



'''simple docstring'''





import os
import sys
from pathlib import Path

import torch

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, require_torch_multi_gpu
from utils import load_json


lowercase_			=  torch.cuda.is_available()
lowercase_			=  [" Sam ate lunch today.", "Sams lunch ingredients."]
lowercase_			=  ["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
lowercase_			=  {
    "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 lowerCAmelCase  (__A  ,     __A):



     """simple docstring"""



     _a     =  '''\n'''.join(__A)
     Path(__A).open('''w''').writelines(__A)




def lowerCAmelCase  (__A):



     """simple docstring"""



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






class __A      (  A						):



 '''simple docstring'''







 @classmethod
 def  a__  (cls	)   ->					List[Any]:


      """simple docstring"""



      return cls



 @require_torch_multi_gpu
 def  a__  (self	)   ->					Tuple:


      """simple docstring"""



      _a     =  {
          '''no_teacher''': True,
          '''freeze_encoder''': True,
          '''gpus''': 2,
          '''overwrite_output_dir''': True,
          '''sortish_sampler''': True,
      }
      self._test_distiller_cli_fork(A			,       check_contents=A	)



 def  a__  (self			,       A			,       A=True	)   ->					Union[str, Any]:


      """simple docstring"""



      _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(A	)
      _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=A			,       output_dir=A			,       **A	)

      def convert(A			,       A	):
           if k in ["tgt_suffix", "server_ip", "server_port", "out", "n_tpu_cores"]:
                return ""
           if v is False or v is None:
                return ""
           if v is True:  # or len(str(v))==0:
                return f'''--{k}'''
           return f'''--{k}={v}'''

      _a     =  [x for x in (convert(A			,       A	) for k, v in args_d.items()) if len(A	)]
      _a     =  [sys.executable, f'''{self.test_file_dir}/distillation.py'''] + cli_args
      execute_subprocess_async(A			,       env=self.get_env()	)

      _a     =  os.listdir(A	)
      _a     =  {os.path.basename(A	) for p in contents}
      _a     =  [p for p in contents if p.endswith('''ckpt'''	)]
      assert len(A	) > 0

      self.assertIn('''test_generations.txt'''			,       A	)
      self.assertIn('''test_results.txt'''			,       A	)

      # get the following from the module, (we don't have access to `model` here)
      _a     =  os.path.join(A			,       '''metrics.json'''	)
      _a     =  '''rouge2'''

      _a     =  load_json(A	)
      # {'test': [{'test_avg_loss': 10.63731575012207, 'test_avg_rouge1': 0.0, 'test_avg_rouge2': 0.0, 'test_avg_rougeL': 0.0, 'test_avg_gen_time': 0.1822289228439331, 'test_avg_gen_len': 142.0, 'step_count': 1}]}
      print(A	)
      _a     =  metrics['''val'''][-1]
      self.assertGreaterEqual(last_step_stats['''val_avg_gen_time''']			,       0.01	)
      self.assertIsInstance(last_step_stats[f'''val_avg_{val_metric}''']			,       A	)
      self.assertEqual(len(metrics['''test''']	)			,       1	)
      _a     =  int(args_d['''max_epochs'''] * (1 / args_d['''val_check_interval''']) / 2 + 1	)
      self.assertEqual(len(metrics['''val''']	)			,       A	)








'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


lowercase_			=  {
    "configuration_segformer": ["SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "SegformerConfig", "SegformerOnnxConfig"]
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_			=  ["SegformerFeatureExtractor"]
  lowercase_			=  ["SegformerImageProcessor"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_			=  [
      "SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "SegformerDecodeHead",
      "SegformerForImageClassification",
      "SegformerForSemanticSegmentation",
      "SegformerLayer",
      "SegformerModel",
      "SegformerPreTrainedModel",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_			=  [
      "TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFSegformerDecodeHead",
      "TFSegformerForImageClassification",
      "TFSegformerForSemanticSegmentation",
      "TFSegformerModel",
      "TFSegformerPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_segformer import SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SegformerConfig, SegformerOnnxConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_segformer import SegformerFeatureExtractor
    from .image_processing_segformer import SegformerImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_segformer import (
        SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        SegformerDecodeHead,
        SegformerForImageClassification,
        SegformerForSemanticSegmentation,
        SegformerLayer,
        SegformerModel,
        SegformerPreTrainedModel,
    )
  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_segformer import (
        TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFSegformerDecodeHead,
        TFSegformerForImageClassification,
        TFSegformerForSemanticSegmentation,
        TFSegformerModel,
        TFSegformerPreTrainedModel,
    )

else:
  import sys

  lowercase_			=  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



