
'''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       =      torch.cuda.is_available()
__A       =      [" Sam ate lunch today.", "Sams lunch ingredients."]
__A       =      ["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
__A       =      {
    "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-0_8,
    "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						(							lowercase__						,						lowercase__		):
						lowercase__								=    """\n""".join(lowercase__		)
						Path(lowercase__		).open("""w"""		).writelines(lowercase__		)





def 	_A						(							lowercase__		):
						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      A							(    __UpperCAmelCase			):



			@classmethod
			def A__	(   cls    )     ->						Any:




									'''simple docstring'''



									return cls



			@require_torch_multi_gpu
			def A__	(   self    )     ->						Any:




									'''simple docstring'''



									lowercase__								=    {
									    """no_teacher""": True,
									    """freeze_encoder""": True,
									    """gpus""": 2,
									    """overwrite_output_dir""": True,
									    """sortish_sampler""": True,
									}
									self._test_distiller_cli_fork(lowerCamelCase__    ,       check_contents=lowerCamelCase__    )



			def A__	(   self    ,       lowerCamelCase__    ,       lowerCamelCase__=True    )     ->						Optional[Any]:




									'''simple docstring'''



									lowercase__								=    {
									    """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(lowerCamelCase__    )
									lowercase__								=    CHEAP_ARGS.copy()
									lowercase__								=    make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()    )
									lowercase__								=    self.get_auto_remove_tmp_dir()
									args_d.update(data_dir=lowerCamelCase__    ,       output_dir=lowerCamelCase__    ,       **lowerCamelCase__    )

									def convert(lowerCamelCase__    ,       lowerCamelCase__    ):
															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}'''

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

									lowercase__								=    os.listdir(lowerCamelCase__    )
									lowercase__								=    {os.path.basename(lowerCamelCase__    ) for p in contents}
									lowercase__								=    [p for p in contents if p.endswith("""ckpt"""    )]
									assert len(lowerCamelCase__    ) > 0

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

									# get the following from the module, (we don't have access to `model` here)
									lowercase__								=    os.path.join(lowerCamelCase__    ,       """metrics.json"""    )
									lowercase__								=    """rouge2"""

									lowercase__								=    load_json(lowerCamelCase__    )
									# {'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(lowerCamelCase__    )
									lowercase__								=    metrics["""val"""][-1]
									self.assertGreaterEqual(last_step_stats["""val_avg_gen_time"""]    ,       0.01    )
									self.assertIsInstance(last_step_stats[F'''val_avg_{val_metric}''']    ,       lowerCamelCase__    )
									self.assertEqual(len(metrics["""test"""]    )    ,       1    )
									lowercase__								=    int(args_d["""max_epochs"""] * (1 / args_d["""val_check_interval"""]) / 2 + 1    )
									self.assertEqual(len(metrics["""val"""]    )    ,       lowerCamelCase__    )








'''simple docstring'''



import json
import os
import shutil
import tempfile
import unittest

from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor, WavaVecaProcessor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_wavaveca import floats_list





class      A							(    unittest.TestCase			):



			def A__	(   self    )     ->						Union[str, Any]:




									'''simple docstring'''



									lowercase__								=    """<pad> <s> </s> <unk> | E T A O N I H S R D L U M W C F G Y P B V K ' X J Q Z""".split(""" """    )
									lowercase__								=    dict(zip(lowerCamelCase__    ,       range(len(lowerCamelCase__    )    )    )    )

									lowercase__								=    {
									    """pad_token""": """<pad>""",
									    """unk_token""": """<unk>""",
									    """bos_token""": """<s>""",
									    """eos_token""": """</s>""",
									}
									lowercase__								=    {
									    """feature_size""": 1,
									    """padding_value""": 0.0,
									    """sampling_rate""": 16_000,
									    """return_attention_mask""": False,
									    """do_normalize""": True,
									}

									lowercase__								=    tempfile.mkdtemp()
									lowercase__								=    os.path.join(self.tmpdirname    ,       VOCAB_FILES_NAMES["""vocab_file"""]    )
									lowercase__								=    os.path.join(self.tmpdirname    ,       lowerCamelCase__    )
									with open(self.vocab_file    ,       """w"""    ,       encoding="""utf-8"""    ) as fp:
															fp.write(json.dumps(lowerCamelCase__    ) + """\n"""    )

									with open(self.feature_extraction_file    ,       """w"""    ,       encoding="""utf-8"""    ) as fp:
															fp.write(json.dumps(lowerCamelCase__    ) + """\n"""    )



			def A__	(   self    ,       **lowerCamelCase__    )     ->						int:




									'''simple docstring'''



									lowercase__								=    self.add_kwargs_tokens_map.copy()
									kwargs.update(lowerCamelCase__    )
									return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname    ,       **lowerCamelCase__    )



			def A__	(   self    ,       **lowerCamelCase__    )     ->						Dict:




									'''simple docstring'''



									return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname    ,       **lowerCamelCase__    )



			def A__	(   self    )     ->						Dict:




									'''simple docstring'''



									shutil.rmtree(self.tmpdirname    )



			def A__	(   self    )     ->						str:




									'''simple docstring'''



									lowercase__								=    self.get_tokenizer()
									lowercase__								=    self.get_feature_extractor()

									lowercase__								=    WavaVecaProcessor(tokenizer=lowerCamelCase__    ,       feature_extractor=lowerCamelCase__    )

									processor.save_pretrained(self.tmpdirname    )
									lowercase__								=    WavaVecaProcessor.from_pretrained(self.tmpdirname    )

									self.assertEqual(processor.tokenizer.get_vocab()    ,       tokenizer.get_vocab()    )
									self.assertIsInstance(processor.tokenizer    ,       lowerCamelCase__    )

									self.assertEqual(processor.feature_extractor.to_json_string()    ,       feature_extractor.to_json_string()    )
									self.assertIsInstance(processor.feature_extractor    ,       lowerCamelCase__    )



			def A__	(   self    )     ->						Tuple:




									'''simple docstring'''



									lowercase__								=    WavaVecaProcessor(tokenizer=self.get_tokenizer()    ,       feature_extractor=self.get_feature_extractor()    )
									processor.save_pretrained(self.tmpdirname    )

									lowercase__								=    self.get_tokenizer(bos_token="""(BOS)"""    ,       eos_token="""(EOS)"""    )
									lowercase__								=    self.get_feature_extractor(do_normalize=lowerCamelCase__    ,       padding_value=1.0    )

									lowercase__								=    WavaVecaProcessor.from_pretrained(
									    self.tmpdirname    ,       bos_token="""(BOS)"""    ,       eos_token="""(EOS)"""    ,       do_normalize=lowerCamelCase__    ,       padding_value=1.0    )

									self.assertEqual(processor.tokenizer.get_vocab()    ,       tokenizer_add_kwargs.get_vocab()    )
									self.assertIsInstance(processor.tokenizer    ,       lowerCamelCase__    )

									self.assertEqual(processor.feature_extractor.to_json_string()    ,       feature_extractor_add_kwargs.to_json_string()    )
									self.assertIsInstance(processor.feature_extractor    ,       lowerCamelCase__    )



			def A__	(   self    )     ->						List[str]:




									'''simple docstring'''



									lowercase__								=    self.get_feature_extractor()
									lowercase__								=    self.get_tokenizer()

									lowercase__								=    WavaVecaProcessor(tokenizer=lowerCamelCase__    ,       feature_extractor=lowerCamelCase__    )

									lowercase__								=    floats_list((3, 1_000)    )

									lowercase__								=    feature_extractor(lowerCamelCase__    ,       return_tensors="""np"""    )
									lowercase__								=    processor(lowerCamelCase__    ,       return_tensors="""np"""    )

									for key in input_feat_extract.keys():
															self.assertAlmostEqual(input_feat_extract[key].sum()    ,       input_processor[key].sum()    ,       delta=1e-2    )



			def A__	(   self    )     ->						Optional[Any]:




									'''simple docstring'''



									lowercase__								=    self.get_feature_extractor()
									lowercase__								=    self.get_tokenizer()

									lowercase__								=    WavaVecaProcessor(tokenizer=lowerCamelCase__    ,       feature_extractor=lowerCamelCase__    )

									lowercase__								=    """This is a test string"""

									lowercase__								=    processor(text=lowerCamelCase__    )

									lowercase__								=    tokenizer(lowerCamelCase__    )

									for key in encoded_tok.keys():
															self.assertListEqual(encoded_tok[key]    ,       encoded_processor[key]    )



			def A__	(   self    )     ->						List[Any]:




									'''simple docstring'''



									lowercase__								=    self.get_feature_extractor()
									lowercase__								=    self.get_tokenizer()

									lowercase__								=    WavaVecaProcessor(tokenizer=lowerCamelCase__    ,       feature_extractor=lowerCamelCase__    )

									lowercase__								=    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

									lowercase__								=    processor.batch_decode(lowerCamelCase__    )
									lowercase__								=    tokenizer.batch_decode(lowerCamelCase__    )

									self.assertListEqual(lowerCamelCase__    ,       lowerCamelCase__    )



			def A__	(   self    )     ->						List[str]:




									'''simple docstring'''



									lowercase__								=    self.get_feature_extractor()
									lowercase__								=    self.get_tokenizer()

									lowercase__								=    WavaVecaProcessor(tokenizer=lowerCamelCase__    ,       feature_extractor=lowerCamelCase__    )

									self.assertListEqual(
									    processor.model_input_names    ,       feature_extractor.model_input_names    ,       msg="""`processor` and `feature_extractor` model input names do not match"""    ,       )






