


"""simple docstring"""






import inspect
import unittest

from transformers import ResNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch
   from torch import nn

   from transformers import ResNetBackbone, ResNetForImageClassification, ResNetModel
   from transformers.models.resnet.modeling_resnet import RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import AutoImageProcessor


class      __lowerCamelCase	:

     '''simple docstring'''


     def __init__(   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase=3     ,					__UpperCAmelCase=32     ,					__UpperCAmelCase=3     ,					__UpperCAmelCase=10     ,					__UpperCAmelCase=[10, 20, 30, 40]     ,					__UpperCAmelCase=[1, 1, 2, 1]     ,					__UpperCAmelCase=True     ,					__UpperCAmelCase=True     ,					__UpperCAmelCase="relu"     ,					__UpperCAmelCase=3     ,					__UpperCAmelCase=None     ,					__UpperCAmelCase=["stage2", "stage3", "stage4"]     ,					__UpperCAmelCase=[2, 3, 4]     ,					)							->				Tuple:
          _a     						=					parent
          _a     						=					batch_size
          _a     						=					image_size
          _a     						=					num_channels
          _a     						=					embeddings_size
          _a     						=					hidden_sizes
          _a     						=					depths
          _a     						=					is_training
          _a     						=					use_labels
          _a     						=					hidden_act
          _a     						=					num_labels
          _a     						=					scope
          _a     						=					len(__UpperCAmelCase				)
          _a     						=					out_features
          _a     						=					out_indices


     def 				_UpperCAmelCase   (   self				)							->				Optional[Any]:
          _a     						=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

          _a     						=					None
          if self.use_labels:
               _a     						=					ids_tensor([self.batch_size]     ,					self.num_labels				)

          _a     						=					self.get_config()

          return config, pixel_values, labels


     def 				_UpperCAmelCase   (   self				)							->				Union[str, Any]:
          return ResNetConfig(
              num_channels=self.num_channels     ,					embeddings_size=self.embeddings_size     ,					hidden_sizes=self.hidden_sizes     ,					depths=self.depths     ,					hidden_act=self.hidden_act     ,					num_labels=self.num_labels     ,					out_features=self.out_features     ,					out_indices=self.out_indices     ,					)


     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				)							->				int:
          _a     						=					ResNetModel(config=__UpperCAmelCase				)
          model.to(__UpperCAmelCase				)
          model.eval()
          _a     						=					model(__UpperCAmelCase				)
          # expected last hidden states: B, C, H // 32, W // 32
          self.parent.assertEqual(
              result.last_hidden_state.shape     ,					(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)     ,					)


     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				)							->				Optional[int]:
          _a     						=					self.num_labels
          _a     						=					ResNetForImageClassification(__UpperCAmelCase				)
          model.to(__UpperCAmelCase				)
          model.eval()
          _a     						=					model(__UpperCAmelCase     ,					labels=__UpperCAmelCase				)
          self.parent.assertEqual(result.logits.shape     ,					(self.batch_size, self.num_labels)				)


     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				)							->				Dict:
          _a     						=					ResNetBackbone(config=__UpperCAmelCase				)
          model.to(__UpperCAmelCase				)
          model.eval()
          _a     						=					model(__UpperCAmelCase				)

          # verify feature maps
          self.parent.assertEqual(len(result.feature_maps				)     ,					len(config.out_features				)				)
          self.parent.assertListEqual(list(result.feature_maps[0].shape				)     ,					[self.batch_size, self.hidden_sizes[1], 4, 4]				)

          # verify channels
          self.parent.assertEqual(len(model.channels				)     ,					len(config.out_features				)				)
          self.parent.assertListEqual(model.channels     ,					config.hidden_sizes[1:]				)

          # verify backbone works with out_features=None
          _a     						=					None
          _a     						=					ResNetBackbone(config=__UpperCAmelCase				)
          model.to(__UpperCAmelCase				)
          model.eval()
          _a     						=					model(__UpperCAmelCase				)

          # verify feature maps
          self.parent.assertEqual(len(result.feature_maps				)     ,					1				)
          self.parent.assertListEqual(list(result.feature_maps[0].shape				)     ,					[self.batch_size, self.hidden_sizes[-1], 1, 1]				)

          # verify channels
          self.parent.assertEqual(len(model.channels				)     ,					1				)
          self.parent.assertListEqual(model.channels     ,					[config.hidden_sizes[-1]]				)





     def 				_UpperCAmelCase   (   self				)							->				List[str]:
          _a     						=					self.prepare_config_and_inputs()
          _a						,   _a						,   _a     						=					config_and_inputs
          _a     						=					{'''pixel_values''': pixel_values}
          return config, inputs_dict







@require_torch
class      __lowerCamelCase	(       lowercase_	,       lowercase_	,       unittest.TestCase					):

     '''simple docstring'''
     A_							:						Optional[int]     =		(
         (
             ResNetModel,
             ResNetForImageClassification,
             ResNetBackbone,
         )
         if is_torch_available()
         else ()
     )
     A_							:						Optional[int]     =		(
         {"""feature-extraction""": ResNetModel, """image-classification""": ResNetForImageClassification}
         if is_torch_available()
         else {}
     )

     A_							:						List[Any]     =		True
     A_							:						str     =		False
     A_							:						List[str]     =		False
     A_							:						Union[str, Any]     =		False
     A_							:						Tuple     =		False


     def 				_UpperCAmelCase   (   self				)							->				Tuple:
          _a     						=					ResNetModelTester(self				)
          _a     						=					ConfigTester(self     ,					config_class=__UpperCAmelCase     ,					has_text_modality=__UpperCAmelCase				)


     def 				_UpperCAmelCase   (   self				)							->				List[Any]:
          self.create_and_test_config_common_properties()
          self.config_tester.create_and_test_config_to_json_string()
          self.config_tester.create_and_test_config_to_json_file()
          self.config_tester.create_and_test_config_from_and_save_pretrained()
          self.config_tester.create_and_test_config_with_num_labels()
          self.config_tester.check_config_can_be_init_without_params()
          self.config_tester.check_config_arguments_init()


     def 				_UpperCAmelCase   (   self				)							->				str:
          return


     @unittest.skip(reason='''ResNet does not use inputs_embeds'''				)
     def 				_UpperCAmelCase   (   self				)							->				int:
          pass


     @unittest.skip(reason='''ResNet does not support input and output embeddings'''				)
     def 				_UpperCAmelCase   (   self				)							->				Union[str, Any]:
          pass


     def 				_UpperCAmelCase   (   self				)							->				Tuple:
          _a						,   _a     						=					self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               _a     						=					model_class(__UpperCAmelCase				)
               _a     						=					inspect.signature(model.forward				)
               # signature.parameters is an OrderedDict => so arg_names order is deterministic
               _a     						=					[*signature.parameters.keys()]

               _a     						=					['''pixel_values''']
               self.assertListEqual(arg_names[:1]     ,					__UpperCAmelCase				)


     def 				_UpperCAmelCase   (   self				)							->				List[Any]:
          _a     						=					self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*__UpperCAmelCase				)


     def 				_UpperCAmelCase   (   self				)							->				Union[str, Any]:
          _a     						=					self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_backbone(*__UpperCAmelCase				)


     def 				_UpperCAmelCase   (   self				)							->				Any:
          _a						,   _a     						=					self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               _a     						=					model_class(config=__UpperCAmelCase				)
               for name, module in model.named_modules():
                    if isinstance(__UpperCAmelCase     ,					(nn.BatchNormad, nn.GroupNorm)				):
                         self.assertTrue(
                             torch.all(module.weight == 1				)     ,					msg=F'Parameter {name} of model {model_class} seems not properly initialized'     ,					)
                         self.assertTrue(
                             torch.all(module.bias == 0				)     ,					msg=F'Parameter {name} of model {model_class} seems not properly initialized'     ,					)


     def 				_UpperCAmelCase   (   self				)							->				str:
          def check_hidden_states_output(__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				):
               _a     						=					model_class(__UpperCAmelCase				)
               model.to(__UpperCAmelCase				)
               model.eval()

               with torch.no_grad():
                    _a     						=					model(**self._prepare_for_class(__UpperCAmelCase     ,					__UpperCAmelCase				)				)

               _a     						=					outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

               _a     						=					self.model_tester.num_stages
               self.assertEqual(len(__UpperCAmelCase				)     ,					expected_num_stages + 1				)

               # ResNet's feature maps are of shape (batch_size, num_channels, height, width)
               self.assertListEqual(
                   list(hidden_states[0].shape[-2:]				)     ,					[self.model_tester.image_size // 4, self.model_tester.image_size // 4]     ,					)

          _a						,   _a     						=					self.model_tester.prepare_config_and_inputs_for_common()
          _a     						=					['''basic''', '''bottleneck''']
          for model_class in self.all_model_classes:
               for layer_type in layers_type:
                    _a     						=					layer_type
                    _a     						=					True
                    check_hidden_states_output(__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				)

                    # check that output_hidden_states also work using config
                    del inputs_dict["output_hidden_states"]
                    _a     						=					True

                    check_hidden_states_output(__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				)


     @unittest.skip(reason='''ResNet does not use feedforward chunking'''				)
     def 				_UpperCAmelCase   (   self				)							->				Union[str, Any]:
          pass


     def 				_UpperCAmelCase   (   self				)							->				Any:
          _a     						=					self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase				)





     @slow
     def 				_UpperCAmelCase   (   self				)							->				List[str]:
          for model_name in RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               _a     						=					ResNetModel.from_pretrained(__UpperCAmelCase				)
               self.assertIsNotNone(__UpperCAmelCase				)







def    A_ ( ):






     """simple docstring"""

     _a     						=					Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
     return image


@require_torch
@require_vision
class      __lowerCamelCase	(       unittest.TestCase					):

     '''simple docstring'''


     @cached_property
     def 				_UpperCAmelCase   (   self				)							->				Optional[Any]:
          return (
              AutoImageProcessor.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]				)
              if is_vision_available()
              else None
          )





     @slow
     def 				_UpperCAmelCase   (   self				)							->				int:
          _a     						=					ResNetForImageClassification.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]				).to(__UpperCAmelCase				)

          _a     						=					self.default_image_processor
          _a     						=					prepare_img()
          _a     						=					image_processor(images=__UpperCAmelCase     ,					return_tensors='''pt'''				).to(__UpperCAmelCase				)

          # forward pass
          with torch.no_grad():
               _a     						=					model(**__UpperCAmelCase				)

          # verify the logits
          _a     						=					torch.Size((1, 1000)				)
          self.assertEqual(outputs.logits.shape     ,					__UpperCAmelCase				)

          _a     						=					torch.tensor([-11.1069, -9.7877, -8.3777]				).to(__UpperCAmelCase				)

          self.assertTrue(torch.allclose(outputs.logits[0, :3]     ,					__UpperCAmelCase     ,					atol=1e-4				)				)







@require_torch
class      __lowerCamelCase	(       lowercase_	,       unittest.TestCase					):

     '''simple docstring'''
     A_							:						Any     =		(ResNetBackbone,) if is_torch_available() else ()
     A_							:						List[str]     =		False
     A_							:						Optional[int]     =		ResNetConfig


     def 				_UpperCAmelCase   (   self				)							->				str:
          _a     						=					ResNetModelTester(self				)





"""simple docstring"""

import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


UpperCamelCase__			:     str			       =				logging.get_logger(__name__)


UpperCamelCase__			:     Any			       =				{"vocab_file": "vocab.json", "merges_file": "merges.txt"}

# See all MVP models at https://huggingface.co/models?filter=mvp
UpperCamelCase__			:     str			       =				{
    "vocab_file": {
        "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json",
    },
    "added_tokens.json": {
        "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json",
    },
    "merges_file": {
        "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt",
    },
}

UpperCamelCase__			:     str			       =				{
    "RUCAIBox/mvp": 10_24,
}




@lru_cache()
def    snake_case__						(							):
					__snake_case        =						(
					    list(range(ord("""!"""		)					,       ord("""~"""		) + 1		)		) + list(range(ord("""¡"""		)					,       ord("""¬"""		) + 1		)		) + list(range(ord("""®"""		)					,       ord("""ÿ"""		) + 1		)		)
					)
					__snake_case        =						bs[:]
					__snake_case        =						0
					for b in range(2**8		):
										if b not in bs:
															bs.append(lowerCAmelCase		)
															cs.append(2**8 + n		)
															n += 1
					__snake_case        =						[chr(lowerCAmelCase		) for n in cs]
					return dict(zip(lowerCAmelCase					,       lowerCAmelCase		)		)




def    snake_case__						(							lowerCAmelCase :		Optional[int]		):
					__snake_case        =						set()
					__snake_case        =						word[0]
					for char in word[1:]:
										pairs.add((prev_char, char)		)
										__snake_case        =						char
					return pairs

class      A__   (   lowercase_  ):
	A_				:					List[str]      =   VOCAB_FILES_NAMES
	A_				:					Any      =   PRETRAINED_VOCAB_FILES_MAP
	A_				:					Optional[Any]      =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	A_				:					Union[str, Any]      =   ["""input_ids""", """attention_mask"""]




	def __init__( self	:						Tuple   ,     lowercase	:						Optional[int]   ,     lowercase	:						Union[str, Any]   ,     lowercase	:						Union[str, Any]="replace"   ,     lowercase	:						Dict="<s>"   ,     lowercase	:						Any="</s>"   ,     lowercase	:						List[Any]="</s>"   ,     lowercase	:						List[Any]="<s>"   ,     lowercase	:						Optional[Any]="<unk>"   ,     lowercase	:						int="<pad>"   ,     lowercase	:						List[str]="<mask>"   ,     lowercase	:						Any=False   ,     **lowercase	:						Optional[int]   ,     )       ->			Tuple:
						__snake_case        =						AddedToken(lowercase   ,     lstrip=lowercase   ,     rstrip=lowercase						) if isinstance(lowercase   ,     lowercase						) else bos_token
						__snake_case        =						AddedToken(lowercase   ,     lstrip=lowercase   ,     rstrip=lowercase						) if isinstance(lowercase   ,     lowercase						) else eos_token
						__snake_case        =						AddedToken(lowercase   ,     lstrip=lowercase   ,     rstrip=lowercase						) if isinstance(lowercase   ,     lowercase						) else sep_token
						__snake_case        =						AddedToken(lowercase   ,     lstrip=lowercase   ,     rstrip=lowercase						) if isinstance(lowercase   ,     lowercase						) else cls_token
						__snake_case        =						AddedToken(lowercase   ,     lstrip=lowercase   ,     rstrip=lowercase						) if isinstance(lowercase   ,     lowercase						) else unk_token
						__snake_case        =						AddedToken(lowercase   ,     lstrip=lowercase   ,     rstrip=lowercase						) if isinstance(lowercase   ,     lowercase						) else pad_token

						# Mask token behave like a normal word, i.e. include the space before it
						__snake_case        =						AddedToken(lowercase   ,     lstrip=lowercase   ,     rstrip=lowercase						) if isinstance(lowercase   ,     lowercase						) else mask_token

						super().__init__(
						    errors=lowercase   ,     bos_token=lowercase   ,     eos_token=lowercase   ,     unk_token=lowercase   ,     sep_token=lowercase   ,     cls_token=lowercase   ,     pad_token=lowercase   ,     mask_token=lowercase   ,     add_prefix_space=lowercase   ,     **lowercase   ,     )
						with open(lowercase   ,     encoding="""utf-8"""						) as vocab_handle:
											__snake_case        =						json.load(lowercase						)
						__snake_case        =						{v: k for k, v in self.encoder.items()}
						__snake_case        =						errors  # how to handle errors in decoding
						__snake_case        =						bytes_to_unicode()
						__snake_case        =						{v: k for k, v in self.byte_encoder.items()}
						with open(lowercase   ,     encoding="""utf-8"""						) as merges_handle:
											__snake_case        =						merges_handle.read().split("""\n"""						)[1:-1]
						__snake_case        =						[tuple(merge.split()						) for merge in bpe_merges]
						__snake_case        =						dict(zip(lowercase   ,     range(len(lowercase						)						)						)						)
						__snake_case        =						{}
						__snake_case        =						add_prefix_space

						# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
						__snake_case        =						re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""						)




	@property
	def 				a_		( self	:						List[str]						)       ->			Any:
						return len(self.encoder						)




	def 				a_		( self	:						str						)       ->			str:
						return dict(self.encoder   ,     **self.added_tokens_encoder						)




	def 				a_		( self	:						int   ,     lowercase	:						Optional[Any]						)       ->			int:
						if token in self.cache:
											return self.cache[token]
						__snake_case        =						tuple(lowercase						)
						__snake_case        =						get_pairs(lowercase						)

						if not pairs:
											return token

						while True:
											__snake_case        =						min(lowercase   ,     key=lambda lowercase						: self.bpe_ranks.get(lowercase   ,     float("""inf"""						)						)						)
											if bigram not in self.bpe_ranks:
																break
											__snake_case   ,   __snake_case        =						bigram
											__snake_case        =						[]
											__snake_case        =						0
											while i < len(lowercase						):
																try:
																					__snake_case        =						word.index(lowercase   ,     lowercase						)
																except ValueError:
																					new_word.extend(word[i:]						)
																					break
																else:
																					new_word.extend(word[i:j]						)
																					__snake_case        =						j

																if word[i] == first and i < len(lowercase						) - 1 and word[i + 1] == second:
																					new_word.append(first + second						)
																					i += 2
																else:
																					new_word.append(word[i]						)
																					i += 1
											__snake_case        =						tuple(lowercase						)
											__snake_case        =						new_word
											if len(lowercase						) == 1:
																break
											else:
																__snake_case        =						get_pairs(lowercase						)
						__snake_case        =						""" """.join(lowercase						)
						__snake_case        =						word
						return word




	def 				a_		( self	:						List[str]   ,     lowercase	:						Optional[int]						)       ->			List[str]:
						__snake_case        =						[]
						for token in re.findall(self.pat   ,     lowercase						):
											__snake_case        =						"""""".join(
											    self.byte_encoder[b] for b in token.encode("""utf-8"""						)						)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
											bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowercase						).split(""" """						)						)
						return bpe_tokens




	def 				a_		( self	:						Dict   ,     lowercase	:						List[Any]						)       ->			List[Any]:
						return self.encoder.get(lowercase   ,     self.encoder.get(self.unk_token						)						)




	def 				a_		( self	:						Optional[int]   ,     lowercase	:						Optional[Any]						)       ->			List[str]:
						return self.decoder.get(lowercase						)




	def 				a_		( self	:						Tuple   ,     lowercase	:						int						)       ->			Any:
						__snake_case        =						"""""".join(lowercase						)
						__snake_case        =						bytearray([self.byte_decoder[c] for c in text]						).decode("""utf-8"""   ,     errors=self.errors						)
						return text




	def 				a_		( self	:						List[Any]   ,     lowercase	:						str   ,     lowercase	:						Optional[str] = None						)       ->			Tuple[str]:
						if not os.path.isdir(lowercase						):
											logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''						)
											return
						__snake_case        =						os.path.join(
						    lowercase   ,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]						)
						__snake_case        =						os.path.join(
						    lowercase   ,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]						)

						with open(lowercase   ,     """w"""   ,     encoding="""utf-8"""						) as f:
											f.write(json.dumps(self.encoder   ,     indent=2   ,     sort_keys=lowercase   ,     ensure_ascii=lowercase						) + """\n"""						)

						__snake_case        =						0
						with open(lowercase   ,     """w"""   ,     encoding="""utf-8"""						) as writer:
											writer.write("""#version: 0.2\n"""						)
											for bpe_tokens, token_index in sorted(self.bpe_ranks.items()   ,     key=lambda lowercase						: kv[1]						):
																if index != token_index:
																					logger.warning(
																					    f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
																					    """ Please check that the tokenizer is not corrupted!"""						)
																					__snake_case        =						token_index
																writer.write(""" """.join(lowercase						) + """\n"""						)
																index += 1

						return vocab_file, merge_file




	def 				a_		( self	:						List[Any]   ,     lowercase	:						List[int]   ,     lowercase	:						Optional[List[int]] = None						)       ->			List[int]:
						if token_ids_a is None:
											return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
						__snake_case        =						[self.cls_token_id]
						__snake_case        =						[self.sep_token_id]
						return cls + token_ids_a + sep + sep + token_ids_a + sep




	def 				a_		( self	:						Optional[Any]   ,     lowercase	:						List[int]   ,     lowercase	:						Optional[List[int]] = None   ,     lowercase	:						bool = False						)       ->			List[int]:
						if already_has_special_tokens:
											return super().get_special_tokens_mask(
											    token_ids_a=lowercase   ,     token_ids_a=lowercase   ,     already_has_special_tokens=lowercase						)

						if token_ids_a is None:
											return [1] + ([0] * len(lowercase						)) + [1]
						return [1] + ([0] * len(lowercase						)) + [1, 1] + ([0] * len(lowercase						)) + [1]




	def 				a_		( self	:						Tuple   ,     lowercase	:						List[int]   ,     lowercase	:						Optional[List[int]] = None						)       ->			List[int]:
						__snake_case        =						[self.sep_token_id]
						__snake_case        =						[self.cls_token_id]

						if token_ids_a is None:
											return len(cls + token_ids_a + sep						) * [0]
						return len(cls + token_ids_a + sep + sep + token_ids_a + sep						) * [0]




	def 				a_		( self	:						Union[str, Any]   ,     lowercase	:						List[Any]   ,     lowercase	:						int=False   ,     **lowercase	:						str						)       ->			Tuple:
						__snake_case        =						kwargs.pop("""add_prefix_space"""   ,     self.add_prefix_space						)
						if (is_split_into_words or add_prefix_space) and (len(lowercase						) > 0 and not text[0].isspace()):
											__snake_case        =						""" """ + text
						return (text, kwargs)


