



import unittest

from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


SCREAMING_SNAKE_CASE__   			=   get_tests_dir('''fixtures/test_sentencepiece_with_bytefallback.model''')


@require_sentencepiece
@require_tokenizers
class   __lowerCAmelCase  (		UpperCAmelCase_				,  unittest.TestCase   ):
					"""simple docstring"""






					A__					:		Union[str, Any]      							=     GPTSwaTokenizer
					A__					:		Dict      							=     False
					A__					:		List[str]      							=     True
					A__					:		Optional[Any]      							=     False



					def      _a				(  self       :							str       ):






								"""simple docstring"""


								super().setUp()

								# We have a SentencePiece fixture for testing
								A__	  =     GPTSwaTokenizer(_snake_case					,       eos_token='<unk>'					,       bos_token='<unk>'					,       pad_token='<unk>'       )

								tokenizer.save_pretrained(self.tmpdirname       )



					def      _a				(  self       :							Any					,       _snake_case       :							Tuple       ):






								"""simple docstring"""


								A__	  =     'This is a test'
								A__	  =     'This is a test'
								return input_text, output_text



					def      _a				(  self       :							Any       ):






								"""simple docstring"""


								A__	  =     '<s>'
								A__	  =     1

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



					def      _a				(  self       :							Optional[Any]       ):






								"""simple docstring"""


								A__	  =     list(self.get_tokenizer().get_vocab().keys()       )

								self.assertEqual(vocab_keys[0]					,       '<unk>'       )
								self.assertEqual(vocab_keys[1]					,       '<s>'       )
								self.assertEqual(vocab_keys[-1]					,       'j'       )
								self.assertEqual(len(_snake_case       )					,       20_00       )



					def      _a				(  self       :							Any       ):






								"""simple docstring"""


								self.assertEqual(self.get_tokenizer().vocab_size					,       20_00       )



					def      _a				(  self       :							str       ):






								"""simple docstring"""


								A__	  =     GPTSwaTokenizer(_snake_case       )

								A__	  =     tokenizer.tokenize('This is a test'       )
								self.assertListEqual(_snake_case					,       ['▁This', '▁is', '▁a', '▁t', 'est']       )

								self.assertListEqual(tokenizer.convert_tokens_to_ids(_snake_case       )					,       [4_65, 2_87, 2_65, 6_31, 8_42]       )

								A__	  =     tokenizer.tokenize('I was born in 92000, and this is falsé.'       )
								# fmt: off
								self.assertListEqual(
								    _snake_case					,       ['▁I', '▁was', '▁bor', 'n', '▁in', '▁', '<0x39>', '2', '0', '0', '0', ',', '▁and', '▁this', '▁is', '▁f', 'al', 's', '<0xC3>', '<0xA9>', '.']					,       )
								# fmt: on

								A__	  =     tokenizer.convert_tokens_to_ids(_snake_case       )
								self.assertListEqual(
								    _snake_case					,       [2_62, 2_72, 15_25, 2_86, 2_71, 2_68, 60, 9_16, 6_33, 6_33, 6_33, 2_59, 2_66, 3_01, 2_87, 3_84, 3_67, 2_63, 1_98, 1_72, 2_60]					,       )

								A__	  =     tokenizer.convert_ids_to_tokens(_snake_case       )
								# fmt: off
								self.assertListEqual(
								    _snake_case					,       ['▁I', '▁was', '▁bor', 'n', '▁in', '▁', '<0x39>', '2', '0', '0', '0', ',', '▁and', '▁this', '▁is', '▁f', 'al', 's', '<0xC3>', '<0xA9>', '.']       )
								# fmt: on



					def      _a				(  self       :							Tuple       ):






								"""simple docstring"""


								A__	  =     GPTSwaTokenizer(_snake_case       )
								A__	  =     ['This is a test', 'I was born in 92000, and this is falsé.']
								A__	  =     [
								    [4_65, 2_87, 2_65, 6_31, 8_42],
								    [2_62, 2_72, 15_25, 2_86, 2_71, 2_68, 60, 9_16, 6_33, 6_33, 6_33, 2_59, 2_66, 3_01, 2_87, 3_84, 3_67, 2_63, 1_98, 1_72, 2_60],
								]

								# Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
								for text, expected_ids in zip(_snake_case					,       _snake_case       ):
											self.assertListEqual(tokenizer.encode_fast(_snake_case       )					,       _snake_case       )

								# Test that decode_fast returns the input text
								for text, token_ids in zip(_snake_case					,       _snake_case       ):
											self.assertEqual(tokenizer.decode_fast(_snake_case       )					,       _snake_case       )



					@slow
					def      _a				(  self       :							List[Any]       ):






								"""simple docstring"""


								A__	  =     [
								    '<|python|>def fibonacci(n)\n    if n < 0:\n        print(\'Incorrect input\')',
								    'Hey there, how are you doing this fine day?',
								    'This is a text with a trailing spaces followed by a dot     .',
								    'Häj sväjs lillebrör! =)',
								    'Det är inget fel på Mr. Cool',
								]

								# fmt: off
								A__	  =     {'input_ids': [[6_34_23, 5, 68_11, 1_49_54, 2_82, 8_16, 38_21, 6_34_66, 6_34_25, 6_34_62, 18, 6_39_78, 6_78, 3_01, 13_20, 6_34_23, 6_34_55, 6_34_58, 18, 6_39_82, 42_46, 39_40, 19_01, 4_77_89, 55_47, 1_89_94], [1_96_30, 11_00, 6_34_46, 13_42, 6_33, 5_44, 44_88, 5_93, 51_02, 24_16, 6_34_95, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [16_52, 4_28, 2_68, 19_36, 5_15, 2_68, 5_85_93, 2_24_13, 91_06, 5_46, 2_68, 3_32_13, 6_39_79, 6_98, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_51_30, 6_34_50, 9_24, 6_34_49, 22_49, 40_62, 15_58, 3_18, 6_35_04, 2_14_98, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_09, 3_77, 28_27, 25_59, 3_32, 65_75, 6_34_43, 2_68_01, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '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]], '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, 1, 1, 1, 1, 1, 1, 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, 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], [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]]}
								# fmt: on
								self.tokenizer_integration_test_util(
								    expected_encoding=_snake_case					,       model_name='AI-Sweden/gpt-sw3-126m'					,       sequences=_snake_case					,       )







import os
import re
import shutil
import sys
import tempfile
import unittest

import black


lowercase_   							=  os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If BertLMPredictionHead is changed in modeling_bert.py, this code needs to be manually updated.
lowercase_   							=  '    def __init__(self, config):\n        super().__init__()\n        self.transform = BertPredictionHeadTransform(config)\n\n        # The output weights are the same as the input embeddings, but there is\n        # an output-only bias for each token.\n        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)\n\n        self.bias = nn.Parameter(torch.zeros(config.vocab_size))\n\n        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`\n        self.decoder.bias = self.bias\n\n    def forward(self, hidden_states):\n        hidden_states = self.transform(hidden_states)\n        hidden_states = self.decoder(hidden_states)\n        return hidden_states\n'





class 					__lowerCAmelCase	(   unittest.TestCase				):




  def     A__	(							self   )    ->					Tuple:
        '''simple docstring'''






        _lowercase      			=tempfile.mkdtemp()
        os.makedirs(os.path.join(self.transformer_dir				,  'models/bert/'   )   )
        _lowercase      			=self.transformer_dir
        shutil.copy(
            os.path.join(lowerCAmelCase				,  'src/transformers/models/bert/modeling_bert.py'   )				,  os.path.join(self.transformer_dir				,  'models/bert/modeling_bert.py'   )				,  )




  def     A__	(							self   )    ->					Optional[int]:
        '''simple docstring'''






        _lowercase      			='src/transformers'
        shutil.rmtree(self.transformer_dir   )




  def     A__	(							self				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase=None   )    ->					str:
        '''simple docstring'''






        _lowercase      			=comment + F'''\nclass {class_name}(nn.Module):\n''' + class_code
        if overwrite_result is not None:
              _lowercase      			=comment + F'''\nclass {class_name}(nn.Module):\n''' + overwrite_result
        _lowercase      			=black.Mode(target_versions={black.TargetVersion.PYaa}				,  line_length=119   )
        _lowercase      			=black.format_str(lowerCAmelCase				,  mode=lowerCAmelCase   )
        _lowercase      			=os.path.join(self.transformer_dir				,  'new_code.py'   )
        with open(lowerCAmelCase				,  'w'				,  newline='\n'   ) as f:
              f.write(lowerCAmelCase   )
        if overwrite_result is None:
              self.assertTrue(len(check_copies.is_copy_consistent(lowerCAmelCase   )   ) == 0   )
        else:
              check_copies.is_copy_consistent(f.name				,  overwrite=lowerCAmelCase   )
              with open(lowerCAmelCase				,  'r'   ) as f:
                    self.assertTrue(f.read()				,  lowerCAmelCase   )




  def     A__	(							self   )    ->					Dict:
        '''simple docstring'''






        _lowercase      			=check_copies.find_code_in_transformers('models.bert.modeling_bert.BertLMPredictionHead'   )
        self.assertEqual(lowerCAmelCase				,  lowerCAmelCase   )




  def     A__	(							self   )    ->					str:
        '''simple docstring'''






        self.check_copy_consistency(
            '# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead'				,  'BertLMPredictionHead'				,  REFERENCE_CODE + '\n'				,  )

        # With no empty line at the end
        self.check_copy_consistency(
            '# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead'				,  'BertLMPredictionHead'				,  lowerCAmelCase				,  )

        # Copy consistency with rename
        self.check_copy_consistency(
            '# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel'				,  'TestModelLMPredictionHead'				,  re.sub('Bert'				,  'TestModel'				,  lowerCAmelCase   )				,  )

        # Copy consistency with a really long name
        _lowercase      			='TestModelWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'
        self.check_copy_consistency(
            F'''# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->{long_class_name}'''				,  F'''{long_class_name}LMPredictionHead'''				,  re.sub('Bert'				,  lowerCAmelCase				,  lowerCAmelCase   )				,  )

        # Copy consistency with overwrite
        self.check_copy_consistency(
            '# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel'				,  'TestModelLMPredictionHead'				,  lowerCAmelCase				,  overwrite_result=re.sub('Bert'				,  'TestModel'				,  lowerCAmelCase   )				,  )




  def     A__	(							self   )    ->					List[str]:
        '''simple docstring'''






        _lowercase      			=check_copies.LOCALIZED_READMES['README_zh-hans.md']

        _lowercase      			=(
            '1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the'
            ' Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for'
            ' Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong'
            ' Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.\n1.'
            ' **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (from HuggingFace),'
            ' released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and'
            ' lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same'
            ' method has been applied to compress GPT2 into'
            ' [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into'
            ' [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),'
            ' Multilingual BERT into'
            ' [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German'
            ' version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)**'
            ' (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders'
            ' as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang'
            ' Luong, Quoc V. Le, Christopher D. Manning.'
        )
        _lowercase      			=(
            '1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'
            ' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
            ' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
            ' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'
        )
        _lowercase      			=(
            '1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'
            ' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
            ' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
            ' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n1.'
            ' **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (来自 HuggingFace) 伴随论文'
            ' [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and'
            ' lighter](https://arxiv.org/abs/1910.01108) 由 Victor Sanh, Lysandre Debut and Thomas Wolf 发布。 The same'
            ' method has been applied to compress GPT2 into'
            ' [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into'
            ' [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),'
            ' Multilingual BERT into'
            ' [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German'
            ' version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)** (来自'
            ' Google Research/Stanford University) 伴随论文 [ELECTRA: Pre-training text encoders as discriminators rather'
            ' than generators](https://arxiv.org/abs/2003.10555) 由 Kevin Clark, Minh-Thang Luong, Quoc V. Le,'
            ' Christopher D. Manning 发布。\n'
        )

        _lowercase			,			_lowercase      			=check_copies.convert_to_localized_md(
            lowerCAmelCase				,  lowerCAmelCase				,  localized_readme['format_model_list']   )

        self.assertFalse(lowerCAmelCase   )
        self.assertEqual(lowerCAmelCase				,  lowerCAmelCase   )

        _lowercase			,			_lowercase      			=check_copies.convert_to_localized_md(
            lowerCAmelCase				,  lowerCAmelCase				,  localized_readme['format_model_list']   )

        # Check whether the number of models is equal to README.md after conversion.
        self.assertTrue(lowerCAmelCase   )

        _lowercase      			=(
            '1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the'
            ' Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for'
            ' Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong'
            ' Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.'
        )
        _lowercase      			=(
            '1. **[ALBERT](https://huggingface.co/transformers/main/model_doc/albert.html)** (来自 Google Research and'
            ' the Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
            ' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
            ' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'
        )
        _lowercase      			=(
            '1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'
            ' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
            ' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
            ' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'
        )

        _lowercase			,			_lowercase      			=check_copies.convert_to_localized_md(
            lowerCAmelCase				,  lowerCAmelCase				,  localized_readme['format_model_list']   )

        # Check if the model link is synchronized.
        self.assertEqual(lowerCAmelCase				,  lowerCAmelCase   )


