

import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params


a_     =			getLogger(__name__)


a_     =			'''cuda''' if torch.cuda.is_available() else '''cpu'''

def        lowerCamelCase__       (    _a							,	_a							,	_a							,	_a = 8							,	_a = DEFAULT_DEVICE							,	_a=False							,	_a="summarization"							,	_a=None							,	**_a							,	):
	SCREAMING_SNAKE_CASE      :					int    =				Path(__snake_case).open("w"							,	encoding="utf-8")
	SCREAMING_SNAKE_CASE      :					Tuple    =				str(__snake_case)
	SCREAMING_SNAKE_CASE      :					Any    =				AutoModelForSeqaSeqLM.from_pretrained(__snake_case).to(__snake_case)
	if fpaa:
		SCREAMING_SNAKE_CASE      :					List[Any]    =				model.half()

	SCREAMING_SNAKE_CASE      :					int    =				AutoTokenizer.from_pretrained(__snake_case)
	logger.info(f"Inferred tokenizer type: {tokenizer.__class__}")  # if this is wrong, check config.model_type.

	SCREAMING_SNAKE_CASE      :					Tuple    =				time.time()
	# update config with task specific params
	use_task_specific_params(__snake_case							,	__snake_case)
	if prefix is None:
		SCREAMING_SNAKE_CASE      :					Tuple    =				prefix or getattr(model.config							,	"prefix"							,	"") or ''
	for examples_chunk in tqdm(list(chunks(__snake_case							,	__snake_case))):
		SCREAMING_SNAKE_CASE      :					Optional[int]    =				[prefix + text for text in examples_chunk]
		SCREAMING_SNAKE_CASE      :					int    =				tokenizer(__snake_case							,	return_tensors="pt"							,	truncation=__snake_case							,	padding="longest").to(__snake_case)
		SCREAMING_SNAKE_CASE      :					List[str]    =				model.generate(
		    input_ids=batch.input_ids							,	attention_mask=batch.attention_mask							,	**__snake_case							,	)
		SCREAMING_SNAKE_CASE      :					Dict    =				tokenizer.batch_decode(__snake_case							,	skip_special_tokens=__snake_case							,	clean_up_tokenization_spaces=__snake_case)
		for hypothesis in dec:
			fout.write(hypothesis + "\n")
			fout.flush()
	fout.close()
	SCREAMING_SNAKE_CASE      :					int    =				int(time.time() - start_time)  # seconds
	SCREAMING_SNAKE_CASE      :					Optional[int]    =				len(__snake_case)
	return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs							,	4)}

def        lowerCamelCase__       (    ):
	return datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")

def        lowerCamelCase__       (    _a=True):
	SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				argparse.ArgumentParser()
	parser.add_argument("model_name"							,	type=__snake_case							,	help="like facebook/bart-large-cnn,t5-base, etc.")
	parser.add_argument("input_path"							,	type=__snake_case							,	help="like cnn_dm/test.source")
	parser.add_argument("save_path"							,	type=__snake_case							,	help="where to save summaries")
	parser.add_argument("--reference_path"							,	type=__snake_case							,	required=__snake_case							,	help="like cnn_dm/test.target")
	parser.add_argument("--score_path"							,	type=__snake_case							,	required=__snake_case							,	default="metrics.json"							,	help="where to save metrics")
	parser.add_argument("--device"							,	type=__snake_case							,	required=__snake_case							,	default=__snake_case							,	help="cuda, cuda:1, cpu etc.")
	parser.add_argument(
	    "--prefix"							,	type=__snake_case							,	required=__snake_case							,	default=__snake_case							,	help="will be added to the begininng of src examples")
	parser.add_argument("--task"							,	type=__snake_case							,	default="summarization"							,	help="used for task_specific_params + metrics")
	parser.add_argument("--bs"							,	type=__snake_case							,	default=8							,	required=__snake_case							,	help="batch size")
	parser.add_argument(
	    "--n_obs"							,	type=__snake_case							,	default=-1							,	required=__snake_case							,	help="How many observations. Defaults to all.")
	parser.add_argument("--fp16"							,	action="store_true")
	parser.add_argument("--dump-args"							,	action="store_true"							,	help="print the custom hparams with the results")
	parser.add_argument(
	    "--info"							,	nargs="?"							,	type=__snake_case							,	const=datetime_now()							,	help=(
	        "use in conjunction w/ --dump-args to print with the results whatever other info you\'d like, e.g."
	        " lang=en-ru. If no value is passed, the current datetime string will be used."
	    )							,	)
	# Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
	SCREAMING_SNAKE_CASE      :					Tuple    =				parser.parse_known_args()
	SCREAMING_SNAKE_CASE      :					List[Any]    =				parse_numeric_n_bool_cl_kwargs(__snake_case)
	if parsed_args and verbose:
		print(f"parsed the following generate kwargs: {parsed_args}")
	SCREAMING_SNAKE_CASE      :					Dict    =				[' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in open(args.input_path).readlines()]
	if args.n_obs > 0:
		SCREAMING_SNAKE_CASE      :					List[str]    =				examples[: args.n_obs]
	Path(args.save_path).parent.mkdir(exist_ok=__snake_case)

	if args.reference_path is None and Path(args.score_path).exists():
		warnings.warn(f"score_path {args.score_path} will be overwritten unless you type ctrl-c.")

	if args.device == "cpu" and args.fpaa:
		# this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
		raise ValueError("Can\'t mix --fp16 and --device cpu")

	SCREAMING_SNAKE_CASE      :					int    =				generate_summaries_or_translations(
	    __snake_case							,	args.save_path							,	args.model_name							,	batch_size=args.bs							,	device=args.device							,	fpaa=args.fpaa							,	task=args.task							,	prefix=args.prefix							,	**__snake_case							,	)

	if args.reference_path is None:
		return {}

	# Compute scores
	SCREAMING_SNAKE_CASE      :					Any    =				calculate_bleu if 'translation' in args.task else calculate_rouge
	SCREAMING_SNAKE_CASE      :					Optional[Any]    =				[x.rstrip() for x in open(args.save_path).readlines()]
	SCREAMING_SNAKE_CASE      :					List[str]    =				[x.rstrip() for x in open(args.reference_path).readlines()][: len(__snake_case)]
	SCREAMING_SNAKE_CASE      :					dict    =				score_fn(__snake_case							,	__snake_case)
	scores.update(__snake_case)

	if args.dump_args:
		scores.update(__snake_case)
	if args.info:
		SCREAMING_SNAKE_CASE      :					Tuple    =				args.info

	if verbose:
		print(__snake_case)

	if args.score_path is not None:
		json.dump(__snake_case							,	open(args.score_path							,	"w"))

	return scores


if __name__ == "__main__":
			# Usage for MT:
			# python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
			run_generate(verbose=True)






'''simple docstring'''




import math

import qiskit






def        _lowerCAmelCase			(  __snake_case  : int = 1 ,							__snake_case  : int = 1 ,							__snake_case  : int = 1			)     ->     qiskit.result.counts.Counts:
     if (
         isinstance(__snake_case ,							__snake_case			)
         or isinstance(__snake_case ,							__snake_case			)
         or isinstance(__snake_case ,							__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(__snake_case			) != input_a)
         or (math.floor(__snake_case			) != input_a)
         or (math.floor(__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
     __A						: int      =       qiskit.QuantumRegister(4 ,							'qr'			)
     __A						: Optional[int]      =       qiskit.ClassicalRegister(2 ,							'cr'			)
     # list the entries
     __A						: Union[str, Any]      =       [input_a, input_a, carry_in]

     __A						: Dict      =       qiskit.QuantumCircuit(__snake_case ,							__snake_case			)

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

     __A						: str      =       qiskit.Aer.get_backend('aer_simulator'			)
     __A						: Any      =       qiskit.execute(__snake_case ,							__snake_case ,							shots=10_00			)

     return job.result().get_counts(__snake_case			)


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