

'''simple docstring'''
import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel


def  _lowerCAmelCase      (		_UpperCamelCase		:  Tuple							,				_UpperCamelCase		:  str       )   ->    List[str]:
     """simple docstring"""




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

     _SCREAMING_SNAKE_CASE												=pt_attn_layer.out_proj.weight
     _SCREAMING_SNAKE_CASE												=pt_attn_layer.out_proj.bias

     _SCREAMING_SNAKE_CASE												=q_proj
     _SCREAMING_SNAKE_CASE												=q_proj_bias

     _SCREAMING_SNAKE_CASE												=k_proj
     _SCREAMING_SNAKE_CASE												=k_proj_bias

     _SCREAMING_SNAKE_CASE												=v_proj
     _SCREAMING_SNAKE_CASE												=v_proj_bias

     _SCREAMING_SNAKE_CASE												=out_proj_weights
     _SCREAMING_SNAKE_CASE												=out_proj_bias


def  _lowerCAmelCase      (		_UpperCamelCase		:  Union[str, Any]							,				_UpperCamelCase		:  Tuple       )   ->    Any:
     """simple docstring"""




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


def  _lowerCAmelCase      (		_UpperCamelCase		:  List[str]							,				_UpperCamelCase		:  Tuple       )   ->    Optional[Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE												=pt_linear.weight
     _SCREAMING_SNAKE_CASE												=pt_linear.bias


def  _lowerCAmelCase      (		_UpperCamelCase		:  Union[str, Any]							,				_UpperCamelCase		:  List[Any]       )   ->    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  _lowerCAmelCase      (		_UpperCamelCase		:  Dict							,				_UpperCamelCase		:  int       )   ->    Dict:
     """simple docstring"""




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


def  _lowerCAmelCase      (		_UpperCamelCase		:  List[Any]							,				_UpperCamelCase		:  str       )   ->    List[str]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE												=pt_model.token_embedding.weight
     _SCREAMING_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  _lowerCAmelCase      (		_UpperCamelCase		:  Tuple							,				_UpperCamelCase		:  List[Any]       )   ->    str:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE												=pt_model.text_projection.data.T

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


def  _lowerCAmelCase      (		_UpperCamelCase		:  int							,				_UpperCamelCase		:  Optional[int]       )   ->    Dict:
     """simple docstring"""




     _SCREAMING_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
     _SCREAMING_SNAKE_CASE												=pt_model.visual.conva.weight.data
     _SCREAMING_SNAKE_CASE												=pt_model.visual.class_embedding
     _SCREAMING_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  _lowerCAmelCase      (		_UpperCamelCase		:  str							,				_UpperCamelCase		:  Tuple							,				_UpperCamelCase		:  List[Any]=None       )   ->    List[Any]:
     """simple docstring"""




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

     _SCREAMING_SNAKE_CASE												=CLIPModel(_UpperCamelCase       ).eval()

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

     copy_text_model_and_projection(_UpperCamelCase							,				_UpperCamelCase       )
     copy_vison_model_and_projection(_UpperCamelCase							,				_UpperCamelCase       )
     _SCREAMING_SNAKE_CASE												=pt_model.logit_scale

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

     _SCREAMING_SNAKE_CASE												=hf_model(input_ids=_UpperCamelCase							,				pixel_values=_UpperCamelCase							,				return_dict=_UpperCamelCase       )
     _SCREAMING_SNAKE_CASE												=hf_outputs.logits_per_image
     _SCREAMING_SNAKE_CASE												=hf_outputs.logits_per_text
     _SCREAMING_SNAKE_CASE					,		_SCREAMING_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__":
     lowerCamelCase				:   Union[str, 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")
     lowerCamelCase				:   int      				=  parser.parse_args()

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



'''simple docstring'''
def  _lowerCAmelCase      (		_UpperCamelCase		:  list       )   ->    list:
     """simple docstring"""




     for i, _ in enumerate(_UpperCamelCase       ):
          if (i % 2 == 1) == (nums[i - 1] > nums[i]):
               _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE												=nums[i], nums[i - 1]

     return nums


if __name__ == "__main__":
     print("Enter the array elements:")
     lowerCamelCase				:   Tuple      				=  list(map(int, input().split()))
     print("The unsorted array is:")
     print(array)
     print("Array after Wiggle sort:")
     print(wiggle_sort(array))
