"""simple docstring"""


import math




class 					UpperCamelCase   :







       def __init__(					self,				lowerCAmelCase__) -> str:
            snake_case_   			=   len(lowerCAmelCase__)
            snake_case_   			=   [0] * (
                4 * self.N
            )  # approximate the overall size of segment tree with array N
            self.build(1,				0,				self.N - 1)







       def 						a_		(					self,				lowerCAmelCase__) -> Optional[Any]:
            return idx * 2







       def 						a_		(					self,				lowerCAmelCase__) -> Any:
            return idx * 2 + 1







       def 						a_		(					self,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__) -> str:  # noqa: E741
            if l == r:
                 snake_case_   			=   A[l]
            else:
                 snake_case_   			=   (l + r) // 2
                 self.build(self.left(lowerCAmelCase__),				lowerCAmelCase__,				lowerCAmelCase__)
                 self.build(self.right(lowerCAmelCase__),				mid + 1,				lowerCAmelCase__)
                 snake_case_   			=   max(self.st[self.left(lowerCAmelCase__)],				self.st[self.right(lowerCAmelCase__)])







       def 						a_		(					self,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__) -> Any:
            return self.update_recursive(1,				0,				self.N - 1,				a - 1,				b - 1,				lowerCAmelCase__)







       def 						a_		(					self,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__) -> Optional[Any]:  # noqa: E741
            if r < a or l > b:
                 return True
            if l == r:
                 snake_case_   			=   val
                 return True
            snake_case_   			=   (l + r) // 2
            self.update_recursive(self.left(lowerCAmelCase__),				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__)
            self.update_recursive(self.right(lowerCAmelCase__),				mid + 1,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__)
            snake_case_   			=   max(self.st[self.left(lowerCAmelCase__)],				self.st[self.right(lowerCAmelCase__)])
            return True







       def 						a_		(					self,				lowerCAmelCase__,				lowerCAmelCase__) -> int:
            return self.query_recursive(1,				0,				self.N - 1,				a - 1,				b - 1)







       def 						a_		(					self,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__) -> Any:  # noqa: E741
            if r < a or l > b:
                 return -math.inf
            if l >= a and r <= b:
                 return self.st[idx]
            snake_case_   			=   (l + r) // 2
            snake_case_   			=   self.query_recursive(self.left(lowerCAmelCase__),				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__)
            snake_case_   			=   self.query_recursive(self.right(lowerCAmelCase__),				mid + 1,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__)
            return max(lowerCAmelCase__,				lowerCAmelCase__)







       def 						a_		(					self) -> Dict:
            snake_case_   			=   []
            for i in range(1,				N + 1):
                 show_list += [self.query(lowerCAmelCase__,				lowerCAmelCase__)]
            print(lowerCAmelCase__)


if __name__ == "__main__":
    __UpperCamelCase										=     [1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
    __UpperCamelCase										=     15
    __UpperCamelCase										=     SegmentTree(A)
    print(segt.query(4, 6))
    print(segt.query(7, 11))
    print(segt.query(7, 12))
    segt.update(1, 3, 111)
    print(segt.query(1, 15))
    segt.update(7, 8, 235)
    segt.show_data()


"""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)

