






import csv
import dataclasses
import json
from dataclasses import dataclass
from typing import List, Optional, Union

from ...utils import is_tf_available, is_torch_available, logging


_a       =    logging.get_logger(__name__)





@dataclass
class __lowerCamelCase					:





	"""simple docstring"""






	UpperCamelCase__								= 42
	UpperCamelCase__								= 42
	UpperCamelCase__								= None
	UpperCamelCase__								= None

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							return json.dumps(dataclasses.asdict(self       )			,       indent=2       ) + "\n"





@dataclass(frozen=snake_case__)
class __lowerCamelCase					:





	"""simple docstring"""






	UpperCamelCase__								= 42
	UpperCamelCase__								= None
	UpperCamelCase__								= None
	UpperCamelCase__								= None

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							return json.dumps(dataclasses.asdict(self       )       ) + "\n"





class __lowerCamelCase					:





	"""simple docstring"""






	def 			UpperCamelCase       (				self			,       UpperCAmelCase       ):
							"""simple docstring"""







							raise NotImplementedError()

	def 			UpperCamelCase       (				self			,       UpperCAmelCase       ):
							"""simple docstring"""







							raise NotImplementedError()

	def 			UpperCamelCase       (				self			,       UpperCAmelCase       ):
							"""simple docstring"""







							raise NotImplementedError()

	def 			UpperCamelCase       (				self			,       UpperCAmelCase       ):
							"""simple docstring"""







							raise NotImplementedError()

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							raise NotImplementedError()

	def 			UpperCamelCase       (				self			,       UpperCAmelCase       ):
							"""simple docstring"""







							if len(self.get_labels()       ) > 1:
													_UpperCAmelCase							   =     self.get_labels()[int(example.label       )]
							return example

	@classmethod
	def 			UpperCamelCase       (				cls			,       UpperCAmelCase			,       UpperCAmelCase=None       ):
							"""simple docstring"""







							with open(UpperCAmelCase			,       'r'			,       encoding='utf-8-sig'       ) as f:
													return list(csv.reader(UpperCAmelCase			,       delimiter='\t'			,       quotechar=UpperCAmelCase       )       )





class __lowerCamelCase					( snake_case__):





	"""simple docstring"""






	def __init__(				self			,       UpperCAmelCase=None			,       UpperCAmelCase=None			,       UpperCAmelCase="classification"			,       UpperCAmelCase=False       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     [] if labels is None else labels
							_UpperCAmelCase							   =     [] if examples is None else examples
							_UpperCAmelCase							   =     mode
							_UpperCAmelCase							   =     verbose

	def __len__(				self       ):
							"""simple docstring"""







							return len(self.examples       )

	def __getitem__(				self			,       UpperCAmelCase       ):
							"""simple docstring"""







							if isinstance(UpperCAmelCase			,       UpperCAmelCase       ):
													return SingleSentenceClassificationProcessor(labels=self.labels			,       examples=self.examples[idx]       )
							return self.examples[idx]

	@classmethod
	def 			UpperCamelCase       (				cls			,       UpperCAmelCase			,       UpperCAmelCase=""			,       UpperCAmelCase=0			,       UpperCAmelCase=1			,       UpperCAmelCase=None			,       UpperCAmelCase=False			,       **UpperCAmelCase       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     cls(**UpperCAmelCase       )
							processor.add_examples_from_csv(
							    UpperCAmelCase			,       split_name=UpperCAmelCase			,       column_label=UpperCAmelCase			,       column_text=UpperCAmelCase			,       column_id=UpperCAmelCase			,       skip_first_row=UpperCAmelCase			,       overwrite_labels=UpperCAmelCase			,       overwrite_examples=UpperCAmelCase			,       )
							return processor

	@classmethod
	def 			UpperCamelCase       (				cls			,       UpperCAmelCase			,       UpperCAmelCase=None			,       **UpperCAmelCase       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     cls(**UpperCAmelCase       )
							processor.add_examples(UpperCAmelCase			,       labels=UpperCAmelCase       )
							return processor

	def 			UpperCamelCase       (				self			,       UpperCAmelCase			,       UpperCAmelCase=""			,       UpperCAmelCase=0			,       UpperCAmelCase=1			,       UpperCAmelCase=None			,       UpperCAmelCase=False			,       UpperCAmelCase=False			,       UpperCAmelCase=False			,       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     self._read_tsv(UpperCAmelCase       )
							if skip_first_row:
													_UpperCAmelCase							   =     lines[1:]
							_UpperCAmelCase							   =     []
							_UpperCAmelCase							   =     []
							_UpperCAmelCase							   =     []
							for i, line in enumerate(UpperCAmelCase       ):
													texts.append(line[column_text]       )
													labels.append(line[column_label]       )
													if column_id is not None:
																			ids.append(line[column_id]       )
													else:
																			_UpperCAmelCase							   =     F"""{split_name}-{i}""" if split_name else str(UpperCAmelCase       )
																			ids.append(UpperCAmelCase       )

							return self.add_examples(
							    UpperCAmelCase			,       UpperCAmelCase			,       UpperCAmelCase			,       overwrite_labels=UpperCAmelCase			,       overwrite_examples=UpperCAmelCase       )

	def 			UpperCamelCase       (				self			,       UpperCAmelCase			,       UpperCAmelCase=None			,       UpperCAmelCase=None			,       UpperCAmelCase=False			,       UpperCAmelCase=False       ):
							"""simple docstring"""







							if labels is not None and len(UpperCAmelCase       ) != len(UpperCAmelCase       ):
													raise ValueError(
													    F"""Text and labels have mismatched lengths {len(UpperCAmelCase       )} and {len(UpperCAmelCase       )}"""       )
							if ids is not None and len(UpperCAmelCase       ) != len(UpperCAmelCase       ):
													raise ValueError(F"""Text and ids have mismatched lengths {len(UpperCAmelCase       )} and {len(UpperCAmelCase       )}"""       )
							if ids is None:
													_UpperCAmelCase							   =     [None] * len(UpperCAmelCase       )
							if labels is None:
													_UpperCAmelCase							   =     [None] * len(UpperCAmelCase       )
							_UpperCAmelCase							   =     []
							_UpperCAmelCase							   =     set()
							for text_or_text_and_label, label, guid in zip(UpperCAmelCase			,       UpperCAmelCase			,       UpperCAmelCase       ):
													if isinstance(UpperCAmelCase			,       (tuple, list)       ) and label is None:
																			_UpperCAmelCase   ,					_UpperCAmelCase							   =     text_or_text_and_label
													else:
																			_UpperCAmelCase							   =     text_or_text_and_label
													added_labels.add(UpperCAmelCase       )
													examples.append(InputExample(guid=UpperCAmelCase			,       text_a=UpperCAmelCase			,       text_b=UpperCAmelCase			,       label=UpperCAmelCase       )       )

							# Update examples
							if overwrite_examples:
													_UpperCAmelCase							   =     examples
							else:
													self.examples.extend(UpperCAmelCase       )

							# Update labels
							if overwrite_labels:
													_UpperCAmelCase							   =     list(UpperCAmelCase       )
							else:
													_UpperCAmelCase							   =     list(set(self.labels       ).union(UpperCAmelCase       )       )

							return self.examples

	def 			UpperCamelCase       (				self			,       UpperCAmelCase			,       UpperCAmelCase=None			,       UpperCAmelCase=False			,       UpperCAmelCase=0			,       UpperCAmelCase=True			,       UpperCAmelCase=None			,       ):
							"""simple docstring"""







							if max_length is None:
													_UpperCAmelCase							   =     tokenizer.max_len

							_UpperCAmelCase							   =     {label: i for i, label in enumerate(self.labels       )}

							_UpperCAmelCase							   =     []
							for ex_index, example in enumerate(self.examples       ):
													if ex_index % 1_0000 == 0:
																			logger.info(F"""Tokenizing example {ex_index}"""       )

													_UpperCAmelCase							   =     tokenizer.encode(
													    example.text_a			,       add_special_tokens=UpperCAmelCase			,       max_length=min(UpperCAmelCase			,       tokenizer.max_len       )			,       )
													all_input_ids.append(UpperCAmelCase       )

							_UpperCAmelCase							   =     max(len(UpperCAmelCase       ) for input_ids in all_input_ids       )

							_UpperCAmelCase							   =     []
							for ex_index, (input_ids, example) in enumerate(zip(UpperCAmelCase			,       self.examples       )       ):
													if ex_index % 1_0000 == 0:
																			logger.info(F"""Writing example {ex_index}/{len(self.examples       )}"""       )
													# The mask has 1 for real tokens and 0 for padding tokens. Only real
													# tokens are attended to.
													_UpperCAmelCase							   =     [1 if mask_padding_with_zero else 0] * len(UpperCAmelCase       )

													# Zero-pad up to the sequence length.
													_UpperCAmelCase							   =     batch_length - len(UpperCAmelCase       )
													if pad_on_left:
																			_UpperCAmelCase							   =     ([pad_token] * padding_length) + input_ids
																			_UpperCAmelCase							   =     ([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
													else:
																			_UpperCAmelCase							   =     input_ids + ([pad_token] * padding_length)
																			_UpperCAmelCase							   =     attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)

													if len(UpperCAmelCase       ) != batch_length:
																			raise ValueError(F"""Error with input length {len(UpperCAmelCase       )} vs {batch_length}"""       )
													if len(UpperCAmelCase       ) != batch_length:
																			raise ValueError(F"""Error with input length {len(UpperCAmelCase       )} vs {batch_length}"""       )

													if self.mode == "classification":
																			_UpperCAmelCase							   =     label_map[example.label]
													elif self.mode == "regression":
																			_UpperCAmelCase							   =     float(example.label       )
													else:
																			raise ValueError(self.mode       )

													if ex_index < 5 and self.verbose:
																			logger.info('*** Example ***'       )
																			logger.info(F"""guid: {example.guid}"""       )
																			logger.info(F"""input_ids: {" ".join([str(UpperCAmelCase       ) for x in input_ids]       )}"""       )
																			logger.info(F"""attention_mask: {" ".join([str(UpperCAmelCase       ) for x in attention_mask]       )}"""       )
																			logger.info(F"""label: {example.label} (id = {label})"""       )

													features.append(InputFeatures(input_ids=UpperCAmelCase			,       attention_mask=UpperCAmelCase			,       label=UpperCAmelCase       )       )

							if return_tensors is None:
													return features
							elif return_tensors == "tf":
													if not is_tf_available():
																			raise RuntimeError('return_tensors set to \'tf\' but TensorFlow 2.0 can\'t be imported'       )
													import tensorflow as tf

													def gen():
																			for ex in features:
																									yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)

													_UpperCAmelCase							   =     tf.data.Dataset.from_generator(
													    UpperCAmelCase			,       ({'input_ids': tf.intaa, 'attention_mask': tf.intaa}, tf.intaa)			,       ({'input_ids': tf.TensorShape([None]       ), 'attention_mask': tf.TensorShape([None]       )}, tf.TensorShape([]       ))			,       )
													return dataset
							elif return_tensors == "pt":
													if not is_torch_available():
																			raise RuntimeError('return_tensors set to \'pt\' but PyTorch can\'t be imported'       )
													import torch
													from torch.utils.data import TensorDataset

													_UpperCAmelCase							   =     torch.tensor([f.input_ids for f in features]			,       dtype=torch.long       )
													_UpperCAmelCase							   =     torch.tensor([f.attention_mask for f in features]			,       dtype=torch.long       )
													if self.mode == "classification":
																			_UpperCAmelCase							   =     torch.tensor([f.label for f in features]			,       dtype=torch.long       )
													elif self.mode == "regression":
																			_UpperCAmelCase							   =     torch.tensor([f.label for f in features]			,       dtype=torch.float       )

													_UpperCAmelCase							   =     TensorDataset(UpperCAmelCase			,       UpperCAmelCase			,       UpperCAmelCase       )
													return dataset
							else:
													raise ValueError('return_tensors should be one of \'tf\' or \'pt\''       )








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)

_a       =    logging.getLogger()
_a       =    torch.cuda.is_available()
_a       =    {
    '''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''': 42,
    '''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-08,
    '''warmup_steps''': 0,
    '''max_epochs''': 1,
    '''train_batch_size''': 2,
    '''eval_batch_size''': 2,
    '''max_source_length''': 12,
    '''max_target_length''': 12,
    '''val_max_target_length''': 12,
    '''test_max_target_length''': 12,
    '''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 __A       ( __lowerCAmelCase					,						__lowerCAmelCase      )->    Dict:





						"""simple docstring"""



						_UpperCAmelCase							   =     '\n'.join(__lowerCAmelCase      )
						Path(__lowerCAmelCase      ).open('w'      ).writelines(__lowerCAmelCase      )


_a       =    [''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
_a       =    ['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
_a       =    '''patrickvonplaten/t5-tiny-random'''
_a       =    '''sshleifer/t5-tinier-random'''
_a       =    '''sshleifer/bart-tiny-random'''
_a       =    '''sshleifer/tiny-mbart'''
_a       =    '''sshleifer/tiny-marian-en-de'''
_a       =    '''stas/tiny-wmt19-en-de'''


_a       =    logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks


def __A       ( __lowerCAmelCase      )->    Tuple:





						"""simple docstring"""



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





class __lowerCamelCase					( snake_case__):





	"""simple docstring"""






	@classmethod
	def 			UpperCamelCase       (				cls       ):
							"""simple docstring"""







							logging.disable(logging.CRITICAL       )  # remove noisy download output from tracebacks
							return cls

	@slow
	@require_torch_gpu
	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     list_models()
							_UpperCAmelCase							   =     'sshleifer'
							_UpperCAmelCase							   =     [x.modelId for x in model_list if x.modelId.startswith(UpperCAmelCase       )]
							_UpperCAmelCase							   =     ['sshleifer/blenderbot-3B', 'sshleifer/blenderbot-90M']
							_UpperCAmelCase							   =     []
							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 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     {'student_encoder_layers': 2, 'student_decoder_layers': 1, 'no_teacher': True}
							self._test_distiller_cli(UpperCAmelCase       )

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     {
							    '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',
							}
							_UpperCAmelCase							   =     self._test_distiller_cli(UpperCAmelCase			,       check_contents=UpperCAmelCase       )

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

							_UpperCAmelCase							   =     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 			UpperCamelCase       (				self       ):
							"""simple docstring"""







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

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

							_UpperCAmelCase							   =     nn.functional.log_softmax(UpperCAmelCase			,       dim=-1       )
							_UpperCAmelCase   ,					_UpperCAmelCase							   =     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 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     {
							    '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',
							}
							_UpperCAmelCase							   =     self._test_distiller_cli(UpperCAmelCase			,       check_contents=UpperCAmelCase       )
							assert model.model.config.model_type == "mbart"

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

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     {
							    '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 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     {
							    'teacher': T5_TINY,
							    'student': T5_TINIER,
							    'model_name_or_path': T5_TINIER,
							    'tokenizer_name': T5_TINIER,
							}
							self._test_distiller_cli(UpperCAmelCase       )

	def 			UpperCamelCase       (				self			,       UpperCAmelCase			,       UpperCAmelCase=True       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     {
							    '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       )
							_UpperCAmelCase							   =     CHEAP_ARGS.copy()
							_UpperCAmelCase							   =     make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
							_UpperCAmelCase							   =     self.get_auto_remove_tmp_dir()

							args_d.update(data_dir=UpperCAmelCase			,       output_dir=UpperCAmelCase			,       **UpperCAmelCase       )
							_UpperCAmelCase							   =     distill_main(argparse.Namespace(**UpperCAmelCase       )       )
							if not check_contents:
													return model
							_UpperCAmelCase							   =     os.listdir(UpperCAmelCase       )
							_UpperCAmelCase							   =     {os.path.basename(UpperCAmelCase       ) for p in contents}
							_UpperCAmelCase							   =     [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       )

							_UpperCAmelCase							   =     load_json(model.metrics_save_path       )
							_UpperCAmelCase							   =     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       )
							_UpperCAmelCase							   =     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					( snake_case__):





	"""simple docstring"""






	@parameterized.expand(
	    [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]			,       )
	def 			UpperCamelCase       (				self			,       UpperCAmelCase       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     CHEAP_ARGS.copy()
							_UpperCAmelCase							   =     'translation' if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else 'summarization'
							_UpperCAmelCase							   =     0.1 if task == 'translation' else 0

							_UpperCAmelCase							   =     make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
							_UpperCAmelCase							   =     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
							_UpperCAmelCase							   =     argparse.Namespace(**UpperCAmelCase       )
							_UpperCAmelCase							   =     main(UpperCAmelCase       )

							_UpperCAmelCase							   =     module.model.get_input_embeddings()
							assert not input_embeds.weight.requires_grad
							if model == T5_TINY:
													_UpperCAmelCase							   =     module.model.lm_head
													assert not lm_head.weight.requires_grad
													assert (lm_head.weight == input_embeds.weight).all().item()
							elif model == FSMT_TINY:
													_UpperCAmelCase							   =     module.model.model
													_UpperCAmelCase							   =     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:
													_UpperCAmelCase							   =     module.model.model
													_UpperCAmelCase							   =     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

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

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     CHEAP_ARGS.copy()

							_UpperCAmelCase							   =     'summarization'
							_UpperCAmelCase							   =     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
							_UpperCAmelCase							   =     BART_TINY
							_UpperCAmelCase							   =     self.get_auto_remove_tmp_dir()
							_UpperCAmelCase							   =     args_d.copy()
							args_da.update(
							    model_name_or_path=UpperCAmelCase			,       output_dir=UpperCAmelCase			,       )
							_UpperCAmelCase							   =     ('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
							for p in extra_model_params:
													_UpperCAmelCase							   =     0.5
							_UpperCAmelCase							   =     argparse.Namespace(**UpperCAmelCase       )
							_UpperCAmelCase							   =     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
							_UpperCAmelCase							   =     T5_TINY
							_UpperCAmelCase							   =     self.get_auto_remove_tmp_dir()
							_UpperCAmelCase							   =     args_d.copy()
							args_da.update(
							    model_name_or_path=UpperCAmelCase			,       output_dir=UpperCAmelCase			,       )
							_UpperCAmelCase							   =     'encoder_layerdrop'
							_UpperCAmelCase							   =     0.5
							_UpperCAmelCase							   =     argparse.Namespace(**UpperCAmelCase       )
							with pytest.raises(UpperCAmelCase       ) as excinfo:
													_UpperCAmelCase							   =     main(UpperCAmelCase       )
							assert str(excinfo.value       ) == F"""model config doesn't have a `{unsupported_param}` attribute"""

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     CHEAP_ARGS.copy()

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

							_UpperCAmelCase							   =     BART_TINY
							_UpperCAmelCase							   =     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
							_UpperCAmelCase							   =     argparse.ArgumentParser()
							_UpperCAmelCase							   =     pl.Trainer.add_argparse_args(UpperCAmelCase       )
							_UpperCAmelCase							   =     SummarizationModule.add_model_specific_args(UpperCAmelCase			,       os.getcwd()       )
							_UpperCAmelCase							   =     {'--help': True}

							# --help test
							with pytest.raises(UpperCAmelCase       ) as excinfo:
													with CaptureStdout() as cs:
																			_UpperCAmelCase							   =     parser.parse_args(UpperCAmelCase       )
													assert False, "--help is expected to sys.exit"
							assert excinfo.type == SystemExit
							_UpperCAmelCase							   =     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
							_UpperCAmelCase							   =     'non_existing_scheduler'
							_UpperCAmelCase							   =     {F"""--lr_scheduler={unsupported_param}"""}
							with pytest.raises(UpperCAmelCase       ) as excinfo:
													with CaptureStderr() as cs:
																			_UpperCAmelCase							   =     parser.parse_args(UpperCAmelCase       )
													assert False, "invalid argument is expected to sys.exit"
							assert excinfo.type == SystemExit
							_UpperCAmelCase							   =     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
							_UpperCAmelCase							   =     'cosine'
							_UpperCAmelCase							   =     args_d.copy()
							_UpperCAmelCase							   =     supported_param
							_UpperCAmelCase							   =     argparse.Namespace(**UpperCAmelCase       )
							_UpperCAmelCase							   =     main(UpperCAmelCase       )
							assert (
							    getattr(model.hparams			,       'lr_scheduler'       ) == supported_param
							), F"""lr_scheduler={supported_param} shouldn't fail"""
