

"""simple docstring"""

import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel





def 					__UpperCamelCase (     a__:			Optional[Any]			,						a__:			Optional[Any]      )					->      List[Any]:




					"""simple docstring"""



					__lowercase        =      pt_attn_layer.in_proj_weight.chunk(3			,						dim=0      )
					__lowercase        =      pt_attn_layer.in_proj_bias.chunk(3			,						dim=0      )

					__lowercase        =      pt_attn_layer.out_proj.weight
					__lowercase        =      pt_attn_layer.out_proj.bias

					__lowercase        =      q_proj
					__lowercase        =      q_proj_bias

					__lowercase        =      k_proj
					__lowercase        =      k_proj_bias

					__lowercase        =      v_proj
					__lowercase        =      v_proj_bias

					__lowercase        =      out_proj_weights
					__lowercase        =      out_proj_bias





def 					__UpperCamelCase (     a__:			List[str]			,						a__:			List[Any]      )					->      Union[str, Any]:




					"""simple docstring"""



					copy_linear(hf_mlp.fca			,						pt_mlp.c_fc      )
					copy_linear(hf_mlp.fca			,						pt_mlp.c_proj      )





def 					__UpperCamelCase (     a__:			Optional[Any]			,						a__:			Optional[int]      )					->      List[str]:




					"""simple docstring"""



					__lowercase        =      pt_linear.weight
					__lowercase        =      pt_linear.bias





def 					__UpperCamelCase (     a__:			List[Any]			,						a__:			List[str]      )					->      Union[str, Any]:




					"""simple docstring"""



					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 (     a__:			Any			,						a__:			str      )					->      Union[str, Any]:




					"""simple docstring"""



					for hf_layer, pt_layer in zip(a__			,						a__      ):
										copy_layer(a__			,						a__      )





def 					__UpperCamelCase (     a__:			List[str]			,						a__:			List[str]      )					->      Optional[int]:




					"""simple docstring"""



					__lowercase        =      pt_model.token_embedding.weight
					__lowercase        =      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 (     a__:			Optional[Any]			,						a__:			str      )					->      List[str]:




					"""simple docstring"""



					__lowercase        =      pt_model.text_projection.data.T

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





def 					__UpperCamelCase (     a__:			Dict			,						a__:			List[Any]      )					->      Dict:




					"""simple docstring"""



					__lowercase        =      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
					__lowercase        =      pt_model.visual.conva.weight.data
					__lowercase        =      pt_model.visual.class_embedding
					__lowercase        =      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 (     a__:			Optional[Any]			,						a__:			Tuple			,						a__:			List[str]=None      )					->      str:




					"""simple docstring"""



					if config_path is not None:
										__lowercase        =      CLIPConfig.from_pretrained(a__      )
					else:
										__lowercase        =      CLIPConfig(projection_dim=5_12			,						text_config={}			,						vision_config={}      )

					__lowercase        =      CLIPModel(a__      ).eval()

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

					copy_text_model_and_projection(a__			,						a__      )
					copy_vison_model_and_projection(a__			,						a__      )
					__lowercase        =      pt_model.logit_scale

					__lowercase        =      torch.arange(0			,						77      ).unsqueeze(0      )
					__lowercase        =      torch.randn(1			,						3			,						2_24			,						2_24      )

					__lowercase        =      hf_model(input_ids=a__			,						pixel_values=a__			,						return_dict=a__      )
					__lowercase        =      hf_outputs.logits_per_image
					__lowercase        =      hf_outputs.logits_per_text
					__lowercase        =      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__":
							a__			:						Optional[Any]							=			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""")
							a__			:						Any							=			parser.parse_args()

							convert_clip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)








'''simple docstring'''






from __future__ import annotations

from collections.abc import Iterable, Iterator
from typing import Any, Generic, TypeVar

lowercase						:   Union[str, Any]      						=							TypeVar('''T''', bound=bool)

class 	SCREAMING_SNAKE_CASE_		(							Generic[T]					):



 def __init__(	self ,		A			):






        '''simple docstring'''




        lowercase	:		T    =   value
        lowercase	:		SkewNode[T] | None    =   None
        lowercase	:		SkewNode[T] | None    =   None



 @property
 def  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




        return self._value





 @staticmethod
 def  SCREAMING_SNAKE_CASE(	A ,		A			):






        '''simple docstring'''




        if not roota:
               return roota

        if not roota:
               return roota

        if roota.value > roota.value:
               lowercase,  lowercase	:		Union[str, Any]    =   roota, roota

        lowercase	:		Optional[int]    =   roota
        lowercase	:		Tuple    =   roota.right
        lowercase	:		Union[str, Any]    =   roota.left
        lowercase	:		Any    =   SkewNode.merge(A ,		A			)

        return result






class 	SCREAMING_SNAKE_CASE_		(							Generic[T]					):



 def __init__(	self ,		A = ()			):






        '''simple docstring'''




        lowercase	:		SkewNode[T] | None    =   None
        if data:
               for item in data:
                      self.insert(A			)



 def __bool__(	self			):






        '''simple docstring'''




        return self._root is not None



 def __iter__(	self			):






        '''simple docstring'''




        lowercase	:		list[Any]    =   []
        while self:
               result.append(self.pop()			)

        # Pushing items back to the heap not to clear it.
        for item in result:
               self.insert(A			)

        return iter(A			)



 def  SCREAMING_SNAKE_CASE(	self ,		A			):






        '''simple docstring'''




        lowercase	:		List[str]    =   SkewNode.merge(self._root ,		SkewNode(A			)			)



 def  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




        lowercase	:		List[Any]    =   self.top()
        lowercase	:		int    =   (
            SkewNode.merge(self._root.left ,		self._root.right			) if self._root else None
        )

        return result



 def  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




        if not self._root:
               raise IndexError('''Can\'t get top element for the empty heap.'''			)
        return self._root.value





 def  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




        lowercase	:		Optional[Any]    =   None


if __name__ == "__main__":
      import doctest

      doctest.testmod()




