


"""simple docstring"""







import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
     import os

     # The slow tests are often failing with OOM error on GPU
     # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
     # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
     lowerCAmelCase__				    =       '''platform'''

     import jax
     import jax.numpy as jnp

     from transformers import BlenderbotTokenizer
     from transformers.models.blenderbot.modeling_flax_blenderbot import (
         FlaxBlenderbotForConditionalGeneration,
         FlaxBlenderbotModel,
         shift_tokens_right,
     )
def  a__      (    SCREAMING_SNAKE_CASE   :   Optional[Any]    ,				SCREAMING_SNAKE_CASE   :   int    ,				SCREAMING_SNAKE_CASE   :   Any=None    ,				SCREAMING_SNAKE_CASE   :   Tuple=None    ,				SCREAMING_SNAKE_CASE   :   int=None    ,				SCREAMING_SNAKE_CASE   :   Dict=None    ,				SCREAMING_SNAKE_CASE   :   int=None    ,				SCREAMING_SNAKE_CASE   :   Dict=None    ,				):


  '''simple docstring'''







  if attention_mask is None:
    lowerCAmelCase					:					int								=							np.where(input_ids != config.pad_token_id    ,				1    ,				0    )
  if decoder_attention_mask is None:
    lowerCAmelCase					:					Union[str, Any]								=							np.where(decoder_input_ids != config.pad_token_id    ,				1    ,				0    )
  if head_mask is None:
    lowerCAmelCase					:					str								=							np.ones((config.encoder_layers, config.encoder_attention_heads)    )
  if decoder_head_mask is None:
    lowerCAmelCase					:					Any								=							np.ones((config.decoder_layers, config.decoder_attention_heads)    )
  if cross_attn_head_mask is None:
    lowerCAmelCase					:					int								=							np.ones((config.decoder_layers, config.decoder_attention_heads)    )
  return {
      "input_ids": input_ids,
      "decoder_input_ids": decoder_input_ids,
      "attention_mask": attention_mask,
      "decoder_attention_mask": attention_mask,
  }


class 				SCREAMING_SNAKE_CASE__    :
       """simple docstring"""


       def __init__(						self		,					snake_case__		,					snake_case__=13		,					snake_case__=7		,					snake_case__=True		,					snake_case__=False		,					snake_case__=99		,					snake_case__=16		,					snake_case__=2		,					snake_case__=4		,					snake_case__=4		,					snake_case__="gelu"		,					snake_case__=0.1		,					snake_case__=0.1		,					snake_case__=32		,					snake_case__=2		,					snake_case__=1		,					snake_case__=0		,					snake_case__=0.02		,					):






         """simple docstring"""





         lowerCAmelCase					:					Dict								=							parent
         lowerCAmelCase					:					Optional[Any]								=							batch_size
         lowerCAmelCase					:					Optional[int]								=							seq_length
         lowerCAmelCase					:					Tuple								=							is_training
         lowerCAmelCase					:					List[Any]								=							use_labels
         lowerCAmelCase					:					List[str]								=							vocab_size
         lowerCAmelCase					:					str								=							hidden_size
         lowerCAmelCase					:					List[str]								=							num_hidden_layers
         lowerCAmelCase					:					Optional[Any]								=							num_attention_heads
         lowerCAmelCase					:					List[Any]								=							intermediate_size
         lowerCAmelCase					:					List[str]								=							hidden_act
         lowerCAmelCase					:					Tuple								=							hidden_dropout_prob
         lowerCAmelCase					:					List[str]								=							attention_probs_dropout_prob
         lowerCAmelCase					:					Tuple								=							max_position_embeddings
         lowerCAmelCase					:					Union[str, Any]								=							eos_token_id
         lowerCAmelCase					:					Tuple								=							pad_token_id
         lowerCAmelCase					:					Optional[int]								=							bos_token_id
         lowerCAmelCase					:					Optional[Any]								=							initializer_range

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					List[Any]								=							np.clip(ids_tensor([self.batch_size, self.seq_length - 1]		,					self.vocab_size						)		,					3		,					self.vocab_size						)
         lowerCAmelCase					:					int								=							np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)		,					dtype=np.intaa						))		,					-1						)

         lowerCAmelCase					:					Tuple								=							shift_tokens_right(snake_case__		,					1		,					2						)

         lowerCAmelCase					:					List[str]								=							BlenderbotConfig(
             vocab_size=self.vocab_size		,					d_model=self.hidden_size		,					encoder_layers=self.num_hidden_layers		,					decoder_layers=self.num_hidden_layers		,					encoder_attention_heads=self.num_attention_heads		,					decoder_attention_heads=self.num_attention_heads		,					encoder_ffn_dim=self.intermediate_size		,					decoder_ffn_dim=self.intermediate_size		,					dropout=self.hidden_dropout_prob		,					attention_dropout=self.attention_probs_dropout_prob		,					max_position_embeddings=self.max_position_embeddings		,					eos_token_id=self.eos_token_id		,					bos_token_id=self.bos_token_id		,					pad_token_id=self.pad_token_id		,					initializer_range=self.initializer_range		,					use_cache=snake_case__		,					)
         lowerCAmelCase					:					Dict								=							prepare_blenderbot_inputs_dict(snake_case__		,					snake_case__		,					snake_case__						)
         return config, inputs_dict

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase						,  lowerCAmelCase					:					Union[str, Any]								=							self.prepare_config_and_inputs()
         return config, inputs_dict

       def 	lowercase__							(						self		,					snake_case__		,					snake_case__		,					snake_case__						):






         """simple docstring"""





         lowerCAmelCase					:					Dict								=							20
         lowerCAmelCase					:					Union[str, Any]								=							model_class_name(snake_case__						)

         lowerCAmelCase					:					List[str]								=							model.encode(inputs_dict["input_ids"]						)

         lowerCAmelCase						,  lowerCAmelCase					:					Optional[int]								=							(
             inputs_dict["decoder_input_ids"],
             inputs_dict["decoder_attention_mask"],
         )

         lowerCAmelCase					:					Tuple								=							model.init_cache(decoder_input_ids.shape[0]		,					snake_case__		,					snake_case__						)
         lowerCAmelCase					:					Optional[Any]								=							jnp.ones((decoder_input_ids.shape[0], max_decoder_length)		,					dtype="i4"						)

         lowerCAmelCase					:					List[Any]								=							jnp.broadcast_to(
             jnp.arange(decoder_input_ids.shape[-1] - 1						)[None, :]		,					(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)		,					)
         lowerCAmelCase					:					Tuple								=							model.decode(
             decoder_input_ids[:, :-1]		,					snake_case__		,					decoder_attention_mask=snake_case__		,					past_key_values=snake_case__		,					decoder_position_ids=snake_case__		,					)

         lowerCAmelCase					:					Dict								=							jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]		,					dtype="i4"						)
         lowerCAmelCase					:					Optional[Any]								=							model.decode(
             decoder_input_ids[:, -1:]		,					snake_case__		,					decoder_attention_mask=snake_case__		,					past_key_values=outputs_cache.past_key_values		,					decoder_position_ids=snake_case__		,					)

         lowerCAmelCase					:					Tuple								=							model.decode(snake_case__		,					snake_case__						)

         lowerCAmelCase					:					int								=							np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])						)						)
         self.parent.assertTrue(diff < 1e-3		,					msg=f"""Max diff is {diff}"""						)







       def 	lowercase__							(						self		,					snake_case__		,					snake_case__		,					snake_case__						):






         """simple docstring"""





         lowerCAmelCase					:					List[Any]								=							20
         lowerCAmelCase					:					int								=							model_class_name(snake_case__						)

         lowerCAmelCase					:					Tuple								=							model.encode(inputs_dict["input_ids"]						)

         lowerCAmelCase						,  lowerCAmelCase					:					int								=							(
             inputs_dict["decoder_input_ids"],
             inputs_dict["decoder_attention_mask"],
         )

         lowerCAmelCase					:					Optional[int]								=							jnp.concatenate(
             [
                 decoder_attention_mask,
                 jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])						),
             ]		,					axis=-1		,					)

         lowerCAmelCase					:					Dict								=							model.init_cache(decoder_input_ids.shape[0]		,					snake_case__		,					snake_case__						)
         lowerCAmelCase					:					Dict								=							jnp.broadcast_to(
             jnp.arange(decoder_input_ids.shape[-1] - 1						)[None, :]		,					(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)		,					)

         lowerCAmelCase					:					List[str]								=							model.decode(
             decoder_input_ids[:, :-1]		,					snake_case__		,					decoder_attention_mask=snake_case__		,					past_key_values=snake_case__		,					decoder_position_ids=snake_case__		,					)
         lowerCAmelCase					:					Tuple								=							jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]		,					dtype="i4"						)
         lowerCAmelCase					:					List[Any]								=							model.decode(
             decoder_input_ids[:, -1:]		,					snake_case__		,					past_key_values=outputs_cache.past_key_values		,					decoder_attention_mask=snake_case__		,					decoder_position_ids=snake_case__		,					)

         lowerCAmelCase					:					List[Any]								=							model.decode(snake_case__		,					snake_case__		,					decoder_attention_mask=snake_case__						)

         lowerCAmelCase					:					int								=							np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])						)						)
         self.parent.assertTrue(diff < 1e-3		,					msg=f"""Max diff is {diff}"""						)


@require_flax
class 				SCREAMING_SNAKE_CASE__    (		unittest.TestCase    ):
       """simple docstring"""


       a  :    List[Any]       =99

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					str								=							np.array(
             [
                 [71, 82, 18, 33, 46, 91, 2],
                 [68, 34, 26, 58, 30, 82, 2],
                 [5, 97, 17, 39, 94, 40, 2],
                 [76, 83, 94, 25, 70, 78, 2],
                 [87, 59, 41, 35, 48, 66, 2],
                 [55, 13, 16, 58, 5, 2, 1],  # note padding
                 [64, 27, 31, 51, 12, 75, 2],
                 [52, 64, 86, 17, 83, 39, 2],
                 [48, 61, 9, 24, 71, 82, 2],
                 [26, 1, 60, 48, 22, 13, 2],
                 [21, 5, 62, 28, 14, 76, 2],
                 [45, 98, 37, 86, 59, 48, 2],
                 [70, 70, 50, 9, 28, 0, 2],
             ]		,					dtype=np.intaa		,					)

         lowerCAmelCase					:					Union[str, Any]								=							input_ids.shape[0]
         lowerCAmelCase					:					Optional[Any]								=							BlenderbotConfig(
             vocab_size=self.vocab_size		,					d_model=24		,					encoder_layers=2		,					decoder_layers=2		,					encoder_attention_heads=2		,					decoder_attention_heads=2		,					encoder_ffn_dim=32		,					decoder_ffn_dim=32		,					max_position_embeddings=48		,					eos_token_id=2		,					pad_token_id=1		,					bos_token_id=0		,					)
         return config, input_ids, batch_size

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase					:					int								=							self._get_config_and_data()
         lowerCAmelCase					:					str								=							FlaxBlenderbotForConditionalGeneration(snake_case__						)
         lowerCAmelCase					:					List[Any]								=							lm_model(input_ids=snake_case__						)
         lowerCAmelCase					:					Dict								=							(batch_size, input_ids.shape[1], config.vocab_size)
         self.assertEqual(outputs["logits"].shape		,					snake_case__						)

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					Tuple								=							BlenderbotConfig(
             vocab_size=self.vocab_size		,					d_model=14		,					encoder_layers=2		,					decoder_layers=2		,					encoder_attention_heads=2		,					decoder_attention_heads=2		,					encoder_ffn_dim=8		,					decoder_ffn_dim=8		,					max_position_embeddings=48		,					)
         lowerCAmelCase					:					Tuple								=							FlaxBlenderbotForConditionalGeneration(snake_case__						)
         lowerCAmelCase					:					int								=							np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]		,					dtype=np.intaa						)
         lowerCAmelCase					:					Optional[Any]								=							np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]		,					dtype=np.intaa						)
         lowerCAmelCase					:					Tuple								=							lm_model(input_ids=snake_case__		,					decoder_input_ids=snake_case__						)
         lowerCAmelCase					:					int								=							(*summary.shape, config.vocab_size)
         self.assertEqual(outputs["logits"].shape		,					snake_case__						)







       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					List[str]								=							np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]		,					dtype=np.intaa						)
         lowerCAmelCase					:					str								=							shift_tokens_right(snake_case__		,					1		,					2						)
         lowerCAmelCase					:					List[Any]								=							np.equal(snake_case__		,					1						).astype(np.floataa						).sum()
         lowerCAmelCase					:					Tuple								=							np.equal(snake_case__		,					1						).astype(np.floataa						).sum()
         self.assertEqual(shifted.shape		,					input_ids.shape						)
         self.assertEqual(snake_case__		,					n_pad_before - 1						)
         self.assertTrue(np.equal(shifted[:, 0]		,					2						).all()						)


@require_flax
class 				SCREAMING_SNAKE_CASE__    (		lowercase       ,   unittest.TestCase       ,   lowercase    ):
       """simple docstring"""


       a  :    str       =True
       a  :    List[Any]       =(
           (
               FlaxBlenderbotModel,
               FlaxBlenderbotForConditionalGeneration,
           )
           if is_flax_available()
           else ()
       )
       a  :    Dict       =(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					Optional[Any]								=							FlaxBlenderbotModelTester(self						)

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase						,  lowerCAmelCase					:					Union[str, Any]								=							self.model_tester.prepare_config_and_inputs()
         for model_class in self.all_model_classes:
           self.model_tester.check_use_cache_forward(snake_case__		,					snake_case__		,					snake_case__						)

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase						,  lowerCAmelCase					:					List[str]								=							self.model_tester.prepare_config_and_inputs()
         for model_class in self.all_model_classes:
           self.model_tester.check_use_cache_forward_with_attn_mask(snake_case__		,					snake_case__		,					snake_case__						)

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase						,  lowerCAmelCase					:					Optional[Any]								=							self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
           with self.subTest(model_class.__name__						):
             lowerCAmelCase					:					Optional[Any]								=							self._prepare_for_class(snake_case__		,					snake_case__						)
             lowerCAmelCase					:					Optional[int]								=							model_class(snake_case__						)

             @jax.jit
             def encode_jitted(snake_case__		,					snake_case__=None		,					**snake_case__						):
               return model.encode(input_ids=snake_case__		,					attention_mask=snake_case__						)

             with self.subTest("JIT Enabled"						):
               lowerCAmelCase					:					Dict								=							encode_jitted(**snake_case__						).to_tuple()

             with self.subTest("JIT Disabled"						):
               with jax.disable_jit():
                 lowerCAmelCase					:					Dict								=							encode_jitted(**snake_case__						).to_tuple()

             self.assertEqual(len(snake_case__						)		,					len(snake_case__						)						)
             for jitted_output, output in zip(snake_case__		,					snake_case__						):
               self.assertEqual(jitted_output.shape		,					output.shape						)

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase						,  lowerCAmelCase					:					Any								=							self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
           with self.subTest(model_class.__name__						):
             lowerCAmelCase					:					int								=							model_class(snake_case__						)
             lowerCAmelCase					:					List[str]								=							model.encode(inputs_dict["input_ids"]		,					inputs_dict["attention_mask"]						)

             lowerCAmelCase					:					Optional[int]								=							{
                 "decoder_input_ids": inputs_dict["decoder_input_ids"],
                 "decoder_attention_mask": inputs_dict["decoder_attention_mask"],
                 "encoder_outputs": encoder_outputs,
             }

             @jax.jit
             def decode_jitted(snake_case__		,					snake_case__		,					snake_case__						):
               return model.decode(
                   decoder_input_ids=snake_case__		,					decoder_attention_mask=snake_case__		,					encoder_outputs=snake_case__		,					)

             with self.subTest("JIT Enabled"						):
               lowerCAmelCase					:					Union[str, Any]								=							decode_jitted(**snake_case__						).to_tuple()

             with self.subTest("JIT Disabled"						):
               with jax.disable_jit():
                 lowerCAmelCase					:					Union[str, Any]								=							decode_jitted(**snake_case__						).to_tuple()

             self.assertEqual(len(snake_case__						)		,					len(snake_case__						)						)
             for jitted_output, output in zip(snake_case__		,					snake_case__						):
               self.assertEqual(jitted_output.shape		,					output.shape						)

       @slow
       def 	lowercase__							(						self						):






         """simple docstring"""





         for model_class_name in self.all_model_classes:
           lowerCAmelCase					:					Optional[Any]								=							model_class_name.from_pretrained("facebook/blenderbot-400M-distill"						)
           # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
           lowerCAmelCase					:					Any								=							np.ones((1, 1)						) * model.config.eos_token_id
           lowerCAmelCase					:					Union[str, Any]								=							model(snake_case__						)
           self.assertIsNotNone(snake_case__						)







       @unittest.skipUnless(jax_device != "cpu"		,					"3B test too slow on CPU."						)
       @slow
       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					Optional[Any]								=							{"num_beams": 1, "early_stopping": True, "min_length": 15, "max_length": 25}
         lowerCAmelCase					:					List[Any]								=							{"skip_special_tokens": True, "clean_up_tokenization_spaces": True}

         lowerCAmelCase					:					Dict								=							FlaxBlenderbotForConditionalGeneration.from_pretrained("facebook/blenderbot-3B"		,					from_pt=snake_case__						)
         lowerCAmelCase					:					Any								=							BlenderbotTokenizer.from_pretrained("facebook/blenderbot-3B"						)

         lowerCAmelCase					:					str								=							["Sam"]
         lowerCAmelCase					:					Tuple								=							tokenizer(snake_case__		,					return_tensors="jax"						)

         lowerCAmelCase					:					Any								=							model.generate(**snake_case__		,					**snake_case__						)
         lowerCAmelCase					:					Any								=							"Sam is a great name. It means \"sun\" in Gaelic."

         lowerCAmelCase					:					int								=							tokenizer.batch_decode(snake_case__		,					**snake_case__						)
         assert generated_txt[0].strip() == tgt_text









"""simple docstring"""







import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class 				SCREAMING_SNAKE_CASE__    (		lowercase       ,   unittest.TestCase    ):
       """simple docstring"""


       a  :    List[str]       =None
       a  :    List[Any]       =BloomTokenizerFast
       a  :    Optional[int]       =BloomTokenizerFast
       a  :    Optional[Any]       =True
       a  :    Dict       =False
       a  :    Optional[Any]       ="tokenizer_file"
       a  :    Optional[int]       ={"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>"}

       def 	lowercase__							(						self						):






         """simple docstring"""





         super().setUp()
         lowerCAmelCase					:					Tuple								=							BloomTokenizerFast.from_pretrained("bigscience/tokenizer"						)
         tokenizer.save_pretrained(self.tmpdirname						)

       def 	lowercase__							(						self		,					**snake_case__						):






         """simple docstring"""





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

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					int								=							self.get_rust_tokenizer()

         lowerCAmelCase					:					List[Any]								=							["The quick brown fox</s>", "jumps over the lazy dog</s>"]
         lowerCAmelCase					:					str								=							[[2_175, 23_714, 73_173, 144_252, 2], [77, 132_619, 3_478, 368, 109_586, 35_433, 2]]

         lowerCAmelCase					:					Optional[int]								=							tokenizer.batch_encode_plus(snake_case__						)["input_ids"]
         self.assertListEqual(snake_case__		,					snake_case__						)

         lowerCAmelCase					:					Optional[int]								=							tokenizer.batch_decode(snake_case__						)
         self.assertListEqual(snake_case__		,					snake_case__						)

       def 	lowercase__							(						self		,					snake_case__=6						):






         """simple docstring"""





         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
           with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""						):
             lowerCAmelCase					:					Tuple								=							self.rust_tokenizer_class.from_pretrained(snake_case__		,					**snake_case__						)
             # tokenizer_r.pad_token = None # Hotfixing padding = None
             # Simple input
             lowerCAmelCase					:					str								=							"This is a simple input"
             lowerCAmelCase					:					Tuple								=							["This is a simple input 1", "This is a simple input 2"]
             lowerCAmelCase					:					Any								=							("This is a simple input", "This is a pair")
             lowerCAmelCase					:					Tuple								=							[
                 ("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
             try:
               tokenizer_r.encode(snake_case__		,					max_length=snake_case__						)
               tokenizer_r.encode_plus(snake_case__		,					max_length=snake_case__						)

               tokenizer_r.batch_encode_plus(snake_case__		,					max_length=snake_case__						)
               tokenizer_r.encode(snake_case__		,					max_length=snake_case__						)
               tokenizer_r.batch_encode_plus(snake_case__		,					max_length=snake_case__						)
             except ValueError:
               self.fail("Bloom Tokenizer should be able to deal with padding"						)

             lowerCAmelCase					:					Tuple								=							None  # Hotfixing padding = None
             self.assertRaises(snake_case__		,					tokenizer_r.encode		,					snake_case__		,					max_length=snake_case__		,					padding="max_length"						)

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

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

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

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

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

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					int								=							self.get_rust_tokenizer()
         lowerCAmelCase					:					int								=							load_dataset("xnli"		,					"all_languages"		,					split="test"		,					streaming=snake_case__						)

         lowerCAmelCase					:					Tuple								=							next(iter(snake_case__						)						)["premise"]  # pick up one data
         lowerCAmelCase					:					Optional[Any]								=							list(sample_data.values()						)

         lowerCAmelCase					:					int								=							list(map(tokenizer.encode		,					snake_case__						)						)
         lowerCAmelCase					:					List[Any]								=							[tokenizer.decode(snake_case__		,					clean_up_tokenization_spaces=snake_case__						) for x in output_tokens]
         self.assertListEqual(snake_case__		,					snake_case__						)







       def 	lowercase__							(						self						):






         """simple docstring"""





         self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map						)		,					1						)
         self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values()						)[0]						)		,					1						)





