

"""simple docstring"""




import unittest

from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
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_							:			Dict											= get_tests_dir("fixtures/test_sentencepiece.model")





@require_sentencepiece
@require_tokenizers
class    lowerCamelCase						(A__			,unittest.TestCase			):
			lowerCamelCase__   :		Any							=							ReformerTokenizer
			lowerCamelCase__   :		Any							=							ReformerTokenizerFast
			lowerCamelCase__   :		Optional[Any]							=							True
			lowerCamelCase__   :		Any							=							False
			lowerCamelCase__   :		Union[str, Any]							=							True

			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple       )   ->    List[str]:
						super().setUp()

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

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[Any]       )   ->    List[str]:
						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				:						List[str]       )   ->    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]    ,				"""j"""       )
						self.assertEqual(len(__UpperCAmelCase       )    ,				1_0_0_0       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]       )   ->    Dict:
						self.assertEqual(self.get_tokenizer().vocab_size    ,				1_0_0_0       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]       )   ->    Union[str, Any]:
						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				:						int    ,				__UpperCAmelCase				:						Any=1_5       )   ->    Dict:
						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""       ):
												SCREAMING_SNAKE_CASE__								=      self.rust_tokenizer_class.from_pretrained(__UpperCAmelCase    ,				**__UpperCAmelCase       )

												# Simple input
												SCREAMING_SNAKE_CASE__								=      """This is a simple input"""
												SCREAMING_SNAKE_CASE__								=      ["""This is a simple input 1""", """This is a simple input 2"""]
												SCREAMING_SNAKE_CASE__								=      ("""This is a simple input""", """This is a pair""")
												SCREAMING_SNAKE_CASE__								=      [
												    ("""This is a simple input 1""", """This is a simple input 2"""),
												    ("""This is a simple pair 1""", """This is a simple pair 2"""),
												]

												# Simple input tests
												self.assertRaises(__UpperCAmelCase    ,				tokenizer_r.encode    ,				__UpperCAmelCase    ,				max_length=__UpperCAmelCase    ,				padding="""max_length"""       )

												# Simple input
												self.assertRaises(__UpperCAmelCase    ,				tokenizer_r.encode_plus    ,				__UpperCAmelCase    ,				max_length=__UpperCAmelCase    ,				padding="""max_length"""       )

												# Simple input
												self.assertRaises(
												    __UpperCAmelCase    ,				tokenizer_r.batch_encode_plus    ,				__UpperCAmelCase    ,				max_length=__UpperCAmelCase    ,				padding="""max_length"""    ,				)

												# Pair input
												self.assertRaises(__UpperCAmelCase    ,				tokenizer_r.encode    ,				__UpperCAmelCase    ,				max_length=__UpperCAmelCase    ,				padding="""max_length"""       )

												# Pair input
												self.assertRaises(__UpperCAmelCase    ,				tokenizer_r.encode_plus    ,				__UpperCAmelCase    ,				max_length=__UpperCAmelCase    ,				padding="""max_length"""       )

												# Pair input
												self.assertRaises(
												    __UpperCAmelCase    ,				tokenizer_r.batch_encode_plus    ,				__UpperCAmelCase    ,				max_length=__UpperCAmelCase    ,				padding="""max_length"""    ,				)

			def 					SCREAMING_SNAKE_CASE				(					self				:						Dict       )   ->    List[str]:
						pass

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]       )   ->    int:
						SCREAMING_SNAKE_CASE__								=      ReformerTokenizer(__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				:						List[Any]       )   ->    List[Any]:
						return ReformerTokenizer.from_pretrained("""google/reformer-crime-and-punishment"""       )

			@slow
			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]       )   ->    str:
						SCREAMING_SNAKE_CASE__								=      """Hello World!"""
						SCREAMING_SNAKE_CASE__								=      [1_2_6, 3_2, 2_6_2, 1_5_2, 3_8, 7_2, 2_8_7]

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

			@slow
			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]       )   ->    Dict:
						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"""
						)
						SCREAMING_SNAKE_CASE__								=      [
						    1_0_8,
						    2_6_5,
						    2_4,
						    1_1_1,
						    4,
						    2_5_8,
						    1_5_6,
						    3_5,
						    2_8,
						    2_7_5,
						    3,
						    2_5_9,
						    2_9_7,
						    2_6_0,
						    8_4,
						    4,
						    3_5,
						    1_1_0,
						    4_4,
						    8,
						    2_5_9,
						    9_1,
						    2_6_8,
						    2_1,
						    1_1,
						    2_0_9,
						    2_7_4,
						    1_0_9,
						    2_6_6,
						    2_7_7,
						    1_1_7,
						    8_6,
						    9_3,
						    3_1_5,
						    2_5_8,
						    2_7_8,
						    2_5_8,
						    2_7_7,
						    2_5_8,
						    0,
						    2_5_8,
						    2_8_8,
						    2_5_8,
						    3_1_9,
						    2_5_8,
						    0,
						    2_5_8,
						    0,
						    2_5_8,
						    0,
						    2_5_8,
						    0,
						    2_5_8,
						    2_8_7,
						    2_5_8,
						    3_1_5,
						    2_5_8,
						    2_8_9,
						    2_5_8,
						    2_7_8,
						    9_9,
						    2_6_9,
						    2_6_6,
						    2_6_2,
						    8,
						    2_5_9,
						    2_4_1,
						    4,
						    2_1_7,
						    2_3_0,
						    2_6_8,
						    2_6_6,
						    5_5,
						    1_6_8,
						    1_0_6,
						    7_5,
						    1_9_3,
						    2_6_6,
						    2_2_3,
						    2_7,
						    4_9,
						    2_6,
						    2_8_2,
						    2_5,
						    2_6_4,
						    2_9_9,
						    1_9,
						    2_6,
						    0,
						    2_5_8,
						    2_7_7,
						    1_1_7,
						    8_6,
						    9_3,
						    1_7_6,
						    1_8_3,
						    2_7_0,
						    1_1,
						    2_6_2,
						    4_2,
						    6_1,
						    2_6_5,
						]

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

			@require_torch
			@slow
			def 					SCREAMING_SNAKE_CASE				(					self				:						int       )   ->    str:
						import torch

						from transformers import ReformerConfig, ReformerModel

						# 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"""       )
						SCREAMING_SNAKE_CASE__								=      self.big_tokenizer.batch_encode_plus([sequence, sequence]    ,				return_tensors="""pt"""       )

						SCREAMING_SNAKE_CASE__								=      ReformerConfig()
						# The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
						SCREAMING_SNAKE_CASE__								=      encoded_sequence["""input_ids"""].shape
						SCREAMING_SNAKE_CASE__								=      ReformerModel(__UpperCAmelCase       )

						# Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
						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				:						List[str]       )   ->    Optional[int]:
						# fmt: off
						SCREAMING_SNAKE_CASE__								=      {"""input_ids""": [[1_0_8, 2_6_5, 2_4, 1_1_1, 4, 2_5_8, 1_5_6, 7, 5_1, 2_7_9, 5_8, 7, 7_6, 2_5, 6_9, 2_7_8], [1_4_0, 2_4_3, 2_6_4, 1_3_4, 1_7, 2_6_7, 7_7, 2_6_3, 2_2, 2_6_2, 2_9_7, 2_5_8, 3_0_4, 1_7_7, 2_7_9, 2_6_6, 1_4, 8_9, 1_3, 3_5, 2_6_1, 2_9_9, 2_7_2, 1_3_7, 2_7_5, 2_7_8]], """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]]}  # noqa: E501
						# fmt: on

						# This tokenizer does not know some characters like ")".
						# That is the reason why we use very simple texts here.
						# Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
						SCREAMING_SNAKE_CASE__								=      [
						    """This is a very simple sentence.""",
						    """The quick brown fox jumps over the lazy dog.""",
						]

						self.tokenizer_integration_test_util(
						    expected_encoding=__UpperCAmelCase    ,				model_name="""google/reformer-crime-and-punishment"""    ,				revision="""0e6c3decb8211d49bf881013425dc8b0448b3f5a"""    ,				padding=__UpperCAmelCase    ,				sequences=__UpperCAmelCase    ,				)





"""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


