
import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin






class     UpperCAmelCase						(		A_			,unittest.TestCase					):
   A__   :						str			=	DebertaTokenizer
   A__   :						Any			=	True
   A__   :						Dict			=	DebertaTokenizerFast



   def 							_SCREAMING_SNAKE_CASE  (self						:   int					)						->      List[Any]:


          '''simple docstring'''


          super().setUp()

          # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
          snake_case			:  int     							= [
              "l",
              "o",
              "w",
              "e",
              "r",
              "s",
              "t",
              "i",
              "d",
              "n",
              "\u0120",
              "\u0120l",
              "\u0120n",
              "\u0120lo",
              "\u0120low",
              "er",
              "\u0120lowest",
              "\u0120newer",
              "\u0120wider",
              "[UNK]",
          ]
          snake_case			:  List[Any]     							= dict(zip(snake_case__       ,		range(len(snake_case__					)					)					)					)
          snake_case			:  int     							= ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
          snake_case			:  int     							= {"unk_token": "[UNK]"}

          snake_case			:  Union[str, Any]     							= os.path.join(self.tmpdirname       ,		VOCAB_FILES_NAMES["vocab_file"]					)
          snake_case			:  Tuple     							= os.path.join(self.tmpdirname       ,		VOCAB_FILES_NAMES["merges_file"]					)
          with open(self.vocab_file       ,		"w"       ,		encoding="utf-8"					) as fp:
                 fp.write(json.dumps(snake_case__					) + "\n"					)
          with open(self.merges_file       ,		"w"       ,		encoding="utf-8"					) as fp:
                 fp.write("\n".join(snake_case__					)					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   Union[str, Any]       ,		**snake_case__						:   List[str]					)						->      Optional[Any]:


          '''simple docstring'''


          kwargs.update(self.special_tokens_map					)
          return self.tokenizer_class.from_pretrained(self.tmpdirname       ,		**snake_case__					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   Dict       ,		snake_case__						:   Tuple					)						->      Union[str, Any]:


          '''simple docstring'''


          snake_case			:  Optional[int]     							= "lower newer"
          snake_case			:  Tuple     							= "lower newer"
          return input_text, output_text



   def 							_SCREAMING_SNAKE_CASE  (self						:   Union[str, Any]					)						->      str:


          '''simple docstring'''


          snake_case			:  Union[str, Any]     							= self.get_tokenizer()
          snake_case			:  List[Any]     							= "lower newer"
          snake_case			:  Optional[Any]     							= ["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
          snake_case			:  Union[str, Any]     							= tokenizer.tokenize(snake_case__					)
          self.assertListEqual(snake_case__       ,		snake_case__					)

          snake_case			:  Tuple     							= tokens + [tokenizer.unk_token]
          snake_case			:  Optional[Any]     							= [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case__					)       ,		snake_case__					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   List[str]					)						->      List[str]:


          '''simple docstring'''


          snake_case			:  Any     							= self.get_tokenizer()
          snake_case			:  str     							= tokenizer("Hello"       ,		"World"					)
          snake_case			:  List[Any]     							= [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
          self.assertListEqual(tokd["token_type_ids"]       ,		snake_case__					)



   @slow
   def 							_SCREAMING_SNAKE_CASE  (self						:   List[str]					)						->      List[str]:


          '''simple docstring'''


          snake_case			:  List[str]     							= self.tokenizer_class.from_pretrained("microsoft/deberta-base"					)

          snake_case			:  Tuple     							= tokenizer.encode("sequence builders"       ,		add_special_tokens=snake_case__					)
          snake_case			:  int     							= tokenizer.encode("multi-sequence build"       ,		add_special_tokens=snake_case__					)

          snake_case			:  Dict     							= tokenizer.encode(
              "sequence builders"       ,		add_special_tokens=snake_case__       ,		add_prefix_space=snake_case__					)
          snake_case			:  Union[str, Any]     							= tokenizer.encode(
              "sequence builders"       ,		"multi-sequence build"       ,		add_special_tokens=snake_case__       ,		add_prefix_space=snake_case__					)

          snake_case			:  Any     							= tokenizer.build_inputs_with_special_tokens(snake_case__					)
          snake_case			:  Dict     							= tokenizer.build_inputs_with_special_tokens(snake_case__       ,		snake_case__					)

          assert encoded_sentence == encoded_text_from_decode
          assert encoded_pair == encoded_pair_from_decode



   @slow
   def 							_SCREAMING_SNAKE_CASE  (self						:   Optional[int]					)						->      Union[str, Any]:


          '''simple docstring'''


          snake_case			:  Dict     							= [self.tokenizer_class]
          if self.test_rust_tokenizer:
                 tokenizer_classes.append(self.rust_tokenizer_class					)

          for tokenizer_class in tokenizer_classes:
                 snake_case			:  Union[str, Any]     							= tokenizer_class.from_pretrained("microsoft/deberta-base"					)

                 snake_case			:  int     							= [
                     "ALBERT: A Lite BERT for Self-supervised Learning of Language Representations",
                     "ALBERT incorporates two parameter reduction techniques",
                     "The first one is a factorized embedding parameterization. By decomposing the large vocabulary"
                     " embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"
                     " vocabulary embedding.",
                 ]

                 snake_case			:  Tuple     							= tokenizer(snake_case__       ,		padding=snake_case__					)
                 snake_case			:  Any     							= [tokenizer.decode(snake_case__       ,		skip_special_tokens=snake_case__					) for seq in encoding["input_ids"]]

                 # fmt: off
                 snake_case			:  Optional[int]     							= {
                     "input_ids": [
                         [1, 21_18, 1_11_26, 5_65, 35, 83, 2_51_91, 1_63, 1_88_54, 13, 1_21_56, 12, 1_61_01, 2_53_76, 1_38_07, 9, 2_22_05, 2_78_93, 16_35, 2, 0, 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, 21_18, 1_11_26, 5_65, 2_45_36, 80, 4_37_97, 48_78, 73_73, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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_33, 78, 65, 16, 10, 37_24, 15_38, 3_31_83, 1_13_03, 4_37_97, 19_38, 4, 8_70, 2_41_65, 2_91_05, 5, 7_39, 3_26_44, 3_31_83, 1_13_03, 3_61_73, 88, 80, 6_50, 78_21, 4_59_40, 6, 52, 25_59, 5, 18_36, 9, 5, 73_97, 1_31_71, 31, 5, 18_36, 9, 3_26_44, 3_31_83, 1_13_03, 4, 2]
                     ],
                     "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]
                     ],
                     "attention_mask": [
                         [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],
                         [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],
                         [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
                     ]
                 }
                 # fmt: on

                 snake_case			:  List[Any]     							= [
                     "ALBERT: A Lite BERT for Self-supervised Learning of Language Representations",
                     "ALBERT incorporates two parameter reduction techniques",
                     "The first one is a factorized embedding parameterization. By decomposing the large vocabulary"
                     " embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"
                     " vocabulary embedding.",
                 ]

                 self.assertDictEqual(encoding.data       ,		snake_case__					)

                 for expected, decoded in zip(snake_case__       ,		snake_case__					):
                        self.assertEqual(snake_case__       ,		snake_case__					)







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





