


'''simple docstring'''
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
  import torch




class      lowerCamelCase_  (      TensorFormatter[Mapping, 'torch.Tensor', Mapping] ):






      def __init__(   self							:       Tuple		,		_A							:       Tuple=None		,		**_A							:       Tuple					):


          '''simple docstring'''


          super().__init__(features=_A					)
          UpperCAmelCase__     :						List[str]						=							torch_tensor_kwargs
          import torch  # noqa import torch at initialization






      def lowercase_				(   self							:       Tuple		,		_A							:       Any					):


          '''simple docstring'''


          import torch

          if isinstance(_A		,		_A					) and column:
              if all(
                  isinstance(_A		,		torch.Tensor					) and x.shape == column[0].shape and x.dtype == column[0].dtype
                  for x in column					):
                  return torch.stack(_A					)
          return column






      def lowercase_				(   self							:       Any		,		_A							:       Tuple					):


          '''simple docstring'''


          import torch

          if isinstance(_A		,		(str, bytes, type(_A					))					):
              return value
          elif isinstance(_A		,		(np.character, np.ndarray)					) and np.issubdtype(value.dtype		,		np.character					):
              return value.tolist()

          UpperCAmelCase__     :						List[Any]						=							{}

          if isinstance(_A		,		(np.number, np.ndarray)					) and np.issubdtype(value.dtype		,		np.integer					):
              UpperCAmelCase__     :						str						=							{'''dtype''': torch.intaa}
          elif isinstance(_A		,		(np.number, np.ndarray)					) and np.issubdtype(value.dtype		,		np.floating					):
              UpperCAmelCase__     :						Optional[int]						=							{'''dtype''': torch.floataa}
          elif config.PIL_AVAILABLE and "PIL" in sys.modules:
              import PIL.Image

              if isinstance(_A		,		PIL.Image.Image					):
                  UpperCAmelCase__     :						List[str]						=							np.asarray(_A					)
          return torch.tensor(_A		,		**{**default_dtype, **self.torch_tensor_kwargs}					)






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


          '''simple docstring'''


          import torch

          # support for torch, tf, jax etc.
          if hasattr(_A		,		'''__array__'''					) and not isinstance(_A		,		torch.Tensor					):
              UpperCAmelCase__     :						Tuple						=							data_struct.__array__()
          # support for nested types like struct of list of struct
          if isinstance(_A		,		np.ndarray					):
              if data_struct.dtype == object:  # torch tensors cannot be instantied from an array of objects
                  return self._consolidate([self.recursive_tensorize(_A					) for substruct in data_struct]					)
          elif isinstance(_A		,		(list, tuple)					):
              return self._consolidate([self.recursive_tensorize(_A					) for substruct in data_struct]					)
          return self._tensorize(_A					)






      def lowercase_				(   self							:       List[str]		,		_A							:       dict					):


          '''simple docstring'''


          return map_nested(self._recursive_tensorize		,		_A		,		map_list=_A					)






      def lowercase_				(   self							:       Optional[int]		,		_A							:       pa.Table					):


          '''simple docstring'''


          UpperCAmelCase__     :						Union[str, Any]						=							self.numpy_arrow_extractor().extract_row(_A					)
          UpperCAmelCase__     :						List[str]						=							self.python_features_decoder.decode_row(_A					)
          return self.recursive_tensorize(_A					)






      def lowercase_				(   self							:       Dict		,		_A							:       pa.Table					):


          '''simple docstring'''


          UpperCAmelCase__     :						Optional[int]						=							self.numpy_arrow_extractor().extract_column(_A					)
          UpperCAmelCase__     :						str						=							self.python_features_decoder.decode_column(_A		,		pa_table.column_names[0]					)
          UpperCAmelCase__     :						Union[str, Any]						=							self.recursive_tensorize(_A					)
          UpperCAmelCase__     :						List[str]						=							self._consolidate(_A					)
          return column






      def lowercase_				(   self							:       Dict		,		_A							:       pa.Table					):


          '''simple docstring'''


          UpperCAmelCase__     :						Union[str, Any]						=							self.numpy_arrow_extractor().extract_batch(_A					)
          UpperCAmelCase__     :						Tuple						=							self.python_features_decoder.decode_batch(_A					)
          UpperCAmelCase__     :						str						=							self.recursive_tensorize(_A					)
          for column_name in batch:
              UpperCAmelCase__     :						List[Any]						=							self._consolidate(batch[column_name]					)
          return batch





'''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)






