





'''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    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase   )->			List[Any]:
      UpperCamelCase      = 1.5
      UpperCamelCase      = int(factor * num_class_images   )
      UpperCamelCase      = 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:
            UpperCamelCase      = client.query(text=__lowerCAmelCase   )
            if len(__lowerCAmelCase   ) >= factor * num_class_images or num_images > 1E4:
                  break
            else:
                  UpperCamelCase      = int(factor * num_images   )
                  UpperCamelCase      = ClipClient(
                      url="""https://knn.laion.ai/knn-service"""		,						indice_name="""laion_400m"""		,						num_images=__lowerCAmelCase		,						aesthetic_weight=0.1		,						)

      UpperCamelCase      = 0
      UpperCamelCase      = 0
      UpperCamelCase      = 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:
                  UpperCamelCase      = class_images[count]
                  count += 1
                  try:
                        UpperCamelCase      = requests.get(images["""url"""]   )
                        if img.status_code == 200:
                              UpperCamelCase      = 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    lowercase__			(				)->			List[str]:
      UpperCamelCase      = 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__":
     SCREAMING_SNAKE_CASE__     =       parse_args()
     retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)








'''simple docstring'''

import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field

import torch

from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging


SCREAMING_SNAKE_CASE__     =       logging.get_logger(__name__)







def    lowercase__			(				)->			Tuple:
      # Get the sagemaker specific mp parameters from smp_options variable.
      UpperCamelCase      = os.getenv("""SM_HP_MP_PARAMETERS"""		,						"""{}"""   )
      try:
            # Parse it and check the field "partitions" is included, it is required for model parallel.
            UpperCamelCase      = json.loads(__UpperCamelCase   )
            if "partitions" not in smp_options:
                  return False
      except json.JSONDecodeError:
            return False

      # Get the sagemaker specific framework parameters from mpi_options variable.
      UpperCamelCase      = os.getenv("""SM_FRAMEWORK_PARAMS"""		,						"""{}"""   )
      try:
            # Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
            UpperCamelCase      = json.loads(__UpperCamelCase   )
            if not mpi_options.get("""sagemaker_mpi_enabled"""		,						__UpperCamelCase   ):
                  return False
      except json.JSONDecodeError:
            return False
      # Lastly, check if the `smdistributed` module is present.
      return importlib.util.find_spec("""smdistributed"""   ) is not None


if is_sagemaker_model_parallel_available():
     import smdistributed.modelparallel.torch as smp

     smp.init()






@dataclass
class     a_						(      lowerCamelCase    ):
  lowercase							=				field(
      default=""""""     ,	metadata={"""help""": """Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer"""}     ,	)

  def   A__						(				self				)  ->				Tuple:



        """simple docstring"""






        super().__post_init__()
        warnings.warn(
            """`SageMakerTrainingArguments` is deprecated and will be removed in v5 of Transformers. You can use """
            """`TrainingArguments` instead."""      ,		_SCREAMING_SNAKE_CASE      ,		)

  @cached_property
  def   A__						(				self				)  ->				"torch.device":



        """simple docstring"""






        logger.info("""PyTorch: setting up devices"""				)
        if torch.distributed.is_available() and torch.distributed.is_initialized() and self.local_rank == -1:
              logger.warning(
                  """torch.distributed process group is initialized, but local_rank == -1. """
                  """In order to use Torch DDP, launch your script with `python -m torch.distributed.launch"""				)
        if self.no_cuda:
              UpperCamelCase      = torch.device("""cpu"""				)
              UpperCamelCase      = 0
        elif is_sagemaker_model_parallel_available():
              UpperCamelCase      = smp.local_rank()
              UpperCamelCase      = torch.device("""cuda"""      ,		_SCREAMING_SNAKE_CASE				)
              UpperCamelCase      = 1
        elif is_sagemaker_dp_enabled():
              import smdistributed.dataparallel.torch.torch_smddp  # noqa: F401

              torch.distributed.init_process_group(backend="""smddp"""      ,		timeout=self.ddp_timeout_delta				)
              UpperCamelCase      = int(os.getenv("""SMDATAPARALLEL_LOCAL_RANK"""				)				)
              UpperCamelCase      = torch.device("""cuda"""      ,		self.local_rank				)
              UpperCamelCase      = 1
        elif self.local_rank == -1:
              # if n_gpu is > 1 we'll use nn.DataParallel.
              # If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
              # Explicitly set CUDA to the first (index 0) CUDA device, otherwise `set_device` will
              # trigger an error that a device index is missing. Index 0 takes into account the
              # GPUs available in the environment, so `CUDA_VISIBLE_DEVICES=1,2` with `cuda:0`
              # will use the first GPU in that env, i.e. GPU#1
              UpperCamelCase      = torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu"""				)
              # Sometimes the line in the postinit has not been run before we end up here, so just checking we're not at
              # the default value.
              UpperCamelCase      = torch.cuda.device_count()
        else:
              # Here, we'll use torch.distributed.
              # Initializes the distributed backend which will take care of synchronizing nodes/GPUs
              if not torch.distributed.is_initialized():
                    torch.distributed.init_process_group(backend="""nccl"""      ,		timeout=self.ddp_timeout_delta				)
              UpperCamelCase      = torch.device("""cuda"""      ,		self.local_rank				)
              UpperCamelCase      = 1

        if device.type == "cuda":
              torch.cuda.set_device(_SCREAMING_SNAKE_CASE				)

        return device

  @property
  def   A__						(				self				)  ->				Tuple:



        """simple docstring"""






        if is_sagemaker_model_parallel_available():
              return smp.dp_size()

        return super().world_size

  @property
  def   A__						(				self				)  ->				Optional[Any]:



        """simple docstring"""






        return not is_sagemaker_model_parallel_available()


  @property
  def   A__						(				self				)  ->				str:



        """simple docstring"""






        return False
