

'''simple docstring'''







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_a      :				Tuple											=					logging.get_logger(__name__)

_a      :				str											=					{
    """google/mobilebert-uncased""": """https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json"""
}





class 					_UpperCAmelCase							(		lowerCAmelCase_     ):
		a     :     List[Any]     						=MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
		a     :     List[Any]     						="""mobilebert"""
		def __init__(						self,__SCREAMING_SNAKE_CASE=3_05_22,__SCREAMING_SNAKE_CASE=5_12,__SCREAMING_SNAKE_CASE=24,__SCREAMING_SNAKE_CASE=4,__SCREAMING_SNAKE_CASE=5_12,__SCREAMING_SNAKE_CASE="relu",__SCREAMING_SNAKE_CASE=0.0,__SCREAMING_SNAKE_CASE=0.1,__SCREAMING_SNAKE_CASE=5_12,__SCREAMING_SNAKE_CASE=2,__SCREAMING_SNAKE_CASE=0.02,__SCREAMING_SNAKE_CASE=1e-12,__SCREAMING_SNAKE_CASE=0,__SCREAMING_SNAKE_CASE=1_28,__SCREAMING_SNAKE_CASE=True,__SCREAMING_SNAKE_CASE=True,__SCREAMING_SNAKE_CASE=1_28,__SCREAMING_SNAKE_CASE=False,__SCREAMING_SNAKE_CASE=True,__SCREAMING_SNAKE_CASE=4,__SCREAMING_SNAKE_CASE="no_norm",__SCREAMING_SNAKE_CASE=True,__SCREAMING_SNAKE_CASE=None,**__SCREAMING_SNAKE_CASE,):



					'''simple docstring'''

					super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE    )

					__lowerCAmelCase      		=       vocab_size
					__lowerCAmelCase      		=       hidden_size
					__lowerCAmelCase      		=       num_hidden_layers
					__lowerCAmelCase      		=       num_attention_heads
					__lowerCAmelCase      		=       hidden_act
					__lowerCAmelCase      		=       intermediate_size
					__lowerCAmelCase      		=       hidden_dropout_prob
					__lowerCAmelCase      		=       attention_probs_dropout_prob
					__lowerCAmelCase      		=       max_position_embeddings
					__lowerCAmelCase      		=       type_vocab_size
					__lowerCAmelCase      		=       initializer_range
					__lowerCAmelCase      		=       layer_norm_eps
					__lowerCAmelCase      		=       embedding_size
					__lowerCAmelCase      		=       trigram_input
					__lowerCAmelCase      		=       use_bottleneck
					__lowerCAmelCase      		=       intra_bottleneck_size
					__lowerCAmelCase      		=       use_bottleneck_attention
					__lowerCAmelCase      		=       key_query_shared_bottleneck
					__lowerCAmelCase      		=       num_feedforward_networks
					__lowerCAmelCase      		=       normalization_type
					__lowerCAmelCase      		=       classifier_activation

					if self.use_bottleneck:
								__lowerCAmelCase      		=       intra_bottleneck_size
					else:
								__lowerCAmelCase      		=       hidden_size

					__lowerCAmelCase      		=       classifier_dropout





class 					_UpperCAmelCase							(		lowerCAmelCase_     ):
		@property
		def 						lowerCamelCase__    (						self    ):



					'''simple docstring'''

					if self.task == "multiple-choice":
								__lowerCAmelCase      		=       {0: """batch""", 1: """choice""", 2: """sequence"""}
					else:
								__lowerCAmelCase      		=       {0: """batch""", 1: """sequence"""}
					return OrderedDict(
					    [
					        ("""input_ids""", dynamic_axis),
					        ("""attention_mask""", dynamic_axis),
					        ("""token_type_ids""", dynamic_axis),
					    ]    )








'''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      :				Dict											=					[""" Sam ate lunch today.""", """Sams lunch ingredients."""]
_a      :				str											=					["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
_a      :				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""": 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      _lowerCAmelCase		(		lowercase      ,			lowercase			)      ->      Any:
			__lowerCAmelCase      		=       """\n""".join(lowercase			)
			Path(lowercase			).open("""w"""			).writelines(lowercase			)





def      _lowerCAmelCase		(		lowercase			)      ->      Optional[int]:
			for split in ["train", "val", "test"]:
						_dump_articles(os.path.join(lowercase      ,			f'{split}.source'			)      ,			lowercase			)
						_dump_articles(os.path.join(lowercase      ,			f'{split}.target'			)      ,			lowercase			)
			return tmp_dir







class 					_UpperCAmelCase							(		lowerCAmelCase_     ):
		@classmethod
		def 						lowerCamelCase__    (						cls    ):



					'''simple docstring'''

					return cls
		@require_torch_multi_gpu
		def 						lowerCamelCase__    (						self    ):



					'''simple docstring'''

					__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 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=True    ):



					'''simple docstring'''

					__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"""],0.01    )
					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    )

