"""simple docstring"""






import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors
@require_tokenizers
class 				snake_case       ( __UpperCAmelCase      ,    unittest.TestCase				):




					"""simple docstring"""

					snake_case__				    = MvpTokenizer
					snake_case__				    = MvpTokenizerFast
					snake_case__				    = True
					snake_case__				    = filter_roberta_detectors







					def 		__lowerCAmelCase						(				self   :	Dict			):
									super().setUp()
									UpperCAmelCase__             =							[
									    'l',
									    'o',
									    'w',
									    'e',
									    'r',
									    's',
									    't',
									    'i',
									    'd',
									    'n',
									    '\u0120',
									    '\u0120l',
									    '\u0120n',
									    '\u0120lo',
									    '\u0120low',
									    'er',
									    '\u0120lowest',
									    '\u0120newer',
									    '\u0120wider',
									    '<unk>',
									]
									UpperCAmelCase__             =							dict(zip(lowerCamelCase__    ,range(len(lowerCamelCase__			)			)			)			)
									UpperCAmelCase__             =							['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
									UpperCAmelCase__             =							{'unk_token': '<unk>'}

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







					def 		__lowerCAmelCase						(				self   :	Union[str, Any]    ,**lowerCamelCase__   :	List[Any]			):
									kwargs.update(self.special_tokens_map			)
									return self.tokenizer_class.from_pretrained(self.tmpdirname    ,**lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	str    ,**lowerCamelCase__   :	Optional[Any]			):
									kwargs.update(self.special_tokens_map			)
									return self.rust_tokenizer_class.from_pretrained(self.tmpdirname    ,**lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	Dict    ,lowerCamelCase__   :	List[Any]			):
									return "lower newer", "lower newer"







					@cached_property
					def 		__lowerCAmelCase						(				self   :	Optional[int]			):
									return MvpTokenizer.from_pretrained('RUCAIBox/mvp'			)







					@cached_property
					def 		__lowerCAmelCase						(				self   :	Dict			):
									return MvpTokenizerFast.from_pretrained('RUCAIBox/mvp'			)







					@require_torch
					def 		__lowerCAmelCase						(				self   :	Union[str, Any]			):
									UpperCAmelCase__             =							['A long paragraph for summarization.', 'Another paragraph for summarization.']
									UpperCAmelCase__             =							[0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
													UpperCAmelCase__             =							tokenizer(lowerCamelCase__    ,max_length=len(lowerCamelCase__			)    ,padding=lowerCamelCase__    ,return_tensors='pt'			)
													self.assertIsInstance(lowerCamelCase__    ,lowerCamelCase__			)

													self.assertEqual((2, 9)    ,batch.input_ids.shape			)
													self.assertEqual((2, 9)    ,batch.attention_mask.shape			)
													UpperCAmelCase__             =							batch.input_ids.tolist()[0]
													self.assertListEqual(lowerCamelCase__    ,lowerCamelCase__			)
													# Test that special tokens are reset







					@require_torch
					def 		__lowerCAmelCase						(				self   :	Tuple			):
									UpperCAmelCase__             =							['A long paragraph for summarization.', 'Another paragraph for summarization.']
									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
													UpperCAmelCase__             =							tokenizer(lowerCamelCase__    ,padding=lowerCamelCase__    ,return_tensors='pt'			)
													# check if input_ids are returned and no labels
													self.assertIn('input_ids'    ,lowerCamelCase__			)
													self.assertIn('attention_mask'    ,lowerCamelCase__			)
													self.assertNotIn('labels'    ,lowerCamelCase__			)
													self.assertNotIn('decoder_attention_mask'    ,lowerCamelCase__			)







					@require_torch
					def 		__lowerCAmelCase						(				self   :	Union[str, Any]			):
									UpperCAmelCase__             =							[
									    'Summary of the text.',
									    'Another summary.',
									]
									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
													UpperCAmelCase__             =							tokenizer(text_target=lowerCamelCase__    ,max_length=32    ,padding='max_length'    ,return_tensors='pt'			)
													self.assertEqual(32    ,targets['input_ids'].shape[1]			)







					@require_torch
					def 		__lowerCAmelCase						(				self   :	List[str]			):
									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
													UpperCAmelCase__             =							tokenizer(
													    ['I am a small frog' * 1_024, 'I am a small frog']    ,padding=lowerCamelCase__    ,truncation=lowerCamelCase__    ,return_tensors='pt'			)
													self.assertIsInstance(lowerCamelCase__    ,lowerCamelCase__			)
													self.assertEqual(batch.input_ids.shape    ,(2, 1_024)			)







					@require_torch
					def 		__lowerCAmelCase						(				self   :	Dict			):
									UpperCAmelCase__             =							['A long paragraph for summarization.']
									UpperCAmelCase__             =							[
									    'Summary of the text.',
									]
									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
													UpperCAmelCase__             =							tokenizer(lowerCamelCase__    ,text_target=lowerCamelCase__    ,return_tensors='pt'			)
													UpperCAmelCase__             =							inputs['input_ids']
													UpperCAmelCase__             =							inputs['labels']
													self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()			)
													self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()			)
													self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()			)
													self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()			)







					def 		__lowerCAmelCase						(				self   :	Any			):
									pass







					def 		__lowerCAmelCase						(				self   :	Tuple			):
									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
													with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''			):
																	UpperCAmelCase__             =							self.rust_tokenizer_class.from_pretrained(lowerCamelCase__    ,**lowerCamelCase__			)
																	UpperCAmelCase__             =							self.tokenizer_class.from_pretrained(lowerCamelCase__    ,**lowerCamelCase__			)
																	UpperCAmelCase__             =							'A, <mask> AllenNLP sentence.'
																	UpperCAmelCase__             =							tokenizer_r.encode_plus(lowerCamelCase__    ,add_special_tokens=lowerCamelCase__    ,return_token_type_ids=lowerCamelCase__			)
																	UpperCAmelCase__             =							tokenizer_p.encode_plus(lowerCamelCase__    ,add_special_tokens=lowerCamelCase__    ,return_token_type_ids=lowerCamelCase__			)

																	# token_type_ids should put 0 everywhere
																	self.assertEqual(sum(tokens_r['token_type_ids']			)    ,sum(tokens_p['token_type_ids']			)			)

																	# attention_mask should put 1 everywhere, so sum over length should be 1
																	self.assertEqual(
																	    sum(tokens_r['attention_mask']			) / len(tokens_r['attention_mask']			)    ,sum(tokens_p['attention_mask']			) / len(tokens_p['attention_mask']			)    ,)

																	UpperCAmelCase__             =							tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']			)
																	UpperCAmelCase__             =							tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']			)

																	# Rust correctly handles the space before the mask while python doesnt
																	self.assertSequenceEqual(tokens_p['input_ids']    ,[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]			)
																	self.assertSequenceEqual(tokens_r['input_ids']    ,[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]			)

																	self.assertSequenceEqual(
																	    lowerCamelCase__    ,['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']			)
																	self.assertSequenceEqual(
																	    lowerCamelCase__    ,['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']			)




"""simple docstring"""






import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_deebert

from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow


logging.basicConfig(level=logging.DEBUG)

lowerCAmelCase__  :  Optional[Any]						     =	logging.getLogger()



def   a_						(					):
				UpperCAmelCase__             =							argparse.ArgumentParser()
				parser.add_argument('-f'       )
				UpperCAmelCase__             =							parser.parse_args()
				return args.f







class 				snake_case       ( __UpperCAmelCase				):




					"""simple docstring"""

					def 		__lowerCAmelCase						(				self   :	int			):
									UpperCAmelCase__             =							logging.StreamHandler(sys.stdout			)
									logger.addHandler(lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	Optional[Any]    ,lowerCamelCase__   :	Union[str, Any]			):
									UpperCAmelCase__             =							get_gpu_count()

									if n_gpu > 1:
													pass
													# XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
													# script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
													# distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
													# cmd = [sys.executable] + distributed_args + args
													# execute_subprocess_async(cmd, env=self.get_env())
													# XXX: test the results - need to save them first into .json file
									else:
													args.insert(0    ,'run_glue_deebert.py'			)
													with patch.object(lowerCamelCase__    ,'argv'    ,lowerCamelCase__			):
																	UpperCAmelCase__             =							run_glue_deebert.main()
																	for value in result.values():
																					self.assertGreaterEqual(lowerCamelCase__    ,0.6_6_6			)







					@slow
					@require_torch_non_multi_gpu
					def 		__lowerCAmelCase						(				self   :	List[str]			):
									UpperCAmelCase__             =							'\n            --model_type roberta\n            --model_name_or_path roberta-base\n            --task_name MRPC\n            --do_train\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --max_seq_length 128\n            --per_gpu_eval_batch_size=1\n            --per_gpu_train_batch_size=8\n            --learning_rate 2e-4\n            --num_train_epochs 3\n            --overwrite_output_dir\n            --seed 42\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --save_steps 0\n            --overwrite_cache\n            --eval_after_first_stage\n            '.split()
									self.run_and_check(lowerCamelCase__			)

									UpperCAmelCase__             =							'\n            --model_type roberta\n            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --task_name MRPC\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --max_seq_length 128\n            --eval_each_highway\n            --eval_highway\n            --overwrite_cache\n            --per_gpu_eval_batch_size=1\n            '.split()
									self.run_and_check(lowerCamelCase__			)

									UpperCAmelCase__             =							'\n            --model_type roberta\n            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --task_name MRPC\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --max_seq_length 128\n            --early_exit_entropy 0.1\n            --eval_highway\n            --overwrite_cache\n            --per_gpu_eval_batch_size=1\n            '.split()
									self.run_and_check(lowerCamelCase__			)



