
import unittest

import torch
from torch import nn

from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory

def  __snake_case							(	):







				'''simple docstring'''


				raise RuntimeError("CUDA out of memory."						)





class   UpperCamelCase_      (      nn.Module		):







						'''simple docstring'''



						def __init__(			self							:str     )  ->Any:
										super().__init__()
										lowercase         =      nn.Linear(3		,    4     )
										lowercase         =      nn.BatchNormad(4     )
										lowercase         =      nn.Linear(4		,    5     )





						def        SCREAMING_SNAKE_CASE(			self							:List[Any]		,    lowerCAmelCase__							:Any     )  ->Dict:
										return self.lineara(self.batchnorm(self.lineara(SCREAMING_SNAKE_CASE_     )     )     )





class   UpperCamelCase_      (      unittest.TestCase		):







						'''simple docstring'''



						def        SCREAMING_SNAKE_CASE(			self							:Optional[Any]     )  ->int:
										lowercase         =      []

										@find_executable_batch_size(starting_batch_size=128     )
										def mock_training_loop_function(lowerCAmelCase__							:str     ):
														nonlocal batch_sizes
														batch_sizes.append(SCREAMING_SNAKE_CASE_     )
														if batch_size != 8:
																		raise_fake_out_of_memory()

										mock_training_loop_function()
										self.assertListEqual(SCREAMING_SNAKE_CASE_		,    [128, 64, 32, 16, 8]     )





						def        SCREAMING_SNAKE_CASE(			self							:List[str]     )  ->Optional[int]:
										lowercase         =      []

										@find_executable_batch_size(starting_batch_size=128     )
										def mock_training_loop_function(lowerCAmelCase__							:Optional[int]		,    lowerCAmelCase__							:Union[str, Any]     ):
														nonlocal batch_sizes
														batch_sizes.append(SCREAMING_SNAKE_CASE_     )
														if batch_size != 8:
																		raise_fake_out_of_memory()
														return batch_size, arga

										lowercase         =      mock_training_loop_function("hello"     )
										self.assertListEqual(SCREAMING_SNAKE_CASE_		,    [128, 64, 32, 16, 8]     )
										self.assertListEqual([bs, arga]		,    [8, "hello"]     )





						def        SCREAMING_SNAKE_CASE(			self							:Tuple     )  ->List[Any]:




										@find_executable_batch_size(starting_batch_size=0     )
										def mock_training_loop_function(lowerCAmelCase__							:Any     ):
														pass

										with self.assertRaises(SCREAMING_SNAKE_CASE_     ) as cm:
														mock_training_loop_function()
														self.assertIn("No executable batch size found, reached zero."		,    cm.exception.args[0]     )





						def        SCREAMING_SNAKE_CASE(			self							:Any     )  ->int:




										@find_executable_batch_size(starting_batch_size=16     )
										def mock_training_loop_function(lowerCAmelCase__							:str     ):
														if batch_size > 0:
																		raise_fake_out_of_memory()
														pass

										with self.assertRaises(SCREAMING_SNAKE_CASE_     ) as cm:
														mock_training_loop_function()
														self.assertIn("No executable batch size found, reached zero."		,    cm.exception.args[0]     )





						def        SCREAMING_SNAKE_CASE(			self							:Any     )  ->List[str]:




										@find_executable_batch_size(starting_batch_size=128     )
										def mock_training_loop_function(lowerCAmelCase__							:Optional[int]		,    lowerCAmelCase__							:List[Any]		,    lowerCAmelCase__							:Union[str, Any]     ):
														if batch_size != 8:
																		raise raise_fake_out_of_memory()

										with self.assertRaises(SCREAMING_SNAKE_CASE_     ) as cm:
														mock_training_loop_function(128		,    "hello"		,    "world"     )
														self.assertIn("Batch size was passed into `f`"		,    cm.exception.args[0]     )
														self.assertIn("`f(arg1='hello', arg2='world')"		,    cm.exception.args[0]     )





						def        SCREAMING_SNAKE_CASE(			self							:Any     )  ->List[str]:




										@find_executable_batch_size(starting_batch_size=16     )
										def mock_training_loop_function(lowerCAmelCase__							:Any     ):
														raise ValueError("Oops, we had an error!"     )

										with self.assertRaises(SCREAMING_SNAKE_CASE_     ) as cm:
														mock_training_loop_function()
														self.assertIn("Oops, we had an error!"		,    cm.exception.args[0]     )





						@require_cuda
						def        SCREAMING_SNAKE_CASE(			self							:Dict     )  ->int:
										lowercase         =      torch.cuda.memory_allocated()
										lowercase         =      ModelForTest()
										model.cuda()
										self.assertGreater(torch.cuda.memory_allocated()		,    SCREAMING_SNAKE_CASE_     )
										lowercase         =      release_memory(SCREAMING_SNAKE_CASE_     )
										self.assertEqual(torch.cuda.memory_allocated()		,    SCREAMING_SNAKE_CASE_     )


import argparse
import os

import torch

from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint

def 		UpperCamelCase						(   lowercase_ )		->						Union[str, Any]:


  '''simple docstring'''






  return sum(param.float().sum() if """encoder.embeddings""" not in key else 0 for key, param in state_dict.items() )

def 		UpperCamelCase						(   lowercase_			,			lowercase_ )		->						List[Any]:


  '''simple docstring'''






  lowercase__	: int     		=   {}

  for key, value in state_dict.items():
    if "text_encoder.embeddings" in key or "image_encoder.embeddings" in key:
      continue

    lowercase__	: Optional[Any]     		=   key.replace("""heads.cmd.mim_head.cls.predictions"""			,			"""mmm_image_head""" )
    lowercase__	: Optional[Any]     		=   key.replace("""heads.cmd.mlm_head.cls.predictions"""			,			"""mmm_text_head""" )
    lowercase__	: Optional[Any]     		=   key.replace("""heads.cmd.itm_head.cls"""			,			"""itm_head""" )
    lowercase__	: Tuple     		=   key.replace("""heads.cmd.itm_head.pooler"""			,			"""itm_head.pooler""" )
    lowercase__	: Optional[Any]     		=   key.replace("""heads.cmd.clip_head.logit_scale"""			,			"""flava.logit_scale""" )
    lowercase__	: Optional[int]     		=   key.replace("""heads.fairseq_mlm.cls.predictions"""			,			"""mlm_head""" )
    lowercase__	: List[Any]     		=   key.replace("""heads.imagenet.mim_head.cls.predictions"""			,			"""mim_head""" )
    lowercase__	: int     		=   key.replace("""mm_text_projection"""			,			"""flava.text_to_mm_projection""" )
    lowercase__	: Optional[Any]     		=   key.replace("""mm_image_projection"""			,			"""flava.image_to_mm_projection""" )
    lowercase__	: Optional[Any]     		=   key.replace("""image_encoder.module"""			,			"""flava.image_model""" )
    lowercase__	: Any     		=   key.replace("""text_encoder.module"""			,			"""flava.text_model""" )
    lowercase__	: Optional[Any]     		=   key.replace("""mm_encoder.module.encoder.cls_token"""			,			"""flava.multimodal_model.cls_token""" )
    lowercase__	: Tuple     		=   key.replace("""mm_encoder.module"""			,			"""flava.multimodal_model""" )
    lowercase__	: Any     		=   key.replace("""text_projection"""			,			"""flava.text_projection""" )
    lowercase__	: List[Any]     		=   key.replace("""image_projection"""			,			"""flava.image_projection""" )

    lowercase__	: str     		=   value.float()

  for key, value in codebook_state_dict.items():
    lowercase__	: Any     		=   value

  return upgrade

@torch.no_grad()
def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_			,			lowercase_=None )		->						Union[str, Any]:


  '''simple docstring'''






  if config_path is not None:
    lowercase__	: int     		=   FlavaConfig.from_pretrained(lowercase_ )
  else:
    lowercase__	: Optional[int]     		=   FlavaConfig()

  lowercase__	: List[Any]     		=   FlavaForPreTraining(lowercase_ ).eval()

  lowercase__	: Dict     		=   convert_dalle_checkpoint(lowercase_			,			lowercase_			,			save_checkpoint=lowercase_ )

  if os.path.exists(lowercase_ ):
    lowercase__	: Dict     		=   torch.load(lowercase_			,			map_location="""cpu""" )
  else:
    lowercase__	: Dict     		=   torch.hub.load_state_dict_from_url(lowercase_			,			map_location="""cpu""" )

  lowercase__	: int     		=   upgrade_state_dict(lowercase_			,			lowercase_ )
  hf_model.load_state_dict(lowercase_ )
  lowercase__	: Optional[int]     		=   hf_model.state_dict()
  lowercase__	: Optional[int]     		=   count_parameters(lowercase_ )
  lowercase__	: Any     		=   count_parameters(lowercase_ ) + count_parameters(lowercase_ )

  assert torch.allclose(lowercase_			,			lowercase_			,			atol=1E-3 )

  hf_model.save_pretrained(lowercase_ )


if __name__ == "__main__":
     lowerCamelCase__     :		int        = 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("""--codebook_path""", default=None, type=str, help="""Path to flava codebook checkpoint""")
     parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
     lowerCamelCase__     :		List[str]        = parser.parse_args()

     convert_flava_checkpoint(args.checkpoint_path, args.codebook_path, args.pytorch_dump_folder_path, args.config_path)






