




import argparse

import torch

from transformers import ChineseCLIPConfig, ChineseCLIPModel






def  snake_case       (							A						,       A						,       A      )							->							Any:




	'''simple docstring'''


	lowerCamelCase_       ,      lowerCamelCase_       ,      lowerCamelCase_							:		Dict							       =							pt_weights[f"{prefix}.in_proj_weight"].chunk(3						,       dim=0      )
	lowerCamelCase_       ,      lowerCamelCase_       ,      lowerCamelCase_							:		str							       =							pt_weights[f"{prefix}.in_proj_bias"].chunk(3						,       dim=0      )

	lowerCamelCase_							:		str							       =							pt_weights[f"{prefix}.out_proj.weight"]
	lowerCamelCase_							:		List[str]							       =							pt_weights[f"{prefix}.out_proj.bias"]

	lowerCamelCase_							:		Optional[int]							       =							q_proj
	lowerCamelCase_							:		int							       =							q_proj_bias

	lowerCamelCase_							:		Any							       =							k_proj
	lowerCamelCase_							:		List[Any]							       =							k_proj_bias

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

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






def  snake_case       (							A						,       A						,       A      )							->							Any:




	'''simple docstring'''


	copy_linear(hf_mlp.fca						,       lowercase__						,       f"{prefix}.c_fc"      )
	copy_linear(hf_mlp.fca						,       lowercase__						,       f"{prefix}.c_proj"      )






def  snake_case       (							A						,       A						,       A      )							->							Tuple:




	'''simple docstring'''


	lowerCamelCase_							:		List[str]							       =							pt_weights[f"{prefix}.weight"].data
	lowerCamelCase_							:		List[str]							       =							pt_weights[f"{prefix}.bias"].data






def  snake_case       (							A						,       A						,       A      )							->							Optional[int]:




	'''simple docstring'''


	copy_linear(hf_layer.layer_norma						,       lowercase__						,       f"{prefix}.ln_1"      )
	copy_linear(hf_layer.layer_norma						,       lowercase__						,       f"{prefix}.ln_2"      )

	# copy MLP
	copy_mlp(hf_layer.mlp						,       lowercase__						,       f"{prefix}.mlp"      )

	# copy attn
	copy_attn_layer(hf_layer.self_attn						,       lowercase__						,       f"{prefix}.attn"      )






def  snake_case       (							A						,       A						,       A      )							->							Tuple:




	'''simple docstring'''


	for layer_id, hf_layer in enumerate(lowercase__      ):
		copy_layer(lowercase__						,       lowercase__						,       f"{prefix}.{layer_id}"      )






def  snake_case       (							A						,       A      )							->							Dict:




	'''simple docstring'''


	lowerCamelCase_							:		int							       =							pt_weights['text_projection'].data.T

	# copy text encoder
	for name, param in hf_model.text_model.named_parameters():
		lowerCamelCase_							:		Optional[Any]							       =							pt_weights[f"bert.{name}"].data






def  snake_case       (							A						,       A      )							->							Optional[int]:




	'''simple docstring'''


	lowerCamelCase_							:		List[Any]							       =							pt_weights['visual.proj'].data.T

	# copy layer norms
	copy_linear(hf_model.vision_model.pre_layrnorm						,       lowercase__						,       'visual.ln_pre'      )
	copy_linear(hf_model.vision_model.post_layernorm						,       lowercase__						,       'visual.ln_post'      )

	# copy embeddings
	lowerCamelCase_							:		List[Any]							       =							pt_weights['visual.conv1.weight'].data
	lowerCamelCase_							:		List[str]							       =							pt_weights['visual.class_embedding'].data
	lowerCamelCase_							:		int							       =							pt_weights['visual.positional_embedding'].data

	# copy encoder
	copy_layers(hf_model.vision_model.encoder.layers						,       lowercase__						,       'visual.transformer.resblocks'      )






@torch.no_grad()
def  snake_case       (							A						,       A						,       A=None      )							->							Tuple:




	'''simple docstring'''


	assert config_path is not None, "Please specify the ChineseCLIP model config of the corresponding model size."
	lowerCamelCase_							:		str							       =							ChineseCLIPConfig.from_pretrained(lowercase__      )

	lowerCamelCase_							:		Optional[Any]							       =							ChineseCLIPModel(lowercase__      ).eval()

	lowerCamelCase_							:		str							       =							torch.load(lowercase__						,       map_location='cpu'      )['state_dict']
	lowerCamelCase_							:		Optional[int]							       =							{(name[7:] if name.startswith('module.'      ) else name): value for name, value in pt_weights.items()}

	copy_text_model_and_projection(lowercase__						,       lowercase__      )
	copy_vision_model_and_projection(lowercase__						,       lowercase__      )
	lowerCamelCase_							:		Optional[int]							       =							pt_weights['logit_scale'].data

	hf_model.save_pretrained(lowercase__      )


if __name__ == "__main__":
							UpperCamelCase__		=						argparse.ArgumentParser()
							parser.add_argument(
							    '--pytorch_dump_folder_path',
							    default=None,
							    type=str,
							    help='Path to the output folder storing converted hf PyTorch model.',
							)
							parser.add_argument(
							    '--checkpoint_path', default=None, type=str, help='Path to original github format ChineseCLIP checkpoint.'
							)
							parser.add_argument(
							    '--config_path', default=None, required=True, type=str, help='Path to hf config.json of model to convert.'
							)
							UpperCamelCase__		=						parser.parse_args()

							convert_chinese_clip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
							print('The conversion is finished!')






import sys

from transformers import AutoTokenizer


UpperCamelCase__		=						sys.argv[1]
UpperCamelCase__		=						sys.argv[2]
UpperCamelCase__		=						int(sys.argv[3])

UpperCamelCase__		=						0

UpperCamelCase__		=						AutoTokenizer.from_pretrained(model_name_or_path)
max_len -= tokenizer.num_special_tokens_to_add()

with open(dataset, 'rt') as f_p:
							for line in f_p:
														UpperCamelCase__		=						line.rstrip()

														if not line:
																					print(line)
																					UpperCamelCase__		=						0
																					continue

														UpperCamelCase__		=						line.split()[0]

														UpperCamelCase__		=						len(tokenizer.tokenize(token))

														# Token contains strange control characters like \x96 or \x95
														# Just filter out the complete line
														if current_subwords_len == 0:
																					continue

														if (subword_len_counter + current_subwords_len) > max_len:
																					print('')
																					print(line)
																					UpperCamelCase__		=						current_subwords_len
																					continue

														subword_len_counter += current_subwords_len

														print(line)
