import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class      __lowercase					(    _A       ,     unittest.TestCase    ):
						lowercase					 =     BarthezTokenizer
						lowercase					 =     BarthezTokenizerFast
						lowercase					 =     True
						lowercase					 =     True

						def        __a		(	self				:      Dict   )							->		Optional[Any]:


									'''simple docstring'''

									super().setUp()

									lowercase							=		BarthezTokenizerFast.from_pretrained('''moussaKam/mbarthez'''   )
									tokenizer.save_pretrained(self.tmpdirname   )
									tokenizer.save_pretrained(self.tmpdirname							,							legacy_format=__lowerCamelCase   )
									lowercase							=		tokenizer

						def        __a		(	self				:      Tuple   )							->		Any:


									'''simple docstring'''

									lowercase							=		'''<pad>'''
									lowercase							=		1

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

						def        __a		(	self				:      List[Any]   )							->		Optional[int]:


									'''simple docstring'''

									lowercase							=		list(self.get_tokenizer().get_vocab().keys()   )

									self.assertEqual(vocab_keys[0]							,							'''<s>'''   )
									self.assertEqual(vocab_keys[1]							,							'''<pad>'''   )
									self.assertEqual(vocab_keys[-1]							,							'''<mask>'''   )
									self.assertEqual(len(__lowerCamelCase   )							,							10_11_22   )

						def        __a		(	self				:      Dict   )							->		List[Any]:


									'''simple docstring'''

									self.assertEqual(self.get_tokenizer().vocab_size							,							10_11_22   )

						@require_torch
						def        __a		(	self				:      str   )							->		Optional[int]:


									'''simple docstring'''

									lowercase							=		['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
									lowercase							=		[0, 57, 30_18, 7_03_07, 91, 2]

									lowercase							=		self.tokenizer(
									    __lowerCamelCase							,							max_length=len(__lowerCamelCase   )							,							padding=__lowerCamelCase							,							truncation=__lowerCamelCase							,							return_tensors='''pt'''   )
									self.assertIsInstance(__lowerCamelCase							,							__lowerCamelCase   )

									self.assertEqual((2, 6)							,							batch.input_ids.shape   )
									self.assertEqual((2, 6)							,							batch.attention_mask.shape   )
									lowercase							=		batch.input_ids.tolist()[0]
									self.assertListEqual(__lowerCamelCase							,							__lowerCamelCase   )

						def        __a		(	self				:      Any   )							->		Optional[Any]:


									'''simple docstring'''

									if not self.test_rust_tokenizer:
												return

									lowercase							=		self.get_tokenizer()
									lowercase							=		self.get_rust_tokenizer()

									lowercase							=		'''I was born in 92000, and this is falsé.'''

									lowercase							=		tokenizer.tokenize(__lowerCamelCase   )
									lowercase							=		rust_tokenizer.tokenize(__lowerCamelCase   )
									self.assertListEqual(__lowerCamelCase							,							__lowerCamelCase   )

									lowercase							=		tokenizer.encode(__lowerCamelCase							,							add_special_tokens=__lowerCamelCase   )
									lowercase							=		rust_tokenizer.encode(__lowerCamelCase							,							add_special_tokens=__lowerCamelCase   )
									self.assertListEqual(__lowerCamelCase							,							__lowerCamelCase   )

									lowercase							=		self.get_rust_tokenizer()
									lowercase							=		tokenizer.encode(__lowerCamelCase   )
									lowercase							=		rust_tokenizer.encode(__lowerCamelCase   )
									self.assertListEqual(__lowerCamelCase							,							__lowerCamelCase   )



						@slow
						def        __a		(	self				:      Tuple   )							->		Optional[int]:


									'''simple docstring'''

									lowercase							=		{'''input_ids''': [[0, 4_90, 1_43_28, 45_07, 3_54, 47, 4_36_69, 95, 25, 7_81_17, 2_02_15, 1_97_79, 1_90, 22, 4_00, 4, 3_53_43, 8_03_10, 6_03, 86, 2_49_37, 1_05, 3_34_38, 9_47_62, 1_96, 3_96_42, 7, 15, 1_59_33, 1_73, 2, 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, 1_05_34, 87, 25, 66, 33_58, 1_96, 5_52_89, 8, 8_29_61, 81, 22_04, 7_52_03, 7, 15, 7_63, 1_29_56, 2_16, 1_78, 1_43_28, 95_95, 13_77, 6_96_93, 7, 4_48, 7_10_21, 1_96, 1_81_06, 14_37, 1_39_74, 1_08, 90_83, 4, 4_93_15, 7, 39, 86, 13_26, 27_93, 4_63_33, 4, 4_48, 1_96, 7_45_88, 7, 4_93_15, 7, 39, 21, 8_22, 3_84_70, 74, 21, 6_67_23, 6_24_80, 8, 2_20_50, 5, 2]], '''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, 0, 0, 0, 0, 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, 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]]}  # noqa: E501
									# fmt: on

									# moussaKam/mbarthez is a french model. So we also use french texts.
									lowercase							=		[
									    '''Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '''
									    '''utilisé principalement dans le domaine du traitement automatique des langues (TAL).''',
									    '''À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '''
									    '''pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '''
									    '''telles que la traduction et la synthèse de texte.''',
									]

									self.tokenizer_integration_test_util(
									    expected_encoding=__lowerCamelCase							,							model_name='''moussaKam/mbarthez'''							,							revision='''c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6'''							,							sequences=__lowerCamelCase							,							)






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      __lowercase					:

						def __init__(	self				:      Dict							,							__lowerCamelCase				:      Union[str, Any]							,							__lowerCamelCase				:      Union[str, Any]=99							,							__lowerCamelCase				:      int=13							,							__lowerCamelCase				:      Dict=7							,							__lowerCamelCase				:      Any=9							,							__lowerCamelCase				:      str=True							,							__lowerCamelCase				:      List[str]=True							,							__lowerCamelCase				:      Optional[int]=False							,							__lowerCamelCase				:      Union[str, Any]=32							,							__lowerCamelCase				:      List[Any]=5							,							__lowerCamelCase				:      Tuple=4							,							__lowerCamelCase				:      Dict=37							,							__lowerCamelCase				:      Optional[Any]=8							,							__lowerCamelCase				:      Tuple=0.1							,							__lowerCamelCase				:      List[Any]=0.002							,							__lowerCamelCase				:      Optional[int]=1							,							__lowerCamelCase				:      List[Any]=0							,							__lowerCamelCase				:      Union[str, Any]=0							,							__lowerCamelCase				:      int=None							,							__lowerCamelCase				:      Optional[int]=None							,							)							->		Dict:


									'''simple docstring'''

									lowercase							=		parent
									lowercase							=		batch_size
									lowercase							=		encoder_seq_length
									lowercase							=		decoder_seq_length
									# For common tests
									lowercase							=		self.decoder_seq_length
									lowercase							=		is_training
									lowercase							=		use_attention_mask
									lowercase							=		use_labels
									lowercase							=		vocab_size
									lowercase							=		hidden_size
									lowercase							=		num_hidden_layers
									lowercase							=		num_attention_heads
									lowercase							=		d_ff
									lowercase							=		relative_attention_num_buckets
									lowercase							=		dropout_rate
									lowercase							=		initializer_factor
									lowercase							=		eos_token_id
									lowercase							=		pad_token_id
									lowercase							=		decoder_start_token_id
									lowercase							=		None
									lowercase							=		decoder_layers

						def        __a		(	self				:      Optional[Any]   )							->		Any:


									'''simple docstring'''

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

						def        __a		(	self				:      Optional[Any]							,							__lowerCamelCase				:      List[str]							,							__lowerCamelCase				:      Tuple							,							__lowerCamelCase				:      str							,							__lowerCamelCase				:      Optional[Any]=None							,							__lowerCamelCase				:      List[str]=None							,							__lowerCamelCase				:      List[Any]=None							,							__lowerCamelCase				:      str=None							,							__lowerCamelCase				:      List[str]=None							,							)							->		Optional[int]:


									'''simple docstring'''

									if attention_mask is None:
												lowercase							=		input_ids.ne(config.pad_token_id   )
									if decoder_attention_mask is None:
												lowercase							=		decoder_input_ids.ne(config.pad_token_id   )
									if head_mask is None:
												lowercase							=		torch.ones(config.num_hidden_layers							,							config.num_attention_heads							,							device=__lowerCamelCase   )
									if decoder_head_mask is None:
												lowercase							=		torch.ones(config.num_decoder_layers							,							config.num_attention_heads							,							device=__lowerCamelCase   )
									if cross_attn_head_mask is None:
												lowercase							=		torch.ones(
												    config.num_decoder_layers							,							config.num_attention_heads							,							device=__lowerCamelCase   )
									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				:      int   )							->		Dict:


									'''simple docstring'''

									lowercase							=		ids_tensor([self.batch_size, self.encoder_seq_length]							,							self.vocab_size   )
									lowercase							=		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
									lowercase							=		input_ids.clamp(self.pad_token_id + 1   )
									lowercase							=		decoder_input_ids.clamp(self.pad_token_id + 1   )

									lowercase							=		self.get_config()
									lowercase							=		config.num_attention_heads
									lowercase							=		self.prepare_inputs_dict(__lowerCamelCase							,							__lowerCamelCase							,							__lowerCamelCase   )
									return config, input_dict

						def        __a		(	self				:      Optional[Any]   )							->		Dict:


									'''simple docstring'''

									lowercase		,lowercase							=		self.prepare_config_and_inputs()
									return config, inputs_dict

						def        __a		(	self				:      List[str]   )							->		Union[str, Any]:


									'''simple docstring'''

									return TaConfig(
									    vocab_size=1_66							,							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				:      str   )							->		Any:


									'''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        __a		(	self				:      Optional[int]							,							__lowerCamelCase				:      List[str]							,							__lowerCamelCase				:      Optional[int]							,							__lowerCamelCase				:      Any							,							__lowerCamelCase				:      int							,							__lowerCamelCase				:      Dict							,							__lowerCamelCase				:      str							,							)							->		Union[str, Any]:


									'''simple docstring'''

									lowercase							=		UMTaModel(config=__lowerCamelCase   )
									model.to(__lowerCamelCase   )
									model.eval()
									lowercase							=		model(
									    input_ids=__lowerCamelCase							,							decoder_input_ids=__lowerCamelCase							,							attention_mask=__lowerCamelCase							,							decoder_attention_mask=__lowerCamelCase							,							)
									lowercase							=		model(input_ids=__lowerCamelCase							,							decoder_input_ids=__lowerCamelCase   )
									lowercase							=		result.last_hidden_state
									lowercase							=		result.past_key_values
									lowercase							=		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(__lowerCamelCase   )							,							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]							,							__lowerCamelCase				:      List[str]							,							__lowerCamelCase				:      Tuple							,							__lowerCamelCase				:      Any							,							__lowerCamelCase				:      int							,							__lowerCamelCase				:      Optional[Any]							,							__lowerCamelCase				:      Union[str, Any]							,							)							->		List[Any]:


									'''simple docstring'''

									lowercase							=		UMTaModel(config=__lowerCamelCase   ).get_decoder().to(__lowerCamelCase   ).eval()
									# first forward pass
									lowercase							=		model(__lowerCamelCase							,							use_cache=__lowerCamelCase   )
									lowercase							=		model(__lowerCamelCase   )
									lowercase							=		model(__lowerCamelCase							,							use_cache=__lowerCamelCase   )

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

									lowercase		,lowercase							=		outputs.to_tuple()

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

									# append to next input_ids and
									lowercase							=		torch.cat([input_ids, next_tokens]							,							dim=-1   )

									lowercase							=		model(__lowerCamelCase   )['''last_hidden_state''']
									lowercase							=		model(__lowerCamelCase							,							past_key_values=__lowerCamelCase   )['''last_hidden_state''']

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

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



						def        __a		(	self				:      Any							,							__lowerCamelCase				:      Tuple							,							__lowerCamelCase				:      Any							,							)							->		str:


									'''simple docstring'''

									lowercase							=		UMTaModel(config=__lowerCamelCase   ).to(__lowerCamelCase   ).half().eval()
									lowercase							=		model(**__lowerCamelCase   )['''last_hidden_state''']
									self.parent.assertFalse(torch.isnan(__lowerCamelCase   ).any().item()   )






@require_torch
class      __lowercase					(    _A       ,     _A       ,     _A       ,     unittest.TestCase    ):
						lowercase					 =     (
						    (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
						)
						lowercase					 =     (UMTaForConditionalGeneration,) if is_torch_available() else ()
						lowercase					 =     (
						    {
						        'conversational': UMTaForConditionalGeneration,
						        'feature-extraction': UMTaModel,
						        'summarization': UMTaForConditionalGeneration,
						        'text2text-generation': UMTaForConditionalGeneration,
						        'translation': UMTaForConditionalGeneration,
						        'question-answering': UMTaForQuestionAnswering,
						    }
						    if is_torch_available()
						    else {}
						)
						lowercase					 =     True
						lowercase					 =     False
						lowercase					 =     False
						lowercase					 =     True
						lowercase					 =     True
						# The small UMT5 model needs higher percentages for CPU/MP tests
						lowercase					 =     [0.8, 0.9]

						def        __a		(	self				:      int   )							->		Optional[int]:


									'''simple docstring'''

									lowercase							=		UMTaModelTester(self   )

						@unittest.skip('''Test has a segmentation fault on torch 1.8.0'''   )
						def        __a		(	self				:      Any   )							->		Any:


									'''simple docstring'''

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

						@unittest.skipIf(torch_device == '''cpu'''							,							'''Cant do half precision'''   )
						def        __a		(	self				:      Dict   )							->		Any:


									'''simple docstring'''

									lowercase							=		self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model_fpaa_forward(*__lowerCamelCase   )

						def        __a		(	self				:      Tuple   )							->		List[Any]:


									'''simple docstring'''

									lowercase							=		['''encoder_attentions''', '''decoder_attentions''', '''cross_attentions''']
									lowercase							=		self.model_tester.prepare_config_and_inputs()
									lowercase							=		config_and_inputs[0]
									lowercase							=		UMTaForConditionalGeneration(__lowerCamelCase   ).eval()
									model.to(__lowerCamelCase   )

									lowercase							=		{
									    '''head_mask''': torch.zeros(config.num_layers							,							config.num_heads							,							device=__lowerCamelCase   ),
									    '''decoder_head_mask''': torch.zeros(config.num_decoder_layers							,							config.num_heads							,							device=__lowerCamelCase   ),
									    '''cross_attn_head_mask''': torch.zeros(config.num_decoder_layers							,							config.num_heads							,							device=__lowerCamelCase   ),
									}

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

												lowercase							=		model.generate(
												    config_and_inputs[1]['''input_ids''']							,							num_beams=1							,							max_length=3							,							output_attentions=__lowerCamelCase							,							return_dict_in_generate=__lowerCamelCase							,							**__lowerCamelCase							,							)
												# We check the state of decoder_attentions and cross_attentions just from the last step
												lowercase							=		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				:      List[Any]   )							->		Optional[int]:


									'''simple docstring'''

									pass






@require_torch
@require_sentencepiece
@require_tokenizers
class      __lowercase					(    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   )							->		List[Any]:


									'''simple docstring'''

									lowercase							=		UMTaForConditionalGeneration.from_pretrained('''google/umt5-small'''							,							return_dict=__lowerCamelCase   ).to(__lowerCamelCase   )
									lowercase							=		AutoTokenizer.from_pretrained('''google/umt5-small'''							,							use_fast=__lowerCamelCase							,							legacy=__lowerCamelCase   )
									lowercase							=		[
									    '''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>.''',
									]
									lowercase							=		tokenizer(__lowerCamelCase							,							return_tensors='''pt'''							,							padding=__lowerCamelCase   ).input_ids
									# fmt: off
									lowercase							=		torch.tensor(
									    [
									        [ 3_85_30, 21_07_03, 25_62_99, 14_10, 25_62_98, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
									        [   8_26, 3_21, 6_71, 2_59_22, 25_62_99, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
									        [  14_60, 3_39, 3_12, 1_90_14, 1_06_20, 7_58, 25_62_99, 23_55,2_74, 1, 0, 0, 0, 0, 0, 0,0, 0],
									        [   5_17, 25_62_99, 1_48_69, 2_81, 3_01, 25_62_98, 2_75, 11_99_83,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
									        [   3_20, 25_62_99, 1_48_69, 2_81, 22_34, 2_89, 22_75, 3_33,6_13_91, 2_89, 25_62_98, 5_43, 25_62_97, 16_87_14, 3_29, 25_62_96,2_74, 1],
									    ]   )
									# fmt: on
									torch.testing.assert_allclose(__lowerCamelCase							,							__lowerCamelCase   )

									lowercase							=		model.generate(input_ids.to(__lowerCamelCase   )   )
									lowercase							=		[
									    '''<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>''',
									]
									lowercase							=		tokenizer.batch_decode(__lowerCamelCase   )
									self.assertEqual(__lowerCamelCase							,							__lowerCamelCase   )





