



"""simple docstring"""






import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel


def 							A__ (     UpperCamelCase__       ,							UpperCamelCase__    ):


  '''simple docstring'''


  _SCREAMING_SNAKE_CASE        =			pt_attn_layer.in_proj_weight.chunk(3       ,							dim=0    )
  _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 							A__ (     UpperCamelCase__       ,							UpperCamelCase__    ):


  '''simple docstring'''


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


def 							A__ (     UpperCamelCase__       ,							UpperCamelCase__    ):


  '''simple docstring'''


  _SCREAMING_SNAKE_CASE        =			pt_linear.weight
  _SCREAMING_SNAKE_CASE        =			pt_linear.bias


def 							A__ (     UpperCamelCase__       ,							UpperCamelCase__    ):


  '''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 							A__ (     UpperCamelCase__       ,							UpperCamelCase__    ):


  '''simple docstring'''


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


def 							A__ (     UpperCamelCase__       ,							UpperCamelCase__    ):


  '''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 							A__ (     UpperCamelCase__       ,							UpperCamelCase__    ):


  '''simple docstring'''


  _SCREAMING_SNAKE_CASE        =			pt_model.text_projection.data.T

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


def 							A__ (     UpperCamelCase__       ,							UpperCamelCase__    ):


  '''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 							A__ (     UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__=None    ):


  '''simple docstring'''


  if config_path is not None:
    _SCREAMING_SNAKE_CASE        =			CLIPConfig.from_pretrained(_snake_case    )
  else:
    _SCREAMING_SNAKE_CASE        =			CLIPConfig(projection_dim=512       ,							text_config={}       ,							vision_config={}    )

  _SCREAMING_SNAKE_CASE        =			CLIPModel(_snake_case    ).eval()

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

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

  _SCREAMING_SNAKE_CASE        =			torch.arange(0       ,							77    ).unsqueeze(0    )
  _SCREAMING_SNAKE_CASE        =			torch.randn(1       ,							3       ,							224       ,							224    )

  _SCREAMING_SNAKE_CASE        =			hf_model(input_ids=_snake_case       ,							pixel_values=_snake_case       ,							return_dict=_snake_case    )
  _SCREAMING_SNAKE_CASE        =			hf_outputs.logits_per_image
  _SCREAMING_SNAKE_CASE        =			hf_outputs.logits_per_text
  _SCREAMING_SNAKE_CASE        =			pt_model(_snake_case       ,							_snake_case    )

  assert torch.allclose(_snake_case       ,							_snake_case       ,							atol=1e-3    )
  assert torch.allclose(_snake_case       ,							_snake_case       ,							atol=1e-3    )

  hf_model.save_pretrained(_snake_case    )


if __name__ == "__main__":
    lowerCamelCase				:     List[str]								=  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				:     Optional[Any]								=  parser.parse_args()

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




"""simple docstring"""
SCREAMING_SNAKE_CASE					:       List[Any]							   =							1000






def 						lowercase				(	_snake_case		: float	)	->float:
	"""simple docstring"""





	return x * x






def 						lowercase				(	_snake_case		: Union[str, Any]	,  _snake_case		: float	,  _snake_case		: float	,  _snake_case		: int = 4	)	->float:
	"""simple docstring"""





	assert callable(
	    _snake_case	), f"""the function(object) passed should be callable your input : {function}"""
	assert isinstance(_snake_case	,  (float, int)	), f"""a should be float or integer your input : {a}"""
	assert isinstance(function(_snake_case	)	,  (float, int)	), (
	    "the function should return integer or float return type of your function, "
	    f"""{type(_snake_case	)}"""
	)
	assert isinstance(_snake_case	,  (float, int)	), f"""b should be float or integer your input : {b}"""
	assert (
	    isinstance(_snake_case	,  _snake_case	) and precision > 0
	), f"""precision should be positive integer your input : {precision}"""

	# just applying the formula of simpson for approximate integration written in
	# mentioned article in first comment of this file and above this function

	__snake_case      :		Tuple         =							(b - a) / N_STEPS
	__snake_case      :		Union[str, Any]         =							function(_snake_case	) + function(_snake_case	)

	for i in range(1	,  _snake_case	):
		__snake_case      :		List[str]         =							a + h * i
		result += function(_snake_case	) * (4 if i % 2 else 2)

	result *= h / 3
	return round(_snake_case	,  _snake_case	)


if __name__ == "__main__":
			import doctest

			doctest.testmod()





