

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     :       Optional[int]        =       torch.cuda.is_available()
lowercase     :       Tuple        =       [''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
lowercase     :       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-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  UpperCAmelCase_   (_lowerCAmelCase :		Path     ,			_lowerCAmelCase :		list		):
			__UpperCamelCase    :    Union[str, Any]								=      '\n'.join(_lowerCAmelCase		)
			Path(_lowerCAmelCase		).open("w"		).writelines(_lowerCAmelCase		)







def  UpperCAmelCase_   (_lowerCAmelCase :		Optional[int]		):







			for split in ["train", "val", "test"]:
						_dump_articles(os.path.join(_lowerCAmelCase     ,			F'''{split}.source'''		)     ,			_lowerCAmelCase		)
						_dump_articles(os.path.join(_lowerCAmelCase     ,			F'''{split}.target'''		)     ,			_lowerCAmelCase		)
			return tmp_dir

class 		SCREAMING_SNAKE_CASE__					(		lowercase__   ):







						"""simple docstring"""




						@classmethod
						def __lowerCamelCase					(				cls    )							->	List[str]:




									'''simple docstring'''


									return cls

						@require_torch_multi_gpu
						def __lowerCamelCase					(				self    )							->	Optional[Any]:




									'''simple docstring'''


									__UpperCamelCase    :    Optional[int]								=      {
									    '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 , __UpperCamelCase , __UpperCamelCase=True    )							->	str:




									'''simple docstring'''


									__UpperCamelCase    :    Tuple								=      {
									    '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    :    dict								=      CHEAP_ARGS.copy()
									__UpperCamelCase    :    Tuple								=      make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()    )
									__UpperCamelCase    :    Optional[int]								=      self.get_auto_remove_tmp_dir()
									args_d.update(data_dir=_A , output_dir=_A , **_A    )

									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}'''

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

									__UpperCamelCase    :    Optional[Any]								=      os.listdir(_A    )
									__UpperCamelCase    :    Tuple								=      {os.path.basename(_A    ) for p in contents}
									__UpperCamelCase    :    Union[str, Any]								=      [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    :    str								=      os.path.join(_A , "metrics.json"    )
									__UpperCamelCase    :    int								=      'rouge2'

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


import os
import pickle
import tempfile
import unittest
from typing import Callable, Optional

import numpy as np

from transformers import (
    BatchEncoding,
    BertTokenizer,
    BertTokenizerFast,
    PreTrainedTokenizer,
    PreTrainedTokenizerFast,
    TensorType,
    TokenSpan,
    is_tokenizers_available,
)
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import CaptureStderr, require_flax, require_tf, require_tokenizers, require_torch, slow


if is_tokenizers_available():
					from tokenizers import Tokenizer
					from tokenizers.models import WordPiece
class 		SCREAMING_SNAKE_CASE__					(		unittest.TestCase   ):







						"""simple docstring"""




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




									'''simple docstring'''


									__UpperCamelCase    :    List[str]								=      list(tokenizer_class.max_model_input_sizes.keys()    )
									for model_name in sa_models[:1]:
												__UpperCamelCase    :    int								=      tokenizer_class.from_pretrained(__UpperCamelCase    )
												self.assertIsNotNone(__UpperCamelCase    )
												self.assertIsInstance(__UpperCamelCase , __UpperCamelCase    )
												self.assertIsInstance(__UpperCamelCase , __UpperCamelCase    )

												for special_tok in tokenizer.all_special_tokens:
															self.assertIsInstance(__UpperCamelCase , __UpperCamelCase    )
															__UpperCamelCase    :    str								=      tokenizer.convert_tokens_to_ids(__UpperCamelCase    )
															self.assertIsInstance(__UpperCamelCase , __UpperCamelCase    )

						def __lowerCamelCase					(				self , __UpperCamelCase , __UpperCamelCase = None    )							->	Tuple:




									'''simple docstring'''


									__UpperCamelCase    :    Optional[Any]								=      pickle.dumps(__UpperCamelCase    )
									self.assertIsNotNone(__UpperCamelCase    )

									__UpperCamelCase    :    List[str]								=      pickle.loads(__UpperCamelCase    )

									# Ensure is_fast is correctly restored
									self.assertEqual(be_restored.is_fast , be_original.is_fast    )

									# Ensure encodings are potentially correctly restored
									if be_original.is_fast:
												self.assertIsNotNone(be_restored.encodings    )
									else:
												self.assertIsNone(be_restored.encodings    )

									# Ensure the keys are the same
									for original_v, restored_v in zip(be_original.values() , be_restored.values()    ):
												if equal_op:
															self.assertTrue(equal_op(__UpperCamelCase , __UpperCamelCase    )    )
												else:
															self.assertEqual(__UpperCamelCase , __UpperCamelCase    )

						@slow
						def __lowerCamelCase					(				self    )							->	List[Any]:




									'''simple docstring'''


									self.check_tokenizer_from_pretrained(__UpperCamelCase    )

						def __lowerCamelCase					(				self    )							->	Optional[int]:




									'''simple docstring'''


									self.assertEqual(TensorType("tf"    ) , TensorType.TENSORFLOW    )
									self.assertEqual(TensorType("pt"    ) , TensorType.PYTORCH    )
									self.assertEqual(TensorType("np"    ) , TensorType.NUMPY    )

						@require_tokenizers
						def __lowerCamelCase					(				self    )							->	List[str]:




									'''simple docstring'''


									import numpy as np

									__UpperCamelCase    :    str								=      BertTokenizer.from_pretrained("bert-base-cased"    )
									__UpperCamelCase    :    Optional[Any]								=      BertTokenizerFast.from_pretrained("bert-base-cased"    )

									# Python no tensor
									with self.subTest("BatchEncoding (Python, return_tensors=None)"    ):
												self.assert_dump_and_restore(tokenizer_p("Small example to encode"    )    )

									with self.subTest("BatchEncoding (Python, return_tensors=NUMPY)"    ):
												self.assert_dump_and_restore(
												    tokenizer_p("Small example to encode" , return_tensors=TensorType.NUMPY    ) , np.array_equal    )

									with self.subTest("BatchEncoding (Rust, return_tensors=None)"    ):
												self.assert_dump_and_restore(tokenizer_r("Small example to encode"    )    )

									with self.subTest("BatchEncoding (Rust, return_tensors=NUMPY)"    ):
												self.assert_dump_and_restore(
												    tokenizer_r("Small example to encode" , return_tensors=TensorType.NUMPY    ) , np.array_equal    )

						@require_tf
						@require_tokenizers
						def __lowerCamelCase					(				self    )							->	Union[str, Any]:




									'''simple docstring'''


									import tensorflow as tf

									def tf_array_equals(__UpperCamelCase , __UpperCamelCase    ):
												return tf.reduce_all(tf.equal(__UpperCamelCase , __UpperCamelCase    )    )

									__UpperCamelCase    :    int								=      BertTokenizer.from_pretrained("bert-base-cased"    )
									__UpperCamelCase    :    Dict								=      BertTokenizerFast.from_pretrained("bert-base-cased"    )

									with self.subTest("BatchEncoding (Python, return_tensors=TENSORFLOW)"    ):
												self.assert_dump_and_restore(
												    tokenizer_p("Small example to encode" , return_tensors=TensorType.TENSORFLOW    ) , __UpperCamelCase    )

									with self.subTest("BatchEncoding (Rust, return_tensors=TENSORFLOW)"    ):
												self.assert_dump_and_restore(
												    tokenizer_r("Small example to encode" , return_tensors=TensorType.TENSORFLOW    ) , __UpperCamelCase    )

						@require_torch
						@require_tokenizers
						def __lowerCamelCase					(				self    )							->	Any:




									'''simple docstring'''


									import torch

									__UpperCamelCase    :    List[Any]								=      BertTokenizer.from_pretrained("bert-base-cased"    )
									__UpperCamelCase    :    Optional[Any]								=      BertTokenizerFast.from_pretrained("bert-base-cased"    )

									with self.subTest("BatchEncoding (Python, return_tensors=PYTORCH)"    ):
												self.assert_dump_and_restore(
												    tokenizer_p("Small example to encode" , return_tensors=TensorType.PYTORCH    ) , torch.equal    )

									with self.subTest("BatchEncoding (Rust, return_tensors=PYTORCH)"    ):
												self.assert_dump_and_restore(
												    tokenizer_r("Small example to encode" , return_tensors=TensorType.PYTORCH    ) , torch.equal    )

						@require_tokenizers
						def __lowerCamelCase					(				self    )							->	Any:




									'''simple docstring'''


									__UpperCamelCase    :    Optional[int]								=      BertTokenizer.from_pretrained("bert-base-cased"    )
									__UpperCamelCase    :    Tuple								=      BertTokenizerFast.from_pretrained("bert-base-cased"    )

									with self.subTest("Python Tokenizer"    ):
												self.assertFalse(tokenizer_p("Small example to_encode"    ).is_fast    )

									with self.subTest("Rust Tokenizer"    ):
												self.assertTrue(tokenizer_r("Small example to_encode"    ).is_fast    )

						@require_tokenizers
						def __lowerCamelCase					(				self    )							->	Union[str, Any]:




									'''simple docstring'''


									__UpperCamelCase    :    Any								=      BertTokenizerFast.from_pretrained("bert-base-cased"    )
									__UpperCamelCase    :    Dict								=      tokenizer_r(["Test", "\xad", "test"] , is_split_into_words=__UpperCamelCase    )

									self.assertEqual(encoded.word_to_tokens(0    ) , TokenSpan(start=1 , end=2    )    )
									self.assertEqual(encoded.word_to_tokens(1    ) , __UpperCamelCase    )
									self.assertEqual(encoded.word_to_tokens(2    ) , TokenSpan(start=2 , end=3    )    )

						def __lowerCamelCase					(				self    )							->	Optional[int]:




									'''simple docstring'''


									__UpperCamelCase    :    str								=      BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]}    )
									__UpperCamelCase    :    List[str]								=      batch.convert_to_tensors(tensor_type="np"    )
									self.assertEqual(tensor_batch["inputs"].shape , (2, 3)    )
									self.assertEqual(tensor_batch["labels"].shape , (2,)    )
									# test converting the converted
									with CaptureStderr() as cs:
												__UpperCamelCase    :    Optional[Any]								=      batch.convert_to_tensors(tensor_type="np"    )
									self.assertFalse(len(cs.err    ) , msg=f'''should have no warning, but got {cs.err}'''    )

									__UpperCamelCase    :    Union[str, Any]								=      BatchEncoding({"inputs": [1, 2, 3], "labels": 0}    )
									__UpperCamelCase    :    int								=      batch.convert_to_tensors(tensor_type="np" , prepend_batch_axis=__UpperCamelCase    )
									self.assertEqual(tensor_batch["inputs"].shape , (1, 3)    )
									self.assertEqual(tensor_batch["labels"].shape , (1,)    )

						@require_torch
						def __lowerCamelCase					(				self    )							->	List[str]:




									'''simple docstring'''


									__UpperCamelCase    :    Dict								=      BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]}    )
									__UpperCamelCase    :    List[Any]								=      batch.convert_to_tensors(tensor_type="pt"    )
									self.assertEqual(tensor_batch["inputs"].shape , (2, 3)    )
									self.assertEqual(tensor_batch["labels"].shape , (2,)    )
									# test converting the converted
									with CaptureStderr() as cs:
												__UpperCamelCase    :    Tuple								=      batch.convert_to_tensors(tensor_type="pt"    )
									self.assertFalse(len(cs.err    ) , msg=f'''should have no warning, but got {cs.err}'''    )

									__UpperCamelCase    :    Union[str, Any]								=      BatchEncoding({"inputs": [1, 2, 3], "labels": 0}    )
									__UpperCamelCase    :    List[Any]								=      batch.convert_to_tensors(tensor_type="pt" , prepend_batch_axis=__UpperCamelCase    )
									self.assertEqual(tensor_batch["inputs"].shape , (1, 3)    )
									self.assertEqual(tensor_batch["labels"].shape , (1,)    )

						@require_tf
						def __lowerCamelCase					(				self    )							->	Optional[Any]:




									'''simple docstring'''


									__UpperCamelCase    :    List[Any]								=      BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]}    )
									__UpperCamelCase    :    Tuple								=      batch.convert_to_tensors(tensor_type="tf"    )
									self.assertEqual(tensor_batch["inputs"].shape , (2, 3)    )
									self.assertEqual(tensor_batch["labels"].shape , (2,)    )
									# test converting the converted
									with CaptureStderr() as cs:
												__UpperCamelCase    :    Optional[int]								=      batch.convert_to_tensors(tensor_type="tf"    )
									self.assertFalse(len(cs.err    ) , msg=f'''should have no warning, but got {cs.err}'''    )

									__UpperCamelCase    :    str								=      BatchEncoding({"inputs": [1, 2, 3], "labels": 0}    )
									__UpperCamelCase    :    Union[str, Any]								=      batch.convert_to_tensors(tensor_type="tf" , prepend_batch_axis=__UpperCamelCase    )
									self.assertEqual(tensor_batch["inputs"].shape , (1, 3)    )
									self.assertEqual(tensor_batch["labels"].shape , (1,)    )

						@require_flax
						def __lowerCamelCase					(				self    )							->	List[Any]:




									'''simple docstring'''


									__UpperCamelCase    :    str								=      BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]}    )
									__UpperCamelCase    :    int								=      batch.convert_to_tensors(tensor_type="jax"    )
									self.assertEqual(tensor_batch["inputs"].shape , (2, 3)    )
									self.assertEqual(tensor_batch["labels"].shape , (2,)    )
									# test converting the converted
									with CaptureStderr() as cs:
												__UpperCamelCase    :    Dict								=      batch.convert_to_tensors(tensor_type="jax"    )
									self.assertFalse(len(cs.err    ) , msg=f'''should have no warning, but got {cs.err}'''    )

									__UpperCamelCase    :    Tuple								=      BatchEncoding({"inputs": [1, 2, 3], "labels": 0}    )
									__UpperCamelCase    :    Union[str, Any]								=      batch.convert_to_tensors(tensor_type="jax" , prepend_batch_axis=__UpperCamelCase    )
									self.assertEqual(tensor_batch["inputs"].shape , (1, 3)    )
									self.assertEqual(tensor_batch["labels"].shape , (1,)    )

						def __lowerCamelCase					(				self    )							->	int:




									'''simple docstring'''


									__UpperCamelCase    :    str								=      [{"input_ids": np.array([0, 1, 2]    )}, {"input_ids": np.array([0, 1, 2, 3]    )}]
									__UpperCamelCase    :    Any								=      BertTokenizer.from_pretrained("bert-base-cased"    )

									__UpperCamelCase    :    Optional[Any]								=      tokenizer.pad(__UpperCamelCase , padding=__UpperCamelCase    )
									self.assertTrue(isinstance(batch["input_ids"] , np.ndarray    )    )
									self.assertEqual(batch["input_ids"].tolist() , [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]    )
									__UpperCamelCase    :    Optional[Any]								=      tokenizer.pad(__UpperCamelCase , padding=__UpperCamelCase , return_tensors="np"    )
									self.assertTrue(isinstance(batch["input_ids"] , np.ndarray    )    )
									self.assertEqual(batch["input_ids"].tolist() , [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]    )

						@require_torch
						def __lowerCamelCase					(				self    )							->	Optional[int]:




									'''simple docstring'''


									import torch

									__UpperCamelCase    :    List[str]								=      [{"input_ids": torch.tensor([0, 1, 2]    )}, {"input_ids": torch.tensor([0, 1, 2, 3]    )}]
									__UpperCamelCase    :    str								=      BertTokenizer.from_pretrained("bert-base-cased"    )

									__UpperCamelCase    :    str								=      tokenizer.pad(__UpperCamelCase , padding=__UpperCamelCase    )
									self.assertTrue(isinstance(batch["input_ids"] , torch.Tensor    )    )
									self.assertEqual(batch["input_ids"].tolist() , [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]    )
									__UpperCamelCase    :    int								=      tokenizer.pad(__UpperCamelCase , padding=__UpperCamelCase , return_tensors="pt"    )
									self.assertTrue(isinstance(batch["input_ids"] , torch.Tensor    )    )
									self.assertEqual(batch["input_ids"].tolist() , [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]    )

						@require_tf
						def __lowerCamelCase					(				self    )							->	List[Any]:




									'''simple docstring'''


									import tensorflow as tf

									__UpperCamelCase    :    List[Any]								=      [{"input_ids": tf.constant([0, 1, 2]    )}, {"input_ids": tf.constant([0, 1, 2, 3]    )}]
									__UpperCamelCase    :    int								=      BertTokenizer.from_pretrained("bert-base-cased"    )

									__UpperCamelCase    :    Optional[Any]								=      tokenizer.pad(__UpperCamelCase , padding=__UpperCamelCase    )
									self.assertTrue(isinstance(batch["input_ids"] , tf.Tensor    )    )
									self.assertEqual(batch["input_ids"].numpy().tolist() , [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]    )
									__UpperCamelCase    :    List[str]								=      tokenizer.pad(__UpperCamelCase , padding=__UpperCamelCase , return_tensors="tf"    )
									self.assertTrue(isinstance(batch["input_ids"] , tf.Tensor    )    )
									self.assertEqual(batch["input_ids"].numpy().tolist() , [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]    )

						@require_tokenizers
						def __lowerCamelCase					(				self    )							->	int:




									'''simple docstring'''


									__UpperCamelCase    :    List[Any]								=      Tokenizer(WordPiece(unk_token="[UNK]"    )    )
									PreTrainedTokenizerFast(tokenizer_object=__UpperCamelCase    )




						@require_tokenizers
						def __lowerCamelCase					(				self    )							->	Optional[int]:




									'''simple docstring'''


									__UpperCamelCase    :    Union[str, Any]								=      Tokenizer(WordPiece(unk_token="[UNK]"    )    )
									with tempfile.TemporaryDirectory() as tmpdirname:
												bert_tokenizer.save(os.path.join(__UpperCamelCase , "tokenizer.json"    )    )
												PreTrainedTokenizerFast(tokenizer_file=os.path.join(__UpperCamelCase , "tokenizer.json"    )    )