
"""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
      lowercase_       :						List[str]     					=	'''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    _lowerCAmelCase    (     lowerCamelCase__							:							Union[str, Any],						lowerCamelCase__							:							List[str],						lowerCamelCase__							:							Optional[Any]=None,						lowerCamelCase__							:							List[Any]=None,						lowerCamelCase__							:							List[str]=None,						lowerCamelCase__							:							Optional[int]=None,						lowerCamelCase__							:							Tuple=None,						lowerCamelCase__							:							str=None,						)  ->    int:






  if attention_mask is None:
    _SCREAMING_SNAKE_CASE   :				str							    = np.where(input_ids != config.pad_token_id,						1,						0				)
  if decoder_attention_mask is None:
    _SCREAMING_SNAKE_CASE   :				Dict							    = np.where(decoder_input_ids != config.pad_token_id,						1,						0				)
  if head_mask is None:
    _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = np.ones((config.encoder_layers, config.encoder_attention_heads)				)
  if decoder_head_mask is None:
    _SCREAMING_SNAKE_CASE   :				Tuple							    = np.ones((config.decoder_layers, config.decoder_attention_heads)				)
  if cross_attn_head_mask is None:
    _SCREAMING_SNAKE_CASE   :				List[Any]							    = 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 			UpperCamelCase      :
     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"""


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




       """simple docstring"""


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

       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = shift_tokens_right(lowerCAmelCase__  ,     1  ,     2				)

       _SCREAMING_SNAKE_CASE   :				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=lowerCAmelCase__  ,     )
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = prepare_blenderbot_inputs_dict(lowerCAmelCase__  ,     lowerCAmelCase__  ,     lowerCAmelCase__				)
       return config, inputs_dict
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				List[str]							    = self.prepare_config_and_inputs()
       return config, inputs_dict
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Dict							    = 20
       _SCREAMING_SNAKE_CASE   :				Dict							    = model_class_name(lowerCAmelCase__				)

       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = model.encode(inputs_dict["input_ids"]				)

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

       _SCREAMING_SNAKE_CASE   :				List[Any]							    = model.init_cache(decoder_input_ids.shape[0]  ,     lowerCAmelCase__  ,     lowerCAmelCase__				)
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = jnp.ones((decoder_input_ids.shape[0], max_decoder_length)  ,     dtype="i4"				)

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

       _SCREAMING_SNAKE_CASE   :				Tuple							    = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]  ,     dtype="i4"				)
       _SCREAMING_SNAKE_CASE   :				Dict							    = model.decode(
           decoder_input_ids[:, -1:]  ,     lowerCAmelCase__  ,     decoder_attention_mask=lowerCAmelCase__  ,     past_key_values=outputs_cache.past_key_values  ,     decoder_position_ids=lowerCAmelCase__  ,     )

       _SCREAMING_SNAKE_CASE   :				List[Any]							    = model.decode(lowerCAmelCase__  ,     lowerCAmelCase__				)

       _SCREAMING_SNAKE_CASE   :				List[str]							    = 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 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				List[Any]							    = 20
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = model_class_name(lowerCAmelCase__				)

       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = model.encode(inputs_dict["input_ids"]				)

       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = (
           inputs_dict["decoder_input_ids"],
           inputs_dict["decoder_attention_mask"],
       )

       _SCREAMING_SNAKE_CASE   :				List[str]							    = jnp.concatenate(
           [
               decoder_attention_mask,
               jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])				),
           ]  ,     axis=-1  ,     )

       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = model.init_cache(decoder_input_ids.shape[0]  ,     lowerCAmelCase__  ,     lowerCAmelCase__				)
       _SCREAMING_SNAKE_CASE   :				Dict							    = jnp.broadcast_to(
           jnp.arange(decoder_input_ids.shape[-1] - 1				)[None, :]  ,     (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)  ,     )

       _SCREAMING_SNAKE_CASE   :				int							    = model.decode(
           decoder_input_ids[:, :-1]  ,     lowerCAmelCase__  ,     decoder_attention_mask=lowerCAmelCase__  ,     past_key_values=lowerCAmelCase__  ,     decoder_position_ids=lowerCAmelCase__  ,     )
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]  ,     dtype="i4"				)
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = model.decode(
           decoder_input_ids[:, -1:]  ,     lowerCAmelCase__  ,     past_key_values=outputs_cache.past_key_values  ,     decoder_attention_mask=lowerCAmelCase__  ,     decoder_position_ids=lowerCAmelCase__  ,     )

       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = model.decode(lowerCAmelCase__  ,     lowerCAmelCase__  ,     decoder_attention_mask=lowerCAmelCase__				)

       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = 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 			UpperCamelCase      (							unittest.TestCase  ):
     A__      =			99
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				int							    = 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  ,     )

       _SCREAMING_SNAKE_CASE   :				List[Any]							    = input_ids.shape[0]
       _SCREAMING_SNAKE_CASE   :				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 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				str							    = self._get_config_and_data()
       _SCREAMING_SNAKE_CASE   :				str							    = FlaxBlenderbotForConditionalGeneration(lowerCAmelCase__				)
       _SCREAMING_SNAKE_CASE   :				List[str]							    = lm_model(input_ids=lowerCAmelCase__				)
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = (batch_size, input_ids.shape[1], config.vocab_size)
       self.assertEqual(outputs["logits"].shape  ,     lowerCAmelCase__				)
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Dict							    = 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  ,     )
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = FlaxBlenderbotForConditionalGeneration(lowerCAmelCase__				)
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]  ,     dtype=np.intaa				)
       _SCREAMING_SNAKE_CASE   :				str							    = np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]  ,     dtype=np.intaa				)
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = lm_model(input_ids=lowerCAmelCase__  ,     decoder_input_ids=lowerCAmelCase__				)
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = (*summary.shape, config.vocab_size)
       self.assertEqual(outputs["logits"].shape  ,     lowerCAmelCase__				)




     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]  ,     dtype=np.intaa				)
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = shift_tokens_right(lowerCAmelCase__  ,     1  ,     2				)
       _SCREAMING_SNAKE_CASE   :				Dict							    = np.equal(lowerCAmelCase__  ,     1				).astype(np.floataa				).sum()
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = np.equal(lowerCAmelCase__  ,     1				).astype(np.floataa				).sum()
       self.assertEqual(shifted.shape  ,     input_ids.shape				)
       self.assertEqual(lowerCAmelCase__  ,     n_pad_before - 1				)
       self.assertTrue(np.equal(shifted[:, 0]  ,     2				).all()				)
@require_flax
class 			UpperCamelCase      (							a__      ,       unittest.TestCase      ,       a__  ):
     A__      =			True
     A__      =			(
         (
             FlaxBlenderbotModel,
             FlaxBlenderbotForConditionalGeneration,
         )
         if is_flax_available()
         else ()
     )
     A__      =			(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = FlaxBlenderbotModelTester(self				)
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Tuple							    = self.model_tester.prepare_config_and_inputs()
       for model_class in self.all_model_classes:
         self.model_tester.check_use_cache_forward(lowerCAmelCase__  ,     lowerCAmelCase__  ,     lowerCAmelCase__				)
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Dict							    = 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(lowerCAmelCase__  ,     lowerCAmelCase__  ,     lowerCAmelCase__				)
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Any							    = self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
         with self.subTest(model_class.__name__				):
           _SCREAMING_SNAKE_CASE   :				Any							    = self._prepare_for_class(lowerCAmelCase__  ,     lowerCAmelCase__				)
           _SCREAMING_SNAKE_CASE   :				int							    = model_class(lowerCAmelCase__				)

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

           with self.subTest("JIT Enabled"				):
             _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = encode_jitted(**lowerCAmelCase__				).to_tuple()

           with self.subTest("JIT Disabled"				):
             with jax.disable_jit():
               _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = encode_jitted(**lowerCAmelCase__				).to_tuple()

           self.assertEqual(len(lowerCAmelCase__				)  ,     len(lowerCAmelCase__				)				)
           for jitted_output, output in zip(lowerCAmelCase__  ,     lowerCAmelCase__				):
             self.assertEqual(jitted_output.shape  ,     output.shape				)
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				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__				):
           _SCREAMING_SNAKE_CASE   :				Optional[int]							    = model_class(lowerCAmelCase__				)
           _SCREAMING_SNAKE_CASE   :				List[str]							    = model.encode(inputs_dict["input_ids"]  ,     inputs_dict["attention_mask"]				)

           _SCREAMING_SNAKE_CASE   :				Dict							    = {
               "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=lowerCAmelCase__  ,     decoder_attention_mask=lowerCAmelCase__  ,     encoder_outputs=lowerCAmelCase__  ,     )

           with self.subTest("JIT Enabled"				):
             _SCREAMING_SNAKE_CASE   :				List[str]							    = decode_jitted(**lowerCAmelCase__				).to_tuple()

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

           self.assertEqual(len(lowerCAmelCase__				)  ,     len(lowerCAmelCase__				)				)
           for jitted_output, output in zip(lowerCAmelCase__  ,     lowerCAmelCase__				):
             self.assertEqual(jitted_output.shape  ,     output.shape				)
     @slow
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


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




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




       """simple docstring"""


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

       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = FlaxBlenderbotForConditionalGeneration.from_pretrained("facebook/blenderbot-3B"  ,     from_pt=lowerCAmelCase__				)
       _SCREAMING_SNAKE_CASE   :				Tuple							    = BlenderbotTokenizer.from_pretrained("facebook/blenderbot-3B"				)

       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = ["Sam"]
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = tokenizer(lowerCAmelCase__  ,     return_tensors="jax"				)

       _SCREAMING_SNAKE_CASE   :				int							    = model.generate(**lowerCAmelCase__  ,     **lowerCAmelCase__				)
       _SCREAMING_SNAKE_CASE   :				str							    = "Sam is a great name. It means \"sun\" in Gaelic."

       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = tokenizer.batch_decode(lowerCAmelCase__  ,     **lowerCAmelCase__				)
       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 			UpperCamelCase      (							__SCREAMING_SNAKE_CASE      ,       unittest.TestCase  ):
     A__      =			None
     A__      =			BloomTokenizerFast
     A__      =			BloomTokenizerFast
     A__      =			True
     A__      =			False
     A__      =			"""tokenizer_file"""
     A__      =			{"""bos_token""": """<s>""", """eos_token""": """</s>""", """unk_token""": """<unk>""", """pad_token""": """<pad>"""}
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       super().setUp()
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = BloomTokenizerFast.from_pretrained("bigscience/tokenizer"				)
       tokenizer.save_pretrained(self.tmpdirname				)
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     **snake_case__				):




       """simple docstring"""


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




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				int							    = self.get_rust_tokenizer()

       _SCREAMING_SNAKE_CASE   :				Tuple							    = ["The quick brown fox</s>", "jumps over the lazy dog</s>"]
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = [[2175, 23714, 73173, 144252, 2], [77, 132619, 3478, 368, 109586, 35433, 2]]

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

       _SCREAMING_SNAKE_CASE   :				Tuple							    = tokenizer.batch_decode(snake_case__				)
       self.assertListEqual(snake_case__  ,     snake_case__				)
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__=6				):




       """simple docstring"""


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

           _SCREAMING_SNAKE_CASE   :				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 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = self.get_rust_tokenizer()
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = load_dataset("xnli"  ,     "all_languages"  ,     split="test"  ,     streaming=snake_case__				)

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

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




     def 				__SCREAMING_SNAKE_CASE							(     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				)



