


"""simple docstring"""


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


a_       							=    "▁"

a_       							=    get_tests_dir("fixtures/test_sentencepiece.model")
@require_sentencepiece
class snake_case      (  _UpperCamelCase     ,				unittest.TestCase):
		__UpperCamelCase           =			BertGenerationTokenizer
		__UpperCamelCase           =			False
		__UpperCamelCase           =			True







		def  a_ (					self      :       Any		)       ->						Optional[int]:





						'''simple docstring'''







						super().setUp()

						_A					  =			BertGenerationTokenizer(a__    ,   keep_accents=a__		)
						tokenizer.save_pretrained(self.tmpdirname		)







		def  a_ (					self      :       Dict		)       ->						Dict:





						'''simple docstring'''







						_A					  =			"<s>"
						_A					  =			1

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







		def  a_ (					self      :       int		)       ->						int:





						'''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]    ,   "<pad>"		)
						self.assertEqual(len(a__		)    ,   10_02		)







		def  a_ (					self      :       List[Any]		)       ->						List[Any]:





						'''simple docstring'''







						self.assertEqual(self.get_tokenizer().vocab_size    ,   10_00		)







		def  a_ (					self      :       List[str]		)       ->						Tuple:





						'''simple docstring'''







						_A					  =			BertGenerationTokenizer(a__    ,   keep_accents=a__		)

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

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

						_A					  =			tokenizer.tokenize("I was born in 92000, and this is falsé."		)
						self.assertListEqual(
						    a__    ,   [
						        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",
						        ".",
						    ]    ,   )
						_A					  =			tokenizer.convert_tokens_to_ids(a__		)
						self.assertListEqual(
						    a__    ,   [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]    ,   )

						_A					  =			tokenizer.convert_ids_to_tokens(a__		)
						self.assertListEqual(
						    a__    ,   [
						        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  a_ (					self      :       str		)       ->						List[Any]:





						'''simple docstring'''







						return BertGenerationTokenizer.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder"		)







		@slow
		def  a_ (					self      :       List[Any]		)       ->						Tuple:





						'''simple docstring'''







						_A					  =			"Hello World!"
						_A					  =			[1_85_36, 22_60, 1_01]

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







		@slow
		def  a_ (					self      :       Optional[Any]		)       ->						Optional[Any]:





						'''simple docstring'''







						_A					  =			(
						    "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"
						)
						_A					  =			[
						    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(a__    ,   self.big_tokenizer.encode(a__		)		)







		@require_torch
		@slow
		def  a_ (					self      :       Dict		)       ->						List[str]:





						'''simple docstring'''







						import torch

						from transformers import BertGenerationConfig, BertGenerationEncoder

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

						_A					  =			BertGenerationConfig()
						_A					  =			BertGenerationEncoder(a__		)

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

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







		@slow
		def  a_ (					self      :       Tuple		)       ->						Optional[int]:





						'''simple docstring'''







						_A					  =			{"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=a__    ,   model_name="google/bert_for_seq_generation_L-24_bbc_encoder"    ,   revision="c817d1fd1be2ffa69431227a1fe320544943d4db"    ,   )



"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a_       							=    {
    "configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoFormerConfig", "RoFormerOnnxConfig"],
    "tokenization_roformer": ["RoFormerTokenizer"],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a_       							=    ["RoFormerTokenizerFast"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a_       							=    [
		    "ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "RoFormerForCausalLM",
		    "RoFormerForMaskedLM",
		    "RoFormerForMultipleChoice",
		    "RoFormerForQuestionAnswering",
		    "RoFormerForSequenceClassification",
		    "RoFormerForTokenClassification",
		    "RoFormerLayer",
		    "RoFormerModel",
		    "RoFormerPreTrainedModel",
		    "load_tf_weights_in_roformer",
		]


try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a_       							=    [
		    "TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "TFRoFormerForCausalLM",
		    "TFRoFormerForMaskedLM",
		    "TFRoFormerForMultipleChoice",
		    "TFRoFormerForQuestionAnswering",
		    "TFRoFormerForSequenceClassification",
		    "TFRoFormerForTokenClassification",
		    "TFRoFormerLayer",
		    "TFRoFormerModel",
		    "TFRoFormerPreTrainedModel",
		]


try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a_       							=    [
		    "FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "FlaxRoFormerForMaskedLM",
		    "FlaxRoFormerForMultipleChoice",
		    "FlaxRoFormerForQuestionAnswering",
		    "FlaxRoFormerForSequenceClassification",
		    "FlaxRoFormerForTokenClassification",
		    "FlaxRoFormerModel",
		    "FlaxRoFormerPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
		from .tokenization_roformer import RoFormerTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_roformer_fast import RoFormerTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_roformer import (
				    ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    RoFormerForCausalLM,
				    RoFormerForMaskedLM,
				    RoFormerForMultipleChoice,
				    RoFormerForQuestionAnswering,
				    RoFormerForSequenceClassification,
				    RoFormerForTokenClassification,
				    RoFormerLayer,
				    RoFormerModel,
				    RoFormerPreTrainedModel,
				    load_tf_weights_in_roformer,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_roformer import (
				    TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFRoFormerForCausalLM,
				    TFRoFormerForMaskedLM,
				    TFRoFormerForMultipleChoice,
				    TFRoFormerForQuestionAnswering,
				    TFRoFormerForSequenceClassification,
				    TFRoFormerForTokenClassification,
				    TFRoFormerLayer,
				    TFRoFormerModel,
				    TFRoFormerPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_roformer import (
				    FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    FlaxRoFormerForMaskedLM,
				    FlaxRoFormerForMultipleChoice,
				    FlaxRoFormerForQuestionAnswering,
				    FlaxRoFormerForSequenceClassification,
				    FlaxRoFormerForTokenClassification,
				    FlaxRoFormerModel,
				    FlaxRoFormerPreTrainedModel,
				)


else:
		import sys

		a_       							=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)