



'''simple docstring'''





import subprocess
import sys

from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch






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




  @require_torch
  def 		_lowercase	(					self :      Optional[int]  )   ->   Dict:
    # this test is a bit tricky since TRANSFORMERS_OFFLINE can only be changed before
    # `transformers` is loaded, and it's too late for inside pytest - so we are changing it
    # while running an external program

    # python one-liner segments

    # this must be loaded before socket.socket is monkey-patched
    __lowerCamelCase			:			List[Any]       			=					'\nfrom transformers import BertConfig, BertModel, BertTokenizer, pipeline\n        '

    __lowerCamelCase			:			Tuple       			=					'\nmname = "hf-internal-testing/tiny-random-bert"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nBertTokenizer.from_pretrained(mname)\npipe = pipeline(task="fill-mask", model=mname)\nprint("success")\n        '

    __lowerCamelCase			:			List[str]       			=					'\nimport socket\ndef offline_socket(*args, **kwargs): raise RuntimeError("Offline mode is enabled, we shouldn\'t access internet")\nsocket.socket = offline_socket\n        '

    # Force fetching the files so that we can use the cache
    __lowerCamelCase			:			Optional[Any]       			=					'hf-internal-testing/tiny-random-bert'
    BertConfig.from_pretrained(_a  )
    BertModel.from_pretrained(_a  )
    BertTokenizer.from_pretrained(_a  )
    pipeline(task='fill-mask'     ,      model=_a  )

    # baseline - just load from_pretrained with normal network
    __lowerCamelCase			:			str       			=					[sys.executable, '-c', '\n'.join([load, run, mock]  )]

    # should succeed
    __lowerCamelCase			:			List[str]       			=					self.get_env()
    # should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
    __lowerCamelCase			:			Optional[int]       			=					'1'
    __lowerCamelCase			:			int       			=					subprocess.run(_a     ,      env=_a     ,      check=_a     ,      capture_output=_a  )
    self.assertEqual(result.returncode     ,      0     ,      result.stderr  )
    self.assertIn('success'     ,      result.stdout.decode()  )




  @require_torch
  def 		_lowercase	(					self :      Union[str, Any]  )   ->   Union[str, Any]:
    # python one-liner segments
    # this must be loaded before socket.socket is monkey-patched
    __lowerCamelCase			:			Any       			=					'\nfrom transformers import BertConfig, BertModel, BertTokenizer, pipeline\n        '

    __lowerCamelCase			:			Union[str, Any]       			=					'\nmname = "hf-internal-testing/tiny-random-bert"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nBertTokenizer.from_pretrained(mname)\npipe = pipeline(task="fill-mask", model=mname)\nprint("success")\n        '

    __lowerCamelCase			:			Any       			=					'\nimport socket\ndef offline_socket(*args, **kwargs): raise socket.error("Faking flaky internet")\nsocket.socket = offline_socket\n        '

    # Force fetching the files so that we can use the cache
    __lowerCamelCase			:			Tuple       			=					'hf-internal-testing/tiny-random-bert'
    BertConfig.from_pretrained(_a  )
    BertModel.from_pretrained(_a  )
    BertTokenizer.from_pretrained(_a  )
    pipeline(task='fill-mask'     ,      model=_a  )

    # baseline - just load from_pretrained with normal network
    __lowerCamelCase			:			Any       			=					[sys.executable, '-c', '\n'.join([load, run, mock]  )]

    # should succeed
    __lowerCamelCase			:			Union[str, Any]       			=					self.get_env()
    __lowerCamelCase			:			Dict       			=					subprocess.run(_a     ,      env=_a     ,      check=_a     ,      capture_output=_a  )
    self.assertEqual(result.returncode     ,      0     ,      result.stderr  )
    self.assertIn('success'     ,      result.stdout.decode()  )




  @require_torch
  def 		_lowercase	(					self :      int  )   ->   List[str]:
    # this test is a bit tricky since TRANSFORMERS_OFFLINE can only be changed before
    # `transformers` is loaded, and it's too late for inside pytest - so we are changing it
    # while running an external program

    # python one-liner segments

    # this must be loaded before socket.socket is monkey-patched
    __lowerCamelCase			:			Optional[Any]       			=					'\nfrom transformers import BertConfig, BertModel, BertTokenizer\n        '

    __lowerCamelCase			:			Tuple       			=					'\nmname = "hf-internal-testing/tiny-random-bert-sharded"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nprint("success")\n        '

    __lowerCamelCase			:			Union[str, Any]       			=					'\nimport socket\ndef offline_socket(*args, **kwargs): raise ValueError("Offline mode is enabled")\nsocket.socket = offline_socket\n        '

    # baseline - just load from_pretrained with normal network
    __lowerCamelCase			:			str       			=					[sys.executable, '-c', '\n'.join([load, run]  )]

    # should succeed
    __lowerCamelCase			:			Dict       			=					self.get_env()
    __lowerCamelCase			:			str       			=					subprocess.run(_a     ,      env=_a     ,      check=_a     ,      capture_output=_a  )
    self.assertEqual(result.returncode     ,      0     ,      result.stderr  )
    self.assertIn('success'     ,      result.stdout.decode()  )

    # next emulate no network
    __lowerCamelCase			:			Optional[int]       			=					[sys.executable, '-c', '\n'.join([load, mock, run]  )]

    # Doesn't fail anymore since the model is in the cache due to other tests, so commenting this.
    # env["TRANSFORMERS_OFFLINE"] = "0"
    # result = subprocess.run(cmd, env=env, check=False, capture_output=True)
    # self.assertEqual(result.returncode, 1, result.stderr)

    # should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
    __lowerCamelCase			:			Optional[int]       			=					'1'
    __lowerCamelCase			:			str       			=					subprocess.run(_a     ,      env=_a     ,      check=_a     ,      capture_output=_a  )
    self.assertEqual(result.returncode     ,      0     ,      result.stderr  )
    self.assertIn('success'     ,      result.stdout.decode()  )




  @require_torch
  def 		_lowercase	(					self :      List[str]  )   ->   Tuple:
    __lowerCamelCase			:			int       			=					'\nfrom transformers import pipeline\n        '
    __lowerCamelCase			:			Optional[Any]       			=					'\nmname = "hf-internal-testing/tiny-random-bert"\npipe = pipeline(model=mname)\n        '

    __lowerCamelCase			:			int       			=					'\nimport socket\ndef offline_socket(*args, **kwargs): raise socket.error("Offline mode is enabled")\nsocket.socket = offline_socket\n        '
    __lowerCamelCase			:			Tuple       			=					self.get_env()
    __lowerCamelCase			:			Optional[Any]       			=					'1'
    __lowerCamelCase			:			Union[str, Any]       			=					[sys.executable, '-c', '\n'.join([load, mock, run]  )]
    __lowerCamelCase			:			str       			=					subprocess.run(_a     ,      env=_a     ,      check=_a     ,      capture_output=_a  )
    self.assertEqual(result.returncode     ,      1     ,      result.stderr  )
    self.assertIn(
        'You cannot infer task automatically within `pipeline` when using offline mode'     ,      result.stderr.decode().replace('\n'     ,      ''  )     ,      )




  @require_torch
  def 		_lowercase	(					self :      Any  )   ->   Tuple:
    __lowerCamelCase			:			Any       			=					'\nfrom transformers import AutoModel\n        '
    __lowerCamelCase			:			str       			=					'\nmname = "hf-internal-testing/test_dynamic_model"\nAutoModel.from_pretrained(mname, trust_remote_code=True)\nprint("success")\n        '

    # baseline - just load from_pretrained with normal network
    __lowerCamelCase			:			List[Any]       			=					[sys.executable, '-c', '\n'.join([load, run]  )]

    # should succeed
    __lowerCamelCase			:			List[str]       			=					self.get_env()
    __lowerCamelCase			:			Any       			=					subprocess.run(_a     ,      env=_a     ,      check=_a     ,      capture_output=_a  )
    self.assertEqual(result.returncode     ,      0     ,      result.stderr  )
    self.assertIn('success'     ,      result.stdout.decode()  )

    # should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
    __lowerCamelCase			:			str       			=					'1'
    __lowerCamelCase			:			Dict       			=					subprocess.run(_a     ,      env=_a     ,      check=_a     ,      capture_output=_a  )
    self.assertEqual(result.returncode     ,      0     ,      result.stderr  )
    self.assertIn('success'     ,      result.stdout.decode()  )











'''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)






