


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


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



def 			_a      (    __UpperCamelCase			):
     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 	a__						(    lowerCAmelCase_     ):

       @classmethod
       def     UpperCAmelCase( cls   :  Any     ):
            return cls

       @require_torch_multi_gpu
       def     UpperCAmelCase( self   :  Any     ):
            a_     :  Any				      =     {
                """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     UpperCAmelCase( self   :  Any   ,				lowerCamelCase_   :  int   ,				lowerCamelCase_   :  str=True     ):
            a_     :  Union[str, 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(lowerCamelCase_     )
            a_     :  dict				      =     CHEAP_ARGS.copy()
            a_     :  Union[str, Any]				      =     make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     )
            a_     :  Union[str, Any]				      =     self.get_auto_remove_tmp_dir()
            args_d.update(data_dir=lowerCamelCase_   ,				output_dir=lowerCamelCase_   ,				**lowerCamelCase_     )

            def convert(lowerCamelCase_   :  Optional[Any]   ,				lowerCamelCase_   :  Dict     ):
                 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_     :  Optional[int]				      =     [x for x in (convert(lowerCamelCase_   ,				lowerCamelCase_     ) for k, v in args_d.items()) if len(lowerCamelCase_     )]
            a_     :  str				      =     [sys.executable, F'''{self.test_file_dir}/distillation.py'''] + cli_args
            execute_subprocess_async(lowerCamelCase_   ,				env=self.get_env()     )

            a_     :  Any				      =     os.listdir(lowerCamelCase_     )
            a_     :  List[Any]				      =     {os.path.basename(lowerCamelCase_     ) for p in contents}
            a_     :  Dict				      =     [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)
            a_     :  List[str]				      =     os.path.join(lowerCamelCase_   ,				"""metrics.json"""     )
            a_     :  Union[str, Any]				      =     """rouge2"""

            a_     :  Tuple				      =     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_     )
            a_     :  Any				      =     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}''']   ,				lowerCamelCase_     )
            self.assertEqual(len(metrics["""test"""]     )   ,				1     )
            a_     :  str				      =     int(args_d["""max_epochs"""] * (1 / args_d["""val_check_interval"""]) / 2 + 1     )
            self.assertEqual(len(metrics["""val"""]     )   ,				lowerCamelCase_     )







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 			lowerCAmelCase__(     unittest.TestCase    ):






    '''simple docstring'''





    def __init__(						self					: Any							, __snake_case					: Tuple							, __snake_case					: Union[str, Any]=13							, __snake_case					: Tuple=7							, __snake_case					: str=True							, __snake_case					: List[Any]=True							, __snake_case					: Optional[int]=True							, __snake_case					: List[str]=True							, __snake_case					: Tuple=99							, __snake_case					: int=24							, __snake_case					: int=32							, __snake_case					: Tuple=5							, __snake_case					: int=4							, __snake_case					: Any=37							, __snake_case					: int="gelu"							, __snake_case					: Union[str, Any]=0.1							, __snake_case					: Optional[Any]=0.1							, __snake_case					: str=512							, __snake_case					: List[Any]=16							, __snake_case					: Tuple=2							, __snake_case					: List[str]=0.02							, __snake_case					: Any=4							, ):





       '''simple docstring'''
       UpperCAmelCase_				:   Union[str, Any]							= parent
       UpperCAmelCase_				:   Dict							= batch_size
       UpperCAmelCase_				:   Union[str, Any]							= seq_length
       UpperCAmelCase_				:   Any							= is_training
       UpperCAmelCase_				:   Tuple							= use_attention_mask
       UpperCAmelCase_				:   Dict							= use_token_type_ids
       UpperCAmelCase_				:   str							= use_labels
       UpperCAmelCase_				:   Union[str, Any]							= vocab_size
       UpperCAmelCase_				:   Union[str, Any]							= hidden_size
       UpperCAmelCase_				:   Union[str, Any]							= embedding_size
       UpperCAmelCase_				:   Optional[int]							= num_hidden_layers
       UpperCAmelCase_				:   Tuple							= num_attention_heads
       UpperCAmelCase_				:   List[Any]							= intermediate_size
       UpperCAmelCase_				:   Union[str, Any]							= hidden_act
       UpperCAmelCase_				:   Any							= hidden_dropout_prob
       UpperCAmelCase_				:   List[str]							= attention_probs_dropout_prob
       UpperCAmelCase_				:   List[str]							= max_position_embeddings
       UpperCAmelCase_				:   Dict							= type_vocab_size
       UpperCAmelCase_				:   Tuple							= type_sequence_label_size
       UpperCAmelCase_				:   Dict							= initializer_range
       UpperCAmelCase_				:   Any							= num_choices





    def        _lowerCamelCase			(						self					: Optional[int]						):





       '''simple docstring'''
       UpperCAmelCase_				:   Any							= ids_tensor([self.batch_size, self.seq_length]							, self.vocab_size						)

       UpperCAmelCase_				:   Any							= None
       if self.use_attention_mask:
          UpperCAmelCase_				:   List[str]							= random_attention_mask([self.batch_size, self.seq_length]						)

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

       UpperCAmelCase_				:   int							= 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					: int						):





       '''simple docstring'''
       UpperCAmelCase_				:   List[str]							= self.prepare_config_and_inputs()
       UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_				:   Union[str, Any]							= config_and_inputs
       UpperCAmelCase_				:   Optional[Any]							= {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
       return config, inputs_dict





@require_flax
class 			lowerCAmelCase__(     snake_case__     ,     unittest.TestCase    ):






    '''simple docstring'''





    A_	:  Any								=			True

    A_	:  int								=			(
        (
            FlaxElectraModel,
            FlaxElectraForCausalLM,
            FlaxElectraForMaskedLM,
            FlaxElectraForPreTraining,
            FlaxElectraForTokenClassification,
            FlaxElectraForQuestionAnswering,
            FlaxElectraForMultipleChoice,
            FlaxElectraForSequenceClassification,
        )
        if is_flax_available()
        else ()
    )





    def        _lowerCamelCase			(						self					: Optional[int]						):





       '''simple docstring'''
       UpperCAmelCase_				:   List[str]							= FlaxElectraModelTester(self						)





    @slow
    def        _lowerCamelCase			(						self					: Union[str, Any]						):





       '''simple docstring'''
       for model_class_name in self.all_model_classes:
          if model_class_name == FlaxElectraForMaskedLM:
             UpperCAmelCase_				:   List[str]							= model_class_name.from_pretrained('''google/electra-small-generator'''						)
          else:
             UpperCAmelCase_				:   Optional[Any]							= model_class_name.from_pretrained('''google/electra-small-discriminator'''						)
          UpperCAmelCase_				:   Tuple							= model(np.ones((1, 1)						)						)
          self.assertIsNotNone(__snake_case						)