






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


snake_case_   :	int							      =logging.getLogger(__name__)



def      UpperCAmelCase						( lowerCAmelCase__	):


		'''simple docstring'''

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



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__	):


		'''simple docstring'''

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



def      UpperCAmelCase						( lowerCAmelCase__="gpt2"	):


		'''simple docstring'''

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



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__	):


		'''simple docstring'''

		__A    =							copy.deepcopy(lowerCAmelCase__	)
		model.to(lowerCAmelCase__	)

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



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__	):


		'''simple docstring'''

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



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__="dev.jbl"					,     lowerCAmelCase__=recopy_gpta					,     ):


		'''simple docstring'''

		__A    =							[]
		__A    =							[]
		__A    =							[]

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

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

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

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

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

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

		intermittent_save(lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__	)



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__="data/tokenized_stories_train_wikitext103.jbl"					,     lowerCAmelCase__=100					,     lowerCAmelCase__=1026					,     lowerCAmelCase__=True	):


		'''simple docstring'''

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

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



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__=50					,     lowerCAmelCase__="secondary_learner.pt"	):


		'''simple docstring'''

		__A    =							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
		__A    =							train_dataset[:512]
		__A    =							train_dataset[512:]
		__A    =							DataLoader(lowerCAmelCase__					,     shuffle=lowerCAmelCase__					,     batch_size=lowerCAmelCase__	)
		__A    =							DataLoader(lowerCAmelCase__					,     shuffle=lowerCAmelCase__					,     batch_size=lowerCAmelCase__	)

		# secondary learner model set up
		__A    =							nn.MSELoss()
		__A    =							nn.MSELoss(reduction="sum"	)
		secondary_learner.to(lowerCAmelCase__	)
		__A    =							torch.optim.Adam(secondary_learner.parameters()					,     lr=0.00_001	)
		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
		__A    =							float("inf"	)
		# Iterate through batches until we've used max_steps batches
		for epoch in range(int(lowerCAmelCase__	)	):
				__A    =							0.0
				secondary_learner.train()
				for step, batch in enumerate(lowerCAmelCase__	):
						__A    =							batch[0].to(lowerCAmelCase__	)
						__A    =							batch[1].to(lowerCAmelCase__	)
						__A    =							secondary_learner(lowerCAmelCase__	)
						q_optimizer.zero_grad()
						__A    =							loss(lowerCAmelCase__					,     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(lowerCAmelCase__	)):
								__A    =							tr_q_loss / (step + 1)

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

						secondary_learner.train()
		return secondary_learner





class        a__      (	nn.Module		):






		def __init__(      self							, lowercase__						)				->				Union[str, Any]:
				super(lowercase__							, self						).__init__()
				__A    =							model.transformer.wte
				__A    =							copy.deepcopy(model.transformer.wte.weight						)
				__A    =							nn.Convad(self.embeddings.weight.size(1						)							, 256							, 3							, padding=1						)
				__A    =							nn.Sequential(nn.Linear(256							, 32						)							, nn.Dropout(p=0.1						)							, nn.Linear(32							, 32						)							, nn.Linear(32							, 1						)						)






		def 					_lowerCamelCase					(      self							, lowercase__						)				->				str:
				__A    =							torch.max(self.conv(self.embeddings(lowercase__						).squeeze(1						).transpose(1							, 2						)						)							, 2						)[0]
				__A    =							self.fc(lowercase__						)
				return qs.squeeze(1						)






		@classmethod
		def 					_lowerCamelCase					(      cls							, lowercase__							, lowercase__						)				->				List[str]:
				__A    =							cls(lowercase__						)  # this calls __init__
				__A    =							torch.load(lowercase__						)
				secondary_learner.load_state_dict(lowercase__						)
				__A    =							model.transformer.wte
				__A    =							copy.deepcopy(model.transformer.wte.weight						)
				return secondary_learner









# Usage:
# ./gen-card-allenai-wmt19.py

import os
from pathlib import Path



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__	):


		'''simple docstring'''

		__A    =							{
		    "en": "Machine learning is great, isn't it?",
		    "ru": "Машинное обучение - это здорово, не так ли?",
		    "de": "Maschinelles Lernen ist großartig, nicht wahr?",
		}

		# BLUE scores as follows:
		# "pair": [fairseq, transformers]
		__A    =							{
		    "wmt19-de-en-6-6-base": [0, 38.37],
		    "wmt19-de-en-6-6-big": [0, 39.90],
		}
		__A    =							F"""{src_lang}-{tgt_lang}"""

		__A    =							F"""
---

language:
- {src_lang}
- {tgt_lang}
thumbnail:
tags:
- translation
- wmt19
- allenai
license: apache-2.0
datasets:
- wmt19
metrics:
- bleu
---

# FSMT

## Model description

This is a ported version of fairseq-based [wmt19 transformer](https://github.com/jungokasai/deep-shallow/) for {src_lang}-{tgt_lang}.

For more details, please, see [Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation](https://arxiv.org/abs/2006.10369).

2 models are available:

* [wmt19-de-en-6-6-big](https://huggingface.co/allenai/wmt19-de-en-6-6-big)
* [wmt19-de-en-6-6-base](https://huggingface.co/allenai/wmt19-de-en-6-6-base)


## Intended uses & limitations

#### How to use

```python
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
mname = \"allenai/{model_name}\"
tokenizer = FSMTTokenizer.from_pretrained(mname)
model = FSMTForConditionalGeneration.from_pretrained(mname)

input = \"{texts[src_lang]}\"
input_ids = tokenizer.encode(input, return_tensors=\"pt\")
outputs = model.generate(input_ids)
decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(decoded) # {texts[tgt_lang]}

```

#### Limitations and bias


## Training data

Pretrained weights were left identical to the original model released by allenai. For more details, please, see the [paper](https://arxiv.org/abs/2006.10369).

## Eval results

Here are the BLEU scores:

model   |  transformers
-------|---------
{model_name}  |  {scores[model_name][1]}

The score was calculated using this code:

```bash
git clone https://github.com/huggingface/transformers
cd transformers
export PAIR={pair}
export DATA_DIR=data/$PAIR
export SAVE_DIR=data/$PAIR
export BS=8
export NUM_BEAMS=5
mkdir -p $DATA_DIR
sacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source
sacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target
echo $PAIR
PYTHONPATH=\"src:examples/seq2seq\" python examples/seq2seq/run_eval.py allenai/{model_name} $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS
```

## Data Sources

- [training, etc.](http://www.statmt.org/wmt19/)
- [test set](http://matrix.statmt.org/test_sets/newstest2019.tgz?1556572561)


### BibTeX entry and citation info

```
@misc{{kasai2020deep,
    title={{Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation}},
    author={{Jungo Kasai and Nikolaos Pappas and Hao Peng and James Cross and Noah A. Smith}},
    year={{2020}},
    eprint={{2006.10369}},
    archivePrefix={{arXiv}},
    primaryClass={{cs.CL}}
}}
```

"""
		model_card_dir.mkdir(parents=lowerCAmelCase__					,     exist_ok=lowerCAmelCase__	)
		__A    =							os.path.join(lowerCAmelCase__					,     "README.md"	)
		print(F"""Generating {path}"""	)
		with open(lowerCAmelCase__					,     "w"					,     encoding="utf-8"	) as f:
				f.write(lowerCAmelCase__	)


# make sure we are under the root of the project
snake_case_   :	Optional[int]							      =Path(__file__).resolve().parent.parent.parent
snake_case_   :	Dict							      =repo_dir / '''model_cards'''

for model_name in ["wmt19-de-en-6-6-base", "wmt19-de-en-6-6-big"]:
					snake_case_   :	Union[str, Any]							      =model_cards_dir / '''allenai''' / model_name
					write_model_card(model_card_dir, src_lang='''de''', tgt_lang='''en''', model_name=model_name)

