

"""simple docstring"""


import importlib.util
import weakref
from functools import partialmethod

from .dependency_versions_check import dep_version_check
from .utils import is_accelerate_available, is_torch_available, logging


if is_torch_available():
   import torch

__A		     =    logging.get_logger(__name__)






def 					a__					(					)			->			Dict:
  return importlib.util.find_spec("deepspeed"				) is not None


if is_accelerate_available() and is_deepspeed_available():
   from accelerate.utils.deepspeed import HfDeepSpeedConfig as DeepSpeedConfig
else:
   # Inherits from a dummy `object` if accelerate is not available, so that python succeeds to import this file.
   # Deepspeed glue code will never inherit this dummy object as it checks if accelerate is available.
   from builtins import object as DeepSpeedConfig





class 					snake_case			(       A_						):

    def __init__(			self							:       int  ,						UpperCamelCase__							:       List[Any])->     Any:



      '''simple docstring'''


      set_hf_deepspeed_config(self)
      dep_version_check("accelerate")
      dep_version_check("deepspeed")
      super().__init__(UpperCamelCase__)





class 					snake_case			(       A_						):

    def __init__(			self							:       Optional[int]  ,						UpperCamelCase__							:       Dict)->     Tuple:



      '''simple docstring'''


      super().__init__(UpperCamelCase__)
      __lowerCAmelCase: Union[str, Any]					   =    None
      __lowerCAmelCase: str					   =    []

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



      '''simple docstring'''


      if self._dtype is None:
        raise ValueError("trainer_config_process() wasn\'t called yet to tell dtype")
      return self._dtype

    def 							lowercase_  (			self							:       int  ,						UpperCamelCase__							:       List[str])->     Optional[Any]:



      '''simple docstring'''


      __lowerCAmelCase: List[str]					   =    self.get_value(UpperCamelCase__)
      if val is None:
        return False
      else:
        return val == "auto"

    def 							lowercase_  (			self							:       int  ,						UpperCamelCase__							:       Union[str, Any]  ,						UpperCamelCase__							:       Any  ,						UpperCamelCase__							:       str=None  ,						UpperCamelCase__							:       Dict=True)->     List[Any]:



      '''simple docstring'''


      __lowerCAmelCase   ,				__lowerCAmelCase: Any					   =    self.find_config_node(UpperCamelCase__)
      if config is None:
        return

      if config.get(UpperCamelCase__) == "auto":
        __lowerCAmelCase: List[str]					   =    hf_val
        return

      if not must_match:
        return

      __lowerCAmelCase: Optional[int]					   =    config.get(UpperCamelCase__)
      if ds_val is not None and ds_val != hf_val:
        self.mismatches.append(f"- ds {ds_key_long}={ds_val} vs hf {hf_key}={hf_val}")

    SCREAMING_SNAKE_CASE_     :   str        =						partialmethod(A_,							must_match=A_						)

    def 							lowercase_  (			self							:       Tuple  ,						UpperCamelCase__							:       int)->     int:



      '''simple docstring'''


      __lowerCAmelCase: Optional[Any]					   =    args.world_size * args.per_device_train_batch_size * args.gradient_accumulation_steps
      self.fill_match(
          "train_micro_batch_size_per_gpu"  ,						args.per_device_train_batch_size  ,						"per_device_train_batch_size")
      self.fill_match("gradient_accumulation_steps"  ,						args.gradient_accumulation_steps  ,						"gradient_accumulation_steps")
      self.fill_match("train_batch_size"  ,						UpperCamelCase__  ,						"train_batch_size (calculated)")
      self.fill_match("gradient_clipping"  ,						args.max_grad_norm  ,						"max_grad_norm")

      self.fill_match("optimizer.params.lr"  ,						args.learning_rate  ,						"learning_rate")
      self.fill_match("optimizer.params.betas"  ,						[args.adam_betaa, args.adam_betaa]  ,						"adam_beta1+adam_beta2")
      self.fill_match("optimizer.params.eps"  ,						args.adam_epsilon  ,						"adam_epsilon")
      self.fill_match("optimizer.params.weight_decay"  ,						args.weight_decay  ,						"weight_decay")

      self.fill_only("scheduler.params.warmup_min_lr"  ,						0)  # not a trainer arg
      self.fill_match("scheduler.params.warmup_max_lr"  ,						args.learning_rate  ,						"learning_rate")
      # total_num_steps - will get set in trainer_config_finalize

      # fp16
      if args.fpaa or args.fpaa_full_eval:
        __lowerCAmelCase: Optional[Any]					   =    "apex" if args.fpaa_backend == "apex" else "amp"
      else:
        __lowerCAmelCase: Optional[int]					   =    None

      if args.save_on_each_node:
        # deepspeed uses shared storage by default. Let's override this setting if save_on_each_node == True
        __lowerCAmelCase: List[str]					   =    self.config.get("checkpoint"  ,						{})
        __lowerCAmelCase: List[str]					   =    args.save_on_each_node

      # amp: similar to the pytorch native amp - it has a bunch of optional params but we won't set
      # any here unless the user did the work
      self.fill_match(
          "fp16.enabled"  ,						((args.fpaa or args.fpaa_full_eval) and fpaa_backend == "amp")  ,						"fp16|fp16_full_eval+fp16_backend(amp)"  ,						)

      # apex: delegates amp work to apex (which needs to be available), but it cannot be used with any
      # ZeRO features
      self.fill_match("amp.enabled"  ,						fpaa_backend == "apex"  ,						"fp16+fp16_backend(apex)")
      self.fill_match("amp.opt_level"  ,						args.fpaa_opt_level  ,						"fp16_opt_level")

      self.fill_match("bf16.enabled"  ,						(args.bfaa or args.bfaa_full_eval)  ,						"bf16|bf16_full_eval")

      # deepspeed's default mode is fp16 unless there is a config that says differently
      if self.is_true("bf16.enabled"):
        __lowerCAmelCase: str					   =    torch.bfloataa
      elif self.is_false("fp16.enabled"):
        __lowerCAmelCase: Any					   =    torch.floataa
      else:
        __lowerCAmelCase: int					   =    torch.floataa




    def 							lowercase_  (			self							:       Optional[Any]  ,						UpperCamelCase__							:       str  ,						UpperCamelCase__							:       Union[str, Any]  ,						UpperCamelCase__							:       Tuple)->     str:



      '''simple docstring'''


      __lowerCAmelCase: Any					   =    [
          "zero_optimization.reduce_bucket_size",
          "zero_optimization.stage3_prefetch_bucket_size",
          "zero_optimization.stage3_param_persistence_threshold",
      ]
      __lowerCAmelCase: Any					   =    [x for x in hidden_size_based_keys if self.is_auto(UpperCamelCase__)]

      if len(UpperCamelCase__) > 0:
        if hasattr(model.config  ,						"hidden_size"):
          __lowerCAmelCase: Tuple					   =    model.config.hidden_size
        elif hasattr(model.config  ,						"hidden_sizes"):
          # if there are many hidden sizes pick the largest one
          __lowerCAmelCase: Dict					   =    max(model.config.hidden_sizes)
        else:
          raise ValueError(
              "The model\'s config file has neither `hidden_size` nor `hidden_sizes` entry, "
              "therefore it\'s not possible to automatically fill out the following `auto` entries "
              f"in the DeepSpeed config file: {hidden_size_auto_keys}. You can fix that by replacing "
              "`auto` values for these keys with an integer value of your choice.")

        self.fill_only("zero_optimization.reduce_bucket_size"  ,						hidden_size * hidden_size)
        if self.is_zeroa():
          # automatically assign the optimal config values based on model config
          self.fill_only("zero_optimization.stage3_prefetch_bucket_size"  ,						0.9 * hidden_size * hidden_size)
          self.fill_only("zero_optimization.stage3_param_persistence_threshold"  ,						1_0 * hidden_size)

        # scheduler
      self.fill_match("scheduler.params.total_num_steps"  ,						UpperCamelCase__  ,						"num_training_steps (calculated)")
      self.fill_match("scheduler.params.warmup_num_steps"  ,						args.get_warmup_steps(UpperCamelCase__)  ,						"warmup_steps")

      if len(self.mismatches) > 0:
        __lowerCAmelCase: Dict					   =    "\n".join(self.mismatches)
        raise ValueError(
            "Please correct the following DeepSpeed config values that mismatch TrainingArguments"
            f" values:\n{mismatches}\nThe easiest method is to set these DeepSpeed config values to \'auto\'.")


__A		     =    None






def 					a__					(					__SCREAMING_SNAKE_CASE				)			->			str:
  global _hf_deepspeed_config_weak_ref
  # will go away automatically when HfDeepSpeedConfig is destroyed (when TrainingArguments is destroyed)
  __lowerCAmelCase: Tuple					   =    weakref.ref(lowercase__				)






def 					a__					(					)			->			Dict:
  global _hf_deepspeed_config_weak_ref
  __lowerCAmelCase: Dict					   =    None






def 					a__					(					)			->			Optional[Any]:







  if _hf_deepspeed_config_weak_ref is not None and _hf_deepspeed_config_weak_ref() is not None:
    return _hf_deepspeed_config_weak_ref().is_zeroa()
  else:
    return False






def 					a__					(					)			->			int:







  if _hf_deepspeed_config_weak_ref is not None and _hf_deepspeed_config_weak_ref() is not None:
    return _hf_deepspeed_config_weak_ref().config
  else:
    return None






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)			->			List[str]:
  from accelerate.utils import DummyOptim, DummyScheduler

  __lowerCAmelCase: str					   =    hf_deepspeed_config.config

  # Optimizer + Scheduler
  # Currently supported combos:
  # 1. DS scheduler + DS optimizer: Yes
  # 2. HF scheduler + HF optimizer: Yes
  # 3. DS scheduler + HF optimizer: Yes
  # 4. HF scheduler + DS optimizer: No
  #
  # Unless Offload is enabled in which case it's:
  # 1. DS scheduler + DS optimizer: Yes
  # 2. HF scheduler + HF optimizer: Mostly*
  # 3. DS scheduler + HF optimizer: Mostly*
  # 4. HF scheduler + DS optimizer: No
  #
  # Mostly*: All non-native DeepSpeed optimizers that have both CPU and GPU implementation should work (except LAMB)

  __lowerCAmelCase: Dict					   =    None
  if "optimizer" in config:
    if args.adafactor:
      raise ValueError(
          "--adafactor was passed, but also found `optimizer` configured in the DeepSpeed config. "
          "Only one optimizer can be configured."				)
    __lowerCAmelCase: Dict					   =    DummyOptim(params=lowercase__				)
  else:
    if hf_deepspeed_config.is_offload():
      logger.info(
          "Detected ZeRO Offload and non-DeepSpeed optimizers: This combination should work as long as the"
          " custom optimizer has both CPU and GPU implementation (except LAMB)"				)

    # ds supports Adam, OneBitAdam, and Lamb optimizers and can import other optimizers from torch.
    # But trainer uses AdamW by default.
    __lowerCAmelCase: Optional[Any]					   =    trainer.create_optimizer()
    # To use other optimizers requires voiding warranty with: `zero_allow_untested_optimizer`
    __lowerCAmelCase: List[str]					   =    True

  __lowerCAmelCase: Union[str, Any]					   =    None
  if "scheduler" in config:
    __lowerCAmelCase: Optional[int]					   =    DummyScheduler(lowercase__				)
  else:
    if isinstance(lowercase__      ,				lowercase__				):
      raise ValueError(
          "Found `optimizer` configured in the DeepSpeed config, but no `scheduler`. "
          "Please configure a scheduler in the DeepSpeed config."				)
    __lowerCAmelCase: List[Any]					   =    trainer.create_scheduler(num_training_steps=lowercase__      ,				optimizer=lowercase__				)

  return optimizer, lr_scheduler






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE=False				)			->			List[Any]:
  from deepspeed.utils import logger as ds_logger

  __lowerCAmelCase: Dict					   =    trainer.model
  __lowerCAmelCase: Optional[int]					   =    trainer.args

  __lowerCAmelCase: Union[str, Any]					   =    trainer.accelerator.state.deepspeed_plugin.hf_ds_config

  # resume config update - some bits like `model` and `num_training_steps` only become available during train
  hf_deepspeed_config.trainer_config_finalize(lowercase__      ,				lowercase__      ,				lowercase__				)

  # set the Deepspeed log level consistent with the Trainer
  ds_logger.setLevel(args.get_process_log_level()				)

  if inference:
    # only Z3 makes sense for the inference
    if not hf_deepspeed_config.is_zeroa():
      raise ValueError("ZeRO inference only makes sense with ZeRO Stage 3 - please adjust your config"				)

    # in case the training config is re-used for inference
    hf_deepspeed_config.del_config_sub_tree("optimizer"				)
    hf_deepspeed_config.del_config_sub_tree("lr_scheduler"				)
    __lowerCAmelCase   ,				__lowerCAmelCase: str					   =    None, None
    __lowerCAmelCase: Optional[Any]					   =    None
  else:
    __lowerCAmelCase: Tuple					   =    None  # important for when deepspeed_init is used as re-init
    __lowerCAmelCase: Union[str, Any]					   =    list(filter(lambda __SCREAMING_SNAKE_CASE				: p.requires_grad      ,				model.parameters()				)				)
    __lowerCAmelCase   ,				__lowerCAmelCase: List[Any]					   =    deepspeed_optim_sched(
        lowercase__      ,				lowercase__      ,				lowercase__      ,				lowercase__      ,				lowercase__				)

  # keep for quick debug:
  # from pprint import pprint; pprint(config)

  return optimizer, lr_scheduler






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)			->			str:
  import glob

  __lowerCAmelCase: List[str]					   =    sorted(glob.glob(F"{checkpoint_path}/global_step*"				)				)

  if len(lowercase__				) > 0:
    logger.info(F"Attempting to resume from {checkpoint_path}"				)
    # this magically updates self.optimizer and self.lr_scheduler
    __lowerCAmelCase   ,				__lowerCAmelCase: List[str]					   =    deepspeed_engine.load_checkpoint(
        lowercase__      ,				load_optimizer_states=lowercase__      ,				load_lr_scheduler_states=lowercase__				)
    if load_path is None:
      raise ValueError(F"[deepspeed] failed to resume from checkpoint {checkpoint_path}"				)
  else:
    raise ValueError(F"Can\'t find a valid checkpoint at {checkpoint_path}"				)





"""simple docstring"""


import math

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






def 					a__					(					__SCREAMING_SNAKE_CASE = 1      ,				__SCREAMING_SNAKE_CASE = 1				)			->			qiskit.result.counts.Counts:
		if isinstance(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				) or isinstance(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_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(__SCREAMING_SNAKE_CASE				) != bit_a) or (math.floor(__SCREAMING_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
		__lowerCAmelCase: str					   =    QuantumRegister(2      ,				"qr"				)
		__lowerCAmelCase: Optional[Any]					   =    ClassicalRegister(2      ,				"cr"				)

		__lowerCAmelCase: Dict					   =    QuantumCircuit(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)

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

		# send the information
		__lowerCAmelCase: Dict					   =    str(__SCREAMING_SNAKE_CASE				) + str(__SCREAMING_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(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)

		__lowerCAmelCase: List[str]					   =    Aer.get_backend("aer_simulator"				)
		__lowerCAmelCase: Dict					   =    execute(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				shots=1_0_0_0				)

		return job.result().get_counts(__SCREAMING_SNAKE_CASE				)


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


