





"""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__        =      logging.getLogger(__name__)


def    a__       (       _SCREAMING_SNAKE_CASE     ):




		"""simple docstring"""


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


def    a__       (       _SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE     ):




		"""simple docstring"""


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


def    a__       (       _SCREAMING_SNAKE_CASE="gpt2"     ):




		"""simple docstring"""


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


def    a__       (       _SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE     ):




		"""simple docstring"""


		UpperCamelCase =						copy.deepcopy(_SCREAMING_SNAKE_CASE     )
		model.to(_SCREAMING_SNAKE_CASE     )

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


def    a__       (       _SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE     ):




		"""simple docstring"""


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


def    a__       (       _SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE="dev.jbl"	,						_SCREAMING_SNAKE_CASE=recopy_gpta	,						):




		"""simple docstring"""


		UpperCamelCase =						[]
		UpperCamelCase =						[]
		UpperCamelCase =						[]

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

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

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

				# Periodically save the stored (X, IG(X)) pairs
				if step % 1_000 == 0 and step > 1:
						intermittent_save(_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE     )

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

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

		intermittent_save(_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE     )


def    a__       (       _SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE="data/tokenized_stories_train_wikitext103.jbl"	,						_SCREAMING_SNAKE_CASE=100	,						_SCREAMING_SNAKE_CASE=1_026	,						_SCREAMING_SNAKE_CASE=True     ):




		"""simple docstring"""


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

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


def    a__       (       _SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE=50	,						_SCREAMING_SNAKE_CASE="secondary_learner.pt"     ):




		"""simple docstring"""


		UpperCamelCase =						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
		UpperCamelCase =						train_dataset[:512]
		UpperCamelCase =						train_dataset[512:]
		UpperCamelCase =						DataLoader(_SCREAMING_SNAKE_CASE	,						shuffle=_SCREAMING_SNAKE_CASE	,						batch_size=_SCREAMING_SNAKE_CASE     )
		UpperCamelCase =						DataLoader(_SCREAMING_SNAKE_CASE	,						shuffle=_SCREAMING_SNAKE_CASE	,						batch_size=_SCREAMING_SNAKE_CASE     )

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

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

						secondary_learner.train()
		return secondary_learner






class 							_lowerCamelCase   (							nn.Module			):


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


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




						@classmethod
						def      snake_case_ (cls ,				__a ,				__a		)			-> Any:
								UpperCamelCase =						cls(__a		)  # this calls __init__
								UpperCamelCase =						torch.load(__a		)
								secondary_learner.load_state_dict(__a		)
								UpperCamelCase =						model.transformer.wte
								UpperCamelCase =						copy.deepcopy(model.transformer.wte.weight		)
								return secondary_learner













import argparse
import logging
import os
import sys
import tempfile
from pathlib import Path

import lightning_base
import pytest
import pytorch_lightning as pl
import torch
from convert_pl_checkpoint_to_hf import convert_pl_to_hf
from distillation import distill_main
from finetune import SummarizationModule, main
from huggingface_hub import list_models
from parameterized import parameterized
from run_eval import generate_summaries_or_translations
from torch import nn

from transformers import AutoConfig, AutoModelForSeqaSeqLM
from transformers.testing_utils import CaptureStderr, CaptureStdout, TestCasePlus, require_torch_gpu, slow
from utils import label_smoothed_nll_loss, lmap, load_json


logging.basicConfig(level=logging.DEBUG)

_UpperCamelCase       :				Tuple           =			logging.getLogger()
_UpperCamelCase       :				Optional[int]           =			torch.cuda.is_available()
_UpperCamelCase       :				List[str]           =			{
    """max_tokens_per_batch""": None,
    """supervise_forward""": True,
    """normalize_hidden""": True,
    """label_smoothing""": 0.2,
    """eval_max_gen_length""": None,
    """eval_beams""": 1,
    """val_metric""": """loss""",
    """save_top_k""": 1,
    """adafactor""": True,
    """early_stopping_patience""": 2,
    """logger_name""": """default""",
    """length_penalty""": 0.5,
    """cache_dir""": """""",
    """task""": """summarization""",
    """num_workers""": 2,
    """alpha_hid""": 0,
    """freeze_embeds""": True,
    """enc_only""": False,
    """tgt_suffix""": """""",
    """resume_from_checkpoint""": None,
    """sortish_sampler""": True,
    """student_decoder_layers""": 1,
    """val_check_interval""": 1.0,
    """output_dir""": """""",
    """fp16""": False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
    """no_teacher""": False,
    """fp16_opt_level""": """O1""",
    """gpus""": 1 if CUDA_AVAILABLE else 0,
    """n_tpu_cores""": 0,
    """max_grad_norm""": 1.0,
    """do_train""": True,
    """do_predict""": True,
    """accumulate_grad_batches""": 1,
    """server_ip""": """""",
    """server_port""": """""",
    """seed""": 4_2,
    """model_name_or_path""": """sshleifer/bart-tiny-random""",
    """config_name""": """""",
    """tokenizer_name""": """facebook/bart-large""",
    """do_lower_case""": False,
    """learning_rate""": 0.3,
    """lr_scheduler""": """linear""",
    """weight_decay""": 0.0,
    """adam_epsilon""": 1E-0_8,
    """warmup_steps""": 0,
    """max_epochs""": 1,
    """train_batch_size""": 2,
    """eval_batch_size""": 2,
    """max_source_length""": 1_2,
    """max_target_length""": 1_2,
    """val_max_target_length""": 1_2,
    """test_max_target_length""": 1_2,
    """fast_dev_run""": False,
    """no_cache""": False,
    """n_train""": -1,
    """n_val""": -1,
    """n_test""": -1,
    """student_encoder_layers""": 1,
    """freeze_encoder""": False,
    """auto_scale_batch_size""": False,
    """overwrite_output_dir""": False,
    """student""": None,
}
def  __UpperCamelCase    (						snake_case							,			snake_case      )    ->		List[Any]:


   '''simple docstring'''







   __A    =  '''\n'''.join(snake_case      )
   Path(snake_case      ).open('''w'''      ).writelines(snake_case      )


_UpperCamelCase       :				List[Any]           =			[""" Sam ate lunch today.""", """Sams lunch ingredients."""]
_UpperCamelCase       :				int           =			["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
_UpperCamelCase       :				List[str]           =			"""patrickvonplaten/t5-tiny-random"""
_UpperCamelCase       :				int           =			"""sshleifer/t5-tinier-random"""
_UpperCamelCase       :				Dict           =			"""sshleifer/bart-tiny-random"""
_UpperCamelCase       :				int           =			"""sshleifer/tiny-mbart"""
_UpperCamelCase       :				Optional[int]           =			"""sshleifer/tiny-marian-en-de"""
_UpperCamelCase       :				List[Any]           =			"""stas/tiny-wmt19-en-de"""


_UpperCamelCase       :				Optional[Any]           =			logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks
def  __UpperCamelCase    (						snake_case      )    ->		Optional[int]:


   '''simple docstring'''







   for split in ["train", "val", "test"]:
      _dump_articles(os.path.join(snake_case							,			F"{split}.source"      )							,			snake_case      )
      _dump_articles(os.path.join(snake_case							,			F"{split}.target"      )							,			snake_case      )
   return tmp_dir







class 		_lowerCAmelCase(		_a):

  """simple docstring"""







  @classmethod
  def 						SCREAMING_SNAKE_CASE__			(   cls      )->   List[str]:
     logging.disable(logging.CRITICAL      )  # remove noisy download output from tracebacks
     return cls

  @slow
  @require_torch_gpu
  def 						SCREAMING_SNAKE_CASE__			(   self      )->   Optional[int]:
     __A    =  list_models()
     __A    =  '''sshleifer'''
     __A    =  [x.modelId for x in model_list if x.modelId.startswith(UpperCAmelCase      )]
     __A    =  ['''sshleifer/blenderbot-3B''', '''sshleifer/blenderbot-90M''']
     __A    =  []
     for m in model_ids:
        if m in allowed_to_be_broken:
           continue
        try:
           AutoConfig.from_pretrained(UpperCAmelCase      )
        except Exception:
           failures.append(UpperCAmelCase      )
     assert not failures, f"The following models could not be loaded through AutoConfig: {failures}"

  def 						SCREAMING_SNAKE_CASE__			(   self      )->   List[str]:
     __A    =  {'''student_encoder_layers''': 2, '''student_decoder_layers''': 1, '''no_teacher''': True}
     self._test_distiller_cli(UpperCAmelCase      )

  def 						SCREAMING_SNAKE_CASE__			(   self      )->   int:
     __A    =  {
         '''student_encoder_layers''': 2,
         '''student_decoder_layers''': 1,
         '''max_epochs''': 4,
         '''val_check_interval''': 0.25,
         '''alpha_hid''': 2.0,
         '''model_name_or_path''': '''IGNORE_THIS_IT_DOESNT_GET_USED''',
     }
     __A    =  self._test_distiller_cli(UpperCAmelCase    ,							check_contents=UpperCAmelCase      )

     __A    =  list(Path(model.output_dir      ).glob('''*.ckpt'''      )      )
     self.assertEqual(1    ,							len(UpperCAmelCase      )      )
     __A    =  list(Path(model.output_dir      ).glob('''**/*.bin'''      )      )
     self.assertEqual(len(UpperCAmelCase      )    ,							2      )
     __A    =  lmap(str.strip    ,							Path(model.hparams.data_dir      ).joinpath('''test.source'''      ).open().readlines()      )
     __A    =  tempfile.mktemp()  # XXX: not being cleaned up
     generate_summaries_or_translations(UpperCAmelCase    ,							UpperCAmelCase    ,							str(model.output_dir / '''best_tfmr'''      )      )
     self.assertTrue(Path(UpperCAmelCase      ).exists()      )

     __A    =  self.get_auto_remove_tmp_dir()
     convert_pl_to_hf(ckpts[0]    ,							transformer_ckpts[0].parent    ,							UpperCAmelCase      )
     assert os.path.exists(os.path.join(UpperCAmelCase    ,							'''pytorch_model.bin'''      )      )

  def 						SCREAMING_SNAKE_CASE__			(   self      )->   List[str]:
     __A    =  AutoModelForSeqaSeqLM.from_pretrained(UpperCAmelCase      )
     __A   ,		__A    =  model.dummy_inputs['''input_ids'''], model.dummy_inputs['''attention_mask''']
     __A    =  torch.tensor([[0, 4, 8, 2], [0, 8, 2, 1]]    ,							dtype=torch.long    ,							device=model.device      )
     __A    =  target_ids[:, :-1].contiguous()  # Why this line?
     __A    =  target_ids[:, 1:].clone()  # why clone?
     __A    =  model(
         UpperCAmelCase    ,							attention_mask=UpperCAmelCase    ,							decoder_input_ids=UpperCAmelCase    ,							labels=UpperCAmelCase    ,							use_cache=UpperCAmelCase      ).loss

     __A    =  model(UpperCAmelCase    ,							attention_mask=UpperCAmelCase    ,							decoder_input_ids=UpperCAmelCase    ,							use_cache=UpperCAmelCase      ).logits

     __A    =  nn.functional.log_softmax(UpperCAmelCase    ,							dim=-1      )
     __A   ,		__A    =  label_smoothed_nll_loss(
         UpperCAmelCase    ,							UpperCAmelCase    ,							0.1    ,							ignore_index=model.config.pad_token_id      )
     with self.assertRaises(UpperCAmelCase      ):
        # TODO: understand why this breaks
        self.assertEqual(UpperCAmelCase    ,							UpperCAmelCase      )

  def 						SCREAMING_SNAKE_CASE__			(   self      )->   Dict:
     __A    =  {
         '''student_encoder_layers''': 2,
         '''student_decoder_layers''': 1,
         '''num_train_epochs''': 4,
         '''val_check_interval''': 0.25,
         '''alpha_hid''': 2.0,
         '''task''': '''translation''',
         '''model_name_or_path''': '''IGNORE_THIS_IT_DOESNT_GET_USED''',
         '''tokenizer_name''': MBART_TINY,
         '''teacher''': MBART_TINY,
         '''src_lang''': '''en_XX''',
         '''tgt_lang''': '''ro_RO''',
     }
     __A    =  self._test_distiller_cli(UpperCAmelCase    ,							check_contents=UpperCAmelCase      )
     assert model.model.config.model_type == "mbart"

     __A    =  list(Path(model.output_dir      ).glob('''*.ckpt'''      )      )
     self.assertEqual(1    ,							len(UpperCAmelCase      )      )
     __A    =  list(Path(model.output_dir      ).glob('''**/*.bin'''      )      )
     __A    =  list(Path(model.output_dir      ).glob('''best_tfmr/*'''      )      )
     assert len(UpperCAmelCase      ) > 2
     self.assertEqual(len(UpperCAmelCase      )    ,							2      )

  def 						SCREAMING_SNAKE_CASE__			(   self      )->   str:
     __A    =  {
         '''student_encoder_layers''': 1,
         '''student_decoder_layers''': 1,
         '''alpha_hid''': 2.0,
         '''teacher''': T5_TINY,
         '''model_name_or_path''': T5_TINY,
         '''tokenizer_name''': T5_TINY,
     }
     self._test_distiller_cli(UpperCAmelCase      )

  def 						SCREAMING_SNAKE_CASE__			(   self      )->   Tuple:
     __A    =  {
         '''teacher''': T5_TINY,
         '''student''': T5_TINIER,
         '''model_name_or_path''': T5_TINIER,
         '''tokenizer_name''': T5_TINIER,
     }
     self._test_distiller_cli(UpperCAmelCase      )





  def 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase    ,							UpperCAmelCase=True      )->   Tuple:
     __A    =  {
         '''label_smoothing''': 0.0,
         '''early_stopping_patience''': -1,
         '''train_batch_size''': 1,
         '''eval_batch_size''': 2,
         '''max_epochs''': 2,
         '''alpha_mlm''': 0.2,
         '''alpha_ce''': 0.8,
         '''do_predict''': True,
         '''model_name_or_path''': '''sshleifer/tinier_bart''',
         '''teacher''': CHEAP_ARGS['''model_name_or_path'''],
         '''val_check_interval''': 0.5,
     }
     default_updates.update(UpperCAmelCase      )
     __A    =  CHEAP_ARGS.copy()
     __A    =  make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )
     __A    =  self.get_auto_remove_tmp_dir()

     args_d.update(data_dir=UpperCAmelCase    ,							output_dir=UpperCAmelCase    ,							**UpperCAmelCase      )
     __A    =  distill_main(argparse.Namespace(**UpperCAmelCase      )      )
     if not check_contents:
        return model
     __A    =  os.listdir(UpperCAmelCase      )
     __A    =  {os.path.basename(UpperCAmelCase      ) for p in contents}
     __A    =  [p for p in contents if p.endswith('''ckpt'''      )]
     assert len(UpperCAmelCase      ) > 0

     self.assertIn('''test_generations.txt'''    ,							UpperCAmelCase      )
     self.assertIn('''test_results.txt'''    ,							UpperCAmelCase      )

     __A    =  load_json(model.metrics_save_path      )
     __A    =  metrics['''val'''][-1]
     self.assertGreaterEqual(last_step_stats['''val_avg_gen_time''']    ,							0.01      )
     self.assertGreaterEqual(1.0    ,							last_step_stats['''val_avg_gen_time''']      )
     self.assertIsInstance(last_step_stats[f"val_avg_{model.val_metric}"]    ,							UpperCAmelCase      )
     __A    =  int(args_d['''max_epochs'''] * (1 / args_d['''val_check_interval''']) + 1      )
     self.assertEqual(len(metrics['''val''']      )    ,							UpperCAmelCase      )
     self.assertEqual(len(metrics['''test''']      )    ,							1      )
     return model







class 		_lowerCAmelCase(		_a):

  """simple docstring"""







  @parameterized.expand(
      [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]    ,							)
  def 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase      )->   List[str]:
     __A    =  CHEAP_ARGS.copy()
     __A    =  '''translation''' if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else '''summarization'''
     __A    =  0.1 if task == '''translation''' else 0

     __A    =  make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )
     __A    =  self.get_auto_remove_tmp_dir()
     args_d.update(
         data_dir=UpperCAmelCase    ,							model_name_or_path=UpperCAmelCase    ,							tokenizer_name=UpperCAmelCase    ,							train_batch_size=2    ,							eval_batch_size=2    ,							output_dir=UpperCAmelCase    ,							do_predict=UpperCAmelCase    ,							task=UpperCAmelCase    ,							src_lang='''en_XX'''    ,							tgt_lang='''ro_RO'''    ,							freeze_encoder=UpperCAmelCase    ,							freeze_embeds=UpperCAmelCase    ,							)
     assert "n_train" in args_d
     __A    =  argparse.Namespace(**UpperCAmelCase      )
     __A    =  main(UpperCAmelCase      )

     __A    =  module.model.get_input_embeddings()
     assert not input_embeds.weight.requires_grad
     if model == T5_TINY:
        __A    =  module.model.lm_head
        assert not lm_head.weight.requires_grad
        assert (lm_head.weight == input_embeds.weight).all().item()
     elif model == FSMT_TINY:
        __A    =  module.model.model
        __A    =  fsmt.decoder.embed_positions
        assert not embed_pos.weight.requires_grad
        assert not fsmt.decoder.embed_tokens.weight.requires_grad
        # check that embeds are not the same
        assert fsmt.decoder.embed_tokens != fsmt.encoder.embed_tokens
     else:
        __A    =  module.model.model
        __A    =  bart.decoder.embed_positions
        assert not embed_pos.weight.requires_grad
        assert not bart.shared.weight.requires_grad
        # check that embeds are the same
        assert bart.decoder.embed_tokens == bart.encoder.embed_tokens
        assert bart.decoder.embed_tokens == bart.shared

     __A    =  load_json(module.output_dir / '''text_batch.json'''      )
     assert isinstance(UpperCAmelCase    ,							UpperCAmelCase      )
     assert len(UpperCAmelCase      ) >= 4

  def 						SCREAMING_SNAKE_CASE__			(   self      )->   str:
     __A    =  CHEAP_ARGS.copy()

     __A    =  '''summarization'''
     __A    =  make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )

     args_d.update(
         data_dir=UpperCAmelCase    ,							tokenizer_name=UpperCAmelCase    ,							train_batch_size=2    ,							eval_batch_size=2    ,							do_predict=UpperCAmelCase    ,							task=UpperCAmelCase    ,							src_lang='''en_XX'''    ,							tgt_lang='''ro_RO'''    ,							freeze_encoder=UpperCAmelCase    ,							freeze_embeds=UpperCAmelCase    ,							)

     # test models whose config includes the extra_model_args
     __A    =  BART_TINY
     __A    =  self.get_auto_remove_tmp_dir()
     __A    =  args_d.copy()
     args_da.update(
         model_name_or_path=UpperCAmelCase    ,							output_dir=UpperCAmelCase    ,							)
     __A    =  ('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
     for p in extra_model_params:
        __A    =  0.5
     __A    =  argparse.Namespace(**UpperCAmelCase      )
     __A    =  main(UpperCAmelCase      )
     for p in extra_model_params:
        assert getattr(model.config    ,							UpperCAmelCase      ) == 0.5, f"failed to override the model config for param {p}"

     # test models whose config doesn't include the extra_model_args
     __A    =  T5_TINY
     __A    =  self.get_auto_remove_tmp_dir()
     __A    =  args_d.copy()
     args_da.update(
         model_name_or_path=UpperCAmelCase    ,							output_dir=UpperCAmelCase    ,							)
     __A    =  '''encoder_layerdrop'''
     __A    =  0.5
     __A    =  argparse.Namespace(**UpperCAmelCase      )
     with pytest.raises(UpperCAmelCase      ) as excinfo:
        __A    =  main(UpperCAmelCase      )
     assert str(excinfo.value      ) == f"model config doesn't have a `{unsupported_param}` attribute"





  def 						SCREAMING_SNAKE_CASE__			(   self      )->   int:
     __A    =  CHEAP_ARGS.copy()

     __A    =  '''summarization'''
     __A    =  make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )

     __A    =  BART_TINY
     __A    =  self.get_auto_remove_tmp_dir()

     args_d.update(
         data_dir=UpperCAmelCase    ,							model_name_or_path=UpperCAmelCase    ,							output_dir=UpperCAmelCase    ,							tokenizer_name=UpperCAmelCase    ,							train_batch_size=2    ,							eval_batch_size=2    ,							do_predict=UpperCAmelCase    ,							task=UpperCAmelCase    ,							src_lang='''en_XX'''    ,							tgt_lang='''ro_RO'''    ,							freeze_encoder=UpperCAmelCase    ,							freeze_embeds=UpperCAmelCase    ,							)

     # emulate finetune.py
     __A    =  argparse.ArgumentParser()
     __A    =  pl.Trainer.add_argparse_args(UpperCAmelCase      )
     __A    =  SummarizationModule.add_model_specific_args(UpperCAmelCase    ,							os.getcwd()      )
     __A    =  {'''--help''': True}

     # --help test
     with pytest.raises(UpperCAmelCase      ) as excinfo:
        with CaptureStdout() as cs:
           __A    =  parser.parse_args(UpperCAmelCase      )
        assert False, "--help is expected to sys.exit"
     assert excinfo.type == SystemExit
     __A    =  lightning_base.arg_to_scheduler_metavar
     assert expected in cs.out, "--help is expected to list the supported schedulers"

     # --lr_scheduler=non_existing_scheduler test
     __A    =  '''non_existing_scheduler'''
     __A    =  {f"--lr_scheduler={unsupported_param}"}
     with pytest.raises(UpperCAmelCase      ) as excinfo:
        with CaptureStderr() as cs:
           __A    =  parser.parse_args(UpperCAmelCase      )
        assert False, "invalid argument is expected to sys.exit"
     assert excinfo.type == SystemExit
     __A    =  f"invalid choice: '{unsupported_param}'"
     assert expected in cs.err, f"should have bailed on invalid choice of scheduler {unsupported_param}"

     # --lr_scheduler=existing_scheduler test
     __A    =  '''cosine'''
     __A    =  args_d.copy()
     __A    =  supported_param
     __A    =  argparse.Namespace(**UpperCAmelCase      )
     __A    =  main(UpperCAmelCase      )
     assert (
         getattr(model.hparams    ,							'''lr_scheduler'''      ) == supported_param
     ), f"lr_scheduler={supported_param} shouldn't fail"

