
"""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


__A       						=       logging.getLogger(__name__)



def     _lowerCamelCase(__UpperCamelCase       )				-> Any:
	random.seed(__UpperCamelCase       )
	np.random.seed(__UpperCamelCase       )
	torch.manual_seed(__UpperCamelCase       )
	torch.cuda.manual_seed_all(__UpperCamelCase       )



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



def     _lowerCamelCase(__UpperCamelCase="gpt2"       )				-> List[Any]:
	_lowerCAmelCase								=GPTaLMHeadModel.from_pretrained(__UpperCamelCase    , output_hidden_states=__UpperCamelCase       )
	torch.save(model.state_dict()    , model_name + """local.pt"""       )
	return model



def     _lowerCamelCase(__UpperCamelCase    , __UpperCamelCase    , __UpperCamelCase       )				-> Any:
	_lowerCAmelCase								=copy.deepcopy(__UpperCamelCase       )
	model.to(__UpperCamelCase       )

	_lowerCAmelCase								=["""bias""", """LayerNorm.weight"""]
	_lowerCAmelCase								=[
	    {
	        """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},
	]
	_lowerCAmelCase								=AdamW(__UpperCamelCase    , lr=5E-5    , eps=1E-8       )
	_lowerCAmelCase								=get_linear_schedule_with_warmup(__UpperCamelCase    , 0    , __UpperCamelCase       )
	torch.cuda.empty_cache()
	return model, lm_optimizer, lm_scheduler



def     _lowerCamelCase(__UpperCamelCase    , __UpperCamelCase    , __UpperCamelCase    , __UpperCamelCase       )				-> Tuple:
	_lowerCAmelCase								=np.array(__UpperCamelCase       ).mean()
	_lowerCAmelCase								=np.array(__UpperCamelCase       ).std()
	_lowerCAmelCase								=(real_perps - avg) / std
	_lowerCAmelCase								=list(zip(__UpperCamelCase    , __UpperCamelCase    , __UpperCamelCase       )       )
	joblib.dump(__UpperCamelCase    , __UpperCamelCase       )



def     _lowerCamelCase(__UpperCamelCase    , __UpperCamelCase    , __UpperCamelCase    , __UpperCamelCase    , __UpperCamelCase    , __UpperCamelCase    , __UpperCamelCase    , __UpperCamelCase="dev.jbl"    , __UpperCamelCase=recopy_gpta    , )				-> Union[str, Any]:
	_lowerCAmelCase								=[]
	_lowerCAmelCase								=[]
	_lowerCAmelCase								=[]

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

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

	for step in tqdm(range(__UpperCamelCase       )       ):
		_lowerCAmelCase								=torch.zeros((1, context_len)    , dtype=torch.long    , device=__UpperCamelCase       )
		_lowerCAmelCase								=random.choice(__UpperCamelCase       )
		_lowerCAmelCase								=random.randint(0    , len(story[0]       ) - context_len - 1       )
		_lowerCAmelCase								=story[0][start : start + context_len]
		lm_optimizer.zero_grad()
		_lowerCAmelCase								=model(__UpperCamelCase    , labels=__UpperCamelCase       )
		_lowerCAmelCase								=outputs[0]
		_lowerCAmelCase								=compute_perplexity(__UpperCamelCase    , __UpperCamelCase    , __UpperCamelCase       )
		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
		_lowerCAmelCase								=compute_perplexity(__UpperCamelCase    , __UpperCamelCase    , __UpperCamelCase       )

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

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

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

	intermittent_save(__UpperCamelCase    , __UpperCamelCase    , __UpperCamelCase    , __UpperCamelCase       )



def     _lowerCamelCase(__UpperCamelCase    , __UpperCamelCase="data/tokenized_stories_train_wikitext103.jbl"    , __UpperCamelCase=100    , __UpperCamelCase=1026    , __UpperCamelCase=True       )				-> Any:
	_lowerCAmelCase								=joblib.load(__UpperCamelCase       )
	print("""data loaded"""       )
	_lowerCAmelCase								=[]
	if trim:
		for i, example in enumerate(__UpperCamelCase       ):
			if len(example[0]       ) > min_len:
				_lowerCAmelCase								=random.randint(0    , len(example[0]       ) - context_len - 1       )
				objective_set.append(example[0, start : start + context_len]       )
			if len(__UpperCamelCase       ) >= number:
				break
		_lowerCAmelCase								=[]
		for j in range(i + 1    , len(__UpperCamelCase       )       ):
			if len(data[j][0]       ) > min_len:
				train_data.append(data[j]       )
	else:
		_lowerCAmelCase								=data[0:number]
		_lowerCAmelCase								=data[number:]

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



def     _lowerCamelCase(__UpperCamelCase    , __UpperCamelCase    , __UpperCamelCase    , __UpperCamelCase    , __UpperCamelCase=50    , __UpperCamelCase="secondary_learner.pt"       )				-> Optional[Any]:
	_lowerCAmelCase								=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
	_lowerCAmelCase								=train_dataset[:512]
	_lowerCAmelCase								=train_dataset[512:]
	_lowerCAmelCase								=DataLoader(__UpperCamelCase    , shuffle=__UpperCamelCase    , batch_size=__UpperCamelCase       )
	_lowerCAmelCase								=DataLoader(__UpperCamelCase    , shuffle=__UpperCamelCase    , batch_size=__UpperCamelCase       )

	# secondary learner model set up
	_lowerCAmelCase								=nn.MSELoss()
	_lowerCAmelCase								=nn.MSELoss(reduction="""sum"""       )
	secondary_learner.to(__UpperCamelCase       )
	_lowerCAmelCase								=torch.optim.Adam(secondary_learner.parameters()    , lr=0.0_00_01       )
	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
	_lowerCAmelCase								=float("""inf"""       )
	# Iterate through batches until we've used max_steps batches
	for epoch in range(int(__UpperCamelCase       )       ):
		_lowerCAmelCase								=0.0
		secondary_learner.train()
		for step, batch in enumerate(__UpperCamelCase       ):
			_lowerCAmelCase								=batch[0].to(__UpperCamelCase       )
			_lowerCAmelCase								=batch[1].to(__UpperCamelCase       )
			_lowerCAmelCase								=secondary_learner(__UpperCamelCase       )
			q_optimizer.zero_grad()
			_lowerCAmelCase								=loss(__UpperCamelCase    , 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(__UpperCamelCase       )):
				_lowerCAmelCase								=tr_q_loss / (step + 1)

				secondary_learner.eval()
				_lowerCAmelCase								=0.0
				_lowerCAmelCase								=0.0
				_lowerCAmelCase								=[]
				_lowerCAmelCase								=[]
				# Compute performance of the secondary learner after this batch
				for stepa, batcha in enumerate(__UpperCamelCase       ):
					_lowerCAmelCase								=batcha[0].to(__UpperCamelCase       )
					_lowerCAmelCase								=batcha[1].to(__UpperCamelCase       )
					_lowerCAmelCase								=secondary_learner(__UpperCamelCase       )
					q_lossa += test_loss(__UpperCamelCase    , __UpperCamelCase       ).item()
					sum_qa += torch.sum(__UpperCamelCase       ).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(__UpperCamelCase       )
				print(
				    """Epoch: """    , __UpperCamelCase    , """step: """    , __UpperCamelCase    , """Avg. q:"""    , sum_qa / len(__UpperCamelCase       )    , """Train Loss: """    , __UpperCamelCase    , """Test Loss: """    , __UpperCamelCase    , )
				if q_lossa < best_test_loss:
					joblib.dump((predicted, actual)    , """pred_vs_actual.jbl"""       )
					torch.save(secondary_learner.state_dict()    , __UpperCamelCase       )
					_lowerCAmelCase								=q_lossa

			secondary_learner.train()
	return secondary_learner







class 		lowerCamelCase__					(					nn.Module				):





			'''simple docstring'''



			def __init__(							self     ,     __UpperCAmelCase  ) ->     Union[str, Any]:
				super(__UpperCAmelCase     ,     self  ).__init__()
				_lowerCAmelCase								=model.transformer.wte
				_lowerCAmelCase								=copy.deepcopy(model.transformer.wte.weight  )
				_lowerCAmelCase								=nn.Convad(self.embeddings.weight.size(1  )     ,     2_56     ,     3     ,     padding=1  )
				_lowerCAmelCase								=nn.Sequential(nn.Linear(2_56     ,     32  )     ,     nn.Dropout(p=0.1  )     ,     nn.Linear(32     ,     32  )     ,     nn.Linear(32     ,     1  )  )



			def 					_lowerCAmelCase		(							self     ,     __UpperCAmelCase  ) ->     int:
				_lowerCAmelCase								=torch.max(self.conv(self.embeddings(__UpperCAmelCase  ).squeeze(1  ).transpose(1     ,     2  )  )     ,     2  )[0]
				_lowerCAmelCase								=self.fc(__UpperCAmelCase  )
				return qs.squeeze(1  )



			@classmethod
			def 					_lowerCAmelCase		(							cls     ,     __UpperCAmelCase     ,     __UpperCAmelCase  ) ->     str:
				_lowerCAmelCase								=cls(__UpperCAmelCase  )  # this calls __init__
				_lowerCAmelCase								=torch.load(__UpperCAmelCase  )
				secondary_learner.load_state_dict(__UpperCAmelCase  )
				_lowerCAmelCase								=model.transformer.wte
				_lowerCAmelCase								=copy.deepcopy(model.transformer.wte.weight  )
				return secondary_learner





"""simple docstring"""


import argparse
import os
import pathlib

import fairseq
import torch
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import (
    DataaVecTextConfig,
    DataaVecTextForMaskedLM,
    DataaVecTextForSequenceClassification,
    DataaVecTextModel,
)
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)

# IMPORTANT: In order for this script to run, please make sure to download the dictionary: `dict.txt` from wget https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.tar.gz
# File copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_text.py
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('0.9.0'):
			raise Exception('requires fairseq >= 0.9.0')


logging.set_verbosity_info()
__A       						=       logging.get_logger(__name__)

__A       						=       'Hello world! cécé herlolip'



def     _lowerCamelCase(__UpperCamelCase    , __UpperCamelCase    , __UpperCamelCase       )				-> Any:
	_lowerCAmelCase						,							_lowerCAmelCase								=os.path.split(__UpperCamelCase       )
	_lowerCAmelCase								=DataaVecTextModel.from_pretrained(
	    __UpperCamelCase    , checkpoint_file=__UpperCamelCase       )
	dataavec.eval()  # disable dropout
	_lowerCAmelCase								=dataavec.models[0]
	_lowerCAmelCase								=dataavec_model.encoder.sentence_encoder
	_lowerCAmelCase								=DataaVecTextConfig(
	    vocab_size=dataavec_sent_encoder.embed_tokens.num_embeddings    , hidden_size=dataavec_model.args.encoder_embed_dim    , num_hidden_layers=dataavec_model.args.encoder_layers    , num_attention_heads=dataavec_model.args.encoder_attention_heads    , intermediate_size=dataavec_model.args.encoder_ffn_embed_dim    , max_position_embeddings=514    , type_vocab_size=1    , layer_norm_eps=1E-5    , )
	if classification_head:
		_lowerCAmelCase								=dataavec.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
	print("""Our BERT config:"""    , __UpperCamelCase       )

	_lowerCAmelCase								=DataaVecTextForSequenceClassification(__UpperCamelCase       ) if classification_head else DataaVecTextForMaskedLM(__UpperCamelCase       )
	model.eval()

	# Now let's copy all the weights.
	# Embeddings
	_lowerCAmelCase								=dataavec_sent_encoder.embed_tokens.weight
	_lowerCAmelCase								=dataavec_sent_encoder.embed_positions.weight
	_lowerCAmelCase								=torch.zeros_like(
	    model.dataavec_text.embeddings.token_type_embeddings.weight       )  # just zero them out b/c data2vec doesn't use them.
	_lowerCAmelCase								=dataavec_sent_encoder.layernorm_embedding.weight
	_lowerCAmelCase								=dataavec_sent_encoder.layernorm_embedding.bias

	for i in range(config.num_hidden_layers       ):
		# Encoder: start of layer
		_lowerCAmelCase								=model.dataavec_text.encoder.layer[i]
		_lowerCAmelCase								=dataavec_sent_encoder.layers[i]

		# self attention
		_lowerCAmelCase								=layer.attention.self
		assert dataavec_layer.self_attn.k_proj.weight.data.shape == torch.Size(
		    (config.hidden_size, config.hidden_size)       ), (
		    "Shape for data2vec_layer.self_attn.k_proj.weight.data should be"
		    F''' {torch.Size((config.hidden_size, config.hidden_size)       )}'''
		)
		assert dataavec_layer.self_attn.q_proj.weight.data.shape == torch.Size(
		    (config.hidden_size, config.hidden_size)       ), (
		    "Shape for data2vec_layer.self_attn.q_proj.weight.data should be"
		    F''' {torch.Size((config.hidden_size, config.hidden_size)       )}'''
		)
		assert dataavec_layer.self_attn.v_proj.weight.data.shape == torch.Size(
		    (config.hidden_size, config.hidden_size)       ), (
		    "Shape for data2vec_layer.self_attn.v_proj.weight.data should be"
		    F''' {torch.Size((config.hidden_size, config.hidden_size)       )}'''
		)

		_lowerCAmelCase								=dataavec_layer.self_attn.q_proj.weight
		_lowerCAmelCase								=dataavec_layer.self_attn.q_proj.bias
		_lowerCAmelCase								=dataavec_layer.self_attn.k_proj.weight
		_lowerCAmelCase								=dataavec_layer.self_attn.k_proj.bias
		_lowerCAmelCase								=dataavec_layer.self_attn.v_proj.weight
		_lowerCAmelCase								=dataavec_layer.self_attn.v_proj.bias

		# self-attention output
		_lowerCAmelCase								=layer.attention.output
		assert (
		    self_output.dense.weight.shape == dataavec_layer.self_attn.out_proj.weight.shape
		), F'''Shape for self_output.dense.weight should be {dataavec_layer.self_attn.out_proj.weight.shape}'''
		_lowerCAmelCase								=dataavec_layer.self_attn.out_proj.weight
		_lowerCAmelCase								=dataavec_layer.self_attn.out_proj.bias
		_lowerCAmelCase								=dataavec_layer.self_attn_layer_norm.weight
		_lowerCAmelCase								=dataavec_layer.self_attn_layer_norm.bias

		# intermediate
		_lowerCAmelCase								=layer.intermediate
		assert (
		    intermediate.dense.weight.shape == dataavec_layer.fca.weight.shape
		), F'''Shape for intermediate.dense.weight should be {dataavec_layer.fca.weight.shape}'''
		_lowerCAmelCase								=dataavec_layer.fca.weight
		_lowerCAmelCase								=dataavec_layer.fca.bias

		# output
		_lowerCAmelCase								=layer.output
		assert (
		    bert_output.dense.weight.shape == dataavec_layer.fca.weight.shape
		), F'''Shape for bert_output.dense.weight should be {dataavec_layer.fca.weight.shape}'''
		_lowerCAmelCase								=dataavec_layer.fca.weight
		_lowerCAmelCase								=dataavec_layer.fca.bias
		_lowerCAmelCase								=dataavec_layer.final_layer_norm.weight
		_lowerCAmelCase								=dataavec_layer.final_layer_norm.bias
		# end of layer

	if classification_head:
		_lowerCAmelCase								=dataavec.model.classification_heads["""mnli"""].dense.weight
		_lowerCAmelCase								=dataavec.model.classification_heads["""mnli"""].dense.bias
		_lowerCAmelCase								=dataavec.model.classification_heads["""mnli"""].out_proj.weight
		_lowerCAmelCase								=dataavec.model.classification_heads["""mnli"""].out_proj.bias
	else:
		# LM Head
		_lowerCAmelCase								=dataavec_model.encoder.lm_head.dense.weight
		_lowerCAmelCase								=dataavec_model.encoder.lm_head.dense.bias
		_lowerCAmelCase								=dataavec_model.encoder.lm_head.layer_norm.weight
		_lowerCAmelCase								=dataavec_model.encoder.lm_head.layer_norm.bias
		_lowerCAmelCase								=dataavec_model.encoder.lm_head.weight
		_lowerCAmelCase								=dataavec_model.encoder.lm_head.bias

	# Let's check that we get the same results.
	_lowerCAmelCase								=dataavec.encode(__UpperCamelCase       ).unsqueeze(0       )  # batch of size 1

	_lowerCAmelCase								=model(__UpperCamelCase       )[0]
	if classification_head:
		_lowerCAmelCase								=dataavec.model.classification_heads["""mnli"""](dataavec.extract_features(__UpperCamelCase       )       )
	else:
		_lowerCAmelCase								=dataavec_model(__UpperCamelCase       )[0]
	print(our_output.shape    , their_output.shape       )
	_lowerCAmelCase								=torch.max(torch.abs(our_output - their_output       )       ).item()
	print(F'''max_absolute_diff = {max_absolute_diff}'''       )  # ~ 1e-7
	_lowerCAmelCase								=torch.allclose(__UpperCamelCase    , __UpperCamelCase    , atol=1E-3       )
	print("""Do both models output the same tensors?"""    , """🔥""" if success else """💩"""       )
	if not success:
		raise Exception("""Something went wRoNg"""       )

	pathlib.Path(__UpperCamelCase       ).mkdir(parents=__UpperCamelCase    , exist_ok=__UpperCamelCase       )
	print(F'''Saving model to {pytorch_dump_folder_path}'''       )
	model.save_pretrained(__UpperCamelCase       )


if __name__ == "__main__":
			__A       						=       argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '--checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
			)
			parser.add_argument(
			    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
			)
			parser.add_argument(
			    '--classification_head', action='store_true', help='Whether to convert a final classification head.'
			)
			__A       						=       parser.parse_args()
			convert_dataavec_checkpoint_to_pytorch(
			    args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
			)



