'''simple docstring'''

import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel
class   SCREAMING_SNAKE_CASE			:







  """simple docstring"""






  def __init__(   self		,						__A		,						__A=99		,						__A=13		,						__A=7		,						__A=9		,						__A=True		,						__A=True		,						__A=False		,						__A=32		,						__A=5		,						__A=4		,						__A=37		,						__A=8		,						__A=0.1		,						__A=0.002		,						__A=1		,						__A=0		,						__A=0		,						__A=None		,						__A=None		,						) ->     List[Any]:
        _lowerCAmelCase										=parent
        _lowerCAmelCase										=batch_size
        _lowerCAmelCase										=encoder_seq_length
        _lowerCAmelCase										=decoder_seq_length
        # For common tests
        _lowerCAmelCase										=self.decoder_seq_length
        _lowerCAmelCase										=is_training
        _lowerCAmelCase										=use_attention_mask
        _lowerCAmelCase										=use_labels
        _lowerCAmelCase										=vocab_size
        _lowerCAmelCase										=hidden_size
        _lowerCAmelCase										=num_hidden_layers
        _lowerCAmelCase										=num_attention_heads
        _lowerCAmelCase										=d_ff
        _lowerCAmelCase										=relative_attention_num_buckets
        _lowerCAmelCase										=dropout_rate
        _lowerCAmelCase										=initializer_factor
        _lowerCAmelCase										=eos_token_id
        _lowerCAmelCase										=pad_token_id
        _lowerCAmelCase										=decoder_start_token_id
        _lowerCAmelCase										=None
        _lowerCAmelCase										=decoder_layers






  def 		UpperCamelCase__       (   self				) ->     Union[str, Any]:
        return TaConfig.from_pretrained('google/umt5-base'				)






  def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A		,						__A=None		,						__A=None		,						__A=None		,						__A=None		,						__A=None		,						) ->     int:






        if attention_mask is None:
              _lowerCAmelCase										=input_ids.ne(config.pad_token_id				)
        if decoder_attention_mask is None:
              _lowerCAmelCase										=decoder_input_ids.ne(config.pad_token_id				)
        if head_mask is None:
              _lowerCAmelCase										=torch.ones(config.num_hidden_layers		,						config.num_attention_heads		,						device=__UpperCamelCase				)
        if decoder_head_mask is None:
              _lowerCAmelCase										=torch.ones(config.num_decoder_layers		,						config.num_attention_heads		,						device=__UpperCamelCase				)
        if cross_attn_head_mask is None:
              _lowerCAmelCase										=torch.ones(
                  config.num_decoder_layers		,						config.num_attention_heads		,						device=__UpperCamelCase				)
        return {
            "input_ids": input_ids,
            "decoder_input_ids": decoder_input_ids,
            "attention_mask": attention_mask,
            "decoder_attention_mask": decoder_attention_mask,
            "head_mask": head_mask,
            "decoder_head_mask": decoder_head_mask,
            "cross_attn_head_mask": cross_attn_head_mask,
        }






  def 		UpperCamelCase__       (   self				) ->     Union[str, Any]:
        _lowerCAmelCase										=ids_tensor([self.batch_size, self.encoder_seq_length]		,						self.vocab_size				)
        _lowerCAmelCase										=ids_tensor([self.batch_size, self.decoder_seq_length]		,						self.vocab_size				)

        # we need to clamp the input ids here to avoid having pad token in between
        # this is because for NllbMoe the position_ids are prepared such that
        # all pad tokens have pos id = 2 and rest are between 2..seq_length
        # and the seq_length here is seq_length - num_pad_tokens
        # but when using past, there is no way of knowing if the past input ids had
        # pad tokens in them, which results in incorrect seq_lenth and which in turn results in
        # position_ids being off by num_pad_tokens in past input
        _lowerCAmelCase										=input_ids.clamp(self.pad_token_id + 1				)
        _lowerCAmelCase										=decoder_input_ids.clamp(self.pad_token_id + 1				)

        _lowerCAmelCase										=self.get_config()
        _lowerCAmelCase										=config.num_attention_heads
        _lowerCAmelCase										=self.prepare_inputs_dict(__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase				)
        return config, input_dict






  def 		UpperCamelCase__       (   self				) ->     Any:
        _lowerCAmelCase						,				_lowerCAmelCase										=self.prepare_config_and_inputs()
        return config, inputs_dict






  def 		UpperCamelCase__       (   self				) ->     List[str]:
        return TaConfig(
            vocab_size=166		,						d_model=self.hidden_size		,						d_ff=self.d_ff		,						d_kv=self.hidden_size // self.num_attention_heads		,						num_layers=self.num_hidden_layers		,						num_decoder_layers=self.decoder_layers		,						num_heads=self.num_attention_heads		,						relative_attention_num_buckets=self.relative_attention_num_buckets		,						dropout_rate=self.dropout_rate		,						initializer_factor=self.initializer_factor		,						eos_token_id=self.eos_token_id		,						bos_token_id=self.pad_token_id		,						pad_token_id=self.pad_token_id		,						decoder_start_token_id=self.decoder_start_token_id		,						)






  def 		UpperCamelCase__       (   self				) ->     Dict:
        return TaConfig(
            vocab_size=self.vocab_size		,						d_model=self.hidden_size		,						d_ff=self.d_ff		,						d_kv=self.hidden_size // self.num_attention_heads		,						num_layers=self.num_hidden_layers		,						num_decoder_layers=self.decoder_layers		,						num_heads=self.num_attention_heads		,						relative_attention_num_buckets=self.relative_attention_num_buckets		,						dropout_rate=self.dropout_rate		,						initializer_factor=self.initializer_factor		,						eos_token_id=self.eos_token_id		,						bos_token_id=self.pad_token_id		,						pad_token_id=self.pad_token_id		,						decoder_start_token_id=self.decoder_start_token_id		,						)






  def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A		,						__A		,						__A		,						__A		,						) ->     List[str]:
        _lowerCAmelCase										=UMTaModel(config=__UpperCamelCase				)
        model.to(__UpperCamelCase				)
        model.eval()
        _lowerCAmelCase										=model(
            input_ids=__UpperCamelCase		,						decoder_input_ids=__UpperCamelCase		,						attention_mask=__UpperCamelCase		,						decoder_attention_mask=__UpperCamelCase		,						)
        _lowerCAmelCase										=model(input_ids=__UpperCamelCase		,						decoder_input_ids=__UpperCamelCase				)
        _lowerCAmelCase										=result.last_hidden_state
        _lowerCAmelCase										=result.past_key_values
        _lowerCAmelCase										=result.encoder_last_hidden_state

        self.parent.assertEqual(encoder_output.size()		,						(self.batch_size, self.encoder_seq_length, self.hidden_size)				)
        self.parent.assertEqual(decoder_output.size()		,						(self.batch_size, self.decoder_seq_length, self.hidden_size)				)
        # There should be `num_layers` key value embeddings stored in decoder_past
        self.parent.assertEqual(len(__UpperCamelCase				)		,						config.num_layers				)
        # There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
        self.parent.assertEqual(len(decoder_past[0]				)		,						4				)






  def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A		,						__A		,						__A		,						__A		,						) ->     str:
        _lowerCAmelCase										=UMTaModel(config=__UpperCamelCase				).get_decoder().to(__UpperCamelCase				).eval()
        # first forward pass
        _lowerCAmelCase										=model(__UpperCamelCase		,						use_cache=__UpperCamelCase				)
        _lowerCAmelCase										=model(__UpperCamelCase				)
        _lowerCAmelCase										=model(__UpperCamelCase		,						use_cache=__UpperCamelCase				)

        self.parent.assertTrue(len(__UpperCamelCase				) == len(__UpperCamelCase				)				)
        self.parent.assertTrue(len(__UpperCamelCase				) == len(__UpperCamelCase				) + 1				)

        _lowerCAmelCase						,				_lowerCAmelCase										=outputs.to_tuple()

        # create hypothetical next token and extent to next_input_ids
        _lowerCAmelCase										=ids_tensor((self.batch_size, 1)		,						config.vocab_size				)

        # append to next input_ids and
        _lowerCAmelCase										=torch.cat([input_ids, next_tokens]		,						dim=-1				)

        _lowerCAmelCase										=model(__UpperCamelCase				)['last_hidden_state']
        _lowerCAmelCase										=model(__UpperCamelCase		,						past_key_values=__UpperCamelCase				)['last_hidden_state']

        # select random slice
        _lowerCAmelCase										=ids_tensor((1,)		,						output_from_past.shape[-1]				).item()
        _lowerCAmelCase										=output_from_no_past[:, -1, random_slice_idx].detach()
        _lowerCAmelCase										=output_from_past[:, 0, random_slice_idx].detach()

        # test that outputs are equal for slice
        self.parent.assertTrue(torch.allclose(__UpperCamelCase		,						__UpperCamelCase		,						atol=1E-3				)				)






  def 		UpperCamelCase__       (   self		,						__A		,						__A		,						) ->     List[str]:
        _lowerCAmelCase										=UMTaModel(config=__UpperCamelCase				).to(__UpperCamelCase				).half().eval()
        _lowerCAmelCase										=model(**__UpperCamelCase				)['last_hidden_state']
        self.parent.assertFalse(torch.isnan(__UpperCamelCase				).any().item()				)

@require_torch
class   SCREAMING_SNAKE_CASE			(						_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			unittest.TestCase):







  """simple docstring"""
  lowercase   :      List[str]       =							(
      (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
  )
  lowercase   :      Tuple       =							(UMTaForConditionalGeneration,) if is_torch_available() else ()
  lowercase   :      Optional[Any]       =							(
      {
          'conversational': UMTaForConditionalGeneration,
          'feature-extraction': UMTaModel,
          'summarization': UMTaForConditionalGeneration,
          'text2text-generation': UMTaForConditionalGeneration,
          'translation': UMTaForConditionalGeneration,
          'question-answering': UMTaForQuestionAnswering,
      }
      if is_torch_available()
      else {}
  )
  lowercase   :      Any       =							True
  lowercase   :      Optional[int]       =							False
  lowercase   :      Any       =							False
  lowercase   :      Optional[int]       =							True
  lowercase   :      Optional[Any]       =							True
  # The small UMT5 model needs higher percentages for CPU/MP tests
  lowercase   :      int       =							[0.8, 0.9]






  def 		UpperCamelCase__       (   self				) ->     Union[str, Any]:
        _lowerCAmelCase										=UMTaModelTester(self				)






  @unittest.skip('Test has a segmentation fault on torch 1.8.0'				)
  def 		UpperCamelCase__       (   self				) ->     Union[str, Any]:
        _lowerCAmelCase										=self.model_tester.prepare_config_and_inputs()
        _lowerCAmelCase										=UMTaModel(config_and_inputs[0]				).to(__UpperCamelCase				)
        with tempfile.TemporaryDirectory() as tmpdirname:
              torch.onnx.export(
                  __UpperCamelCase		,						(config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])		,						F'''{tmpdirname}/t5_test.onnx'''		,						export_params=__UpperCamelCase		,						opset_version=9		,						input_names=['input_ids', 'decoder_input_ids']		,						)






  @unittest.skipIf(torch_device == 'cpu'		,						'Cant do half precision'				)
  def 		UpperCamelCase__       (   self				) ->     Optional[int]:
        _lowerCAmelCase										=self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model_fpaa_forward(*__UpperCamelCase				)






  def 		UpperCamelCase__       (   self				) ->     Any:
        _lowerCAmelCase										=['encoder_attentions', 'decoder_attentions', 'cross_attentions']
        _lowerCAmelCase										=self.model_tester.prepare_config_and_inputs()
        _lowerCAmelCase										=config_and_inputs[0]
        _lowerCAmelCase										=UMTaForConditionalGeneration(__UpperCamelCase				).eval()
        model.to(__UpperCamelCase				)

        _lowerCAmelCase										={
            'head_mask': torch.zeros(config.num_layers		,						config.num_heads		,						device=__UpperCamelCase				),
            'decoder_head_mask': torch.zeros(config.num_decoder_layers		,						config.num_heads		,						device=__UpperCamelCase				),
            'cross_attn_head_mask': torch.zeros(config.num_decoder_layers		,						config.num_heads		,						device=__UpperCamelCase				),
        }

        for attn_name, (name, mask) in zip(__UpperCamelCase		,						head_masking.items()				):
              _lowerCAmelCase										={name: mask}
              # Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
              if name == "head_mask":
                    _lowerCAmelCase										=torch.ones(
                        config.num_decoder_layers		,						config.num_heads		,						device=__UpperCamelCase				)

              _lowerCAmelCase										=model.generate(
                  config_and_inputs[1]['input_ids']		,						num_beams=1		,						max_length=3		,						output_attentions=__UpperCamelCase		,						return_dict_in_generate=__UpperCamelCase		,						**__UpperCamelCase		,						)
              # We check the state of decoder_attentions and cross_attentions just from the last step
              _lowerCAmelCase										=out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
              self.assertEqual(sum([w.sum().item() for w in attn_weights]				)		,						0.0				)






  @unittest.skip('Does not work on the tiny model as we keep hitting edge cases.'				)
  def 		UpperCamelCase__       (   self				) ->     List[Any]:
        pass

@require_torch
@require_sentencepiece
@require_tokenizers
class   SCREAMING_SNAKE_CASE			(						unittest.TestCase):







  """simple docstring"""






  @slow
  @unittest.skip(
      'Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged'				)
  def 		UpperCamelCase__       (   self				) ->     Optional[Any]:
        _lowerCAmelCase										=UMTaForConditionalGeneration.from_pretrained('google/umt5-small'		,						return_dict=__UpperCamelCase				).to(__UpperCamelCase				)
        _lowerCAmelCase										=AutoTokenizer.from_pretrained('google/umt5-small'		,						use_fast=__UpperCamelCase		,						legacy=__UpperCamelCase				)
        _lowerCAmelCase										=[
            'Bonjour monsieur <extra_id_0> bien <extra_id_1>.',
            'No se como puedo <extra_id_0>.',
            'This is the reason why we <extra_id_0> them.',
            'The <extra_id_0> walks in <extra_id_1>, seats',
            'A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.',
        ]
        _lowerCAmelCase										=tokenizer(__UpperCamelCase		,						return_tensors='pt'		,						padding=__UpperCamelCase				).input_ids
        # fmt: off
        _lowerCAmelCase										=torch.tensor(
            [
                [ 3_8530, 21_0703, 25_6299, 1410, 25_6298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                [   826, 321, 671, 2_5922, 25_6299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                [  1460, 339, 312, 1_9014, 1_0620, 758, 25_6299, 2355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
                [   517, 25_6299, 1_4869, 281, 301, 25_6298, 275, 11_9983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
                [   320, 25_6299, 1_4869, 281, 2234, 289, 2275, 333,6_1391, 289, 25_6298, 543, 25_6297, 16_8714, 329, 25_6296,274, 1],
            ]				)
        # fmt: on
        torch.testing.assert_allclose(__UpperCamelCase		,						__UpperCamelCase				)

        _lowerCAmelCase										=model.generate(input_ids.to(__UpperCamelCase				)				)
        _lowerCAmelCase										=[
            '<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>',
            '<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
            '<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
            '<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
            '<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
        ]
        _lowerCAmelCase										=tokenizer.batch_decode(__UpperCamelCase				)
        self.assertEqual(__UpperCamelCase		,						__UpperCamelCase				)






'''simple docstring'''

import argparse
import os
import sys
from unittest.mock import patch

import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main

from transformers import MarianMTModel
from transformers.file_utils import cached_path
from transformers.testing_utils import TestCasePlus, require_torch_gpu, slow
from utils import load_json


lowercase_     		= '''sshleifer/mar_enro_6_3_student'''
class   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""






		def 		UpperCamelCase__       (   self				) ->     Optional[Any]:
								super().setUp()

								_lowerCAmelCase										=cached_path(
								    'https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz'		,						extract_compressed_file=__A		,						)
								_lowerCAmelCase										=F'''{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k'''






		@slow
		@require_torch_gpu
		def 		UpperCamelCase__       (   self				) ->     Union[str, Any]:
								MarianMTModel.from_pretrained(__A				)






		@slow
		@require_torch_gpu
		def 		UpperCamelCase__       (   self				) ->     Union[str, Any]:
								_lowerCAmelCase										={
								    '$MAX_LEN': 64,
								    '$BS': 64,
								    '$GAS': 1,
								    '$ENRO_DIR': self.data_dir,
								    'facebook/mbart-large-cc25': MARIAN_MODEL,
								    # "val_check_interval=0.25": "val_check_interval=1.0",
								    '--learning_rate=3e-5': '--learning_rate 3e-4',
								    '--num_train_epochs 6': '--num_train_epochs 1',
								}

								# Clean up bash script
								_lowerCAmelCase										=(self.test_file_dir / 'train_mbart_cc25_enro.sh').open().read().split('finetune.py'				)[1].strip()
								_lowerCAmelCase										=bash_script.replace('\\\n'		,						''				).strip().replace('"$@"'		,						''				)
								for k, v in env_vars_to_replace.items():
														_lowerCAmelCase										=bash_script.replace(__A		,						str(__A				)				)
								_lowerCAmelCase										=self.get_auto_remove_tmp_dir()

								# bash_script = bash_script.replace("--fp16 ", "")
								_lowerCAmelCase										=F'''
            --output_dir {output_dir}
            --tokenizer_name Helsinki-NLP/opus-mt-en-ro
            --sortish_sampler
            --do_predict
            --gpus 1
            --freeze_encoder
            --n_train 40000
            --n_val 500
            --n_test 500
            --fp16_opt_level O1
            --num_sanity_val_steps 0
            --eval_beams 2
        '''.split()
								# XXX: args.gpus > 1 : handle multi_gpu in the future

								_lowerCAmelCase										=['finetune.py'] + bash_script.split() + args
								with patch.object(__A		,						'argv'		,						__A				):
														_lowerCAmelCase										=argparse.ArgumentParser()
														_lowerCAmelCase										=pl.Trainer.add_argparse_args(__A				)
														_lowerCAmelCase										=SummarizationModule.add_model_specific_args(__A		,						os.getcwd()				)
														_lowerCAmelCase										=parser.parse_args()
														_lowerCAmelCase										=main(__A				)

								# Check metrics
								_lowerCAmelCase										=load_json(model.metrics_save_path				)
								_lowerCAmelCase										=metrics['val'][0]
								_lowerCAmelCase										=metrics['val'][-1]
								self.assertEqual(len(metrics['val']				)		,						(args.max_epochs / args.val_check_interval)				)
								assert isinstance(last_step_stats[F'''val_avg_{model.val_metric}''']		,						__A				)

								self.assertGreater(last_step_stats['val_avg_gen_time']		,						0.01				)
								# model hanging on generate. Maybe bad config was saved. (XXX: old comment/assert?)
								self.assertLessEqual(last_step_stats['val_avg_gen_time']		,						1.0				)

								# test learning requirements:

								# 1. BLEU improves over the course of training by more than 2 pts
								self.assertGreater(last_step_stats['val_avg_bleu'] - first_step_stats['val_avg_bleu']		,						2				)

								# 2. BLEU finishes above 17
								self.assertGreater(last_step_stats['val_avg_bleu']		,						17				)

								# 3. test BLEU and val BLEU within ~1.1 pt.
								self.assertLess(abs(metrics['val'][-1]['val_avg_bleu'] - metrics['test'][-1]['test_avg_bleu']				)		,						1.1				)

								# check lightning ckpt can be loaded and has a reasonable statedict
								_lowerCAmelCase										=os.listdir(__A				)
								_lowerCAmelCase										=[x for x in contents if x.endswith('.ckpt'				)][0]
								_lowerCAmelCase										=os.path.join(args.output_dir		,						__A				)
								_lowerCAmelCase										=torch.load(__A		,						map_location='cpu'				)
								_lowerCAmelCase										='model.model.decoder.layers.0.encoder_attn_layer_norm.weight'
								assert expected_key in ckpt["state_dict"]
								assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

								# TODO: turn on args.do_predict when PL bug fixed.
								if args.do_predict:
														_lowerCAmelCase										={os.path.basename(__A				) for p in contents}
														assert "test_generations.txt" in contents
														assert "test_results.txt" in contents
														# assert len(metrics["val"]) ==  desired_n_evals
														assert len(metrics['test']				) == 1

class   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""






		@timeout_decorator.timeout(600				)
		@slow
		@require_torch_gpu
		def 		UpperCamelCase__       (   self				) ->     Tuple:
								_lowerCAmelCase										=F'''{self.test_file_dir_str}/test_data/wmt_en_ro'''
								_lowerCAmelCase										={
								    '--fp16_opt_level=O1': '',
								    '$MAX_LEN': 128,
								    '$BS': 16,
								    '$GAS': 1,
								    '$ENRO_DIR': data_dir,
								    '$m': 'sshleifer/student_marian_en_ro_6_1',
								    'val_check_interval=0.25': 'val_check_interval=1.0',
								}

								# Clean up bash script
								_lowerCAmelCase										=(
								    (self.test_file_dir / 'distil_marian_no_teacher.sh').open().read().split('distillation.py'				)[1].strip()
								)
								_lowerCAmelCase										=bash_script.replace('\\\n'		,						''				).strip().replace('"$@"'		,						''				)
								_lowerCAmelCase										=bash_script.replace('--fp16 '		,						' '				)

								for k, v in env_vars_to_replace.items():
														_lowerCAmelCase										=bash_script.replace(__A		,						str(__A				)				)
								_lowerCAmelCase										=self.get_auto_remove_tmp_dir()
								_lowerCAmelCase										=bash_script.replace('--fp16'		,						''				)
								_lowerCAmelCase										=6
								_lowerCAmelCase										=(
								    ['distillation.py']
								    + bash_script.split()
								    + [
								        F'''--output_dir={output_dir}''',
								        '--gpus=1',
								        '--learning_rate=1e-3',
								        F'''--num_train_epochs={epochs}''',
								        '--warmup_steps=10',
								        '--val_check_interval=1.0',
								        '--do_predict',
								    ]
								)
								with patch.object(__A		,						'argv'		,						__A				):
														_lowerCAmelCase										=argparse.ArgumentParser()
														_lowerCAmelCase										=pl.Trainer.add_argparse_args(__A				)
														_lowerCAmelCase										=SummarizationDistiller.add_model_specific_args(__A		,						os.getcwd()				)
														_lowerCAmelCase										=parser.parse_args()
														# assert args.gpus == gpus THIS BREAKS for multi_gpu

														_lowerCAmelCase										=distill_main(__A				)

								# Check metrics
								_lowerCAmelCase										=load_json(model.metrics_save_path				)
								_lowerCAmelCase										=metrics['val'][0]
								_lowerCAmelCase										=metrics['val'][-1]
								assert len(metrics['val']				) >= (args.max_epochs / args.val_check_interval)  # +1 accounts for val_sanity_check

								assert last_step_stats["val_avg_gen_time"] >= 0.01

								assert first_step_stats["val_avg_bleu"] < last_step_stats["val_avg_bleu"]  # model learned nothing
								assert 1.0 >= last_step_stats["val_avg_gen_time"]  # model hanging on generate. Maybe bad config was saved.
								assert isinstance(last_step_stats[F'''val_avg_{model.val_metric}''']		,						__A				)

								# check lightning ckpt can be loaded and has a reasonable statedict
								_lowerCAmelCase										=os.listdir(__A				)
								_lowerCAmelCase										=[x for x in contents if x.endswith('.ckpt'				)][0]
								_lowerCAmelCase										=os.path.join(args.output_dir		,						__A				)
								_lowerCAmelCase										=torch.load(__A		,						map_location='cpu'				)
								_lowerCAmelCase										='model.model.decoder.layers.0.encoder_attn_layer_norm.weight'
								assert expected_key in ckpt["state_dict"]
								assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

								# TODO: turn on args.do_predict when PL bug fixed.
								if args.do_predict:
														_lowerCAmelCase										={os.path.basename(__A				) for p in contents}
														assert "test_generations.txt" in contents
														assert "test_results.txt" in contents
														# assert len(metrics["val"]) ==  desired_n_evals
														assert len(metrics['test']				) == 1





