code: 
  """simple docstring"""
  import unittest
  
  import numpy as np
  
  from transformers import (
      GPTSanJapaneseConfig,
      GPTSanJapaneseForConditionalGeneration,
      GPTSanJapaneseModel,
      GPTSanJapaneseTokenizer,
      is_torch_available,
  )
  from transformers.generation import GenerationConfig
  from transformers.testing_utils import require_torch, slow, tooslow, torch_device
  
  from ...generation.test_utils import GenerationTesterMixin
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, ids_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
  
  
  class    _lowerCAmelCase						:
  
  
  
  							def __init__(      self ,						a_ ,						a_=36000 ,						a_=13 ,						a_=7 ,						a_=True ,						a_=32 ,						a_=42 ,						a_=5 ,						a_=2 ,						a_=4 ,						a_=2 ,						a_=32 ,						a_=80 ,						a_=33 ,						a_=0.0 ,						a_=1e-6 ,						a_=100 ,						a_=0.0 ,						)							->							Optional[Any]:
  								_UpperCAmelCase =						vocab_size
  								_UpperCAmelCase =						parent
  								_UpperCAmelCase =						batch_size
  								_UpperCAmelCase =						num_contexts
  								# For common tests
  								_UpperCAmelCase =						self.num_contexts
  								_UpperCAmelCase =						is_training
  								_UpperCAmelCase =						hidden_size
  								_UpperCAmelCase =						num_ext_layers
  								_UpperCAmelCase =						ext_size
  								_UpperCAmelCase =						num_hidden_layers
  								_UpperCAmelCase =						num_attention_heads
  								_UpperCAmelCase =						num_experts
  								_UpperCAmelCase =						d_ff
  								_UpperCAmelCase =						d_ext
  								_UpperCAmelCase =						d_spout
  								_UpperCAmelCase =						dropout_rate
  								_UpperCAmelCase =						layer_norm_epsilon
  								_UpperCAmelCase =						expert_capacity
  								_UpperCAmelCase =						router_jitter_noise
  
  
  
  							def       _a   (      self   )							->							List[str]:
  								return GPTSanJapaneseConfig.from_pretrained("Tanrei/GPTSAN-japanese"   )
  
  
  
  							def       _a   (      self   )							->							Any:
  								_UpperCAmelCase =						ids_tensor([self.batch_size, self.seq_length] ,						self.vocab_size   )
  
  								_UpperCAmelCase =						self.get_config()
  
  								return (config, input_ids)
  
  
  
  							def       _a   (      self   )							->							List[Any]:
  								_UpperCAmelCase =						ids_tensor([self.batch_size, self.seq_length] ,						self.vocab_size   )
  
  								_UpperCAmelCase =						self.get_config()
  
  								return (config, {"input_ids": input_ids})
  
  
  
  							def       _a   (      self   )							->							str:
  								return GPTSanJapaneseConfig(
  								    vocab_size=self.vocab_size ,						num_contexts=self.seq_length ,						d_model=self.hidden_size ,						d_ff=self.d_ff ,						d_ext=self.d_ext ,						d_spout=self.d_spout ,						num_switch_layers=self.num_hidden_layers - self.num_ext_layers ,						num_ext_layers=self.num_ext_layers ,						num_heads=self.num_attention_heads ,						num_experts=self.num_experts ,						expert_capacity=self.expert_capacity ,						dropout_rate=self.dropout_rate ,						layer_norm_epsilon=self.layer_norm_epsilon ,						router_jitter_noise=self.router_jitter_noise ,						)
  
  
  
  							def       _a   (      self ,						a_ ,						a_ ,						)							->							Any:
  								_UpperCAmelCase =						GPTSanJapaneseForConditionalGeneration(config=a_   )
  								model.to(a_   )
  								model.eval()
  								_UpperCAmelCase =						model(
  								    input_ids=a_ ,						)
  								self.parent.assertIsNotNone(a_   )
  
  
  
  
  
  @require_torch
  class    _lowerCAmelCase						(						lowerCamelCase    ,       lowerCamelCase    ,       unittest.TestCase						):
  							lowercase_     :     Any       =							(GPTSanJapaneseModel,) if is_torch_available() else ()
  							lowercase_     :     Union[str, Any]       =							(
  							    {
  							        '''conversational''': GPTSanJapaneseForConditionalGeneration,
  							        '''feature-extraction''': GPTSanJapaneseForConditionalGeneration,
  							        '''summarization''': GPTSanJapaneseForConditionalGeneration,
  							        '''text2text-generation''': GPTSanJapaneseForConditionalGeneration,
  							        '''translation''': GPTSanJapaneseForConditionalGeneration,
  							    }
  							    if is_torch_available()
  							    else {}
  							)
  							lowercase_     :     Optional[int]       =							False
  							lowercase_     :     str       =							False
  							lowercase_     :     Dict       =							False
  							lowercase_     :     Optional[Any]       =							False
  							lowercase_     :     Dict       =							False
  							lowercase_     :     Union[str, Any]       =							False
  							lowercase_     :     int       =							False
  							lowercase_     :     int       =							False
  							# The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
  							lowercase_     :     Any       =							[0.8, 0.9]
  
  
  
  							def       _a   (      self ,						a_ ,						a_ ,						a_ ,						a_ ,						a_   )							->							int:
  								if pipeline_test_casse_name == "SummarizationPipelineTests":
  									# TODO: fix `_reorder_cache` is not implemented for this model
  									return True
  								elif pipeline_test_casse_name == "Text2TextGenerationPipelineTests":
  									# TODO: check this.
  									return True
  
  								return False
  
  
  
  							def       _a   (      self   )							->							Union[str, Any]:
  								_UpperCAmelCase =						GPTSanJapaneseTester(self   )
  								_UpperCAmelCase =						ConfigTester(self ,						config_class=a_ ,						d_model=37   )
  
  
  
  							def       _a   (      self   )							->							Optional[Any]:
  								GPTSanJapaneseConfig()
  
  
  
  							def       _a   (      self   )							->							Dict:
  								_UpperCAmelCase =						self.model_tester.prepare_config_and_inputs()
  								self.model_tester.create_and_check_model(*a_   )
  
  
  
  							@unittest.skip(
  							    reason="skip for now as the computed `max_memory` by `model_split_percents` in the test method will be changed inside `from_pretrained`"   )
  							def       _a   (      self   )							->							Any:
  								super().test_model_parallelism()
  
  
  
  							@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."   )
  							def       _a   (      self   )							->							int:
  								pass
  
  
  
  
  
  @require_torch
  class    _lowerCAmelCase						(						lowerCamelCase    ,       lowerCamelCase    ,       unittest.TestCase						):
  							lowercase_     :     Optional[int]       =							(GPTSanJapaneseForConditionalGeneration,) if is_torch_available() else ()
  							lowercase_     :     Optional[int]       =							False
  							lowercase_     :     Union[str, Any]       =							False
  							lowercase_     :     Optional[int]       =							False
  							lowercase_     :     Any       =							False
  							lowercase_     :     Tuple       =							False
  							lowercase_     :     Union[str, Any]       =							False
  							# The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
  							lowercase_     :     List[Any]       =							[0.8, 0.9]
  
  
  
  							def       _a   (      self   )							->							Dict:
  								_UpperCAmelCase =						GPTSanJapaneseTester(self   )
  								_UpperCAmelCase =						ConfigTester(self ,						config_class=a_ ,						d_model=37   )
  
  
  
  							def       _a   (      self   )							->							Tuple:
  								GPTSanJapaneseConfig()
  
  
  
  							def       _a   (      self   )							->							str:
  								_UpperCAmelCase =						self.model_tester.prepare_config_and_inputs()
  								self.model_tester.create_and_check_model(*a_   )
  
  
  
  							@unittest.skip(
  							    reason="skip for now as the computed `max_memory` by `model_split_percents` in the test method will be changed inside `from_pretrained`"   )
  							def       _a   (      self   )							->							Any:
  								super().test_model_parallelism()
  
  
  
  							@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."   )
  							def       _a   (      self   )							->							Tuple:
  								pass
  
  
  
  							@slow
  							def       _a   (      self   )							->							Dict:
  								_UpperCAmelCase =						GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"   )
  								_UpperCAmelCase =						GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"   )
  								_UpperCAmelCase =						tokenizer.encode("武田信玄は" ,						return_tensors="pt"   )
  								_UpperCAmelCase =						model(a_   )
  								_UpperCAmelCase =						outputs.logits.detach().cpu().numpy()
  								# Output of original model created with mesh-tensoflow
  								_UpperCAmelCase =						[
  								    # fmt: off
  								    [-12.037839889526367, -12.433061599731445, -14.333840370178223, -12.450345993041992, -11.1661376953125,
  								    -11.930137634277344, -10.659740447998047, -12.909574508666992, -13.241043090820312, -13.398579597473145,
  								    -11.107524871826172, -12.3685941696167, -22.97943115234375, -10.481067657470703, -12.484030723571777,
  								    -12.807360649108887, -14.769700050354004, -12.233579635620117, -13.428145408630371, -22.624177932739258],
  								    [-7.511149883270264, -8.281851768493652, -7.943127155303955, -7.55021333694458, -6.49869966506958,
  								    -7.586796283721924, -6.978085994720459, -7.839145183563232, -8.21964168548584, -8.695091247558594,
  								    -6.706910610198975, -6.6585798263549805, -19.565698623657227, -5.353842735290527, -8.350686073303223,
  								    -8.039388656616211, -10.856569290161133, -7.75154447555542, -8.819022178649902, -19.51532745361328],
  								    [-9.73066234588623, -10.223922729492188, -9.932981491088867, -11.857836723327637, -7.662626266479492,
  								    -11.13529109954834, -7.765097618103027, -11.472923278808594, -9.543149948120117, -11.905633926391602,
  								    -9.366164207458496, -11.5734281539917, -23.699003219604492, -9.429590225219727, -10.42839241027832,
  								    -10.585240364074707, -10.94771957397461, -11.095416069030762, -10.390240669250488, -23.769372940063477],
  								    [-9.728265762329102, -9.859712600708008, -10.09729290008545, -9.678522109985352, -6.879519939422607,
  								    -9.68487548828125, -4.2803425788879395, -10.018914222717285, -9.308445930480957, -10.63394546508789,
  								    -8.083646774291992, -9.06301498413086, -21.904266357421875, -8.90160846710205, -8.841876029968262,
  								    -11.856719970703125, -12.079398155212402, -11.233753204345703, -10.177338600158691, -21.87256622314453],
  								    [-9.669764518737793, -9.614198684692383, -9.814510345458984, -9.996501922607422, -11.375690460205078,
  								    -10.113405227661133, -10.546867370605469, -10.04369068145752, -10.907809257507324, -10.504216194152832,
  								    -11.129199028015137, -10.151124000549316, -21.96586799621582, -9.086349487304688, -11.730339050292969,
  								    -10.460667610168457, -10.298049926757812, -10.784148216247559, -10.840693473815918, -22.03152847290039],
  								    # fmt: on
  								]
  								_UpperCAmelCase =						np.array(a_   ).flatten()
  								_UpperCAmelCase =						output_logits[0, :, :20].flatten()
  
  								def check(a_ ,						a_ ,						a_=5e-4   ):
  									return abs(a - b   ) < epsilon * max(abs(a_   ) ,						abs(a_   )   )
  
  								self.assertTrue(np.all([check(target[i] ,						predict[i]   ) for i in range(len(a_   )   )]   )   )
  
  
  
  							@slow
  							def       _a   (      self   )							->							Optional[Any]:
  								_UpperCAmelCase =						GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"   )
  								_UpperCAmelCase =						GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"   )
  								model.to(a_   )
  
  								# set deterministically
  								_UpperCAmelCase =						GenerationConfig.from_pretrained("Tanrei/GPTSAN-japanese"   )
  								_UpperCAmelCase =						1
  
  								# use different length sentences to test batching
  								_UpperCAmelCase =						[
  								    "甲斐なら武田と言うほど",
  								    "織田信長は、",
  								]
  
  								_UpperCAmelCase =						"left"
  								_UpperCAmelCase =						tokenizer(a_ ,						return_tensors="pt" ,						padding=a_   )
  								_UpperCAmelCase =						inputs["input_ids"].to(a_   )
  
  								self.assertNotEqual(inputs["attention_mask"][0].numpy().tolist() ,						inputs["attention_mask"][1].numpy().tolist()   )
  
  								_UpperCAmelCase =						model.generate(
  								    input_ids=a_ ,						attention_mask=inputs["attention_mask"].to(a_   ) ,						max_new_tokens=3 ,						generation_config=a_ ,						)
  
  								_UpperCAmelCase =						tokenizer(sentences[0] ,						return_tensors="pt"   ).input_ids.to(a_   )
  								_UpperCAmelCase =						model.generate(
  								    input_ids=a_ ,						max_new_tokens=3 ,						generation_config=a_   )
  
  								_UpperCAmelCase =						tokenizer(sentences[1] ,						return_tensors="pt"   ).input_ids.to(a_   )
  								_UpperCAmelCase =						model.generate(input_ids=a_ ,						max_new_tokens=3 ,						generation_config=a_   )
  
  								self.assertNotEqual(inputs_non_padded.shape ,						inputs_padded.shape   )
  
  								_UpperCAmelCase =						tokenizer.batch_decode(a_ ,						skip_special_tokens=a_   )
  								_UpperCAmelCase =						tokenizer.decode(output_non_padded[0] ,						skip_special_tokens=a_   )
  								_UpperCAmelCase =						tokenizer.decode(output_padded[0] ,						skip_special_tokens=a_   )
  
  								_UpperCAmelCase =						[
  								    "甲斐なら武田と言うほど甲斐の武田",
  								    "織田信長は、このような",
  								]
  								self.assertListEqual(a_ ,						a_   )
  								self.assertListEqual(a_ ,						[non_padded_sentence, padded_sentence]   )
  
  
  
  							@tooslow
  							def       _a   (      self   )							->							List[Any]:
  								_UpperCAmelCase =						GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"   )
  								_UpperCAmelCase =						GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"   )
  								# Output of original model created with mesh-tensoflow
  								_UpperCAmelCase =						[
  								    ("武田信玄は", 35675),
  								    ("武田信玄は、", 45),
  								    ("武田信玄は、この", 29),
  								    ("武田信玄は、このよう", 30642),
  								    ("武田信玄は、このような", 35680),
  								    ("武田信玄は、このような「", 8640),
  								    ("武田信玄は、このような「武田", 31617),
  								    ("武田信玄は、このような「武田家", 30646),
  								    ("武田信玄は、このような「武田家の", 31617),
  								    ("武田信玄は、このような「武田家の家", 31381),
  								]
  								for input, output in target:
  									_UpperCAmelCase =						tokenizer.encode(a_ ,						return_tensors="pt"   )
  									_UpperCAmelCase =						model(a_   )
  									_UpperCAmelCase =						outputs.logits.detach().cpu().numpy()[0]
  									_UpperCAmelCase =						np.argmax(output_logits[-1]   )
  									self.assertEqual(a_ ,						a_   )
  
  
  
  							@slow
  							def       _a   (      self   )							->							Dict:
  								_UpperCAmelCase =						GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"   )
  								_UpperCAmelCase =						GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"   )
  								model.to(a_   )
  
  								# set deterministically
  								_UpperCAmelCase =						GenerationConfig.from_pretrained("Tanrei/GPTSAN-japanese"   )
  								_UpperCAmelCase =						1
  
  								_UpperCAmelCase =						"武田信玄は、"
  								_UpperCAmelCase =						tokenizer(a_ ,						return_tensors="pt"   ).input_ids.to(a_   )
  								_UpperCAmelCase =						tokenizer([input_text, input_text] ,						return_tensors="pt"   ).input_ids.to(a_   )
  
  								# spout from uniform and one-hot
  								_UpperCAmelCase =						[
  								    # fmt: off
  								    [0.87882208, 0.38426396, 0.33220248, 0.43890406, 0.16562252,
  								    0.04803985, 0.211572  , 0.23188473, 0.37153068, 0.7836377 ,
  								    0.02160172, 0.38761719, 0.75290772, 0.90198857, 0.34365777,
  								    0.64168169, 0.44318471, 0.14575746, 0.92562881, 0.40812148,
  								    0.29019122, 0.88861599, 0.65524846, 0.43563456, 0.38177187,
  								    0.70832965, 0.81527892, 0.68832812, 0.38833192, 0.4561522 ,
  								    0.14828817, 0.47248213, 0.54357335, 0.82009566, 0.1338884 ,
  								    0.02755417, 0.19764677, 0.2422084 , 0.04757674, 0.65409606,
  								    0.0824589 , 0.03304383, 0.94387689, 0.98764509, 0.82433901,
  								    0.27646741, 0.64907493, 0.76009406, 0.30087915, 0.17904689,
  								    0.41601714, 0.67046398, 0.10422822, 0.08447374, 0.07354344,
  								    0.61423565, 0.70284866, 0.7532333 , 0.1972038 , 0.29575659,
  								    0.90583886, 0.29265307, 0.50000175, 0.70407655, 0.889363  ,
  								    0.81904418, 0.66829128, 0.64468815, 0.56563723, 0.85601875,
  								    0.94924672, 0.00166762, 0.25220643, 0.74540219, 0.67993247,
  								    0.1549675 , 0.39385352, 0.92153607, 0.63745931, 0.27759043,
  								    0.84702295, 0.65904271, 0.58676614, 0.8666936 , 0.39607438,
  								    0.79954983, 0.42220697, 0.39650381, 0.7849864 , 0.56150201,
  								    0.15678925, 0.14746032, 0.34542114, 0.47026783, 0.11956489,
  								    0.25421435, 0.33788901, 0.68934842, 0.36424685, 0.71737898,
  								    0.38983449, 0.94393779, 0.39575588, 0.36616553, 0.87104665,
  								    0.64630203, 0.22516905, 0.88270804, 0.15031338, 0.75144345,
  								    0.46459025, 0.85396454, 0.86355643, 0.65139851, 0.70266061,
  								    0.30241389, 0.81056497, 0.88865969, 0.38773807, 0.70635849,
  								    0.90718459, 0.43245789, 0.28000654, 0.45935562, 0.08773519,
  								    0.9552151 , 0.93901511, 0.22489288], # uniform
  								    [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., 0., 0., 0., 0., 0.,
  								     0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
  								     0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
  								     0., 0., 0., 0., 0., 0., 0., 0.],
  								    # fmt: on
  								]
  
  								_UpperCAmelCase =						model.generate(
  								    input_ids=a_ ,						spout=spouts[0] ,						max_new_tokens=20 ,						generation_config=a_ ,						)
  
  								_UpperCAmelCase =						model.generate(
  								    input_ids=a_ ,						spout=spouts[1] ,						max_new_tokens=20 ,						generation_config=a_ ,						)
  
  								_UpperCAmelCase =						model.generate(
  								    input_ids=a_ ,						spout=a_ ,						max_new_tokens=20 ,						generation_config=a_ ,						)
  
  								_UpperCAmelCase =						tokenizer.decode(outputa[0]   )
  								_UpperCAmelCase =						tokenizer.decode(outputa[0]   )
  								_UpperCAmelCase =						tokenizer.batch_decode(a_   )
  
  								_UpperCAmelCase =						[
  								    "武田信玄は、武田氏の滅亡後、武田氏の居城であった甲斐武田氏の居城である",
  								    "武田信玄は、武田家の滅亡を防ぐため、武田家の家臣である武田信虎を討",
  								]
  								self.assertListEqual(a_ ,						a_   )
  								self.assertListEqual(a_ ,						[outa_sentence, outa_sentence]   )
  
  
  
  							@slow
  							def       _a   (      self   )							->							int:
  								_UpperCAmelCase =						GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"   )
  								_UpperCAmelCase =						GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"   )
  								model.to(a_   )
  
  								# set deterministically
  								_UpperCAmelCase =						GenerationConfig.from_pretrained("Tanrei/GPTSAN-japanese"   )
  								_UpperCAmelCase =						1
  
  								_UpperCAmelCase =						"武田信玄"
  								_UpperCAmelCase =						"織田信長"
  								_UpperCAmelCase =						"は、"
  								_UpperCAmelCase =						"が、"
  								_UpperCAmelCase =						tokenizer(a_ ,						prefix_text=a_ ,						return_tensors="pt"   )
  								_UpperCAmelCase =						tokenizer(a_ ,						prefix_text=a_ ,						return_tensors="pt"   )
  								_UpperCAmelCase =						tokenizer([[prefix_text_a, input_text_a], [prefix_text_a, input_text_a]] ,						return_tensors="pt"   )
  
  								_UpperCAmelCase =						model.generate(
  								    input_ids=input_tok_a.input_ids.to(a_   ) ,						token_type_ids=input_tok_a.token_type_ids.to(a_   ) ,						max_new_tokens=20 ,						generation_config=a_ ,						)
  
  								_UpperCAmelCase =						model.generate(
  								    input_ids=input_tok_a.input_ids.to(a_   ) ,						token_type_ids=input_tok_a.token_type_ids.to(a_   ) ,						max_new_tokens=20 ,						generation_config=a_ ,						)
  
  								_UpperCAmelCase =						model.generate(
  								    input_ids=input_tok_a.input_ids.to(a_   ) ,						token_type_ids=input_tok_a.token_type_ids.to(a_   ) ,						attention_mask=input_tok_a.attention_mask.to(a_   ) ,						max_new_tokens=20 ,						generation_config=a_ ,						)
  
  								_UpperCAmelCase =						tokenizer.decode(outputa[0]   )
  								_UpperCAmelCase =						tokenizer.decode(outputa[0]   )
  								_UpperCAmelCase =						tokenizer.batch_decode(a_   )
  
  								_UpperCAmelCase =						[
  								    "武田信玄は、武田氏の祖である武田信虎を、その子・武田信友を擁して",
  								    "織田信長が、織田信長の妻・お市の方を妻として迎えたという逸話が残",
  								]
  								self.assertListEqual(a_ ,						a_   )
  								self.assertListEqual(a_ ,						[outa_sentence, outa_sentence]   )
  
  
  
  
  
style_context: 
  """simple docstring"""
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available
  
  
  __magic_name__		       =				{}
  
  try:
  			if not is_sentencepiece_available():
  						raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  			pass
  else:
  			__magic_name__		       =				['''CpmTokenizer''']
  
  try:
  			if not is_tokenizers_available():
  						raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  			pass
  else:
  			__magic_name__		       =				['''CpmTokenizerFast''']
  
  
  if TYPE_CHECKING:
  			try:
  						if not is_sentencepiece_available():
  									raise OptionalDependencyNotAvailable()
  			except OptionalDependencyNotAvailable:
  						pass
  			else:
  						from .tokenization_cpm import CpmTokenizer
  
  			try:
  						if not is_tokenizers_available():
  									raise OptionalDependencyNotAvailable()
  			except OptionalDependencyNotAvailable:
  						pass
  			else:
  						from .tokenization_cpm_fast import CpmTokenizerFast
  
  else:
  			import sys
  
  			__magic_name__		       =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  
  
  
  
  