



import json
import os
import subprocess
import sys
import warnings
from argparse import ArgumentParser
from contextlib import AbstractContextManager
from typing import Dict, List, Optional

import requests

from ..utils import logging
from . import BaseTransformersCLICommand


_lowerCamelCase         =	logging.get_logger(__name__)  # pylint: disable=invalid-name


_lowerCamelCase         =	"""lfs-multipart-upload"""

class    _SCREAMING_SNAKE_CASE						(UpperCamelCase			):





     @staticmethod
     def 						__snake_case     (							UpperCamelCase			:				ArgumentParser					)->Any:
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						parser.add_parser(
          "lfs-enable-largefiles"  ,	help=(
              "Deprecated: use `huggingface-cli` instead. Configure your repository to enable upload of files > 5GB."
          )  ,	)
      enable_parser.add_argument("path"  ,	type=UpperCamelCase  ,	help="Local path to repository you want to configure."					)
      enable_parser.set_defaults(func=lambda UpperCamelCase					: LfsEnableCommand(UpperCamelCase					)					)

      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						parser.add_parser(
          UpperCamelCase  ,	help=(
              "Deprecated: use `huggingface-cli` instead. "
              "Command will get called by git-lfs, do not call it directly."
          )  ,	)
      upload_parser.set_defaults(func=lambda UpperCamelCase					: LfsUploadCommand(UpperCamelCase					)					)






class    _SCREAMING_SNAKE_CASE						:





     def __init__(							self			:				Union[str, Any]  ,	UpperCamelCase			:				List[Any]					)->List[str]:
      __SCREAMING_SNAKE_CASE							:     str							=						args






     def 						__snake_case     (							self			:				Union[str, Any]					)->List[Any]:
      warnings.warn(
          "Managing repositories through transformers-cli is deprecated. Please use `huggingface-cli` instead."					)
      __SCREAMING_SNAKE_CASE							:     List[Any]							=						os.path.abspath(self.args.path					)
      if not os.path.isdir(UpperCamelCase					):
       print("This does not look like a valid git repo."					)
       exit(1					)
      subprocess.run(
          "git config lfs.customtransfer.multipart.path transformers-cli".split()  ,	check=UpperCamelCase  ,	cwd=UpperCamelCase					)
      subprocess.run(
          F"""git config lfs.customtransfer.multipart.args {LFS_MULTIPART_UPLOAD_COMMAND}""".split()  ,	check=UpperCamelCase  ,	cwd=UpperCamelCase  ,	)
      print("Local repo set up for largefiles"					)






def 						_lowerCAmelCase    (					__lowerCamelCase :  Dict  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     List[str]							=						json.dumps(__lowerCamelCase  ) + "\n"
 sys.stdout.write(__lowerCamelCase  )
 sys.stdout.flush()






def 						_lowerCAmelCase    (					):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     Optional[int]							=						json.loads(sys.stdin.readline().strip()  )

 if "terminate" in (msg.get("type"  ), msg.get("event"  )):
  # terminate message received
  return None

 if msg.get("event"  ) not in ("download", "upload"):
  logger.critical("Received unexpected message"  )
  sys.exit(1  )

 return msg

class    _SCREAMING_SNAKE_CASE						(UpperCamelCase			):





     def __init__(							self			:				str  ,	UpperCamelCase			:				str  ,	UpperCamelCase			:				int  ,	UpperCamelCase			:				int					)->int:
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						filepath
      __SCREAMING_SNAKE_CASE							:     List[str]							=						seek_from
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						read_limit
      __SCREAMING_SNAKE_CASE							:     int							=						0





     def __enter__(							self			:				Optional[Any]					)->int:
      __SCREAMING_SNAKE_CASE							:     int							=						open(self.filepath  ,	"rb"					)
      self.f.seek(self.seek_from					)
      return self





     def __len__(							self			:				int					)->Dict:
      __SCREAMING_SNAKE_CASE							:     str							=						os.fstat(self.f.fileno()					).st_size
      return min(self.read_limit  ,	total_length - self.seek_from					)





     def 						__snake_case     (							self			:				Any  ,	UpperCamelCase			:				List[str]=-1					)->Optional[Any]:
      if self.n_seen >= self.read_limit:
       return b""
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						self.read_limit - self.n_seen
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						self.f.read(remaining_amount if n < 0 else min(UpperCamelCase  ,	UpperCamelCase					)					)
      self.n_seen += len(UpperCamelCase					)
      return data





     def __iter__(							self			:				Optional[Any]					)->Any:
      yield self.read(n=4 * 1_0_2_4 * 1_0_2_4					)






     def __exit__(							self			:				Tuple  ,	*UpperCamelCase			:				int					)->str:
      self.f.close()






class    _SCREAMING_SNAKE_CASE						:





     def __init__(							self			:				List[str]  ,	UpperCamelCase			:				Optional[int]					)->Optional[int]:
      __SCREAMING_SNAKE_CASE							:     Any							=						args






     def 						__snake_case     (							self			:				int					)->int:
      # Immediately after invoking a custom transfer process, git-lfs
      # sends initiation data to the process over stdin.
      # This tells the process useful information about the configuration.
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						json.loads(sys.stdin.readline().strip()					)
      if not (init_msg.get("event"					) == "init" and init_msg.get("operation"					) == "upload"):
       write_msg({"error": {"code": 3_2, "message": "Wrong lfs init operation"}}					)
       sys.exit(1					)

      # The transfer process should use the information it needs from the
      # initiation structure, and also perform any one-off setup tasks it
      # needs to do. It should then respond on stdout with a simple empty
      # confirmation structure, as follows:
      write_msg({}					)

      # After the initiation exchange, git-lfs will send any number of
      # transfer requests to the stdin of the transfer process, in a serial sequence.
      while True:
       __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						read_msg()
       if msg is None:
        # When all transfers have been processed, git-lfs will send
        # a terminate event to the stdin of the transfer process.
        # On receiving this message the transfer process should
        # clean up and terminate. No response is expected.
        sys.exit(0					)

       __SCREAMING_SNAKE_CASE							:     Dict							=						msg["oid"]
       __SCREAMING_SNAKE_CASE							:     List[str]							=						msg["path"]
       __SCREAMING_SNAKE_CASE							:     Tuple							=						msg["action"]["href"]
       __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						msg["action"]["header"]
       __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						int(header.pop("chunk_size"					)					)
       __SCREAMING_SNAKE_CASE							:     List[str]							=						list(header.values()					)

       __SCREAMING_SNAKE_CASE							:     List[str]							=						[]
       for i, presigned_url in enumerate(UpperCamelCase					):
        with FileSlice(UpperCamelCase  ,	seek_from=i * chunk_size  ,	read_limit=UpperCamelCase					) as data:
         __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						requests.put(UpperCamelCase  ,	data=UpperCamelCase					)
         r.raise_for_status()
         parts.append(
             {
                 "etag": r.headers.get("etag"					),
                 "partNumber": i + 1,
             }					)
         # In order to support progress reporting while data is uploading / downloading,
         # the transfer process should post messages to stdout
         write_msg(
             {
                 "event": "progress",
                 "oid": oid,
                 "bytesSoFar": (i + 1) * chunk_size,
                 "bytesSinceLast": chunk_size,
             }					)
         # Not precise but that's ok.

       __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						requests.post(
           UpperCamelCase  ,	json={
               "oid": oid,
               "parts": parts,
           }  ,	)
       r.raise_for_status()

       write_msg({"event": "complete", "oid": oid}					)













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"

