





'''simple docstring'''


import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


lowerCAmelCase					:     Tuple												=get_tests_dir('''fixtures/test_sentencepiece.model''')
lowerCAmelCase					:     List[Any]												=get_tests_dir('''fixtures/test_sentencepiece_bpe.model''')

lowerCAmelCase					:     List[str]												='''pt''' if is_torch_available() else '''tf'''
@require_sentencepiece
@require_tokenizers
class       a_   (				_lowerCAmelCase	,						unittest.TestCase							):
     __A      =			CamembertTokenizer
     __A      =			CamembertTokenizerFast
     __A      =			True
     __A      =			True





     def 							lowercase__      ( self		:					Tuple						):





        """simple docstring"""




        super().setUp()

        # We have a SentencePiece fixture for testing
        lowercase_			:Tuple    			= CamembertTokenizer(lowercase						)
        tokenizer.save_pretrained(self.tmpdirname						)





     def 							lowercase__      ( self		:					Union[str, Any]						):





        """simple docstring"""




        lowercase_			:Optional[Any]    			= "<pad>"
        lowercase_			:Tuple    			= 1

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





     def 							lowercase__      ( self		:					Dict						):





        """simple docstring"""




        lowercase_			:int    			= list(self.get_tokenizer().get_vocab().keys()						)

        self.assertEqual(vocab_keys[0]	,      "<s>NOTUSED"						)
        self.assertEqual(vocab_keys[1]	,      "<pad>"						)
        self.assertEqual(vocab_keys[-1]	,      "<mask>"						)
        self.assertEqual(len(lowercase						)	,      1_004						)





     def 							lowercase__      ( self		:					List[Any]						):





        """simple docstring"""




        self.assertEqual(self.get_tokenizer().vocab_size	,      1_005						)





     def 							lowercase__      ( self		:					Dict						):





        """simple docstring"""




        lowercase_			:str    			= CamembertTokenizer(lowercase						)
        tokenizer.save_pretrained(self.tmpdirname						)
        lowercase_			:Optional[Any]    			= CamembertTokenizerFast.from_pretrained(self.tmpdirname						)

        lowercase_			:Dict    			= "I was born in 92000, and this is falsé."

        lowercase_			:Tuple    			= tokenizer.encode(lowercase						)
        lowercase_			:List[Any]    			= rust_tokenizer.encode(lowercase						)
        self.assertListEqual(lowercase	,      lowercase						)

        lowercase_			:Any    			= tokenizer.encode(lowercase	,      add_special_tokens=lowercase						)
        lowercase_			:Optional[Any]    			= rust_tokenizer.encode(lowercase	,      add_special_tokens=lowercase						)
        self.assertListEqual(lowercase	,      lowercase						)

        # <unk> tokens are not the same for `rust` than for `slow`.
        # Because spm gives back raw token instead of `unk` in EncodeAsPieces
        # tokens = tokenizer.tokenize(sequence)
        lowercase_			:int    			= tokenizer.convert_ids_to_tokens(lowercase						)
        lowercase_			:Optional[Any]    			= rust_tokenizer.tokenize(lowercase						)
        self.assertListEqual(lowercase	,      lowercase						)





     def 							lowercase__      ( self		:					Optional[int]						):





        """simple docstring"""




        if not self.test_rust_tokenizer:
           return

        lowercase_			:Optional[Any]    			= self.get_tokenizer()
        lowercase_			:Any    			= self.get_rust_tokenizer()

        lowercase_			:Union[str, Any]    			= "I was born in 92000, and this is falsé."

        lowercase_			:Tuple    			= tokenizer.tokenize(lowercase						)
        lowercase_			:Tuple    			= rust_tokenizer.tokenize(lowercase						)
        self.assertListEqual(lowercase	,      lowercase						)

        lowercase_			:Tuple    			= tokenizer.encode(lowercase	,      add_special_tokens=lowercase						)
        lowercase_			:Tuple    			= rust_tokenizer.encode(lowercase	,      add_special_tokens=lowercase						)
        self.assertListEqual(lowercase	,      lowercase						)

        lowercase_			:Dict    			= self.get_rust_tokenizer()
        lowercase_			:Union[str, Any]    			= tokenizer.encode(lowercase						)
        lowercase_			:Optional[int]    			= rust_tokenizer.encode(lowercase						)
        self.assertListEqual(lowercase	,      lowercase						)







     @slow
     def 							lowercase__      ( self		:					Optional[Any]						):





        """simple docstring"""




        lowercase_			:Dict    			= {"input_ids": [[5, 54, 7_196, 297, 30, 23, 776, 18, 11, 3_215, 3_705, 8_252, 22, 3_164, 1_181, 2_116, 29, 16, 813, 25, 791, 3_314, 20, 3_446, 38, 27_575, 120, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 468, 17, 11, 9_088, 20, 1_517, 8, 22_804, 18_818, 10, 38, 629, 607, 607, 142, 19, 7_196, 867, 56, 10_326, 24, 2_267, 20, 416, 5_072, 15_612, 233, 734, 7, 2_399, 27, 16, 3_015, 1_649, 7, 24, 20, 4_338, 2_399, 27, 13, 3_400, 14, 13, 6_189, 8, 930, 9, 6]], "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, 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]]}  # noqa: E501
        # fmt: on

        # camembert is a french model. So we also use french texts.
        lowercase_			:Optional[Any]    			= [
            "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=lowercase	,      model_name="camembert-base"	,      revision="3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf"	,      sequences=lowercase	,      )









'''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[int]												=logging.getLogger()
def 							UpperCAmelCase_  (						):
   lowercase_			:Union[str, Any]    			= argparse.ArgumentParser()
   parser.add_argument("-f" )
   lowercase_			:Any    			= parser.parse_args()
   return args.f


class       a_   (				_lowerCAmelCase							):





     def 							lowercase__      ( self		:					List[Any]						):





        """simple docstring"""




        lowercase_			:List[Any]    			= logging.StreamHandler(sys.stdout						)
        logger.addHandler(lowercase						)





     def 							lowercase__      ( self		:					str	,      lowercase		:					str						):





        """simple docstring"""




        lowercase_			:Dict    			= 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(lowercase	,      "argv"	,      lowercase						):
              lowercase_			:int    			= run_glue_deebert.main()
              for value in result.values():
                 self.assertGreaterEqual(lowercase	,      0.6_66						)







     @slow
     @require_torch_non_multi_gpu
     def 							lowercase__      ( self		:					Tuple						):





        """simple docstring"""




        lowercase_			:List[Any]    			= "\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(lowercase						)

        lowercase_			:List[str]    			= "\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(lowercase						)

        lowercase_			:Any    			= "\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(lowercase						)


