'''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


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







     """simple docstring"""
     a					:							Union[str, Any]							= '\n'.join(snake_case  )
     Path(snake_case  ).open('w'  ).writelines(snake_case  )






def SCREAMING_SNAKE_CASE__  (				snake_case       :				List[Any]  )   ->				Dict:







     """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   UpperCamelCase					(       a_       ):







    """simple docstring"""





    @classmethod
    def    SCREAMING_SNAKE_CASE_     (					cls     :						str):


         """simple docstring"""







         return cls





    @require_torch_multi_gpu
    def    SCREAMING_SNAKE_CASE_     (					self     :						List[Any]):


         """simple docstring"""







         a					:							List[Any]							= {
             '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    SCREAMING_SNAKE_CASE_     (					self     :						Dict							,							UpperCAmelCase_     :						Dict							,							UpperCAmelCase_     :						Any=True):


         """simple docstring"""







         a					:							Optional[Any]							= {
             '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_)
         a					:							dict							= CHEAP_ARGS.copy()
         a					:							List[str]							= make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
         a					:							Any							= self.get_auto_remove_tmp_dir()
         args_d.update(data_dir=UpperCAmelCase_							,							output_dir=UpperCAmelCase_							,							**UpperCAmelCase_)

         def convert(UpperCAmelCase_     :						int							,							UpperCAmelCase_     :						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}"""

         a					:							Any							= [x for x in (convert(UpperCAmelCase_							,							UpperCAmelCase_) for k, v in args_d.items()) if len(UpperCAmelCase_)]
         a					:							str							= [sys.executable, f"""{self.test_file_dir}/distillation.py"""] + cli_args
         execute_subprocess_async(UpperCAmelCase_							,							env=self.get_env())

         a					:							Optional[int]							= os.listdir(UpperCAmelCase_)
         a					:							Dict							= {os.path.basename(UpperCAmelCase_) for p in contents}
         a					:							Tuple							= [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)
         a					:							Dict							= os.path.join(UpperCAmelCase_							,							'metrics.json')
         a					:							List[Any]							= 'rouge2'

         a					:							Dict							= 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_)
         a					:							List[Any]							= 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)
         a					:							List[str]							= int(args_d['max_epochs'] * (1 / args_d['val_check_interval']) / 2 + 1)
         self.assertEqual(len(metrics['val'])							,							UpperCAmelCase_)


'''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__)

UpperCamelCase       :     Tuple			=    {
    """google/long-t5-local-base""": """https://huggingface.co/google/long-t5-local-base/blob/main/config.json""",
    """google/long-t5-local-large""": """https://huggingface.co/google/long-t5-local-large/blob/main/config.json""",
    """google/long-t5-tglobal-base""": """https://huggingface.co/google/long-t5-tglobal-base/blob/main/config.json""",
    """google/long-t5-tglobal-large""": """https://huggingface.co/google/long-t5-tglobal-large/blob/main/config.json""",
}



class   UpperCamelCase					(       a_       ):







    """simple docstring"""





    A     :    Union[str, Any]				=			"longt5"
    A     :    List[str]				=			["past_key_values"]
    A     :    Dict				=			{"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}





    def __init__(					self     :						int							,							UpperCAmelCase_     :						Union[str, Any]=3_2_1_2_8							,							UpperCAmelCase_     :						List[Any]=5_1_2							,							UpperCAmelCase_     :						List[Any]=6_4							,							UpperCAmelCase_     :						List[str]=2_0_4_8							,							UpperCAmelCase_     :						List[str]=6							,							UpperCAmelCase_     :						Tuple=None							,							UpperCAmelCase_     :						Tuple=8							,							UpperCAmelCase_     :						str=1_2_7							,							UpperCAmelCase_     :						Dict=1_6							,							UpperCAmelCase_     :						Optional[int]=3_2							,							UpperCAmelCase_     :						Union[str, Any]=1_2_8							,							UpperCAmelCase_     :						str=0.1							,							UpperCAmelCase_     :						Optional[int]=1e-6							,							UpperCAmelCase_     :						Tuple=1.0							,							UpperCAmelCase_     :						str="relu"							,							UpperCAmelCase_     :						str=True							,							UpperCAmelCase_     :						Dict="local"							,							UpperCAmelCase_     :						Optional[int]=True							,							UpperCAmelCase_     :						Dict=0							,							UpperCAmelCase_     :						Union[str, Any]=1							,							**UpperCAmelCase_     :						Any							,							):


         """simple docstring"""







         a					:							Optional[int]							= vocab_size
         a					:							Tuple							= d_model
         a					:							str							= d_kv
         a					:							str							= d_ff
         a					:							Union[str, Any]							= num_layers
         # default = symmetry
         a					:							Optional[Any]							= num_decoder_layers if num_decoder_layers is not None else self.num_layers
         a					:							List[Any]							= num_heads
         a					:							List[str]							= local_radius
         a					:							Optional[Any]							= global_block_size
         a					:							Optional[int]							= relative_attention_num_buckets
         a					:							List[Any]							= relative_attention_max_distance
         a					:							int							= dropout_rate
         a					:							List[str]							= layer_norm_epsilon
         a					:							Optional[Any]							= initializer_factor
         a					:							List[Any]							= feed_forward_proj
         a					:							Dict							= encoder_attention_type
         a					:							int							= use_cache

         a					:							Optional[Any]							= self.feed_forward_proj.split('-')
         a					:							List[Any]							= act_info[-1]
         a					:							str							= act_info[0] == 'gated'

         if len(UpperCAmelCase_) > 1 and act_info[0] != "gated" or len(UpperCAmelCase_) > 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":
              a					:							Any							= 'gelu_new'

         super().__init__(
             pad_token_id=UpperCAmelCase_							,							eos_token_id=UpperCAmelCase_							,							is_encoder_decoder=UpperCAmelCase_							,							**UpperCAmelCase_							,							)






class   UpperCamelCase					(       a_       ):







    """simple docstring"""





    @property
    def    SCREAMING_SNAKE_CASE_     (					self     :						Union[str, Any]):


         """simple docstring"""







         a					:							List[Any]							= {
             'input_ids': {0: 'batch', 1: 'encoder_sequence'},
             'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
         }
         if self.use_past:
              a					:							List[str]							= 'past_encoder_sequence + sequence'
              a					:							str							= {0: 'batch'}
              a					:							List[Any]							= {0: 'batch', 1: 'past_decoder_sequence + sequence'}
         else:
              a					:							Optional[Any]							= {0: 'batch', 1: 'decoder_sequence'}
              a					:							str							= {0: 'batch', 1: 'decoder_sequence'}

         if self.use_past:
              self.fill_with_past_key_values_(UpperCAmelCase_							,							direction='inputs')

         return common_inputs







    @property
    def    SCREAMING_SNAKE_CASE_     (					self     :						Union[str, Any]):


         """simple docstring"""







         return 1_3

