


'''simple docstring'''







import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel



def 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__		):






				"""simple docstring"""



				__magic_name__       ,     __magic_name__       ,     __magic_name__				:					List[str]						       =       pt_attn_layer.in_proj_weight.chunk(3     ,   dim=0		)
				__magic_name__       ,     __magic_name__       ,     __magic_name__				:					Union[str, Any]						       =       pt_attn_layer.in_proj_bias.chunk(3     ,   dim=0		)

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

				__magic_name__				:					Optional[int]						       =       q_proj
				__magic_name__				:					Any						       =       q_proj_bias

				__magic_name__				:					Union[str, Any]						       =       k_proj
				__magic_name__				:					Optional[Any]						       =       k_proj_bias

				__magic_name__				:					Union[str, Any]						       =       v_proj
				__magic_name__				:					Any						       =       v_proj_bias

				__magic_name__				:					Union[str, Any]						       =       out_proj_weights
				__magic_name__				:					Tuple						       =       out_proj_bias



def 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__		):






				"""simple docstring"""



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



def 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__		):






				"""simple docstring"""



				__magic_name__				:					List[str]						       =       pt_linear.weight
				__magic_name__				:					int						       =       pt_linear.bias



def 						_UpperCamelCase							( 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 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__		):






				"""simple docstring"""



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



def 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__		):






				"""simple docstring"""



				__magic_name__				:					Union[str, Any]						       =       pt_model.token_embedding.weight
				__magic_name__				:					Dict						       =       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 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__		):






				"""simple docstring"""



				__magic_name__				:					List[str]						       =       pt_model.text_projection.data.T

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



def 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__		):






				"""simple docstring"""



				__magic_name__				:					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
				__magic_name__				:					Optional[int]						       =       pt_model.visual.conva.weight.data
				__magic_name__				:					Any						       =       pt_model.visual.class_embedding
				__magic_name__				:					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 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__=None		):






				"""simple docstring"""



				if config_path is not None:
								__magic_name__				:					Optional[Any]						       =       CLIPConfig.from_pretrained(UpperCamelCase__		)
				else:
								__magic_name__				:					Union[str, Any]						       =       CLIPConfig(projection_dim=512     ,   text_config={}     ,   vision_config={}		)

				__magic_name__				:					List[str]						       =       CLIPModel(UpperCamelCase__		).eval()

				__magic_name__       ,     __magic_name__				:					Optional[Any]						       =       load(UpperCamelCase__     ,   device="cpu"     ,   jit=UpperCamelCase__		)
				__magic_name__				:					List[str]						       =       pt_model.eval()

				copy_text_model_and_projection(UpperCamelCase__     ,   UpperCamelCase__		)
				copy_vison_model_and_projection(UpperCamelCase__     ,   UpperCamelCase__		)
				__magic_name__				:					Optional[Any]						       =       pt_model.logit_scale

				__magic_name__				:					Optional[int]						       =       torch.arange(0     ,   77		).unsqueeze(0		)
				__magic_name__				:					Optional[Any]						       =       torch.randn(1     ,   3     ,   224     ,   224		)

				__magic_name__				:					Union[str, Any]						       =       hf_model(input_ids=UpperCamelCase__     ,   pixel_values=UpperCamelCase__     ,   return_dict=UpperCamelCase__		)
				__magic_name__				:					Union[str, Any]						       =       hf_outputs.logits_per_image
				__magic_name__				:					Tuple						       =       hf_outputs.logits_per_text
				__magic_name__       ,     __magic_name__				:					List[str]						       =       pt_model(UpperCamelCase__     ,   UpperCamelCase__		)

				assert torch.allclose(UpperCamelCase__     ,   UpperCamelCase__     ,   atol=1E-3		)
				assert torch.allclose(UpperCamelCase__     ,   UpperCamelCase__     ,   atol=1E-3		)

				hf_model.save_pretrained(UpperCamelCase__		)


if __name__ == "__main__":
		_SCREAMING_SNAKE_CASE       :   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")
		_SCREAMING_SNAKE_CASE       :   Optional[int]        = parser.parse_args()

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



'''simple docstring'''







import unittest

from transformers import BlenderbotTokenizer, BlenderbotTokenizerFast
from transformers.utils import cached_property






class 		_snake_case    (				unittest.TestCase		):






						'''simple docstring'''







						@cached_property
						def lowerCAmelCase__ (  self:	Optional[Any]     )							->	List[Any]:
										return BlenderbotTokenizer.from_pretrained("facebook/blenderbot-3B"     )







						@cached_property
						def lowerCAmelCase__ (  self:	Optional[Any]     )							->	Optional[int]:
										return BlenderbotTokenizerFast.from_pretrained("facebook/blenderbot-3B"     )







						def lowerCAmelCase__ (  self:	Optional[Any]     )							->	Optional[int]:
										__magic_name__				:					Tuple						       =       self.tokenizer_ab
										__magic_name__				:					Optional[int]						       =       " I am a small frog."
										__magic_name__				:					Optional[int]						       =       tok([src_text]      ,							padding=__UpperCamelCase      ,							truncation=__UpperCamelCase     )["input_ids"]
										__magic_name__				:					int						       =       tok.batch_decode(__UpperCamelCase      ,							skip_special_tokens=__UpperCamelCase      ,							clean_up_tokenization_spaces=__UpperCamelCase     )[0]
										assert src_text == decoded







						def lowerCAmelCase__ (  self:	Optional[int]     )							->	Any:
										__magic_name__				:					Tuple						       =       self.rust_tokenizer_ab
										__magic_name__				:					str						       =       " I am a small frog."
										__magic_name__				:					Union[str, Any]						       =       tok([src_text]      ,							padding=__UpperCamelCase      ,							truncation=__UpperCamelCase     )["input_ids"]
										__magic_name__				:					Any						       =       tok.batch_decode(__UpperCamelCase      ,							skip_special_tokens=__UpperCamelCase      ,							clean_up_tokenization_spaces=__UpperCamelCase     )[0]
										assert src_text == decoded







						def lowerCAmelCase__ (  self:	Optional[Any]     )							->	str:
										assert self.tokenizer_ab.add_prefix_space
										assert self.tokenizer_ab([" Sam", "Sam"]     ).input_ids == [[5502, 2], [5502, 2]]







						def lowerCAmelCase__ (  self:	List[str]     )							->	List[Any]:
										assert self.rust_tokenizer_ab.add_prefix_space
										assert self.rust_tokenizer_ab([" Sam", "Sam"]     ).input_ids == [[5502, 2], [5502, 2]]