




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


__SCREAMING_SNAKE_CASE			:      Tuple      			=torch.cuda.is_available()
__SCREAMING_SNAKE_CASE			:      str      			=[""" Sam ate lunch today.""", """Sams lunch ingredients."""]
__SCREAMING_SNAKE_CASE			:      List[str]      			=["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
__SCREAMING_SNAKE_CASE			:      Optional[int]      			={
    """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-0_8,
    """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_	(     lowerCamelCase:				Path   , lowerCamelCase:				list  )    ->	int:


      '''simple docstring'''




      lowerCamelCase__					     = '\n'.join(lowerCamelCase  )
      Path(lowerCamelCase  ).open('w'  ).writelines(lowerCamelCase  )

def 	lowerCAmelCase_	(     lowerCamelCase:				Dict  )    ->	Dict:


      '''simple docstring'''




      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	(						_lowerCAmelCase						):
     """simple docstring"""

     @classmethod
     def        __snake_case      (			cls     :      List[str]	) ->				Dict:
           return cls




     @require_torch_multi_gpu
     def        __snake_case      (			self     :      Optional[Any]	) ->				Tuple:
           lowerCamelCase__					     = {
               '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        __snake_case      (			self     :      Optional[int]		,     A__     :      List[Any]		,     A__     :      List[str]=True	) ->				List[Any]:
           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(A__	)
           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=A__		,     output_dir=A__		,     **A__	)

           def convert(A__     :      Any		,     A__     :      Any	):
                 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(A__		,     A__	) for k, v in args_d.items()) if len(A__	)]
           lowerCamelCase__					     = [sys.executable, F"""{self.test_file_dir}/distillation.py"""] + cli_args
           execute_subprocess_async(A__		,     env=self.get_env()	)

           lowerCamelCase__					     = os.listdir(A__	)
           lowerCamelCase__					     = {os.path.basename(A__	) for p in contents}
           lowerCamelCase__					     = [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)
           lowerCamelCase__					     = os.path.join(A__		,     'metrics.json'	)
           lowerCamelCase__					     = 'rouge2'

           lowerCamelCase__					     = 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__	)
           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}"""]		,     A__	)
           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']	)		,     A__	)

'''simple docstring'''



from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


UpperCamelCase__					:						Any											=     logging.get_logger(__name__)

class       _a (_lowerCamelCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE					=     'mt5'
       SCREAMING_SNAKE_CASE					=     ['past_key_values']






       def __init__(							self				,							A__=25_01_12				,							A__=5_12				,							A__=64				,							A__=10_24				,							A__=8				,							A__=None				,							A__=6				,							A__=32				,							A__=1_28				,							A__=0.1				,							A__=1E-6				,							A__=1.0				,							A__="gated-gelu"				,							A__=True				,							A__=True				,							A__="T5Tokenizer"				,							A__=False				,							A__=0				,							A__=1				,							A__=0				,							**A__				,							)			->	Union[str, Any]:
          super().__init__(
              is_encoder_decoder=A__				,							tokenizer_class=A__				,							tie_word_embeddings=A__				,							pad_token_id=A__				,							eos_token_id=A__				,							decoder_start_token_id=A__				,							**A__				,							)
          _SCREAMING_SNAKE_CASE          = vocab_size
          _SCREAMING_SNAKE_CASE          = d_model
          _SCREAMING_SNAKE_CASE          = d_kv
          _SCREAMING_SNAKE_CASE          = d_ff
          _SCREAMING_SNAKE_CASE          = num_layers
          _SCREAMING_SNAKE_CASE          = (
              num_decoder_layers if num_decoder_layers is not None else self.num_layers
          )  # default = symmetry
          _SCREAMING_SNAKE_CASE          = num_heads
          _SCREAMING_SNAKE_CASE          = relative_attention_num_buckets
          _SCREAMING_SNAKE_CASE          = relative_attention_max_distance
          _SCREAMING_SNAKE_CASE          = dropout_rate
          _SCREAMING_SNAKE_CASE          = layer_norm_epsilon
          _SCREAMING_SNAKE_CASE          = initializer_factor
          _SCREAMING_SNAKE_CASE          = feed_forward_proj
          _SCREAMING_SNAKE_CASE          = use_cache

          _SCREAMING_SNAKE_CASE          = self.feed_forward_proj.split("""-"""					)
          _SCREAMING_SNAKE_CASE          = act_info[-1]
          _SCREAMING_SNAKE_CASE          = act_info[0] == """gated"""

          if len(A__					) > 1 and act_info[0] != "gated" or len(A__					) > 2:
             raise ValueError(
                 F"`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."
                 """Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. """
                 """'gated-gelu' or 'relu'"""					)

          # for backwards compatibility
          if feed_forward_proj == "gated-gelu":
             _SCREAMING_SNAKE_CASE          = """gelu_new"""






       @property
       def 				UpperCamelCase (							self					)			->	int:
          return self.d_model






       @property
       def 				UpperCamelCase (							self					)			->	Optional[Any]:
          return self.num_heads






       @property
       def 				UpperCamelCase (							self					)			->	Dict:
          return self.num_layers






class       _a (_lowerCamelCase):





       """simple docstring"""




       @property
       # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
       def 				UpperCamelCase (							self					)			->	Mapping[str, Mapping[int, str]]:
          _SCREAMING_SNAKE_CASE          = {
              """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
              """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
          }
          if self.use_past:
             _SCREAMING_SNAKE_CASE          = """past_encoder_sequence + sequence"""
             _SCREAMING_SNAKE_CASE          = {0: """batch"""}
             _SCREAMING_SNAKE_CASE          = {0: """batch""", 1: """past_decoder_sequence + sequence"""}
          else:
             _SCREAMING_SNAKE_CASE          = {0: """batch""", 1: """decoder_sequence"""}
             _SCREAMING_SNAKE_CASE          = {0: """batch""", 1: """decoder_sequence"""}

          if self.use_past:
             self.fill_with_past_key_values_(A__				,							direction="""inputs"""					)

          return common_inputs






       @property
       # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
       def 				UpperCamelCase (							self					)			->	int:
          return 13






       @property
       def 				UpperCamelCase (							self					)			->	float:
          return 5E-4






