






import shutil
import tempfile
import unittest
from unittest.mock import patch

from transformers import (
    DefaultFlowCallback,
    IntervalStrategy,
    PrinterCallback,
    ProgressCallback,
    Trainer,
    TrainerCallback,
    TrainingArguments,
    is_torch_available,
)
from transformers.testing_utils import require_torch


if is_torch_available():
     from transformers.trainer import DEFAULT_CALLBACKS

     from .test_trainer import RegressionDataset, RegressionModelConfig, RegressionPreTrainedModel




class 			snake_case__   (							lowercase_):

       '''simple docstring'''



       def __init__(							self    ) ->      Optional[Any]:






            '''simple docstring'''





            __snake_case			:Any          =						[]



       def 					__lowercase						(							self			,						a__			,						a__			,						a__			,						**a__    ) ->      Any:






            '''simple docstring'''





            self.events.append("""on_init_end"""    )



       def 					__lowercase						(							self			,						a__			,						a__			,						a__			,						**a__    ) ->      str:






            '''simple docstring'''





            self.events.append("""on_train_begin"""    )



       def 					__lowercase						(							self			,						a__			,						a__			,						a__			,						**a__    ) ->      Dict:






            '''simple docstring'''





            self.events.append("""on_train_end"""    )



       def 					__lowercase						(							self			,						a__			,						a__			,						a__			,						**a__    ) ->      Any:






            '''simple docstring'''





            self.events.append("""on_epoch_begin"""    )



       def 					__lowercase						(							self			,						a__			,						a__			,						a__			,						**a__    ) ->      Dict:






            '''simple docstring'''





            self.events.append("""on_epoch_end"""    )



       def 					__lowercase						(							self			,						a__			,						a__			,						a__			,						**a__    ) ->      Optional[Any]:






            '''simple docstring'''





            self.events.append("""on_step_begin"""    )



       def 					__lowercase						(							self			,						a__			,						a__			,						a__			,						**a__    ) ->      Tuple:






            '''simple docstring'''





            self.events.append("""on_step_end"""    )



       def 					__lowercase						(							self			,						a__			,						a__			,						a__			,						**a__    ) ->      Any:






            '''simple docstring'''





            self.events.append("""on_evaluate"""    )



       def 					__lowercase						(							self			,						a__			,						a__			,						a__			,						**a__    ) ->      List[str]:






            '''simple docstring'''





            self.events.append("""on_predict"""    )



       def 					__lowercase						(							self			,						a__			,						a__			,						a__			,						**a__    ) ->      int:






            '''simple docstring'''





            self.events.append("""on_save"""    )



       def 					__lowercase						(							self			,						a__			,						a__			,						a__			,						**a__    ) ->      Optional[int]:






            '''simple docstring'''





            self.events.append("""on_log"""    )



       def 					__lowercase						(							self			,						a__			,						a__			,						a__			,						**a__    ) ->      Dict:






            '''simple docstring'''





            self.events.append("""on_prediction_step"""    )




@require_torch
class 			snake_case__   (							unittest.TestCase):

       '''simple docstring'''



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






            '''simple docstring'''





            __snake_case			:int          =						tempfile.mkdtemp()



       def 					__lowercase						(							self    ) ->      Tuple:






            '''simple docstring'''





            shutil.rmtree(self.output_dir    )



       def 					__lowercase						(							self			,						a__=0			,						a__=0			,						a__=64			,						a__=64			,						a__=None			,						a__=False			,						**a__    ) ->      Optional[Any]:






            '''simple docstring'''





            __snake_case			:List[Any]          =						RegressionDataset(length=a__    )
            __snake_case			:int          =						RegressionDataset(length=a__    )
            __snake_case			:str          =						RegressionModelConfig(a=a__			,						b=a__    )
            __snake_case			:str          =						RegressionPreTrainedModel(a__    )

            __snake_case			:Any          =						TrainingArguments(self.output_dir			,						disable_tqdm=a__			,						report_to=[]			,						**a__    )
            return Trainer(
                a__			,						a__			,						train_dataset=a__			,						eval_dataset=a__			,						callbacks=a__			,						)



       def 					__lowercase						(							self			,						a__			,						a__    ) ->      Dict:






            '''simple docstring'''





            self.assertEqual(len(a__    )			,						len(a__    )    )

            # Order doesn't matter
            __snake_case			:Any          =						sorted(a__			,						key=lambda a__    : cb.__name__ if isinstance(a__			,						a__    ) else cb.__class__.__name__    )
            __snake_case			:Any          =						sorted(a__			,						key=lambda a__    : cb.__name__ if isinstance(a__			,						a__    ) else cb.__class__.__name__    )

            for cba, cba in zip(a__			,						a__    ):
                 if isinstance(a__			,						a__    ) and isinstance(a__			,						a__    ):
                      self.assertEqual(a__			,						a__    )
                 elif isinstance(a__			,						a__    ) and not isinstance(a__			,						a__    ):
                      self.assertEqual(a__			,						cba.__class__    )
                 elif not isinstance(a__			,						a__    ) and isinstance(a__			,						a__    ):
                      self.assertEqual(cba.__class__			,						a__    )
                 else:
                      self.assertEqual(a__			,						a__    )



       def 					__lowercase						(							self			,						a__    ) ->      List[str]:






            '''simple docstring'''





            __snake_case			:Union[str, Any]          =						["""on_init_end""", """on_train_begin"""]
            __snake_case			:Optional[int]          =						0
            __snake_case			:Dict          =						len(trainer.get_eval_dataloader()    )
            __snake_case			:Any          =						["""on_prediction_step"""] * len(trainer.get_eval_dataloader()    ) + ["""on_log""", """on_evaluate"""]
            for _ in range(trainer.state.num_train_epochs    ):
                 expected_events.append("""on_epoch_begin"""    )
                 for _ in range(a__    ):
                      step += 1
                      expected_events += ["on_step_begin", "on_step_end"]
                      if step % trainer.args.logging_steps == 0:
                           expected_events.append("""on_log"""    )
                      if trainer.args.evaluation_strategy == IntervalStrategy.STEPS and step % trainer.args.eval_steps == 0:
                           expected_events += evaluation_events.copy()
                      if step % trainer.args.save_steps == 0:
                           expected_events.append("""on_save"""    )
                 expected_events.append("""on_epoch_end"""    )
                 if trainer.args.evaluation_strategy == IntervalStrategy.EPOCH:
                      expected_events += evaluation_events.copy()
            expected_events += ["on_log", "on_train_end"]
            return expected_events



       def 					__lowercase						(							self    ) ->      List[str]:






            '''simple docstring'''





            __snake_case			:List[Any]          =						self.get_trainer()
            __snake_case			:str          =						DEFAULT_CALLBACKS.copy() + [ProgressCallback]
            self.check_callbacks_equality(trainer.callback_handler.callbacks			,						a__    )

            # Callbacks passed at init are added to the default callbacks
            __snake_case			:List[str]          =						self.get_trainer(callbacks=[MyTestTrainerCallback]    )
            expected_callbacks.append(a__    )
            self.check_callbacks_equality(trainer.callback_handler.callbacks			,						a__    )

            # TrainingArguments.disable_tqdm controls if use ProgressCallback or PrinterCallback
            __snake_case			:Tuple          =						self.get_trainer(disable_tqdm=a__    )
            __snake_case			:Any          =						DEFAULT_CALLBACKS.copy() + [PrinterCallback]
            self.check_callbacks_equality(trainer.callback_handler.callbacks			,						a__    )



       def 					__lowercase						(							self    ) ->      Optional[int]:






            '''simple docstring'''





            __snake_case			:List[Any]          =						DEFAULT_CALLBACKS.copy() + [ProgressCallback]
            __snake_case			:List[str]          =						self.get_trainer()

            # We can add, pop, or remove by class name
            trainer.remove_callback(a__    )
            expected_callbacks.remove(a__    )
            self.check_callbacks_equality(trainer.callback_handler.callbacks			,						a__    )

            __snake_case			:str          =						self.get_trainer()
            __snake_case			:Tuple          =						trainer.pop_callback(a__    )
            self.assertEqual(cb.__class__			,						a__    )
            self.check_callbacks_equality(trainer.callback_handler.callbacks			,						a__    )

            trainer.add_callback(a__    )
            expected_callbacks.insert(0			,						a__    )
            self.check_callbacks_equality(trainer.callback_handler.callbacks			,						a__    )

            # We can also add, pop, or remove by instance
            __snake_case			:Dict          =						self.get_trainer()
            __snake_case			:Any          =						trainer.callback_handler.callbacks[0]
            trainer.remove_callback(a__    )
            expected_callbacks.remove(a__    )
            self.check_callbacks_equality(trainer.callback_handler.callbacks			,						a__    )

            __snake_case			:Any          =						self.get_trainer()
            __snake_case			:Optional[int]          =						trainer.callback_handler.callbacks[0]
            __snake_case			:str          =						trainer.pop_callback(a__    )
            self.assertEqual(a__			,						a__    )
            self.check_callbacks_equality(trainer.callback_handler.callbacks			,						a__    )

            trainer.add_callback(a__    )
            expected_callbacks.insert(0			,						a__    )
            self.check_callbacks_equality(trainer.callback_handler.callbacks			,						a__    )



       def 					__lowercase						(							self    ) ->      Dict:






            '''simple docstring'''





            import warnings

            # XXX: for now ignore scatter_gather warnings in this test since it's not relevant to what's being tested
            warnings.simplefilter(action="""ignore"""			,						category=a__    )

            __snake_case			:Any          =						self.get_trainer(callbacks=[MyTestTrainerCallback]    )
            trainer.train()
            __snake_case			:Tuple          =						trainer.callback_handler.callbacks[-2].events
            self.assertEqual(a__			,						self.get_expected_events(a__    )    )

            # Independent log/save/eval
            __snake_case			:Dict          =						self.get_trainer(callbacks=[MyTestTrainerCallback]			,						logging_steps=5    )
            trainer.train()
            __snake_case			:Dict          =						trainer.callback_handler.callbacks[-2].events
            self.assertEqual(a__			,						self.get_expected_events(a__    )    )

            __snake_case			:List[str]          =						self.get_trainer(callbacks=[MyTestTrainerCallback]			,						save_steps=5    )
            trainer.train()
            __snake_case			:Optional[Any]          =						trainer.callback_handler.callbacks[-2].events
            self.assertEqual(a__			,						self.get_expected_events(a__    )    )

            __snake_case			:List[str]          =						self.get_trainer(callbacks=[MyTestTrainerCallback]			,						eval_steps=5			,						evaluation_strategy="""steps"""    )
            trainer.train()
            __snake_case			:Optional[Any]          =						trainer.callback_handler.callbacks[-2].events
            self.assertEqual(a__			,						self.get_expected_events(a__    )    )

            __snake_case			:Tuple          =						self.get_trainer(callbacks=[MyTestTrainerCallback]			,						evaluation_strategy="""epoch"""    )
            trainer.train()
            __snake_case			:Union[str, Any]          =						trainer.callback_handler.callbacks[-2].events
            self.assertEqual(a__			,						self.get_expected_events(a__    )    )

            # A bit of everything
            __snake_case			:int          =						self.get_trainer(
                callbacks=[MyTestTrainerCallback]			,						logging_steps=3			,						save_steps=10			,						eval_steps=5			,						evaluation_strategy="""steps"""			,						)
            trainer.train()
            __snake_case			:Optional[int]          =						trainer.callback_handler.callbacks[-2].events
            self.assertEqual(a__			,						self.get_expected_events(a__    )    )

            # warning should be emitted for duplicated callbacks
            with patch("""transformers.trainer_callback.logger.warning"""    ) as warn_mock:
                 __snake_case			:str          =						self.get_trainer(
                     callbacks=[MyTestTrainerCallback, MyTestTrainerCallback]			,						)
                 assert str(a__    ) in warn_mock.call_args[0][0]










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   UpperCamelCase  (       snake_case__     :   str	,snake_case__     :   Dict	,snake_case__     :   List[str]  ):




     '''simple docstring'''






     __snake_case			:Tuple          =						1.5
     __snake_case			:Any          =						int(factor * num_class_images  )
     __snake_case			:List[str]          =						ClipClient(
         url="""https://knn.laion.ai/knn-service"""	,indice_name="""laion_400m"""	,num_images=snake_case__	,aesthetic_weight=0.1  )

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

     while True:
          __snake_case			:Optional[Any]          =						client.query(text=snake_case__  )
          if len(snake_case__  ) >= factor * num_class_images or num_images > 1e4:
               break
          else:
               __snake_case			:Tuple          =						int(factor * num_images  )
               __snake_case			:Any          =						ClipClient(
                   url="""https://knn.laion.ai/knn-service"""	,indice_name="""laion_400m"""	,num_images=snake_case__	,aesthetic_weight=0.1	,)

     __snake_case			:Dict          =						0
     __snake_case			:Tuple          =						0
     __snake_case			:Dict          =						tqdm(desc="""downloading real regularization images"""	,total=snake_case__  )

     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:
               __snake_case			:List[Any]          =						class_images[count]
               count += 1
               try:
                    __snake_case			:str          =						requests.get(images["""url"""]  )
                    if img.status_code == 200:
                         __snake_case			:Any          =						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   UpperCamelCase  (       ):




     '''simple docstring'''






     __snake_case			:List[str]          =						argparse.ArgumentParser(""""""	,add_help=snake_case__  )
     parser.add_argument("""--class_prompt"""	,help="""text prompt to retrieve images"""	,required=snake_case__	,type=snake_case__  )
     parser.add_argument("""--class_data_dir"""	,help="""path to save images"""	,required=snake_case__	,type=snake_case__  )
     parser.add_argument("""--num_class_images"""	,help="""number of images to download"""	,default=200	,type=snake_case__  )
     return parser.parse_args()


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


