



'''simple docstring'''





import baseaa
import io
import json
import os
from copy import deepcopy

from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler






class    lowerCamelCase_       :
  """simple docstring"""




  def __init__(					self :      Any     ,      _a :      Union[str, Any]  )   ->   Tuple:
    if isinstance(_a     ,      _a  ):
      # Don't modify user's data should they want to reuse it (e.g. in tests), because once we
      # modified it, it will not be accepted here again, since `auto` values would have been overridden
      __lowerCamelCase			:			Tuple       			=					deepcopy(_a  )
    elif os.path.exists(_a  ):
      with io.open(_a     ,      'r'     ,      encoding='utf-8'  ) as f:
        __lowerCamelCase			:			Any       			=					json.load(_a  )
    else:
      try:
        __lowerCamelCase			:			List[str]       			=					baseaa.urlsafe_baadecode(_a  ).decode('utf-8'  )
        __lowerCamelCase			:			int       			=					json.loads(_a  )
      except (UnicodeDecodeError, AttributeError, ValueError):
        raise ValueError(
            f'Expected a string path to an existing deepspeed config, or a dictionary, or a base64 encoded string. Received: {config_file_or_dict}'  )

    __lowerCamelCase			:			List[Any]       			=					config

    self.set_stage_and_offload()




  def 		_lowercase	(					self :      str  )   ->   int:
    # zero stage - this is done as early as possible, before model is created, to allow
    # ``is_deepspeed_zero3_enabled`` query and getting to the early deepspeed config object
    # during ``zero.Init()`` which needs to know the dtype, and some other hparams.
    __lowerCamelCase			:			Any       			=					self.get_value('zero_optimization.stage'     ,      -1  )

    # offload
    __lowerCamelCase			:			str       			=					False
    if self.is_zeroa() or self.is_zeroa():
      __lowerCamelCase			:			int       			=					set(['cpu', 'nvme']  )
      __lowerCamelCase			:			Dict       			=					set(
          [
              self.get_value('zero_optimization.offload_optimizer.device'  ),
              self.get_value('zero_optimization.offload_param.device'  ),
          ]  )
      if len(offload_devices & offload_devices_valid  ) > 0:
        __lowerCamelCase			:			Union[str, Any]       			=					True




  def 		_lowercase	(					self :      Dict     ,      _a :      Optional[int]  )   ->   str:
    __lowerCamelCase			:			List[Any]       			=					self.config

    # find the config node of interest if it exists
    __lowerCamelCase			:			List[Any]       			=					ds_key_long.split('.'  )
    __lowerCamelCase			:			str       			=					nodes.pop()
    for node in nodes:
      __lowerCamelCase			:			Any       			=					config.get(_a  )
      if config is None:
        return None, ds_key

    return config, ds_key




  def 		_lowercase	(					self :      Dict     ,      _a :      Optional[Any]     ,      _a :      Any=None  )   ->   str:
    __lowerCamelCase			,__lowerCamelCase			:			int       			=					self.find_config_node(_a  )
    if config is None:
      return default
    return config.get(_a     ,      _a  )




  def 		_lowercase	(					self :      Union[str, Any]     ,      _a :      List[Any]     ,      _a :      Optional[int]=False  )   ->   str:
    __lowerCamelCase			:			Union[str, Any]       			=					self.config

    # find the config node of interest if it exists
    __lowerCamelCase			:			Optional[Any]       			=					ds_key_long.split('.'  )
    for node in nodes:
      __lowerCamelCase			:			Optional[int]       			=					config
      __lowerCamelCase			:			str       			=					config.get(_a  )
      if config is None:
        if must_exist:
          raise ValueError(f'Can\'t find {ds_key_long} entry in the config: {self.config}'  )
        else:
          return

        # if found remove it
    if parent_config is not None:
      parent_config.pop(_a  )




  def 		_lowercase	(					self :      int     ,      _a :      Optional[Any]  )   ->   List[Any]:
    __lowerCamelCase			:			Optional[Any]       			=					self.get_value(_a  )
    return False if value is None else bool(_a  )




  def 		_lowercase	(					self :      Tuple     ,      _a :      Optional[int]  )   ->   List[str]:
    __lowerCamelCase			:			Union[str, Any]       			=					self.get_value(_a  )
    return False if value is None else not bool(_a  )




  def 		_lowercase	(					self :      List[str]  )   ->   Optional[int]:
    return self._stage == 2




  def 		_lowercase	(					self :      str  )   ->   Union[str, Any]:
    return self._stage == 3




  def 		_lowercase	(					self :      List[Any]  )   ->   List[str]:
    return self._offload






class    lowerCamelCase_       :
  """simple docstring"""




  def __init__(					self :      List[Any]     ,      _a :      Any  )   ->   Optional[int]:
    __lowerCamelCase			:			Tuple       			=					engine




  def 		_lowercase	(					self :      str     ,      _a :      str     ,      **_a :      Tuple  )   ->   List[str]:
    # runs backpropagation and handles mixed precision
    self.engine.backward(_a     ,      **_a  )

    # Deepspeed's `engine.step` performs the following operations:
    # - gradient accumulation check
    # - gradient clipping
    # - optimizer step
    # - zero grad
    # - checking overflow
    # - lr_scheduler step (only if engine.lr_scheduler is not None)
    self.engine.step()
    # and this plugin overrides the above calls with no-ops when Accelerate runs under
    # Deepspeed, but allows normal functionality for non-Deepspeed cases thus enabling a simple
    # training loop that works transparently under many training regimes.






class    lowerCamelCase_       (							SCREAMING_SNAKE_CASE_	):
  """simple docstring"""




  def __init__(					self :      List[str]     ,      _a :      List[Any]  )   ->   int:
    super().__init__(_a     ,      device_placement=_a     ,      scaler=_a  )
    __lowerCamelCase			:			str       			=					hasattr(self.optimizer     ,      'overflow'  )




  def 		_lowercase	(					self :      int     ,      _a :      int=None  )   ->   Any:
    pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed




  def 		_lowercase	(					self :      Tuple  )   ->   Any:
    pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed




  @property
  def 		_lowercase	(					self :      Optional[Any]  )   ->   str:
    if self.__has_overflow__:
      return self.optimizer.overflow
    return False






class    lowerCamelCase_       (							SCREAMING_SNAKE_CASE_	):
  """simple docstring"""




  def __init__(					self :      int     ,      _a :      int     ,      _a :      int  )   ->   List[str]:
    super().__init__(_a     ,      _a  )




  def 		_lowercase	(					self :      Optional[int]  )   ->   int:
    pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed






class    lowerCamelCase_       :
  """simple docstring"""




  def __init__(					self :      List[Any]     ,      _a :      List[Any]     ,      _a :      Optional[int]=0.001     ,      _a :      List[str]=0     ,      **_a :      Tuple  )   ->   Tuple:
    __lowerCamelCase			:			Dict       			=					params
    __lowerCamelCase			:			Optional[Any]       			=					lr
    __lowerCamelCase			:			int       			=					weight_decay
    __lowerCamelCase			:			List[Any]       			=					kwargs






class    lowerCamelCase_       :
  """simple docstring"""




  def __init__(					self :      List[str]     ,      _a :      Optional[Any]     ,      _a :      Dict=None     ,      _a :      Dict=0     ,      **_a :      Optional[int]  )   ->   Dict:
    __lowerCamelCase			:			int       			=					optimizer
    __lowerCamelCase			:			Any       			=					total_num_steps
    __lowerCamelCase			:			List[Any]       			=					warmup_num_steps
    __lowerCamelCase			:			List[str]       			=					kwargs











'''simple docstring'''





import argparse
import os
from io import BytesIO
from pathlib import Path

import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm

def 			a_	(				_lowerCAmelCase			,_lowerCAmelCase			,_lowerCAmelCase			) -> int:
  __lowerCamelCase			:			Optional[Any]       			=					1.5
  __lowerCamelCase			:			Tuple       			=					int(factor * num_class_images			)
  __lowerCamelCase			:			Tuple       			=					ClipClient(
      url='https://knn.laion.ai/knn-service'			,indice_name='laion_400m'			,num_images=_lowerCAmelCase			,aesthetic_weight=0.1			)

  os.makedirs(F'{class_data_dir}/images'			,exist_ok=_lowerCAmelCase			)
  if len(list(Path(F'{class_data_dir}/images'			).iterdir()			)			) >= num_class_images:
    return

  while True:
    __lowerCamelCase			:			Dict       			=					client.query(text=_lowerCAmelCase			)
    if len(_lowerCAmelCase			) >= factor * num_class_images or num_images > 1E4:
      break
    else:
      __lowerCamelCase			:			Union[str, Any]       			=					int(factor * num_images			)
      __lowerCamelCase			:			List[str]       			=					ClipClient(
          url='https://knn.laion.ai/knn-service'			,indice_name='laion_400m'			,num_images=_lowerCAmelCase			,aesthetic_weight=0.1			,)

  __lowerCamelCase			:			List[Any]       			=					0
  __lowerCamelCase			:			Tuple       			=					0
  __lowerCamelCase			:			int       			=					tqdm(desc='downloading real regularization images'			,total=_lowerCAmelCase			)

  with open(F'{class_data_dir}/caption.txt'			,'w'			) as fa, open(F'{class_data_dir}/urls.txt'			,'w'			) as fa, open(
      F'{class_data_dir}/images.txt'			,'w'			) as fa:
    while total < num_class_images:
      __lowerCamelCase			:			List[str]       			=					class_images[count]
      count += 1
      try:
        __lowerCamelCase			:			int       			=					requests.get(images['url']			)
        if img.status_code == 200:
          __lowerCamelCase			:			str       			=					Image.open(BytesIO(img.content			)			)
          with open(F'{class_data_dir}/images/{total}.jpg'			,'wb'			) as f:
            f.write(img.content			)
          fa.write(images['caption'] + '\n'			)
          fa.write(images['url'] + '\n'			)
          fa.write(F'{class_data_dir}/images/{total}.jpg' + '\n'			)
          total += 1
          pbar.update(1			)
        else:
          continue
      except Exception:
        continue
  return

def 			a_	(				) -> Dict:
  __lowerCamelCase			:			Any       			=					argparse.ArgumentParser(''			,add_help=_lowerCAmelCase			)
  parser.add_argument('--class_prompt'			,help='text prompt to retrieve images'			,required=_lowerCAmelCase			,type=_lowerCAmelCase			)
  parser.add_argument('--class_data_dir'			,help='path to save images'			,required=_lowerCAmelCase			,type=_lowerCAmelCase			)
  parser.add_argument('--num_class_images'			,help='number of images to download'			,default=200			,type=_lowerCAmelCase			)
  return parser.parse_args()


if __name__ == "__main__":
   _UpperCamelCase											=		parse_args()
   retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)






