

import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel


def  __a    (				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE ) ->       List[str]:






				'''simple docstring'''
				__UpperCAmelCase   ,       __UpperCAmelCase   ,       __UpperCAmelCase			   =     pt_attn_layer.in_proj_weight.chunk(3    ,				dim=0 )
				__UpperCAmelCase   ,       __UpperCAmelCase   ,       __UpperCAmelCase			   =     pt_attn_layer.in_proj_bias.chunk(3    ,				dim=0 )

				__UpperCAmelCase			   =     pt_attn_layer.out_proj.weight
				__UpperCAmelCase			   =     pt_attn_layer.out_proj.bias

				__UpperCAmelCase			   =     q_proj
				__UpperCAmelCase			   =     q_proj_bias

				__UpperCAmelCase			   =     k_proj
				__UpperCAmelCase			   =     k_proj_bias

				__UpperCAmelCase			   =     v_proj
				__UpperCAmelCase			   =     v_proj_bias

				__UpperCAmelCase			   =     out_proj_weights
				__UpperCAmelCase			   =     out_proj_bias


def  __a    (				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE ) ->       List[Any]:






				'''simple docstring'''
				copy_linear(hf_mlp.fca    ,				pt_mlp.c_fc )
				copy_linear(hf_mlp.fca    ,				pt_mlp.c_proj )


def  __a    (				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE ) ->       Union[str, Any]:






				'''simple docstring'''
				__UpperCAmelCase			   =     pt_linear.weight
				__UpperCAmelCase			   =     pt_linear.bias


def  __a    (				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE ) ->       List[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  __a    (				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE ) ->       Union[str, Any]:






				'''simple docstring'''
				for hf_layer, pt_layer in zip(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE ):
								copy_layer(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE )


def  __a    (				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE ) ->       Optional[int]:






				'''simple docstring'''
				__UpperCAmelCase			   =     pt_model.token_embedding.weight
				__UpperCAmelCase			   =     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    (				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE ) ->       Tuple:






				'''simple docstring'''
				__UpperCAmelCase			   =     pt_model.text_projection.data.T

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


def  __a    (				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE ) ->       int:






				'''simple docstring'''
				__UpperCAmelCase			   =     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
				__UpperCAmelCase			   =     pt_model.visual.conva.weight.data
				__UpperCAmelCase			   =     pt_model.visual.class_embedding
				__UpperCAmelCase			   =     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    (				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE=None ) ->       Any:






				'''simple docstring'''
				if config_path is not None:
								__UpperCAmelCase			   =     CLIPConfig.from_pretrained(SCREAMING_SNAKE_CASE )
				else:
								__UpperCAmelCase			   =     CLIPConfig(projection_dim=5_1_2    ,				text_config={}    ,				vision_config={} )

				__UpperCAmelCase			   =     CLIPModel(SCREAMING_SNAKE_CASE ).eval()

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

				copy_text_model_and_projection(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE )
				copy_vison_model_and_projection(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE )
				__UpperCAmelCase			   =     pt_model.logit_scale

				__UpperCAmelCase			   =     torch.arange(0    ,				7_7 ).unsqueeze(0 )
				__UpperCAmelCase			   =     torch.randn(1    ,				3    ,				2_2_4    ,				2_2_4 )

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

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

				hf_model.save_pretrained(SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
							A_						:     List[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_						:     Union[str, Any]			  =				parser.parse_args()

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







from dataclasses import dataclass
from typing import Callable, Optional

import datasets
@dataclass
class    A_				( datasets.BuilderConfig  ):


    '''simple docstring'''






    a__       							=    None
    a__       							=    None
    a__       							=    None


    def        lowerCAmelCase_  (self					)       -> Dict:
        assert self.generator is not None, "generator must be specified"

        if self.gen_kwargs is None:
            __UpperCAmelCase			   =     {}






class    A_				( datasets.GeneratorBasedBuilder  ):


    '''simple docstring'''






    a__       							=    GeneratorConfig


    def        lowerCAmelCase_  (self					)       -> Dict:
        return datasets.DatasetInfo(features=self.config.features					)


    def        lowerCAmelCase_  (self       , lowercase__					)       -> List[str]:
        return [datasets.SplitGenerator(name=datasets.Split.TRAIN       , gen_kwargs=self.config.gen_kwargs					)]






    def        lowerCAmelCase_  (self       , **lowercase__					)       -> str:
        for idx, ex in enumerate(self.config.generator(**lowercase__					)					):
            yield idx, ex




