from collections import Counter
from pathlib import Path
from typing import Optional, Tuple

import yaml






class      __lowercase					(    yaml.SafeLoader    ):

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


									'''simple docstring'''

									lowercase							=		[self.constructed_objects[key_node] for key_node, _ in node.value]
									lowercase							=		[tuple(__UpperCamelCase   ) if isinstance(__UpperCamelCase							,							__UpperCamelCase   ) else key for key in keys]
									lowercase							=		Counter(__UpperCamelCase   )
									lowercase							=		[key for key in counter if counter[key] > 1]
									if duplicate_keys:
												raise TypeError(f'Got duplicate yaml keys: {duplicate_keys}'   )



						def        __a		(	self				:      Dict							,							__lowerCamelCase				:      int							,							__lowerCamelCase				:      Dict=False   )							->		str:


									'''simple docstring'''

									lowercase							=		super().construct_mapping(__UpperCamelCase							,							deep=__UpperCamelCase   )
									self._check_no_duplicates_on_constructed_node(__UpperCamelCase   )
									return mapping






def        __UpperCAmelCase    (       UpperCAmelCase       )->       Tuple[Optional[str], str]:



			"""simple docstring"""







			lowercase							=		list(readme_content.splitlines()       )
			if full_content and full_content[0] == "---" and "---" in full_content[1:]:
						lowercase							=		full_content[1:].index('''---'''       ) + 1
						lowercase							=		'''\n'''.join(full_content[1:sep_idx]       )
						return yamlblock, "\n".join(full_content[sep_idx + 1 :]       )

			return None, "\n".join(UpperCAmelCase__       )






class      __lowercase					(    _A    ):
						# class attributes
						lowercase					 =     {'train_eval_index'}  # train-eval-index in the YAML metadata

						@classmethod
						def        __a		(	cls				:      List[Any]							,							__lowerCamelCase				:      Path   )							->		Union[str, Any]:


									'''simple docstring'''

									with open(__UpperCamelCase							,							encoding='''utf-8'''   ) as readme_file:
												lowercase		,lowercase							=		_split_yaml_from_readme(readme_file.read()   )
									if yaml_string is not None:
												return cls.from_yaml_string(__UpperCamelCase   )
									else:
												return cls()

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


									'''simple docstring'''

									if path.exists():
												with open(__UpperCamelCase							,							encoding='''utf-8'''   ) as readme_file:
															lowercase							=		readme_file.read()
									else:
												lowercase							=		None
									lowercase							=		self._to_readme(__UpperCamelCase   )
									with open(__UpperCamelCase							,							'''w'''							,							encoding='''utf-8'''   ) as readme_file:
												readme_file.write(__UpperCamelCase   )

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


									'''simple docstring'''

									if readme_content is not None:
												lowercase		,lowercase							=		_split_yaml_from_readme(__UpperCamelCase   )
												lowercase							=		'''---\n''' + self.to_yaml_string() + '''---\n''' + content
									else:
												lowercase							=		'''---\n''' + self.to_yaml_string() + '''---\n'''
									return full_content

						@classmethod
						def        __a		(	cls				:      List[Any]							,							__lowerCamelCase				:      str   )							->		Any:


									'''simple docstring'''

									lowercase							=		yaml.load(__UpperCamelCase							,							Loader=_NoDuplicateSafeLoader   ) or {}

									# Convert the YAML keys to DatasetMetadata fields
									lowercase							=		{
									    (key.replace('''-'''							,							'''_'''   ) if key.replace('''-'''							,							'''_'''   ) in cls._FIELDS_WITH_DASHES else key): value
									    for key, value in metadata_dict.items()
									}
									return cls(**__UpperCamelCase   )



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


									'''simple docstring'''

									return yaml.safe_dump(
									    {
									        (key.replace('''_'''							,							'''-'''   ) if key in self._FIELDS_WITH_DASHES else key): value
									        for key, value in self.items()
									    }							,							sort_keys=__UpperCamelCase							,							allow_unicode=__UpperCamelCase							,							encoding='''utf-8'''							,							).decode('''utf-8'''   )





A_      =      {
    "image-classification": [],
    "translation": [],
    "image-segmentation": [],
    "fill-mask": [],
    "automatic-speech-recognition": [],
    "token-classification": [],
    "sentence-similarity": [],
    "audio-classification": [],
    "question-answering": [],
    "summarization": [],
    "zero-shot-classification": [],
    "table-to-text": [],
    "feature-extraction": [],
    "other": [],
    "multiple-choice": [],
    "text-classification": [],
    "text-to-image": [],
    "text2text-generation": [],
    "zero-shot-image-classification": [],
    "tabular-classification": [],
    "tabular-regression": [],
    "image-to-image": [],
    "tabular-to-text": [],
    "unconditional-image-generation": [],
    "text-retrieval": [],
    "text-to-speech": [],
    "object-detection": [],
    "audio-to-audio": [],
    "text-generation": [],
    "conversational": [],
    "table-question-answering": [],
    "visual-question-answering": [],
    "image-to-text": [],
    "reinforcement-learning": [],
    "voice-activity-detection": [],
    "time-series-forecasting": [],
    "document-question-answering": [],
}


if __name__ == "__main__":
							from argparse import ArgumentParser

							A_      =      ArgumentParser(usage="Validate the yaml metadata block of a README.md file.")
							ap.add_argument("readme_filepath")
							A_      =      ap.parse_args()

							A_      =      Path(args.readme_filepath)
							A_      =      DatasetMetadata.from_readme(readme_filepath)
							print(dataset_metadata)
							dataset_metadata.to_readme(readme_filepath)






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   )





