
import unittest

import numpy as np

def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_			,			lowercase_ = None			,			)		->						np.ndarray:


  '''simple docstring'''






  lowercase__	: Tuple     		=   np.shape(lowercase_ )
  lowercase__	: Tuple     		=   np.shape(lowercase_ )
  lowercase__	: Optional[int]     		=   np.shape(lowercase_ )

  if shape_a[0] != shape_b[0]:
    lowercase__	: List[Any]     		=   (
        """Expected the same number of rows for A and B. """
        F'Instead found A of size {shape_a} and B of size {shape_b}'
    )
    raise ValueError(lowercase_ )

  if shape_b[1] != shape_c[1]:
    lowercase__	: Any     		=   (
        """Expected the same number of columns for B and C. """
        F'Instead found B of size {shape_b} and C of size {shape_c}'
    )
    raise ValueError(lowercase_ )

  lowercase__	: List[str]     		=   pseudo_inv
  if a_inv is None:
    try:
      lowercase__	: List[str]     		=   np.linalg.inv(lowercase_ )
    except np.linalg.LinAlgError:
      raise ValueError(
          """Input matrix A is not invertible. Cannot compute Schur complement.""" )

  return mat_c - mat_b.T @ a_inv @ mat_b



class    _snake_case (						unittest.TestCase				):


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: List[str]     		=   np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]])
   lowercase__	: List[str]     		=   np.array([[0, 3], [3, 0], [2, 3]])
   lowercase__	: Tuple     		=   np.array([[2, 1], [6, 3]])

   lowercase__	: List[Any]     		=   schur_complement(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)

   lowercase__	: Any     		=   np.block([[a, b], [b.T, c]])

   lowercase__	: List[Any]     		=   np.linalg.det(SCREAMING_SNAKE_CASE_)
   lowercase__	: List[Any]     		=   np.linalg.det(SCREAMING_SNAKE_CASE_)
   lowercase__	: int     		=   np.linalg.det(SCREAMING_SNAKE_CASE_)

   self.assertAlmostEqual(SCREAMING_SNAKE_CASE_       ,	det_a * det_s)


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Optional[Any]     		=   np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]])
   lowercase__	: Dict     		=   np.array([[0, 3], [3, 0], [2, 3]])
   lowercase__	: int     		=   np.array([[2, 1], [6, 3]])

   with self.assertRaises(SCREAMING_SNAKE_CASE_):
     schur_complement(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)



 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Tuple     		=   np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]])
   lowercase__	: Union[str, Any]     		=   np.array([[0, 3], [3, 0], [2, 3]])
   lowercase__	: str     		=   np.array([[2, 1, 3], [6, 3, 5]])

   with self.assertRaises(SCREAMING_SNAKE_CASE_):
     schur_complement(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     unittest.main()








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)






