

"""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						:   str							   =	torch.cuda.is_available()
__UpperCamelCase						:   Union[str, Any]							   =	[''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
__UpperCamelCase						:   List[Any]							   =	['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
__UpperCamelCase						:   Dict							   =	{
    '''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						(   A_		,  A_				):
	lowerCAmelCase__			:		Any						       =			'''\n'''.join(A_				)
	Path(A_				).open('''w'''				).writelines(A_				)

def 						__SCREAMING_SNAKE_CASE						(   A_				):
	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_ ):






					"""simple docstring"""



					@classmethod
					def  __lowerCAmelCase				( cls    :       Union[str, Any]	):
						return cls







					@require_torch_multi_gpu
					def  __lowerCAmelCase				( self    :       str	):
						lowerCAmelCase__			:		List[str]						       =			{
						    '''no_teacher''': True,
						    '''freeze_encoder''': True,
						    '''gpus''': 2,
						    '''overwrite_output_dir''': True,
						    '''sortish_sampler''': True,
						}
						self._test_distiller_cli_fork(lowercase_     ,check_contents=lowercase_	)







					def  __lowerCAmelCase				( self    :       Tuple     ,lowercase_    :       Dict     ,lowercase_    :       List[str]=True	):
						lowerCAmelCase__			:		str						       =			{
						    '''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(lowercase_	)
						lowerCAmelCase__			:		dict						       =			CHEAP_ARGS.copy()
						lowerCAmelCase__			:		Tuple						       =			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()	)
						lowerCAmelCase__			:		Union[str, Any]						       =			self.get_auto_remove_tmp_dir()
						args_d.update(data_dir=lowercase_     ,output_dir=lowercase_     ,**lowercase_	)

						def convert(lowercase_    :       str     ,lowercase_    :       Optional[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__			:		List[str]						       =			[x for x in (convert(lowercase_     ,lowercase_	) for k, v in args_d.items()) if len(lowercase_	)]
						lowerCAmelCase__			:		Optional[Any]						       =			[sys.executable, F'{self.test_file_dir}/distillation.py'] + cli_args
						execute_subprocess_async(lowercase_     ,env=self.get_env()	)

						lowerCAmelCase__			:		Tuple						       =			os.listdir(lowercase_	)
						lowerCAmelCase__			:		List[Any]						       =			{os.path.basename(lowercase_	) for p in contents}
						lowerCAmelCase__			:		str						       =			[p for p in contents if p.endswith('''ckpt'''	)]
						assert len(lowercase_	) > 0

						self.assertIn('''test_generations.txt'''     ,lowercase_	)
						self.assertIn('''test_results.txt'''     ,lowercase_	)

						# get the following from the module, (we don't have access to `model` here)
						lowerCAmelCase__			:		List[str]						       =			os.path.join(lowercase_     ,'''metrics.json'''	)
						lowerCAmelCase__			:		Dict						       =			'''rouge2'''

						lowerCAmelCase__			:		Optional[Any]						       =			load_json(lowercase_	)
						# {'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(lowercase_	)
						lowerCAmelCase__			:		Union[str, 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}']     ,lowercase_	)
						self.assertEqual(len(metrics['''test''']	)     ,1	)
						lowerCAmelCase__			:		int						       =			int(args_d['''max_epochs'''] * (1 / args_d['''val_check_interval''']) / 2 + 1	)
						self.assertEqual(len(metrics['''val''']	)     ,lowercase_	)







"""simple docstring"""





from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


__UpperCamelCase						:   str							   =	logging.get_logger(__name__)

__UpperCamelCase						:   Union[str, Any]							   =	{
    '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/config.json''',
    '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/config.json''',
    '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/config.json''',
    '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/config.json''',
    '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/config.json''',
}







class        SCREAMING_SNAKE_CASE      (	a_ ):






					"""simple docstring"""



					lowercase__       				= "gpt2"
					lowercase__       				= ["past_key_values"]
					lowercase__       				= {
					    "hidden_size": "n_embd",
					    "max_position_embeddings": "n_positions",
					    "num_attention_heads": "n_head",
					    "num_hidden_layers": "n_layer",
					}







					def __init__( self    :       List[str]     ,lowercase_    :       Dict=5_0_2_5_7     ,lowercase_    :       Dict=1_0_2_4     ,lowercase_    :       Any=7_6_8     ,lowercase_    :       str=1_2     ,lowercase_    :       int=1_2     ,lowercase_    :       List[Any]=None     ,lowercase_    :       Dict="gelu_new"     ,lowercase_    :       Any=0.1     ,lowercase_    :       Optional[Any]=0.1     ,lowercase_    :       Tuple=0.1     ,lowercase_    :       Dict=1E-5     ,lowercase_    :       List[Any]=0.02     ,lowercase_    :       Tuple="cls_index"     ,lowercase_    :       Optional[int]=True     ,lowercase_    :       Union[str, Any]=None     ,lowercase_    :       Optional[Any]=True     ,lowercase_    :       List[Any]=0.1     ,lowercase_    :       List[str]=True     ,lowercase_    :       int=True     ,lowercase_    :       Optional[Any]=5_0_2_5_6     ,lowercase_    :       Dict=5_0_2_5_6     ,lowercase_    :       Union[str, Any]=False     ,lowercase_    :       Optional[Any]=False     ,**lowercase_    :       Dict     ,):
						lowerCAmelCase__			:		Tuple						       =			vocab_size
						lowerCAmelCase__			:		List[Any]						       =			n_positions
						lowerCAmelCase__			:		Optional[int]						       =			n_embd
						lowerCAmelCase__			:		Tuple						       =			n_layer
						lowerCAmelCase__			:		Optional[Any]						       =			n_head
						lowerCAmelCase__			:		List[Any]						       =			n_inner
						lowerCAmelCase__			:		Any						       =			activation_function
						lowerCAmelCase__			:		Optional[int]						       =			resid_pdrop
						lowerCAmelCase__			:		str						       =			embd_pdrop
						lowerCAmelCase__			:		Tuple						       =			attn_pdrop
						lowerCAmelCase__			:		List[str]						       =			layer_norm_epsilon
						lowerCAmelCase__			:		Union[str, Any]						       =			initializer_range
						lowerCAmelCase__			:		Optional[int]						       =			summary_type
						lowerCAmelCase__			:		Optional[Any]						       =			summary_use_proj
						lowerCAmelCase__			:		List[str]						       =			summary_activation
						lowerCAmelCase__			:		Optional[int]						       =			summary_first_dropout
						lowerCAmelCase__			:		Dict						       =			summary_proj_to_labels
						lowerCAmelCase__			:		Dict						       =			scale_attn_weights
						lowerCAmelCase__			:		Dict						       =			use_cache
						lowerCAmelCase__			:		List[Any]						       =			scale_attn_by_inverse_layer_idx
						lowerCAmelCase__			:		int						       =			reorder_and_upcast_attn

						lowerCAmelCase__			:		List[str]						       =			bos_token_id
						lowerCAmelCase__			:		List[str]						       =			eos_token_id

						super().__init__(bos_token_id=lowercase_     ,eos_token_id=lowercase_     ,**lowercase_	)







class        SCREAMING_SNAKE_CASE      (	a_ ):






					"""simple docstring"""



					def __init__( self    :       Optional[int]     ,lowercase_    :       PretrainedConfig     ,lowercase_    :       str = "default"     ,lowercase_    :       List[PatchingSpec] = None     ,lowercase_    :       bool = False     ,):
						super().__init__(lowercase_     ,task=lowercase_     ,patching_specs=lowercase_     ,use_past=lowercase_	)
						if not getattr(self._config     ,'''pad_token_id'''     ,lowercase_	):
							# TODO: how to do that better?
							lowerCAmelCase__			:		int						       =			0







					@property
					def  __lowerCAmelCase				( self    :       List[str]	):
						lowerCAmelCase__			:		Any						       =			OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}	)
						if self.use_past:
							self.fill_with_past_key_values_(lowercase_     ,direction='''inputs'''	)
							lowerCAmelCase__			:		str						       =			{0: '''batch''', 1: '''past_sequence + sequence'''}
						else:
							lowerCAmelCase__			:		List[str]						       =			{0: '''batch''', 1: '''sequence'''}

						return common_inputs







					@property
					def  __lowerCAmelCase				( self    :       Any	):
						return self._config.n_layer







					@property
					def  __lowerCAmelCase				( self    :       List[str]	):
						return self._config.n_head







					def  __lowerCAmelCase				( self    :       Union[str, Any]     ,lowercase_    :       PreTrainedTokenizer     ,lowercase_    :       int = -1     ,lowercase_    :       int = -1     ,lowercase_    :       bool = False     ,lowercase_    :       Optional[TensorType] = None     ,):
						lowerCAmelCase__			:		Any						       =			super(lowercase_     ,self	).generate_dummy_inputs(
						    lowercase_     ,batch_size=lowercase_     ,seq_length=lowercase_     ,is_pair=lowercase_     ,framework=lowercase_	)

						# We need to order the input in the way they appears in the forward()
						lowerCAmelCase__			:		List[Any]						       =			OrderedDict({'''input_ids''': common_inputs['''input_ids''']}	)

						# Need to add the past_keys
						if self.use_past:
							if not is_torch_available():
								raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''	)
							else:
								import torch

								lowerCAmelCase__       ,lowerCAmelCase__			:		List[Any]						       =			common_inputs['''input_ids'''].shape
								# Not using the same length for past_key_values
								lowerCAmelCase__			:		Dict						       =			seqlen + 2
								lowerCAmelCase__			:		Optional[Any]						       =			(
								    batch,
								    self.num_attention_heads,
								    past_key_values_length,
								    self._config.hidden_size // self.num_attention_heads,
								)
								lowerCAmelCase__			:		Tuple						       =			[
								    (torch.zeros(lowercase_	), torch.zeros(lowercase_	)) for _ in range(self.num_layers	)
								]

						lowerCAmelCase__			:		Any						       =			common_inputs['''attention_mask''']
						if self.use_past:
							lowerCAmelCase__			:		Optional[Any]						       =			ordered_inputs['''attention_mask'''].dtype
							lowerCAmelCase__			:		Union[str, Any]						       =			torch.cat(
							    [ordered_inputs['''attention_mask'''], torch.ones(lowercase_     ,lowercase_     ,dtype=lowercase_	)]     ,dim=1	)

						return ordered_inputs







					@property
					def  __lowerCAmelCase				( self    :       str	):
						return 1_3




