

import inspect
import unittest

from transformers import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

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 transformers import MODEL_MAPPING, PoolFormerConfig, PoolFormerForImageClassification, PoolFormerModel
      from transformers.models.poolformer.modeling_poolformer import POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import PoolFormerImageProcessor


class      lowerCamelCase_ (			_lowerCAmelCase):




 def     a       (			self							):
   __UpperCAmelCase	:  Union[str, Any]		=     self.config_class(**self.inputs_dict							)
   self.parent.assertTrue(hasattr(_UpperCAmelCase  ,  'hidden_sizes'							)							)
   self.parent.assertTrue(hasattr(_UpperCAmelCase  ,  'num_encoder_blocks'							)							)



class      lowerCamelCase_ :




 def __init__(			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase=13  ,  _UpperCAmelCase=64  ,  _UpperCAmelCase=3  ,  _UpperCAmelCase=4  ,  _UpperCAmelCase=[2, 2, 2, 2]  ,  _UpperCAmelCase=[8, 4, 2, 1]  ,  _UpperCAmelCase=[16, 32, 64, 128]  ,  _UpperCAmelCase=[1, 4, 8, 16]  ,  _UpperCAmelCase=False  ,  _UpperCAmelCase=True  ,  _UpperCAmelCase="gelu"  ,  _UpperCAmelCase=0.1  ,  _UpperCAmelCase=0.0_2  ,  _UpperCAmelCase=3  ,  _UpperCAmelCase=None  ,  ):
   __UpperCAmelCase	:  Optional[int]		=     parent
   __UpperCAmelCase	:  str		=     batch_size
   __UpperCAmelCase	:  Optional[int]		=     image_size
   __UpperCAmelCase	:  Dict		=     num_channels
   __UpperCAmelCase	:  int		=     num_encoder_blocks
   __UpperCAmelCase	:  str		=     sr_ratios
   __UpperCAmelCase	:  List[Any]		=     depths
   __UpperCAmelCase	:  Optional[int]		=     hidden_sizes
   __UpperCAmelCase	:  List[str]		=     downsampling_rates
   __UpperCAmelCase	:  Dict		=     is_training
   __UpperCAmelCase	:  Optional[int]		=     use_labels
   __UpperCAmelCase	:  Optional[Any]		=     hidden_act
   __UpperCAmelCase	:  int		=     hidden_dropout_prob
   __UpperCAmelCase	:  Tuple		=     initializer_range
   __UpperCAmelCase	:  str		=     num_labels
   __UpperCAmelCase	:  Union[str, Any]		=     scope




 def     a       (			self							):
   __UpperCAmelCase	:  Optional[int]		=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

   __UpperCAmelCase	:  Optional[Any]		=     None
   if self.use_labels:
     __UpperCAmelCase	:  Optional[int]		=     ids_tensor([self.batch_size, self.image_size, self.image_size]  ,  self.num_labels							)

   __UpperCAmelCase	:  Tuple		=     PoolFormerConfig(
       image_size=self.image_size  ,  num_channels=self.num_channels  ,  num_encoder_blocks=self.num_encoder_blocks  ,  depths=self.depths  ,  hidden_sizes=self.hidden_sizes  ,  hidden_act=self.hidden_act  ,  hidden_dropout_prob=self.hidden_dropout_prob  ,  initializer_range=self.initializer_range  ,  )

   return config, pixel_values, labels




 def     a       (			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase							):
   __UpperCAmelCase	:  Any		=     PoolFormerModel(config=_UpperCAmelCase							)
   model.to(_UpperCAmelCase							)
   model.eval()
   __UpperCAmelCase	:  int		=     model(_UpperCAmelCase							)
   __UpperCAmelCase	:  Dict		=     self.image_size // 3_2.0
   self.parent.assertEqual(
       result.last_hidden_state.shape  ,  (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)							)




 def     a       (			self							):
   __UpperCAmelCase	:  Tuple		=     self.prepare_config_and_inputs()
   __UpperCAmelCase,			__UpperCAmelCase,			__UpperCAmelCase	:  Union[str, Any]		=     config_and_inputs
   __UpperCAmelCase	:  str		=     {'pixel_values': pixel_values}
   return config, inputs_dict



@require_torch
class      lowerCamelCase_ (			_lowerCAmelCase	,				_lowerCAmelCase	,				unittest.TestCase):
 _lowerCAmelCase									=	(PoolFormerModel, PoolFormerForImageClassification) if is_torch_available() else ()
 _lowerCAmelCase									=	(
     {'''feature-extraction''': PoolFormerModel, '''image-classification''': PoolFormerForImageClassification}
     if is_torch_available()
     else {}
 )

 _lowerCAmelCase									=	False
 _lowerCAmelCase									=	False
 _lowerCAmelCase									=	False
 _lowerCAmelCase									=	False
 _lowerCAmelCase									=	False




 def     a       (			self							):
   __UpperCAmelCase	:  str		=     PoolFormerModelTester(self							)
   __UpperCAmelCase	:  List[str]		=     PoolFormerConfigTester(self  ,  config_class=_UpperCAmelCase							)




 def     a       (			self							):
   self.config_tester.run_common_tests()




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




 @unittest.skip('PoolFormer does not use inputs_embeds'							)
 def     a       (			self							):
   pass




 @unittest.skip('PoolFormer does not have get_input_embeddings method and get_output_embeddings methods'							)
 def     a       (			self							):
   pass




 def     a       (			self							):
   def check_hidden_states_output(_UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase							):
     __UpperCAmelCase	:  Union[str, Any]		=     model_class(_UpperCAmelCase							)
     model.to(_UpperCAmelCase							)
     model.eval()

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

     __UpperCAmelCase	:  Tuple		=     outputs.hidden_states

     __UpperCAmelCase	:  Tuple		=     self.model_tester.num_encoder_blocks
     self.assertEqual(len(_UpperCAmelCase							)  ,  _UpperCAmelCase							)

     # verify the first hidden states (first block)
     self.assertListEqual(
         list(hidden_states[0].shape[-3:]							)  ,  [
             self.model_tester.hidden_sizes[0],
             self.model_tester.image_size // 4,
             self.model_tester.image_size // 4,
         ]  ,  )

   __UpperCAmelCase,			__UpperCAmelCase	:  int		=     self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     __UpperCAmelCase	:  Optional[int]		=     True
     check_hidden_states_output(_UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase							)

     # check that output_hidden_states also work using config
     del inputs_dict["output_hidden_states"]
     __UpperCAmelCase	:  int		=     True

     check_hidden_states_output(_UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase							)




 def     a       (			self							):
   if not self.model_tester.is_training:
     return

   __UpperCAmelCase,			__UpperCAmelCase	:  int		=     self.model_tester.prepare_config_and_inputs_for_common()
   __UpperCAmelCase	:  Dict		=     True

   for model_class in self.all_model_classes:
     if model_class in get_values(_UpperCAmelCase							):
       continue
     __UpperCAmelCase	:  Union[str, Any]		=     model_class(_UpperCAmelCase							)
     model.to(_UpperCAmelCase							)
     model.train()
     __UpperCAmelCase	:  Any		=     self._prepare_for_class(_UpperCAmelCase  ,  _UpperCAmelCase  ,  return_labels=_UpperCAmelCase							)
     __UpperCAmelCase	:  Optional[Any]		=     model(**_UpperCAmelCase							).loss
     loss.backward()




 def     a       (			self							):
   __UpperCAmelCase,			__UpperCAmelCase	:  Dict		=     self.model_tester.prepare_config_and_inputs_for_common()

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

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




 @slow
 def     a       (			self							):
   for model_name in POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     __UpperCAmelCase	:  Dict		=     PoolFormerModel.from_pretrained(_UpperCAmelCase							)
     self.assertIsNotNone(_UpperCAmelCase							)





def  _snake_case		()							->		str:
  __UpperCAmelCase	:  Union[str, Any]		=     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
  return image







@require_torch
class      lowerCamelCase_ (			unittest.TestCase):




 @slow
 def     a       (			self							):
   __UpperCAmelCase	:  Optional[Any]		=     PoolFormerImageProcessor()
   __UpperCAmelCase	:  Tuple		=     PoolFormerForImageClassification.from_pretrained('sail/poolformer_s12'							).to(_UpperCAmelCase							)

   __UpperCAmelCase	:  Optional[Any]		=     image_processor(images=prepare_img()  ,  return_tensors='pt'							).to(_UpperCAmelCase							)

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

   # verify the logits
   __UpperCAmelCase	:  Optional[Any]		=     torch.Size((1, 1_000)							)
   self.assertEqual(outputs.logits.shape  ,  _UpperCAmelCase							)

   __UpperCAmelCase	:  Dict		=     torch.tensor([-0.6_1_1_3, 0.1_6_8_5, -0.0_4_9_2]							).to(_UpperCAmelCase							)
   self.assertTrue(torch.allclose(outputs.logits[0, :3]  ,  _UpperCAmelCase  ,  atol=1E-4							)							)









'''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


_lowerCAmelCase	:		Tuple		  =						logging.get_logger(__name__)

_lowerCAmelCase	:		Dict		  =						{
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
}

_lowerCAmelCase	:		Optional[int]		  =						{
    'vocab_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/vocab.json',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/vocab.json',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/vocab.json',
        'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json',
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/merges.txt',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/merges.txt',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/merges.txt',
        'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt',
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt'
        ),
    },
}

_lowerCAmelCase	:		Tuple		  =						{
    'roberta-base': 512,
    'roberta-large': 512,
    'roberta-large-mnli': 512,
    'distilroberta-base': 512,
    'roberta-base-openai-detector': 512,
    'roberta-large-openai-detector': 512,
}

@lru_cache()
def       __UpperCamelCase     (     )     ->   Any:




	"""simple docstring"""





	lowerCAmelCase       :     Any   				=			(
	    list(range(ord('!'					)       , ord('~'					) + 1					)					) + list(range(ord('¡'					)       , ord('¬'					) + 1					)					) + list(range(ord('®'					)       , ord('ÿ'					) + 1					)					)
	)
	lowerCAmelCase       :     Optional[int]   				=			bs[:]
	lowerCAmelCase       :     Optional[int]   				=			0
	for b in range(2**8					):
		if b not in bs:
			bs.append(_A					)
			cs.append(2**8 + n					)
			n += 1
	lowerCAmelCase       :     int   				=			[chr(_A					) for n in cs]
	return dict(zip(_A       , _A					)					)

def       __UpperCamelCase     (     _A     :					Union[str, Any]					)     ->   Any:




	"""simple docstring"""





	lowerCAmelCase       :     Tuple   				=			set()
	lowerCAmelCase       :     List[Any]   				=			word[0]
	for char in word[1:]:
		pairs.add((prev_char, char)					)
		lowerCAmelCase       :     Dict   				=			char
	return pairs






class 			lowerCAmelCase					(					a				):
	_lowerCamelCase :       int         =			VOCAB_FILES_NAMES
	_lowerCamelCase :       Any         =			PRETRAINED_VOCAB_FILES_MAP
	_lowerCamelCase :       int         =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_lowerCamelCase :       Optional[Any]         =			["""input_ids""", """attention_mask"""]



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

		# Mask token behave like a normal word, i.e. include the space before it
		lowerCAmelCase       :     List[str]   				=			AddedToken(snake_case__			,       lstrip=snake_case__			,       rstrip=snake_case__     ) if isinstance(snake_case__			,       snake_case__     ) else mask_token

		super().__init__(
		    errors=snake_case__			,       bos_token=snake_case__			,       eos_token=snake_case__			,       unk_token=snake_case__			,       sep_token=snake_case__			,       cls_token=snake_case__			,       pad_token=snake_case__			,       mask_token=snake_case__			,       add_prefix_space=snake_case__			,       **snake_case__			,       )

		with open(snake_case__			,       encoding='utf-8'     ) as vocab_handle:
			lowerCAmelCase       :     Tuple   				=			json.load(snake_case__     )
		lowerCAmelCase       :     Union[str, Any]   				=			{v: k for k, v in self.encoder.items()}
		lowerCAmelCase       :     List[str]   				=			errors  # how to handle errors in decoding
		lowerCAmelCase       :     Tuple   				=			bytes_to_unicode()
		lowerCAmelCase       :     Dict   				=			{v: k for k, v in self.byte_encoder.items()}
		with open(snake_case__			,       encoding='utf-8'     ) as merges_handle:
			lowerCAmelCase       :     List[str]   				=			merges_handle.read().split('\n'     )[1:-1]
		lowerCAmelCase       :     Tuple   				=			[tuple(merge.split()     ) for merge in bpe_merges]
		lowerCAmelCase       :     Optional[Any]   				=			dict(zip(snake_case__			,       range(len(snake_case__     )     )     )     )
		lowerCAmelCase       :     List[str]   				=			{}
		lowerCAmelCase       :     Dict   				=			add_prefix_space

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



	@property
	def 					lowercase  (       self     ):
		return len(self.encoder     )



	def 					lowercase  (       self     ):
		return dict(self.encoder			,       **self.added_tokens_encoder     )



	def 					lowercase  (       self			,       snake_case__     ):
		if token in self.cache:
			return self.cache[token]
		lowerCAmelCase       :     List[str]   				=			tuple(snake_case__     )
		lowerCAmelCase       :     Union[str, Any]   				=			get_pairs(snake_case__     )

		if not pairs:
			return token

		while True:
			lowerCAmelCase       :     str   				=			min(snake_case__			,       key=lambda snake_case__     : self.bpe_ranks.get(snake_case__			,       float('inf'     )     )     )
			if bigram not in self.bpe_ranks:
				break
			lowerCAmelCase				, lowerCAmelCase       :     List[Any]   				=			bigram
			lowerCAmelCase       :     Optional[int]   				=			[]
			lowerCAmelCase       :     Union[str, Any]   				=			0
			while i < len(snake_case__     ):
				try:
					lowerCAmelCase       :     Union[str, Any]   				=			word.index(snake_case__			,       snake_case__     )
				except ValueError:
					new_word.extend(word[i:]     )
					break
				else:
					new_word.extend(word[i:j]     )
					lowerCAmelCase       :     List[str]   				=			j

				if word[i] == first and i < len(snake_case__     ) - 1 and word[i + 1] == second:
					new_word.append(first + second     )
					i += 2
				else:
					new_word.append(word[i]     )
					i += 1
			lowerCAmelCase       :     int   				=			tuple(snake_case__     )
			lowerCAmelCase       :     List[Any]   				=			new_word
			if len(snake_case__     ) == 1:
				break
			else:
				lowerCAmelCase       :     Union[str, Any]   				=			get_pairs(snake_case__     )
		lowerCAmelCase       :     Optional[Any]   				=			' '.join(snake_case__     )
		lowerCAmelCase       :     Any   				=			word
		return word



	def 					lowercase  (       self			,       snake_case__     ):
		lowerCAmelCase       :     List[Any]   				=			[]
		for token in re.findall(self.pat			,       snake_case__     ):
			lowerCAmelCase       :     Dict   				=			''.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(snake_case__     ).split(' '     )     )
		return bpe_tokens



	def 					lowercase  (       self			,       snake_case__     ):
		return self.encoder.get(snake_case__			,       self.encoder.get(self.unk_token     )     )



	def 					lowercase  (       self			,       snake_case__     ):
		return self.decoder.get(snake_case__     )



	def 					lowercase  (       self			,       snake_case__     ):
		lowerCAmelCase       :     Union[str, Any]   				=			''.join(snake_case__     )
		lowerCAmelCase       :     Any   				=			bytearray([self.byte_decoder[c] for c in text]     ).decode('utf-8'			,       errors=self.errors     )
		return text



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

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

		lowerCAmelCase       :     List[Any]   				=			0
		with open(snake_case__			,       '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 snake_case__     : 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!'     )
					lowerCAmelCase       :     int   				=			token_index
				writer.write(' '.join(snake_case__     ) + '\n'     )
				index += 1

		return vocab_file, merge_file



	def 					lowercase  (       self			,       snake_case__			,       snake_case__ = None     ):
		if token_ids_a is None:
			return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
		lowerCAmelCase       :     Optional[Any]   				=			[self.cls_token_id]
		lowerCAmelCase       :     List[str]   				=			[self.sep_token_id]
		return cls + token_ids_a + sep + sep + token_ids_a + sep



	def 					lowercase  (       self			,       snake_case__			,       snake_case__ = None			,       snake_case__ = False     ):
		if already_has_special_tokens:
			return super().get_special_tokens_mask(
			    token_ids_a=snake_case__			,       token_ids_a=snake_case__			,       already_has_special_tokens=snake_case__     )

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



	def 					lowercase  (       self			,       snake_case__			,       snake_case__ = None     ):
		lowerCAmelCase       :     Union[str, Any]   				=			[self.sep_token_id]
		lowerCAmelCase       :     Tuple   				=			[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 					lowercase  (       self			,       snake_case__			,       snake_case__=False			,       **snake_case__     ):
		lowerCAmelCase       :     List[str]   				=			kwargs.pop('add_prefix_space'			,       self.add_prefix_space     )
		if (is_split_into_words or add_prefix_space) and (len(snake_case__     ) > 0 and not text[0].isspace()):
			lowerCAmelCase       :     Optional[Any]   				=			' ' + text
		return (text, kwargs)
