
import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel




def 						A_			(				snake_case				: List[str]			,   snake_case				: int			)   ->     Dict:



						'''simple docstring'''

						__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase  	=      pt_attn_layer.in_proj_weight.chunk(3			,   dim=0			)
						__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase  	=      pt_attn_layer.in_proj_bias.chunk(3			,   dim=0			)

						__UpperCamelCase  	=      pt_attn_layer.out_proj.weight
						__UpperCamelCase  	=      pt_attn_layer.out_proj.bias

						__UpperCamelCase  	=      q_proj
						__UpperCamelCase  	=      q_proj_bias

						__UpperCamelCase  	=      k_proj
						__UpperCamelCase  	=      k_proj_bias

						__UpperCamelCase  	=      v_proj
						__UpperCamelCase  	=      v_proj_bias

						__UpperCamelCase  	=      out_proj_weights
						__UpperCamelCase  	=      out_proj_bias




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



						'''simple docstring'''

						copy_linear(hf_mlp.fca			,   pt_mlp.c_fc			)
						copy_linear(hf_mlp.fca			,   pt_mlp.c_proj			)




def 						A_			(				snake_case				: Union[str, Any]			,   snake_case				: Optional[Any]			)   ->     Union[str, Any]:



						'''simple docstring'''

						__UpperCamelCase  	=      pt_linear.weight
						__UpperCamelCase  	=      pt_linear.bias




def 						A_			(				snake_case				: Tuple			,   snake_case				: int			)   ->     str:



						'''simple docstring'''

						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 						A_			(				snake_case				: List[Any]			,   snake_case				: Any			)   ->     Optional[int]:



						'''simple docstring'''

						for hf_layer, pt_layer in zip(snake_case			,   snake_case			):
												copy_layer(snake_case			,   snake_case			)




def 						A_			(				snake_case				: str			,   snake_case				: Dict			)   ->     List[str]:



						'''simple docstring'''

						__UpperCamelCase  	=      pt_model.token_embedding.weight
						__UpperCamelCase  	=      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 						A_			(				snake_case				: str			,   snake_case				: List[str]			)   ->     Dict:



						'''simple docstring'''

						__UpperCamelCase  	=      pt_model.text_projection.data.T

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




def 						A_			(				snake_case				: List[str]			,   snake_case				: Any			)   ->     str:



						'''simple docstring'''

						__UpperCamelCase  	=      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
						__UpperCamelCase  	=      pt_model.visual.conva.weight.data
						__UpperCamelCase  	=      pt_model.visual.class_embedding
						__UpperCamelCase  	=      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 						A_			(				snake_case				: List[Any]			,   snake_case				: int			,   snake_case				: Any=None			)   ->     List[Any]:



						'''simple docstring'''

						if config_path is not None:
												__UpperCamelCase  	=      CLIPConfig.from_pretrained(snake_case			)
						else:
												__UpperCamelCase  	=      CLIPConfig(projection_dim=512			,   text_config={}			,   vision_config={}			)

						__UpperCamelCase  	=      CLIPModel(snake_case			).eval()

						__UpperCamelCase	,				__UpperCamelCase  	=      load(snake_case			,   device='''cpu'''			,   jit=snake_case			)
						__UpperCamelCase  	=      pt_model.eval()

						copy_text_model_and_projection(snake_case			,   snake_case			)
						copy_vison_model_and_projection(snake_case			,   snake_case			)
						__UpperCamelCase  	=      pt_model.logit_scale

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

						__UpperCamelCase  	=      hf_model(input_ids=snake_case			,   pixel_values=snake_case			,   return_dict=snake_case			)
						__UpperCamelCase  	=      hf_outputs.logits_per_image
						__UpperCamelCase  	=      hf_outputs.logits_per_text
						__UpperCamelCase	,				__UpperCamelCase  	=      pt_model(snake_case			,   snake_case			)

						assert torch.allclose(snake_case			,   snake_case			,   atol=1e-3			)
						assert torch.allclose(snake_case			,   snake_case			,   atol=1e-3			)

						hf_model.save_pretrained(snake_case			)


if __name__ == "__main__":
				lowercase__				:				Union[str, Any]						=      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")
				lowercase__				:				str						=      parser.parse_args()

				convert_clip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)





"""simple docstring"""






import numpy as np



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



def     a_		(   lowerCamelCase   ,     lowerCamelCase="utf-8"   ,     lowerCamelCase="surrogatepass"			):
				UpperCAmelCase__			 =				int(lowerCamelCase   ,     2			)
				return n.to_bytes((n.bit_length() + 7) // 8   ,     'big'			).decode(lowerCamelCase   ,     lowerCamelCase			) or "\0"



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

				UpperCAmelCase__			 =				[]
				UpperCAmelCase__			 =				[]
				UpperCAmelCase__			 =				[bin(lowerCamelCase			)[2:] for x in range(1   ,     size_par + len(lowerCamelCase			) + 1			)]

				# sorted information data for the size of the output data
				UpperCAmelCase__			 =				[]
				# data position template + parity
				UpperCAmelCase__			 =				[]
				# parity bit counter
				UpperCAmelCase__			 =				0
				# counter position of data bits
				UpperCAmelCase__			 =				0

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

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

								qtd_bp += 1

				# Mount the message
				UpperCAmelCase__			 =				0  # parity bit counter
				for x in range(0   ,     size_par + len(lowerCamelCase			)			):
								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     a_		(   lowerCamelCase   ,     lowerCamelCase			):
				UpperCAmelCase__			 =				[]
				# Parity bit counter
				UpperCAmelCase__			 =				0
				# Counter p data bit reading
				UpperCAmelCase__			 =				0
				# list of parity received
				UpperCAmelCase__			 =				[]
				UpperCAmelCase__			 =				[]

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

				#  sorted information data for the size of the output data
				UpperCAmelCase__			 =				[]
				# Data position feedback + parity
				UpperCAmelCase__			 =				[]
				# Parity bit counter
				UpperCAmelCase__			 =				0
				# Counter p data bit reading
				UpperCAmelCase__			 =				0

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

    # Calculates parity
				UpperCAmelCase__			 =				0  # parity bit counter
				for bp in range(1   ,     size_par + 1			):
								# Bit counter one for a certain parity
								UpperCAmelCase__			 =				0
								# Counter to control loop reading
								UpperCAmelCase__			 =				0
								for x in data_ord:
												if x is not None:
																try:
																				UpperCAmelCase__			 =				(bin_pos[cont_loop])[-1 * (bp)]
																except IndexError:
																				UpperCAmelCase__			 =				'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
				UpperCAmelCase__			 =				0  # Parity bit counter
				for x in range(0   ,     size_par + len(lowerCamelCase			)			):
								if data_ord[x] is None:
												data_out.append(str(parity[cont_bp]			)			)
												cont_bp += 1
								else:
												data_out.append(data_ord[x]			)

				UpperCAmelCase__			 =				parity_received == parity
				return data_output, ack



