

'''simple docstring'''


import json
import os
import unittest

from transformers.models.roc_bert.tokenization_roc_bert import (
    VOCAB_FILES_NAMES,
    RoCBertBasicTokenizer,
    RoCBertTokenizer,
    RoCBertWordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english




@require_tokenizers
class     a			(				_a			,			unittest.TestCase       ):






						"""simple docstring"""




						__UpperCAmelCase			     =    RoCBertTokenizer
						__UpperCAmelCase			     =    None
						__UpperCAmelCase			     =    False
						__UpperCAmelCase			     =    True
						__UpperCAmelCase			     =    filter_non_english







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



								'''simple docstring'''



								super().setUp()

								snake_case__						:       Optional[int]       						=  ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''你''', '''好''', '''是''', '''谁''', '''a''', '''b''', '''c''', '''d''']
								snake_case__						:       Any       						=  {}
								snake_case__						:       List[Any]       						=  {}
								for i, value in enumerate(_A  ):
										snake_case__						:       List[Any]       						=  i
										snake_case__						:       List[str]       						=  i
								snake_case__						:       Dict       						=  os.path.join(self.tmpdirname			,			VOCAB_FILES_NAMES['''vocab_file''']  )
								snake_case__						:       int       						=  os.path.join(self.tmpdirname			,			VOCAB_FILES_NAMES['''word_shape_file''']  )
								snake_case__						:       Union[str, Any]       						=  os.path.join(self.tmpdirname			,			VOCAB_FILES_NAMES['''word_pronunciation_file''']  )
								with open(self.vocab_file			,			'''w'''			,			encoding='''utf-8'''  ) as vocab_writer:
										vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]  )  )
								with open(self.word_shape_file			,			'''w'''			,			encoding='''utf-8'''  ) as word_shape_writer:
										json.dump(_A			,			_A			,			ensure_ascii=_A  )
								with open(self.word_pronunciation_file			,			'''w'''			,			encoding='''utf-8'''  ) as word_pronunciation_writer:
										json.dump(_A			,			_A			,			ensure_ascii=_A  )







						def       __magic_name__							(							self					:      Optional[int]  ):



								'''simple docstring'''



								snake_case__						:       List[Any]       						=  self.tokenizer_class(self.vocab_file			,			self.word_shape_file			,			self.word_pronunciation_file  )

								snake_case__						:       str       						=  tokenizer.tokenize('''你好[SEP]你是谁'''  )
								self.assertListEqual(_A			,			['''你''', '''好''', '''[SEP]''', '''你''', '''是''', '''谁''']  )
								self.assertListEqual(tokenizer.convert_tokens_to_ids(_A  )			,			[5, 6, 2, 5, 7, 8]  )
								self.assertListEqual(tokenizer.convert_tokens_to_shape_ids(_A  )			,			[5, 6, 2, 5, 7, 8]  )
								self.assertListEqual(tokenizer.convert_tokens_to_pronunciation_ids(_A  )			,			[5, 6, 2, 5, 7, 8]  )







						def       __magic_name__							(							self					:      Optional[Any]  ):



								'''simple docstring'''



								snake_case__						:       List[Any]       						=  RoCBertBasicTokenizer()

								self.assertListEqual(tokenizer.tokenize('''ah\u535A\u63A8zz'''  )			,			['''ah''', '''\u535A''', '''\u63A8''', '''zz''']  )







						def       __magic_name__							(							self					:      Any  ):



								'''simple docstring'''



								snake_case__						:       int       						=  RoCBertBasicTokenizer(do_lower_case=_A  )

								self.assertListEqual(
								    tokenizer.tokenize(''' \tHeLLo!how  \n Are yoU?  '''  )			,			['''hello''', '''!''', '''how''', '''are''', '''you''', '''?''']  )
								self.assertListEqual(tokenizer.tokenize('''H\u00E9llo'''  )			,			['''hello''']  )







						def       __magic_name__							(							self					:      List[Any]  ):



								'''simple docstring'''



								snake_case__						:       str       						=  RoCBertBasicTokenizer(do_lower_case=_A			,			strip_accents=_A  )

								self.assertListEqual(
								    tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  '''  )			,			['''hällo''', '''!''', '''how''', '''are''', '''you''', '''?''']  )
								self.assertListEqual(tokenizer.tokenize('''H\u00E9llo'''  )			,			['''h\u00E9llo''']  )







						def       __magic_name__							(							self					:      Dict  ):



								'''simple docstring'''



								snake_case__						:       Dict       						=  RoCBertBasicTokenizer(do_lower_case=_A			,			strip_accents=_A  )

								self.assertListEqual(
								    tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  '''  )			,			['''hallo''', '''!''', '''how''', '''are''', '''you''', '''?''']  )
								self.assertListEqual(tokenizer.tokenize('''H\u00E9llo'''  )			,			['''hello''']  )







						def       __magic_name__							(							self					:      str  ):



								'''simple docstring'''



								snake_case__						:       str       						=  RoCBertBasicTokenizer(do_lower_case=_A  )

								self.assertListEqual(
								    tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  '''  )			,			['''hallo''', '''!''', '''how''', '''are''', '''you''', '''?''']  )
								self.assertListEqual(tokenizer.tokenize('''H\u00E9llo'''  )			,			['''hello''']  )







						def       __magic_name__							(							self					:      Optional[int]  ):



								'''simple docstring'''



								snake_case__						:       Tuple       						=  RoCBertBasicTokenizer(do_lower_case=_A  )

								self.assertListEqual(
								    tokenizer.tokenize(''' \tHeLLo!how  \n Are yoU?  '''  )			,			['''HeLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?''']  )







						def       __magic_name__							(							self					:      int  ):



								'''simple docstring'''



								snake_case__						:       Union[str, Any]       						=  RoCBertBasicTokenizer(do_lower_case=_A			,			strip_accents=_A  )

								self.assertListEqual(
								    tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  '''  )			,			['''HäLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?''']  )







						def       __magic_name__							(							self					:      int  ):



								'''simple docstring'''



								snake_case__						:       Dict       						=  RoCBertBasicTokenizer(do_lower_case=_A			,			strip_accents=_A  )

								self.assertListEqual(
								    tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  '''  )			,			['''HaLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?''']  )







						def       __magic_name__							(							self					:      int  ):



								'''simple docstring'''



								snake_case__						:       Any       						=  RoCBertBasicTokenizer(do_lower_case=_A			,			never_split=['''[UNK]''']  )

								self.assertListEqual(
								    tokenizer.tokenize(''' \tHeLLo!how  \n Are yoU? [UNK]'''  )			,			['''HeLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?''', '''[UNK]''']  )







						def       __magic_name__							(							self					:      List[str]  ):



								'''simple docstring'''



								snake_case__						:       Tuple       						=  ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''']

								snake_case__						:       str       						=  {}
								for i, token in enumerate(_A  ):
										snake_case__						:       List[str]       						=  i
								snake_case__						:       Optional[int]       						=  RoCBertWordpieceTokenizer(vocab=_A			,			unk_token='''[UNK]'''  )

								self.assertListEqual(tokenizer.tokenize(''''''  )			,			[]  )

								self.assertListEqual(tokenizer.tokenize('''unwanted running'''  )			,			['''un''', '''##want''', '''##ed''', '''runn''', '''##ing''']  )

								self.assertListEqual(tokenizer.tokenize('''unwantedX running'''  )			,			['''[UNK]''', '''runn''', '''##ing''']  )







						def       __magic_name__							(							self					:      Dict  ):



								'''simple docstring'''



								self.assertTrue(_is_whitespace(''' '''  )  )
								self.assertTrue(_is_whitespace('''\t'''  )  )
								self.assertTrue(_is_whitespace('''\r'''  )  )
								self.assertTrue(_is_whitespace('''\n'''  )  )
								self.assertTrue(_is_whitespace('''\u00A0'''  )  )

								self.assertFalse(_is_whitespace('''A'''  )  )
								self.assertFalse(_is_whitespace('''-'''  )  )







						def       __magic_name__							(							self					:      Tuple  ):



								'''simple docstring'''



								self.assertTrue(_is_control('''\u0005'''  )  )

								self.assertFalse(_is_control('''A'''  )  )
								self.assertFalse(_is_control(''' '''  )  )
								self.assertFalse(_is_control('''\t'''  )  )
								self.assertFalse(_is_control('''\r'''  )  )







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



								'''simple docstring'''



								self.assertTrue(_is_punctuation('''-'''  )  )
								self.assertTrue(_is_punctuation('''$'''  )  )
								self.assertTrue(_is_punctuation('''`'''  )  )
								self.assertTrue(_is_punctuation('''.'''  )  )

								self.assertFalse(_is_punctuation('''A'''  )  )
								self.assertFalse(_is_punctuation(''' '''  )  )







						def       __magic_name__							(							self					:      Dict  ):



								'''simple docstring'''



								snake_case__						:       Optional[int]       						=  self.get_tokenizer()

								# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
								self.assertListEqual([tokenizer.tokenize(_A  ) for t in ['''Test''', '''\xad''', '''test''']]			,			[['''[UNK]'''], [], ['''[UNK]''']]  )

								if self.test_rust_tokenizer:
										snake_case__						:       Dict       						=  self.get_rust_tokenizer()
										self.assertListEqual(
										    [rust_tokenizer.tokenize(_A  ) for t in ['''Test''', '''\xad''', '''test''']]			,			[['''[UNK]'''], [], ['''[UNK]''']]  )







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



								'''simple docstring'''



								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
										with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""  ):
												snake_case__						:       Union[str, Any]       						=  self.rust_tokenizer_class.from_pretrained(_A			,			**_A  )

												snake_case__						:       List[Any]       						=  F"""A, naïve {tokenizer_r.mask_token} AllenNLP sentence."""
												snake_case__						:       Union[str, Any]       						=  tokenizer_r.encode_plus(
												    _A			,			return_attention_mask=_A			,			return_token_type_ids=_A			,			return_offsets_mapping=_A			,			add_special_tokens=_A			,			)

												snake_case__						:       Dict       						=  tokenizer_r.do_lower_case if hasattr(_A			,			'''do_lower_case'''  ) else False
												snake_case__						:       Union[str, Any]       						=  (
												    [
												        ((0, 0), tokenizer_r.cls_token),
												        ((0, 1), '''A'''),
												        ((1, 2), ''','''),
												        ((3, 5), '''na'''),
												        ((5, 6), '''##ï'''),
												        ((6, 8), '''##ve'''),
												        ((9, 1_5), tokenizer_r.mask_token),
												        ((1_6, 2_1), '''Allen'''),
												        ((2_1, 2_3), '''##NL'''),
												        ((2_3, 2_4), '''##P'''),
												        ((2_5, 3_3), '''sentence'''),
												        ((3_3, 3_4), '''.'''),
												        ((0, 0), tokenizer_r.sep_token),
												    ]
												    if not do_lower_case
												    else [
												        ((0, 0), tokenizer_r.cls_token),
												        ((0, 1), '''a'''),
												        ((1, 2), ''','''),
												        ((3, 8), '''naive'''),
												        ((9, 1_5), tokenizer_r.mask_token),
												        ((1_6, 2_1), '''allen'''),
												        ((2_1, 2_3), '''##nl'''),
												        ((2_3, 2_4), '''##p'''),
												        ((2_5, 3_3), '''sentence'''),
												        ((3_3, 3_4), '''.'''),
												        ((0, 0), tokenizer_r.sep_token),
												    ]
												)

												self.assertEqual(
												    [e[1] for e in expected_results]			,			tokenizer_r.convert_ids_to_tokens(tokens['''input_ids''']  )  )
												self.assertEqual([e[0] for e in expected_results]			,			tokens['''offset_mapping''']  )







						def       __magic_name__							(							self					:      List[str]  ):



								'''simple docstring'''



								snake_case__						:       Dict       						=  ['''的''', '''人''', '''有''']
								snake_case__						:       Tuple       						=  ''''''.join(_A  )
								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
										with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""  ):
												snake_case__						:       Dict       						=  True
												snake_case__						:       List[Any]       						=  self.tokenizer_class.from_pretrained(_A			,			**_A  )
												snake_case__						:       str       						=  self.rust_tokenizer_class.from_pretrained(_A			,			**_A  )

												snake_case__						:       int       						=  tokenizer_p.encode(_A			,			add_special_tokens=_A  )
												snake_case__						:       str       						=  tokenizer_r.encode(_A			,			add_special_tokens=_A  )

												snake_case__						:       str       						=  tokenizer_r.convert_ids_to_tokens(_A  )
												snake_case__						:       str       						=  tokenizer_p.convert_ids_to_tokens(_A  )

												# it is expected that each Chinese character is not preceded by "##"
												self.assertListEqual(_A			,			_A  )
												self.assertListEqual(_A			,			_A  )

												snake_case__						:       Tuple       						=  False
												snake_case__						:       List[Any]       						=  self.rust_tokenizer_class.from_pretrained(_A			,			**_A  )
												snake_case__						:       Tuple       						=  self.tokenizer_class.from_pretrained(_A			,			**_A  )

												snake_case__						:       int       						=  tokenizer_r.encode(_A			,			add_special_tokens=_A  )
												snake_case__						:       str       						=  tokenizer_p.encode(_A			,			add_special_tokens=_A  )

												snake_case__						:       str       						=  tokenizer_r.convert_ids_to_tokens(_A  )
												snake_case__						:       List[str]       						=  tokenizer_p.convert_ids_to_tokens(_A  )

												# it is expected that only the first Chinese character is not preceded by "##".
												snake_case__						:       Union[str, Any]       						=  [
												    F"""##{token}""" if idx != 0 else token for idx, token in enumerate(_A  )
												]
												self.assertListEqual(_A			,			_A  )
												self.assertListEqual(_A			,			_A  )







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



								'''simple docstring'''



								snake_case__						:       Dict       						=  self.tokenizer_class(self.vocab_file			,			self.word_shape_file			,			self.word_pronunciation_file  )

								snake_case__						:       Optional[Any]       						=  tokenizer.encode('''你好'''			,			add_special_tokens=_A  )
								snake_case__						:       Tuple       						=  tokenizer.encode('''你是谁'''			,			add_special_tokens=_A  )

								snake_case__						:       int       						=  tokenizer.build_inputs_with_special_tokens(_A  )
								snake_case__						:       Any       						=  tokenizer.build_inputs_with_special_tokens(_A			,			_A  )

								assert encoded_sentence == [1] + text + [2]
								assert encoded_pair == [1] + text + [2] + text_a + [2]







						def       __magic_name__							(							self					:      Any  ):



								'''simple docstring'''



								snake_case__						:       Any       						=  self.get_tokenizers(do_lower_case=_A  )
								for tokenizer in tokenizers:
										with self.subTest(F"""{tokenizer.__class__.__name__}"""  ):
												snake_case__						:       Any       						=  '''你好，你是谁'''
												snake_case__						:       Optional[Any]       						=  tokenizer.tokenize(_A  )
												snake_case__						:       Optional[int]       						=  tokenizer.convert_tokens_to_ids(_A  )
												snake_case__						:       Dict       						=  tokenizer.convert_tokens_to_shape_ids(_A  )
												snake_case__						:       str       						=  tokenizer.convert_tokens_to_pronunciation_ids(_A  )
												snake_case__						:       str       						=  tokenizer.prepare_for_model(
												    _A			,			_A			,			_A			,			add_special_tokens=_A  )

												snake_case__						:       Union[str, Any]       						=  tokenizer.encode_plus(_A			,			add_special_tokens=_A  )

												self.assertEqual(_A			,			_A  )






'''simple docstring'''


import argparse

from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging


logging.set_verbosity_info()




def 	_a       (				__lowerCAmelCase		:      Union[str, Any]   ,							__lowerCAmelCase		:      Any   ,							__lowerCAmelCase		:      Optional[int]   ,							__lowerCAmelCase		:      List[Any]     ):

		"""simple docstring"""

		snake_case__						:       Any       						=  BigBirdConfig.from_json_file(__lowerCAmelCase     )
		print(F"""Building PyTorch model from configuration: {config}"""     )

		if is_trivia_qa:
				snake_case__						:       str       						=  BigBirdForQuestionAnswering(__lowerCAmelCase     )
		else:
				snake_case__						:       Tuple       						=  BigBirdForPreTraining(__lowerCAmelCase     )

		# Load weights from tf checkpoint
		load_tf_weights_in_big_bird(__lowerCAmelCase   ,							__lowerCAmelCase   ,							is_trivia_qa=__lowerCAmelCase     )

		# Save pytorch-model
		print(F"""Save PyTorch model to {pytorch_dump_path}"""     )
		model.save_pretrained(__lowerCAmelCase     )


if __name__ == "__main__":
					lowerCAmelCase__						:       Union[str, Any]										=						argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
					)
					parser.add_argument(
					    """--big_bird_config_file""",
					    default=None,
					    type=str,
					    required=True,
					    help=(
					        """The config json file corresponding to the pre-trained BERT model. \n"""
					        """This specifies the model architecture."""
					    ),
					)
					parser.add_argument(
					    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
					)
					parser.add_argument(
					    """--is_trivia_qa""", action="""store_true""", help="""Whether to convert a model with a trivia_qa head."""
					)
					lowerCAmelCase__						:       Optional[int]										=						parser.parse_args()
					convert_tf_checkpoint_to_pytorch(
					    args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
					)



