



import timeit



class 			_lowercase							:





	"""simple docstring"""




	def __init__(self	, lowerCamelCase_	):
						"""simple docstring"""

						if isinstance(snake_case_	, snake_case_	):  # Initializes a matrix identical to the one provided.
											a        =     arg
											a        =     len(snake_case_	)
						else:  # Initializes a square matrix of the given size and set values to zero.
											a        =     arg
											a        =     [[0 for _ in range(self.n	)] for _ in range(self.n	)]




	def __mul__(self	, lowerCamelCase_	):
						"""simple docstring"""

						a        =     Matrix(self.n	)
						for i in range(self.n	):
											for j in range(self.n	):
																for k in range(self.n	):
																					matrix.t[i][j] += self.t[i][k] * b.t[k][j]
						return matrix






def 					a(   A :					Dict				,  A :					List[str] )	-> List[str]:
					"""simple docstring"""





					a        =     Matrix([[1, 0], [0, 1]] )
					while b > 0:
										if b & 1:
															matrix *= a
										a *= a
										b >>= 1
					return matrix






def 					a(   A :					Optional[int]				,  A :					Tuple				,  A :					Optional[Any] )	-> Union[str, Any]:
					"""simple docstring"""





					if n == 1:
										return fa
					elif n == 2:
										return fa
					a        =     Matrix([[1, 1], [1, 0]] )
					a        =     modular_exponentiation(__a				,  n - 2 )
					return fa * matrix.t[0][0] + fa * matrix.t[0][1]






def 					a(   A :					Optional[Any]				,  A :					Optional[Any]				,  A :					List[str] )	-> List[Any]:
					"""simple docstring"""





					if n == 1:
										return fa
					elif n == 2:
										return fa

					a        =     fa
					a        =     fa
					n -= 2

					while n > 0:
										a        =     fn_a + fn_a, fn_a
										n -= 1

					return fn_a






def 					a(   )	-> str:
					"""simple docstring"""





					a        =     '''
from random import randint
from __main__ import fibonacci_with_matrix_exponentiation
'''
					a        =     '''fibonacci_with_matrix_exponentiation(randint(1,70000), 1, 1)'''
					a        =     timeit.timeit(setup=__a				,  stmt=__a				,  number=100 )
					print("With matrix exponentiation the average execution time is "				,  exec_time / 100 )
					return exec_time






def 					a(   )	-> int:
					"""simple docstring"""





					a        =     '''
from random import randint
from __main__ import simple_fibonacci
'''
					a        =     '''simple_fibonacci(randint(1,70000), 1, 1)'''
					a        =     timeit.timeit(setup=__a				,  stmt=__a				,  number=100 )
					print(
					    "Without matrix exponentiation the average execution time is "				,  exec_time / 100 )
					return exec_time






def 					a(   )	-> List[str]:
					"""simple docstring"""





					matrix_exponentiation_time()
					simple_fibonacci_time()


if __name__ == "__main__":
		main()





import argparse

import torch

from transformers import ChineseCLIPConfig, ChineseCLIPModel






def 					a(   A :					Union[str, Any]				,  A :					List[str]				,  A :					Any )	-> Tuple:
					"""simple docstring"""





					a  ,       a  ,       a        =     pt_weights[f'''{prefix}.in_proj_weight'''].chunk(3				,  dim=0 )
					a  ,       a  ,       a        =     pt_weights[f'''{prefix}.in_proj_bias'''].chunk(3				,  dim=0 )

					a        =     pt_weights[f'''{prefix}.out_proj.weight''']
					a        =     pt_weights[f'''{prefix}.out_proj.bias''']

					a        =     q_proj
					a        =     q_proj_bias

					a        =     k_proj
					a        =     k_proj_bias

					a        =     v_proj
					a        =     v_proj_bias

					a        =     out_proj_weights
					a        =     out_proj_bias






def 					a(   A :					List[str]				,  A :					List[Any]				,  A :					int )	-> List[Any]:
					"""simple docstring"""





					copy_linear(hf_mlp.fca				,  A				,  f'''{prefix}.c_fc''' )
					copy_linear(hf_mlp.fca				,  A				,  f'''{prefix}.c_proj''' )






def 					a(   A :					Optional[Any]				,  A :					List[Any]				,  A :					List[str] )	-> Dict:
					"""simple docstring"""





					a        =     pt_weights[f'''{prefix}.weight'''].data
					a        =     pt_weights[f'''{prefix}.bias'''].data






def 					a(   A :					int				,  A :					Optional[Any]				,  A :					List[Any] )	-> Union[str, Any]:
					"""simple docstring"""





					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 					a(   A :					str				,  A :					Optional[int]				,  A :					List[str] )	-> Dict:
					"""simple docstring"""





					for layer_id, hf_layer in enumerate(A ):
										copy_layer(A				,  A				,  f'''{prefix}.{layer_id}''' )






def 					a(   A :					Dict				,  A :					Union[str, Any] )	-> Optional[Any]:
					"""simple docstring"""





					a        =     pt_weights["text_projection"].data.T

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






def 					a(   A :					Optional[Any]				,  A :					Optional[Any] )	-> List[str]:
					"""simple docstring"""





					a        =     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
					a        =     pt_weights["visual.conv1.weight"].data
					a        =     pt_weights["visual.class_embedding"].data
					a        =     pt_weights["visual.positional_embedding"].data

					# copy encoder
					copy_layers(hf_model.vision_model.encoder.layers				,  A				,  "visual.transformer.resblocks" )






@torch.no_grad()
def 					a(   A :					str				,  A :					List[str]				,  A :					Union[str, Any]=None )	-> Tuple:
					"""simple docstring"""





					assert config_path is not None, "Please specify the ChineseCLIP model config of the corresponding model size."
					a        =     ChineseCLIPConfig.from_pretrained(A )

					a        =     ChineseCLIPModel(A ).eval()

					a        =     torch.load(A				,  map_location="cpu" )["state_dict"]
					a        =     {(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 )
					a        =     pt_weights["logit_scale"].data

					hf_model.save_pretrained(A )


if __name__ == "__main__":
				_lowercase:				List[Any]       =	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."
				)
				_lowercase:				int       =	parser.parse_args()

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