


'''simple docstring'''

import json
import os
import tempfile
import unittest

from transformers.modelcard import ModelCard







class  UpperCAmelCase (       unittest.TestCase):

 def 		lowercase_						( self :	Tuple					):





    """simple docstring"""



    UpperCamelCase__ 						=						{
        "model_details": {
            "Organization": "testing",
            "Model date": "today",
            "Model version": "v2.1, Developed by Test Corp in 2019.",
            "Architecture": "Convolutional Neural Network.",
        },
        "metrics": "BLEU and ROUGE-1",
        "evaluation_data": {
            "Datasets": {"BLEU": "My-great-dataset-v1", "ROUGE-1": "My-short-dataset-v2.1"},
            "Preprocessing": "See details on https://arxiv.org/pdf/1810.03993.pdf",
        },
        "training_data": {
            "Dataset": "English Wikipedia dump dated 2018-12-01",
            "Preprocessing": (
                "Using SentencePiece vocabulary of size 52k tokens. See details on"
                " https://arxiv.org/pdf/1810.03993.pdf"
            ),
        },
        "quantitative_analyses": {"BLEU": 55.1, "ROUGE-1": 76},
    }

 def 		lowercase_						( self :	Optional[Any]					):





    """simple docstring"""



    UpperCamelCase__ 						=						ModelCard.from_dict(self.inputs_dict					)
    self.assertTrue(hasattr(a_,					"model_details"					)					)
    self.assertTrue(hasattr(a_,					"intended_use"					)					)
    self.assertTrue(hasattr(a_,					"factors"					)					)
    self.assertTrue(hasattr(a_,					"metrics"					)					)
    self.assertTrue(hasattr(a_,					"evaluation_data"					)					)
    self.assertTrue(hasattr(a_,					"training_data"					)					)
    self.assertTrue(hasattr(a_,					"quantitative_analyses"					)					)
    self.assertTrue(hasattr(a_,					"ethical_considerations"					)					)
    self.assertTrue(hasattr(a_,					"caveats_and_recommendations"					)					)

 def 		lowercase_						( self :	List[Any]					):





    """simple docstring"""



    UpperCamelCase__ 						=						ModelCard.from_dict(self.inputs_dict					)
    UpperCamelCase__ 						=						json.loads(modelcard.to_json_string()					)
    for key, value in self.inputs_dict.items():
       self.assertEqual(obj[key],					a_					)

 def 		lowercase_						( self :	Optional[Any]					):





    """simple docstring"""



    UpperCamelCase__ 						=						ModelCard.from_dict(self.inputs_dict					)

    with tempfile.TemporaryDirectory() as tmpdirname:
       UpperCamelCase__ 						=						os.path.join(a_,					"modelcard.json"					)
       model_card_first.to_json_file(a_					)
       UpperCamelCase__ 						=						ModelCard.from_json_file(a_					)

    self.assertEqual(model_card_second.to_dict(),					model_card_first.to_dict()					)

 def 		lowercase_						( self :	Optional[int]					):





    """simple docstring"""



    UpperCamelCase__ 						=						ModelCard.from_dict(self.inputs_dict					)

    with tempfile.TemporaryDirectory() as tmpdirname:
       model_card_first.save_pretrained(a_					)
       UpperCamelCase__ 						=						ModelCard.from_pretrained(a_					)

    self.assertEqual(model_card_second.to_dict(),					model_card_first.to_dict()					)



'''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


__lowercase:		Union[str, Any]							=     torch.cuda.is_available()
__lowercase:		str							=     [" Sam ate lunch today.", "Sams lunch ingredients."]
__lowercase:		Optional[Any]							=     ["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
__lowercase:		Optional[int]							=     {
    "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        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				Path    ,		_UpperCamelCase      :				list   )		->					Optional[Any]:





   '''simple docstring'''







   UpperCamelCase__ 						=						"\n".join(_UpperCamelCase   )
   Path(_UpperCamelCase   ).open("w"   ).writelines(_UpperCamelCase   )



def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				Optional[Any]   )		->					List[str]:





   '''simple docstring'''







   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  UpperCAmelCase (       SCREAMING_SNAKE_CASE__):

 @classmethod
 def 		lowercase_						( cls :	List[Any]					):





    """simple docstring"""



    return cls

 @require_torch_multi_gpu
 def 		lowercase_						( self :	List[Any]					):





    """simple docstring"""



    UpperCamelCase__ 						=						{
        "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 		lowercase_						( self :	Tuple,					a_ :	Optional[Any],					a_ :	Optional[int]=True					):





    """simple docstring"""



    UpperCamelCase__ 						=						{
        "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_					)
    UpperCamelCase__ 						=						CHEAP_ARGS.copy()
    UpperCamelCase__ 						=						make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)
    UpperCamelCase__ 						=						self.get_auto_remove_tmp_dir()
    args_d.update(data_dir=a_,					output_dir=a_,					**a_					)

    def convert(a_ :	str,					a_ :	Any					):
       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}'

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

    UpperCamelCase__ 						=						os.listdir(a_					)
    UpperCamelCase__ 						=						{os.path.basename(a_					) for p in contents}
    UpperCamelCase__ 						=						[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)
    UpperCamelCase__ 						=						os.path.join(a_,					"metrics.json"					)
    UpperCamelCase__ 						=						"rouge2"

    UpperCamelCase__ 						=						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_					)
    UpperCamelCase__ 						=						metrics["val"][-1]
    self.assertGreaterEqual(last_step_stats["val_avg_gen_time"],					0.01					)
    self.assertIsInstance(last_step_stats[f'val_avg_{val_metric}'],					a_					)
    self.assertEqual(len(metrics["test"]					),					1					)
    UpperCamelCase__ 						=						int(args_d["max_epochs"] * (1 / args_d["val_check_interval"]) / 2 + 1					)
    self.assertEqual(len(metrics["val"]					),					a_					)