



import unittest

from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


_lowercase     							=						get_tests_dir('''fixtures/spiece.model''')
@require_sentencepiece
@require_tokenizers
class 				__A					( A_					, unittest.TestCase      ):
			UpperCamelCase					:int     =			AlbertTokenizer
			UpperCamelCase					:Optional[int]     =			AlbertTokenizerFast
			UpperCamelCase					:Tuple     =			True
			UpperCamelCase					:Any     =			True
			UpperCamelCase					:Optional[int]     =			True





			def        _snake_case    (self				):
						super().setUp()

						# We have a SentencePiece fixture for testing
						lowerCamelCase__ :      Dict								=		AlbertTokenizer(__magic_name__				)
						tokenizer.save_pretrained(self.tmpdirname				)





			def        _snake_case    (self							,    __magic_name__				):
						lowerCamelCase__ :      Optional[Any]								=		"""this is a test"""
						lowerCamelCase__ :      str								=		"""this is a test"""
						return input_text, output_text





			def        _snake_case    (self				):
						lowerCamelCase__ :      Any								=		"""<pad>"""
						lowerCamelCase__ :      List[str]								=		0

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





			def        _snake_case    (self				):
						lowerCamelCase__ :      int								=		list(self.get_tokenizer().get_vocab().keys()				)

						self.assertEqual(vocab_keys[0]							,    """<pad>"""				)
						self.assertEqual(vocab_keys[1]							,    """<unk>"""				)
						self.assertEqual(vocab_keys[-1]							,    """▁eloquent"""				)
						self.assertEqual(len(__magic_name__				)							,    30000				)





			def        _snake_case    (self				):
						self.assertEqual(self.get_tokenizer().vocab_size							,    30000				)





			def        _snake_case    (self				):
						if not self.test_rust_tokenizer:
									return

						lowerCamelCase__ :      Union[str, Any]								=		self.get_tokenizer()
						lowerCamelCase__ :      Union[str, Any]								=		self.get_rust_tokenizer()

						lowerCamelCase__ :      str								=		"""I was born in 92000, and this is falsé."""

						lowerCamelCase__ :      Optional[Any]								=		tokenizer.tokenize(__magic_name__				)
						lowerCamelCase__ :      Any								=		rust_tokenizer.tokenize(__magic_name__				)
						self.assertListEqual(__magic_name__							,    __magic_name__				)

						lowerCamelCase__ :      Any								=		tokenizer.encode(__magic_name__							,    add_special_tokens=__magic_name__				)
						lowerCamelCase__ :      Optional[int]								=		rust_tokenizer.encode(__magic_name__							,    add_special_tokens=__magic_name__				)
						self.assertListEqual(__magic_name__							,    __magic_name__				)

						lowerCamelCase__ :      List[Any]								=		self.get_rust_tokenizer()
						lowerCamelCase__ :      Any								=		tokenizer.encode(__magic_name__				)
						lowerCamelCase__ :      Any								=		rust_tokenizer.encode(__magic_name__				)
						self.assertListEqual(__magic_name__							,    __magic_name__				)





			def        _snake_case    (self				):
						lowerCamelCase__ :      List[Any]								=		AlbertTokenizer(__magic_name__							,    keep_accents=__magic_name__				)

						lowerCamelCase__ :      Any								=		tokenizer.tokenize("""This is a test"""				)
						self.assertListEqual(__magic_name__							,    ["""▁this""", """▁is""", """▁a""", """▁test"""]				)

						self.assertListEqual(tokenizer.convert_tokens_to_ids(__magic_name__				)							,    [48, 25, 21, 1289]				)

						lowerCamelCase__ :      List[str]								=		tokenizer.tokenize("""I was born in 92000, and this is falsé."""				)
						self.assertListEqual(
						    __magic_name__							,    ["""▁i""", """▁was""", """▁born""", """▁in""", """▁9""", """2000""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """é""", """."""]				)
						lowerCamelCase__ :      Dict								=		tokenizer.convert_tokens_to_ids(__magic_name__				)
						self.assertListEqual(__magic_name__							,    [31, 23, 386, 19, 561, 3050, 15, 17, 48, 25, 8256, 18, 1, 9]				)

						lowerCamelCase__ :      List[Any]								=		tokenizer.convert_ids_to_tokens(__magic_name__				)
						self.assertListEqual(
						    __magic_name__							,    ["""▁i""", """▁was""", """▁born""", """▁in""", """▁9""", """2000""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """<unk>""", """."""]							,    )





			def        _snake_case    (self				):
						lowerCamelCase__ :      Any								=		AlbertTokenizer(__magic_name__				)

						lowerCamelCase__ :      Any								=		tokenizer.encode("""sequence builders"""				)
						lowerCamelCase__ :      Any								=		tokenizer.encode("""multi-sequence build"""				)

						lowerCamelCase__ :      List[Any]								=		tokenizer.build_inputs_with_special_tokens(__magic_name__				)
						lowerCamelCase__ :      Dict								=		tokenizer.build_inputs_with_special_tokens(__magic_name__							,    __magic_name__				)

						assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
						assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
						    tokenizer.sep_token_id
						]





			@slow
			def        _snake_case    (self				):
						# fmt: off
						lowerCamelCase__ :      str								=		{"""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, 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, 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, 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, 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, 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]], """input_ids""": [[2, 21970, 13, 5, 6092, 167, 28, 7103, 2153, 673, 8, 7028, 12051, 18, 17, 7103, 2153, 673, 8, 3515, 18684, 8, 4461, 6, 1927, 297, 8, 12060, 2607, 18, 13, 5, 4461, 15, 10538, 38, 8, 135, 15, 822, 58, 15, 993, 10363, 15, 1460, 8005, 4461, 15, 993, 255, 2328, 9, 9, 9, 6, 26, 1112, 816, 3260, 13, 5, 103, 2377, 6, 17, 1112, 816, 2782, 13, 5, 103, 10641, 6, 29, 84, 2512, 2430, 782, 18684, 2761, 19, 808, 2430, 2556, 17, 855, 1480, 9477, 4091, 128, 11712, 15, 7103, 2153, 673, 17, 24883, 9990, 9, 3], [2, 11502, 25, 1006, 20, 782, 8, 11809, 855, 1732, 19393, 18667, 37, 367, 21018, 69, 1854, 34, 11860, 19124, 27, 156, 225, 17, 193, 4141, 19, 65, 9124, 9, 3, 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], [2, 14, 2231, 886, 2385, 17659, 84, 14, 16792, 1952, 9, 3, 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]], """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, 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, 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]]}  # noqa: E501
						# fmt: on

						self.tokenizer_integration_test_util(
						    expected_encoding=__magic_name__							,    model_name="""albert-base-v2"""							,    revision="""6b6560eaf5ff2e250b00c50f380c5389a9c2d82e"""							,    )






import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()






def      _A				(UpperCamelCase		:      Any      ,			UpperCamelCase		:      Union[str, Any]      ,			UpperCamelCase		:      Optional[Any] )  ->Any:



			'''simple docstring'''







			lowerCamelCase__ :      Optional[Any]								=		TaConfig.from_json_file(UpperCamelCase )
			print(f"Building PyTorch model from configuration: {config}" )
			lowerCamelCase__ :      Optional[int]								=		TaForConditionalGeneration(UpperCamelCase )

			# Load weights from tf checkpoint
			load_tf_weights_in_ta(UpperCamelCase      ,			UpperCamelCase      ,			UpperCamelCase )

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


if __name__ == "__main__":
				_lowercase     							=						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(
				    '''--config_file''',
				    default=None,
				    type=str,
				    required=True,
				    help=(
				        '''The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'''
				    ),
				)
				parser.add_argument(
				    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
				)
				_lowercase     							=						parser.parse_args()
				convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)



