"""simple docstring"""


import inspect
import unittest

from transformers import SwinaSRConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch
 from torch import nn

 from transformers import SwinaSRForImageSuperResolution, SwinaSRModel
 from transformers.models.swinasr.modeling_swinasr import SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
 from PIL import Image

 from transformers import SwinaSRImageProcessor






class 					a					:



  def __init__(		self   :    str						,  lowerCamelCase_   :    Dict						,  lowerCamelCase_   :    Union[str, Any]=13						,  lowerCamelCase_   :    List[Any]=32						,  lowerCamelCase_   :    Dict=1						,  lowerCamelCase_   :    int=3						,  lowerCamelCase_   :    Optional[int]=16						,  lowerCamelCase_   :    List[Any]=[1, 2, 1]						,  lowerCamelCase_   :    int=[2, 2, 4]						,  lowerCamelCase_   :    Optional[Any]=2						,  lowerCamelCase_   :    Optional[Any]=2.0						,  lowerCamelCase_   :    Dict=True						,  lowerCamelCase_   :    int=0.0						,  lowerCamelCase_   :    Tuple=0.0						,  lowerCamelCase_   :    List[Any]=0.1						,  lowerCamelCase_   :    str="gelu"						,  lowerCamelCase_   :    str=False						,  lowerCamelCase_   :    Dict=True						,  lowerCamelCase_   :    Optional[Any]=0.02						,  lowerCamelCase_   :    Optional[Any]=1E-5						,  lowerCamelCase_   :    List[str]=True						,  lowerCamelCase_   :    Union[str, Any]=None						,  lowerCamelCase_   :    str=False						,  lowerCamelCase_   :    List[str]=2						,  )      -> Optional[Any]:
      __a			      =						parent
      __a			      =						batch_size
      __a			      =						image_size
      __a			      =						patch_size
      __a			      =						num_channels
      __a			      =						embed_dim
      __a			      =						depths
      __a			      =						num_heads
      __a			      =						window_size
      __a			      =						mlp_ratio
      __a			      =						qkv_bias
      __a			      =						hidden_dropout_prob
      __a			      =						attention_probs_dropout_prob
      __a			      =						drop_path_rate
      __a			      =						hidden_act
      __a			      =						use_absolute_embeddings
      __a			      =						patch_norm
      __a			      =						layer_norm_eps
      __a			      =						initializer_range
      __a			      =						is_training
      __a			      =						scope
      __a			      =						use_labels
      __a			      =						upscale

      # here we set some attributes to make tests pass
      __a			      =						len(lowerCamelCase_     )
      __a			      =						embed_dim
      __a			      =						(image_size // patch_size) ** 2



  def        lowerCAmelCase_				(		self   :    List[Any]     )      -> Optional[int]:
      __a			      =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

      __a			      =						None
      if self.use_labels:
          __a			      =						ids_tensor([self.batch_size]						,  self.type_sequence_label_size     )

      __a			      =						self.get_config()

      return config, pixel_values, labels



  def        lowerCAmelCase_				(		self   :    Tuple     )      -> int:
      return SwinaSRConfig(
          image_size=self.image_size						,  patch_size=self.patch_size						,  num_channels=self.num_channels						,  embed_dim=self.embed_dim						,  depths=self.depths						,  num_heads=self.num_heads						,  window_size=self.window_size						,  mlp_ratio=self.mlp_ratio						,  qkv_bias=self.qkv_bias						,  hidden_dropout_prob=self.hidden_dropout_prob						,  attention_probs_dropout_prob=self.attention_probs_dropout_prob						,  drop_path_rate=self.drop_path_rate						,  hidden_act=self.hidden_act						,  use_absolute_embeddings=self.use_absolute_embeddings						,  path_norm=self.patch_norm						,  layer_norm_eps=self.layer_norm_eps						,  initializer_range=self.initializer_range						,  upscale=self.upscale						,  )



  def        lowerCAmelCase_				(		self   :    Any						,  lowerCamelCase_   :    Dict						,  lowerCamelCase_   :    Optional[int]						,  lowerCamelCase_   :    Any     )      -> Tuple:
      __a			      =						SwinaSRModel(config=lowerCamelCase_     )
      model.to(lowerCamelCase_     )
      model.eval()
      __a			      =						model(lowerCamelCase_     )

      self.parent.assertEqual(
          result.last_hidden_state.shape						,  (self.batch_size, self.embed_dim, self.image_size, self.image_size)     )



  def        lowerCAmelCase_				(		self   :    Tuple						,  lowerCamelCase_   :    Optional[int]						,  lowerCamelCase_   :    Tuple						,  lowerCamelCase_   :    Dict     )      -> Tuple:
      __a			      =						SwinaSRForImageSuperResolution(lowerCamelCase_     )
      model.to(lowerCamelCase_     )
      model.eval()
      __a			      =						model(lowerCamelCase_     )

      __a			      =						self.image_size * self.upscale
      self.parent.assertEqual(
          result.reconstruction.shape						,  (self.batch_size, self.num_channels, expected_image_size, expected_image_size)     )



  def        lowerCAmelCase_				(		self   :    Any     )      -> Optional[Any]:
      __a			      =						self.prepare_config_and_inputs()
      __a      ,  __a      ,  __a			      =						config_and_inputs
      __a			      =						{"""pixel_values""": pixel_values}
      return config, inputs_dict






@require_torch
class 					a					(				A_   ,		A_   ,		unittest.TestCase							):
  A_ :				Union[str, Any] 			=							(SwinaSRModel, SwinaSRForImageSuperResolution) if is_torch_available() else ()
  A_ :				Optional[Any] 			=							{'''feature-extraction''': SwinaSRModel} if is_torch_available() else {}

  A_ :				Any 			=							False
  A_ :				str 			=							False
  A_ :				List[Any] 			=							False
  A_ :				List[str] 			=							False
  A_ :				Union[str, Any] 			=							False



  def        lowerCAmelCase_				(		self   :    int     )      -> Dict:
      __a			      =						SwinaSRModelTester(self     )
      __a			      =						ConfigTester(self						,  config_class=lowerCamelCase_						,  embed_dim=37     )



  def        lowerCAmelCase_				(		self   :    Tuple     )      -> int:
      self.config_tester.create_and_test_config_to_json_string()
      self.config_tester.create_and_test_config_to_json_file()
      self.config_tester.create_and_test_config_from_and_save_pretrained()
      self.config_tester.create_and_test_config_with_num_labels()
      self.config_tester.check_config_can_be_init_without_params()
      self.config_tester.check_config_arguments_init()



  def        lowerCAmelCase_				(		self   :    Any     )      -> Any:
      __a			      =						self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*lowerCamelCase_     )



  def        lowerCAmelCase_				(		self   :    Dict     )      -> int:
      __a			      =						self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_super_resolution(*lowerCamelCase_     )



  @unittest.skip(reason="""Got `CUDA error: misaligned address` with PyTorch 2.0.0."""     )
  def        lowerCAmelCase_				(		self   :    str     )      -> Tuple:
      pass



  @unittest.skip(reason="""Swin2SR does not use inputs_embeds"""     )
  def        lowerCAmelCase_				(		self   :    Optional[int]     )      -> int:
      pass



  @unittest.skip(reason="""Swin2SR does not support training yet"""     )
  def        lowerCAmelCase_				(		self   :    Any     )      -> Tuple:
      pass



  @unittest.skip(reason="""Swin2SR does not support training yet"""     )
  def        lowerCAmelCase_				(		self   :    Tuple     )      -> Optional[Any]:
      pass



  def        lowerCAmelCase_				(		self   :    Optional[Any]     )      -> Union[str, Any]:
      __a      ,  __a			      =						self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
          __a			      =						model_class(lowerCamelCase_     )
          self.assertIsInstance(model.get_input_embeddings()						,  (nn.Module)     )
          __a			      =						model.get_output_embeddings()
          self.assertTrue(x is None or isinstance(lowerCamelCase_						,  nn.Linear     )     )



  def        lowerCAmelCase_				(		self   :    Any     )      -> Dict:
      __a      ,  __a			      =						self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
          __a			      =						model_class(lowerCamelCase_     )
          __a			      =						inspect.signature(model.forward     )
          # signature.parameters is an OrderedDict => so arg_names order is deterministic
          __a			      =						[*signature.parameters.keys()]

          __a			      =						["""pixel_values"""]
          self.assertListEqual(arg_names[:1]						,  lowerCamelCase_     )



  @slow
  def        lowerCAmelCase_				(		self   :    int     )      -> Optional[Any]:
      for model_name in SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          __a			      =						SwinaSRModel.from_pretrained(lowerCamelCase_     )
          self.assertIsNotNone(lowerCamelCase_     )



  def        lowerCAmelCase_				(		self   :    int     )      -> Any:
      __a      ,  __a			      =						self.model_tester.prepare_config_and_inputs_for_common()

      __a			      =						_config_zero_init(lowerCamelCase_     )
      for model_class in self.all_model_classes:
          __a			      =						model_class(config=lowerCamelCase_     )
          for name, param in model.named_parameters():
              if "logit_scale" in name:
                  continue
              if param.requires_grad:
                  self.assertIn(
                      ((param.data.mean() * 1E9).round() / 1E9).item()						,  [0.0, 1.0]						,  msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""						,  )



  def        lowerCAmelCase_				(		self   :    Optional[Any]     )      -> Tuple:
      __a      ,  __a			      =						self.model_tester.prepare_config_and_inputs_for_common()
      __a			      =						True

      for model_class in self.all_model_classes:
          __a			      =						True
          __a			      =						False
          __a			      =						True
          __a			      =						model_class(lowerCamelCase_     )
          model.to(lowerCamelCase_     )
          model.eval()
          with torch.no_grad():
              __a			      =						model(**self._prepare_for_class(lowerCamelCase_						,  lowerCamelCase_     )     )
          __a			      =						outputs.attentions
          __a			      =						len(self.model_tester.depths     )
          self.assertEqual(len(lowerCamelCase_     )						,  lowerCamelCase_     )

          # check that output_attentions also work using config
          del inputs_dict["output_attentions"]
          __a			      =						True
          __a			      =						config.window_size**2
          __a			      =						model_class(lowerCamelCase_     )
          model.to(lowerCamelCase_     )
          model.eval()
          with torch.no_grad():
              __a			      =						model(**self._prepare_for_class(lowerCamelCase_						,  lowerCamelCase_     )     )
          __a			      =						outputs.attentions
          self.assertEqual(len(lowerCamelCase_     )						,  lowerCamelCase_     )

          self.assertListEqual(
              list(attentions[0].shape[-3:]     )						,  [self.model_tester.num_heads[0], window_size_squared, window_size_squared]						,  )
          __a			      =						len(lowerCamelCase_     )

          # Check attention is always last and order is fine
          __a			      =						True
          __a			      =						True
          __a			      =						model_class(lowerCamelCase_     )
          model.to(lowerCamelCase_     )
          model.eval()
          with torch.no_grad():
              __a			      =						model(**self._prepare_for_class(lowerCamelCase_						,  lowerCamelCase_     )     )

          self.assertEqual(out_len + 1						,  len(lowerCamelCase_     )     )

          __a			      =						outputs.attentions

          self.assertEqual(len(lowerCamelCase_     )						,  lowerCamelCase_     )

          self.assertListEqual(
              list(self_attentions[0].shape[-3:]     )						,  [self.model_tester.num_heads[0], window_size_squared, window_size_squared]						,  )






@require_vision
@require_torch
@slow
class 					a					(				unittest.TestCase							):



  def        lowerCAmelCase_				(		self   :    List[str]     )      -> Dict:
      __a			      =						SwinaSRImageProcessor()
      __a			      =						SwinaSRForImageSuperResolution.from_pretrained("""caidas/swin2SR-classical-sr-x2-64"""     ).to(lowerCamelCase_     )

      __a			      =						Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""     )
      __a			      =						processor(images=lowerCamelCase_						,  return_tensors="""pt"""     ).to(lowerCamelCase_     )

      # forward pass
      with torch.no_grad():
          __a			      =						model(**lowerCamelCase_     )

      # verify the logits
      __a			      =						torch.Size([1, 3, 9_76, 12_96]     )
      self.assertEqual(outputs.reconstruction.shape						,  lowerCamelCase_     )
      __a			      =						torch.tensor(
          [[0.54_58, 0.55_46, 0.56_38], [0.55_26, 0.55_65, 0.56_51], [0.53_96, 0.54_26, 0.56_21]]     ).to(lowerCamelCase_     )
      self.assertTrue(torch.allclose(outputs.reconstruction[0, 0, :3, :3]						,  lowerCamelCase_						,  atol=1E-4     )     )

"""simple docstring"""


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 						UpperCamelCase				(	_lowerCAmelCase   :							Path				, _lowerCAmelCase   :							list						):
    __a			      =						"""\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 						UpperCamelCase				(	_lowerCAmelCase   :							List[Any]						):
    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 					a					(				A_							):



  @classmethod
  def        lowerCAmelCase_				(		cls   :    Optional[Any]     )      -> Optional[int]:
      logging.disable(logging.CRITICAL     )  # remove noisy download output from tracebacks
      return cls



  @slow
  @require_torch_gpu
  def        lowerCAmelCase_				(		self   :    List[str]     )      -> str:
      __a			      =						list_models()
      __a			      =						"""sshleifer"""
      __a			      =						[x.modelId for x in model_list if x.modelId.startswith(lowerCamelCase_     )]
      __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(lowerCamelCase_     )
          except Exception:
              failures.append(lowerCamelCase_     )
      assert not failures, F"""The following models could not be loaded through AutoConfig: {failures}"""



  def        lowerCAmelCase_				(		self   :    Any     )      -> Dict:
      __a			      =						{"""student_encoder_layers""": 2, """student_decoder_layers""": 1, """no_teacher""": True}
      self._test_distiller_cli(lowerCamelCase_     )



  def        lowerCAmelCase_				(		self   :    Dict     )      -> Any:
      __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(lowerCamelCase_						,  check_contents=lowerCamelCase_     )

      __a			      =						list(Path(model.output_dir     ).glob("""*.ckpt"""     )     )
      self.assertEqual(1						,  len(lowerCamelCase_     )     )
      __a			      =						list(Path(model.output_dir     ).glob("""**/*.bin"""     )     )
      self.assertEqual(len(lowerCamelCase_     )						,  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(lowerCamelCase_						,  lowerCamelCase_						,  str(model.output_dir / """best_tfmr"""     )     )
      self.assertTrue(Path(lowerCamelCase_     ).exists()     )

      __a			      =						self.get_auto_remove_tmp_dir()
      convert_pl_to_hf(ckpts[0]						,  transformer_ckpts[0].parent						,  lowerCamelCase_     )
      assert os.path.exists(os.path.join(lowerCamelCase_						,  """pytorch_model.bin"""     )     )



  def        lowerCAmelCase_				(		self   :    Optional[int]     )      -> Any:
      __a			      =						AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase_     )
      __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(
          lowerCamelCase_						,  attention_mask=lowerCamelCase_						,  decoder_input_ids=lowerCamelCase_						,  labels=lowerCamelCase_						,  use_cache=lowerCamelCase_     ).loss

      __a			      =						model(lowerCamelCase_						,  attention_mask=lowerCamelCase_						,  decoder_input_ids=lowerCamelCase_						,  use_cache=lowerCamelCase_     ).logits

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



  def        lowerCAmelCase_				(		self   :    str     )      -> int:
      __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(lowerCamelCase_						,  check_contents=lowerCamelCase_     )
      assert model.model.config.model_type == "mbart"

      __a			      =						list(Path(model.output_dir     ).glob("""*.ckpt"""     )     )
      self.assertEqual(1						,  len(lowerCamelCase_     )     )
      __a			      =						list(Path(model.output_dir     ).glob("""**/*.bin"""     )     )
      __a			      =						list(Path(model.output_dir     ).glob("""best_tfmr/*"""     )     )
      assert len(lowerCamelCase_     ) > 2
      self.assertEqual(len(lowerCamelCase_     )						,  2     )



  def        lowerCAmelCase_				(		self   :    Dict     )      -> Union[str, Any]:
      __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(lowerCamelCase_     )



  def        lowerCAmelCase_				(		self   :    List[str]     )      -> List[str]:
      __a			      =						{
          """teacher""": T5_TINY,
          """student""": T5_TINIER,
          """model_name_or_path""": T5_TINIER,
          """tokenizer_name""": T5_TINIER,
      }
      self._test_distiller_cli(lowerCamelCase_     )



  def        lowerCAmelCase_				(		self   :    int						,  lowerCamelCase_   :    List[Any]						,  lowerCamelCase_   :    Union[str, Any]=True     )      -> Optional[Any]:
      __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(lowerCamelCase_     )
      __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=lowerCamelCase_						,  output_dir=lowerCamelCase_						,  **lowerCamelCase_     )
      __a			      =						distill_main(argparse.Namespace(**lowerCamelCase_     )     )
      if not check_contents:
          return model
      __a			      =						os.listdir(lowerCamelCase_     )
      __a			      =						{os.path.basename(lowerCamelCase_     ) for p in contents}
      __a			      =						[p for p in contents if p.endswith("""ckpt"""     )]
      assert len(lowerCamelCase_     ) > 0

      self.assertIn("""test_generations.txt"""						,  lowerCamelCase_     )
      self.assertIn("""test_results.txt"""						,  lowerCamelCase_     )

      __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}"""]						,  lowerCamelCase_     )
      __a			      =						int(args_d["""max_epochs"""] * (1 / args_d["""val_check_interval"""]) + 1     )
      self.assertEqual(len(metrics["""val"""]     )						,  lowerCamelCase_     )
      self.assertEqual(len(metrics["""test"""]     )						,  1     )
      return model






class 					a					(				A_							):



  @parameterized.expand(
      [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]						,  )
  def        lowerCAmelCase_				(		self   :    List[Any]						,  lowerCamelCase_   :    Optional[Any]     )      -> Dict:
      __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=lowerCamelCase_						,  model_name_or_path=lowerCamelCase_						,  tokenizer_name=lowerCamelCase_						,  train_batch_size=2						,  eval_batch_size=2						,  output_dir=lowerCamelCase_						,  do_predict=lowerCamelCase_						,  task=lowerCamelCase_						,  src_lang="""en_XX"""						,  tgt_lang="""ro_RO"""						,  freeze_encoder=lowerCamelCase_						,  freeze_embeds=lowerCamelCase_						,  )
      assert "n_train" in args_d
      __a			      =						argparse.Namespace(**lowerCamelCase_     )
      __a			      =						main(lowerCamelCase_     )

      __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(lowerCamelCase_						,  lowerCamelCase_     )
      assert len(lowerCamelCase_     ) >= 4



  def        lowerCAmelCase_				(		self   :    Dict     )      -> Tuple:
      __a			      =						CHEAP_ARGS.copy()

      __a			      =						"""summarization"""
      __a			      =						make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     )

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

      # 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=lowerCamelCase_						,  output_dir=lowerCamelCase_						,  )
      __a			      =						("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
      for p in extra_model_params:
          __a			      =						0.5
      __a			      =						argparse.Namespace(**lowerCamelCase_     )
      __a			      =						main(lowerCamelCase_     )
      for p in extra_model_params:
          assert getattr(model.config						,  lowerCamelCase_     ) == 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=lowerCamelCase_						,  output_dir=lowerCamelCase_						,  )
      __a			      =						"""encoder_layerdrop"""
      __a			      =						0.5
      __a			      =						argparse.Namespace(**lowerCamelCase_     )
      with pytest.raises(lowerCamelCase_     ) as excinfo:
          __a			      =						main(lowerCamelCase_     )
      assert str(excinfo.value     ) == F"""model config doesn't have a `{unsupported_param}` attribute"""



  def        lowerCAmelCase_				(		self   :    int     )      -> 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=lowerCamelCase_						,  model_name_or_path=lowerCamelCase_						,  output_dir=lowerCamelCase_						,  tokenizer_name=lowerCamelCase_						,  train_batch_size=2						,  eval_batch_size=2						,  do_predict=lowerCamelCase_						,  task=lowerCamelCase_						,  src_lang="""en_XX"""						,  tgt_lang="""ro_RO"""						,  freeze_encoder=lowerCamelCase_						,  freeze_embeds=lowerCamelCase_						,  )

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

      # --help test
      with pytest.raises(lowerCamelCase_     ) as excinfo:
          with CaptureStdout() as cs:
              __a			      =						parser.parse_args(lowerCamelCase_     )
          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(lowerCamelCase_     ) as excinfo:
          with CaptureStderr() as cs:
              __a			      =						parser.parse_args(lowerCamelCase_     )
          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(**lowerCamelCase_     )
      __a			      =						main(lowerCamelCase_     )
      assert (
          getattr(model.hparams						,  """lr_scheduler"""     ) == supported_param
      ), F"""lr_scheduler={supported_param} shouldn't fail"""
