
"""simple docstring"""

import argparse

import torch

from transformers import ChineseCLIPConfig, ChineseCLIPModel
def 						UpperCAmelCase   (						a_,		a_,		a_     ):





		'''simple docstring'''


		lowerCamelCase      ,       lowerCamelCase      ,       lowerCamelCase	:     Tuple            =       pt_weights[F"""{prefix}.in_proj_weight"""].chunk(3,		dim=0     )
		lowerCamelCase      ,       lowerCamelCase      ,       lowerCamelCase	:     Any            =       pt_weights[F"""{prefix}.in_proj_bias"""].chunk(3,		dim=0     )

		lowerCamelCase	:     Optional[Any]            =       pt_weights[F"""{prefix}.out_proj.weight"""]
		lowerCamelCase	:     Optional[int]            =       pt_weights[F"""{prefix}.out_proj.bias"""]

		lowerCamelCase	:     List[Any]            =       q_proj
		lowerCamelCase	:     Any            =       q_proj_bias

		lowerCamelCase	:     List[str]            =       k_proj
		lowerCamelCase	:     List[str]            =       k_proj_bias

		lowerCamelCase	:     str            =       v_proj
		lowerCamelCase	:     str            =       v_proj_bias

		lowerCamelCase	:     List[str]            =       out_proj_weights
		lowerCamelCase	:     int            =       out_proj_bias
def 						UpperCAmelCase   (						a_,		a_,		a_     ):





		'''simple docstring'''


		copy_linear(hf_mlp.fca,		a_,		F"""{prefix}.c_fc"""     )
		copy_linear(hf_mlp.fca,		a_,		F"""{prefix}.c_proj"""     )
def 						UpperCAmelCase   (						a_,		a_,		a_     ):





		'''simple docstring'''


		lowerCamelCase	:     str            =       pt_weights[F"""{prefix}.weight"""].data
		lowerCamelCase	:     List[str]            =       pt_weights[F"""{prefix}.bias"""].data
def 						UpperCAmelCase   (						a_,		a_,		a_     ):





		'''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 						UpperCAmelCase   (						a_,		a_,		a_     ):





		'''simple docstring'''


		for layer_id, hf_layer in enumerate(a_     ):
				copy_layer(a_,		a_,		F"""{prefix}.{layer_id}"""     )
def 						UpperCAmelCase   (						a_,		a_     ):





		'''simple docstring'''


		lowerCamelCase	:     Optional[int]            =       pt_weights['text_projection'].data.T

		# copy text encoder
		for name, param in hf_model.text_model.named_parameters():
				lowerCamelCase	:     List[Any]            =       pt_weights[F"""bert.{name}"""].data
def 						UpperCAmelCase   (						a_,		a_     ):





		'''simple docstring'''


		lowerCamelCase	:     int            =       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
		lowerCamelCase	:     Optional[Any]            =       pt_weights['visual.conv1.weight'].data
		lowerCamelCase	:     Tuple            =       pt_weights['visual.class_embedding'].data
		lowerCamelCase	:     List[Any]            =       pt_weights['visual.positional_embedding'].data

		# copy encoder
		copy_layers(hf_model.vision_model.encoder.layers,		a_,		'visual.transformer.resblocks'     )
@torch.no_grad()
def 						UpperCAmelCase   (						a_,		a_,		a_=None     ):





		'''simple docstring'''


		assert config_path is not None, "Please specify the ChineseCLIP model config of the corresponding model size."
		lowerCamelCase	:     Dict            =       ChineseCLIPConfig.from_pretrained(a_     )

		lowerCamelCase	:     Any            =       ChineseCLIPModel(a_     ).eval()

		lowerCamelCase	:     List[Any]            =       torch.load(a_,		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(a_,		a_     )
		copy_vision_model_and_projection(a_,		a_     )
		lowerCamelCase	:     Optional[Any]            =       pt_weights['logit_scale'].data

		hf_model.save_pretrained(a_     )


if __name__ == "__main__":
					_A				=					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.'
					)
					_A				=					parser.parse_args()

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








"""simple docstring"""

import numpy as np
def 						UpperCAmelCase   (						a_,		a_="utf-8",		a_="surrogatepass"     ):





		'''simple docstring'''


		lowerCamelCase	:     Dict            =       bin(int.from_bytes(text.encode(a_,		a_     ),		'big'     )     )[2:]
		return bits.zfill(8 * ((len(a_     ) + 7) // 8)     )
def 						UpperCAmelCase   (						a_,		a_="utf-8",		a_="surrogatepass"     ):





		'''simple docstring'''


		lowerCamelCase	:     Union[str, Any]            =       int(a_,		2     )
		return n.to_bytes((n.bit_length() + 7) // 8,		'big'     ).decode(a_,		a_     ) or "\0"
def 						UpperCAmelCase   (						a_,		a_     ):





		'''simple docstring'''


		if size_par + len(a_     ) <= 2**size_par - (len(a_     ) - 1):
				raise ValueError('size of parity don\'t match with size of data'     )

		lowerCamelCase	:     Optional[Any]            =       []
		lowerCamelCase	:     Dict            =       []
		lowerCamelCase	:     str            =       [bin(a_     )[2:] for x in range(1,		size_par + len(a_     ) + 1     )]

		# sorted information data for the size of the output data
		lowerCamelCase	:     List[Any]            =       []
		# data position template + parity
		lowerCamelCase	:     Union[str, Any]            =       []
		# parity bit counter
		lowerCamelCase	:     Union[str, Any]            =       0
		# counter position of data bits
		lowerCamelCase	:     str            =       0

		for x in range(1,		size_par + len(a_     ) + 1     ):
				# Performs a template of bit positions - who should be given,
				# and who should be parity
				if qtd_bp < size_par:
						if (np.log(a_     ) / np.log(2     )).is_integer():
								data_out_gab.append('P'     )
								lowerCamelCase	:     int            =       qtd_bp + 1
						else:
								data_out_gab.append('D'     )
				else:
						data_out_gab.append('D'     )

				# Sorts the data to the new output size
				if data_out_gab[-1] == "D":
						data_ord.append(data[cont_data]     )
						cont_data += 1
				else:
						data_ord.append(a_     )

    # Calculates parity
		lowerCamelCase	:     Union[str, Any]            =       0  # parity bit counter
		for bp in range(1,		size_par + 1     ):
				# Bit counter one for a given parity
				lowerCamelCase	:     Optional[int]            =       0
				# counter to control the loop reading
				lowerCamelCase	:     int            =       0
				for x in data_ord:
						if x is not None:
								try:
										lowerCamelCase	:     Optional[int]            =       (bin_pos[cont_loop])[-1 * (bp)]
								except IndexError:
										lowerCamelCase	:     List[Any]            =       '0'
								if aux == "1" and x == "1":
										cont_bo += 1
						cont_loop += 1
				parity.append(cont_bo % 2     )

				qtd_bp += 1

		# Mount the message
		lowerCamelCase	:     Dict            =       0  # parity bit counter
		for x in range(0,		size_par + len(a_     )     ):
				if data_ord[x] is None:
						data_out.append(str(parity[cont_bp]     )     )
						cont_bp += 1
				else:
						data_out.append(data_ord[x]     )

		return data_out
def 						UpperCAmelCase   (						a_,		a_     ):





		'''simple docstring'''


		lowerCamelCase	:     int            =       []
		# Parity bit counter
		lowerCamelCase	:     Optional[Any]            =       0
		# Counter p data bit reading
		lowerCamelCase	:     int            =       0
		# list of parity received
		lowerCamelCase	:     Any            =       []
		lowerCamelCase	:     Optional[int]            =       []

		for x in range(1,		len(a_     ) + 1     ):
				# Performs a template of bit positions - who should be given,
				#  and who should be parity
				if qtd_bp < size_par and (np.log(a_     ) / np.log(2     )).is_integer():
						data_out_gab.append('P'     )
						lowerCamelCase	:     Tuple            =       qtd_bp + 1
				else:
						data_out_gab.append('D'     )

				# Sorts the data to the new output size
				if data_out_gab[-1] == "D":
						data_output.append(data[cont_data]     )
				else:
						parity_received.append(data[cont_data]     )
				cont_data += 1

		# -----------calculates the parity with the data
		lowerCamelCase	:     Tuple            =       []
		lowerCamelCase	:     str            =       []
		lowerCamelCase	:     Optional[Any]            =       [bin(a_     )[2:] for x in range(1,		size_par + len(a_     ) + 1     )]

		#  sorted information data for the size of the output data
		lowerCamelCase	:     Dict            =       []
		# Data position feedback + parity
		lowerCamelCase	:     Union[str, Any]            =       []
		# Parity bit counter
		lowerCamelCase	:     Union[str, Any]            =       0
		# Counter p data bit reading
		lowerCamelCase	:     List[Any]            =       0

		for x in range(1,		size_par + len(a_     ) + 1     ):
				# Performs a template position of bits - who should be given,
				# and who should be parity
				if qtd_bp < size_par and (np.log(a_     ) / np.log(2     )).is_integer():
						data_out_gab.append('P'     )
						lowerCamelCase	:     Any            =       qtd_bp + 1
				else:
						data_out_gab.append('D'     )

				# Sorts the data to the new output size
				if data_out_gab[-1] == "D":
						data_ord.append(data_output[cont_data]     )
						cont_data += 1
				else:
						data_ord.append(a_     )

    # Calculates parity
		lowerCamelCase	:     Optional[int]            =       0  # parity bit counter
		for bp in range(1,		size_par + 1     ):
				# Bit counter one for a certain parity
				lowerCamelCase	:     Tuple            =       0
				# Counter to control loop reading
				lowerCamelCase	:     Any            =       0
				for x in data_ord:
						if x is not None:
								try:
										lowerCamelCase	:     List[str]            =       (bin_pos[cont_loop])[-1 * (bp)]
								except IndexError:
										lowerCamelCase	:     str            =       '0'
								if aux == "1" and x == "1":
										cont_bo += 1
						cont_loop += 1
				parity.append(str(cont_bo % 2     )     )

				qtd_bp += 1

		# Mount the message
		lowerCamelCase	:     Optional[int]            =       0  # Parity bit counter
		for x in range(0,		size_par + len(a_     )     ):
				if data_ord[x] is None:
						data_out.append(str(parity[cont_bp]     )     )
						cont_bp += 1
				else:
						data_out.append(data_ord[x]     )

		lowerCamelCase	:     Any            =       parity_received == parity
		return data_output, ack






