

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_       :				List[Any]		   =      [''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
UpperCAmelCase_       :				Optional[Any]		   =      ['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
UpperCAmelCase_       :				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        __SCREAMING_SNAKE_CASE    (      a__						:      Path ,a__						:      list					)				->							List[Any]:
	__A   : Tuple								= """\n""".join(a__					)
	Path(a__					).open("""w"""					).writelines(a__					)
def        __SCREAMING_SNAKE_CASE    (      a__						:      Tuple					)				->							Union[str, Any]:
	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     lowerCamelCase_			(  _lowercase  ):




	@classmethod
	def 					lowerCAmelCase_					(    cls	:    Optional[Any]       ):
		return cls




	@require_torch_multi_gpu
	def 					lowerCAmelCase_					(    self	:    Optional[Any]       ):
		__A   : Any								= {
		    """no_teacher""": True,
		    """freeze_encoder""": True,
		    """gpus""": 2,
		    """overwrite_output_dir""": True,
		    """sortish_sampler""": True,
		}
		self._test_distiller_cli_fork(__A				,			check_contents=__A       )




	def 					lowerCAmelCase_					(    self	:    Tuple				,			__A	:    List[str]				,			__A	:    Optional[int]=True       ):
		__A   : List[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(__A       )
		__A   : dict								= CHEAP_ARGS.copy()
		__A   : int								= make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
		__A   : int								= self.get_auto_remove_tmp_dir()
		args_d.update(data_dir=__A				,			output_dir=__A				,			**__A       )

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

		__A   : Union[str, Any]								= os.listdir(__A       )
		__A   : Optional[Any]								= {os.path.basename(__A       ) for p in contents}
		__A   : Optional[int]								= [p for p in contents if p.endswith("""ckpt"""       )]
		assert len(__A       ) > 0

		self.assertIn("""test_generations.txt"""				,			__A       )
		self.assertIn("""test_results.txt"""				,			__A       )

		# get the following from the module, (we don't have access to `model` here)
		__A   : Dict								= os.path.join(__A				,			"""metrics.json"""       )
		__A   : Optional[int]								= """rouge2"""

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








import os
import tempfile
import unittest
from typing import List

from transformers.models.esm.tokenization_esm import VOCAB_FILES_NAMES, EsmTokenizer
from transformers.testing_utils import require_tokenizers
from transformers.tokenization_utils import PreTrainedTokenizer
from transformers.tokenization_utils_base import PreTrainedTokenizerBase






@require_tokenizers
class     lowerCamelCase_			(  unittest.TestCase  ):
	_lowercase		:      List[str]    				=							EsmTokenizer




	def 					lowerCAmelCase_					(    self	:    List[Any]       ):
		super().setUp()
		__A   : Dict								= tempfile.mkdtemp()
		# fmt: off
		__A   : List[str]								= ["<cls>", "<pad>", "<eos>", "<unk>", "L", "A", "G", "V", "S", "E", "R", "T", "I", "D", "P", "K", "Q", "N", "F", "Y", "M", "H", "W", "C", "X", "B", "U", "Z", "O", ".", "-", "<null_1>", "<mask>"]  # noqa: E501
		# fmt: on
		__A   : int								= os.path.join(self.tmpdirname				,			VOCAB_FILES_NAMES["""vocab_file"""]       )
		with open(self.vocab_file				,			"""w"""				,			encoding="""utf-8"""       ) as vocab_writer:
			vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]       )       )




	def 					lowerCAmelCase_					(    self	:    Dict				,			**__A	:    int       ):
		return [self.get_tokenizer(**__A       )]




	def 					lowerCAmelCase_					(    self	:    Optional[int]				,			**__A	:    List[Any]       ):
		return self.tokenizer_class.from_pretrained(self.tmpdirname				,			**__A       )




	def 					lowerCAmelCase_					(    self	:    List[str]       ):
		__A   : Union[str, Any]								= self.tokenizer_class(self.vocab_file       )

		__A   : int								= tokenizer.tokenize("""LAGVS"""       )
		self.assertListEqual(__A				,			["""L""", """A""", """G""", """V""", """S"""]       )
		self.assertListEqual(tokenizer.convert_tokens_to_ids(__A       )				,			[4, 5, 6, 7, 8]       )




	def 					lowerCAmelCase_					(    self	:    str       ):
		__A   : Union[str, Any]								= self.tokenizer_class(self.vocab_file       )

		__A   : int								= """LAGVS"""
		self.assertListEqual(tokenizer.encode(__A       )				,			[0, 4, 5, 6, 7, 8, 2]       )




	def 					lowerCAmelCase_					(    self	:    Dict       ):
		__A   : str								= self.tokenizer_class(self.vocab_file       )

		__A   : Tuple								= ["""LAGVS""", """WCB"""]
		__A   : str								= tokenizer(__A				,			padding=__A       )["""input_ids"""]

		self.assertListEqual(__A				,			[[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2]]       )




	def 					lowerCAmelCase_					(    self	:    List[Any]       ):
		__A   : Dict								= self.tokenizer_class(self.vocab_file       )

		__A   : Any								= ["""LAGVS""", """WCB"""]
		__A   : str								= tokenizer(__A				,			padding=__A       )["""input_ids"""]

		self.assertListEqual(__A				,			[[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2, 1, 1]]       )




	def 					lowerCAmelCase_					(    self	:    List[str]       ):
		__A   : Optional[int]								= self.get_tokenizers(fast=__A       )
		for tokenizer in tokenizers:
			with self.subTest(F"""{tokenizer.__class__.__name__}"""       ):
				__A   : Any								= """<unk>"""
				__A   : str								= """<mask>"""

				__A   : Optional[Any]								= tokenizer.tokenize(__A       )
				__A   : str								= tokenizer.tokenize(__A       )

				self.assertEqual(len(__A       )				,			1       )
				self.assertEqual(len(__A       )				,			1       )
				self.assertEqual(token_a[0]				,			__A       )
				self.assertEqual(token_a[0]				,			__A       )





