
from typing import Tuple, Union

from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig


if is_timm_available():
     import timm


if is_torch_available():
     from torch import Tensor



class    _snake_case (						UpperCAmelCase_	,    UpperCAmelCase_				):
 __lowerCAmelCase	:  List[Any]	   =      'pixel_values'
 __lowerCAmelCase	:  Union[str, Any]	   =      False
 __lowerCAmelCase	:  List[Any]	   =      TimmBackboneConfig


 def __init__(       self       ,	SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   requires_backends(self       ,	"""timm""")
   super().__init__(SCREAMING_SNAKE_CASE_)
   lowercase__	: List[Any]     		=   config

   if config.backbone is None:
     raise ValueError("""backbone is not set in the config. Please set it to a timm model name.""")

   if config.backbone not in timm.list_models():
     raise ValueError(f'backbone {config.backbone} is not supported by timm.')

   if hasattr(SCREAMING_SNAKE_CASE_       ,	"""out_features""") and config.out_features is not None:
     raise ValueError("""out_features is not supported by TimmBackbone. Please use out_indices instead.""")

   lowercase__	: Any     		=   getattr(SCREAMING_SNAKE_CASE_       ,	"""use_pretrained_backbone"""       ,	SCREAMING_SNAKE_CASE_)
   if pretrained is None:
     raise ValueError("""use_pretrained_backbone is not set in the config. Please set it to True or False.""")

   # We just take the final layer by default. This matches the default for the transformers models.
   lowercase__	: Optional[int]     		=   config.out_indices if getattr(SCREAMING_SNAKE_CASE_       ,	"""out_indices"""       ,	SCREAMING_SNAKE_CASE_) is not None else (-1,)

   lowercase__	: List[str]     		=   timm.create_model(
       config.backbone       ,	pretrained=SCREAMING_SNAKE_CASE_       ,	features_only=config.features_only       ,	in_chans=config.num_channels       ,	out_indices=SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_       ,	)
   # These are used to control the output of the model when called. If output_hidden_states is True, then
   # return_layers is modified to include all layers.
   lowercase__	: List[str]     		=   self._backbone.return_layers
   lowercase__	: Union[str, Any]     		=   {layer["""module"""]: str(SCREAMING_SNAKE_CASE_) for i, layer in enumerate(self._backbone.feature_info.info)}
   super()._init_backbone(SCREAMING_SNAKE_CASE_)


 @classmethod
 def   lowercase__  (       cls       ,	SCREAMING_SNAKE_CASE_       ,	*SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   requires_backends(cls       ,	["""vision""", """timm"""])
   from ...models.timm_backbone import TimmBackboneConfig

   lowercase__	: Dict     		=   kwargs.pop("""config"""       ,	TimmBackboneConfig())

   lowercase__	: List[str]     		=   kwargs.pop("""use_timm_backbone"""       ,	SCREAMING_SNAKE_CASE_)
   if not use_timm:
     raise ValueError("""use_timm_backbone must be True for timm backbones""")

   lowercase__	: Union[str, Any]     		=   kwargs.pop("""num_channels"""       ,	config.num_channels)
   lowercase__	: Optional[int]     		=   kwargs.pop("""features_only"""       ,	config.features_only)
   lowercase__	: int     		=   kwargs.pop("""use_pretrained_backbone"""       ,	config.use_pretrained_backbone)
   lowercase__	: Any     		=   kwargs.pop("""out_indices"""       ,	config.out_indices)
   lowercase__	: List[str]     		=   TimmBackboneConfig(
       backbone=SCREAMING_SNAKE_CASE_       ,	num_channels=SCREAMING_SNAKE_CASE_       ,	features_only=SCREAMING_SNAKE_CASE_       ,	use_pretrained_backbone=SCREAMING_SNAKE_CASE_       ,	out_indices=SCREAMING_SNAKE_CASE_       ,	)
   return super()._from_config(SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   pass



 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_=None       ,	SCREAMING_SNAKE_CASE_=None       ,	SCREAMING_SNAKE_CASE_=None       ,	**SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: Union[str, Any]     		=   return_dict if return_dict is not None else self.config.use_return_dict
   lowercase__	: List[Any]     		=   (
       output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
   )
   lowercase__	: Optional[Any]     		=   output_attentions if output_attentions is not None else self.config.output_attentions

   if output_attentions:
     raise ValueError("""Cannot output attentions for timm backbones at the moment""")

   if output_hidden_states:
     # We modify the return layers to include all the stages of the backbone
     lowercase__	: List[str]     		=   self._all_layers
     lowercase__	: Optional[int]     		=   self._backbone(SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_)
     lowercase__	: Optional[int]     		=   self._return_layers
     lowercase__	: Optional[Any]     		=   tuple(hidden_states[i] for i in self.out_indices)
   else:
     lowercase__	: int     		=   self._backbone(SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_)
     lowercase__	: List[str]     		=   None

   lowercase__	: Optional[Any]     		=   tuple(SCREAMING_SNAKE_CASE_)
   lowercase__	: int     		=   tuple(SCREAMING_SNAKE_CASE_) if hidden_states is not None else None

   if not return_dict:
     lowercase__	: Union[str, Any]     		=   (feature_maps,)
     if output_hidden_states:
       lowercase__	: Optional[Any]     		=   output + (hidden_states,)
     return output

   return BackboneOutput(feature_maps=SCREAMING_SNAKE_CASE_       ,	hidden_states=SCREAMING_SNAKE_CASE_       ,	attentions=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)






