






import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel

def        _lowerCamelCase		(  __A      :							Any						,						__A      :							Union[str, Any]	) ->   Any:
    _UpperCAmelCase					,	_UpperCAmelCase					,	_UpperCAmelCase			: Tuple     					=			pt_attn_layer.in_proj_weight.chunk(3						,						dim=0	)
    _UpperCAmelCase					,	_UpperCAmelCase					,	_UpperCAmelCase			: Union[str, Any]     					=			pt_attn_layer.in_proj_bias.chunk(3						,						dim=0	)

    _UpperCAmelCase			: Union[str, Any]     					=			pt_attn_layer.out_proj.weight
    _UpperCAmelCase			: Union[str, Any]     					=			pt_attn_layer.out_proj.bias

    _UpperCAmelCase			: int     					=			q_proj
    _UpperCAmelCase			: List[Any]     					=			q_proj_bias

    _UpperCAmelCase			: Union[str, Any]     					=			k_proj
    _UpperCAmelCase			: Tuple     					=			k_proj_bias

    _UpperCAmelCase			: Optional[Any]     					=			v_proj
    _UpperCAmelCase			: Optional[Any]     					=			v_proj_bias

    _UpperCAmelCase			: str     					=			out_proj_weights
    _UpperCAmelCase			: Tuple     					=			out_proj_bias

def        _lowerCamelCase		(  __A      :							Optional[Any]						,						__A      :							Union[str, Any]	) ->   Tuple:
    copy_linear(hf_mlp.fca						,						pt_mlp.c_fc	)
    copy_linear(hf_mlp.fca						,						pt_mlp.c_proj	)

def        _lowerCamelCase		(  __A      :							int						,						__A      :							int	) ->   int:
    _UpperCAmelCase			: Optional[Any]     					=			pt_linear.weight
    _UpperCAmelCase			: Any     					=			pt_linear.bias

def        _lowerCamelCase		(  __A      :							str						,						__A      :							str	) ->   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        _lowerCamelCase		(  __A      :							Optional[Any]						,						__A      :							Tuple	) ->   List[str]:
    for hf_layer, pt_layer in zip(__A						,						__A	):
        copy_layer(__A						,						__A	)

def        _lowerCamelCase		(  __A      :							Optional[int]						,						__A      :							Union[str, Any]	) ->   List[Any]:
    # copy  embeds
    _UpperCAmelCase			: Dict     					=			pt_model.token_embedding.weight
    _UpperCAmelCase			: List[str]     					=			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		(  __A      :							List[Any]						,						__A      :							Any	) ->   Dict:
    # copy projection
    _UpperCAmelCase			: List[Any]     					=			pt_model.text_projection.data.T

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

def        _lowerCamelCase		(  __A      :							List[Any]						,						__A      :							Tuple	) ->   Optional[int]:
    # copy projection
    _UpperCAmelCase			: List[str]     					=			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			: Dict     					=			pt_model.visual.conva.weight.data
    _UpperCAmelCase			: Union[str, Any]     					=			pt_model.visual.class_embedding
    _UpperCAmelCase			: List[Any]     					=			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		(  __A      :							Any						,						__A      :							List[Any]						,						__A      :							List[Any]=None	) ->   int:
    if config_path is not None:
        _UpperCAmelCase			: Any     					=			CLIPConfig.from_pretrained(__A	)
    else:
        _UpperCAmelCase			: Tuple     					=			CLIPConfig(projection_dim=512						,						text_config={}						,						vision_config={}	)

    _UpperCAmelCase			: Union[str, Any]     					=			CLIPModel(__A	).eval()

    _UpperCAmelCase					,	_UpperCAmelCase			: Tuple     					=			load(__A						,						device='''cpu'''						,						jit=__A	)
    _UpperCAmelCase			: Union[str, Any]     					=			pt_model.eval()

    copy_text_model_and_projection(__A						,						__A	)
    copy_vison_model_and_projection(__A						,						__A	)
    _UpperCAmelCase			: List[Any]     					=			pt_model.logit_scale

    _UpperCAmelCase			: Union[str, Any]     					=			torch.arange(0						,						77	).unsqueeze(0	)
    _UpperCAmelCase			: Union[str, Any]     					=			torch.randn(1						,						3						,						224						,						224	)

    _UpperCAmelCase			: Tuple     					=			hf_model(input_ids=__A						,						pixel_values=__A						,						return_dict=__A	)
    _UpperCAmelCase			: str     					=			hf_outputs.logits_per_image
    _UpperCAmelCase			: Dict     					=			hf_outputs.logits_per_text
    _UpperCAmelCase					,	_UpperCAmelCase			: str     					=			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__":
  SCREAMING_SNAKE_CASE								=  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')
  SCREAMING_SNAKE_CASE								=  parser.parse_args()

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












from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE								=  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE								=  {
    'MBZUAI/swiftformer-xs': 'https://huggingface.co/MBZUAI/swiftformer-xs/resolve/main/config.json',
}
class 							A_      (  __lowercase     ):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:     Dict	=   "swiftformer"


 def __init__(		self							,					_A=3							,					_A=[3, 3, 6, 4]							,					_A=[48, 56, 112, 220]							,					_A=4							,					_A=[True, True, True, True]							,					_A="gelu"							,					_A=3							,					_A=2							,					_A=1							,					_A=0.0							,					_A=True							,					_A=1e-5							,					_A=1e-5							,					**_A							,					)	->						List[Any]:

     """simple docstring"""



     super().__init__(**_A)
     _UpperCAmelCase			: Tuple     					=			num_channels
     _UpperCAmelCase			: Optional[int]     					=			depths
     _UpperCAmelCase			: Union[str, Any]     					=			embed_dims
     _UpperCAmelCase			: Dict     					=			mlp_ratio
     _UpperCAmelCase			: Tuple     					=			downsamples
     _UpperCAmelCase			: str     					=			hidden_act
     _UpperCAmelCase			: int     					=			down_patch_size
     _UpperCAmelCase			: Dict     					=			down_stride
     _UpperCAmelCase			: Dict     					=			down_pad
     _UpperCAmelCase			: Tuple     					=			drop_path_rate
     _UpperCAmelCase			: Tuple     					=			use_layer_scale
     _UpperCAmelCase			: int     					=			layer_scale_init_value
     _UpperCAmelCase			: int     					=			batch_norm_eps



class 							A_      (  __lowercase     ):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:     List[Any]	=   version.parse("1.11"     )


 @property
 def 	snake_case__			(		self)	->						Mapping[str, Mapping[int, str]]:

     """simple docstring"""



     return OrderedDict(
         [
             ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
         ])


 @property
 def 	snake_case__			(		self)	->						float:

     """simple docstring"""



     return 1e-4




