





import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__				,					unittest.TestCase      ):
 __SCREAMING_SNAKE_CASE							       =	BarthezTokenizer
 __SCREAMING_SNAKE_CASE							       =	BarthezTokenizerFast
 __SCREAMING_SNAKE_CASE							       =	True
 __SCREAMING_SNAKE_CASE							       =	True



 def 				UpperCamelCase			(							self ):
        super().setUp()

        A__											=						BarthezTokenizerFast.from_pretrained('''moussaKam/mbarthez''' )
        tokenizer.save_pretrained(self.tmpdirname )
        tokenizer.save_pretrained(self.tmpdirname,legacy_format=__lowerCamelCase )
        A__											=						tokenizer



 def 				UpperCamelCase			(							self ):
        A__											=						'''<pad>'''
        A__											=						1

        self.assertEqual(self.get_tokenizer()._convert_token_to_id(__lowerCamelCase ),__lowerCamelCase )
        self.assertEqual(self.get_tokenizer()._convert_id_to_token(__lowerCamelCase ),__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        A__											=						list(self.get_tokenizer().get_vocab().keys() )

        self.assertEqual(vocab_keys[0],'''<s>''' )
        self.assertEqual(vocab_keys[1],'''<pad>''' )
        self.assertEqual(vocab_keys[-1],'''<mask>''' )
        self.assertEqual(len(__lowerCamelCase ),10_1122 )



 def 				UpperCamelCase			(							self ):
        self.assertEqual(self.get_tokenizer().vocab_size,10_1122 )



 @require_torch
 def 				UpperCamelCase			(							self ):
        A__											=						['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
        A__											=						[0, 57, 3018, 7_0307, 91, 2]

        A__											=						self.tokenizer(
            __lowerCamelCase,max_length=len(__lowerCamelCase ),padding=__lowerCamelCase,truncation=__lowerCamelCase,return_tensors='''pt''' )
        self.assertIsInstance(__lowerCamelCase,__lowerCamelCase )

        self.assertEqual((2, 6),batch.input_ids.shape )
        self.assertEqual((2, 6),batch.attention_mask.shape )
        A__											=						batch.input_ids.tolist()[0]
        self.assertListEqual(__lowerCamelCase,__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        if not self.test_rust_tokenizer:
               return

        A__											=						self.get_tokenizer()
        A__											=						self.get_rust_tokenizer()

        A__											=						'''I was born in 92000, and this is falsé.'''

        A__											=						tokenizer.tokenize(__lowerCamelCase )
        A__											=						rust_tokenizer.tokenize(__lowerCamelCase )
        self.assertListEqual(__lowerCamelCase,__lowerCamelCase )

        A__											=						tokenizer.encode(__lowerCamelCase,add_special_tokens=__lowerCamelCase )
        A__											=						rust_tokenizer.encode(__lowerCamelCase,add_special_tokens=__lowerCamelCase )
        self.assertListEqual(__lowerCamelCase,__lowerCamelCase )

        A__											=						self.get_rust_tokenizer()
        A__											=						tokenizer.encode(__lowerCamelCase )
        A__											=						rust_tokenizer.encode(__lowerCamelCase )
        self.assertListEqual(__lowerCamelCase,__lowerCamelCase )




 @slow
 def 				UpperCamelCase			(							self ):
        # fmt: off
        A__											=						{'''input_ids''': [[0, 490, 1_4328, 4507, 354, 47, 4_3669, 95, 25, 7_8117, 2_0215, 1_9779, 190, 22, 400, 4, 3_5343, 8_0310, 603, 86, 2_4937, 105, 3_3438, 9_4762, 196, 3_9642, 7, 15, 1_5933, 173, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 1_0534, 87, 25, 66, 3358, 196, 5_5289, 8, 8_2961, 81, 2204, 7_5203, 7, 15, 763, 1_2956, 216, 178, 1_4328, 9595, 1377, 6_9693, 7, 448, 7_1021, 196, 1_8106, 1437, 1_3974, 108, 9083, 4, 4_9315, 7, 39, 86, 1326, 2793, 4_6333, 4, 448, 196, 7_4588, 7, 4_9315, 7, 39, 21, 822, 3_8470, 74, 21, 6_6723, 6_2480, 8, 2_2050, 5, 2]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
        # fmt: on

        # moussaKam/mbarthez is a french model. So we also use french texts.
        A__											=						[
            '''Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '''
            '''utilisé principalement dans le domaine du traitement automatique des langues (TAL).''',
            '''À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '''
            '''pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '''
            '''telles que la traduction et la synthèse de texte.''',
        ]

        self.tokenizer_integration_test_util(
            expected_encoding=__lowerCamelCase,model_name='''moussaKam/mbarthez''',revision='''c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6''',sequences=__lowerCamelCase,)












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)

a__:      List[Any]									=		logging.getLogger()


def 	UpperCamelCase__(       )->Union[str, Any]:
       A__											=						argparse.ArgumentParser()
       parser.add_argument('''-f'''  )
       A__											=						parser.parse_args()
       return args.f




class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__      ):



 def 				UpperCamelCase			(							self ):
        A__											=						logging.StreamHandler(sys.stdout )
        logger.addHandler(__lowerCamelCase )



 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        A__											=						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 ):
                      A__											=						run_glue_deebert.main()
                      for value in result.values():
                             self.assertGreaterEqual(__lowerCamelCase,0.666 )




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

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

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





