

"""simple docstring"""




import unittest

from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


A_							:			List[Any]											= "▁"

A_							:			Tuple											= get_tests_dir("fixtures/test_sentencepiece.model")





@require_sentencepiece
@require_tokenizers
class    lowerCamelCase						(A__			,unittest.TestCase			):
			lowerCamelCase__   :		List[str]							=							BigBirdTokenizer
			lowerCamelCase__   :		Union[str, Any]							=							BigBirdTokenizerFast
			lowerCamelCase__   :		Tuple							=							True
			lowerCamelCase__   :		str							=							True

			def 					SCREAMING_SNAKE_CASE				(					self				:						int       )   ->    Optional[int]:
						super().setUp()

						SCREAMING_SNAKE_CASE__								=      self.tokenizer_class(__UpperCAmelCase    ,				keep_accents=__UpperCAmelCase       )
						tokenizer.save_pretrained(self.tmpdirname       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						str       )   ->    Optional[int]:
						SCREAMING_SNAKE_CASE__								=      """<s>"""
						SCREAMING_SNAKE_CASE__								=      1

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

			def 					SCREAMING_SNAKE_CASE				(					self				:						int       )   ->    Tuple:
						SCREAMING_SNAKE_CASE__								=      list(self.get_tokenizer().get_vocab().keys()       )

						self.assertEqual(vocab_keys[0]    ,				"""<unk>"""       )
						self.assertEqual(vocab_keys[1]    ,				"""<s>"""       )
						self.assertEqual(vocab_keys[-1]    ,				"""[MASK]"""       )
						self.assertEqual(len(__UpperCAmelCase       )    ,				1_0_0_4       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Dict       )   ->    Any:
						self.assertEqual(self.get_tokenizer().vocab_size    ,				1_0_0_0       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple       )   ->    Dict:
						if not self.test_rust_tokenizer:
									return

						SCREAMING_SNAKE_CASE__								=      self.get_tokenizer()
						SCREAMING_SNAKE_CASE__								=      self.get_rust_tokenizer()

						SCREAMING_SNAKE_CASE__								=      """I was born in 92000, and this is falsé."""

						SCREAMING_SNAKE_CASE__								=      tokenizer.tokenize(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      rust_tokenizer.tokenize(__UpperCAmelCase       )
						self.assertListEqual(__UpperCAmelCase    ,				__UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__								=      tokenizer.encode(__UpperCAmelCase    ,				add_special_tokens=__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      rust_tokenizer.encode(__UpperCAmelCase    ,				add_special_tokens=__UpperCAmelCase       )
						self.assertListEqual(__UpperCAmelCase    ,				__UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__								=      self.get_rust_tokenizer()
						SCREAMING_SNAKE_CASE__								=      tokenizer.encode(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      rust_tokenizer.encode(__UpperCAmelCase       )
						self.assertListEqual(__UpperCAmelCase    ,				__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Union[str, Any]       )   ->    List[str]:
						SCREAMING_SNAKE_CASE__								=      BigBirdTokenizer(__UpperCAmelCase    ,				keep_accents=__UpperCAmelCase       )

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

						self.assertListEqual(
						    tokenizer.convert_tokens_to_ids(__UpperCAmelCase       )    ,				[2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]    ,				)

						SCREAMING_SNAKE_CASE__								=      tokenizer.tokenize("""I was born in 92000, and this is falsé."""       )
						self.assertListEqual(
						    __UpperCAmelCase    ,				[
						        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""",
						        """.""",
						    ]    ,				)
						SCREAMING_SNAKE_CASE__								=      tokenizer.convert_tokens_to_ids(__UpperCAmelCase       )
						self.assertListEqual(
						    __UpperCAmelCase    ,				[8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 0, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 0, 4]    ,				)

						SCREAMING_SNAKE_CASE__								=      tokenizer.convert_ids_to_tokens(__UpperCAmelCase       )
						self.assertListEqual(
						    __UpperCAmelCase    ,				[
						        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 					SCREAMING_SNAKE_CASE				(					self				:						Tuple       )   ->    Optional[Any]:
						return BigBirdTokenizer.from_pretrained("""google/bigbird-roberta-base"""       )

			@slow
			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]       )   ->    int:
						SCREAMING_SNAKE_CASE__								=      """Hello World!"""
						SCREAMING_SNAKE_CASE__								=      [6_5, 1_8_5_3_6, 2_2_6_0, 1_0_1, 6_6]

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

			@slow
			def 					SCREAMING_SNAKE_CASE				(					self				:						List[Any]       )   ->    Optional[int]:
						SCREAMING_SNAKE_CASE__								=      (
						    """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"""
						)
						# fmt: off
						SCREAMING_SNAKE_CASE__								=      [6_5, 8_7_1, 4_1_9, 3_5_8, 9_4_6, 9_9_1, 2_5_2_1, 4_5_2, 3_5_8, 1_3_5_7, 3_8_7, 7_7_5_1, 3_5_3_6, 1_1_2, 9_8_5, 4_5_6, 1_2_6, 8_6_5, 9_3_8, 5_4_0_0, 5_7_3_4, 4_5_8, 1_3_6_8, 4_6_7, 7_8_6, 2_4_6_2, 5_2_4_6, 1_1_5_9, 6_3_3, 8_6_5, 4_5_1_9, 4_5_7, 5_8_2, 8_5_2, 2_5_5_7, 4_2_7, 9_1_6, 5_0_8, 4_0_5, 3_4_3_2_4, 4_9_7, 3_9_1, 4_0_8, 1_1_3_4_2, 1_2_4_4, 3_8_5, 1_0_0, 9_3_8, 9_8_5, 4_5_6, 5_7_4, 3_6_2, 1_2_5_9_7, 3_2_0_0, 3_1_2_9, 1_1_7_2, 6_6]  # noqa: E231
						# fmt: on
						self.assertListEqual(__UpperCAmelCase    ,				self.big_tokenizer.encode(__UpperCAmelCase       )       )

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

						from transformers import BigBirdConfig, BigBirdModel

						# Build sequence
						SCREAMING_SNAKE_CASE__								=      list(self.big_tokenizer.get_vocab().keys()       )[:1_0]
						SCREAMING_SNAKE_CASE__								=      """ """.join(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      self.big_tokenizer.encode_plus(__UpperCAmelCase    ,				return_tensors="""pt"""    ,				return_token_type_ids=__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      self.big_tokenizer.batch_encode_plus(
						    [sequence + """ """ + sequence]    ,				return_tensors="""pt"""    ,				return_token_type_ids=__UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__								=      BigBirdConfig(attention_type="""original_full"""       )
						SCREAMING_SNAKE_CASE__								=      BigBirdModel(__UpperCAmelCase       )

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

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

			@slow
			def 					SCREAMING_SNAKE_CASE				(					self				:						int       )   ->    int:
						SCREAMING_SNAKE_CASE__								=      BigBirdTokenizer.from_pretrained("""google/bigbird-roberta-base"""       )
						SCREAMING_SNAKE_CASE__								=      tokenizer.decode(tokenizer("""Paris is the [MASK]."""       ).input_ids       )

						self.assertTrue(decoded_text == """[CLS] Paris is the[MASK].[SEP]"""       )





			@slow
			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]       )   ->    Union[str, Any]:
						# fmt: off
						SCREAMING_SNAKE_CASE__								=      {"""input_ids""": [[6_5, 3_9_2_8_6, 4_5_8, 3_6_3_3_5, 2_0_0_1, 4_5_6, 1_3_0_7_3, 1_3_2_6_6, 4_5_5, 1_1_3, 7_7_4_6, 1_7_4_1, 1_1_1_5_7, 3_9_1, 1_3_0_7_3, 1_3_2_6_6, 4_5_5, 1_1_3, 3_9_6_7, 3_5_4_1_2, 1_1_3, 4_9_3_6, 1_0_9, 3_8_7_0, 2_3_7_7, 1_1_3, 3_0_0_8_4, 4_5_7_2_0, 4_5_8, 1_3_4, 1_7_4_9_6, 1_1_2, 5_0_3, 1_1_6_7_2, 1_1_3, 1_1_8, 1_1_2, 5_6_6_5, 1_3_3_4_7, 3_8_6_8_7, 1_1_2, 1_4_9_6, 3_1_3_8_9, 1_1_2, 3_2_6_8, 4_7_2_6_4, 1_3_4, 9_6_2, 1_1_2, 1_6_3_7_7, 8_0_3_5, 2_3_1_3_0, 4_3_0, 1_2_1_6_9, 1_5_5_1_8, 2_8_5_9_2, 4_5_8, 1_4_6, 4_1_6_9_7, 1_0_9, 3_9_1, 1_2_1_6_9, 1_5_5_1_8, 1_6_6_8_9, 4_5_8, 1_4_6, 4_1_3_5_8, 1_0_9, 4_5_2, 7_2_6, 4_0_3_4, 1_1_1, 7_6_3, 3_5_4_1_2, 5_0_8_2, 3_8_8, 1_9_0_3, 1_1_1, 9_0_5_1, 3_9_1, 2_8_7_0, 4_8_9_1_8, 1_9_0_0, 1_1_2_3, 5_5_0, 9_9_8, 1_1_2, 9_5_8_6, 1_5_9_8_5, 4_5_5, 3_9_1, 4_1_0, 2_2_9_5_5, 3_7_6_3_6, 1_1_4, 6_6], [6_5, 4_4_8, 1_7_4_9_6, 4_1_9, 3_6_6_3, 3_8_5, 7_6_3, 1_1_3, 2_7_5_3_3, 2_8_7_0, 3_2_8_3, 1_3_0_4_3, 1_6_3_9, 2_4_7_1_3, 5_2_3, 6_5_6, 2_4_0_1_3, 1_8_5_5_0, 2_5_2_1, 5_1_7, 2_7_0_1_4, 2_1_2_4_4, 4_2_0, 1_2_1_2, 1_4_6_5, 3_9_1, 9_2_7, 4_8_3_3, 3_8_8, 5_7_8, 1_1_7_8_6, 1_1_4, 6_6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [6_5, 4_8_4, 2_1_6_9, 7_6_8_7, 2_1_9_3_2, 1_8_1_4_6, 7_2_6, 3_6_3, 1_7_0_3_2, 3_3_9_1, 1_1_4, 6_6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 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, 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=__UpperCAmelCase    ,				model_name="""google/bigbird-roberta-base"""    ,				revision="""215c99f1600e06f83acce68422f2035b2b5c3510"""    ,				)





"""simple docstring"""




from ..utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_scipy_available,
    is_torch_available,
    is_torchsde_available,
)


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from ..utils.dummy_pt_objects import *  # noqa F403
else:
						from .scheduling_consistency_models import CMStochasticIterativeScheduler
						from .scheduling_ddim import DDIMScheduler
						from .scheduling_ddim_inverse import DDIMInverseScheduler
						from .scheduling_ddim_parallel import DDIMParallelScheduler
						from .scheduling_ddpm import DDPMScheduler
						from .scheduling_ddpm_parallel import DDPMParallelScheduler
						from .scheduling_deis_multistep import DEISMultistepScheduler
						from .scheduling_dpmsolver_multistep import DPMSolverMultistepScheduler
						from .scheduling_dpmsolver_multistep_inverse import DPMSolverMultistepInverseScheduler
						from .scheduling_dpmsolver_singlestep import DPMSolverSinglestepScheduler
						from .scheduling_euler_ancestral_discrete import EulerAncestralDiscreteScheduler
						from .scheduling_euler_discrete import EulerDiscreteScheduler
						from .scheduling_heun_discrete import HeunDiscreteScheduler
						from .scheduling_ipndm import IPNDMScheduler
						from .scheduling_k_dpm_2_ancestral_discrete import KDPMaAncestralDiscreteScheduler
						from .scheduling_k_dpm_2_discrete import KDPMaDiscreteScheduler
						from .scheduling_karras_ve import KarrasVeScheduler
						from .scheduling_pndm import PNDMScheduler
						from .scheduling_repaint import RePaintScheduler
						from .scheduling_sde_ve import ScoreSdeVeScheduler
						from .scheduling_sde_vp import ScoreSdeVpScheduler
						from .scheduling_unclip import UnCLIPScheduler
						from .scheduling_unipc_multistep import UniPCMultistepScheduler
						from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
						from .scheduling_vq_diffusion import VQDiffusionScheduler

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from ..utils.dummy_flax_objects import *  # noqa F403
else:
						from .scheduling_ddim_flax import FlaxDDIMScheduler
						from .scheduling_ddpm_flax import FlaxDDPMScheduler
						from .scheduling_dpmsolver_multistep_flax import FlaxDPMSolverMultistepScheduler
						from .scheduling_karras_ve_flax import FlaxKarrasVeScheduler
						from .scheduling_lms_discrete_flax import FlaxLMSDiscreteScheduler
						from .scheduling_pndm_flax import FlaxPNDMScheduler
						from .scheduling_sde_ve_flax import FlaxScoreSdeVeScheduler
						from .scheduling_utils_flax import (
						    FlaxKarrasDiffusionSchedulers,
						    FlaxSchedulerMixin,
						    FlaxSchedulerOutput,
						    broadcast_to_shape_from_left,
						)


try:
						if not (is_torch_available() and is_scipy_available()):
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from ..utils.dummy_torch_and_scipy_objects import *  # noqa F403
else:
						from .scheduling_lms_discrete import LMSDiscreteScheduler

try:
						if not (is_torch_available() and is_torchsde_available()):
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from ..utils.dummy_torch_and_torchsde_objects import *  # noqa F403
else:
						from .scheduling_dpmsolver_sde import DPMSolverSDEScheduler


