lowerCAmelCase_   )						->List[Any]:
   UpperCAmelCase							=     {"""streaming""": True}
   UpperCAmelCase							=     load_dataset(args.dataset_name  ,						split="""train"""  ,						**lowerCAmelCase_   )
   UpperCAmelCase							=     ConstantLengthDataset(lowerCAmelCase_  ,						lowerCAmelCase_  ,						seq_length=args.seq_length   )
   UpperCAmelCase							=     DataLoader(lowerCAmelCase_  ,						batch_size=args.batch_size   )
   return eval_dataloader
def   _UpperCamelCase				(						lowerCAmelCase_   )						->Optional[int]:
   model.eval()
   UpperCAmelCase							=     []
   for step, batch in enumerate(lowerCAmelCase_   ):
      with torch.no_grad():
         UpperCAmelCase							=     model(lowerCAmelCase_  ,						labels=lowerCAmelCase_   )
      UpperCAmelCase							=     outputs.loss.repeat(args.batch_size   )
      losses.append(accelerator.gather(lowerCAmelCase_   )   )

      if args.max_eval_steps > 0 and step >= args.max_eval_steps:
         break
   UpperCAmelCase							=     torch.mean(torch.cat(lowerCAmelCase_   )   )
   try:
      UpperCAmelCase							=     torch.exp(lowerCAmelCase_   )
   except OverflowError:
      UpperCAmelCase							=     float("""inf"""   )
   return loss.item(), perplexity.item()


# Setup Accelerator
__a     =			Accelerator()

# Parse configuration
__a     =			HfArgumentParser(EvaluationArguments)
__a     =			parser.parse_args()
set_seed(args.seed)

# Logging
__a     =			logging.getLogger(__name__)
logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)

# Load model and tokenizer
__a     =			AutoModelForCausalLM.from_pretrained(args.model_ckpt)
__a     =			AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
__a     =			create_dataloader(args)

# Prepare everything with our `accelerator`.
__a   ,					__a     =			accelerator.prepare(model, eval_dataloader)

# Evaluate and save the last checkpoint
logger.info("""Evaluating and saving model after training""")
__a   ,					__a     =			evaluate(args)







logger.info(F"""loss/eval: {eval_loss}, perplexity: {perplexity}""")







import math

import qiskit
def   _UpperCamelCase				(						lowerCAmelCase_ = 1  ,						lowerCAmelCase_ = 1  ,						lowerCAmelCase_ = 1   )						->qiskit.result.counts.Counts:
   if (
       isinstance(lowerCAmelCase_  ,						lowerCAmelCase_   )
       or isinstance(lowerCAmelCase_  ,						lowerCAmelCase_   )
       or isinstance(lowerCAmelCase_  ,						lowerCAmelCase_   )
   ):
      raise TypeError("""inputs must be integers."""   )

   if (input_a < 0) or (input_a < 0) or (carry_in < 0):
      raise ValueError("""inputs must be positive."""   )

   if (
       (math.floor(lowerCAmelCase_   ) != input_a)
       or (math.floor(lowerCAmelCase_   ) != input_a)
       or (math.floor(lowerCAmelCase_   ) != carry_in)
   ):
      raise ValueError("""inputs must be exact integers."""   )

   if (input_a > 2) or (input_a > 2) or (carry_in > 2):
      raise ValueError("""inputs must be less or equal to 2."""   )

   # build registers
   UpperCAmelCase							=     qiskit.QuantumRegister(4  ,						"""qr"""   )
   UpperCAmelCase							=     qiskit.ClassicalRegister(2  ,						"""cr"""   )
   # list the entries
   UpperCAmelCase							=     [input_a, input_a, carry_in]

   UpperCAmelCase							=     qiskit.QuantumCircuit(lowerCAmelCase_  ,						lowerCAmelCase_   )

   for i in range(0  ,						3   ):
      if entry[i] == 2:
         quantum_circuit.h(lowerCAmelCase_   )  # for hadamard entries
      elif entry[i] == 1:
         quantum_circuit.x(lowerCAmelCase_   )  # for 1 entries
      elif entry[i] == 0:
         quantum_circuit.i(lowerCAmelCase_   )  # for 0 entries

    # build the circuit
   quantum_circuit.ccx(0  ,						1  ,						3   )  # ccx = toffoli gate
   quantum_circuit.cx(0  ,						1   )
   quantum_circuit.ccx(1  ,						2  ,						3   )
   quantum_circuit.cx(1  ,						2   )
   quantum_circuit.cx(0  ,						1   )

   quantum_circuit.measure([2, 3]  ,						lowerCAmelCase_   )  # measure the last two qbits

   UpperCAmelCase							=     qiskit.Aer.get_backend("""aer_simulator"""   )
   UpperCAmelCase							=     qiskit.execute(lowerCAmelCase_  ,						lowerCAmelCase_  ,						shots=1_0_0_0   )

   return job.result().get_counts(lowerCAmelCase_   )


if __name__ == "__main__":
 print(F"""Total sum count for state is: {quantum_full_adder(1, 1, 1)}""")



