






import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig
def 						_a    (      a :Optional[Any]	,							a :int	,							a :List[str]	,							a :List[str] )							->			Tuple:
 a		  =      s.rsplit(a	,							a )
 return new.join(a )
def 						_a    (      a :Any )							->			List[Any]:
 # encoder.embeddings are double copied in original FLAVA
 return sum(param.float().sum() if '''encoder.embeddings''' not in key else 0 for key, param in state_dict.items() )
def 						_a    (      a :Any )							->			List[Any]:
 a		  =      {}

 a		  =      ['''group_1''', '''group_2''', '''group_3''', '''group_4''']
 for key, value in state_dict.items():
  for group_key in group_keys:
   if group_key in key:
    a		  =      key.replace(F"""{group_key}."""	,							F"""{group_key}.group.""" )

  if "res_path" in key:
   a		  =      key.replace('''res_path.'''	,							'''res_path.path.''' )

  if key.endswith('''.w''' ):
   a		  =      rreplace(a	,							'''.w'''	,							'''.weight'''	,							1 )
  if key.endswith('''.b''' ):
   a		  =      rreplace(a	,							'''.b'''	,							'''.bias'''	,							1 )

  a		  =      value.float()

 return upgrade
@torch.no_grad()
def 						_a    (      a :Union[str, Any]	,							a :Optional[Any]	,							a :Optional[int]=None	,							a :str=True )							->			Tuple:
 from dall_e import Encoder

 a		  =      Encoder()
 if os.path.exists(a ):
  a		  =      torch.load(a )
 else:
  a		  =      torch.hub.load_state_dict_from_url(a )

 if isinstance(a	,							a ):
  a		  =      ckpt.state_dict()
 encoder.load_state_dict(a )

 if config_path is not None:
  a		  =      FlavaImageCodebookConfig.from_pretrained(a )
 else:
  a		  =      FlavaImageCodebookConfig()

 a		  =      FlavaImageCodebook(a ).eval()
 a		  =      encoder.state_dict()

 a		  =      upgrade_state_dict(a )
 hf_model.load_state_dict(a )
 a		  =      hf_model.state_dict()
 a		  =      count_parameters(a )
 a		  =      count_parameters(a )

 assert torch.allclose(a	,							a	,							atol=1e-3 )

 if save_checkpoint:
  hf_model.save_pretrained(a )
 else:
  return hf_state_dict


if __name__ == "__main__":
     UpperCAmelCase__				   =		argparse.ArgumentParser()
     parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
     parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to flava checkpoint")
     parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
     UpperCAmelCase__				   =		parser.parse_args()

     convert_dalle_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)









import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_deebert

from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow


logging.basicConfig(level=logging.DEBUG)

a__:      List[Any]									=		logging.getLogger()


def 	UpperCamelCase__(       )->Union[str, Any]:
       A__											=						argparse.ArgumentParser()
       parser.add_argument('''-f'''  )
       A__											=						parser.parse_args()
       return args.f




class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__      ):



 def 				UpperCamelCase			(							self ):
        A__											=						logging.StreamHandler(sys.stdout )
        logger.addHandler(__lowerCamelCase )



 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        A__											=						get_gpu_count()

        if n_gpu > 1:
               pass
               # XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
               # script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
               # distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
               # cmd = [sys.executable] + distributed_args + args
               # execute_subprocess_async(cmd, env=self.get_env())
               # XXX: test the results - need to save them first into .json file
        else:
               args.insert(0,'''run_glue_deebert.py''' )
               with patch.object(__lowerCamelCase,'''argv''',__lowerCamelCase ):
                      A__											=						run_glue_deebert.main()
                      for value in result.values():
                             self.assertGreaterEqual(__lowerCamelCase,0.666 )




 @slow
 @require_torch_non_multi_gpu
 def 				UpperCamelCase			(							self ):
        A__											=						'''
            --model_type roberta
            --model_name_or_path roberta-base
            --task_name MRPC
            --do_train
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --max_seq_length 128
            --per_gpu_eval_batch_size=1
            --per_gpu_train_batch_size=8
            --learning_rate 2e-4
            --num_train_epochs 3
            --overwrite_output_dir
            --seed 42
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --save_steps 0
            --overwrite_cache
            --eval_after_first_stage
            '''.split()
        self.run_and_check(__lowerCamelCase )

        A__											=						'''
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --eval_each_highway
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            '''.split()
        self.run_and_check(__lowerCamelCase )

        A__											=						'''
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --early_exit_entropy 0.1
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            '''.split()
        self.run_and_check(__lowerCamelCase )





