






import copy
import inspect
import unittest

from transformers import PretrainedConfig, SwiftFormerConfig
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_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 SwiftFormerForImageClassification, SwiftFormerModel
					from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import ViTImageProcessor


class       __SCREAMING_SNAKE_CASE   :






						def __init__(		self       ,  _UpperCamelCase       ,  _UpperCamelCase=13       ,  _UpperCamelCase=3       ,  _UpperCamelCase=True       ,  _UpperCamelCase=True       ,  _UpperCamelCase=0.1       ,  _UpperCamelCase=0.1       ,  _UpperCamelCase=2_24       ,  _UpperCamelCase=10_00       ,  _UpperCamelCase=[3, 3, 6, 4]       ,  _UpperCamelCase=[48, 56, 1_12, 2_20]       ,  ):






												"""simple docstring"""
												lowerCAmelCase__        =  parent
												lowerCAmelCase__        =  batch_size
												lowerCAmelCase__        =  num_channels
												lowerCAmelCase__        =  is_training
												lowerCAmelCase__        =  use_labels
												lowerCAmelCase__        =  hidden_dropout_prob
												lowerCAmelCase__        =  attention_probs_dropout_prob
												lowerCAmelCase__        =  num_labels
												lowerCAmelCase__        =  image_size
												lowerCAmelCase__        =  layer_depths
												lowerCAmelCase__        =  embed_dims






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

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

												lowerCAmelCase__        =  self.get_config()

												return config, pixel_values, labels






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												return SwiftFormerConfig(
												    depths=self.layer_depths       ,  embed_dims=self.embed_dims       ,  mlp_ratio=4       ,  downsamples=[True, True, True, True]       ,  hidden_act='gelu'       ,  num_labels=self.num_labels       ,  down_patch_size=3       ,  down_stride=2       ,  down_pad=1       ,  drop_rate=0.0       ,  drop_path_rate=0.0       ,  use_layer_scale=_UpperCamelCase       ,  layer_scale_init_value=1E-5       ,  )






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase				):






												"""simple docstring"""
												lowerCAmelCase__        =  SwiftFormerModel(config=_UpperCamelCase				)
												model.to(_UpperCamelCase				)
												model.eval()
												lowerCAmelCase__        =  model(_UpperCamelCase				)
												self.parent.assertEqual(result.last_hidden_state.shape       ,  (self.batch_size, self.embed_dims[-1], 7, 7)				)






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase				):






												"""simple docstring"""
												lowerCAmelCase__        =  self.num_labels
												lowerCAmelCase__        =  SwiftFormerForImageClassification(_UpperCamelCase				)
												model.to(_UpperCamelCase				)
												model.eval()
												lowerCAmelCase__        =  model(_UpperCamelCase       ,  labels=_UpperCamelCase				)
												self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.num_labels)				)

												lowerCAmelCase__        =  SwiftFormerForImageClassification(_UpperCamelCase				)
												model.to(_UpperCamelCase				)
												model.eval()

												lowerCAmelCase__        =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)
												lowerCAmelCase__        =  model(_UpperCamelCase				)
												self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.num_labels)				)







						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												((lowerCAmelCase__)  ,    (lowerCAmelCase__)  ,    (lowerCAmelCase__))        =  self.prepare_config_and_inputs()
												lowerCAmelCase__        =  {'pixel_values': pixel_values}
												return config, inputs_dict




@require_torch
class       __SCREAMING_SNAKE_CASE   (				__lowercase						,   __lowercase						,   unittest.TestCase):
						_SCREAMING_SNAKE_CASE		:    List[Any]						=   (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
						_SCREAMING_SNAKE_CASE		:    List[str]						=   (
						    {'''feature-extraction''': SwiftFormerModel, '''image-classification''': SwiftFormerForImageClassification}
						    if is_torch_available()
						    else {}
						)

						_SCREAMING_SNAKE_CASE		:    Optional[int]						=   False
						_SCREAMING_SNAKE_CASE		:    List[Any]						=   False
						_SCREAMING_SNAKE_CASE		:    Optional[Any]						=   False
						_SCREAMING_SNAKE_CASE		:    List[str]						=   False
						_SCREAMING_SNAKE_CASE		:    Union[str, Any]						=   False






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  SwiftFormerModelTester(self				)
												lowerCAmelCase__        =  ConfigTester(
												    self       ,  config_class=_UpperCamelCase       ,  has_text_modality=_UpperCamelCase       ,  hidden_size=37       ,  num_attention_heads=12       ,  num_hidden_layers=12       ,  )






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												self.config_tester.run_common_tests()






						@unittest.skip(reason='SwiftFormer does not use inputs_embeds'				)
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												pass






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__  ,    lowerCAmelCase__        =  self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																		lowerCAmelCase__        =  model_class(_UpperCamelCase				)
																		lowerCAmelCase__        =  model.get_output_embeddings()
																		self.assertTrue(x is None or isinstance(_UpperCamelCase       ,  nn.Linear				)				)






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__  ,    lowerCAmelCase__        =  self.model_tester.prepare_config_and_inputs_for_common()

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

																		lowerCAmelCase__        =  ['pixel_values']
																		self.assertListEqual(arg_names[:1]       ,  _UpperCamelCase				)






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*_UpperCamelCase				)






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*_UpperCamelCase				)






						@slow
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		lowerCAmelCase__        =  SwiftFormerModel.from_pretrained(_UpperCamelCase				)
																		self.assertIsNotNone(_UpperCamelCase				)






						@unittest.skip(reason='SwiftFormer does not output attentions'				)
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												pass






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												def check_hidden_states_output(_UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase				):
																		lowerCAmelCase__        =  model_class(_UpperCamelCase				)
																		model.to(_UpperCamelCase				)
																		model.eval()

																		with torch.no_grad():
																								lowerCAmelCase__        =  model(**self._prepare_for_class(_UpperCamelCase       ,  _UpperCamelCase				)				)

																		lowerCAmelCase__        =  outputs.hidden_states

																		lowerCAmelCase__        =  8
																		self.assertEqual(len(_UpperCamelCase				)       ,  _UpperCamelCase				)  # TODO

																		# SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
																		# with the width and height being successively divided by 2, after every 2 blocks
																		for i in range(len(_UpperCamelCase				)				):
																								self.assertEqual(
																								    hidden_states[i].shape       ,  torch.Size(
																								        [
																								            self.model_tester.batch_size,
																								            self.model_tester.embed_dims[i // 2],
																								            (self.model_tester.image_size // 4) // 2 ** (i // 2),
																								            (self.model_tester.image_size // 4) // 2 ** (i // 2),
																								        ]				)       ,  )

												lowerCAmelCase__  ,    lowerCAmelCase__        =  self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																		lowerCAmelCase__        =  True
																		check_hidden_states_output(_UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase				)

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

																		check_hidden_states_output(_UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase				)






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												def _config_zero_init(_UpperCamelCase				):
																		lowerCAmelCase__        =  copy.deepcopy(_UpperCamelCase				)
																		for key in configs_no_init.__dict__.keys():
																								if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
																														setattr(_UpperCamelCase       ,  _UpperCamelCase       ,  1E-10				)
																								if isinstance(getattr(_UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase				)       ,  _UpperCamelCase				):
																														lowerCAmelCase__        =  _config_zero_init(getattr(_UpperCamelCase       ,  _UpperCamelCase				)				)
																														setattr(_UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase				)
																		return configs_no_init

												lowerCAmelCase__  ,    lowerCAmelCase__        =  self.model_tester.prepare_config_and_inputs_for_common()

												lowerCAmelCase__        =  _config_zero_init(_UpperCamelCase				)
												for model_class in self.all_model_classes:
																		lowerCAmelCase__        =  model_class(config=_UpperCamelCase				)
																		for name, param in model.named_parameters():
																								if param.requires_grad:
																														self.assertIn(
																														    ((param.data.mean() * 1E9) / 1E9).round().item()       ,  [0.0, 1.0]       ,  msg=F"Parameter {name} of model {model_class} seems not properly initialized"       ,  )







						@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'				)
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												pass







def 		_UpperCamelCase				(      )					->				Optional[int]:

						"""simple docstring"""



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


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






						@cached_property
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												return ViTImageProcessor.from_pretrained('MBZUAI/swiftformer-xs'				) if is_vision_available() else None







						@slow
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  SwiftFormerForImageClassification.from_pretrained('MBZUAI/swiftformer-xs'				).to(_UpperCamelCase				)

												lowerCAmelCase__        =  self.default_image_processor
												lowerCAmelCase__        =  prepare_img()
												lowerCAmelCase__        =  image_processor(images=_UpperCamelCase       ,  return_tensors='pt'				).to(_UpperCamelCase				)

												# forward pass
												with torch.no_grad():
																		lowerCAmelCase__        =  model(**_UpperCamelCase				)

												# verify the logits
												lowerCAmelCase__        =  torch.Size((1, 10_00)				)
												self.assertEqual(outputs.logits.shape       ,  _UpperCamelCase				)

												lowerCAmelCase__        =  torch.tensor([[-2.1703E00, 2.1107E00, -2.0811E00]]				).to(_UpperCamelCase				)
												self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,  _UpperCamelCase       ,  atol=1E-4				)				)













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

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging


__snake_case      :      Any             =       logging.get_logger(__name__)


__snake_case      :      Union[str, Any]             =       {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt"""}

# See all LED models at https://huggingface.co/models?filter=LED
__snake_case      :      Optional[Any]             =       {
    """vocab_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json""",
    },
}

__snake_case      :      List[str]             =       {
    """allenai/led-base-16384""": 1_63_84,
}







@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def 		_UpperCamelCase				(      )					->				int:

						"""simple docstring"""



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







def 		_UpperCamelCase				(      UpperCamelCase_    :   Optional[Any]						)					->				Dict:

						"""simple docstring"""



						lowerCAmelCase__        =  set()
						lowerCAmelCase__        =  word[0]
						for char in word[1:]:
												pairs.add((prev_char, char)						)
												lowerCAmelCase__        =  char
						return pairs


class       __SCREAMING_SNAKE_CASE   (				__lowercase):
						_SCREAMING_SNAKE_CASE		:    Optional[int]						=   VOCAB_FILES_NAMES
						_SCREAMING_SNAKE_CASE		:    Optional[int]						=   PRETRAINED_VOCAB_FILES_MAP
						_SCREAMING_SNAKE_CASE		:    Optional[Any]						=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_SCREAMING_SNAKE_CASE		:    Any						=   ['''input_ids''', '''attention_mask''']






						def __init__(		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase="replace"       ,  _UpperCamelCase="<s>"       ,  _UpperCamelCase="</s>"       ,  _UpperCamelCase="</s>"       ,  _UpperCamelCase="<s>"       ,  _UpperCamelCase="<unk>"       ,  _UpperCamelCase="<pad>"       ,  _UpperCamelCase="<mask>"       ,  _UpperCamelCase=False       ,  **_UpperCamelCase       ,  ):






												"""simple docstring"""
												lowerCAmelCase__        =  AddedToken(_UpperCamelCase       ,  lstrip=_UpperCamelCase       ,  rstrip=_UpperCamelCase				) if isinstance(_UpperCamelCase       ,  _UpperCamelCase				) else bos_token
												lowerCAmelCase__        =  AddedToken(_UpperCamelCase       ,  lstrip=_UpperCamelCase       ,  rstrip=_UpperCamelCase				) if isinstance(_UpperCamelCase       ,  _UpperCamelCase				) else eos_token
												lowerCAmelCase__        =  AddedToken(_UpperCamelCase       ,  lstrip=_UpperCamelCase       ,  rstrip=_UpperCamelCase				) if isinstance(_UpperCamelCase       ,  _UpperCamelCase				) else sep_token
												lowerCAmelCase__        =  AddedToken(_UpperCamelCase       ,  lstrip=_UpperCamelCase       ,  rstrip=_UpperCamelCase				) if isinstance(_UpperCamelCase       ,  _UpperCamelCase				) else cls_token
												lowerCAmelCase__        =  AddedToken(_UpperCamelCase       ,  lstrip=_UpperCamelCase       ,  rstrip=_UpperCamelCase				) if isinstance(_UpperCamelCase       ,  _UpperCamelCase				) else unk_token
												lowerCAmelCase__        =  AddedToken(_UpperCamelCase       ,  lstrip=_UpperCamelCase       ,  rstrip=_UpperCamelCase				) if isinstance(_UpperCamelCase       ,  _UpperCamelCase				) else pad_token

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

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

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

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






						@property
						# Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												return len(self.encoder				)






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												return dict(self.encoder       ,  **self.added_tokens_encoder				)






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase				):






												"""simple docstring"""
												if token in self.cache:
																		return self.cache[token]
												lowerCAmelCase__        =  tuple(_UpperCamelCase				)
												lowerCAmelCase__        =  get_pairs(_UpperCamelCase				)

												if not pairs:
																		return token

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

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






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase				):






												"""simple docstring"""
												lowerCAmelCase__        =  []
												for token in re.findall(self.pat       ,  _UpperCamelCase				):
																		lowerCAmelCase__        =  ''.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(_UpperCamelCase				).split(' '				)				)
												return bpe_tokens






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase				):






												"""simple docstring"""
												return self.encoder.get(_UpperCamelCase       ,  self.encoder.get(self.unk_token				)				)






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase				):






												"""simple docstring"""
												return self.decoder.get(_UpperCamelCase				)






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase				):






												"""simple docstring"""
												lowerCAmelCase__        =  ''.join(_UpperCamelCase				)
												lowerCAmelCase__        =  bytearray([self.byte_decoder[c] for c in text]				).decode('utf-8'       ,  errors=self.errors				)
												return text






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase = None				):






												"""simple docstring"""
												if not os.path.isdir(_UpperCamelCase				):
																		logger.error(F"Vocabulary path ({save_directory}) should be a directory"				)
																		return
												lowerCAmelCase__        =  os.path.join(
												    _UpperCamelCase       ,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']				)
												lowerCAmelCase__        =  os.path.join(
												    _UpperCamelCase       ,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']				)

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

												lowerCAmelCase__        =  0
												with open(_UpperCamelCase       ,  '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 _UpperCamelCase				: 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__        =  token_index
																								writer.write(' '.join(_UpperCamelCase				) + '\n'				)
																								index += 1

												return vocab_file, merge_file






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase = None				):






												"""simple docstring"""
												if token_ids_a is None:
																		return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
												lowerCAmelCase__        =  [self.cls_token_id]
												lowerCAmelCase__        =  [self.sep_token_id]
												return cls + token_ids_a + sep + sep + token_ids_a + sep






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase = None       ,  _UpperCamelCase = False				):






												"""simple docstring"""
												if already_has_special_tokens:
																		return super().get_special_tokens_mask(
																		    token_ids_a=_UpperCamelCase       ,  token_ids_a=_UpperCamelCase       ,  already_has_special_tokens=_UpperCamelCase				)

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






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase = None				):






												"""simple docstring"""
												lowerCAmelCase__        =  [self.sep_token_id]
												lowerCAmelCase__        =  [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  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase=False       ,  **_UpperCamelCase				):






												"""simple docstring"""
												lowerCAmelCase__        =  kwargs.pop('add_prefix_space'       ,  self.add_prefix_space				)
												if (is_split_into_words or add_prefix_space) and (len(_UpperCamelCase				) > 0 and not text[0].isspace()):
																		lowerCAmelCase__        =  ' ' + text
												return (text, kwargs)







						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase = None       ,  _UpperCamelCase = PaddingStrategy.DO_NOT_PAD       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  ):






												"""simple docstring"""
												lowerCAmelCase__        =  super()._pad(
												    encoded_inputs=_UpperCamelCase       ,  max_length=_UpperCamelCase       ,  padding_strategy=_UpperCamelCase       ,  pad_to_multiple_of=_UpperCamelCase       ,  return_attention_mask=_UpperCamelCase       ,  )

												# Load from model defaults
												if return_attention_mask is None:
																		lowerCAmelCase__        =  'attention_mask' in self.model_input_names

												if return_attention_mask and "global_attention_mask" in encoded_inputs:
																		lowerCAmelCase__        =  encoded_inputs[self.model_input_names[0]]
																		# `global_attention_mask` need to have the same length as other (sequential) inputs.
																		lowerCAmelCase__        =  len(encoded_inputs['global_attention_mask']				) != len(_UpperCamelCase				)

																		if needs_to_be_padded:
																								lowerCAmelCase__        =  len(_UpperCamelCase				) - len(encoded_inputs['global_attention_mask']				)

																								if self.padding_side == "right":
																														# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
																														lowerCAmelCase__        =  (
																														    encoded_inputs['global_attention_mask'] + [-1] * difference
																														)
																								elif self.padding_side == "left":
																														lowerCAmelCase__        =  [-1] * difference + encoded_inputs[
																														    'global_attention_mask'
																														]
																								else:
																														raise ValueError('Invalid padding strategy:' + str(self.padding_side				)				)

												return encoded_inputs





