


'''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


a_  :    int      =			torch.cuda.is_available()
a_  :    List[Any]      =			[""" Sam ate lunch today.""", """Sams lunch ingredients."""]
a_  :    Optional[Any]      =			["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
a_  :    Any      =			{
    """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 __snake_case (				UpperCAmelCase_						:						List[str] ,    UpperCAmelCase_						:						Tuple		):
 lowerCamelCase_								=  "\n".join(__A		)
 Path(__A		).open("w"		).writelines(__A		)





def __snake_case (				UpperCAmelCase_						:						List[str]		):



 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 					snake_case   (   lowercase      ):





  """simple docstring"""




  @classmethod
  def   snake_case		(   cls					):







   """simple docstring"""
   return cls






  @require_torch_multi_gpu
  def   snake_case		(   self					):







   """simple docstring"""
   lowerCamelCase_								=  {
       "no_teacher": True,
       "freeze_encoder": True,
       "gpus": 2,
       "overwrite_output_dir": True,
       "sortish_sampler": True,
   }
   self._test_distiller_cli_fork(UpperCamelCase      ,  check_contents=UpperCamelCase					)







  def   snake_case		(   self      ,  UpperCamelCase      ,  UpperCamelCase=True					):







   """simple docstring"""
   lowerCamelCase_								=  {
       "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					)
   lowerCamelCase_								=  CHEAP_ARGS.copy()
   lowerCamelCase_								=  make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)
   lowerCamelCase_								=  self.get_auto_remove_tmp_dir()
   args_d.update(data_dir=UpperCamelCase      ,  output_dir=UpperCamelCase      ,  **UpperCamelCase					)

   def convert(UpperCamelCase      ,  UpperCamelCase					):
    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}'''

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

   lowerCamelCase_								=  os.listdir(UpperCamelCase					)
   lowerCamelCase_								=  {os.path.basename(UpperCamelCase					) for p in contents}
   lowerCamelCase_								=  [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					)

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

   lowerCamelCase_								=  load_json(UpperCamelCase					)
   # {'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(UpperCamelCase					)
   lowerCamelCase_								=  metrics["val"][-1]
   self.assertGreaterEqual(last_step_stats["val_avg_gen_time"]      ,  0.01					)
   self.assertIsInstance(last_step_stats[f'''val_avg_{val_metric}''']      ,  UpperCamelCase					)
   self.assertEqual(len(metrics["test"]					)      ,  1					)
   lowerCamelCase_								=  int(args_d["max_epochs"] * (1 / args_d["val_check_interval"]) / 2 + 1					)
   self.assertEqual(len(metrics["val"]					)      ,  UpperCamelCase					)







'''simple docstring'''

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a_  :    Union[str, Any]      =			logging.get_logger(__name__)

a_  :    int      =			{
    """uw-madison/nystromformer-512""": """https://huggingface.co/uw-madison/nystromformer-512/resolve/main/config.json""",
    # See all Nystromformer models at https://huggingface.co/models?filter=nystromformer
}





class 					snake_case   (   lowercase      ):





  """simple docstring"""




  _lowerCamelCase		   =  "nystromformer"






  def __init__(   self      ,  UpperCamelCase=3_0000      ,  UpperCamelCase=768      ,  UpperCamelCase=12      ,  UpperCamelCase=12      ,  UpperCamelCase=3072      ,  UpperCamelCase="gelu_new"      ,  UpperCamelCase=0.1      ,  UpperCamelCase=0.1      ,  UpperCamelCase=510      ,  UpperCamelCase=2      ,  UpperCamelCase=64      ,  UpperCamelCase=64      ,  UpperCamelCase=65      ,  UpperCamelCase=False      ,  UpperCamelCase=0.02      ,  UpperCamelCase=1e-5      ,  UpperCamelCase=1      ,  UpperCamelCase=0      ,  UpperCamelCase=2      ,  **UpperCamelCase      ,  ):







   """simple docstring"""
   lowerCamelCase_								=  vocab_size
   lowerCamelCase_								=  max_position_embeddings
   lowerCamelCase_								=  hidden_size
   lowerCamelCase_								=  num_hidden_layers
   lowerCamelCase_								=  num_attention_heads
   lowerCamelCase_								=  intermediate_size
   lowerCamelCase_								=  hidden_act
   lowerCamelCase_								=  hidden_dropout_prob
   lowerCamelCase_								=  attention_probs_dropout_prob
   lowerCamelCase_								=  initializer_range
   lowerCamelCase_								=  type_vocab_size
   lowerCamelCase_								=  segment_means_seq_len
   lowerCamelCase_								=  num_landmarks
   lowerCamelCase_								=  conv_kernel_size
   lowerCamelCase_								=  inv_coeff_init_option
   lowerCamelCase_								=  layer_norm_eps
   super().__init__(pad_token_id=UpperCamelCase      ,  bos_token_id=UpperCamelCase      ,  eos_token_id=UpperCamelCase      ,  **UpperCamelCase					)



