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


lowercase__    :       Tuple						=			torch.cuda.is_available()
lowercase__    :       Union[str, Any]						=			[''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
lowercase__    :       List[Any]						=			['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
lowercase__    :       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''': 4_2,
    '''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''': 1_2,
    '''max_target_length''': 1_2,
    '''val_max_target_length''': 1_2,
    '''test_max_target_length''': 1_2,
    '''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__     ,						snake_case__							)  ->			Union[str, Any]:
							lowerCAmelCase				=	'''\n'''.join(snake_case__							)
							Path(snake_case__							).open('''w'''							).writelines(snake_case__							)

def      SCREAMING_SNAKE_CASE_  (		snake_case__							)  ->			List[str]:
							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      lowercase_						(						UpperCamelCase_		):



	"""simple docstring"""





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


	@require_torch_multi_gpu
	def     SCREAMING_SNAKE_CASE_       (							self       )		->str:
								lowerCAmelCase				=	{
								    '''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     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE=True       )		->str:
								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(__SCREAMING_SNAKE_CASE       )
								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=__SCREAMING_SNAKE_CASE	,    output_dir=__SCREAMING_SNAKE_CASE	,    **__SCREAMING_SNAKE_CASE       )

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

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

								lowerCAmelCase				=	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       )
								lowerCAmelCase				=	metrics['''val'''][-1]
								self.assertGreaterEqual(last_step_stats['''val_avg_gen_time''']	,    0.0_1       )
								self.assertIsInstance(last_step_stats[F"val_avg_{val_metric}"]	,    __SCREAMING_SNAKE_CASE       )
								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''']       )	,    __SCREAMING_SNAKE_CASE       )




from typing import Callable, Optional, Union

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


lowercase__    :       Optional[Any]						=			logging.get_logger(__name__)

lowercase__    :       Union[str, Any]						=			{
    '''microsoft/prophetnet-large-uncased''': (
        '''https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json'''
    ),
}




class      lowercase_						(						UpperCamelCase_		):



	"""simple docstring"""





	UpperCAmelCase_						:      Optional[int]     =       """prophetnet"""
	UpperCAmelCase_						:      Optional[int]     =       ["""past_key_values"""]
	UpperCAmelCase_						:      Any     =       {
	    """num_attention_heads""": """num_encoder_attention_heads""",
	}


	def __init__(							self	,    __SCREAMING_SNAKE_CASE = 0.1	,    __SCREAMING_SNAKE_CASE = "gelu"	,    __SCREAMING_SNAKE_CASE = 30522	,    __SCREAMING_SNAKE_CASE = 1024	,    __SCREAMING_SNAKE_CASE = 4096	,    __SCREAMING_SNAKE_CASE = 12	,    __SCREAMING_SNAKE_CASE = 16	,    __SCREAMING_SNAKE_CASE = 4096	,    __SCREAMING_SNAKE_CASE = 12	,    __SCREAMING_SNAKE_CASE = 16	,    __SCREAMING_SNAKE_CASE = 0.1	,    __SCREAMING_SNAKE_CASE = 0.1	,    __SCREAMING_SNAKE_CASE = 512	,    __SCREAMING_SNAKE_CASE = 0.0_2	,    __SCREAMING_SNAKE_CASE = True	,    __SCREAMING_SNAKE_CASE = True	,    __SCREAMING_SNAKE_CASE = 0	,    __SCREAMING_SNAKE_CASE = 2	,    __SCREAMING_SNAKE_CASE = 32	,    __SCREAMING_SNAKE_CASE = 128	,    __SCREAMING_SNAKE_CASE = False	,    __SCREAMING_SNAKE_CASE = 0.0	,    __SCREAMING_SNAKE_CASE = True	,    __SCREAMING_SNAKE_CASE = 0	,    __SCREAMING_SNAKE_CASE = 1	,    __SCREAMING_SNAKE_CASE = 2	,    **__SCREAMING_SNAKE_CASE	,    )		->int:
								lowerCAmelCase				=	vocab_size
								lowerCAmelCase				=	hidden_size
								lowerCAmelCase				=	encoder_ffn_dim
								lowerCAmelCase				=	num_encoder_layers
								lowerCAmelCase				=	num_encoder_attention_heads
								lowerCAmelCase				=	decoder_ffn_dim
								lowerCAmelCase				=	num_decoder_layers
								lowerCAmelCase				=	num_decoder_attention_heads
								lowerCAmelCase				=	max_position_embeddings
								lowerCAmelCase				=	init_std  # Normal(0, this parameter)
								lowerCAmelCase				=	activation_function

								# parameters for prophetnet
								lowerCAmelCase				=	ngram
								lowerCAmelCase				=	num_buckets
								lowerCAmelCase				=	relative_max_distance
								lowerCAmelCase				=	disable_ngram_loss
								lowerCAmelCase				=	eps

								# 3 Types of Dropout
								lowerCAmelCase				=	attention_dropout
								lowerCAmelCase				=	activation_dropout
								lowerCAmelCase				=	dropout

								lowerCAmelCase				=	use_cache

								super().__init__(
								    pad_token_id=__SCREAMING_SNAKE_CASE	,    bos_token_id=__SCREAMING_SNAKE_CASE	,    eos_token_id=__SCREAMING_SNAKE_CASE	,    is_encoder_decoder=__SCREAMING_SNAKE_CASE	,    add_cross_attention=__SCREAMING_SNAKE_CASE	,    decoder_start_token_id=__SCREAMING_SNAKE_CASE	,    **__SCREAMING_SNAKE_CASE	,    )


	@property
	def     SCREAMING_SNAKE_CASE_       (							self       )		->int:
								return self.num_encoder_layers + self.num_decoder_layers




	@num_hidden_layers.setter
	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE       )		->Union[str, Any]:
								raise NotImplementedError(
								    '''This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'''
								    ''' `num_decoder_layers`.'''       )



