"""simple docstring"""


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




class 					UpperCamelCase   (							enum.Enum  ):
       SCREAMING_SNAKE_CASE_						      = "timm"
       SCREAMING_SNAKE_CASE_						      = "transformers"







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase		,   UpperCAmelCase   )							-> Union[str, 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(UpperCAmelCase		,   (list,)   ):
               raise ValueError(f'out_features must be a list {type(UpperCAmelCase   )}'   )
          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(UpperCAmelCase		,   (list, tuple)   ):
               raise ValueError(f'out_indices must be a list or tuple, got {type(UpperCAmelCase   )}'   )
          if any(idx >= len(UpperCAmelCase   ) 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(UpperCAmelCase   ) != len(UpperCAmelCase   ):
               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       (						UpperCAmelCase		,   UpperCAmelCase		,   UpperCAmelCase		,   )							-> Any:
     if out_indices is None and out_features is None:
          snake_case_   			=   [len(UpperCAmelCase   ) - 1]
          snake_case_   			=   [stage_names[-1]]
     elif out_indices is None and out_features is not None:
          snake_case_   			=   [stage_names.index(UpperCAmelCase   ) for layer in out_features]
     elif out_features is None and out_indices is not None:
          snake_case_   			=   [stage_names[idx] for idx in out_indices]
     return out_features, out_indices







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase		,   UpperCAmelCase		,   )							-> Tuple[List[str], List[int]]:
     verify_out_features_out_indices(out_features=UpperCAmelCase		,   out_indices=UpperCAmelCase		,   stage_names=UpperCAmelCase   )
     snake_case_			,      snake_case_   			=   _align_output_features_output_indices(
         out_features=UpperCAmelCase		,   out_indices=UpperCAmelCase		,   stage_names=UpperCAmelCase   )
     # Verify that the aligned out_features and out_indices are valid
     verify_out_features_out_indices(out_features=UpperCAmelCase		,   out_indices=UpperCAmelCase		,   stage_names=UpperCAmelCase   )
     return output_features, output_indices




class 					UpperCamelCase   :
       SCREAMING_SNAKE_CASE_						      = None







       def 						a_		(					self,				lowerCAmelCase__) -> None:
            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']
            snake_case_   			=   [stage['module'] for stage in self._backbone.feature_info.info]
            snake_case_   			=   [stage['num_chs'] for stage in self._backbone.feature_info.info]
            snake_case_   			=   self._backbone.feature_info.out_indices
            snake_case_   			=   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)
            snake_case_			,      snake_case_   			=   out_features, out_indices







       def 						a_		(					self,				lowerCAmelCase__) -> None:
            snake_case_   			=   getattr(lowerCAmelCase__,				'stage_names')
            snake_case_   			=   getattr(lowerCAmelCase__,				'out_features',				lowerCAmelCase__)
            snake_case_   			=   getattr(lowerCAmelCase__,				'out_indices',				lowerCAmelCase__)

            snake_case_   			=   stage_names
            snake_case_			,      snake_case_   			=   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
            snake_case_   			=   None







       def 						a_		(					self,				lowerCAmelCase__) -> None:
            snake_case_   			=   config

            snake_case_   			=   getattr(lowerCAmelCase__,				'use_timm_backbone',				lowerCAmelCase__)
            snake_case_   			=   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 						a_		(					self) -> Optional[int]:
            return self._out_features







       @out_features.setter
       def 						a_		(					self,				lowerCAmelCase__) -> str:
            snake_case_			,      snake_case_   			=   get_aligned_output_features_output_indices(
                out_features=lowerCAmelCase__,				out_indices=lowerCAmelCase__,				stage_names=self.stage_names)







       @property
       def 						a_		(					self) -> Tuple:
            return self._out_indices







       @out_indices.setter
       def 						a_		(					self,				lowerCAmelCase__) -> Dict:
            snake_case_			,      snake_case_   			=   get_aligned_output_features_output_indices(
                out_features=lowerCAmelCase__,				out_indices=lowerCAmelCase__,				stage_names=self.stage_names)







       @property
       def 						a_		(					self) -> Any:
            # the current backbones will output the number of channels for each stage
            # even if that stage is not in the out_features list.
            return {stage: self.num_features[i] for i, stage in enumerate(self.stage_names)}







       @property
       def 						a_		(					self) -> List[Any]:
            return [self.out_feature_channels[name] for name in self.out_features]







       def 						a_		(					self,				*lowerCAmelCase__,				**lowerCAmelCase__) -> List[str]:
            snake_case_   			=   dict(inspect.signature(self.forward).parameters)
            snake_case_   			=   {k: v for k, v in kwargs.items() if k in signature}
            return self(*lowerCAmelCase__,				**lowerCAmelCase__)







       def 						a_		(					self,				lowerCAmelCase__,				lowerCAmelCase__ = None,				lowerCAmelCase__ = None,				lowerCAmelCase__ = None,				) -> Tuple:
            raise NotImplementedError('This method should be implemented by the derived class.')







       def 						a_		(					self) -> Union[str, Any]:
            snake_case_   			=   super().to_dict()
            snake_case_   			=   output.pop('_out_features')
            snake_case_   			=   output.pop('_out_indices')
            return output




class 					UpperCamelCase   :







       @property
       def 						a_		(					self) -> Union[str, Any]:
            return self._out_features







       @out_features.setter
       def 						a_		(					self,				lowerCAmelCase__) -> Union[str, Any]:
            snake_case_			,      snake_case_   			=   get_aligned_output_features_output_indices(
                out_features=lowerCAmelCase__,				out_indices=lowerCAmelCase__,				stage_names=self.stage_names)







       @property
       def 						a_		(					self) -> int:
            return self._out_indices







       @out_indices.setter
       def 						a_		(					self,				lowerCAmelCase__) -> List[str]:
            snake_case_			,      snake_case_   			=   get_aligned_output_features_output_indices(
                out_features=lowerCAmelCase__,				out_indices=lowerCAmelCase__,				stage_names=self.stage_names)







       def 						a_		(					self) -> int:
            snake_case_   			=   super().to_dict()
            snake_case_   			=   output.pop('_out_features')
            snake_case_   			=   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)

