


import argparse

import torch

from transformers import ChineseCLIPConfig, ChineseCLIPModel







def   lowerCamelCase(				a__		,a__		,a__):
				_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE     					=pt_weights[f"{prefix}.in_proj_weight"].chunk(3		,dim=0)
				_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE     					=pt_weights[f"{prefix}.in_proj_bias"].chunk(3		,dim=0)

				_SCREAMING_SNAKE_CASE     					=pt_weights[f"{prefix}.out_proj.weight"]
				_SCREAMING_SNAKE_CASE     					=pt_weights[f"{prefix}.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   lowerCamelCase(				a__		,a__		,a__):
				copy_linear(hf_mlp.fca		,_A		,f"{prefix}.c_fc")
				copy_linear(hf_mlp.fca		,_A		,f"{prefix}.c_proj")







def   lowerCamelCase(				a__		,a__		,a__):
				_SCREAMING_SNAKE_CASE     					=pt_weights[f"{prefix}.weight"].data
				_SCREAMING_SNAKE_CASE     					=pt_weights[f"{prefix}.bias"].data







def   lowerCamelCase(				a__		,a__		,a__):
				copy_linear(hf_layer.layer_norma		,_A		,f"{prefix}.ln_1")
				copy_linear(hf_layer.layer_norma		,_A		,f"{prefix}.ln_2")

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

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







def   lowerCamelCase(				a__		,a__		,a__):


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







def   lowerCamelCase(				a__		,a__):
				_SCREAMING_SNAKE_CASE     					=pt_weights['''text_projection'''].data.T

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







def   lowerCamelCase(				a__		,a__):
				_SCREAMING_SNAKE_CASE     					=pt_weights['''visual.proj'''].data.T

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

				# copy embeddings
				_SCREAMING_SNAKE_CASE     					=pt_weights['''visual.conv1.weight'''].data
				_SCREAMING_SNAKE_CASE     					=pt_weights['''visual.class_embedding'''].data
				_SCREAMING_SNAKE_CASE     					=pt_weights['''visual.positional_embedding'''].data

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







@torch.no_grad()
def   lowerCamelCase(				a__		,a__		,a__=None):
				assert config_path is not None, "Please specify the ChineseCLIP model config of the corresponding model size."
				_SCREAMING_SNAKE_CASE     					=ChineseCLIPConfig.from_pretrained(_A)

				_SCREAMING_SNAKE_CASE     					=ChineseCLIPModel(_A).eval()

				_SCREAMING_SNAKE_CASE     					=torch.load(_A		,map_location='''cpu''')['''state_dict''']
				_SCREAMING_SNAKE_CASE     					={(name[7:] if name.startswith('''module.''') else name): value for name, value in pt_weights.items()}

				copy_text_model_and_projection(_A		,_A)
				copy_vision_model_and_projection(_A		,_A)
				_SCREAMING_SNAKE_CASE     					=pt_weights['''logit_scale'''].data

				hf_model.save_pretrained(_A)


if __name__ == "__main__":
						snake_case_  :     Tuple							  =							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.'''
						)
						snake_case_  :     Dict							  =							parser.parse_args()

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



import math







def   lowerCamelCase(				a__):
				_SCREAMING_SNAKE_CASE     					=math.log(2		,10) * number
				_SCREAMING_SNAKE_CASE     					=round((log_number - int(a__))		,15)

				return difference







def   lowerCamelCase(				a__ = 67_8910):
				_SCREAMING_SNAKE_CASE     					=90
				_SCREAMING_SNAKE_CASE     					=0

				_SCREAMING_SNAKE_CASE     					=math.log(1.23		,10)
				_SCREAMING_SNAKE_CASE     					=math.log(1.24		,10)
				_SCREAMING_SNAKE_CASE     					=0

				while position < number:
								_SCREAMING_SNAKE_CASE     					=log_difference(a__)

								if difference >= upper_limit:
												power_iterator += 93

								elif difference < lower_limit:
												power_iterator += 196

								else:
												_SCREAMING_SNAKE_CASE     					=power_iterator
												power_iterator += 196
												position += 1

				return previous_power


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							print(f"""{solution() = }""")