






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[int]							=  torch.cuda.is_available()
__UpperCamelCase   :						Dict							=  [""" Sam ate lunch today.""", """Sams lunch ingredients."""]
__UpperCamelCase   :						Union[str, Any]							=  ["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
__UpperCamelCase   :						Optional[Any]							=  {
    """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        a_	( _A ,				_A					)     ->     List[str]:







				"""simple docstring"""
				snake_case__													=       '\n'.join(_A					)
				Path(_A					).open('w'					).writelines(_A					)
def        a_	( _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 lowerCAmelCase_	(       cls: Tuple  )						->						Optional[int]:
											return cls
							@require_torch_multi_gpu
							def lowerCAmelCase_	(       self: int  )						->						int:
											snake_case__													=       {
											    '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 lowerCAmelCase_	(       self: int    ,		UpperCamelCase: Optional[int]    ,		UpperCamelCase: Tuple=True  )						->						Any:
											snake_case__													=       {
											    '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  )
											snake_case__													=       CHEAP_ARGS.copy()
											snake_case__													=       make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()  )
											snake_case__													=       self.get_auto_remove_tmp_dir()
											args_d.update(data_dir=UpperCamelCase    ,		output_dir=UpperCamelCase    ,		**UpperCamelCase  )

											def convert(UpperCamelCase: Optional[int]    ,		UpperCamelCase: List[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}'''

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

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

											snake_case__													=       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  )
											snake_case__													=       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  )
											snake_case__													=       int(args_d['max_epochs'] * (1 / args_d['val_check_interval']) / 2 + 1  )
											self.assertEqual(len(metrics['val']  )    ,		UpperCamelCase  )












import unittest

import numpy as np

from transformers import ElectraConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
							from transformers.models.electra.modeling_flax_electra import (
							    FlaxElectraForCausalLM,
							    FlaxElectraForMaskedLM,
							    FlaxElectraForMultipleChoice,
							    FlaxElectraForPreTraining,
							    FlaxElectraForQuestionAnswering,
							    FlaxElectraForSequenceClassification,
							    FlaxElectraForTokenClassification,
							    FlaxElectraModel,
							)


class 					__SCREAMING_SNAKE_CASE(		unittest.TestCase		):
							def __init__(       self: int    ,		UpperCamelCase: List[str]    ,		UpperCamelCase: Dict=13    ,		UpperCamelCase: Dict=7    ,		UpperCamelCase: Union[str, Any]=True    ,		UpperCamelCase: List[Any]=True    ,		UpperCamelCase: Optional[int]=True    ,		UpperCamelCase: Union[str, Any]=True    ,		UpperCamelCase: Optional[int]=99    ,		UpperCamelCase: Optional[int]=24    ,		UpperCamelCase: Any=32    ,		UpperCamelCase: Any=5    ,		UpperCamelCase: Any=4    ,		UpperCamelCase: int=37    ,		UpperCamelCase: Union[str, Any]="gelu"    ,		UpperCamelCase: str=0.1    ,		UpperCamelCase: str=0.1    ,		UpperCamelCase: int=5_12    ,		UpperCamelCase: Tuple=16    ,		UpperCamelCase: Dict=2    ,		UpperCamelCase: Any=0.02    ,		UpperCamelCase: List[Any]=4    ,		)						->						int:
											snake_case__													=       parent
											snake_case__													=       batch_size
											snake_case__													=       seq_length
											snake_case__													=       is_training
											snake_case__													=       use_attention_mask
											snake_case__													=       use_token_type_ids
											snake_case__													=       use_labels
											snake_case__													=       vocab_size
											snake_case__													=       hidden_size
											snake_case__													=       embedding_size
											snake_case__													=       num_hidden_layers
											snake_case__													=       num_attention_heads
											snake_case__													=       intermediate_size
											snake_case__													=       hidden_act
											snake_case__													=       hidden_dropout_prob
											snake_case__													=       attention_probs_dropout_prob
											snake_case__													=       max_position_embeddings
											snake_case__													=       type_vocab_size
											snake_case__													=       type_sequence_label_size
											snake_case__													=       initializer_range
											snake_case__													=       num_choices
							def lowerCAmelCase_	(       self: List[str]  )						->						List[str]:
											snake_case__													=       ids_tensor([self.batch_size, self.seq_length]    ,		self.vocab_size  )

											snake_case__													=       None
											if self.use_attention_mask:
															snake_case__													=       random_attention_mask([self.batch_size, self.seq_length]  )

											snake_case__													=       None
											if self.use_token_type_ids:
															snake_case__													=       ids_tensor([self.batch_size, self.seq_length]    ,		self.type_vocab_size  )

											snake_case__													=       ElectraConfig(
											    vocab_size=self.vocab_size    ,		hidden_size=self.hidden_size    ,		embedding_size=self.embedding_size    ,		num_hidden_layers=self.num_hidden_layers    ,		num_attention_heads=self.num_attention_heads    ,		intermediate_size=self.intermediate_size    ,		hidden_act=self.hidden_act    ,		hidden_dropout_prob=self.hidden_dropout_prob    ,		attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,		max_position_embeddings=self.max_position_embeddings    ,		type_vocab_size=self.type_vocab_size    ,		initializer_range=self.initializer_range    ,		)

											return config, input_ids, token_type_ids, attention_mask


							def lowerCAmelCase_	(       self: Tuple  )						->						Any:
											snake_case__													=       self.prepare_config_and_inputs()
											snake_case__							,  snake_case__							,  snake_case__							,  snake_case__													=       config_and_inputs
											snake_case__													=       {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
											return config, inputs_dict


@require_flax
class 					__SCREAMING_SNAKE_CASE(		a_      ,					unittest.TestCase		):
							_UpperCAmelCase	      =    True

							_UpperCAmelCase	      =    (
							    (
							        FlaxElectraModel,
							        FlaxElectraForCausalLM,
							        FlaxElectraForMaskedLM,
							        FlaxElectraForPreTraining,
							        FlaxElectraForTokenClassification,
							        FlaxElectraForQuestionAnswering,
							        FlaxElectraForMultipleChoice,
							        FlaxElectraForSequenceClassification,
							    )
							    if is_flax_available()
							    else ()
							)
							def lowerCAmelCase_	(       self: List[Any]  )						->						Dict:
											snake_case__													=       FlaxElectraModelTester(self  )


							@slow
							def lowerCAmelCase_	(       self: int  )						->						Optional[int]:
											for model_class_name in self.all_model_classes:
															if model_class_name == FlaxElectraForMaskedLM:
																			snake_case__													=       model_class_name.from_pretrained('google/electra-small-generator'  )
															else:
																			snake_case__													=       model_class_name.from_pretrained('google/electra-small-discriminator'  )
															snake_case__													=       model(np.ones((1, 1)  )  )
															self.assertIsNotNone(UpperCamelCase  )




