





import logging
import pathlib
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Callable, Dict, List, Optional, Set, Union

import datasets
import librosa
import numpy as np
import torch
from lang_trans import arabic
from packaging import version
from torch import nn

from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaProcessor,
    is_apex_available,
    trainer_utils,
)


if is_apex_available():
    from apex import amp

if version.parse(version.parse(torch.__version__).base_version) >= version.parse("""1.6"""):
    _UpperCamelCase       :				List[Any]           =			True
    from torch.cuda.amp import autocast


_UpperCamelCase       :				Optional[Any]           =			logging.getLogger(__name__)







@dataclass
class 		_lowerCAmelCase:

  """simple docstring"""







  lowerCamelCase__     	=       field(
      metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''})
  lowerCamelCase__     	=       field(
      default=_a						,       metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}						,       )
  lowerCamelCase__     	=       field(
      default=_a						,       metadata={'''help''': '''Whether to freeze the feature extractor layers of the model.'''})
  lowerCamelCase__     	=       field(
      default=_a						,       metadata={'''help''': '''Whether to log verbose messages or not.'''}						,       )
def  __UpperCamelCase    (						snake_case							,			snake_case      )    ->		int:


   '''simple docstring'''







   logging.basicConfig(
       format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''							,			datefmt='''%m/%d/%Y %H:%M:%S'''							,			handlers=[logging.StreamHandler(sys.stdout      )]							,			)
   __A    =  logging.WARNING
   if model_args.verbose_logging:
      __A    =  logging.DEBUG
   elif trainer_utils.is_main_process(training_args.local_rank      ):
      __A    =  logging.INFO
   logger.setLevel(snake_case      )







@dataclass
class 		_lowerCAmelCase:

  """simple docstring"""







  lowerCamelCase__     	=       field(
      default=_a						,       metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''})
  lowerCamelCase__     	=       field(
      default=_a						,       metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''})
  lowerCamelCase__     	=       field(
      default='''train'''						,       metadata={
          '''help''': '''The name of the training data set split to use (via the datasets library). Defaults to \'train\''''
      }						,       )
  lowerCamelCase__     	=       field(
      default='''validation'''						,       metadata={
          '''help''': (
              '''The name of the validation data set split to use (via the datasets library). Defaults to \'validation\''''
          )
      }						,       )
  lowerCamelCase__     	=       field(
      default='''text'''						,       metadata={'''help''': '''Column in the dataset that contains label (target text). Defaults to \'text\''''}						,       )
  lowerCamelCase__     	=       field(
      default='''file'''						,       metadata={'''help''': '''Column in the dataset that contains speech file path. Defaults to \'file\''''}						,       )
  lowerCamelCase__     	=       field(
      default=_a						,       metadata={'''help''': '''Resample loaded audio to target feature extractor\'s sampling rate or not.'''}						,       )
  lowerCamelCase__     	=       field(
      default=_a						,       metadata={'''help''': '''Filters out examples longer than specified. Defaults to no filtering.'''}						,       )
  lowerCamelCase__     	=       field(
      default='''librispeech'''						,       metadata={
          '''help''': (
              '''Orthography used for normalization and tokenization: \'librispeech\' (default), \'timit\', or'''
              ''' \'buckwalter\'.'''
          )
      }						,       )
  lowerCamelCase__     	=       field(
      default=_a						,       metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''})
  lowerCamelCase__     	=       field(
      default=_a						,       metadata={'''help''': '''The number of processes to use for the preprocessing.'''}						,       )







@dataclass
class 		_lowerCAmelCase:

  """simple docstring"""







  lowerCamelCase__     	=       False
  lowerCamelCase__     	=       None
  lowerCamelCase__     	=       "|"
  lowerCamelCase__     	=       field(default_factory=_a)
  lowerCamelCase__     	=       field(default_factory=_a)
  lowerCamelCase__     	=       None

  @classmethod
  def 						SCREAMING_SNAKE_CASE__			(   cls    ,							UpperCAmelCase      )->   Optional[int]:
     if name == "librispeech":
        return cls()
     if name == "timit":
        return cls(
            do_lower_case=UpperCAmelCase    ,							translation_table=str.maketrans({'''-''': ''' '''}      )    ,							)
     if name == "buckwalter":
        __A    =  {
            '''-''': ''' ''',  # sometimes used to represent pauses
            '''^''': '''v''',  # fixing "tha" in arabic_speech_corpus dataset
        }
        return cls(
            vocab_file=pathlib.Path(UpperCAmelCase      ).parent.joinpath('''vocab/buckwalter.json'''      )    ,							word_delimiter_token='''/'''    ,							translation_table=str.maketrans(UpperCAmelCase      )    ,							words_to_remove={'''sil'''}    ,							untransliterator=arabic.buckwalter.untransliterate    ,							)
     raise ValueError(f"Unsupported orthography: '{name}'."      )

  def 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase      )->   str:
     # TODO(elgeish) return a pipeline (e.g., from jiwer) instead? Or rely on branch predictor as is
     if len(self.translation_table      ) > 0:
        __A    =  text.translate(self.translation_table      )
     if len(self.words_to_remove      ) == 0:
        __A    =  ''' '''.join(text.split()      )  # clean up whitespaces
     else:
        __A    =  ''' '''.join(w for w in text.split() if w not in self.words_to_remove      )  # and clean up whilespaces
     return text





  def 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase      )->   WavaVecaProcessor:
     __A    =  WavaVecaFeatureExtractor.from_pretrained(
         model_args.model_name_or_path    ,							cache_dir=model_args.cache_dir      )
     if self.vocab_file:
        __A    =  WavaVecaCTCTokenizer(
            self.vocab_file    ,							cache_dir=model_args.cache_dir    ,							do_lower_case=self.do_lower_case    ,							word_delimiter_token=self.word_delimiter_token    ,							)
     else:
        __A    =  WavaVecaCTCTokenizer.from_pretrained(
            model_args.model_name_or_path    ,							cache_dir=model_args.cache_dir    ,							do_lower_case=self.do_lower_case    ,							word_delimiter_token=self.word_delimiter_token    ,							)
     return WavaVecaProcessor(UpperCAmelCase    ,							UpperCAmelCase      )







@dataclass
class 		_lowerCAmelCase:

  """simple docstring"""







  lowerCamelCase__     	=       42
  lowerCamelCase__     	=       True
  lowerCamelCase__     	=       None
  lowerCamelCase__     	=       None
  lowerCamelCase__     	=       None
  lowerCamelCase__     	=       None

  def __call__(   self    ,							UpperCAmelCase      )->   Dict[str, torch.Tensor]:
     # split inputs and labels since they have to be of different lenghts and need
     # different padding methods
     __A    =  [{'''input_values''': feature['''input_values''']} for feature in features]
     __A    =  [{'''input_ids''': feature['''labels''']} for feature in features]

     __A    =  self.processor.pad(
         UpperCAmelCase    ,							padding=self.padding    ,							max_length=self.max_length    ,							pad_to_multiple_of=self.pad_to_multiple_of    ,							return_tensors='''pt'''    ,							)
     __A    =  self.processor.pad(
         labels=UpperCAmelCase    ,							padding=self.padding    ,							max_length=self.max_length_labels    ,							pad_to_multiple_of=self.pad_to_multiple_of_labels    ,							return_tensors='''pt'''    ,							)

     # replace padding with -100 to ignore loss correctly
     __A    =  labels_batch['''input_ids'''].masked_fill(labels_batch.attention_mask.ne(1      )    ,							-1_00      )

     __A    =  labels

     return batch







class 		_lowerCAmelCase(		_a):

  """simple docstring"""







  def 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase    ,							UpperCAmelCase      )->   torch.Tensor:
     model.train()
     __A    =  self._prepare_inputs(UpperCAmelCase      )

     if self.use_amp:
        with autocast():
           __A    =  self.compute_loss(UpperCAmelCase    ,							UpperCAmelCase      )
     else:
        __A    =  self.compute_loss(UpperCAmelCase    ,							UpperCAmelCase      )

     if self.args.n_gpu > 1:
        if model.module.config.ctc_loss_reduction == "mean":
           __A    =  loss.mean()
        elif model.module.config.ctc_loss_reduction == "sum":
           __A    =  loss.sum() / (inputs['''labels'''] >= 0).sum()
        else:
           raise ValueError(f"{model.config.ctc_loss_reduction} is not valid. Choose one of ['mean', 'sum']"      )

     if self.args.gradient_accumulation_steps > 1:
        __A    =  loss / self.args.gradient_accumulation_steps

     if self.use_amp:
        self.scaler.scale(UpperCAmelCase      ).backward()
     elif self.use_apex:
        with amp.scale_loss(UpperCAmelCase    ,							self.optimizer      ) as scaled_loss:
           scaled_loss.backward()
     elif self.deepspeed:
        self.deepspeed.backward(UpperCAmelCase      )
     else:
        loss.backward()

     return loss.detach()
def  __UpperCamelCase    (						)    ->		Any:


   '''simple docstring'''







   __A    =  HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)      )

   __A   ,		__A   ,		__A    =  parser.parse_args_into_dataclasses()
   configure_logger(snake_case							,			snake_case      )

   __A    =  Orthography.from_name(data_args.orthography.lower()      )
   __A    =  orthography.create_processor(snake_case      )
   __A    =  WavaVecaForCTC.from_pretrained(
       model_args.model_name_or_path							,			cache_dir=model_args.cache_dir							,			gradient_checkpointing=training_args.gradient_checkpointing							,			vocab_size=len(processor.tokenizer      )							,			)

   __A    =  datasets.load_dataset(
       data_args.dataset_name							,			data_args.dataset_config_name							,			split=data_args.train_split_name      )
   __A    =  datasets.load_dataset(
       data_args.dataset_name							,			data_args.dataset_config_name							,			split=data_args.validation_split_name      )

   __A    =  datasets.load_metric('''wer'''      )
   __A    =  processor.feature_extractor.sampling_rate if data_args.target_feature_extractor_sampling_rate else None
   __A    =  ''''''.join(t for t in processor.tokenizer.get_vocab().keys() if len(snake_case      ) == 1      )
   __A    =  re.compile(  # remove characters not in vocabulary
       RF"[^\s{re.escape(snake_case      )}]"							,			flags=re.IGNORECASE if processor.tokenizer.do_lower_case else 0							,			)
   __A    =  []

   def prepare_example(snake_case      ):  # TODO(elgeish) make use of multiprocessing?
      __A   ,		__A    =  librosa.load(example[data_args.speech_file_column]							,			sr=snake_case      )
      if data_args.max_duration_in_seconds is not None:
         __A    =  len(example['''speech''']      ) / example['''sampling_rate''']
      # Normalize and clean up text; order matters!
      __A    =  orthography.preprocess_for_training(example[data_args.target_text_column]      )
      __A    =  vocabulary_text_cleaner.sub(''''''							,			snake_case      )
      if updated_text != example[data_args.target_text_column]:
         text_updates.append((example[data_args.target_text_column], updated_text)      )
         __A    =  updated_text
      return example

   __A    =  train_dataset.map(snake_case							,			remove_columns=[data_args.speech_file_column]      )
   __A    =  val_dataset.map(snake_case							,			remove_columns=[data_args.speech_file_column]      )

   if data_args.max_duration_in_seconds is not None:

      def filter_by_max_duration(snake_case      ):
         return example["duration_in_seconds"] <= data_args.max_duration_in_seconds

      __A    =  len(snake_case      )
      __A    =  len(snake_case      )
      __A    =  train_dataset.filter(snake_case							,			remove_columns=['''duration_in_seconds''']      )
      __A    =  val_dataset.filter(snake_case							,			remove_columns=['''duration_in_seconds''']      )
      if len(snake_case      ) > old_train_size:
         logger.warning(
             F"Filtered out {len(snake_case      ) - old_train_size} train example(s) longer than"
             F" {data_args.max_duration_in_seconds} second(s)."      )
      if len(snake_case      ) > old_val_size:
         logger.warning(
             F"Filtered out {len(snake_case      ) - old_val_size} validation example(s) longer than"
             F" {data_args.max_duration_in_seconds} second(s)."      )
   logger.info(F"Split sizes: {len(snake_case      )} train and {len(snake_case      )} validation."      )

   logger.warning(F"Updated {len(snake_case      )} transcript(s) using '{data_args.orthography}' orthography rules."      )
   if logger.isEnabledFor(logging.DEBUG      ):
      for original_text, updated_text in text_updates:
         logger.debug(F"Updated text: \"{original_text}\" -> \"{updated_text}\""      )
   __A    =  None

   def prepare_dataset(snake_case      ):
      # check that all files have the correct sampling rate
      assert (
          len(set(batch['''sampling_rate''']      )      ) == 1
      ), F"Make sure all inputs have the same sampling rate of {processor.feature_extractor.sampling_rate}."

      __A    =  processor(
          audio=batch['''speech''']							,			text=batch[data_args.target_text_column]							,			sampling_rate=batch['''sampling_rate'''][0]      )
      batch.update(snake_case      )
      return batch

   __A    =  train_dataset.map(
       snake_case							,			batch_size=training_args.per_device_train_batch_size							,			batched=snake_case							,			num_proc=data_args.preprocessing_num_workers							,			)
   __A    =  val_dataset.map(
       snake_case							,			batch_size=training_args.per_device_train_batch_size							,			batched=snake_case							,			num_proc=data_args.preprocessing_num_workers							,			)

   __A    =  DataCollatorCTCWithPadding(processor=snake_case							,			padding=snake_case      )

   def compute_metrics(snake_case      ):
      __A    =  pred.predictions
      __A    =  np.argmax(snake_case							,			axis=-1      )

      __A    =  processor.tokenizer.pad_token_id

      __A    =  processor.batch_decode(snake_case      )
      # we do not want to group tokens when computing the metrics
      __A    =  processor.batch_decode(pred.label_ids							,			group_tokens=snake_case      )
      if logger.isEnabledFor(logging.DEBUG      ):
         for reference, predicted in zip(snake_case							,			snake_case      ):
            logger.debug(F"reference: \"{reference}\""      )
            logger.debug(F"predicted: \"{predicted}\""      )
            if orthography.untransliterator is not None:
               logger.debug(F"reference (untransliterated): \"{orthography.untransliterator(snake_case      )}\""      )
               logger.debug(F"predicted (untransliterated): \"{orthography.untransliterator(snake_case      )}\""      )

      __A    =  wer_metric.compute(predictions=snake_case							,			references=snake_case      )

      return {"wer": wer}

   if model_args.freeze_feature_extractor:
      model.freeze_feature_extractor()

   __A    =  CTCTrainer(
       model=snake_case							,			data_collator=snake_case							,			args=snake_case							,			compute_metrics=snake_case							,			train_dataset=snake_case							,			eval_dataset=snake_case							,			tokenizer=processor.feature_extractor							,			)

   trainer.train()


if __name__ == "__main__":
    main()








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"

