

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


UpperCamelCase__		   =    torch.cuda.is_available()
UpperCamelCase__		   =    [''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
UpperCamelCase__		   =    ['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
UpperCamelCase__		   =    {
    '''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						:Union[str, Any]  ,__A						:Union[str, Any]		)     ->   Union[str, Any]:


       """simple docstring"""




       __snake_case					  =       """\n""".join(snake_case__		)
       Path(snake_case__		).open("""w"""		).writelines(snake_case__		)







def 	lowerCamelCase__			(   __A						:int		)     ->   List[str]:


       """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 						__snake_case						(							UpperCamelCase_     ):
       """simple docstring"""







       @classmethod
       def  a		(		cls      )			->       List[str]:






              """simple docstring"""


              return cls


       @require_torch_multi_gpu
       def  a		(		self      )			->       str:






              """simple docstring"""


              __snake_case					  =       {
                  """no_teacher""": True,
                  """freeze_encoder""": True,
                  """gpus""": 2,
                  """overwrite_output_dir""": True,
                  """sortish_sampler""": True,
              }
              self._test_distiller_cli_fork(__SCREAMING_SNAKE_CASE			,   check_contents=__SCREAMING_SNAKE_CASE      )






       def  a		(		self			,   _UpperCamelCase			,   _UpperCamelCase=True      )			->       str:






              """simple docstring"""


              __snake_case					  =       {
                  """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(__SCREAMING_SNAKE_CASE      )
              __snake_case					  =       CHEAP_ARGS.copy()
              __snake_case					  =       make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )
              __snake_case					  =       self.get_auto_remove_tmp_dir()
              args_d.update(data_dir=__SCREAMING_SNAKE_CASE			,   output_dir=__SCREAMING_SNAKE_CASE			,   **__SCREAMING_SNAKE_CASE      )

              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}'

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

              __snake_case					  =       os.listdir(__SCREAMING_SNAKE_CASE      )
              __snake_case					  =       {os.path.basename(__SCREAMING_SNAKE_CASE      ) for p in contents}
              __snake_case					  =       [p for p in contents if p.endswith("""ckpt"""      )]
              assert len(__SCREAMING_SNAKE_CASE      ) > 0

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

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

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




import functools
import operator

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


UpperCamelCase__		   =    logging.get_logger(__name__)

UpperCamelCase__		   =    {
    '''facebook/hubert-base-ls960''': '''https://huggingface.co/facebook/hubert-base-ls960/resolve/main/config.json''',
    # See all Hubert models at https://huggingface.co/models?filter=hubert
}

class 						__snake_case						(							snake_case__     ):
       """simple docstring"""







       __SCREAMING_SNAKE_CASE													=       "hubert"


       def __init__(		self			,   _UpperCamelCase=32			,   _UpperCamelCase=7_68			,   _UpperCamelCase=12			,   _UpperCamelCase=12			,   _UpperCamelCase=30_72			,   _UpperCamelCase="gelu"			,   _UpperCamelCase=0.1			,   _UpperCamelCase=0.1			,   _UpperCamelCase=0.1			,   _UpperCamelCase=True			,   _UpperCamelCase=0.0			,   _UpperCamelCase=0.1			,   _UpperCamelCase=0.1			,   _UpperCamelCase=0.02			,   _UpperCamelCase=1E-5			,   _UpperCamelCase="group"			,   _UpperCamelCase="gelu"			,   _UpperCamelCase=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12)			,   _UpperCamelCase=(5, 2, 2, 2, 2, 2, 2)			,   _UpperCamelCase=(10, 3, 3, 3, 3, 2, 2)			,   _UpperCamelCase=False			,   _UpperCamelCase=1_28			,   _UpperCamelCase=16			,   _UpperCamelCase=False			,   _UpperCamelCase=True			,   _UpperCamelCase=0.05			,   _UpperCamelCase=10			,   _UpperCamelCase=2			,   _UpperCamelCase=0.0			,   _UpperCamelCase=10			,   _UpperCamelCase=0			,   _UpperCamelCase="sum"			,   _UpperCamelCase=False			,   _UpperCamelCase=False			,   _UpperCamelCase=2_56			,   _UpperCamelCase=0			,   _UpperCamelCase=1			,   _UpperCamelCase=2			,   **_UpperCamelCase			,   )			->       Any:






              """simple docstring"""


              super().__init__(**_UpperCamelCase			,   pad_token_id=_UpperCamelCase			,   bos_token_id=_UpperCamelCase			,   eos_token_id=_UpperCamelCase      )
              __snake_case					  =       hidden_size
              __snake_case					  =       feat_extract_norm
              __snake_case					  =       feat_extract_activation
              __snake_case					  =       list(_UpperCamelCase      )
              __snake_case					  =       list(_UpperCamelCase      )
              __snake_case					  =       list(_UpperCamelCase      )
              __snake_case					  =       conv_bias
              __snake_case					  =       num_conv_pos_embeddings
              __snake_case					  =       num_conv_pos_embedding_groups
              __snake_case					  =       len(self.conv_dim      )
              __snake_case					  =       num_hidden_layers
              __snake_case					  =       intermediate_size
              __snake_case					  =       hidden_act
              __snake_case					  =       num_attention_heads
              __snake_case					  =       hidden_dropout
              __snake_case					  =       attention_dropout
              __snake_case					  =       activation_dropout
              __snake_case					  =       feat_proj_layer_norm
              __snake_case					  =       feat_proj_dropout
              __snake_case					  =       final_dropout
              __snake_case					  =       layerdrop
              __snake_case					  =       layer_norm_eps
              __snake_case					  =       initializer_range
              __snake_case					  =       vocab_size
              __snake_case					  =       do_stable_layer_norm
              __snake_case					  =       use_weighted_layer_sum
              __snake_case					  =       classifier_proj_size

              if (
                  (len(self.conv_stride      ) != self.num_feat_extract_layers)
                  or (len(self.conv_kernel      ) != self.num_feat_extract_layers)
                  or (len(self.conv_dim      ) != self.num_feat_extract_layers)
              ):
                     raise ValueError(
                         """Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="""
                         """ `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="""
                         F' {len(self.conv_dim      )}`, `len(config.conv_stride) = {len(self.conv_stride      )}`,'
                         F' `len(config.conv_kernel) = {len(self.conv_kernel      )}`.'      )

              # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
              __snake_case					  =       apply_spec_augment
              __snake_case					  =       mask_time_prob
              __snake_case					  =       mask_time_length
              __snake_case					  =       mask_time_min_masks
              __snake_case					  =       mask_feature_prob
              __snake_case					  =       mask_feature_length
              __snake_case					  =       mask_feature_min_masks

              # ctc loss
              __snake_case					  =       ctc_loss_reduction
              __snake_case					  =       ctc_zero_infinity






       @property
       def  a		(		self      )			->       Union[str, Any]:






              """simple docstring"""


              return functools.reduce(operator.mul			,   self.conv_stride			,   1      )

