



import unittest

from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


lowercase_	       =			'▁'

lowercase_	       =			get_tests_dir('''fixtures/test_sentencepiece.model''')






@require_sentencepiece
class 						__a (     UpperCamelCase_	,    unittest.TestCase				):
	SCREAMING_SNAKE_CASE       =       BertGenerationTokenizer
	SCREAMING_SNAKE_CASE       =       False
	SCREAMING_SNAKE_CASE       =       True



	def 		UpperCamelCase							(						self							:    Tuple)->							Tuple:
						super().setUp()

						__lowerCAmelCase	   =BertGenerationTokenizer(snake_case_				,	keep_accents=snake_case_)
						tokenizer.save_pretrained(self.tmpdirname)



	def 		UpperCamelCase							(						self							:    int)->							List[Any]:
						__lowerCAmelCase	   ='''<s>'''
						__lowerCAmelCase	   =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 		UpperCamelCase							(						self							:    Optional[int])->							List[Any]:
						__lowerCAmelCase	   =list(self.get_tokenizer().get_vocab().keys())

						self.assertEqual(vocab_keys[0]				,	"""<unk>""")
						self.assertEqual(vocab_keys[1]				,	"""<s>""")
						self.assertEqual(vocab_keys[-1]				,	"""<pad>""")
						self.assertEqual(len(snake_case_)				,	10_02)



	def 		UpperCamelCase							(						self							:    List[Any])->							List[Any]:
						self.assertEqual(self.get_tokenizer().vocab_size				,	10_00)



	def 		UpperCamelCase							(						self							:    int)->							Tuple:
						__lowerCAmelCase	   =BertGenerationTokenizer(snake_case_				,	keep_accents=snake_case_)

						__lowerCAmelCase	   =tokenizer.tokenize("""This is a test""")
						self.assertListEqual(snake_case_				,	["""▁This""", """▁is""", """▁a""", """▁t""", """est"""])

						self.assertListEqual(
						    tokenizer.convert_tokens_to_ids(snake_case_)				,	[2_85, 46, 10, 1_70, 3_82]				,	)

						__lowerCAmelCase	   =tokenizer.tokenize("""I was born in 92000, and this is falsé.""")
						self.assertListEqual(
						    snake_case_				,	[
						        SPIECE_UNDERLINE + """I""",
						        SPIECE_UNDERLINE + """was""",
						        SPIECE_UNDERLINE + """b""",
						        """or""",
						        """n""",
						        SPIECE_UNDERLINE + """in""",
						        SPIECE_UNDERLINE + """""",
						        """9""",
						        """2""",
						        """0""",
						        """0""",
						        """0""",
						        """,""",
						        SPIECE_UNDERLINE + """and""",
						        SPIECE_UNDERLINE + """this""",
						        SPIECE_UNDERLINE + """is""",
						        SPIECE_UNDERLINE + """f""",
						        """al""",
						        """s""",
						        """é""",
						        """.""",
						    ]				,	)
						__lowerCAmelCase	   =tokenizer.convert_tokens_to_ids(snake_case_)
						self.assertListEqual(
						    snake_case_				,	[8, 21, 84, 55, 24, 19, 7, 0, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 0, 4]				,	)

						__lowerCAmelCase	   =tokenizer.convert_ids_to_tokens(snake_case_)
						self.assertListEqual(
						    snake_case_				,	[
						        SPIECE_UNDERLINE + """I""",
						        SPIECE_UNDERLINE + """was""",
						        SPIECE_UNDERLINE + """b""",
						        """or""",
						        """n""",
						        SPIECE_UNDERLINE + """in""",
						        SPIECE_UNDERLINE + """""",
						        """<unk>""",
						        """2""",
						        """0""",
						        """0""",
						        """0""",
						        """,""",
						        SPIECE_UNDERLINE + """and""",
						        SPIECE_UNDERLINE + """this""",
						        SPIECE_UNDERLINE + """is""",
						        SPIECE_UNDERLINE + """f""",
						        """al""",
						        """s""",
						        """<unk>""",
						        """.""",
						    ]				,	)



	@cached_property
	def 		UpperCamelCase							(						self							:    Any)->							Optional[Any]:
						return BertGenerationTokenizer.from_pretrained("""google/bert_for_seq_generation_L-24_bbc_encoder""")



	@slow
	def 		UpperCamelCase							(						self							:    List[str])->							int:
						__lowerCAmelCase	   ='''Hello World!'''
						__lowerCAmelCase	   =[1_85_36, 22_60, 1_01]

						self.assertListEqual(snake_case_				,	self.big_tokenizer.encode(snake_case_))



	@slow
	def 		UpperCamelCase							(						self							:    Dict)->							str:
						__lowerCAmelCase	   =(
						    '''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
						    ''' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'''
						)
						__lowerCAmelCase	   =[
						    8_71,
						    4_19,
						    3_58,
						    9_46,
						    9_91,
						    25_21,
						    4_52,
						    3_58,
						    13_57,
						    3_87,
						    77_51,
						    35_36,
						    1_12,
						    9_85,
						    4_56,
						    1_26,
						    8_65,
						    9_38,
						    54_00,
						    57_34,
						    4_58,
						    13_68,
						    4_67,
						    7_86,
						    24_62,
						    52_46,
						    11_59,
						    6_33,
						    8_65,
						    45_19,
						    4_57,
						    5_82,
						    8_52,
						    25_57,
						    4_27,
						    9_16,
						    5_08,
						    4_05,
						    3_43_24,
						    4_97,
						    3_91,
						    4_08,
						    1_13_42,
						    12_44,
						    3_85,
						    1_00,
						    9_38,
						    9_85,
						    4_56,
						    5_74,
						    3_62,
						    1_25_97,
						    32_00,
						    31_29,
						    11_72,
						]

						self.assertListEqual(snake_case_				,	self.big_tokenizer.encode(snake_case_))



	@require_torch
	@slow
	def 		UpperCamelCase							(						self							:    List[str])->							List[Any]:
						import torch

						from transformers import BertGenerationConfig, BertGenerationEncoder

						# Build sequence
						__lowerCAmelCase	   =list(self.big_tokenizer.get_vocab().keys())[:10]
						__lowerCAmelCase	   =''' '''.join(snake_case_)
						__lowerCAmelCase	   =self.big_tokenizer.encode_plus(snake_case_				,	return_tensors="""pt"""				,	return_token_type_ids=snake_case_)
						__lowerCAmelCase	   =self.big_tokenizer.batch_encode_plus(
						    [sequence + """ """ + sequence]				,	return_tensors="""pt"""				,	return_token_type_ids=snake_case_)

						__lowerCAmelCase	   =BertGenerationConfig()
						__lowerCAmelCase	   =BertGenerationEncoder(snake_case_)

						assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

						with torch.no_grad():
											model(**snake_case_)
											model(**snake_case_)



	@slow
	def 		UpperCamelCase							(						self							:    Optional[int])->							List[Any]:
						# fmt: off
						__lowerCAmelCase	   ={'''input_ids''': [[3_92_86, 4_58, 3_63_35, 20_01, 4_56, 1_30_73, 1_32_66, 4_55, 1_13, 77_46, 17_41, 1_11_57, 3_91, 1_30_73, 1_32_66, 4_55, 1_13, 39_67, 3_54_12, 1_13, 49_36, 1_09, 38_70, 23_77, 1_13, 3_00_84, 4_57_20, 4_58, 1_34, 1_74_96, 1_12, 5_03, 1_16_72, 1_13, 1_18, 1_12, 56_65, 1_33_47, 3_86_87, 1_12, 14_96, 3_13_89, 1_12, 32_68, 4_72_64, 1_34, 9_62, 1_12, 1_63_77, 80_35, 2_31_30, 4_30, 1_21_69, 1_55_18, 2_85_92, 4_58, 1_46, 4_16_97, 1_09, 3_91, 1_21_69, 1_55_18, 1_66_89, 4_58, 1_46, 4_13_58, 1_09, 4_52, 7_26, 40_34, 1_11, 7_63, 3_54_12, 50_82, 3_88, 19_03, 1_11, 90_51, 3_91, 28_70, 4_89_18, 19_00, 11_23, 5_50, 9_98, 1_12, 95_86, 1_59_85, 4_55, 3_91, 4_10, 2_29_55, 3_76_36, 1_14], [4_48, 1_74_96, 4_19, 36_63, 3_85, 7_63, 1_13, 2_75_33, 28_70, 32_83, 1_30_43, 16_39, 2_47_13, 5_23, 6_56, 2_40_13, 1_85_50, 25_21, 5_17, 2_70_14, 2_12_44, 4_20, 12_12, 14_65, 3_91, 9_27, 48_33, 3_88, 5_78, 1_17_86, 1_14, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [4_84, 21_69, 76_87, 2_19_32, 1_81_46, 7_26, 3_63, 1_70_32, 33_91, 1_14, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 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], [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]]}  # noqa: E501
						# fmt: on

						self.tokenizer_integration_test_util(
						    expected_encoding=snake_case_				,	model_name="""google/bert_for_seq_generation_L-24_bbc_encoder"""				,	revision="""c817d1fd1be2ffa69431227a1fe320544943d4db"""				,	)









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


lowercase_	       =			{
    '''configuration_encodec''': [
        '''ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''EncodecConfig''',
    ],
    '''feature_extraction_encodec''': ['''EncodecFeatureExtractor'''],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_	       =			[
				    '''ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''EncodecModel''',
				    '''EncodecPreTrainedModel''',
				]

if TYPE_CHECKING:
				from .configuration_encodec import (
				    ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    EncodecConfig,
				)
				from .feature_extraction_encodec import EncodecFeatureExtractor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_encodec import (
								    ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
								    EncodecModel,
								    EncodecPreTrainedModel,
								)

else:
				import sys

				lowercase_	       =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




