
import json
import os
import unittest

from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class     lowercase							(	lowerCAmelCase_						,		unittest.TestCase       ):
					__a											=     OpenAIGPTTokenizer
					__a											=     OpenAIGPTTokenizerFast
					__a											=     True
					__a											=     False
					def lowercase_      (		self     ):
								"""simple docstring"""







								super().setUp()

								# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
								lowerCAmelCase__			:     int					   =      [
								    '''l''',
								    '''o''',
								    '''w''',
								    '''e''',
								    '''r''',
								    '''s''',
								    '''t''',
								    '''i''',
								    '''d''',
								    '''n''',
								    '''w</w>''',
								    '''r</w>''',
								    '''t</w>''',
								    '''lo''',
								    '''low''',
								    '''er</w>''',
								    '''low</w>''',
								    '''lowest</w>''',
								    '''newer</w>''',
								    '''wider</w>''',
								    '''<unk>''',
								]
								lowerCAmelCase__			:     Optional[Any]					   =      dict(zip(__lowerCAmelCase			,						range(len(__lowerCAmelCase     )     )     )     )
								lowerCAmelCase__			:     Optional[Any]					   =      ['''#version: 0.2''', '''l o''', '''lo w''', '''e r</w>''', '''''']

								lowerCAmelCase__			:     Tuple					   =      os.path.join(self.tmpdirname			,						VOCAB_FILES_NAMES['''vocab_file''']     )
								lowerCAmelCase__			:     Dict					   =      os.path.join(self.tmpdirname			,						VOCAB_FILES_NAMES['''merges_file''']     )
								with open(self.vocab_file			,						'''w'''     ) as fp:
											fp.write(json.dumps(__lowerCAmelCase     )     )
								with open(self.merges_file			,						'''w'''     ) as fp:
											fp.write('''\n'''.join(__lowerCAmelCase     )     )
					def lowercase_      (		self			,						SCREAMING_SNAKE_CASE__     ):
								"""simple docstring"""







								return "lower newer", "lower newer"
					def lowercase_      (		self     ):
								"""simple docstring"""







								lowerCAmelCase__			:     Optional[Any]					   =      OpenAIGPTTokenizer(self.vocab_file			,						self.merges_file     )

								lowerCAmelCase__			:     Optional[Any]					   =      '''lower'''
								lowerCAmelCase__			:     List[str]					   =      ['''low''', '''er</w>''']
								lowerCAmelCase__			:     Tuple					   =      tokenizer.tokenize(__lowerCAmelCase     )
								self.assertListEqual(__lowerCAmelCase			,						__lowerCAmelCase     )

								lowerCAmelCase__			:     Optional[Any]					   =      tokens + ['''<unk>''']
								lowerCAmelCase__			:     List[str]					   =      [14, 15, 20]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCAmelCase     )			,						__lowerCAmelCase     )
					def lowercase_      (		self			,						SCREAMING_SNAKE_CASE__=15     ):
								"""simple docstring"""







								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''     ):
														lowerCAmelCase__			:     Tuple					   =      self.rust_tokenizer_class.from_pretrained(__lowerCAmelCase			,						**__lowerCAmelCase     )

														# Simple input
														lowerCAmelCase__			:     Dict					   =      '''This is a simple input'''
														lowerCAmelCase__			:     List[Any]					   =      ['''This is a simple input 1''', '''This is a simple input 2''']
														lowerCAmelCase__			:     Union[str, Any]					   =      ('''This is a simple input''', '''This is a pair''')
														lowerCAmelCase__			:     Any					   =      [
														    ('''This is a simple input 1''', '''This is a simple input 2'''),
														    ('''This is a simple pair 1''', '''This is a simple pair 2'''),
														]

														# Simple input tests
														self.assertRaises(__lowerCAmelCase			,						tokenizer_r.encode			,						__lowerCAmelCase			,						max_length=__lowerCAmelCase			,						padding='''max_length'''     )

														# Simple input
														self.assertRaises(__lowerCAmelCase			,						tokenizer_r.encode_plus			,						__lowerCAmelCase			,						max_length=__lowerCAmelCase			,						padding='''max_length'''     )

														# Simple input
														self.assertRaises(
														    __lowerCAmelCase			,						tokenizer_r.batch_encode_plus			,						__lowerCAmelCase			,						max_length=__lowerCAmelCase			,						padding='''max_length'''			,						)

														# Pair input
														self.assertRaises(__lowerCAmelCase			,						tokenizer_r.encode			,						__lowerCAmelCase			,						max_length=__lowerCAmelCase			,						padding='''max_length'''     )

														# Pair input
														self.assertRaises(__lowerCAmelCase			,						tokenizer_r.encode_plus			,						__lowerCAmelCase			,						max_length=__lowerCAmelCase			,						padding='''max_length'''     )

														# Pair input
														self.assertRaises(
														    __lowerCAmelCase			,						tokenizer_r.batch_encode_plus			,						__lowerCAmelCase			,						max_length=__lowerCAmelCase			,						padding='''max_length'''			,						)
					def lowercase_      (		self     ):
								"""simple docstring"""







								pass





@require_ftfy
@require_spacy
@require_tokenizers
class     lowercase							(	lowerCAmelCase_       ):
					pass




"""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 							a					(		lowerCAmelCase_ ):


    @slow
    @require_torch
    def   lowerCAmelCase_	(       self						:	str       ):
      _UpperCAmelCase    			=   EncoderDecoderModel.from_encoder_decoder_pretrained("""prajjwal1/bert-tiny""" ,					"""prajjwal1/bert-tiny"""       )
      _UpperCAmelCase    			=   BertTokenizer.from_pretrained("""bert-base-uncased"""       )

      _UpperCAmelCase    			=   bertabert.config.encoder.vocab_size
      _UpperCAmelCase    			=   tokenizer.sep_token_id
      _UpperCAmelCase    			=   tokenizer.cls_token_id
      _UpperCAmelCase    			=   128

      _UpperCAmelCase    			=   datasets.load_dataset("""cnn_dailymail""" ,					"""3.0.0""" ,					split="""train[:1%]"""       )
      _UpperCAmelCase    			=   datasets.load_dataset("""cnn_dailymail""" ,					"""3.0.0""" ,					split="""validation[:1%]"""       )

      _UpperCAmelCase    			=   train_dataset.select(range(32       )       )
      _UpperCAmelCase    			=   val_dataset.select(range(16       )       )

      _UpperCAmelCase    			=   4

      def _map_to_encoder_decoder_inputs(__lowerCAmelCase						:	Dict       ):
        # Tokenizer will automatically set [BOS] <text> [EOS]
        _UpperCAmelCase    			=   tokenizer(batch["""article"""] ,					padding="""max_length""" ,					truncation=__lowerCAmelCase ,					max_length=512       )
        _UpperCAmelCase    			=   tokenizer(batch["""highlights"""] ,					padding="""max_length""" ,					truncation=__lowerCAmelCase ,					max_length=128       )
        _UpperCAmelCase    			=   inputs.input_ids
        _UpperCAmelCase    			=   inputs.attention_mask

        _UpperCAmelCase    			=   outputs.input_ids
        _UpperCAmelCase    			=   outputs.input_ids.copy()
        _UpperCAmelCase    			=   [
            [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["""labels"""]
        ]
        _UpperCAmelCase    			=   outputs.attention_mask

        assert all(len(__lowerCAmelCase       ) == 512 for x in inputs.input_ids       )
        assert all(len(__lowerCAmelCase       ) == 128 for x in outputs.input_ids       )

        return batch

      def _compute_metrics(__lowerCAmelCase						:	int       ):
        _UpperCAmelCase    			=   pred.label_ids
        _UpperCAmelCase    			=   pred.predictions

        # all unnecessary tokens are removed
        _UpperCAmelCase    			=   tokenizer.batch_decode(__lowerCAmelCase ,					skip_special_tokens=__lowerCAmelCase       )
        _UpperCAmelCase    			=   tokenizer.batch_decode(__lowerCAmelCase ,					skip_special_tokens=__lowerCAmelCase       )

        _UpperCAmelCase    			=   sum([int(pred_str[i] == label_str[i]       ) for i in range(len(__lowerCAmelCase       )       )]       ) / len(__lowerCAmelCase       )

        return {"accuracy": accuracy}

      # map train dataset
      _UpperCAmelCase    			=   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
      _UpperCAmelCase    			=   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"""] ,					)

      _UpperCAmelCase    			=   self.get_auto_remove_tmp_dir()

      _UpperCAmelCase    			=   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
      _UpperCAmelCase    			=   SeqaSeqTrainer(
          model=__lowerCAmelCase ,					args=__lowerCAmelCase ,					compute_metrics=_compute_metrics ,					train_dataset=__lowerCAmelCase ,					eval_dataset=__lowerCAmelCase ,					tokenizer=__lowerCAmelCase ,					)

      # start training
      trainer.train()
