
"""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   _UpperCAmelCase					:
    def __init__(     self    ,     snake_case_    ,     snake_case_=3_60_00    ,     snake_case_=13    ,     snake_case_=7    ,     snake_case_=True    ,     snake_case_=32    ,     snake_case_=42    ,     snake_case_=5    ,     snake_case_=2    ,     snake_case_=4    ,     snake_case_=2    ,     snake_case_=32    ,     snake_case_=80    ,     snake_case_=33    ,     snake_case_=0.0    ,     snake_case_=1E-6    ,     snake_case_=1_00    ,     snake_case_=0.0    ,     ):
      _snake_case  :    Union[str, Any]            = vocab_size
      _snake_case  :    List[str]            = parent
      _snake_case  :    List[str]            = batch_size
      _snake_case  :    Tuple            = num_contexts
      # For common tests
      _snake_case  :    List[str]            = self.num_contexts
      _snake_case  :    List[Any]            = is_training
      _snake_case  :    Tuple            = hidden_size
      _snake_case  :    Any            = num_ext_layers
      _snake_case  :    List[str]            = ext_size
      _snake_case  :    Tuple            = num_hidden_layers
      _snake_case  :    Union[str, Any]            = num_attention_heads
      _snake_case  :    Union[str, Any]            = num_experts
      _snake_case  :    str            = d_ff
      _snake_case  :    Optional[Any]            = d_ext
      _snake_case  :    Any            = d_spout
      _snake_case  :    Optional[Any]            = dropout_rate
      _snake_case  :    int            = layer_norm_epsilon
      _snake_case  :    str            = expert_capacity
      _snake_case  :    int            = router_jitter_noise
    def        lowerCamelCase__						(     self  ):
      return GPTSanJapaneseConfig.from_pretrained("Tanrei/GPTSAN-japanese"  )
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    int            = ids_tensor([self.batch_size, self.seq_length]    ,     self.vocab_size  )

      _snake_case  :    Optional[int]            = self.get_config()

      return (config, input_ids)
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Any            = ids_tensor([self.batch_size, self.seq_length]    ,     self.vocab_size  )

      _snake_case  :    int            = self.get_config()

      return (config, {"input_ids": input_ids})
    def        lowerCamelCase__						(     self  ):
      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        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_    ,     ):
      _snake_case  :    Optional[Any]            = GPTSanJapaneseForConditionalGeneration(config=snake_case_  )
      model.to(snake_case_  )
      model.eval()
      _snake_case  :    str            = model(
          input_ids=snake_case_    ,     )
      self.parent.assertIsNotNone(snake_case_  )






@require_torch
class   _UpperCAmelCase					(       _snake_case		,       _snake_case		,       unittest.TestCase):
    __lowercase				:			str	=       (GPTSanJapaneseModel,) if is_torch_available() else ()
    __lowercase				:			Optional[int]	=       (
        {
            """conversational""": GPTSanJapaneseForConditionalGeneration,
            """feature-extraction""": GPTSanJapaneseForConditionalGeneration,
            """summarization""": GPTSanJapaneseForConditionalGeneration,
            """text2text-generation""": GPTSanJapaneseForConditionalGeneration,
            """translation""": GPTSanJapaneseForConditionalGeneration,
        }
        if is_torch_available()
        else {}
    )
    __lowercase				:			str	=       False
    __lowercase				:			Optional[Any]	=       False
    __lowercase				:			Union[str, Any]	=       False
    __lowercase				:			int	=       False
    __lowercase				:			List[Any]	=       False
    __lowercase				:			List[Any]	=       False
    __lowercase				:			Dict	=       False
    __lowercase				:			Any	=       False
    # The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
    __lowercase				:			Optional[int]	=       [0.8, 0.9]
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_  ):
      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        lowerCamelCase__						(     self  ):
      _snake_case  :    str            = GPTSanJapaneseTester(self  )
      _snake_case  :    Union[str, Any]            = ConfigTester(self    ,     config_class=snake_case_    ,     d_model=37  )
    def        lowerCamelCase__						(     self  ):
      GPTSanJapaneseConfig()
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    str            = self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*snake_case_  )
    @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        lowerCamelCase__						(     self  ):
      super().test_model_parallelism()


    @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."  )
    def        lowerCamelCase__						(     self  ):
      pass






@require_torch
class   _UpperCAmelCase					(       _snake_case		,       _snake_case		,       unittest.TestCase):
    __lowercase				:			Any	=       (GPTSanJapaneseForConditionalGeneration,) if is_torch_available() else ()
    __lowercase				:			Optional[Any]	=       False
    __lowercase				:			int	=       False
    __lowercase				:			int	=       False
    __lowercase				:			List[Any]	=       False
    __lowercase				:			Any	=       False
    __lowercase				:			Any	=       False
    # The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
    __lowercase				:			Optional[int]	=       [0.8, 0.9]
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    str            = GPTSanJapaneseTester(self  )
      _snake_case  :    List[Any]            = ConfigTester(self    ,     config_class=snake_case_    ,     d_model=37  )
    def        lowerCamelCase__						(     self  ):
      GPTSanJapaneseConfig()
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Any            = self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*snake_case_  )
    @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        lowerCamelCase__						(     self  ):
      super().test_model_parallelism()
    @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."  )
    def        lowerCamelCase__						(     self  ):
      pass
    @slow
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Union[str, Any]            = GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"  )
      _snake_case  :    Union[str, Any]            = GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"  )
      _snake_case  :    List[str]            = tokenizer.encode("武田信玄は"    ,     return_tensors="pt"  )
      _snake_case  :    Any            = model(snake_case_  )
      _snake_case  :    Optional[Any]            = outputs.logits.detach().cpu().numpy()
      # Output of original model created with mesh-tensoflow
      _snake_case  :    List[str]            = [
          # 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
      ]
      _snake_case  :    Any            = np.array(snake_case_  ).flatten()
      _snake_case  :    int            = output_logits[0, :, :20].flatten()

      def check(snake_case_    ,     snake_case_    ,     snake_case_=5E-4  ):
        return abs(a - b  ) < epsilon * max(abs(snake_case_  )    ,     abs(snake_case_  )  )

      self.assertTrue(np.all([check(target[i]    ,     predict[i]  ) for i in range(len(snake_case_  )  )]  )  )
    @slow
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Union[str, Any]            = GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"  )
      _snake_case  :    List[str]            = GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"  )
      model.to(snake_case_  )

      # set deterministically
      _snake_case  :    Dict            = GenerationConfig.from_pretrained("Tanrei/GPTSAN-japanese"  )
      _snake_case  :    Optional[Any]            = 1

      # use different length sentences to test batching
      _snake_case  :    int            = [
          "甲斐なら武田と言うほど",
          "織田信長は、",
      ]

      _snake_case  :    Optional[int]            = "left"
      _snake_case  :    int            = tokenizer(snake_case_    ,     return_tensors="pt"    ,     padding=snake_case_  )
      _snake_case  :    str            = inputs["input_ids"].to(snake_case_  )

      self.assertNotEqual(inputs["attention_mask"][0].numpy().tolist()    ,     inputs["attention_mask"][1].numpy().tolist()  )

      _snake_case  :    Dict            = model.generate(
          input_ids=snake_case_    ,     attention_mask=inputs["attention_mask"].to(snake_case_  )    ,     max_new_tokens=3    ,     generation_config=snake_case_    ,     )

      _snake_case  :    Optional[int]            = tokenizer(sentences[0]    ,     return_tensors="pt"  ).input_ids.to(snake_case_  )
      _snake_case  :    List[str]            = model.generate(
          input_ids=snake_case_    ,     max_new_tokens=3    ,     generation_config=snake_case_  )

      _snake_case  :    List[Any]            = tokenizer(sentences[1]    ,     return_tensors="pt"  ).input_ids.to(snake_case_  )
      _snake_case  :    Union[str, Any]            = model.generate(input_ids=snake_case_    ,     max_new_tokens=3    ,     generation_config=snake_case_  )

      self.assertNotEqual(inputs_non_padded.shape    ,     inputs_padded.shape  )

      _snake_case  :    Optional[Any]            = tokenizer.batch_decode(snake_case_    ,     skip_special_tokens=snake_case_  )
      _snake_case  :    Any            = tokenizer.decode(output_non_padded[0]    ,     skip_special_tokens=snake_case_  )
      _snake_case  :    int            = tokenizer.decode(output_padded[0]    ,     skip_special_tokens=snake_case_  )

      _snake_case  :    Tuple            = [
          "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(snake_case_    ,     snake_case_  )
      self.assertListEqual(snake_case_    ,     [non_padded_sentence, padded_sentence]  )
    @tooslow
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Dict            = GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"  )
      _snake_case  :    str            = GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"  )
      # Output of original model created with mesh-tensoflow
      _snake_case  :    Tuple            = [
          ("武田信玄は", 3_56_75),
          ("武田信玄は、", 45),
          ("武田信玄は、この", 29),
          ("武田信玄は、このよう", 3_06_42),
          ("武田信玄は、このような", 3_56_80),
          ("武田信玄は、このような「", 86_40),
          ("武田信玄は、このような「武田", 3_16_17),
          ("武田信玄は、このような「武田家", 3_06_46),
          ("武田信玄は、このような「武田家の", 3_16_17),
          ("武田信玄は、このような「武田家の家", 3_13_81),
      ]
      for input, output in target:
        _snake_case  :    str            = tokenizer.encode(snake_case_    ,     return_tensors="pt"  )
        _snake_case  :    Optional[int]            = model(snake_case_  )
        _snake_case  :    Any            = outputs.logits.detach().cpu().numpy()[0]
        _snake_case  :    Union[str, Any]            = np.argmax(output_logits[-1]  )
        self.assertEqual(snake_case_    ,     snake_case_  )
    @slow
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Union[str, Any]            = GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"  )
      _snake_case  :    Optional[Any]            = GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"  )
      model.to(snake_case_  )

      # set deterministically
      _snake_case  :    List[Any]            = GenerationConfig.from_pretrained("Tanrei/GPTSAN-japanese"  )
      _snake_case  :    str            = 1

      _snake_case  :    int            = "武田信玄は、"
      _snake_case  :    Tuple            = tokenizer(snake_case_    ,     return_tensors="pt"  ).input_ids.to(snake_case_  )
      _snake_case  :    int            = tokenizer([input_text, input_text]    ,     return_tensors="pt"  ).input_ids.to(snake_case_  )

      # spout from uniform and one-hot
      _snake_case  :    int            = [
          # 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
      ]

      _snake_case  :    List[Any]            = model.generate(
          input_ids=snake_case_    ,     spout=spouts[0]    ,     max_new_tokens=20    ,     generation_config=snake_case_    ,     )

      _snake_case  :    Dict            = model.generate(
          input_ids=snake_case_    ,     spout=spouts[1]    ,     max_new_tokens=20    ,     generation_config=snake_case_    ,     )

      _snake_case  :    str            = model.generate(
          input_ids=snake_case_    ,     spout=snake_case_    ,     max_new_tokens=20    ,     generation_config=snake_case_    ,     )

      _snake_case  :    Dict            = tokenizer.decode(outputa[0]  )
      _snake_case  :    Optional[int]            = tokenizer.decode(outputa[0]  )
      _snake_case  :    List[Any]            = tokenizer.batch_decode(snake_case_  )

      _snake_case  :    int            = [
          "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(snake_case_    ,     snake_case_  )
      self.assertListEqual(snake_case_    ,     [outa_sentence, outa_sentence]  )


    @slow
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Dict            = GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"  )
      _snake_case  :    Optional[Any]            = GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"  )
      model.to(snake_case_  )

      # set deterministically
      _snake_case  :    Optional[int]            = GenerationConfig.from_pretrained("Tanrei/GPTSAN-japanese"  )
      _snake_case  :    Optional[int]            = 1

      _snake_case  :    List[str]            = "武田信玄"
      _snake_case  :    Union[str, Any]            = "織田信長"
      _snake_case  :    Any            = "は、"
      _snake_case  :    List[Any]            = "が、"
      _snake_case  :    Tuple            = tokenizer(snake_case_    ,     prefix_text=snake_case_    ,     return_tensors="pt"  )
      _snake_case  :    List[str]            = tokenizer(snake_case_    ,     prefix_text=snake_case_    ,     return_tensors="pt"  )
      _snake_case  :    Any            = tokenizer([[prefix_text_a, input_text_a], [prefix_text_a, input_text_a]]    ,     return_tensors="pt"  )

      _snake_case  :    Dict            = model.generate(
          input_ids=input_tok_a.input_ids.to(snake_case_  )    ,     token_type_ids=input_tok_a.token_type_ids.to(snake_case_  )    ,     max_new_tokens=20    ,     generation_config=snake_case_    ,     )

      _snake_case  :    Any            = model.generate(
          input_ids=input_tok_a.input_ids.to(snake_case_  )    ,     token_type_ids=input_tok_a.token_type_ids.to(snake_case_  )    ,     max_new_tokens=20    ,     generation_config=snake_case_    ,     )

      _snake_case  :    str            = model.generate(
          input_ids=input_tok_a.input_ids.to(snake_case_  )    ,     token_type_ids=input_tok_a.token_type_ids.to(snake_case_  )    ,     attention_mask=input_tok_a.attention_mask.to(snake_case_  )    ,     max_new_tokens=20    ,     generation_config=snake_case_    ,     )

      _snake_case  :    Optional[Any]            = tokenizer.decode(outputa[0]  )
      _snake_case  :    Optional[int]            = tokenizer.decode(outputa[0]  )
      _snake_case  :    Any            = tokenizer.batch_decode(snake_case_  )

      _snake_case  :    Tuple            = [
          "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(snake_case_    ,     snake_case_  )
      self.assertListEqual(snake_case_    ,     [outa_sentence, outa_sentence]  )






import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors




@require_tokenizers
class 				lowerCAmelCase__	(						UpperCamelCase       ,				unittest.TestCase    ):
					__A  :     Any     =    BartTokenizer
					__A  :     str     =    BartTokenizerFast
					__A  :     Optional[int]     =    True
					__A  :     Optional[int]     =    filter_roberta_detectors







					def 			_lowercase      (							self   :  Dict):
											super().setUp()
											A__						:				Union[str, Any]         =			[
											    "l",
											    "o",
											    "w",
											    "e",
											    "r",
											    "s",
											    "t",
											    "i",
											    "d",
											    "n",
											    "\u0120",
											    "\u0120l",
											    "\u0120n",
											    "\u0120lo",
											    "\u0120low",
											    "er",
											    "\u0120lowest",
											    "\u0120newer",
											    "\u0120wider",
											    "<unk>",
											]
											A__						:				List[str]         =			dict(zip(_A      ,		range(len(_A))))
											A__						:				List[str]         =			["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
											A__						:				str         =			{"unk_token": "<unk>"}

											A__						:				Dict         =			os.path.join(self.tmpdirname      ,		VOCAB_FILES_NAMES["vocab_file"])
											A__						:				Optional[Any]         =			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(_A) + "\n")
											with open(self.merges_file      ,		"w"      ,		encoding="utf-8") as fp:
																	fp.write("\n".join(_A))







					def 			_lowercase      (							self   :  Union[str, Any]      ,		**_A   :  str):
											kwargs.update(self.special_tokens_map)
											return self.tokenizer_class.from_pretrained(self.tmpdirname      ,		**_A)







					def 			_lowercase      (							self   :  Union[str, Any]      ,		**_A   :  List[Any]):
											kwargs.update(self.special_tokens_map)
											return self.rust_tokenizer_class.from_pretrained(self.tmpdirname      ,		**_A)







					def 			_lowercase      (							self   :  Tuple      ,		_A   :  Union[str, Any]):
											return "lower newer", "lower newer"







					@cached_property
					def 			_lowercase      (							self   :  Optional[int]):
											return BartTokenizer.from_pretrained("facebook/bart-large")







					@cached_property
					def 			_lowercase      (							self   :  Optional[int]):
											return BartTokenizerFast.from_pretrained("facebook/bart-large")







					@require_torch
					def 			_lowercase      (							self   :  Dict):
											A__						:				Optional[int]         =			["A long paragraph for summarization.", "Another paragraph for summarization."]
											A__						:				List[Any]         =			[0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																	A__						:				Tuple         =			tokenizer(_A      ,		max_length=len(_A)      ,		padding=_A      ,		return_tensors="pt")
																	self.assertIsInstance(_A      ,		_A)

																	self.assertEqual((2, 9)      ,		batch.input_ids.shape)
																	self.assertEqual((2, 9)      ,		batch.attention_mask.shape)
																	A__						:				Dict         =			batch.input_ids.tolist()[0]
																	self.assertListEqual(_A      ,		_A)
																	# Test that special tokens are reset







					@require_torch
					def 			_lowercase      (							self   :  List[Any]):
											A__						:				Optional[Any]         =			["A long paragraph for summarization.", "Another paragraph for summarization."]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																	A__						:				Tuple         =			tokenizer(_A      ,		padding=_A      ,		return_tensors="pt")
																	# check if input_ids are returned and no labels
																	self.assertIn("input_ids"      ,		_A)
																	self.assertIn("attention_mask"      ,		_A)
																	self.assertNotIn("labels"      ,		_A)
																	self.assertNotIn("decoder_attention_mask"      ,		_A)







					@require_torch
					def 			_lowercase      (							self   :  int):
											A__						:				Optional[Any]         =			[
											    "Summary of the text.",
											    "Another summary.",
											]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																	A__						:				int         =			tokenizer(text_target=_A      ,		max_length=32      ,		padding="max_length"      ,		return_tensors="pt")
																	self.assertEqual(32      ,		targets["input_ids"].shape[1])







					@require_torch
					def 			_lowercase      (							self   :  List[str]):
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																	A__						:				List[str]         =			tokenizer(
																	    ["I am a small frog" * 1024, "I am a small frog"]      ,		padding=_A      ,		truncation=_A      ,		return_tensors="pt")
																	self.assertIsInstance(_A      ,		_A)
																	self.assertEqual(batch.input_ids.shape      ,		(2, 1024))







					@require_torch
					def 			_lowercase      (							self   :  List[str]):
											A__						:				int         =			["A long paragraph for summarization."]
											A__						:				List[str]         =			[
											    "Summary of the text.",
											]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																	A__						:				Optional[Any]         =			tokenizer(_A      ,		return_tensors="pt")
																	A__						:				Union[str, Any]         =			tokenizer(text_target=_A      ,		return_tensors="pt")
																	A__						:				Tuple         =			inputs["input_ids"]
																	A__						:				str         =			targets["input_ids"]
																	self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item())
																	self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item())
																	self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item())
																	self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item())







					def 			_lowercase      (							self   :  Optional[int]):
											pass







					def 			_lowercase      (							self   :  Any):
											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																	with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'):
																							A__						:				Union[str, Any]         =			self.rust_tokenizer_class.from_pretrained(_A      ,		**_A)
																							A__						:				int         =			self.tokenizer_class.from_pretrained(_A      ,		**_A)
																							A__						:				Union[str, Any]         =			"A, <mask> AllenNLP sentence."
																							A__						:				Dict         =			tokenizer_r.encode_plus(_A      ,		add_special_tokens=_A      ,		return_token_type_ids=_A)
																							A__						:				Optional[Any]         =			tokenizer_p.encode_plus(_A      ,		add_special_tokens=_A      ,		return_token_type_ids=_A)

																							# token_type_ids should put 0 everywhere
																							self.assertEqual(sum(tokens_r["token_type_ids"])      ,		sum(tokens_p["token_type_ids"]))

																							# attention_mask should put 1 everywhere, so sum over length should be 1
																							self.assertEqual(
																							    sum(tokens_r["attention_mask"]) / len(tokens_r["attention_mask"])      ,		sum(tokens_p["attention_mask"]) / len(tokens_p["attention_mask"])      ,		)

																							A__						:				Optional[int]         =			tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"])
																							A__						:				Dict         =			tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"])

																							# Rust correctly handles the space before the mask while python doesnt
																							self.assertSequenceEqual(tokens_p["input_ids"]      ,		[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2])
																							self.assertSequenceEqual(tokens_r["input_ids"]      ,		[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2])

																							self.assertSequenceEqual(
																							    _A      ,		["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"])
																							self.assertSequenceEqual(
																							    _A      ,		["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"])