






import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel
def 		_a					(   a				:Any			,							a				:List[str]				)							->    Optional[int]:
 a						,  a						,  a							=   pt_attn_layer.in_proj_weight.chunk(3			,							dim=0				)
 a						,  a						,  a							=   pt_attn_layer.in_proj_bias.chunk(3			,							dim=0				)

 a							=   pt_attn_layer.out_proj.weight
 a							=   pt_attn_layer.out_proj.bias

 a							=   q_proj
 a							=   q_proj_bias

 a							=   k_proj
 a							=   k_proj_bias

 a							=   v_proj
 a							=   v_proj_bias

 a							=   out_proj_weights
 a							=   out_proj_bias
def 		_a					(   a				:str			,							a				:Dict				)							->    Optional[int]:
 copy_linear(hf_mlp.fca			,							pt_mlp.c_fc				)
 copy_linear(hf_mlp.fca			,							pt_mlp.c_proj				)
def 		_a					(   a				:List[Any]			,							a				:Union[str, Any]				)							->    Tuple:
 a							=   pt_linear.weight
 a							=   pt_linear.bias
def 		_a					(   a				:Tuple			,							a				:Union[str, Any]				)							->    Tuple:
 # 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 		_a					(   a				:Optional[int]			,							a				:int				)							->    Dict:
 for hf_layer, pt_layer in zip(a			,							a				):
  copy_layer(a			,							a				)
def 		_a					(   a				:Dict			,							a				:Optional[Any]				)							->    Any:
 # copy  embeds
 a							=   pt_model.token_embedding.weight
 a							=   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 		_a					(   a				:Optional[int]			,							a				:Tuple				)							->    str:
 # copy projection
 a							=   pt_model.text_projection.data.T

 # copy text encoder
 copy_encoder(hf_model.text_model			,							a				)
def 		_a					(   a				:Tuple			,							a				:Union[str, Any]				)							->    int:
 # copy projection
 a							=   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
 a							=   pt_model.visual.conva.weight.data
 a							=   pt_model.visual.class_embedding
 a							=   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 		_a					(   a				:str			,							a				:Optional[int]			,							a				:str=None				)							->    Union[str, Any]:
 if config_path is not None:
  a							=   CLIPConfig.from_pretrained(a				)
 else:
  a							=   CLIPConfig(projection_dim=512			,							text_config={}			,							vision_config={}				)

 a							=   CLIPModel(a				).eval()

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

 copy_text_model_and_projection(a			,							a				)
 copy_vison_model_and_projection(a			,							a				)
 a							=   pt_model.logit_scale

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

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

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

 hf_model.save_pretrained(a				)


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)










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






class 	lowercase_      (			enum.Enum     ):






      '''simple docstring'''




      __snake_case  						=						'''timm'''
      __snake_case  						=						'''transformers'''
def 		_a					(   a				:Optional[Iterable[str]]			,							a				:Optional[Iterable[int]]			,							a				:Optional[Iterable[str]]				)							->    Optional[Any]:
 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 		_a					(   a				:Optional[List[str]]			,							a				:Optional[Union[List[int], Tuple[int]]]			,							a				:List[str]			,							)							->    Union[str, Any]:
 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 		_a					(   a				:Optional[List[str]]			,							a				:Optional[Union[List[int], Tuple[int]]]			,							a				:List[str]			,							)							->    Tuple[List[str], List[int]]:
 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 	lowercase_      :






      '''simple docstring'''




      __snake_case  						=						None







      def    __lowerCAmelCase      (   self   :		Any				,      __UpperCAmelCase   :		Tuple						)					->None:




       """simple docstring"""


       if getattr(self				,      '''_backbone'''				,      __UpperCAmelCase						) 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=__UpperCAmelCase				,      out_indices=__UpperCAmelCase				,      stage_names=self.stage_names						)
       a						,  a							=   out_features, out_indices







      def    __lowerCAmelCase      (   self   :		Any				,      __UpperCAmelCase   :		Tuple						)					->None:




       """simple docstring"""


       a							=   getattr(__UpperCAmelCase				,      '''stage_names'''						)
       a							=   getattr(__UpperCAmelCase				,      '''out_features'''				,      __UpperCAmelCase						)
       a							=   getattr(__UpperCAmelCase				,      '''out_indices'''				,      __UpperCAmelCase						)

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







      def    __lowerCAmelCase      (   self   :		str				,      __UpperCAmelCase   :		Union[str, Any]						)					->None:




       """simple docstring"""


       a							=   config

       a							=   getattr(__UpperCAmelCase				,      '''use_timm_backbone'''				,      __UpperCAmelCase						)
       a							=   BackboneType.TIMM if self.use_timm_backbone else BackboneType.TRANSFORMERS

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







      @property
      def    __lowerCAmelCase      (   self   :		Dict						)					->Optional[Any]:




       """simple docstring"""


       return self._out_features







      @out_features.setter
      def    __lowerCAmelCase      (   self   :		Tuple				,      __UpperCAmelCase   :		List[str]						)					->List[Any]:




       """simple docstring"""


       a						,  a							=   get_aligned_output_features_output_indices(
           out_features=__UpperCAmelCase				,      out_indices=__UpperCAmelCase				,      stage_names=self.stage_names						)







      @property
      def    __lowerCAmelCase      (   self   :		Dict						)					->int:




       """simple docstring"""


       return self._out_indices







      @out_indices.setter
      def    __lowerCAmelCase      (   self   :		Tuple				,      __UpperCAmelCase   :		Union[Tuple[int], List[int]]						)					->List[str]:




       """simple docstring"""


       a						,  a							=   get_aligned_output_features_output_indices(
           out_features=__UpperCAmelCase				,      out_indices=__UpperCAmelCase				,      stage_names=self.stage_names						)







      @property
      def    __lowerCAmelCase      (   self   :		Tuple						)					->str:




       """simple docstring"""


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







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




       """simple docstring"""


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







      def    __lowerCAmelCase      (   self   :		Optional[Any]				,      *__UpperCAmelCase   :		List[str]				,      **__UpperCAmelCase   :		Optional[Any]						)					->Optional[int]:




       """simple docstring"""


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







      def    __lowerCAmelCase      (   self   :		List[str]				,      __UpperCAmelCase   :		Dict				,      __UpperCAmelCase   :		Optional[bool] = None				,      __UpperCAmelCase   :		Optional[bool] = None				,      __UpperCAmelCase   :		Optional[bool] = None				,      )					->int:




       """simple docstring"""


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







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




       """simple docstring"""


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






class 	lowercase_      :






      '''simple docstring'''




      @property
      def    __lowerCAmelCase      (   self   :		List[str]						)					->Optional[int]:




       """simple docstring"""


       return self._out_features







      @out_features.setter
      def    __lowerCAmelCase      (   self   :		int				,      __UpperCAmelCase   :		List[str]						)					->int:




       """simple docstring"""


       a						,  a							=   get_aligned_output_features_output_indices(
           out_features=__UpperCAmelCase				,      out_indices=__UpperCAmelCase				,      stage_names=self.stage_names						)







      @property
      def    __lowerCAmelCase      (   self   :		str						)					->str:




       """simple docstring"""


       return self._out_indices







      @out_indices.setter
      def    __lowerCAmelCase      (   self   :		str				,      __UpperCAmelCase   :		Union[Tuple[int], List[int]]						)					->int:




       """simple docstring"""


       a						,  a							=   get_aligned_output_features_output_indices(
           out_features=__UpperCAmelCase				,      out_indices=__UpperCAmelCase				,      stage_names=self.stage_names						)







      def    __lowerCAmelCase      (   self   :		Dict						)					->List[Any]:




       """simple docstring"""


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


