




import argparse

import torch

from transformers import ChineseCLIPConfig, ChineseCLIPModel






def 	_lowercase	(			lowercase_		,							lowercase_		,							lowercase_						):



		'''simple docstring'''







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

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

		_snake_case			 = q_proj
		_snake_case			 = q_proj_bias

		_snake_case			 = k_proj
		_snake_case			 = k_proj_bias

		_snake_case			 = v_proj
		_snake_case			 = v_proj_bias

		_snake_case			 = out_proj_weights
		_snake_case			 = out_proj_bias






def 	_lowercase	(			lowercase_		,							lowercase_		,							lowercase_						):



		'''simple docstring'''







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






def 	_lowercase	(			lowercase_		,							lowercase_		,							lowercase_						):



		'''simple docstring'''







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






def 	_lowercase	(			lowercase_		,							lowercase_		,							lowercase_						):



		'''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 	_lowercase	(			lowercase_		,							lowercase_		,							lowercase_						):



		'''simple docstring'''







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






def 	_lowercase	(			lowercase_		,							lowercase_						):



		'''simple docstring'''







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

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






def 	_lowercase	(			lowercase_		,							lowercase_						):



		'''simple docstring'''







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

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






@torch.no_grad()
def 	_lowercase	(			lowercase_		,							lowercase_		,							lowercase_=None						):



		'''simple docstring'''







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

		_snake_case			 = ChineseCLIPModel(lowercase__						).eval()

		_snake_case			 = torch.load(lowercase__		,							map_location="cpu"						)["state_dict"]
		_snake_case			 = {(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__						)
		_snake_case			 = pt_weights["logit_scale"].data

		hf_model.save_pretrained(lowercase__						)


if __name__ == "__main__":
						_lowerCamelCase							:		str										=						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.'
						)
						_lowerCamelCase							:		Optional[int]										=						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 copy
import logging
import random

import joblib
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AdamW, GPTaLMHeadModel, get_linear_schedule_with_warmup


_lowerCamelCase							:		str										=						logging.getLogger(__name__)






def 	_lowercase	(			lowercase_						):



		'''simple docstring'''







		random.seed(lowercase_						)
		np.random.seed(lowercase_						)
		torch.manual_seed(lowercase_						)
		torch.cuda.manual_seed_all(lowercase_						)






def 	_lowercase	(			lowercase_		,							lowercase_		,							lowercase_						):



		'''simple docstring'''







		model.eval()
		_snake_case			 = next(model.parameters()						).device
		_snake_case			 = 1
		_snake_case			 = torch.zeros((eval_batch_size, context_len)		,							dtype=torch.long		,							device=lowercase_						)
		_snake_case			 = DataLoader(lowercase_		,							shuffle=lowercase_		,							batch_size=lowercase_						)
		_snake_case			 = torch.zeros(1		,							device=lowercase_						)
		_snake_case			 = 0
		for batch in eval_dataloader:
				batch.to(lowercase_						)
				# pad
				context.zero_()
				for i in range(lowercase_						):
						_snake_case			 = batch[i]
				_snake_case			 = model(lowercase_		,							labels=lowercase_						)
				eval_loss += outputs[0].sum().item()
				nb_eval_examples += batch.size(0						)
		_snake_case			 = eval_loss / nb_eval_examples
		_snake_case			 = torch.exp(lowercase_						)
		model.train()
		return perplexity






def 	_lowercase	(			lowercase_="gpt2"						):



		'''simple docstring'''







		_snake_case			 = GPTaLMHeadModel.from_pretrained(lowercase_		,							output_hidden_states=lowercase_						)
		torch.save(model.state_dict()		,							model_name + "local.pt"						)
		return model






def 	_lowercase	(			lowercase_		,							lowercase_		,							lowercase_						):



		'''simple docstring'''







		_snake_case			 = copy.deepcopy(lowercase_						)
		model.to(lowercase_						)

		_snake_case			 = ["bias", "LayerNorm.weight"]
		_snake_case			 = [
		    {
		        "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay						)],
		        "weight_decay": 0.0,
		    },
		    {"params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay						)], "weight_decay": 0.0},
		]
		_snake_case			 = AdamW(lowercase_		,							lr=5e-5		,							eps=1e-8						)
		_snake_case			 = get_linear_schedule_with_warmup(lowercase_		,							0		,							lowercase_						)
		torch.cuda.empty_cache()
		return model, lm_optimizer, lm_scheduler






def 	_lowercase	(			lowercase_		,							lowercase_		,							lowercase_		,							lowercase_						):



		'''simple docstring'''







		_snake_case			 = np.array(lowercase_						).mean()
		_snake_case			 = np.array(lowercase_						).std()
		_snake_case			 = (real_perps - avg) / std
		_snake_case			 = list(zip(lowercase_		,							lowercase_		,							lowercase_						)						)
		joblib.dump(lowercase_		,							lowercase_						)






def 	_lowercase	(			lowercase_		,							lowercase_		,							lowercase_		,							lowercase_		,							lowercase_		,							lowercase_		,							lowercase_		,							lowercase_="dev.jbl"		,							lowercase_=recopy_gpta		,							):



		'''simple docstring'''







		_snake_case			 = []
		_snake_case			 = []
		_snake_case			 = []

		# Initialize the transformer model
		_snake_case			 = copy.deepcopy(lowercase_						)
		orig_model.to(device="cpu"						)
		torch.cuda.empty_cache()

		# Compute perplexity of initial transformer model for comparison
		model.train()
		_snake_case ,					_snake_case ,					_snake_case			 = recopy_model(lowercase_		,							lowercase_		,							lowercase_						)

		for step in tqdm(range(lowercase_						)						):
				_snake_case			 = torch.zeros((1, context_len)		,							dtype=torch.long		,							device=lowercase_						)
				_snake_case			 = random.choice(lowercase_						)
				_snake_case			 = random.randint(0		,							len(story[0]						) - context_len - 1						)
				_snake_case			 = story[0][start : start + context_len]
				lm_optimizer.zero_grad()
				_snake_case			 = model(lowercase_		,							labels=lowercase_						)
				_snake_case			 = outputs[0]
				_snake_case			 = compute_perplexity(lowercase_		,							lowercase_		,							lowercase_						)
				model.train()
				lm_loss.backward()
				# Do LM backprop
				torch.nn.utils.clip_grad_norm_(model.parameters()		,							3.0						)
				lm_optimizer.step()
				lm_scheduler.step()  # Update learning rate schedule

				# Compute perplexity after back-propagating on the selected context
				_snake_case			 = compute_perplexity(lowercase_		,							lowercase_		,							lowercase_						)

				# Periodically save the stored (X, IG(X)) pairs
				if step % 1000 == 0 and step > 1:
						intermittent_save(lowercase_		,							lowercase_		,							lowercase_		,							lowercase_						)

				# Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
				_snake_case ,					_snake_case ,					_snake_case			 = recopy_model(lowercase_		,							lowercase_		,							lowercase_						)

				past_perps.append(past_perp.item()						)
				real_perps.append(orig_perp - real_perp.item()						)
				contexts.append(np.array(context.cpu()						)						)

		intermittent_save(lowercase_		,							lowercase_		,							lowercase_		,							lowercase_						)






def 	_lowercase	(			lowercase_		,							lowercase_="data/tokenized_stories_train_wikitext103.jbl"		,							lowercase_=100		,							lowercase_=1026		,							lowercase_=True						):



		'''simple docstring'''







		_snake_case			 = joblib.load(lowercase_						)
		print("data loaded"						)
		_snake_case			 = []
		if trim:
				for i, example in enumerate(lowercase_						):
						if len(example[0]						) > min_len:
								_snake_case			 = random.randint(0		,							len(example[0]						) - context_len - 1						)
								objective_set.append(example[0, start : start + context_len]						)
						if len(lowercase_						) >= number:
								break
				_snake_case			 = []
				for j in range(i + 1		,							len(lowercase_						)						):
						if len(data[j][0]						) > min_len:
								train_data.append(data[j]						)
		else:
				_snake_case			 = data[0:number]
				_snake_case			 = data[number:]

		joblib.dump(lowercase_		,							"objective_set.jbl"						)
		print("objective set saved"						)
		return train_data, objective_set






def 	_lowercase	(			lowercase_		,							lowercase_		,							lowercase_		,							lowercase_		,							lowercase_=50		,							lowercase_="secondary_learner.pt"						):



		'''simple docstring'''







		_snake_case			 = torch.device("cuda:0" if torch.cuda.is_available() else "cpu"						)
		# We will use the first 512 pairs from our dataset as a test set for
		# our secondary learner and the rest to train
		_snake_case			 = train_dataset[:512]
		_snake_case			 = train_dataset[512:]
		_snake_case			 = DataLoader(lowercase_		,							shuffle=lowercase_		,							batch_size=lowercase_						)
		_snake_case			 = DataLoader(lowercase_		,							shuffle=lowercase_		,							batch_size=lowercase_						)

		# secondary learner model set up
		_snake_case			 = nn.MSELoss()
		_snake_case			 = nn.MSELoss(reduction="sum"						)
		secondary_learner.to(lowercase_						)
		_snake_case			 = torch.optim.Adam(secondary_learner.parameters()		,							lr=0.00001						)
		secondary_learner.train()

		# TODO in original code this is written as number of actual batches seen
		# not number of items seen but other places it is number of items instead.
		# improve consistency! changed this to epochs for clarity
		_snake_case			 = float("inf"						)
		# Iterate through batches until we've used max_steps batches
		for epoch in range(int(lowercase_						)						):
				_snake_case			 = 0.0
				secondary_learner.train()
				for step, batch in enumerate(lowercase_						):
						_snake_case			 = batch[0].to(lowercase_						)
						_snake_case			 = batch[1].to(lowercase_						)
						_snake_case			 = secondary_learner(lowercase_						)
						q_optimizer.zero_grad()
						_snake_case			 = loss(lowercase_		,							real_q.float()						)
						q_loss.backward()
						q_optimizer.step()
						tr_q_loss += q_loss.item()

						# model trains fairly quickly so we won't wait for a full epoch
						# eval is triggered at eval_freq and end of epochs
						if (step % eval_freq == 0 and step > 0) or ((step + 1) == len(lowercase_						)):
								_snake_case			 = tr_q_loss / (step + 1)

								secondary_learner.eval()
								_snake_case			 = 0.0
								_snake_case			 = 0.0
								_snake_case			 = []
								_snake_case			 = []
								# Compute performance of the secondary learner after this batch
								for stepa, batcha in enumerate(lowercase_						):
										_snake_case			 = batcha[0].to(lowercase_						)
										_snake_case			 = batcha[1].to(lowercase_						)
										_snake_case			 = secondary_learner(lowercase_						)
										q_lossa += test_loss(lowercase_		,							lowercase_						).item()
										sum_qa += torch.sum(lowercase_						).item()
										for ei, i in enumerate(predicted_qa.cpu().detach().numpy()						):
												predicted.append(i.item()						)
										for ei, i in enumerate(real_qa.cpu().detach().numpy()						):
												actual.append(i.item()						)

								q_lossa /= len(lowercase_						)
								print(
								    "Epoch: "		,							lowercase_		,							"step: "		,							lowercase_		,							"Avg. q:"		,							sum_qa / len(lowercase_						)		,							"Train Loss: "		,							lowercase_		,							"Test Loss: "		,							lowercase_		,							)
								if q_lossa < best_test_loss:
										joblib.dump((predicted, actual)		,							"pred_vs_actual.jbl"						)
										torch.save(secondary_learner.state_dict()		,							lowercase_						)
										_snake_case			 = q_lossa

						secondary_learner.train()
		return secondary_learner





class   lowerCamelCase_							( nn.Module							):




					'''simple docstring'''






					def __init__(					self,						__UpperCamelCase				):
							super(__UpperCamelCase,						self				).__init__()
							_snake_case			 = model.transformer.wte
							_snake_case			 = copy.deepcopy(model.transformer.wte.weight				)
							_snake_case			 = nn.Convad(self.embeddings.weight.size(1				),						256,						3,						padding=1				)
							_snake_case			 = nn.Sequential(nn.Linear(256,						32				),						nn.Dropout(p=0.1				),						nn.Linear(32,						32				),						nn.Linear(32,						1				)				)






					def        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase				):
							_snake_case			 = torch.max(self.conv(self.embeddings(__UpperCamelCase				).squeeze(1				).transpose(1,						2				)				),						2				)[0]
							_snake_case			 = self.fc(__UpperCamelCase				)
							return qs.squeeze(1				)






					@classmethod
					def        __SCREAMING_SNAKE_CASE			(					cls,						__UpperCamelCase,						__UpperCamelCase				):
							_snake_case			 = cls(__UpperCamelCase				)  # this calls __init__
							_snake_case			 = torch.load(__UpperCamelCase				)
							secondary_learner.load_state_dict(__UpperCamelCase				)
							_snake_case			 = model.transformer.wte
							_snake_case			 = copy.deepcopy(model.transformer.wte.weight				)
							return secondary_learner





