




import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel

def      lowerCAmelCase_     (					lowerCamelCase:    Any       ,			lowerCamelCase:    Tuple  )   ->     Dict:


      '''simple docstring'''




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

      lowerCamelCase__ 					=			pt_attn_layer.out_proj.weight
      lowerCamelCase__ 					=			pt_attn_layer.out_proj.bias

      lowerCamelCase__ 					=			q_proj
      lowerCamelCase__ 					=			q_proj_bias

      lowerCamelCase__ 					=			k_proj
      lowerCamelCase__ 					=			k_proj_bias

      lowerCamelCase__ 					=			v_proj
      lowerCamelCase__ 					=			v_proj_bias

      lowerCamelCase__ 					=			out_proj_weights
      lowerCamelCase__ 					=			out_proj_bias

def      lowerCAmelCase_     (					lowerCamelCase:    Optional[Any]       ,			lowerCamelCase:    List[Any]  )   ->     Dict:


      '''simple docstring'''




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

def      lowerCAmelCase_     (					lowerCamelCase:    List[Any]       ,			lowerCamelCase:    List[Any]  )   ->     Tuple:


      '''simple docstring'''




      lowerCamelCase__ 					=			pt_linear.weight
      lowerCamelCase__ 					=			pt_linear.bias

def      lowerCAmelCase_     (					lowerCamelCase:    Optional[int]       ,			lowerCamelCase:    List[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      lowerCAmelCase_     (					lowerCamelCase:    Optional[int]       ,			lowerCamelCase:    Tuple  )   ->     int:


      '''simple docstring'''




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

def      lowerCAmelCase_     (					lowerCamelCase:    Dict       ,			lowerCamelCase:    str  )   ->     Tuple:


      '''simple docstring'''




      lowerCamelCase__ 					=			pt_model.token_embedding.weight
      lowerCamelCase__ 					=			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_     (					lowerCamelCase:    List[Any]       ,			lowerCamelCase:    Any  )   ->     Optional[Any]:


      '''simple docstring'''




      lowerCamelCase__ 					=			pt_model.text_projection.data.T

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

def      lowerCAmelCase_     (					lowerCamelCase:    int       ,			lowerCamelCase:    List[Any]  )   ->     List[Any]:


      '''simple docstring'''




      lowerCamelCase__ 					=			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
      lowerCamelCase__ 					=			pt_model.visual.conva.weight.data
      lowerCamelCase__ 					=			pt_model.visual.class_embedding
      lowerCamelCase__ 					=			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_     (					lowerCamelCase:    Union[str, Any]       ,			lowerCamelCase:    int       ,			lowerCamelCase:    Tuple=None  )   ->     List[Any]:


      '''simple docstring'''




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

      lowerCamelCase__ 					=			CLIPModel(A_  ).eval()

      lowerCamelCase__					,						lowerCamelCase__ 					=			load(A_       ,			device='cpu'       ,			jit=A_  )
      lowerCamelCase__ 					=			pt_model.eval()

      copy_text_model_and_projection(A_       ,			A_  )
      copy_vison_model_and_projection(A_       ,			A_  )
      lowerCamelCase__ 					=			pt_model.logit_scale

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

      lowerCamelCase__ 					=			hf_model(input_ids=A_       ,			pixel_values=A_       ,			return_dict=A_  )
      lowerCamelCase__ 					=			hf_outputs.logits_per_image
      lowerCamelCase__ 					=			hf_outputs.logits_per_text
      lowerCamelCase__					,						lowerCamelCase__ 					=			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      :				Tuple				      = 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      :				Union[str, Any]				      = parser.parse_args()

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





def      lowerCAmelCase_     (					lowerCamelCase:    list       ,			lowerCamelCase:    int       ,			lowerCamelCase:    int  )   ->     None:


      '''simple docstring'''




      if right <= left:
            return
      lowerCamelCase__ 					=			lowerCamelCase__ 					=			left
      lowerCamelCase__ 					=			right
      lowerCamelCase__ 					=			sorting[left]
      while i <= b:
            if sorting[i] < pivot:
                  lowerCamelCase__					,						lowerCamelCase__ 					=			sorting[i], sorting[a]
                  a += 1
                  i += 1
            elif sorting[i] > pivot:
                  lowerCamelCase__					,						lowerCamelCase__ 					=			sorting[i], sorting[b]
                  b -= 1
            else:
                  i += 1
      quick_sort_apartition(lowerCamelCase       ,			lowerCamelCase       ,			a - 1  )
      quick_sort_apartition(lowerCamelCase       ,			b + 1       ,			lowerCamelCase  )

def      lowerCAmelCase_     (					lowerCamelCase:    list       ,			lowerCamelCase:    int       ,			lowerCamelCase:    int  )   ->     None:


      '''simple docstring'''




      if left < right:
            lowerCamelCase__ 					=			lomuto_partition(lowerCamelCase       ,			lowerCamelCase       ,			lowerCamelCase  )
            quick_sort_lomuto_partition(lowerCamelCase       ,			lowerCamelCase       ,			pivot_index - 1  )
            quick_sort_lomuto_partition(lowerCamelCase       ,			pivot_index + 1       ,			lowerCamelCase  )

def      lowerCAmelCase_     (					lowerCamelCase:    list       ,			lowerCamelCase:    int       ,			lowerCamelCase:    int  )   ->     int:


      '''simple docstring'''




      lowerCamelCase__ 					=			sorting[right]
      lowerCamelCase__ 					=			left
      for i in range(lowerCamelCase       ,			lowerCamelCase  ):
            if sorting[i] < pivot:
                  lowerCamelCase__					,						lowerCamelCase__ 					=			sorting[i], sorting[store_index]
                  store_index += 1
      lowerCamelCase__					,						lowerCamelCase__ 					=			sorting[store_index], sorting[right]
      return store_index

def      lowerCAmelCase_     (					lowerCamelCase:    list  )   ->     list:


      '''simple docstring'''




      if len(lowerCamelCase  ) <= 1:
            return sorting
      return (
          three_way_radix_quicksort([i for i in sorting if i < sorting[0]]  )
          + [i for i in sorting if i == sorting[0]]
          + three_way_radix_quicksort([i for i in sorting if i > sorting[0]]  )
      )


if __name__ == "__main__":
      import doctest

      doctest.testmod(verbose=True)

      __SCREAMING_SNAKE_CASE      :				Optional[int]				      =input("""Enter numbers separated by a comma:\n""").strip()
      __SCREAMING_SNAKE_CASE      :				str				      =[int(item) for item in user_input.split(""",""")]
      quick_sort_apartition(unsorted, 0, len(unsorted) - 1)
      print(unsorted)