




'''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							:      Optional[Any] 	=       torch.cuda.is_available()
A							:      int 	=       [""" Sam ate lunch today.""", """Sams lunch ingredients."""]
A							:      int 	=       ["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
A							:      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-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_  (   a__		:	Path    ,a__		:	list			):



    """simple docstring"""




    __lowercase							=						"""\n""".join(a__			)
    Path(a__			).open("""w"""			).writelines(a__			)
def 				snake_case_  (   a__		:	List[str]			):



    """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        SCREAMING_SNAKE_CASE(				__A     ):

       @classmethod
       def     snake_case__			(    cls     )    ->					Optional[int]:

           """simple docstring"""



           return cls

       @require_torch_multi_gpu
       def     snake_case__			(    self     )    ->					int:

           """simple docstring"""



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




       def     snake_case__			(    self					,		lowerCamelCase__					,		lowerCamelCase__=True     )    ->					Dict:

           """simple docstring"""



           __lowercase							=						{
               """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__     )
           __lowercase							=						CHEAP_ARGS.copy()
           __lowercase							=						make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     )
           __lowercase							=						self.get_auto_remove_tmp_dir()
           args_d.update(data_dir=lowerCamelCase__					,		output_dir=lowerCamelCase__					,		**lowerCamelCase__     )

           def convert(lowerCamelCase__					,		lowerCamelCase__     ):
               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}'

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

           __lowercase							=						os.listdir(lowerCamelCase__     )
           __lowercase							=						{os.path.basename(lowerCamelCase__     ) for p in contents}
           __lowercase							=						[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__     )

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

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










'''simple docstring'''
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import (
    TaBlock,
    TaConfig,
    TaLayerNorm,
)

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin






class        SCREAMING_SNAKE_CASE(				__A   ,      __A   ,      __A     ):

       @register_to_config
       def __init__(    self					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__ = False					,		)    ->					Optional[int]:

           """simple docstring"""



           super().__init__()

           __lowercase							=						nn.Linear(lowerCamelCase__					,		lowerCamelCase__					,		bias=lowerCamelCase__     )

           __lowercase							=						nn.Embedding(lowerCamelCase__					,		lowerCamelCase__     )
           __lowercase							=						False

           __lowercase							=						nn.Dropout(p=lowerCamelCase__     )

           __lowercase							=						TaConfig(
               d_model=lowerCamelCase__					,		num_heads=lowerCamelCase__					,		d_kv=lowerCamelCase__					,		d_ff=lowerCamelCase__					,		feed_forward_proj=lowerCamelCase__					,		dropout_rate=lowerCamelCase__					,		is_decoder=lowerCamelCase__					,		is_encoder_decoder=lowerCamelCase__					,		)
           __lowercase							=						nn.ModuleList()
           for lyr_num in range(lowerCamelCase__     ):
               __lowercase							=						TaBlock(lowerCamelCase__     )
               self.encoders.append(lowerCamelCase__     )

           __lowercase							=						TaLayerNorm(lowerCamelCase__     )
           __lowercase							=						nn.Dropout(p=lowerCamelCase__     )




       def     snake_case__			(    self					,		lowerCamelCase__					,		lowerCamelCase__     )    ->					str:

           """simple docstring"""



           __lowercase							=						self.input_proj(lowerCamelCase__     )

           # terminal relative positional encodings
           __lowercase							=						encoder_inputs.shape[1]
           __lowercase							=						torch.arange(lowerCamelCase__					,		device=encoder_inputs.device     )

           __lowercase							=						encoder_inputs_mask.sum(-1     )
           __lowercase							=						torch.roll(input_positions.unsqueeze(0     )					,		tuple(seq_lens.tolist()     )					,		dims=0     )
           x += self.position_encoding(lowerCamelCase__     )

           __lowercase							=						self.dropout_pre(lowerCamelCase__     )

           # inverted the attention mask
           __lowercase							=						encoder_inputs.size()
           __lowercase							=						self.get_extended_attention_mask(lowerCamelCase__					,		lowerCamelCase__     )

           for lyr in self.encoders:
               __lowercase							=						lyr(lowerCamelCase__					,		lowerCamelCase__     )[0]
           __lowercase							=						self.layer_norm(lowerCamelCase__     )

           return self.dropout_post(lowerCamelCase__     ), encoder_inputs_mask




