


"""simple docstring"""






import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel


def __lowercase  (   lowerCamelCase_	:   Dict    ,       lowerCamelCase_	:   List[str]	):
				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 __lowercase  (   lowerCamelCase_	:   Optional[Any]    ,       lowerCamelCase_	:   str	):
				copy_linear(hf_mlp.fca    ,       pt_mlp.c_fc	)
				copy_linear(hf_mlp.fca    ,       pt_mlp.c_proj	)


def __lowercase  (   lowerCamelCase_	:   Optional[Any]    ,       lowerCamelCase_	:   Union[str, Any]	):
				SCREAMING_SNAKE_CASE__  				=					pt_linear.weight
				SCREAMING_SNAKE_CASE__  				=					pt_linear.bias


def __lowercase  (   lowerCamelCase_	:   Union[str, Any]    ,       lowerCamelCase_	:   Union[str, Any]	):
				# 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 __lowercase  (   lowerCamelCase_	:   Union[str, Any]    ,       lowerCamelCase_	:   List[Any]	):
				for hf_layer, pt_layer in zip(lowerCamelCase_    ,       lowerCamelCase_	):
								copy_layer(lowerCamelCase_    ,       lowerCamelCase_	)


def __lowercase  (   lowerCamelCase_	:   Any    ,       lowerCamelCase_	:   int	):
				# copy  embeds
				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 __lowercase  (   lowerCamelCase_	:   Optional[Any]    ,       lowerCamelCase_	:   Union[str, Any]	):
				# copy projection
				SCREAMING_SNAKE_CASE__  				=					pt_model.text_projection.data.T

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


def __lowercase  (   lowerCamelCase_	:   str    ,       lowerCamelCase_	:   Any	):
				# copy projection
				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 __lowercase  (   lowerCamelCase_	:   Optional[int]    ,       lowerCamelCase_	:   Any    ,       lowerCamelCase_	:   int=None	):
				if config_path is not None:
								SCREAMING_SNAKE_CASE__  				=					CLIPConfig.from_pretrained(lowerCamelCase_	)
				else:
								SCREAMING_SNAKE_CASE__  				=					CLIPConfig(projection_dim=512    ,       text_config={}    ,       vision_config={}	)

				SCREAMING_SNAKE_CASE__  				=					CLIPModel(lowerCamelCase_	).eval()

				SCREAMING_SNAKE_CASE__				,   SCREAMING_SNAKE_CASE__  				=					load(lowerCamelCase_    ,       device="cpu"    ,       jit=lowerCamelCase_	)
				SCREAMING_SNAKE_CASE__  				=					pt_model.eval()

				copy_text_model_and_projection(lowerCamelCase_    ,       lowerCamelCase_	)
				copy_vison_model_and_projection(lowerCamelCase_    ,       lowerCamelCase_	)
				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=lowerCamelCase_    ,       pixel_values=lowerCamelCase_    ,       return_dict=lowerCamelCase_	)
				SCREAMING_SNAKE_CASE__  				=					hf_outputs.logits_per_image
				SCREAMING_SNAKE_CASE__  				=					hf_outputs.logits_per_text
				SCREAMING_SNAKE_CASE__				,   SCREAMING_SNAKE_CASE__  				=					pt_model(lowerCamelCase_    ,       lowerCamelCase_	)

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

				hf_model.save_pretrained(lowerCamelCase_	)


if __name__ == "__main__":
	_lowerCamelCase  =	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  =	parser.parse_args()

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




"""simple docstring"""






def __lowercase  (   lowerCamelCase_	:   list	):
				if not isinstance(lowerCamelCase_    ,       lowerCamelCase_	):
								raise ValueError("Input series is not valid, valid series - [2, 4, 8]"	)
				if len(lowerCamelCase_	) == 0:
								raise ValueError("Input list must be a non empty list"	)
				if len(lowerCamelCase_	) == 1:
								return True
				try:
								SCREAMING_SNAKE_CASE__  				=					series[1] / series[0]
								for index in range(len(lowerCamelCase_	) - 1	):
												if series[index + 1] / series[index] != common_ratio:
																return False
				except ZeroDivisionError:
								return False
				return True


def __lowercase  (   lowerCamelCase_	:   list	):
				if not isinstance(lowerCamelCase_    ,       lowerCamelCase_	):
								raise ValueError("Input series is not valid, valid series - [2, 4, 8]"	)
				if len(lowerCamelCase_	) == 0:
								raise ValueError("Input list must be a non empty list"	)
				SCREAMING_SNAKE_CASE__  				=					1
				for value in series:
								answer *= value
				return pow(lowerCamelCase_    ,       1 / len(lowerCamelCase_	)	)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
