





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    lowerCAmelCase							:
 def __init__(      self  :      Tuple			,     UpperCAmelCase  :      Dict			,     UpperCAmelCase  :      Union[str, Any]=99			,     UpperCAmelCase  :      str=13			,     UpperCAmelCase  :      List[str]=7			,     UpperCAmelCase  :      str=9			,     UpperCAmelCase  :      Optional[int]=True			,     UpperCAmelCase  :      str=True			,     UpperCAmelCase  :      Any=False			,     UpperCAmelCase  :      Union[str, Any]=32			,     UpperCAmelCase  :      List[str]=5			,     UpperCAmelCase  :      Tuple=4			,     UpperCAmelCase  :      Union[str, Any]=37			,     UpperCAmelCase  :      int=8			,     UpperCAmelCase  :      List[str]=0.1			,     UpperCAmelCase  :      Any=0.0_0_2			,     UpperCAmelCase  :      Optional[Any]=1			,     UpperCAmelCase  :      List[Any]=0			,     UpperCAmelCase  :      Union[str, Any]=0			,     UpperCAmelCase  :      Tuple=None			,     UpperCAmelCase  :      Optional[Any]=None			,     )			->	Union[str, Any]:
     lowerCamelCase__    :      int					 =		parent
     lowerCamelCase__    :      Any					 =		batch_size
     lowerCamelCase__    :      Optional[int]					 =		encoder_seq_length
     lowerCamelCase__    :      int					 =		decoder_seq_length
     # For common tests
     lowerCamelCase__    :      List[str]					 =		self.decoder_seq_length
     lowerCamelCase__    :      Optional[int]					 =		is_training
     lowerCamelCase__    :      List[Any]					 =		use_attention_mask
     lowerCamelCase__    :      Optional[Any]					 =		use_labels
     lowerCamelCase__    :      Union[str, Any]					 =		vocab_size
     lowerCamelCase__    :      Union[str, Any]					 =		hidden_size
     lowerCamelCase__    :      Optional[Any]					 =		num_hidden_layers
     lowerCamelCase__    :      Any					 =		num_attention_heads
     lowerCamelCase__    :      str					 =		d_ff
     lowerCamelCase__    :      Optional[Any]					 =		relative_attention_num_buckets
     lowerCamelCase__    :      Any					 =		dropout_rate
     lowerCamelCase__    :      Any					 =		initializer_factor
     lowerCamelCase__    :      Union[str, Any]					 =		eos_token_id
     lowerCamelCase__    :      List[str]					 =		pad_token_id
     lowerCamelCase__    :      List[str]					 =		decoder_start_token_id
     lowerCamelCase__    :      List[Any]					 =		None
     lowerCamelCase__    :      Optional[Any]					 =		decoder_layers
 def      A_	(      self  :      List[Any]	)			->	int:
     return TaConfig.from_pretrained('google/umt5-base'	)
 def      A_	(      self  :      List[Any]			,     UpperCAmelCase  :      str			,     UpperCAmelCase  :      Optional[int]			,     UpperCAmelCase  :      Union[str, Any]			,     UpperCAmelCase  :      Tuple=None			,     UpperCAmelCase  :      List[str]=None			,     UpperCAmelCase  :      str=None			,     UpperCAmelCase  :      Optional[Any]=None			,     UpperCAmelCase  :      Optional[Any]=None			,     )			->	List[str]:
     if attention_mask is None:
         lowerCamelCase__    :      Optional[Any]					 =		input_ids.ne(config.pad_token_id	)
     if decoder_attention_mask is None:
         lowerCamelCase__    :      Optional[Any]					 =		decoder_input_ids.ne(config.pad_token_id	)
     if head_mask is None:
         lowerCamelCase__    :      int					 =		torch.ones(config.num_hidden_layers			,     config.num_attention_heads			,     device=UpperCAmelCase	)
     if decoder_head_mask is None:
         lowerCamelCase__    :      Dict					 =		torch.ones(config.num_decoder_layers			,     config.num_attention_heads			,     device=UpperCAmelCase	)
     if cross_attn_head_mask is None:
         lowerCamelCase__    :      Dict					 =		torch.ones(
             config.num_decoder_layers			,     config.num_attention_heads			,     device=UpperCAmelCase	)
     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      A_	(      self  :      str	)			->	List[str]:
     lowerCamelCase__    :      Any					 =		ids_tensor([self.batch_size, self.encoder_seq_length]			,     self.vocab_size	)
     lowerCamelCase__    :      Optional[Any]					 =		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__    :      List[str]					 =		input_ids.clamp(self.pad_token_id + 1	)
     lowerCamelCase__    :      Union[str, Any]					 =		decoder_input_ids.clamp(self.pad_token_id + 1	)

     lowerCamelCase__    :      Dict					 =		self.get_config()
     lowerCamelCase__    :      Tuple					 =		config.num_attention_heads
     lowerCamelCase__    :      Any					 =		self.prepare_inputs_dict(UpperCAmelCase			,     UpperCAmelCase			,     UpperCAmelCase	)
     return config, input_dict
 def      A_	(      self  :      Tuple	)			->	Union[str, Any]:
     lowerCamelCase__ ,							lowerCamelCase__    :      Dict					 =		self.prepare_config_and_inputs()
     return config, inputs_dict
 def      A_	(      self  :      Optional[int]	)			->	List[str]:
     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      A_	(      self  :      Union[str, Any]	)			->	Dict:
     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      A_	(      self  :      Any			,     UpperCAmelCase  :      Union[str, Any]			,     UpperCAmelCase  :      List[str]			,     UpperCAmelCase  :      int			,     UpperCAmelCase  :      str			,     UpperCAmelCase  :      Any			,     UpperCAmelCase  :      Dict			,     )			->	str:
     lowerCamelCase__    :      Dict					 =		UMTaModel(config=UpperCAmelCase	)
     model.to(UpperCAmelCase	)
     model.eval()
     lowerCamelCase__    :      Optional[int]					 =		model(
         input_ids=UpperCAmelCase			,     decoder_input_ids=UpperCAmelCase			,     attention_mask=UpperCAmelCase			,     decoder_attention_mask=UpperCAmelCase			,     )
     lowerCamelCase__    :      Any					 =		model(input_ids=UpperCAmelCase			,     decoder_input_ids=UpperCAmelCase	)
     lowerCamelCase__    :      Dict					 =		result.last_hidden_state
     lowerCamelCase__    :      Any					 =		result.past_key_values
     lowerCamelCase__    :      List[Any]					 =		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(UpperCAmelCase	)			,     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      A_	(      self  :      Optional[int]			,     UpperCAmelCase  :      str			,     UpperCAmelCase  :      Union[str, Any]			,     UpperCAmelCase  :      List[Any]			,     UpperCAmelCase  :      int			,     UpperCAmelCase  :      List[str]			,     UpperCAmelCase  :      Optional[Any]			,     )			->	Optional[int]:
     lowerCamelCase__    :      List[Any]					 =		UMTaModel(config=UpperCAmelCase	).get_decoder().to(UpperCAmelCase	).eval()
     # first forward pass
     lowerCamelCase__    :      Tuple					 =		model(UpperCAmelCase			,     use_cache=UpperCAmelCase	)
     lowerCamelCase__    :      List[Any]					 =		model(UpperCAmelCase	)
     lowerCamelCase__    :      int					 =		model(UpperCAmelCase			,     use_cache=UpperCAmelCase	)

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

     lowerCamelCase__ ,							lowerCamelCase__    :      Dict					 =		outputs.to_tuple()

     # create hypothetical next token and extent to next_input_ids
     lowerCamelCase__    :      Optional[int]					 =		ids_tensor((self.batch_size, 1)			,     config.vocab_size	)

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

     lowerCamelCase__    :      List[str]					 =		model(UpperCAmelCase	)['last_hidden_state']
     lowerCamelCase__    :      str					 =		model(UpperCAmelCase			,     past_key_values=UpperCAmelCase	)['last_hidden_state']

     # select random slice
     lowerCamelCase__    :      List[str]					 =		ids_tensor((1,)			,     output_from_past.shape[-1]	).item()
     lowerCamelCase__    :      Tuple					 =		output_from_no_past[:, -1, random_slice_idx].detach()
     lowerCamelCase__    :      List[str]					 =		output_from_past[:, 0, random_slice_idx].detach()

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







 def      A_	(      self  :      Optional[int]			,     UpperCAmelCase  :      Optional[Any]			,     UpperCAmelCase  :      List[str]			,     )			->	Tuple:
     lowerCamelCase__    :      Union[str, Any]					 =		UMTaModel(config=UpperCAmelCase	).to(UpperCAmelCase	).half().eval()
     lowerCamelCase__    :      Optional[int]					 =		model(**UpperCAmelCase	)['last_hidden_state']
     self.parent.assertFalse(torch.isnan(UpperCAmelCase	).any().item()	)


@require_torch
class    lowerCAmelCase							(					__UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase,  unittest.TestCase			):
 UpperCAmelCase__													=		(
     (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
 )
 UpperCAmelCase__													=		(UMTaForConditionalGeneration,) if is_torch_available() else ()
 UpperCAmelCase__													=		(
     {
         """conversational""": UMTaForConditionalGeneration,
         """feature-extraction""": UMTaModel,
         """summarization""": UMTaForConditionalGeneration,
         """text2text-generation""": UMTaForConditionalGeneration,
         """translation""": UMTaForConditionalGeneration,
         """question-answering""": UMTaForQuestionAnswering,
     }
     if is_torch_available()
     else {}
 )
 UpperCAmelCase__													=		True
 UpperCAmelCase__													=		False
 UpperCAmelCase__													=		False
 UpperCAmelCase__													=		True
 UpperCAmelCase__													=		True
 # The small UMT5 model needs higher percentages for CPU/MP tests
 UpperCAmelCase__													=		[0.8, 0.9]
 def      A_	(      self  :      Union[str, Any]	)			->	List[Any]:
     lowerCamelCase__    :      Union[str, Any]					 =		UMTaModelTester(self	)
 @unittest.skip('Test has a segmentation fault on torch 1.8.0'	)
 def      A_	(      self  :      Tuple	)			->	int:
     lowerCamelCase__    :      str					 =		self.model_tester.prepare_config_and_inputs()
     lowerCamelCase__    :      Tuple					 =		UMTaModel(config_and_inputs[0]	).to(UpperCAmelCase	)
     with tempfile.TemporaryDirectory() as tmpdirname:
         torch.onnx.export(
             UpperCAmelCase			,     (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])			,     F"""{tmpdirname}/t5_test.onnx"""			,     export_params=UpperCAmelCase			,     opset_version=9			,     input_names=['input_ids', 'decoder_input_ids']			,     )
 @unittest.skipIf(torch_device == 'cpu'			,     'Cant do half precision'	)
 def      A_	(      self  :      Tuple	)			->	Optional[Any]:
     lowerCamelCase__    :      Any					 =		self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model_fpaa_forward(*UpperCAmelCase	)
 def      A_	(      self  :      List[Any]	)			->	str:
     lowerCamelCase__    :      int					 =		['encoder_attentions', 'decoder_attentions', 'cross_attentions']
     lowerCamelCase__    :      str					 =		self.model_tester.prepare_config_and_inputs()
     lowerCamelCase__    :      Any					 =		config_and_inputs[0]
     lowerCamelCase__    :      Any					 =		UMTaForConditionalGeneration(UpperCAmelCase	).eval()
     model.to(UpperCAmelCase	)

     lowerCamelCase__    :      Tuple					 =		{
         'head_mask': torch.zeros(config.num_layers			,     config.num_heads			,     device=UpperCAmelCase	),
         'decoder_head_mask': torch.zeros(config.num_decoder_layers			,     config.num_heads			,     device=UpperCAmelCase	),
         'cross_attn_head_mask': torch.zeros(config.num_decoder_layers			,     config.num_heads			,     device=UpperCAmelCase	),
     }

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

         lowerCamelCase__    :      Tuple					 =		model.generate(
             config_and_inputs[1]['input_ids']			,     num_beams=1			,     max_length=3			,     output_attentions=UpperCAmelCase			,     return_dict_in_generate=UpperCAmelCase			,     **UpperCAmelCase			,     )
         # We check the state of decoder_attentions and cross_attentions just from the last step
         lowerCamelCase__    :      Union[str, 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      A_	(      self  :      Optional[Any]	)			->	Optional[Any]:
     pass


@require_torch
@require_sentencepiece
@require_tokenizers
class    lowerCAmelCase							(					unittest.TestCase			):
 @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      A_	(      self  :      Any	)			->	int:
     lowerCamelCase__    :      Optional[Any]					 =		UMTaForConditionalGeneration.from_pretrained('google/umt5-small'			,     return_dict=UpperCAmelCase	).to(UpperCAmelCase	)
     lowerCamelCase__    :      List[str]					 =		AutoTokenizer.from_pretrained('google/umt5-small'			,     use_fast=UpperCAmelCase			,     legacy=UpperCAmelCase	)
     lowerCamelCase__    :      Dict					 =		[
         '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__    :      Tuple					 =		tokenizer(UpperCAmelCase			,     return_tensors='pt'			,     padding=UpperCAmelCase	).input_ids
     # fmt: off
     lowerCamelCase__    :      Any					 =		torch.tensor(
         [
             [ 38530, 210703, 256299, 1410, 256298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
             [   826, 321, 671, 25922, 256299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
             [  1460, 339, 312, 19014, 10620, 758, 256299, 2355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
             [   517, 256299, 14869, 281, 301, 256298, 275, 119983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
             [   320, 256299, 14869, 281, 2234, 289, 2275, 333,61391, 289, 256298, 543, 256297, 168714, 329, 256296,274, 1],
         ]	)
     # fmt: on
     torch.testing.assert_allclose(UpperCAmelCase			,     UpperCAmelCase	)

     lowerCamelCase__    :      Optional[int]					 =		model.generate(input_ids.to(UpperCAmelCase	)	)
     lowerCamelCase__    :      List[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__    :      Union[str, Any]					 =		tokenizer.batch_decode(UpperCAmelCase	)
     self.assertEqual(UpperCAmelCase			,     UpperCAmelCase	)












import unittest

from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_UpperCAmelCase				:       Optional[Any]	   =				"""▁"""

_UpperCAmelCase				:       Optional[Any]	   =				get_tests_dir("""fixtures/test_sentencepiece.model""")

@require_sentencepiece
class    lowerCAmelCase							(					__UpperCamelCase,  unittest.TestCase			):
 UpperCAmelCase__													=		BertGenerationTokenizer
 UpperCAmelCase__													=		False
 UpperCAmelCase__													=		True
 def      A_	(      self  :      List[Any]	)			->	List[str]:
     super().setUp()

     lowerCamelCase__    :      Dict					 =		BertGenerationTokenizer(UpperCAmelCase			,     keep_accents=UpperCAmelCase	)
     tokenizer.save_pretrained(self.tmpdirname	)
 def      A_	(      self  :      Optional[Any]	)			->	Dict:
     lowerCamelCase__    :      List[str]					 =		'<s>'
     lowerCamelCase__    :      Optional[int]					 =		1

     self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCAmelCase	)			,     UpperCAmelCase	)
     self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCAmelCase	)			,     UpperCAmelCase	)
 def      A_	(      self  :      List[str]	)			->	Optional[int]:
     lowerCamelCase__    :      Optional[Any]					 =		list(self.get_tokenizer().get_vocab().keys()	)

     self.assertEqual(vocab_keys[0]			,     '<unk>'	)
     self.assertEqual(vocab_keys[1]			,     '<s>'	)
     self.assertEqual(vocab_keys[-1]			,     '<pad>'	)
     self.assertEqual(len(UpperCAmelCase	)			,     1002	)
 def      A_	(      self  :      List[Any]	)			->	Optional[Any]:
     self.assertEqual(self.get_tokenizer().vocab_size			,     1000	)
 def      A_	(      self  :      Union[str, Any]	)			->	List[Any]:
     lowerCamelCase__    :      Union[str, Any]					 =		BertGenerationTokenizer(UpperCAmelCase			,     keep_accents=UpperCAmelCase	)

     lowerCamelCase__    :      List[str]					 =		tokenizer.tokenize('This is a test'	)
     self.assertListEqual(UpperCAmelCase			,     ['▁This', '▁is', '▁a', '▁t', 'est']	)

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

     lowerCamelCase__    :      List[str]					 =		tokenizer.tokenize('I was born in 92000, and this is falsé.'	)
     self.assertListEqual(
         UpperCAmelCase			,     [
             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__    :      Optional[int]					 =		tokenizer.convert_tokens_to_ids(UpperCAmelCase	)
     self.assertListEqual(
         UpperCAmelCase			,     [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]			,     )

     lowerCamelCase__    :      Optional[Any]					 =		tokenizer.convert_ids_to_tokens(UpperCAmelCase	)
     self.assertListEqual(
         UpperCAmelCase			,     [
             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      A_	(      self  :      Dict	)			->	Tuple:
     return BertGenerationTokenizer.from_pretrained('google/bert_for_seq_generation_L-24_bbc_encoder'	)
 @slow
 def      A_	(      self  :      Optional[int]	)			->	List[str]:
     lowerCamelCase__    :      Union[str, Any]					 =		'Hello World!'
     lowerCamelCase__    :      Dict					 =		[18536, 2260, 101]

     self.assertListEqual(UpperCAmelCase			,     self.big_tokenizer.encode(UpperCAmelCase	)	)
 @slow
 def      A_	(      self  :      Optional[Any]	)			->	str:
     lowerCamelCase__    :      List[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'
     )
     lowerCamelCase__    :      Any					 =		[
         871,
         419,
         358,
         946,
         991,
         2521,
         452,
         358,
         1357,
         387,
         7751,
         3536,
         112,
         985,
         456,
         126,
         865,
         938,
         5400,
         5734,
         458,
         1368,
         467,
         786,
         2462,
         5246,
         1159,
         633,
         865,
         4519,
         457,
         582,
         852,
         2557,
         427,
         916,
         508,
         405,
         34324,
         497,
         391,
         408,
         11342,
         1244,
         385,
         100,
         938,
         985,
         456,
         574,
         362,
         12597,
         3200,
         3129,
         1172,
     ]

     self.assertListEqual(UpperCAmelCase			,     self.big_tokenizer.encode(UpperCAmelCase	)	)
 @require_torch
 @slow
 def      A_	(      self  :      int	)			->	Optional[Any]:
     import torch

     from transformers import BertGenerationConfig, BertGenerationEncoder

     # Build sequence
     lowerCamelCase__    :      str					 =		list(self.big_tokenizer.get_vocab().keys()	)[:10]
     lowerCamelCase__    :      int					 =		' '.join(UpperCAmelCase	)
     lowerCamelCase__    :      Union[str, Any]					 =		self.big_tokenizer.encode_plus(UpperCAmelCase			,     return_tensors='pt'			,     return_token_type_ids=UpperCAmelCase	)
     lowerCamelCase__    :      Optional[Any]					 =		self.big_tokenizer.batch_encode_plus(
         [sequence + ' ' + sequence]			,     return_tensors='pt'			,     return_token_type_ids=UpperCAmelCase	)

     lowerCamelCase__    :      Tuple					 =		BertGenerationConfig()
     lowerCamelCase__    :      Optional[Any]					 =		BertGenerationEncoder(UpperCAmelCase	)

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

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







 @slow
 def      A_	(      self  :      Optional[int]	)			->	List[Any]:
     # fmt: off
     lowerCamelCase__    :      Any					 =		{'input_ids': [[39286, 458, 36335, 2001, 456, 13073, 13266, 455, 113, 7746, 1741, 11157, 391, 13073, 13266, 455, 113, 3967, 35412, 113, 4936, 109, 3870, 2377, 113, 30084, 45720, 458, 134, 17496, 112, 503, 11672, 113, 118, 112, 5665, 13347, 38687, 112, 1496, 31389, 112, 3268, 47264, 134, 962, 112, 16377, 8035, 23130, 430, 12169, 15518, 28592, 458, 146, 41697, 109, 391, 12169, 15518, 16689, 458, 146, 41358, 109, 452, 726, 4034, 111, 763, 35412, 5082, 388, 1903, 111, 9051, 391, 2870, 48918, 1900, 1123, 550, 998, 112, 9586, 15985, 455, 391, 410, 22955, 37636, 114], [448, 17496, 419, 3663, 385, 763, 113, 27533, 2870, 3283, 13043, 1639, 24713, 523, 656, 24013, 18550, 2521, 517, 27014, 21244, 420, 1212, 1465, 391, 927, 4833, 388, 578, 11786, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [484, 2169, 7687, 21932, 18146, 726, 363, 17032, 3391, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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=UpperCAmelCase			,     model_name='google/bert_for_seq_generation_L-24_bbc_encoder'			,     revision='c817d1fd1be2ffa69431227a1fe320544943d4db'			,     )





