

import enum
import inspect
from typing import Iterable, List, Optional, Tuple, Union







class 		__magic_name__     (				enum.Enum				):

   '''simple docstring'''




   lowerCamelCase__					:      Optional[int]        =  'timm'
   lowerCamelCase__					:      Any        =  'transformers'





def      UpperCAmelCase__							(     _A :					Dict      , _A :					int      , _A :					Optional[Any]				):





  '''simple docstring'''







  if stage_names is None:
    raise ValueError('''Stage_names must be set for transformers backbones'''				)

  if out_features is not None:
    if not isinstance(_A      , (list,)				):
      raise ValueError(F"""out_features must be a list {type(_A				)}"""				)
    if any(feat not in stage_names for feat in out_features				):
      raise ValueError(F"""out_features must be a subset of stage_names: {stage_names} got {out_features}"""				)

  if out_indices is not None:
    if not isinstance(_A      , (list, tuple)				):
      raise ValueError(F"""out_indices must be a list or tuple, got {type(_A				)}"""				)
    if any(idx >= len(_A				) for idx in out_indices				):
      raise ValueError('''out_indices must be valid indices for stage_names {stage_names}, got {out_indices}'''				)

  if out_features is not None and out_indices is not None:
    if len(_A				) != len(_A				):
      raise ValueError('''out_features and out_indices should have the same length if both are set'''				)
    if out_features != [stage_names[idx] for idx in out_indices]:
      raise ValueError('''out_features and out_indices should correspond to the same stages if both are set'''				)




def      UpperCAmelCase__							(     _A :					str      , _A :					Dict      , _A :					Dict      , ):





  '''simple docstring'''







  if out_indices is None and out_features is None:
    a__    =[len(_A				) - 1]
    a__    =[stage_names[-1]]
  elif out_indices is None and out_features is not None:
    a__    =[stage_names.index(_A				) for layer in out_features]
  elif out_features is None and out_indices is not None:
    a__    =[stage_names[idx] for idx in out_indices]
  return out_features, out_indices




def      UpperCAmelCase__							(     _A :					Union[str, Any]      , _A :					Tuple      , _A :					Union[str, Any]      , ):





  '''simple docstring'''







  verify_out_features_out_indices(out_features=_A      , out_indices=_A      , stage_names=_A				)
  a__, a__    =_align_output_features_output_indices(
      out_features=_A      , out_indices=_A      , stage_names=_A				)
  # Verify that the aligned out_features and out_indices are valid
  verify_out_features_out_indices(out_features=_A      , out_indices=_A      , stage_names=_A				)
  return output_features, output_indices







class 		__magic_name__     :

   '''simple docstring'''




   lowerCamelCase__					:      Any        =  None



   def 					_UpperCAmelCase					(							self,       lowercase_			)				->       None:






     """simple docstring"""






     if getattr(self,       '''_backbone''',       lowerCAmelCase__			) is None:
       raise ValueError('''self._backbone must be set before calling _init_timm_backbone'''			)

     # These will diagree with the defaults for the transformers models e.g. for resnet50
     # the transformer model has out_features = ['stem', 'stage1', 'stage2', 'stage3', 'stage4']
     # the timm model has out_features = ['act', 'layer1', 'layer2', 'layer3', 'layer4']
     a__    =[stage['''module'''] for stage in self._backbone.feature_info.info]
     a__    =[stage['''num_chs'''] for stage in self._backbone.feature_info.info]
     a__    =self._backbone.feature_info.out_indices
     a__    =self._backbone.feature_info.module_name()

     # We verify the out indices and out features are valid
     verify_out_features_out_indices(
         out_features=lowerCAmelCase__,       out_indices=lowerCAmelCase__,       stage_names=self.stage_names			)
     a__, a__    =out_features, out_indices



   def 					_UpperCAmelCase					(							self,       lowercase_			)				->       None:






     """simple docstring"""






     a__    =getattr(lowerCAmelCase__,       '''stage_names'''			)
     a__    =getattr(lowerCAmelCase__,       '''out_features''',       lowerCAmelCase__			)
     a__    =getattr(lowerCAmelCase__,       '''out_indices''',       lowerCAmelCase__			)

     a__    =stage_names
     a__, a__    =get_aligned_output_features_output_indices(
         out_features=lowerCAmelCase__,       out_indices=lowerCAmelCase__,       stage_names=lowerCAmelCase__			)
     # Number of channels for each stage. This is set in the transformer backbone model init
     a__    =None



   def 					_UpperCAmelCase					(							self,       lowercase_			)				->       None:






     """simple docstring"""






     a__    =config

     a__    =getattr(lowerCAmelCase__,       '''use_timm_backbone''',       lowerCAmelCase__			)
     a__    =BackboneType.TIMM if self.use_timm_backbone else BackboneType.TRANSFORMERS

     if self.backbone_type == BackboneType.TIMM:
       self._init_timm_backbone(lowerCAmelCase__			)
     elif self.backbone_type == BackboneType.TRANSFORMERS:
       self._init_transformers_backbone(lowerCAmelCase__			)
     else:
       raise ValueError(F"""backbone_type {self.backbone_type} not supported."""			)



   @property
   def 					_UpperCAmelCase					(							self			)				->       Optional[int]:






     """simple docstring"""






     return self._out_features



   @out_features.setter
   def 					_UpperCAmelCase					(							self,       lowercase_			)				->       str:






     """simple docstring"""






     a__, a__    =get_aligned_output_features_output_indices(
         out_features=lowerCAmelCase__,       out_indices=lowerCAmelCase__,       stage_names=self.stage_names			)



   @property
   def 					_UpperCAmelCase					(							self			)				->       Tuple:






     """simple docstring"""






     return self._out_indices



   @out_indices.setter
   def 					_UpperCAmelCase					(							self,       lowercase_			)				->       Dict:






     """simple docstring"""






     a__, a__    =get_aligned_output_features_output_indices(
         out_features=lowerCAmelCase__,       out_indices=lowerCAmelCase__,       stage_names=self.stage_names			)



   @property
   def 					_UpperCAmelCase					(							self			)				->       Any:






     """simple docstring"""






     return {stage: self.num_features[i] for i, stage in enumerate(self.stage_names			)}



   @property
   def 					_UpperCAmelCase					(							self			)				->       List[Any]:






     """simple docstring"""






     return [self.out_feature_channels[name] for name in self.out_features]



   def 					_UpperCAmelCase					(							self,       *lowercase_,       **lowercase_			)				->       List[str]:






     """simple docstring"""






     a__    =dict(inspect.signature(self.forward			).parameters			)
     a__    ={k: v for k, v in kwargs.items() if k in signature}
     return self(*lowerCAmelCase__,       **lowerCAmelCase__			)



   def 					_UpperCAmelCase					(							self,       lowercase_,       lowercase_ = None,       lowercase_ = None,       lowercase_ = None,       )				->       Tuple:






     """simple docstring"""






     raise NotImplementedError('''This method should be implemented by the derived class.'''			)



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






     """simple docstring"""






     a__    =super().to_dict()
     a__    =output.pop('''_out_features'''			)
     a__    =output.pop('''_out_indices'''			)
     return output







class 		__magic_name__     :

   '''simple docstring'''




   @property
   def 					_UpperCAmelCase					(							self			)				->       Union[str, Any]:






     """simple docstring"""






     return self._out_features



   @out_features.setter
   def 					_UpperCAmelCase					(							self,       lowercase_			)				->       Union[str, Any]:






     """simple docstring"""






     a__, a__    =get_aligned_output_features_output_indices(
         out_features=lowerCAmelCase__,       out_indices=lowerCAmelCase__,       stage_names=self.stage_names			)



   @property
   def 					_UpperCAmelCase					(							self			)				->       int:






     """simple docstring"""






     return self._out_indices



   @out_indices.setter
   def 					_UpperCAmelCase					(							self,       lowercase_			)				->       List[str]:






     """simple docstring"""






     a__, a__    =get_aligned_output_features_output_indices(
         out_features=lowerCAmelCase__,       out_indices=lowerCAmelCase__,       stage_names=self.stage_names			)



   def 					_UpperCAmelCase					(							self			)				->       int:






     """simple docstring"""






     a__    =super().to_dict()
     a__    =output.pop('''_out_features'''			)
     a__    =output.pop('''_out_indices'''			)
     return output

"""simple docstring"""


import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase   )							-> Optional[Any]:
     snake_case_			,      snake_case_			,      snake_case_   			=   pt_attn_layer.in_proj_weight.chunk(3		,   dim=0   )
     snake_case_			,      snake_case_			,      snake_case_   			=   pt_attn_layer.in_proj_bias.chunk(3		,   dim=0   )

     snake_case_   			=   pt_attn_layer.out_proj.weight
     snake_case_   			=   pt_attn_layer.out_proj.bias

     snake_case_   			=   q_proj
     snake_case_   			=   q_proj_bias

     snake_case_   			=   k_proj
     snake_case_   			=   k_proj_bias

     snake_case_   			=   v_proj
     snake_case_   			=   v_proj_bias

     snake_case_   			=   out_proj_weights
     snake_case_   			=   out_proj_bias







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase   )							-> int:
     copy_linear(hf_mlp.fca		,   pt_mlp.c_fc   )
     copy_linear(hf_mlp.fca		,   pt_mlp.c_proj   )







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase   )							-> int:
     snake_case_   			=   pt_linear.weight
     snake_case_   			=   pt_linear.bias







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase   )							-> str:
     # copy layer norms
     copy_linear(hf_layer.layer_norma		,   pt_layer.ln_a   )
     copy_linear(hf_layer.layer_norma		,   pt_layer.ln_a   )

     # copy MLP
     copy_mlp(hf_layer.mlp		,   pt_layer.mlp   )

     # copy attn
     copy_attn_layer(hf_layer.self_attn		,   pt_layer.attn   )







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase   )							-> Optional[Any]:
     for hf_layer, pt_layer in zip(UpperCAmelCase		,   UpperCAmelCase   ):
          copy_layer(UpperCAmelCase		,   UpperCAmelCase   )







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase   )							-> Tuple:
     # copy  embeds
     snake_case_   			=   pt_model.token_embedding.weight
     snake_case_   			=   pt_model.positional_embedding

     # copy layer norm
     copy_linear(hf_encoder.final_layer_norm		,   pt_model.ln_final   )

     # copy hidden layers
     copy_layers(hf_encoder.encoder.layers		,   pt_model.transformer.resblocks   )







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase   )							-> int:
     # copy projection
     snake_case_   			=   pt_model.text_projection.data.T

     # copy text encoder
     copy_encoder(hf_model.text_model		,   UpperCAmelCase   )







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase   )							-> List[str]:
     # copy projection
     snake_case_   			=   pt_model.visual.proj.data.T

     # copy layer norms
     copy_linear(hf_model.vision_model.pre_layrnorm		,   pt_model.visual.ln_pre   )
     copy_linear(hf_model.vision_model.post_layernorm		,   pt_model.visual.ln_post   )

     # copy embeds
     snake_case_   			=   pt_model.visual.conva.weight.data
     snake_case_   			=   pt_model.visual.class_embedding
     snake_case_   			=   pt_model.visual.positional_embedding.data

     # copy encoder
     copy_layers(hf_model.vision_model.encoder.layers		,   pt_model.visual.transformer.resblocks   )







@torch.no_grad()
def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase		,   UpperCAmelCase=None   )							-> Optional[Any]:
     if config_path is not None:
          snake_case_   			=   CLIPConfig.from_pretrained(UpperCAmelCase   )
     else:
          snake_case_   			=   CLIPConfig(projection_dim=512		,   text_config={}		,   vision_config={}   )

     snake_case_   			=   CLIPModel(UpperCAmelCase   ).eval()

     snake_case_			,      snake_case_   			=   load(UpperCAmelCase		,   device='cpu'		,   jit=UpperCAmelCase   )
     snake_case_   			=   pt_model.eval()

     copy_text_model_and_projection(UpperCAmelCase		,   UpperCAmelCase   )
     copy_vison_model_and_projection(UpperCAmelCase		,   UpperCAmelCase   )
     snake_case_   			=   pt_model.logit_scale

     snake_case_   			=   torch.arange(0		,   77   ).unsqueeze(0   )
     snake_case_   			=   torch.randn(1		,   3		,   224		,   224   )

     snake_case_   			=   hf_model(input_ids=UpperCAmelCase		,   pixel_values=UpperCAmelCase		,   return_dict=UpperCAmelCase   )
     snake_case_   			=   hf_outputs.logits_per_image
     snake_case_   			=   hf_outputs.logits_per_text
     snake_case_			,      snake_case_   			=   pt_model(UpperCAmelCase		,   UpperCAmelCase   )

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

     hf_model.save_pretrained(UpperCAmelCase   )


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 fairseq 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_clip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)

