





'''simple docstring'''






import unittest

from transformers import YosoConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import (
         YosoForMaskedLM,
         YosoForMultipleChoice,
         YosoForQuestionAnswering,
         YosoForSequenceClassification,
         YosoForTokenClassification,
         YosoModel,
     )
     from transformers.models.yoso.modeling_yoso import YOSO_PRETRAINED_MODEL_ARCHIVE_LIST


class 	_lowercase    :


   def __init__(			self     , _UpperCAmelCase     , _UpperCAmelCase=13     , _UpperCAmelCase=7     , _UpperCAmelCase=True     , _UpperCAmelCase=True     , _UpperCAmelCase=True     , _UpperCAmelCase=True     , _UpperCAmelCase=99     , _UpperCAmelCase=32     , _UpperCAmelCase=5     , _UpperCAmelCase=4     , _UpperCAmelCase=37     , _UpperCAmelCase="gelu"     , _UpperCAmelCase=0.1     , _UpperCAmelCase=0.1     , _UpperCAmelCase=512     , _UpperCAmelCase=16     , _UpperCAmelCase=2     , _UpperCAmelCase=0.02     , _UpperCAmelCase=3     , _UpperCAmelCase=4     , _UpperCAmelCase=None     , ):
       A							:							int				=    parent
       A							:							str				=    batch_size
       A							:							Any				=    seq_length
       A							:							Optional[Any]				=    is_training
       A							:							Any				=    use_input_mask
       A							:							str				=    use_token_type_ids
       A							:							List[str]				=    use_labels
       A							:							Optional[Any]				=    vocab_size
       A							:							Union[str, Any]				=    hidden_size
       A							:							Any				=    num_hidden_layers
       A							:							List[str]				=    num_attention_heads
       A							:							Optional[int]				=    intermediate_size
       A							:							Optional[int]				=    hidden_act
       A							:							Any				=    hidden_dropout_prob
       A							:							Tuple				=    attention_probs_dropout_prob
       A							:							str				=    max_position_embeddings
       A							:							Any				=    type_vocab_size
       A							:							int				=    type_sequence_label_size
       A							:							Optional[int]				=    initializer_range
       A							:							Tuple				=    num_labels
       A							:							str				=    num_choices
       A							:							Any				=    scope


   def        snake_case					(			self       ):
       A							:							Union[str, Any]				=    ids_tensor([self.batch_size, self.seq_length]     , self.vocab_size       )

       A							:							Optional[int]				=    None
       if self.use_input_mask:
           A							:							Dict				=    random_attention_mask([self.batch_size, self.seq_length]       )

       A							:							Tuple				=    None
       if self.use_token_type_ids:
           A							:							List[Any]				=    ids_tensor([self.batch_size, self.seq_length]     , self.type_vocab_size       )

       A							:							Any				=    None
       A							:							int				=    None
       A							:							Any				=    None
       if self.use_labels:
           A							:							str				=    ids_tensor([self.batch_size]     , self.type_sequence_label_size       )
           A							:							List[str]				=    ids_tensor([self.batch_size, self.seq_length]     , self.num_labels       )
           A							:							Optional[int]				=    ids_tensor([self.batch_size]     , self.num_choices       )

       A							:							List[str]				=    self.get_config()

       return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels


   def        snake_case					(			self       ):
       return YosoConfig(
           vocab_size=self.vocab_size     , hidden_size=self.hidden_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     , is_decoder=_UpperCAmelCase     , initializer_range=self.initializer_range     , )


   def        snake_case					(			self       ):
       A							:							Optional[int]				=    self.get_config()
       A							:							Tuple				=    300
       return config


   def        snake_case					(			self       ):
       (
           (
           A
       ),						(
           A
       ),						(
           A
       ),						(
           A
       ),						(
           A
       ),						(
           A
       ),						(
           A
       ),						
       )							:							Optional[Any]				=    self.prepare_config_and_inputs()

       A							:							List[str]				=    True
       A							:							int				=    floats_tensor([self.batch_size, self.seq_length, self.hidden_size]       )
       A							:							Dict				=    ids_tensor([self.batch_size, self.seq_length]     , vocab_size=2       )

       return (
           config,
           input_ids,
           token_type_ids,
           input_mask,
           sequence_labels,
           token_labels,
           choice_labels,
           encoder_hidden_states,
           encoder_attention_mask,
       )


   def        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase       ):
       A							:							List[Any]				=    YosoModel(config=_UpperCAmelCase       )
       model.to(_UpperCAmelCase       )
       model.eval()
       A							:							List[str]				=    model(_UpperCAmelCase     , attention_mask=_UpperCAmelCase     , token_type_ids=_UpperCAmelCase       )
       A							:							List[str]				=    model(_UpperCAmelCase     , token_type_ids=_UpperCAmelCase       )
       A							:							Any				=    model(_UpperCAmelCase       )
       self.parent.assertEqual(result.last_hidden_state.shape     , (self.batch_size, self.seq_length, self.hidden_size)       )


   def        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase     , ):
       A							:							str				=    True
       A							:							Optional[Any]				=    YosoModel(_UpperCAmelCase       )
       model.to(_UpperCAmelCase       )
       model.eval()
       A							:							List[Any]				=    model(
           _UpperCAmelCase     , attention_mask=_UpperCAmelCase     , token_type_ids=_UpperCAmelCase     , encoder_hidden_states=_UpperCAmelCase     , encoder_attention_mask=_UpperCAmelCase     , )
       A							:							Dict				=    model(
           _UpperCAmelCase     , attention_mask=_UpperCAmelCase     , token_type_ids=_UpperCAmelCase     , encoder_hidden_states=_UpperCAmelCase     , )
       A							:							Any				=    model(_UpperCAmelCase     , attention_mask=_UpperCAmelCase     , token_type_ids=_UpperCAmelCase       )
       self.parent.assertEqual(result.last_hidden_state.shape     , (self.batch_size, self.seq_length, self.hidden_size)       )


   def        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase       ):
       A							:							List[Any]				=    YosoForMaskedLM(config=_UpperCAmelCase       )
       model.to(_UpperCAmelCase       )
       model.eval()
       A							:							List[Any]				=    model(_UpperCAmelCase     , attention_mask=_UpperCAmelCase     , token_type_ids=_UpperCAmelCase     , labels=_UpperCAmelCase       )
       self.parent.assertEqual(result.logits.shape     , (self.batch_size, self.seq_length, self.vocab_size)       )


   def        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase       ):
       A							:							Union[str, Any]				=    YosoForQuestionAnswering(config=_UpperCAmelCase       )
       model.to(_UpperCAmelCase       )
       model.eval()
       A							:							Tuple				=    model(
           _UpperCAmelCase     , attention_mask=_UpperCAmelCase     , token_type_ids=_UpperCAmelCase     , start_positions=_UpperCAmelCase     , end_positions=_UpperCAmelCase     , )
       self.parent.assertEqual(result.start_logits.shape     , (self.batch_size, self.seq_length)       )
       self.parent.assertEqual(result.end_logits.shape     , (self.batch_size, self.seq_length)       )


   def        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase       ):
       A							:							List[Any]				=    self.num_labels
       A							:							Optional[int]				=    YosoForSequenceClassification(_UpperCAmelCase       )
       model.to(_UpperCAmelCase       )
       model.eval()
       A							:							Any				=    model(_UpperCAmelCase     , attention_mask=_UpperCAmelCase     , token_type_ids=_UpperCAmelCase     , labels=_UpperCAmelCase       )
       self.parent.assertEqual(result.logits.shape     , (self.batch_size, self.num_labels)       )


   def        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase       ):
       A							:							List[Any]				=    self.num_labels
       A							:							str				=    YosoForTokenClassification(config=_UpperCAmelCase       )
       model.to(_UpperCAmelCase       )
       model.eval()
       A							:							Optional[Any]				=    model(_UpperCAmelCase     , attention_mask=_UpperCAmelCase     , token_type_ids=_UpperCAmelCase     , labels=_UpperCAmelCase       )
       self.parent.assertEqual(result.logits.shape     , (self.batch_size, self.seq_length, self.num_labels)       )


   def        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase       ):
       A							:							List[Any]				=    self.num_choices
       A							:							str				=    YosoForMultipleChoice(config=_UpperCAmelCase       )
       model.to(_UpperCAmelCase       )
       model.eval()
       A							:							Any				=    input_ids.unsqueeze(1       ).expand(-1     , self.num_choices     , -1       ).contiguous()
       A							:							int				=    token_type_ids.unsqueeze(1       ).expand(-1     , self.num_choices     , -1       ).contiguous()
       A							:							List[Any]				=    input_mask.unsqueeze(1       ).expand(-1     , self.num_choices     , -1       ).contiguous()
       A							:							Tuple				=    model(
           _UpperCAmelCase     , attention_mask=_UpperCAmelCase     , token_type_ids=_UpperCAmelCase     , labels=_UpperCAmelCase     , )
       self.parent.assertEqual(result.logits.shape     , (self.batch_size, self.num_choices)       )


   def        snake_case					(			self       ):
       A							:							List[str]				=    self.prepare_config_and_inputs()
       (
           (
           A
       ),						(
           A
       ),						(
           A
       ),						(
           A
       ),						(
           A
       ),						(
           A
       ),						(
           A
       ),						
       )							:							List[Any]				=    config_and_inputs
       A							:							str				=    {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
       return config, inputs_dict







@require_torch
class 	_lowercase    (				a		,       a		,       unittest.TestCase					):
   _UpperCamelCase									= (
       (
           YosoModel,
           YosoForMaskedLM,
           YosoForMultipleChoice,
           YosoForQuestionAnswering,
           YosoForSequenceClassification,
           YosoForTokenClassification,
       )
       if is_torch_available()
       else ()
   )
   _UpperCamelCase									= False
   _UpperCamelCase									= False
   _UpperCamelCase									= False

   _UpperCamelCase									= ()
   _UpperCamelCase									= (
       {
           """feature-extraction""": YosoModel,
           """fill-mask""": YosoForMaskedLM,
           """question-answering""": YosoForQuestionAnswering,
           """text-classification""": YosoForSequenceClassification,
           """token-classification""": YosoForTokenClassification,
           """zero-shot""": YosoForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )


   def        snake_case					(			self       ):
       A							:							Tuple				=    YosoModelTester(self       )
       A							:							str				=    ConfigTester(self     , config_class=_UpperCAmelCase     , hidden_size=37       )


   def        snake_case					(			self       ):
       self.config_tester.run_common_tests()


   def        snake_case					(			self       ):
       A							:							int				=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_UpperCAmelCase       )


   def        snake_case					(			self       ):
       A							:							Union[str, Any]				=    self.model_tester.prepare_config_and_inputs()
       for type in ["absolute", "relative_key", "relative_key_query"]:
           A							:							List[str]				=    type
           self.model_tester.create_and_check_model(*_UpperCAmelCase       )


   def        snake_case					(			self       ):
       A							:							Dict				=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_lm(*_UpperCAmelCase       )


   def        snake_case					(			self       ):
       A							:							List[Any]				=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_multiple_choice(*_UpperCAmelCase       )


   def        snake_case					(			self       ):
       A							:							Any				=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_question_answering(*_UpperCAmelCase       )


   def        snake_case					(			self       ):
       A							:							Optional[Any]				=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_sequence_classification(*_UpperCAmelCase       )


   def        snake_case					(			self       ):
       A							:							List[Any]				=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_token_classification(*_UpperCAmelCase       )


   @slow
   def        snake_case					(			self       ):
       for model_name in YOSO_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           A							:							str				=    YosoModel.from_pretrained(_UpperCAmelCase       )
           self.assertIsNotNone(_UpperCAmelCase       )


   def        snake_case					(			self       ):
       return







@require_torch
class 	_lowercase    (				unittest.TestCase					):


   @slow
   def        snake_case					(			self       ):
       A							:							int				=    YosoModel.from_pretrained('''uw-madison/yoso-4096'''       )
       A							:							Optional[int]				=    torch.tensor([[0, 1, 2, 3, 4, 5]]       )

       with torch.no_grad():
           A							:							List[str]				=    model(_UpperCAmelCase       )[0]

       A							:							Tuple				=    torch.Size((1, 6, 768)       )
       self.assertEqual(output.shape     , _UpperCAmelCase       )

       A							:							str				=    torch.tensor(
           [[[-0.0611, 0.1242, 0.0840], [0.0280, -0.0048, 0.1125], [0.0106, 0.0226, 0.0751]]]       )

       self.assertTrue(torch.allclose(output[:, :3, :3]     , _UpperCAmelCase     , atol=1E-4       )       )


   @slow
   def        snake_case					(			self       ):
       A							:							Tuple				=    YosoForMaskedLM.from_pretrained('''uw-madison/yoso-4096'''       )
       A							:							str				=    torch.tensor([[0, 1, 2, 3, 4, 5]]       )

       with torch.no_grad():
           A							:							int				=    model(_UpperCAmelCase       )[0]

       A							:							Optional[int]				=    50_265

       A							:							int				=    torch.Size((1, 6, vocab_size)       )
       self.assertEqual(output.shape     , _UpperCAmelCase       )

       A							:							List[str]				=    torch.tensor(
           [[[-2.1313, -3.7285, -2.2407], [-2.7047, -3.3314, -2.6408], [0.0629, -2.5166, -0.3356]]]       )

       self.assertTrue(torch.allclose(output[:, :3, :3]     , _UpperCAmelCase     , atol=1E-4       )       )


   @slow
   def        snake_case					(			self       ):
       A							:							Optional[Any]				=    YosoForMaskedLM.from_pretrained('''uw-madison/yoso-4096'''       )
       A							:							List[str]				=    torch.arange(4_096       ).unsqueeze(0       )

       with torch.no_grad():
           A							:							Dict				=    model(_UpperCAmelCase       )[0]

       A							:							List[str]				=    50_265

       A							:							int				=    torch.Size((1, 4_096, vocab_size)       )
       self.assertEqual(output.shape     , _UpperCAmelCase       )

       A							:							Dict				=    torch.tensor(
           [[[-2.3914, -4.3742, -5.0956], [-4.0988, -4.2384, -7.0406], [-3.1427, -3.7192, -6.6800]]]       )

       self.assertTrue(torch.allclose(output[:, :3, :3]     , _UpperCAmelCase     , atol=1E-4       )       )







'''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


snake_case_     			=						torch.cuda.is_available()
snake_case_     			=						[""" Sam ate lunch today.""", """Sams lunch ingredients."""]
snake_case_     			=						["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
snake_case_     			=						{
    """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 				_lowerCamelCase(	UpperCamelCase__     :			Path     ,							UpperCamelCase__     :			list		)		->	str:
    A							:							List[Any]				=    '''\n'''.join(UpperCamelCase__		)
    Path(UpperCamelCase__		).open('''w'''		).writelines(UpperCamelCase__		)
def 				_lowerCamelCase(	UpperCamelCase__     :			List[Any]		)		->	Dict:
    for split in ["train", "val", "test"]:
        _dump_articles(os.path.join(UpperCamelCase__     ,							F'''{split}.source'''		)     ,							UpperCamelCase__		)
        _dump_articles(os.path.join(UpperCamelCase__     ,							F'''{split}.target'''		)     ,							UpperCamelCase__		)
    return tmp_dir





class 	_lowercase    (				a					):


   @classmethod
   def        snake_case					(			cls       ):
       return cls


   @require_torch_multi_gpu
   def        snake_case					(			self       ):
       A							:							Optional[int]				=    {
           '''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        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase=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(_UpperCAmelCase       )
       A							:							dict				=    CHEAP_ARGS.copy()
       A							:							Union[str, Any]				=    make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
       A							:							Optional[int]				=    self.get_auto_remove_tmp_dir()
       args_d.update(data_dir=_UpperCAmelCase     , output_dir=_UpperCAmelCase     , **_UpperCAmelCase       )

       def convert(_UpperCAmelCase     , _UpperCAmelCase       ):
           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							:							int				=    [x for x in (convert(_UpperCAmelCase     , _UpperCAmelCase       ) for k, v in args_d.items()) if len(_UpperCAmelCase       )]
       A							:							Union[str, Any]				=    [sys.executable, f'''{self.test_file_dir}/distillation.py'''] + cli_args
       execute_subprocess_async(_UpperCAmelCase     , env=self.get_env()       )

       A							:							Dict				=    os.listdir(_UpperCAmelCase       )
       A							:							Optional[int]				=    {os.path.basename(_UpperCAmelCase       ) for p in contents}
       A							:							Union[str, Any]				=    [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)
       A							:							Optional[Any]				=    os.path.join(_UpperCAmelCase     , '''metrics.json'''       )
       A							:							List[Any]				=    '''rouge2'''

       A							:							Union[str, Any]				=    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       )
       A							:							Optional[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}''']     , _UpperCAmelCase       )
       self.assertEqual(len(metrics['''test''']       )     , 1       )
       A							:							Tuple				=    int(args_d['''max_epochs'''] * (1 / args_d['''val_check_interval''']) / 2 + 1       )
       self.assertEqual(len(metrics['''val''']       )     , _UpperCAmelCase       )
