
import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel




def 				__magic_name__				(     __lowerCAmelCase			:		Dict	,						__lowerCAmelCase			:		int							)    ->     List[str]:
				__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 				__magic_name__				(     __lowerCAmelCase			:		str	,						__lowerCAmelCase			:		Tuple							)    ->     Dict:
				copy_linear(hf_mlp.fca	,						pt_mlp.c_fc							)
				copy_linear(hf_mlp.fca	,						pt_mlp.c_proj							)




def 				__magic_name__				(     __lowerCAmelCase			:		str	,						__lowerCAmelCase			:		List[Any]							)    ->     Union[str, Any]:
				__lowerCamelCase					      =			pt_linear.weight
				__lowerCamelCase					      =			pt_linear.bias




def 				__magic_name__				(     __lowerCAmelCase			:		str	,						__lowerCAmelCase			:		Tuple							)    ->     Optional[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 				__magic_name__				(     __lowerCAmelCase			:		Any	,						__lowerCAmelCase			:		Any							)    ->     Union[str, Any]:
				for hf_layer, pt_layer in zip(__lowerCAmelCase	,						__lowerCAmelCase							):
								copy_layer(__lowerCAmelCase	,						__lowerCAmelCase							)




def 				__magic_name__				(     __lowerCAmelCase			:		Any	,						__lowerCAmelCase			:		Optional[int]							)    ->     List[Any]:
				# copy  embeds
				__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 				__magic_name__				(     __lowerCAmelCase			:		str	,						__lowerCAmelCase			:		List[Any]							)    ->     Any:
				# copy projection
				__lowerCamelCase					      =			pt_model.text_projection.data.T

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




def 				__magic_name__				(     __lowerCAmelCase			:		Union[str, Any]	,						__lowerCAmelCase			:		List[Any]							)    ->     Any:
				# copy projection
				__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 				__magic_name__				(     __lowerCAmelCase			:		Optional[int]	,						__lowerCAmelCase			:		Optional[int]	,						__lowerCAmelCase			:		List[Any]=None							)    ->     Any:
				if config_path is not None:
								__lowerCamelCase					      =			CLIPConfig.from_pretrained(__lowerCAmelCase							)
				else:
								__lowerCamelCase					      =			CLIPConfig(projection_dim=512	,						text_config={}	,						vision_config={}							)

				__lowerCamelCase					      =			CLIPModel(__lowerCAmelCase							).eval()

				__lowerCamelCase      ,					__lowerCamelCase					      =			load(__lowerCAmelCase	,						device='''cpu'''	,						jit=__lowerCAmelCase							)
				__lowerCamelCase					      =			pt_model.eval()

				copy_text_model_and_projection(__lowerCAmelCase	,						__lowerCAmelCase							)
				copy_vison_model_and_projection(__lowerCAmelCase	,						__lowerCAmelCase							)
				__lowerCamelCase					      =			pt_model.logit_scale

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

				__lowerCamelCase					      =			hf_model(input_ids=__lowerCAmelCase	,						pixel_values=__lowerCAmelCase	,						return_dict=__lowerCAmelCase							)
				__lowerCamelCase					      =			hf_outputs.logits_per_image
				__lowerCamelCase					      =			hf_outputs.logits_per_text
				__lowerCamelCase      ,					__lowerCamelCase					      =			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__":
					SCREAMING_SNAKE_CASE__     :    int      							=					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)





from transformers import SqueezeBertTokenizer, SqueezeBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest

@require_tokenizers
class      lowerCAmelCase__  (			__lowercase    ):
		a__ :		Any   						=	SqueezeBertTokenizer
		a__ :		Union[str, Any]   						=	SqueezeBertTokenizerFast
		a__ :		int   						=	True






		def 				__A (	self	:							Optional[int]							,    **SCREAMING_SNAKE_CASE__	:							Union[str, Any]						)  ->							int:
						return SqueezeBertTokenizerFast.from_pretrained(self.tmpdirname							,    **SCREAMING_SNAKE_CASE__						)







		@slow
		def 				__A (	self	:							int						)  ->							Optional[int]:
						__lowerCamelCase					      =			SqueezeBertTokenizer.from_pretrained('''squeezebert/squeezebert-mnli-headless'''						)

						__lowerCamelCase					      =			tokenizer.encode('''sequence builders'''							,    add_special_tokens=SCREAMING_SNAKE_CASE__						)
						__lowerCamelCase					      =			tokenizer.encode('''multi-sequence build'''							,    add_special_tokens=SCREAMING_SNAKE_CASE__						)

						__lowerCamelCase					      =			tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE__						)
						__lowerCamelCase					      =			tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						)

						assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
						assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
						    tokenizer.sep_token_id
						]



