


"""simple docstring"""







import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel


class       SCREAMING_SNAKE_CASE__				:
       """simple docstring"""


       def __init__(						self		,     snake_case__		,     snake_case__=99		,     snake_case__=13		,     snake_case__=7		,     snake_case__=9		,     snake_case__=True		,     snake_case__=True		,     snake_case__=False		,     snake_case__=32		,     snake_case__=5		,     snake_case__=4		,     snake_case__=37		,     snake_case__=8		,     snake_case__=0.1		,     snake_case__=0.002		,     snake_case__=1		,     snake_case__=0		,     snake_case__=0		,     snake_case__=None		,     snake_case__=None		,     ):






         """simple docstring"""





         lowerCAmelCase     :     Optional[Any]								=							parent
         lowerCAmelCase     :     List[Any]								=							batch_size
         lowerCAmelCase     :     str								=							encoder_seq_length
         lowerCAmelCase     :     List[str]								=							decoder_seq_length
         # For common tests
         lowerCAmelCase     :     Dict								=							self.decoder_seq_length
         lowerCAmelCase     :     List[str]								=							is_training
         lowerCAmelCase     :     str								=							use_attention_mask
         lowerCAmelCase     :     Union[str, Any]								=							use_labels
         lowerCAmelCase     :     List[Any]								=							vocab_size
         lowerCAmelCase     :     Optional[int]								=							hidden_size
         lowerCAmelCase     :     Optional[Any]								=							num_hidden_layers
         lowerCAmelCase     :     Union[str, Any]								=							num_attention_heads
         lowerCAmelCase     :     List[Any]								=							d_ff
         lowerCAmelCase     :     List[str]								=							relative_attention_num_buckets
         lowerCAmelCase     :     Dict								=							dropout_rate
         lowerCAmelCase     :     List[str]								=							initializer_factor
         lowerCAmelCase     :     List[Any]								=							eos_token_id
         lowerCAmelCase     :     Tuple								=							pad_token_id
         lowerCAmelCase     :     str								=							decoder_start_token_id
         lowerCAmelCase     :     Dict								=							None
         lowerCAmelCase     :     Dict								=							decoder_layers

       def        lowercase__							(						self						):






         """simple docstring"""





         return TaConfig.from_pretrained("google/umt5-base"						)

       def        lowercase__							(						self		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__=None		,     snake_case__=None		,     snake_case__=None		,     snake_case__=None		,     snake_case__=None		,     ):






         """simple docstring"""





         if attention_mask is None:
           lowerCAmelCase     :     Dict								=							input_ids.ne(config.pad_token_id						)
         if decoder_attention_mask is None:
           lowerCAmelCase     :     List[Any]								=							decoder_input_ids.ne(config.pad_token_id						)
         if head_mask is None:
           lowerCAmelCase     :     Union[str, Any]								=							torch.ones(config.num_hidden_layers		,     config.num_attention_heads		,     device=snake_case__						)
         if decoder_head_mask is None:
           lowerCAmelCase     :     str								=							torch.ones(config.num_decoder_layers		,     config.num_attention_heads		,     device=snake_case__						)
         if cross_attn_head_mask is None:
           lowerCAmelCase     :     Optional[int]								=							torch.ones(
               config.num_decoder_layers		,     config.num_attention_heads		,     device=snake_case__						)
         return {
             "input_ids": input_ids,
             "decoder_input_ids": decoder_input_ids,
             "attention_mask": attention_mask,
             "decoder_attention_mask": decoder_attention_mask,
             "head_mask": head_mask,
             "decoder_head_mask": decoder_head_mask,
             "cross_attn_head_mask": cross_attn_head_mask,
         }

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     List[Any]								=							ids_tensor([self.batch_size, self.encoder_seq_length]		,     self.vocab_size						)
         lowerCAmelCase     :     Tuple								=							ids_tensor([self.batch_size, self.decoder_seq_length]		,     self.vocab_size						)

         # we need to clamp the input ids here to avoid having pad token in between
         # this is because for NllbMoe the position_ids are prepared such that
         # all pad tokens have pos id = 2 and rest are between 2..seq_length
         # and the seq_length here is seq_length - num_pad_tokens
         # but when using past, there is no way of knowing if the past input ids had
         # pad tokens in them, which results in incorrect seq_lenth and which in turn results in
         # position_ids being off by num_pad_tokens in past input
         lowerCAmelCase     :     Optional[int]								=							input_ids.clamp(self.pad_token_id + 1						)
         lowerCAmelCase     :     List[str]								=							decoder_input_ids.clamp(self.pad_token_id + 1						)

         lowerCAmelCase     :     Any								=							self.get_config()
         lowerCAmelCase     :     Optional[int]								=							config.num_attention_heads
         lowerCAmelCase     :     Union[str, Any]								=							self.prepare_inputs_dict(snake_case__		,     snake_case__		,     snake_case__						)
         return config, input_dict

       def        lowercase__							(						self						):






         """simple docstring"""





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

       def        lowercase__							(						self						):






         """simple docstring"""





         return TaConfig(
             vocab_size=166		,     d_model=self.hidden_size		,     d_ff=self.d_ff		,     d_kv=self.hidden_size // self.num_attention_heads		,     num_layers=self.num_hidden_layers		,     num_decoder_layers=self.decoder_layers		,     num_heads=self.num_attention_heads		,     relative_attention_num_buckets=self.relative_attention_num_buckets		,     dropout_rate=self.dropout_rate		,     initializer_factor=self.initializer_factor		,     eos_token_id=self.eos_token_id		,     bos_token_id=self.pad_token_id		,     pad_token_id=self.pad_token_id		,     decoder_start_token_id=self.decoder_start_token_id		,     )

       def        lowercase__							(						self						):






         """simple docstring"""





         return TaConfig(
             vocab_size=self.vocab_size		,     d_model=self.hidden_size		,     d_ff=self.d_ff		,     d_kv=self.hidden_size // self.num_attention_heads		,     num_layers=self.num_hidden_layers		,     num_decoder_layers=self.decoder_layers		,     num_heads=self.num_attention_heads		,     relative_attention_num_buckets=self.relative_attention_num_buckets		,     dropout_rate=self.dropout_rate		,     initializer_factor=self.initializer_factor		,     eos_token_id=self.eos_token_id		,     bos_token_id=self.pad_token_id		,     pad_token_id=self.pad_token_id		,     decoder_start_token_id=self.decoder_start_token_id		,     )

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






         """simple docstring"""





         lowerCAmelCase     :     int								=							UMTaModel(config=snake_case__						)
         model.to(snake_case__						)
         model.eval()
         lowerCAmelCase     :     Union[str, Any]								=							model(
             input_ids=snake_case__		,     decoder_input_ids=snake_case__		,     attention_mask=snake_case__		,     decoder_attention_mask=snake_case__		,     )
         lowerCAmelCase     :     List[str]								=							model(input_ids=snake_case__		,     decoder_input_ids=snake_case__						)
         lowerCAmelCase     :     int								=							result.last_hidden_state
         lowerCAmelCase     :     Tuple								=							result.past_key_values
         lowerCAmelCase     :     List[str]								=							result.encoder_last_hidden_state

         self.parent.assertEqual(encoder_output.size()		,     (self.batch_size, self.encoder_seq_length, self.hidden_size)						)
         self.parent.assertEqual(decoder_output.size()		,     (self.batch_size, self.decoder_seq_length, self.hidden_size)						)
         # There should be `num_layers` key value embeddings stored in decoder_past
         self.parent.assertEqual(len(snake_case__						)		,     config.num_layers						)
         # There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
         self.parent.assertEqual(len(decoder_past[0]						)		,     4						)

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






         """simple docstring"""





         lowerCAmelCase     :     Optional[int]								=							UMTaModel(config=snake_case__						).get_decoder().to(snake_case__						).eval()
         # first forward pass
         lowerCAmelCase     :     Tuple								=							model(snake_case__		,     use_cache=snake_case__						)
         lowerCAmelCase     :     Optional[Any]								=							model(snake_case__						)
         lowerCAmelCase     :     List[Any]								=							model(snake_case__		,     use_cache=snake_case__						)

         self.parent.assertTrue(len(snake_case__						) == len(snake_case__						)						)
         self.parent.assertTrue(len(snake_case__						) == len(snake_case__						) + 1						)

         lowerCAmelCase						,  lowerCAmelCase     :     List[Any]								=							outputs.to_tuple()

         # create hypothetical next token and extent to next_input_ids
         lowerCAmelCase     :     Any								=							ids_tensor((self.batch_size, 1)		,     config.vocab_size						)

         # append to next input_ids and
         lowerCAmelCase     :     List[str]								=							torch.cat([input_ids, next_tokens]		,     dim=-1						)

         lowerCAmelCase     :     Optional[int]								=							model(snake_case__						)["last_hidden_state"]
         lowerCAmelCase     :     Any								=							model(snake_case__		,     past_key_values=snake_case__						)["last_hidden_state"]

         # select random slice
         lowerCAmelCase     :     Optional[int]								=							ids_tensor((1,)		,     output_from_past.shape[-1]						).item()
         lowerCAmelCase     :     List[Any]								=							output_from_no_past[:, -1, random_slice_idx].detach()
         lowerCAmelCase     :     List[Any]								=							output_from_past[:, 0, random_slice_idx].detach()

         # test that outputs are equal for slice
         self.parent.assertTrue(torch.allclose(snake_case__		,     snake_case__		,     atol=1e-3						)						)







       def        lowercase__							(						self		,     snake_case__		,     snake_case__		,     ):






         """simple docstring"""





         lowerCAmelCase     :     int								=							UMTaModel(config=snake_case__						).to(snake_case__						).half().eval()
         lowerCAmelCase     :     Union[str, Any]								=							model(**snake_case__						)["last_hidden_state"]
         self.parent.assertFalse(torch.isnan(snake_case__						).any().item()						)


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


       a  :				Dict   =(
           (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
       )
       a  :				int   =(UMTaForConditionalGeneration,) if is_torch_available() else ()
       a  :				Any   =(
           {
               "conversational": UMTaForConditionalGeneration,
               "feature-extraction": UMTaModel,
               "summarization": UMTaForConditionalGeneration,
               "text2text-generation": UMTaForConditionalGeneration,
               "translation": UMTaForConditionalGeneration,
               "question-answering": UMTaForQuestionAnswering,
           }
           if is_torch_available()
           else {}
       )
       a  :				int   =True
       a  :				Union[str, Any]   =False
       a  :				List[str]   =False
       a  :				str   =True
       a  :				str   =True
       # The small UMT5 model needs higher percentages for CPU/MP tests
       a  :				Tuple   =[0.8, 0.9]

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Union[str, Any]								=							UMTaModelTester(self						)

       @unittest.skip("Test has a segmentation fault on torch 1.8.0"						)
       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Dict								=							self.model_tester.prepare_config_and_inputs()
         lowerCAmelCase     :     Any								=							UMTaModel(config_and_inputs[0]						).to(snake_case__						)
         with tempfile.TemporaryDirectory() as tmpdirname:
           torch.onnx.export(
               snake_case__		,     (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])		,     f"""{tmpdirname}/t5_test.onnx"""		,     export_params=snake_case__		,     opset_version=9		,     input_names=["input_ids", "decoder_input_ids"]		,     )

       @unittest.skipIf(torch_device == "cpu"		,     "Cant do half precision"						)
       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Union[str, Any]								=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model_fpaa_forward(*snake_case__						)

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     str								=							["encoder_attentions", "decoder_attentions", "cross_attentions"]
         lowerCAmelCase     :     List[str]								=							self.model_tester.prepare_config_and_inputs()
         lowerCAmelCase     :     List[str]								=							config_and_inputs[0]
         lowerCAmelCase     :     List[str]								=							UMTaForConditionalGeneration(snake_case__						).eval()
         model.to(snake_case__						)

         lowerCAmelCase     :     List[Any]								=							{
             "head_mask": torch.zeros(config.num_layers		,     config.num_heads		,     device=snake_case__						),
             "decoder_head_mask": torch.zeros(config.num_decoder_layers		,     config.num_heads		,     device=snake_case__						),
             "cross_attn_head_mask": torch.zeros(config.num_decoder_layers		,     config.num_heads		,     device=snake_case__						),
         }

         for attn_name, (name, mask) in zip(snake_case__		,     head_masking.items()						):
           lowerCAmelCase     :     Tuple								=							{name: mask}
           # Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
           if name == "head_mask":
             lowerCAmelCase     :     Optional[Any]								=							torch.ones(
                 config.num_decoder_layers		,     config.num_heads		,     device=snake_case__						)

           lowerCAmelCase     :     int								=							model.generate(
               config_and_inputs[1]["input_ids"]		,     num_beams=1		,     max_length=3		,     output_attentions=snake_case__		,     return_dict_in_generate=snake_case__		,     **snake_case__		,     )
           # We check the state of decoder_attentions and cross_attentions just from the last step
           lowerCAmelCase     :     Any								=							out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
           self.assertEqual(sum([w.sum().item() for w in attn_weights]						)		,     0.0						)







       @unittest.skip("Does not work on the tiny model as we keep hitting edge cases."						)
       def        lowercase__							(						self						):






         """simple docstring"""





         pass


@require_torch
@require_sentencepiece
@require_tokenizers
class       SCREAMING_SNAKE_CASE__				(		unittest.TestCase				):
       """simple docstring"""


       @slow
       @unittest.skip(
           "Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged"						)
       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     List[str]								=							UMTaForConditionalGeneration.from_pretrained("google/umt5-small"		,     return_dict=snake_case__						).to(snake_case__						)
         lowerCAmelCase     :     Dict								=							AutoTokenizer.from_pretrained("google/umt5-small"		,     use_fast=snake_case__		,     legacy=snake_case__						)
         lowerCAmelCase     :     int								=							[
             "Bonjour monsieur <extra_id_0> bien <extra_id_1>.",
             "No se como puedo <extra_id_0>.",
             "This is the reason why we <extra_id_0> them.",
             "The <extra_id_0> walks in <extra_id_1>, seats",
             "A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.",
         ]
         lowerCAmelCase     :     str								=							tokenizer(snake_case__		,     return_tensors="pt"		,     padding=snake_case__						).input_ids
         # fmt: off
         lowerCAmelCase     :     Tuple								=							torch.tensor(
             [
                 [ 38_530, 210_703, 256_299, 1_410, 256_298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                 [   826, 321, 671, 25_922, 256_299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                 [  1_460, 339, 312, 19_014, 10_620, 758, 256_299, 2_355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
                 [   517, 256_299, 14_869, 281, 301, 256_298, 275, 119_983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
                 [   320, 256_299, 14_869, 281, 2_234, 289, 2_275, 333,61_391, 289, 256_298, 543, 256_297, 168_714, 329, 256_296,274, 1],
             ]						)
         # fmt: on
         torch.testing.assert_allclose(snake_case__		,     snake_case__						)

         lowerCAmelCase     :     Any								=							model.generate(input_ids.to(snake_case__						)						)
         lowerCAmelCase     :     Optional[Any]								=							[
             "<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>",
             "<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
             "<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
             "<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
             "<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
         ]
         lowerCAmelCase     :     int								=							tokenizer.batch_decode(snake_case__						)
         self.assertEqual(snake_case__		,     snake_case__						)









"""simple docstring"""







import unittest

from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


lowerCAmelCase__      				=   '''▁'''

lowerCAmelCase__      				=   get_tests_dir('''fixtures/test_sentencepiece.model''')


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


       a  :				str   =BigBirdTokenizer
       a  :				Union[str, Any]   =BigBirdTokenizerFast
       a  :				Tuple   =True
       a  :				Any   =True

       def        lowercase__							(						self						):






         """simple docstring"""





         super().setUp()

         lowerCAmelCase     :     str								=							self.tokenizer_class(snake_case__		,     keep_accents=snake_case__						)
         tokenizer.save_pretrained(self.tmpdirname						)

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     str								=							"<s>"
         lowerCAmelCase     :     Optional[int]								=							1

         self.assertEqual(self.get_tokenizer()._convert_token_to_id(snake_case__						)		,     snake_case__						)
         self.assertEqual(self.get_tokenizer()._convert_id_to_token(snake_case__						)		,     snake_case__						)

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Dict								=							list(self.get_tokenizer().get_vocab().keys()						)

         self.assertEqual(vocab_keys[0]		,     "<unk>"						)
         self.assertEqual(vocab_keys[1]		,     "<s>"						)
         self.assertEqual(vocab_keys[-1]		,     "[MASK]"						)
         self.assertEqual(len(snake_case__						)		,     1_004						)

       def        lowercase__							(						self						):






         """simple docstring"""





         self.assertEqual(self.get_tokenizer().vocab_size		,     1_000						)

       def        lowercase__							(						self						):






         """simple docstring"""





         if not self.test_rust_tokenizer:
           return

         lowerCAmelCase     :     Tuple								=							self.get_tokenizer()
         lowerCAmelCase     :     Optional[int]								=							self.get_rust_tokenizer()

         lowerCAmelCase     :     Tuple								=							"I was born in 92000, and this is falsé."

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

         lowerCAmelCase     :     Tuple								=							tokenizer.encode(snake_case__		,     add_special_tokens=snake_case__						)
         lowerCAmelCase     :     int								=							rust_tokenizer.encode(snake_case__		,     add_special_tokens=snake_case__						)
         self.assertListEqual(snake_case__		,     snake_case__						)

         lowerCAmelCase     :     List[Any]								=							self.get_rust_tokenizer()
         lowerCAmelCase     :     Tuple								=							tokenizer.encode(snake_case__						)
         lowerCAmelCase     :     List[Any]								=							rust_tokenizer.encode(snake_case__						)
         self.assertListEqual(snake_case__		,     snake_case__						)

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Any								=							BigBirdTokenizer(snake_case__		,     keep_accents=snake_case__						)

         lowerCAmelCase     :     Union[str, Any]								=							tokenizer.tokenize("This is a test"						)
         self.assertListEqual(snake_case__		,     ["▁This", "▁is", "▁a", "▁t", "est"]						)

         self.assertListEqual(
             tokenizer.convert_tokens_to_ids(snake_case__						)		,     [285, 46, 10, 170, 382]		,     )

         lowerCAmelCase     :     Any								=							tokenizer.tokenize("I was born in 92000, and this is falsé."						)
         self.assertListEqual(
             snake_case__		,     [
                 SPIECE_UNDERLINE + "I",
                 SPIECE_UNDERLINE + "was",
                 SPIECE_UNDERLINE + "b",
                 "or",
                 "n",
                 SPIECE_UNDERLINE + "in",
                 SPIECE_UNDERLINE + "",
                 "9",
                 "2",
                 "0",
                 "0",
                 "0",
                 ",",
                 SPIECE_UNDERLINE + "and",
                 SPIECE_UNDERLINE + "this",
                 SPIECE_UNDERLINE + "is",
                 SPIECE_UNDERLINE + "f",
                 "al",
                 "s",
                 "é",
                 ".",
             ]		,     )
         lowerCAmelCase     :     str								=							tokenizer.convert_tokens_to_ids(snake_case__						)
         self.assertListEqual(
             snake_case__		,     [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]		,     )

         lowerCAmelCase     :     List[str]								=							tokenizer.convert_ids_to_tokens(snake_case__						)
         self.assertListEqual(
             snake_case__		,     [
                 SPIECE_UNDERLINE + "I",
                 SPIECE_UNDERLINE + "was",
                 SPIECE_UNDERLINE + "b",
                 "or",
                 "n",
                 SPIECE_UNDERLINE + "in",
                 SPIECE_UNDERLINE + "",
                 "<unk>",
                 "2",
                 "0",
                 "0",
                 "0",
                 ",",
                 SPIECE_UNDERLINE + "and",
                 SPIECE_UNDERLINE + "this",
                 SPIECE_UNDERLINE + "is",
                 SPIECE_UNDERLINE + "f",
                 "al",
                 "s",
                 "<unk>",
                 ".",
             ]		,     )

       @cached_property
       def        lowercase__							(						self						):






         """simple docstring"""





         return BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base"						)

       @slow
       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     List[Any]								=							"Hello World!"
         lowerCAmelCase     :     Any								=							[65, 18_536, 2_260, 101, 66]

         self.assertListEqual(snake_case__		,     self.big_tokenizer.encode(snake_case__						)						)

       @slow
       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Union[str, Any]								=							(
             "This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"
             " add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"
         )
         # fmt: off
         lowerCAmelCase     :     List[str]								=							[65, 871, 419, 358, 946, 991, 2_521, 452, 358, 1_357, 387, 7_751, 3_536, 112, 985, 456, 126, 865, 938, 5_400, 5_734, 458, 1_368, 467, 786, 2_462, 5_246, 1_159, 633, 865, 4_519, 457, 582, 852, 2_557, 427, 916, 508, 405, 34_324, 497, 391, 408, 11_342, 1_244, 385, 100, 938, 985, 456, 574, 362, 12_597, 3_200, 3_129, 1_172, 66]  # noqa: E231
         # fmt: on
         self.assertListEqual(snake_case__		,     self.big_tokenizer.encode(snake_case__						)						)

       @require_torch
       @slow
       def        lowercase__							(						self						):






         """simple docstring"""





         import torch

         from transformers import BigBirdConfig, BigBirdModel

         # Build sequence
         lowerCAmelCase     :     Dict								=							list(self.big_tokenizer.get_vocab().keys()						)[:10]
         lowerCAmelCase     :     int								=							" ".join(snake_case__						)
         lowerCAmelCase     :     Dict								=							self.big_tokenizer.encode_plus(snake_case__		,     return_tensors="pt"		,     return_token_type_ids=snake_case__						)
         lowerCAmelCase     :     Any								=							self.big_tokenizer.batch_encode_plus(
             [sequence + " " + sequence]		,     return_tensors="pt"		,     return_token_type_ids=snake_case__						)

         lowerCAmelCase     :     str								=							BigBirdConfig(attention_type="original_full"						)
         lowerCAmelCase     :     Any								=							BigBirdModel(snake_case__						)

         assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

         with torch.no_grad():
           model(**snake_case__						)
           model(**snake_case__						)

       @slow
       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     List[str]								=							BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base"						)
         lowerCAmelCase     :     Union[str, Any]								=							tokenizer.decode(tokenizer("Paris is the [MASK]."						).input_ids						)

         self.assertTrue(decoded_text == "[CLS] Paris is the[MASK].[SEP]"						)







       @slow
       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Any								=							{"input_ids": [[65, 39_286, 458, 36_335, 2_001, 456, 13_073, 13_266, 455, 113, 7_746, 1_741, 11_157, 391, 13_073, 13_266, 455, 113, 3_967, 35_412, 113, 4_936, 109, 3_870, 2_377, 113, 30_084, 45_720, 458, 134, 17_496, 112, 503, 11_672, 113, 118, 112, 5_665, 13_347, 38_687, 112, 1_496, 31_389, 112, 3_268, 47_264, 134, 962, 112, 16_377, 8_035, 23_130, 430, 12_169, 15_518, 28_592, 458, 146, 41_697, 109, 391, 12_169, 15_518, 16_689, 458, 146, 41_358, 109, 452, 726, 4_034, 111, 763, 35_412, 5_082, 388, 1_903, 111, 9_051, 391, 2_870, 48_918, 1_900, 1_123, 550, 998, 112, 9_586, 15_985, 455, 391, 410, 22_955, 37_636, 114, 66], [65, 448, 17_496, 419, 3_663, 385, 763, 113, 27_533, 2_870, 3_283, 13_043, 1_639, 24_713, 523, 656, 24_013, 18_550, 2_521, 517, 27_014, 21_244, 420, 1_212, 1_465, 391, 927, 4_833, 388, 578, 11_786, 114, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [65, 484, 2_169, 7_687, 21_932, 18_146, 726, 363, 17_032, 3_391, 114, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
         # fmt: on

         self.tokenizer_integration_test_util(
             expected_encoding=snake_case__		,     model_name="google/bigbird-roberta-base"		,     revision="215c99f1600e06f83acce68422f2035b2b5c3510"		,     )





