





"""simple docstring"""





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

import black


__A											=						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.
__A											=						"""    def __init__(self, config):
        super().__init__()
        self.transform = BertPredictionHeadTransform(config)

        # The output weights are the same as the input embeddings, but there is
        # an output-only bias for each token.
        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

        self.bias = nn.Parameter(torch.zeros(config.vocab_size))

        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
        self.decoder.bias = self.bias

    def forward(self, hidden_states):
        hidden_states = self.transform(hidden_states)
        hidden_states = self.decoder(hidden_states)
        return hidden_states
"""






class    _lowerCAmelCase  (	unittest.TestCase			):
       """simple docstring"""




       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[Any]											=				tempfile.mkdtemp()
         os.makedirs(os.path.join(self.transformer_dir       ,					'models/bert/' ) )
         lowerCAmelCase__     :List[Any]											=				self.transformer_dir
         shutil.copy(
             os.path.join(__UpperCAmelCase       ,					'src/transformers/models/bert/modeling_bert.py' )       ,					os.path.join(self.transformer_dir       ,					'models/bert/modeling_bert.py' )       ,					)






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :int											=				'src/transformers'
         shutil.rmtree(self.transformer_dir )






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase=None ):



         '''simple docstring'''

         lowerCAmelCase__     :Union[str, Any]											=				comment + F"\nclass {class_name}(nn.Module):\n" + class_code
         if overwrite_result is not None:
           lowerCAmelCase__     :List[Any]											=				comment + F"\nclass {class_name}(nn.Module):\n" + overwrite_result
         lowerCAmelCase__     :Any											=				black.Mode(target_versions={black.TargetVersion.PYaa}       ,					line_length=1_1_9 )
         lowerCAmelCase__     :int											=				black.format_str(__UpperCAmelCase       ,					mode=__UpperCAmelCase )
         lowerCAmelCase__     :Any											=				os.path.join(self.transformer_dir       ,					'new_code.py' )
         with open(__UpperCAmelCase       ,					'w'       ,					newline='\n' ) as f:
           f.write(__UpperCAmelCase )
         if overwrite_result is None:
           self.assertTrue(len(check_copies.is_copy_consistent(__UpperCAmelCase ) ) == 0 )
         else:
           check_copies.is_copy_consistent(f.name       ,					overwrite=__UpperCAmelCase )
           with open(__UpperCAmelCase       ,					'r' ) as f:
             self.assertTrue(f.read()       ,					__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :str											=				check_copies.find_code_in_transformers('models.bert.modeling_bert.BertLMPredictionHead' )
         self.assertEqual(__UpperCAmelCase       ,					__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''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'       ,					__UpperCAmelCase       ,					)

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

         # Copy consistency with a really long name
         lowerCAmelCase__     :Optional[int]											=				'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'       ,					__UpperCAmelCase       ,					__UpperCAmelCase )       ,					)

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






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Tuple											=				check_copies.LOCALIZED_READMES['README_zh-hans.md']

         lowerCAmelCase__     :Optional[int]											=				(
             '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.'
         )
         lowerCAmelCase__     :Optional[int]											=				(
             '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'
         )
         lowerCAmelCase__     :Any											=				(
             '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'
         )

         lowerCAmelCase__				,							lowerCAmelCase__     :Union[str, Any]											=				check_copies.convert_to_localized_md(
             __UpperCAmelCase       ,					__UpperCAmelCase       ,					localized_readme['format_model_list'] )

         self.assertFalse(__UpperCAmelCase )
         self.assertEqual(__UpperCAmelCase       ,					__UpperCAmelCase )

         lowerCAmelCase__				,							lowerCAmelCase__     :Optional[int]											=				check_copies.convert_to_localized_md(
             __UpperCAmelCase       ,					__UpperCAmelCase       ,					localized_readme['format_model_list'] )

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

         lowerCAmelCase__     :List[str]											=				(
             '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.'
         )
         lowerCAmelCase__     :Dict											=				(
             '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'
         )
         lowerCAmelCase__     :Optional[Any]											=				(
             '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'
         )

         lowerCAmelCase__				,							lowerCAmelCase__     :int											=				check_copies.convert_to_localized_md(
             __UpperCAmelCase       ,					__UpperCAmelCase       ,					localized_readme['format_model_list'] )

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



import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class SCREAMING_SNAKE_CASE	(		__snake_case				,    unittest.TestCase				):






			"""simple docstring"""




			__A    	=	None
			__A    	=	BloomTokenizerFast
			__A    	=	BloomTokenizerFast
			__A    	=	True
			__A    	=	False
			__A    	=	"""tokenizer_file"""
			__A    	=	{"""bos_token""": """<s>""", """eos_token""": """</s>""", """unk_token""": """<unk>""", """pad_token""": """<pad>"""}


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				super().setUp()
				snake_case_   		=			BloomTokenizerFast.from_pretrained('bigscience/tokenizer'				)
				tokenizer.save_pretrained(self.tmpdirname				)


			def       __lowerCAmelCase			(     self			,					**__UpperCamelCase				):

				"""simple docstring"""


				kwargs.update(self.special_tokens_map				)
				return BloomTokenizerFast.from_pretrained(self.tmpdirname			,					**__UpperCamelCase				)


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			self.get_rust_tokenizer()

				snake_case_   		=			['The quick brown fox</s>', 'jumps over the lazy dog</s>']
				snake_case_   		=			[[21_75, 2_37_14, 7_31_73, 14_42_52, 2], [77, 13_26_19, 34_78, 3_68, 10_95_86, 3_54_33, 2]]

				snake_case_   		=			tokenizer.batch_encode_plus(__UpperCamelCase				)['input_ids']
				self.assertListEqual(__UpperCamelCase			,					__UpperCamelCase				)

				snake_case_   		=			tokenizer.batch_decode(__UpperCamelCase				)
				self.assertListEqual(__UpperCamelCase			,					__UpperCamelCase				)


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase=6				):

				"""simple docstring"""


				for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
					with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""				):
						snake_case_   		=			self.rust_tokenizer_class.from_pretrained(__UpperCamelCase			,					**__UpperCamelCase				)
						# tokenizer_r.pad_token = None # Hotfixing padding = None
						# Simple input
						snake_case_   		=			'This is a simple input'
						snake_case_   		=			['This is a simple input 1', 'This is a simple input 2']
						snake_case_   		=			('This is a simple input', 'This is a pair')
						snake_case_   		=			[
						    ('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
						try:
							tokenizer_r.encode(__UpperCamelCase			,					max_length=__UpperCamelCase				)
							tokenizer_r.encode_plus(__UpperCamelCase			,					max_length=__UpperCamelCase				)

							tokenizer_r.batch_encode_plus(__UpperCamelCase			,					max_length=__UpperCamelCase				)
							tokenizer_r.encode(__UpperCamelCase			,					max_length=__UpperCamelCase				)
							tokenizer_r.batch_encode_plus(__UpperCamelCase			,					max_length=__UpperCamelCase				)
						except ValueError:
							self.fail('Bloom Tokenizer should be able to deal with padding'				)

						snake_case_   		=			None  # Hotfixing padding = None
						self.assertRaises(__UpperCamelCase			,					tokenizer_r.encode			,					__UpperCamelCase			,					max_length=__UpperCamelCase			,					padding='max_length'				)

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

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

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

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

						# Pair input
						self.assertRaises(
						    __UpperCamelCase			,					tokenizer_r.batch_encode_plus			,					__UpperCamelCase			,					max_length=__UpperCamelCase			,					padding='max_length'			,					)


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			self.get_rust_tokenizer()
				snake_case_   		=			load_dataset('xnli'			,					'all_languages'			,					split='test'			,					streaming=__UpperCamelCase				)

				snake_case_   		=			next(iter(__UpperCamelCase				)				)['premise']  # pick up one data
				snake_case_   		=			list(sample_data.values()				)

				snake_case_   		=			list(map(tokenizer.encode			,					__UpperCamelCase				)				)
				snake_case_   		=			[tokenizer.decode(__UpperCamelCase			,					clean_up_tokenization_spaces=__UpperCamelCase				) for x in output_tokens]
				self.assertListEqual(__UpperCamelCase			,					__UpperCamelCase				)







			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map				)			,					1				)
				self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values()				)[0]				)			,					1				)
