



"""simple docstring"""






from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
   import datasets






class 			__snake_case			(      _lowercase):




  @slow
  @require_torch
  def 		SCREAMING_SNAKE_CASE							(					self	:		Optional[Any]						):






         """simple docstring"""






         _lowerCamelCase		:							List[Any]            =					EncoderDecoderModel.from_encoder_decoder_pretrained('''prajjwal1/bert-tiny'''		,   '''prajjwal1/bert-tiny'''						)
         _lowerCamelCase		:							Optional[Any]            =					BertTokenizer.from_pretrained('''bert-base-uncased'''						)

         _lowerCamelCase		:							List[Any]            =					bertabert.config.encoder.vocab_size
         _lowerCamelCase		:							Optional[int]            =					tokenizer.sep_token_id
         _lowerCamelCase		:							Any            =					tokenizer.cls_token_id
         _lowerCamelCase		:							Tuple            =					1_2_8

         _lowerCamelCase		:							str            =					datasets.load_dataset('''cnn_dailymail'''		,   '''3.0.0'''		,   split='''train[:1%]'''						)
         _lowerCamelCase		:							List[Any]            =					datasets.load_dataset('''cnn_dailymail'''		,   '''3.0.0'''		,   split='''validation[:1%]'''						)

         _lowerCamelCase		:							Union[str, Any]            =					train_dataset.select(range(3_2						)						)
         _lowerCamelCase		:							Tuple            =					val_dataset.select(range(1_6						)						)

         _lowerCamelCase		:							Optional[Any]            =					4

         def _map_to_encoder_decoder_inputs(__lowerCAmelCase	:		Union[str, Any]						):
                # Tokenizer will automatically set [BOS] <text> [EOS]
                _lowerCamelCase		:							Union[str, Any]            =					tokenizer(batch['''article''']		,   padding='''max_length'''		,   truncation=__lowerCAmelCase		,   max_length=5_1_2						)
                _lowerCamelCase		:							Optional[Any]            =					tokenizer(batch['''highlights''']		,   padding='''max_length'''		,   truncation=__lowerCAmelCase		,   max_length=1_2_8						)
                _lowerCamelCase		:							Union[str, Any]            =					inputs.input_ids
                _lowerCamelCase		:							Dict            =					inputs.attention_mask

                _lowerCamelCase		:							Union[str, Any]            =					outputs.input_ids
                _lowerCamelCase		:							Optional[int]            =					outputs.input_ids.copy()
                _lowerCamelCase		:							List[str]            =					[
                    [-1_0_0 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['''labels''']
                ]
                _lowerCamelCase		:							List[Any]            =					outputs.attention_mask

                assert all(len(__lowerCAmelCase						) == 5_1_2 for x in inputs.input_ids						)
                assert all(len(__lowerCAmelCase						) == 1_2_8 for x in outputs.input_ids						)

                return batch

         def _compute_metrics(__lowerCAmelCase	:		List[str]						):
                _lowerCamelCase		:							Union[str, Any]            =					pred.label_ids
                _lowerCamelCase		:							List[Any]            =					pred.predictions

                # all unnecessary tokens are removed
                _lowerCamelCase		:							Optional[int]            =					tokenizer.batch_decode(__lowerCAmelCase		,   skip_special_tokens=__lowerCAmelCase						)
                _lowerCamelCase		:							Tuple            =					tokenizer.batch_decode(__lowerCAmelCase		,   skip_special_tokens=__lowerCAmelCase						)

                _lowerCamelCase		:							List[str]            =					sum([int(pred_str[i] == label_str[i]						) for i in range(len(__lowerCAmelCase						)						)]						) / len(__lowerCAmelCase						)

                return {"accuracy": accuracy}

         # map train dataset
         _lowerCamelCase		:							Optional[Any]            =					train_dataset.map(
             _map_to_encoder_decoder_inputs		,   batched=__lowerCAmelCase		,   batch_size=__lowerCAmelCase		,   remove_columns=['''article''', '''highlights''']		,   )
         train_dataset.set_format(
             type='''torch'''		,   columns=['''input_ids''', '''attention_mask''', '''decoder_input_ids''', '''decoder_attention_mask''', '''labels''']		,   )

         # same for validation dataset
         _lowerCamelCase		:							Union[str, Any]            =					val_dataset.map(
             _map_to_encoder_decoder_inputs		,   batched=__lowerCAmelCase		,   batch_size=__lowerCAmelCase		,   remove_columns=['''article''', '''highlights''']		,   )
         val_dataset.set_format(
             type='''torch'''		,   columns=['''input_ids''', '''attention_mask''', '''decoder_input_ids''', '''decoder_attention_mask''', '''labels''']		,   )

         _lowerCamelCase		:							Any            =					self.get_auto_remove_tmp_dir()

         _lowerCamelCase		:							Dict            =					SeqaSeqTrainingArguments(
             output_dir=__lowerCAmelCase		,   per_device_train_batch_size=__lowerCAmelCase		,   per_device_eval_batch_size=__lowerCAmelCase		,   predict_with_generate=__lowerCAmelCase		,   evaluation_strategy='''steps'''		,   do_train=__lowerCAmelCase		,   do_eval=__lowerCAmelCase		,   warmup_steps=0		,   eval_steps=2		,   logging_steps=2		,   )

         # instantiate trainer
         _lowerCamelCase		:							Optional[Any]            =					SeqaSeqTrainer(
             model=__lowerCAmelCase		,   args=__lowerCAmelCase		,   compute_metrics=_compute_metrics		,   train_dataset=__lowerCAmelCase		,   eval_dataset=__lowerCAmelCase		,   tokenizer=__lowerCAmelCase		,   )

         # start training
         trainer.train()









'''simple docstring'''






import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin




class 					UpperCAmelCase	(     snake_case_				,						unittest.TestCase  ):
      _lowercase:     List[str]			=  DebertaTokenizer
      _lowercase:     Union[str, Any]			=  True
      _lowercase:     Union[str, Any]			=  DebertaTokenizerFast




      def 							lowercase__       (     self     :					Any    )							-> List[Any]:
            super().setUp()

            # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
            _lowerCAmelCase					=				[
                """l""",
                """o""",
                """w""",
                """e""",
                """r""",
                """s""",
                """t""",
                """i""",
                """d""",
                """n""",
                """\u0120""",
                """\u0120l""",
                """\u0120n""",
                """\u0120lo""",
                """\u0120low""",
                """er""",
                """\u0120lowest""",
                """\u0120newer""",
                """\u0120wider""",
                """[UNK]""",
            ]
            _lowerCAmelCase					=				dict(zip(__snake_case     ,     range(len(__snake_case    )    )    )    )
            _lowerCAmelCase					=				["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
            _lowerCAmelCase					=				{"""unk_token""": """[UNK]"""}

            _lowerCAmelCase					=				os.path.join(self.tmpdirname     ,     VOCAB_FILES_NAMES["""vocab_file"""]    )
            _lowerCAmelCase					=				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(__snake_case    ) + """\n"""    )
            with open(self.merges_file     ,     """w"""     ,     encoding="""utf-8"""    ) as fp:
                  fp.write("""\n""".join(__snake_case    )    )




      def 							lowercase__       (     self     :					List[str]     ,     **__snake_case     :					Any    )							-> Dict:
            kwargs.update(self.special_tokens_map    )
            return self.tokenizer_class.from_pretrained(self.tmpdirname     ,     **__snake_case    )




      def 							lowercase__       (     self     :					Dict     ,     __snake_case     :					Dict    )							-> Optional[Any]:
            _lowerCAmelCase					=				"""lower newer"""
            _lowerCAmelCase					=				"""lower newer"""
            return input_text, output_text




      def 							lowercase__       (     self     :					Union[str, Any]    )							-> List[str]:
            _lowerCAmelCase					=				self.get_tokenizer()
            _lowerCAmelCase					=				"""lower newer"""
            _lowerCAmelCase					=				["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
            _lowerCAmelCase					=				tokenizer.tokenize(__snake_case    )
            self.assertListEqual(__snake_case     ,     __snake_case    )

            _lowerCAmelCase					=				tokens + [tokenizer.unk_token]
            _lowerCAmelCase					=				[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
            self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case    )     ,     __snake_case    )




      def 							lowercase__       (     self     :					Union[str, Any]    )							-> Tuple:
            _lowerCAmelCase					=				self.get_tokenizer()
            _lowerCAmelCase					=				tokenizer("""Hello"""     ,     """World"""    )
            _lowerCAmelCase					=				[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
            self.assertListEqual(tokd["""token_type_ids"""]     ,     __snake_case    )




      @slow
      def 							lowercase__       (     self     :					int    )							-> Union[str, Any]:
            _lowerCAmelCase					=				self.tokenizer_class.from_pretrained("""microsoft/deberta-base"""    )

            _lowerCAmelCase					=				tokenizer.encode("""sequence builders"""     ,     add_special_tokens=__snake_case    )
            _lowerCAmelCase					=				tokenizer.encode("""multi-sequence build"""     ,     add_special_tokens=__snake_case    )

            _lowerCAmelCase					=				tokenizer.encode(
                """sequence builders"""     ,     add_special_tokens=__snake_case     ,     add_prefix_space=__snake_case    )
            _lowerCAmelCase					=				tokenizer.encode(
                """sequence builders"""     ,     """multi-sequence build"""     ,     add_special_tokens=__snake_case     ,     add_prefix_space=__snake_case    )

            _lowerCAmelCase					=				tokenizer.build_inputs_with_special_tokens(__snake_case    )
            _lowerCAmelCase					=				tokenizer.build_inputs_with_special_tokens(__snake_case     ,     __snake_case    )

            assert encoded_sentence == encoded_text_from_decode
            assert encoded_pair == encoded_pair_from_decode




      @slow
      def 							lowercase__       (     self     :					Any    )							-> str:
            _lowerCAmelCase					=				[self.tokenizer_class]
            if self.test_rust_tokenizer:
                  tokenizer_classes.append(self.rust_tokenizer_class    )

            for tokenizer_class in tokenizer_classes:
                  _lowerCAmelCase					=				tokenizer_class.from_pretrained("""microsoft/deberta-base"""    )

                  _lowerCAmelCase					=				[
                      """ALBERT: A Lite BERT for Self-supervised Learning of Language Representations""",
                      """ALBERT incorporates two parameter reduction techniques""",
                      """The first one is a factorized embedding parameterization. By decomposing the large vocabulary"""
                      """ embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"""
                      """ vocabulary embedding.""",
                  ]

                  _lowerCAmelCase					=				tokenizer(__snake_case     ,     padding=__snake_case    )
                  _lowerCAmelCase					=				[tokenizer.decode(__snake_case     ,     skip_special_tokens=__snake_case    ) for seq in encoding["""input_ids"""]]

                  # fmt: off
                  _lowerCAmelCase					=				{
                      """input_ids""": [
                          [1, 21_18, 1_11_26, 5_65, 35, 83, 2_51_91, 1_63, 1_88_54, 13, 1_21_56, 12, 1_61_01, 2_53_76, 1_38_07, 9, 2_22_05, 2_78_93, 16_35, 2, 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, 21_18, 1_11_26, 5_65, 2_45_36, 80, 4_37_97, 48_78, 73_73, 2, 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, 0, 0, 0, 0, 0, 0, 0],
                          [1, 1_33, 78, 65, 16, 10, 37_24, 15_38, 3_31_83, 1_13_03, 4_37_97, 19_38, 4, 8_70, 2_41_65, 2_91_05, 5, 7_39, 3_26_44, 3_31_83, 1_13_03, 3_61_73, 88, 80, 6_50, 78_21, 4_59_40, 6, 52, 25_59, 5, 18_36, 9, 5, 73_97, 1_31_71, 31, 5, 18_36, 9, 3_26_44, 3_31_83, 1_13_03, 4, 2]
                      ],
                      """token_type_ids""": [
                          [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, 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, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
                      ],
                      """attention_mask""": [
                          [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],
                          [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, 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]
                      ]
                  }
                  # fmt: on

                  _lowerCAmelCase					=				[
                      """ALBERT: A Lite BERT for Self-supervised Learning of Language Representations""",
                      """ALBERT incorporates two parameter reduction techniques""",
                      """The first one is a factorized embedding parameterization. By decomposing the large vocabulary"""
                      """ embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"""
                      """ vocabulary embedding.""",
                  ]

                  self.assertDictEqual(encoding.data     ,     __snake_case    )

                  for expected, decoded in zip(__snake_case     ,     __snake_case    ):
                        self.assertEqual(__snake_case     ,     __snake_case    )






