






"""simple docstring"""


import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel




def      snake_case    (A_ :List[Any]      ,     A_ :Any ):







  '''simple docstring'''

  a,	a,	a	:     int							=  pt_attn_layer.in_proj_weight.chunk(3      ,     dim=0 )
  a,	a,	a	:     List[str]							=  pt_attn_layer.in_proj_bias.chunk(3      ,     dim=0 )

  a	:     Dict							=  pt_attn_layer.out_proj.weight
  a	:     Any							=  pt_attn_layer.out_proj.bias

  a	:     Tuple							=  q_proj
  a	:     Optional[int]							=  q_proj_bias

  a	:     Dict							=  k_proj
  a	:     Any							=  k_proj_bias

  a	:     Dict							=  v_proj
  a	:     Optional[int]							=  v_proj_bias

  a	:     Optional[int]							=  out_proj_weights
  a	:     Optional[Any]							=  out_proj_bias




def      snake_case    (A_ :Dict      ,     A_ :str ):







  '''simple docstring'''

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




def      snake_case    (A_ :List[str]      ,     A_ :Union[str, Any] ):







  '''simple docstring'''

  a	:     Tuple							=  pt_linear.weight
  a	:     Optional[Any]							=  pt_linear.bias




def      snake_case    (A_ :List[str]      ,     A_ :Optional[int] ):







  '''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      snake_case    (A_ :Dict      ,     A_ :Any ):







  '''simple docstring'''

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




def      snake_case    (A_ :List[str]      ,     A_ :Any ):







  '''simple docstring'''

  a	:     List[Any]							=  pt_model.token_embedding.weight
  a	:     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      snake_case    (A_ :int      ,     A_ :Any ):







  '''simple docstring'''

  a	:     int							=  pt_model.text_projection.data.T

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




def      snake_case    (A_ :Optional[int]      ,     A_ :str ):







  '''simple docstring'''

  a	:     List[Any]							=  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
  a	:     List[Any]							=  pt_model.visual.conva.weight.data
  a	:     Any							=  pt_model.visual.class_embedding
  a	:     Tuple							=  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      snake_case    (A_ :Any      ,     A_ :Optional[int]      ,     A_ :Any=None ):







  '''simple docstring'''

  if config_path is not None:
    a	:     Dict							=  CLIPConfig.from_pretrained(A_ )
  else:
    a	:     Union[str, Any]							=  CLIPConfig(projection_dim=5_1_2      ,     text_config={}      ,     vision_config={} )

  a	:     Dict							=  CLIPModel(A_ ).eval()

  a,	a	:     Union[str, Any]							=  load(A_      ,     device='cpu'      ,     jit=A_ )
  a	:     List[Any]							=  pt_model.eval()

  copy_text_model_and_projection(A_      ,     A_ )
  copy_vison_model_and_projection(A_      ,     A_ )
  a	:     int							=  pt_model.logit_scale

  a	:     Optional[Any]							=  torch.arange(0      ,     7_7 ).unsqueeze(0 )
  a	:     List[str]							=  torch.randn(1      ,     3      ,     2_2_4      ,     2_2_4 )

  a	:     Any							=  hf_model(input_ids=A_      ,     pixel_values=A_      ,     return_dict=A_ )
  a	:     Tuple							=  hf_outputs.logits_per_image
  a	:     str							=  hf_outputs.logits_per_text
  a,	a	:     List[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__":
   _UpperCamelCase							:       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')
   _UpperCamelCase							:       Union[str, Any]       					=		parser.parse_args()

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












"""simple docstring"""


from __future__ import annotations




def      snake_case    (A_ :list[int | float]      ,     A_ :int      ,     A_ :int ):







  '''simple docstring'''

  if len(A_ ) == 0:
    raise ValueError('find_min() arg is an empty sequence' )
  if (
      left >= len(A_ )
      or left < -len(A_ )
      or right >= len(A_ )
      or right < -len(A_ )
  ):
    raise IndexError('list index out of range' )
  if left == right:
    return nums[left]
  a	:     Optional[Any]							=  (left + right) >> 1  # the middle
  a	:     List[str]							=  find_min(A_      ,     A_      ,     A_ )  # find min in range[left, mid]
  a	:     Union[str, Any]							=  find_min(A_      ,     mid + 1      ,     A_ )  # find min in range[mid + 1, right]

  return left_min if left_min <= right_min else right_min


if __name__ == "__main__":
   import doctest

   doctest.testmod(verbose=True)




