


'''simple docstring'''

import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from datasets import Dataset, load_dataset

import transformers
from transformers import (
    CONFIG_MAPPING,
    MODEL_FOR_MASKED_LM_MAPPING,
    AutoConfig,
    AutoModelForMaskedLM,
    AutoTokenizer,
    DataCollatorForWholeWordMask,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


a_  :				str						= logging.getLogger(__name__)
a_  :				List[str]						= list(MODEL_FOR_MASKED_LM_MAPPING.keys())
a_  :				Any						= tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)





@dataclass
class      snake_case    :





  """simple docstring"""




  _lowerCamelCase		    =  field(
      default=lowercase			,						metadata={
          "help": (
              "The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."
          )
      }			,						)
  _lowerCamelCase		    =  field(
      default=lowercase			,						metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(lowercase			)}			,						)
  _lowerCamelCase		    =  field(
      default=lowercase			,						metadata={
          "help": (
              "Override some existing default config settings when a model is trained from scratch. Example: "
              "n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"
          )
      }			,						)
  _lowerCamelCase		    =  field(
      default=lowercase			,						metadata={"help": "Pretrained config name or path if not the same as model_name"}			)
  _lowerCamelCase		    =  field(
      default=lowercase			,						metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}			)
  _lowerCamelCase		    =  field(
      default=lowercase			,						metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}			,						)
  _lowerCamelCase		    =  field(
      default=lowercase			,						metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."}			,						)
  _lowerCamelCase		    =  field(
      default="main"			,						metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}			,						)
  _lowerCamelCase		    =  field(
      default=lowercase			,						metadata={
          "help": (
              "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
              "with private models)."
          )
      }			,						)






  def   snake_case		(    self     ):







   """simple docstring"""
   if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
    raise ValueError(
        "--config_overrides can't be used in combination with --config_name or --model_name_or_path"     )





@dataclass
class      snake_case    :





  """simple docstring"""




  _lowerCamelCase		    =  field(
      default=lowercase			,						metadata={"help": "The name of the dataset to use (via the datasets library)."}			)
  _lowerCamelCase		    =  field(
      default=lowercase			,						metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}			)
  _lowerCamelCase		    =  field(default=lowercase			,						metadata={"help": "The input training data file (a text file)."}			)
  _lowerCamelCase		    =  field(
      default=lowercase			,						metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."}			,						)
  _lowerCamelCase		    =  field(
      default=lowercase			,						metadata={"help": "An optional input train ref data file for whole word masking in Chinese."}			,						)
  _lowerCamelCase		    =  field(
      default=lowercase			,						metadata={"help": "An optional input validation ref data file for whole word masking in Chinese."}			,						)
  _lowerCamelCase		    =  field(
      default=lowercase			,						metadata={"help": "Overwrite the cached training and evaluation sets"}			)
  _lowerCamelCase		    =  field(
      default=5			,						metadata={
          "help": "The percentage of the train set used as validation set in case there's no validation split"
      }			,						)
  _lowerCamelCase		    =  field(
      default=lowercase			,						metadata={
          "help": (
              "The maximum total input sequence length after tokenization. Sequences longer "
              "than this will be truncated. Default to the max input length of the model."
          )
      }			,						)
  _lowerCamelCase		    =  field(
      default=lowercase			,						metadata={"help": "The number of processes to use for the preprocessing."}			,						)
  _lowerCamelCase		    =  field(
      default=0.15			,						metadata={"help": "Ratio of tokens to mask for masked language modeling loss"}			)
  _lowerCamelCase		    =  field(
      default=lowercase			,						metadata={
          "help": (
              "Whether to pad all samples to `max_seq_length`. "
              "If False, will pad the samples dynamically when batching to the maximum length in the batch."
          )
      }			,						)






  def   snake_case		(    self     ):







   """simple docstring"""
   if self.train_file is not None:
    lowerCamelCase_      =  self.train_file.split("."     )[-1]
    assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
   if self.validation_file is not None:
    lowerCamelCase_      =  self.validation_file.split("."     )[-1]
    assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."





def __snake_case					(      UpperCAmelCase_						:						str					,				UpperCAmelCase_						:						Optional[int]		):
 with open(UpperCAmelCase_					,				"r"					,				encoding="utf-8"		) as f:
  lowerCamelCase_      =  [json.loads(UpperCAmelCase_		) for line in f.read().splitlines() if (len(UpperCAmelCase_		) > 0 and not line.isspace())]
 assert len(UpperCAmelCase_		) == len(UpperCAmelCase_		)

 lowerCamelCase_      =  {c: dataset[c] for c in dataset.column_names}
 lowerCamelCase_      =  refs
 return Dataset.from_dict(UpperCAmelCase_		)





def __snake_case					(      ):
 # See all possible arguments in src/transformers/training_args.py
 # or by passing the --help flag to this script.
 # We now keep distinct sets of args, for a cleaner separation of concerns.

 lowerCamelCase_      =  HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)		)
 if len(sys.argv		) == 2 and sys.argv[1].endswith(".json"		):
  # If we pass only one argument to the script and it's the path to a json file,
  # let's parse it to get our arguments.
  lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_      =  parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]		)		)
 else:
  lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_      =  parser.parse_args_into_dataclasses()

 # Detecting last checkpoint.
 lowerCamelCase_      =  None
 if os.path.isdir(training_args.output_dir		) and training_args.do_train and not training_args.overwrite_output_dir:
  lowerCamelCase_      =  get_last_checkpoint(training_args.output_dir		)
  if last_checkpoint is None and len(os.listdir(training_args.output_dir		)		) > 0:
   raise ValueError(
       F'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
       "Use --overwrite_output_dir to overcome."		)
  elif last_checkpoint is not None:
   logger.info(
       F'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
       "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."		)

    # Setup logging
 logging.basicConfig(
     format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"					,				datefmt="%m/%d/%Y %H:%M:%S"					,				handlers=[logging.StreamHandler(sys.stdout		)]					,				)
 logger.setLevel(logging.INFO if is_main_process(training_args.local_rank		) else logging.WARN		)

 # Log on each process the small summary:
 logger.warning(
     F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
     + F'''distributed training: {bool(training_args.local_rank != -1		)}, 16-bits training: {training_args.fpaa}'''		)
 # Set the verbosity to info of the Transformers logger (on main process only):
 if is_main_process(training_args.local_rank		):
  transformers.utils.logging.set_verbosity_info()
  transformers.utils.logging.enable_default_handler()
  transformers.utils.logging.enable_explicit_format()
 logger.info("Training/evaluation parameters %s"					,				UpperCAmelCase_		)

 # Set seed before initializing model.
 set_seed(training_args.seed		)

 # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
 # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
 # (the dataset will be downloaded automatically from the datasets Hub).
 #
 # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
 # 'text' is found. You can easily tweak this behavior (see below).
 #
 # In distributed training, the load_dataset function guarantee that only one local process can concurrently
 # download the dataset.
 if data_args.dataset_name is not None:
  # Downloading and loading a dataset from the hub.
  lowerCamelCase_      =  load_dataset(data_args.dataset_name					,				data_args.dataset_config_name		)
  if "validation" not in datasets.keys():
   lowerCamelCase_      =  load_dataset(
       data_args.dataset_name					,				data_args.dataset_config_name					,				split=F'''train[:{data_args.validation_split_percentage}%]'''					,				)
   lowerCamelCase_      =  load_dataset(
       data_args.dataset_name					,				data_args.dataset_config_name					,				split=F'''train[{data_args.validation_split_percentage}%:]'''					,				)
 else:
  lowerCamelCase_      =  {}
  if data_args.train_file is not None:
   lowerCamelCase_      =  data_args.train_file
  if data_args.validation_file is not None:
   lowerCamelCase_      =  data_args.validation_file
  lowerCamelCase_      =  data_args.train_file.split("."		)[-1]
  if extension == "txt":
   lowerCamelCase_      =  "text"
  lowerCamelCase_      =  load_dataset(UpperCAmelCase_					,				data_files=UpperCAmelCase_		)
 # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
 # https://huggingface.co/docs/datasets/loading_datasets.html.

 # Load pretrained model and tokenizer
 #
 # Distributed training:
 # The .from_pretrained methods guarantee that only one local process can concurrently
 # download model & vocab.
 lowerCamelCase_      =  {
     "cache_dir": model_args.cache_dir,
     "revision": model_args.model_revision,
     "use_auth_token": True if model_args.use_auth_token else None,
 }
 if model_args.config_name:
  lowerCamelCase_      =  AutoConfig.from_pretrained(model_args.config_name					,				**UpperCAmelCase_		)
 elif model_args.model_name_or_path:
  lowerCamelCase_      =  AutoConfig.from_pretrained(model_args.model_name_or_path					,				**UpperCAmelCase_		)
 else:
  lowerCamelCase_      =  CONFIG_MAPPING[model_args.model_type]()
  logger.warning("You are instantiating a new config instance from scratch."		)
  if model_args.config_overrides is not None:
   logger.info(F'''Overriding config: {model_args.config_overrides}'''		)
   config.update_from_string(model_args.config_overrides		)
   logger.info(F'''New config: {config}'''		)

 lowerCamelCase_      =  {
     "cache_dir": model_args.cache_dir,
     "use_fast": model_args.use_fast_tokenizer,
     "revision": model_args.model_revision,
     "use_auth_token": True if model_args.use_auth_token else None,
 }
 if model_args.tokenizer_name:
  lowerCamelCase_      =  AutoTokenizer.from_pretrained(model_args.tokenizer_name					,				**UpperCAmelCase_		)
 elif model_args.model_name_or_path:
  lowerCamelCase_      =  AutoTokenizer.from_pretrained(model_args.model_name_or_path					,				**UpperCAmelCase_		)
 else:
  raise ValueError(
      "You are instantiating a new tokenizer from scratch. This is not supported by this script."
      "You can do it from another script, save it, and load it from here, using --tokenizer_name."		)

 if model_args.model_name_or_path:
  lowerCamelCase_      =  AutoModelForMaskedLM.from_pretrained(
      model_args.model_name_or_path					,				from_tf=bool(".ckpt" in model_args.model_name_or_path		)					,				config=UpperCAmelCase_					,				cache_dir=model_args.cache_dir					,				revision=model_args.model_revision					,				use_auth_token=True if model_args.use_auth_token else None					,				)
 else:
  logger.info("Training new model from scratch"		)
  lowerCamelCase_      =  AutoModelForMaskedLM.from_config(UpperCAmelCase_		)

 model.resize_token_embeddings(len(UpperCAmelCase_		)		)

 # Preprocessing the datasets.
 # First we tokenize all the texts.
 if training_args.do_train:
  lowerCamelCase_      =  datasets["train"].column_names
 else:
  lowerCamelCase_      =  datasets["validation"].column_names
 lowerCamelCase_      =  "text" if "text" in column_names else column_names[0]

 lowerCamelCase_      =  "max_length" if data_args.pad_to_max_length else False

 def tokenize_function(UpperCAmelCase_						:						Union[str, Any]		):
  # Remove empty lines
  lowerCamelCase_      =  [line for line in examples["text"] if len(UpperCAmelCase_		) > 0 and not line.isspace()]
  return tokenizer(examples["text"]					,				padding=UpperCAmelCase_					,				truncation=UpperCAmelCase_					,				max_length=data_args.max_seq_length		)

 lowerCamelCase_      =  datasets.map(
     UpperCAmelCase_					,				batched=UpperCAmelCase_					,				num_proc=data_args.preprocessing_num_workers					,				remove_columns=[text_column_name]					,				load_from_cache_file=not data_args.overwrite_cache					,				)

 # Add the chinese references if provided
 if data_args.train_ref_file is not None:
  lowerCamelCase_      =  add_chinese_references(tokenized_datasets["train"]					,				data_args.train_ref_file		)
 if data_args.validation_ref_file is not None:
  lowerCamelCase_      =  add_chinese_references(
      tokenized_datasets["validation"]					,				data_args.validation_ref_file		)
 # If we have ref files, need to avoid it removed by trainer
 lowerCamelCase_      =  data_args.train_ref_file or data_args.validation_ref_file
 if has_ref:
  lowerCamelCase_      =  False

 # Data collator
 # This one will take care of randomly masking the tokens.
 lowerCamelCase_      =  DataCollatorForWholeWordMask(tokenizer=UpperCAmelCase_					,				mlm_probability=data_args.mlm_probability		)

 # Initialize our Trainer
 lowerCamelCase_      =  Trainer(
     model=UpperCAmelCase_					,				args=UpperCAmelCase_					,				train_dataset=tokenized_datasets["train"] if training_args.do_train else None					,				eval_dataset=tokenized_datasets["validation"] if training_args.do_eval else None					,				tokenizer=UpperCAmelCase_					,				data_collator=UpperCAmelCase_					,				)

 # Training
 if training_args.do_train:
  if last_checkpoint is not None:
   lowerCamelCase_      =  last_checkpoint
  elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path		):
   lowerCamelCase_      =  model_args.model_name_or_path
  else:
   lowerCamelCase_      =  None
  lowerCamelCase_      =  trainer.train(resume_from_checkpoint=UpperCAmelCase_		)
  trainer.save_model()  # Saves the tokenizer too for easy upload

  lowerCamelCase_      =  os.path.join(training_args.output_dir					,				"train_results.txt"		)
  if trainer.is_world_process_zero():
   with open(UpperCAmelCase_					,				"w"		) as writer:
    logger.info("***** Train results *****"		)
    for key, value in sorted(train_result.metrics.items()		):
     logger.info(F'''  {key} = {value}'''		)
     writer.write(F'''{key} = {value}\n'''		)

            # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
   trainer.state.save_to_json(os.path.join(training_args.output_dir					,				"trainer_state.json"		)		)

    # Evaluation
 lowerCamelCase_      =  {}
 if training_args.do_eval:
  logger.info("*** Evaluate ***"		)

  lowerCamelCase_      =  trainer.evaluate()

  lowerCamelCase_      =  math.exp(eval_output["eval_loss"]		)
  lowerCamelCase_      =  perplexity

  lowerCamelCase_      =  os.path.join(training_args.output_dir					,				"eval_results_mlm_wwm.txt"		)
  if trainer.is_world_process_zero():
   with open(UpperCAmelCase_					,				"w"		) as writer:
    logger.info("***** Eval results *****"		)
    for key, value in sorted(results.items()		):
     logger.info(F'''  {key} = {value}'''		)
     writer.write(F'''{key} = {value}\n'''		)

 return results





def __snake_case					(      UpperCAmelCase_						:						List[Any]		):
 # For xla_spawn (TPUs)
 main()


if __name__ == "__main__":
     main()




import math

import qiskit



def lowerCamelCase  (   SCREAMING_SNAKE_CASE = 1  ,			SCREAMING_SNAKE_CASE = 1  ,			SCREAMING_SNAKE_CASE = 1	):
    '''simple docstring'''







    if (
        isinstance(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)
        or isinstance(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)
        or isinstance(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)
    ):
        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(SCREAMING_SNAKE_CASE	) != input_a)
        or (math.floor(SCREAMING_SNAKE_CASE	) != input_a)
        or (math.floor(SCREAMING_SNAKE_CASE	) != 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     :List[str]			  =		qiskit.QuantumRegister(4  ,			'''qr'''	)
    __UpperCamelCase     :str			  =		qiskit.ClassicalRegister(2  ,			'''cr'''	)
    # list the entries
    __UpperCamelCase     :Tuple			  =		[input_a, input_a, carry_in]

    __UpperCamelCase     :Optional[int]			  =		qiskit.QuantumCircuit(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)

    for i in range(0  ,			3	):
        if entry[i] == 2:
            quantum_circuit.h(SCREAMING_SNAKE_CASE	)  # for hadamard entries
        elif entry[i] == 1:
            quantum_circuit.x(SCREAMING_SNAKE_CASE	)  # for 1 entries
        elif entry[i] == 0:
            quantum_circuit.i(SCREAMING_SNAKE_CASE	)  # 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]  ,			SCREAMING_SNAKE_CASE	)  # measure the last two qbits

    __UpperCamelCase     :Optional[Any]			  =		qiskit.Aer.get_backend('''aer_simulator'''	)
    __UpperCamelCase     :Tuple			  =		qiskit.execute(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			shots=1_000	)

    return job.result().get_counts(SCREAMING_SNAKE_CASE	)


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





