


import collections
import inspect
import unittest
from typing import Dict, List, Tuple

from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_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 MaskFormerSwinBackbone
	from transformers.models.maskformer import MaskFormerSwinModel
class 	lowerCamelCase_       :



					'''simple docstring'''







					def __init__(				self ,       snake_case_ ,       snake_case_=1_3 ,       snake_case_=3_2 ,       snake_case_=2 ,       snake_case_=3 ,       snake_case_=1_6 ,       snake_case_=[1, 2, 1] ,       snake_case_=[2, 2, 4] ,       snake_case_=2 ,       snake_case_=2.0 ,       snake_case_=True ,       snake_case_=0.0 ,       snake_case_=0.0 ,       snake_case_=0.1 ,       snake_case_="gelu" ,       snake_case_=False ,       snake_case_=True ,       snake_case_=0.0_2 ,       snake_case_=1e-5 ,       snake_case_=True ,       snake_case_=None ,       snake_case_=True ,       snake_case_=1_0 ,       snake_case_=8 ,       snake_case_=["stage1", "stage2", "stage3"] ,       snake_case_=[1, 2, 3] ,       )   ->			Any:







								'''simple docstring'''

								__lowercase 					=   parent
								__lowercase 					=   batch_size
								__lowercase 					=   image_size
								__lowercase 					=   patch_size
								__lowercase 					=   num_channels
								__lowercase 					=   embed_dim
								__lowercase 					=   depths
								__lowercase 					=   num_heads
								__lowercase 					=   window_size
								__lowercase 					=   mlp_ratio
								__lowercase 					=   qkv_bias
								__lowercase 					=   hidden_dropout_prob
								__lowercase 					=   attention_probs_dropout_prob
								__lowercase 					=   drop_path_rate
								__lowercase 					=   hidden_act
								__lowercase 					=   use_absolute_embeddings
								__lowercase 					=   patch_norm
								__lowercase 					=   layer_norm_eps
								__lowercase 					=   initializer_range
								__lowercase 					=   is_training
								__lowercase 					=   scope
								__lowercase 					=   use_labels
								__lowercase 					=   type_sequence_label_size
								__lowercase 					=   encoder_stride
								__lowercase 					=   out_features
								__lowercase 					=   out_indices






					def A      (				self						)   ->			int:







								'''simple docstring'''

								__lowercase 					=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

								__lowercase 					=   None
								if self.use_labels:
											__lowercase 					=   ids_tensor([self.batch_size] ,       self.type_sequence_label_size						)

								__lowercase 					=   self.get_config()

								return config, pixel_values, labels






					def A      (				self						)   ->			List[str]:







								'''simple docstring'''

								return MaskFormerSwinConfig(
								    image_size=self.image_size ,       patch_size=self.patch_size ,       num_channels=self.num_channels ,       embed_dim=self.embed_dim ,       depths=self.depths ,       num_heads=self.num_heads ,       window_size=self.window_size ,       mlp_ratio=self.mlp_ratio ,       qkv_bias=self.qkv_bias ,       hidden_dropout_prob=self.hidden_dropout_prob ,       attention_probs_dropout_prob=self.attention_probs_dropout_prob ,       drop_path_rate=self.drop_path_rate ,       hidden_act=self.hidden_act ,       use_absolute_embeddings=self.use_absolute_embeddings ,       path_norm=self.patch_norm ,       layer_norm_eps=self.layer_norm_eps ,       initializer_range=self.initializer_range ,       encoder_stride=self.encoder_stride ,       out_features=self.out_features ,       out_indices=self.out_indices ,       )






					def A      (				self ,       snake_case_ ,       snake_case_ ,       snake_case_						)   ->			Union[str, Any]:







								'''simple docstring'''

								__lowercase 					=   MaskFormerSwinModel(config=snake_case_						)
								model.to(snake_case_						)
								model.eval()
								__lowercase 					=   model(snake_case_						)

								__lowercase 					=   ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths						) - 1))
								__lowercase 					=   int(config.embed_dim * 2 ** (len(config.depths						) - 1)						)

								self.parent.assertEqual(result.last_hidden_state.shape ,       (self.batch_size, expected_seq_len, expected_dim)						)






					def A      (				self ,       snake_case_ ,       snake_case_ ,       snake_case_						)   ->			List[Any]:







								'''simple docstring'''

								__lowercase 					=   MaskFormerSwinBackbone(config=snake_case_						)
								model.to(snake_case_						)
								model.eval()
								__lowercase 					=   model(snake_case_						)

								# verify feature maps
								self.parent.assertEqual(len(result.feature_maps						) ,       len(config.out_features						)						)
								self.parent.assertListEqual(list(result.feature_maps[0].shape						) ,       [1_3, 1_6, 1_6, 1_6]						)

								# verify channels
								self.parent.assertEqual(len(model.channels						) ,       len(config.out_features						)						)
								self.parent.assertListEqual(model.channels ,       [1_6, 3_2, 6_4]						)

								# verify ValueError
								with self.parent.assertRaises(snake_case_						):
											__lowercase 					=   ['''stem''']
											__lowercase 					=   MaskFormerSwinBackbone(config=snake_case_						)






					def A      (				self						)   ->			List[Any]:







								'''simple docstring'''

								__lowercase 					=   self.prepare_config_and_inputs()
								__lowercase					, __lowercase					, __lowercase 					=   config_and_inputs
								__lowercase 					=   {'''pixel_values''': pixel_values}
								return config, inputs_dict




@require_torch
class 	lowerCamelCase_       (				lowerCAmelCase__     ,     lowerCAmelCase__     ,     unittest.TestCase				):



					'''simple docstring'''







					__UpperCAmelCase            =				(
					    (
					        MaskFormerSwinModel,
					        MaskFormerSwinBackbone,
					    )
					    if is_torch_available()
					    else ()
					)
					__UpperCAmelCase            =				{"feature-extraction": MaskFormerSwinModel} if is_torch_available() else {}
					__UpperCAmelCase            =				False
					__UpperCAmelCase            =				False
					__UpperCAmelCase            =				False
					__UpperCAmelCase            =				False
					__UpperCAmelCase            =				False






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







								'''simple docstring'''

								__lowercase 					=   MaskFormerSwinModelTester(self						)
								__lowercase 					=   ConfigTester(self ,       config_class=snake_case_ ,       embed_dim=3_7						)






					@require_torch_multi_gpu
					@unittest.skip(
					    reason=(
					        '''`MaskFormerSwinModel` outputs `hidden_states_spatial_dimensions` which doesn\'t work well with'''
					        ''' `nn.DataParallel`'''
					    )						)
					def A      (				self						)   ->			int:







								'''simple docstring'''

								pass






					def A      (				self						)   ->			str:







								'''simple docstring'''

								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 A      (				self						)   ->			Optional[Any]:







								'''simple docstring'''

								return






					def A      (				self						)   ->			List[str]:







								'''simple docstring'''

								__lowercase 					=   self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*snake_case_						)






					def A      (				self						)   ->			Tuple:







								'''simple docstring'''

								__lowercase 					=   self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_backbone(*snake_case_						)






					@unittest.skip('''Swin does not use inputs_embeds'''						)
					def A      (				self						)   ->			Union[str, Any]:







								'''simple docstring'''

								pass






					@unittest.skip('''Swin does not support feedforward chunking'''						)
					def A      (				self						)   ->			Tuple:







								'''simple docstring'''

								pass






					def A      (				self						)   ->			Any:







								'''simple docstring'''

								__lowercase					, __lowercase 					=   self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
											__lowercase 					=   model_class(snake_case_						)
											self.assertIsInstance(model.get_input_embeddings() ,       (nn.Module)						)
											__lowercase 					=   model.get_output_embeddings()
											self.assertTrue(x is None or isinstance(snake_case_ ,       nn.Linear						)						)






					def A      (				self						)   ->			Any:







								'''simple docstring'''

								__lowercase					, __lowercase 					=   self.model_tester.prepare_config_and_inputs_for_common()

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

											__lowercase 					=   ['''pixel_values''']
											self.assertListEqual(arg_names[:1] ,       snake_case_						)






					@unittest.skip(reason='''MaskFormerSwin is only used as backbone and doesn\'t support output_attentions'''						)
					def A      (				self						)   ->			List[Any]:







								'''simple docstring'''

								pass






					@unittest.skip(reason='''MaskFormerSwin is only used as an internal backbone'''						)
					def A      (				self						)   ->			List[Any]:







								'''simple docstring'''

								pass






					def A      (				self ,       snake_case_ ,       snake_case_ ,       snake_case_ ,       snake_case_						)   ->			List[Any]:







								'''simple docstring'''

								__lowercase 					=   model_class(snake_case_						)
								model.to(snake_case_						)
								model.eval()

								with torch.no_grad():
											__lowercase 					=   model(**self._prepare_for_class(snake_case_ ,       snake_case_						)						)

								__lowercase 					=   outputs.hidden_states

								__lowercase 					=   getattr(
								    self.model_tester ,       '''expected_num_hidden_layers''' ,       len(self.model_tester.depths						) + 1						)
								self.assertEqual(len(snake_case_						) ,       snake_case_						)

								# Swin has a different seq_length
								__lowercase 					=   (
								    config.patch_size
								    if isinstance(config.patch_size ,       collections.abc.Iterable						)
								    else (config.patch_size, config.patch_size)
								)

								__lowercase 					=   (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

								self.assertListEqual(
								    list(hidden_states[0].shape[-2:]						) ,       [num_patches, self.model_tester.embed_dim] ,       )






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







								'''simple docstring'''

								__lowercase					, __lowercase 					=   self.model_tester.prepare_config_and_inputs_for_common()

								__lowercase 					=   (
								    self.model_tester.image_size
								    if isinstance(self.model_tester.image_size ,       collections.abc.Iterable						)
								    else (self.model_tester.image_size, self.model_tester.image_size)
								)

								for model_class in self.all_model_classes:
											__lowercase 					=   True
											self.check_hidden_states_output(snake_case_ ,       snake_case_ ,       snake_case_ ,       snake_case_						)

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

											self.check_hidden_states_output(snake_case_ ,       snake_case_ ,       snake_case_ ,       snake_case_						)






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







								'''simple docstring'''

								__lowercase					, __lowercase 					=   self.model_tester.prepare_config_and_inputs_for_common()
								__lowercase 					=   3

								__lowercase 					=   (
								    self.model_tester.image_size
								    if isinstance(self.model_tester.image_size ,       collections.abc.Iterable						)
								    else (self.model_tester.image_size, self.model_tester.image_size)
								)
								__lowercase 					=   (
								    config.patch_size
								    if isinstance(config.patch_size ,       collections.abc.Iterable						)
								    else (config.patch_size, config.patch_size)
								)

								__lowercase 					=   image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
								__lowercase 					=   image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

								for model_class in self.all_model_classes:
											__lowercase 					=   True
											self.check_hidden_states_output(snake_case_ ,       snake_case_ ,       snake_case_ ,       (padded_height, padded_width)						)

											# check that output_hidden_states also work using config
											del inputs_dict["output_hidden_states"]
											__lowercase 					=   True
											self.check_hidden_states_output(snake_case_ ,       snake_case_ ,       snake_case_ ,       (padded_height, padded_width)						)






					@unittest.skip(reason='''MaskFormerSwin doesn\'t have pretrained checkpoints'''						)
					def A      (				self						)   ->			Any:







								'''simple docstring'''

								pass






					@unittest.skip(reason='''This will be fixed once MaskFormerSwin is replaced by native Swin'''						)
					def A      (				self						)   ->			Tuple:







								'''simple docstring'''

								pass






					@unittest.skip(reason='''This will be fixed once MaskFormerSwin is replaced by native Swin'''						)
					def A      (				self						)   ->			List[Any]:







								'''simple docstring'''

								pass






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







								'''simple docstring'''

								__lowercase					, __lowercase 					=   self.model_tester.prepare_config_and_inputs_for_common()

								def set_nan_tensor_to_zero(snake_case_						):
											__lowercase 					=   0
											return t

								def check_equivalence(snake_case_ ,       snake_case_ ,       snake_case_ ,       snake_case_={}						):
											with torch.no_grad():
														__lowercase 					=   model(**snake_case_ ,       return_dict=snake_case_ ,       **snake_case_						)
														__lowercase 					=   model(**snake_case_ ,       return_dict=snake_case_ ,       **snake_case_						).to_tuple()

														def recursive_check(snake_case_ ,       snake_case_						):
																	if isinstance(snake_case_ ,       (List, Tuple)						):
																				for tuple_iterable_value, dict_iterable_value in zip(snake_case_ ,       snake_case_						):
																							recursive_check(snake_case_ ,       snake_case_						)
																	elif isinstance(snake_case_ ,       snake_case_						):
																				for tuple_iterable_value, dict_iterable_value in zip(
																				    tuple_object.values() ,       dict_object.values()						):
																							recursive_check(snake_case_ ,       snake_case_						)
																	elif tuple_object is None:
																				return
																	else:
																				self.assertTrue(
																				    torch.allclose(
																				        set_nan_tensor_to_zero(snake_case_						) ,       set_nan_tensor_to_zero(snake_case_						) ,       atol=1e-5						) ,       msg=(
																				        '''Tuple and dict output are not equal. Difference:'''
																				        F' {torch.max(torch.abs(tuple_object - dict_object						)						)}. Tuple has `nan`:'
																				        F' {torch.isnan(snake_case_						).any()} and `inf`: {torch.isinf(snake_case_						)}. Dict has'
																				        F' `nan`: {torch.isnan(snake_case_						).any()} and `inf`: {torch.isinf(snake_case_						)}.'
																				    ) ,       )

														recursive_check(snake_case_ ,       snake_case_						)

								for model_class in self.all_model_classes:
											__lowercase 					=   model_class(snake_case_						)
											model.to(snake_case_						)
											model.eval()

											__lowercase 					=   self._prepare_for_class(snake_case_ ,       snake_case_						)
											__lowercase 					=   self._prepare_for_class(snake_case_ ,       snake_case_						)
											check_equivalence(snake_case_ ,       snake_case_ ,       snake_case_						)

											__lowercase 					=   self._prepare_for_class(snake_case_ ,       snake_case_ ,       return_labels=snake_case_						)
											__lowercase 					=   self._prepare_for_class(snake_case_ ,       snake_case_ ,       return_labels=snake_case_						)
											check_equivalence(snake_case_ ,       snake_case_ ,       snake_case_						)

											__lowercase 					=   self._prepare_for_class(snake_case_ ,       snake_case_						)
											__lowercase 					=   self._prepare_for_class(snake_case_ ,       snake_case_						)
											check_equivalence(snake_case_ ,       snake_case_ ,       snake_case_ ,       {'''output_hidden_states''': True}						)

											__lowercase 					=   self._prepare_for_class(snake_case_ ,       snake_case_ ,       return_labels=snake_case_						)
											__lowercase 					=   self._prepare_for_class(snake_case_ ,       snake_case_ ,       return_labels=snake_case_						)
											check_equivalence(snake_case_ ,       snake_case_ ,       snake_case_ ,       {'''output_hidden_states''': True}						)




@require_torch
class 	lowerCamelCase_       (				unittest.TestCase     ,     lowerCAmelCase__				):



					'''simple docstring'''







					__UpperCAmelCase            =				(MaskFormerSwinBackbone,) if is_torch_available() else ()
					__UpperCAmelCase            =				MaskFormerSwinConfig






					def A      (				self						)   ->			Tuple:







								'''simple docstring'''

								__lowercase 					=   MaskFormerSwinModelTester(self						)






					def A      (				self						)   ->			int:







								'''simple docstring'''

								__lowercase					, __lowercase 					=   self.model_tester.prepare_config_and_inputs_for_common()
								__lowercase 					=   inputs_dict['''pixel_values'''].shape[0]

								for backbone_class in self.all_model_classes:
											__lowercase 					=   backbone_class(snake_case_						)
											backbone.to(snake_case_						)
											backbone.eval()

											__lowercase 					=   backbone(**snake_case_						)

											# Test default outputs and verify feature maps
											self.assertIsInstance(outputs.feature_maps ,       snake_case_						)
											self.assertTrue(len(outputs.feature_maps						) == len(backbone.channels						)						)
											for feature_map, n_channels in zip(outputs.feature_maps ,       backbone.channels						):
														self.assertTrue(feature_map.shape[:2] ,       (batch_size, n_channels)						)
											self.assertIsNone(outputs.hidden_states						)
											self.assertIsNone(outputs.attentions						)

											# Test output_hidden_states=True
											__lowercase 					=   backbone(**snake_case_ ,       output_hidden_states=snake_case_						)
											self.assertIsNotNone(outputs.hidden_states						)
											self.assertTrue(len(outputs.hidden_states						) ,       len(backbone.stage_names						)						)
											# We skip the stem layer
											for hidden_states, n_channels in zip(outputs.hidden_states[1:] ,       backbone.channels						):
														for hidden_state in hidden_states:
																	# Hidden states are in the format (batch_size, (height * width), n_channels)
																	__lowercase					, __lowercase					, __lowercase 					=   hidden_state.shape
																	self.assertTrue((h_batch_size, h_n_channels) ,       (batch_size, n_channels)						)

            # Test output_attentions=True
											if self.has_attentions:
														__lowercase 					=   backbone(**snake_case_ ,       output_attentions=snake_case_						)
														self.assertIsNotNone(outputs.attentions						)






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


a : int		=				logging.get_logger(__name__)


a : Any		=				{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}

# See all LED models at https://huggingface.co/models?filter=LED
a : Dict		=				{
    '''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''',
    },
}

a : Optional[int]		=				{
    '''allenai/led-base-16384''': 16384,
}


@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def 		lowercase_  (     ):


			'''simple docstring'''







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


def 		lowercase_  (     _UpperCamelCase  ):


			'''simple docstring'''







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





class 	lowerCamelCase_       (				lowerCAmelCase__				):



					'''simple docstring'''







					__UpperCAmelCase            =				VOCAB_FILES_NAMES
					__UpperCAmelCase            =				PRETRAINED_VOCAB_FILES_MAP
					__UpperCAmelCase            =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					__UpperCAmelCase            =				["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_ ,       )   ->			List[Any]:







								'''simple docstring'''

								__lowercase 					=   AddedToken(snake_case_ ,       lstrip=snake_case_ ,       rstrip=snake_case_						) if isinstance(snake_case_ ,       snake_case_						) else bos_token
								__lowercase 					=   AddedToken(snake_case_ ,       lstrip=snake_case_ ,       rstrip=snake_case_						) if isinstance(snake_case_ ,       snake_case_						) else eos_token
								__lowercase 					=   AddedToken(snake_case_ ,       lstrip=snake_case_ ,       rstrip=snake_case_						) if isinstance(snake_case_ ,       snake_case_						) else sep_token
								__lowercase 					=   AddedToken(snake_case_ ,       lstrip=snake_case_ ,       rstrip=snake_case_						) if isinstance(snake_case_ ,       snake_case_						) else cls_token
								__lowercase 					=   AddedToken(snake_case_ ,       lstrip=snake_case_ ,       rstrip=snake_case_						) if isinstance(snake_case_ ,       snake_case_						) else unk_token
								__lowercase 					=   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
								__lowercase 					=   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:
											__lowercase 					=   json.load(snake_case_						)
								__lowercase 					=   {v: k for k, v in self.encoder.items()}
								__lowercase 					=   errors  # how to handle errors in decoding
								__lowercase 					=   bytes_to_unicode()
								__lowercase 					=   {v: k for k, v in self.byte_encoder.items()}
								with open(snake_case_ ,       encoding='''utf-8'''						) as merges_handle:
											__lowercase 					=   merges_handle.read().split('''\n'''						)[1:-1]
								__lowercase 					=   [tuple(merge.split()						) for merge in bpe_merges]
								__lowercase 					=   dict(zip(snake_case_ ,       range(len(snake_case_						)						)						)						)
								__lowercase 					=   {}
								__lowercase 					=   add_prefix_space

								# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
								__lowercase 					=   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 A      (				self						)   ->			Union[str, Any]:







								'''simple docstring'''

								return len(self.encoder						)






					def A      (				self						)   ->			str:







								'''simple docstring'''

								return dict(self.encoder ,       **self.added_tokens_encoder						)






					def A      (				self ,       snake_case_						)   ->			Optional[int]:







								'''simple docstring'''

								if token in self.cache:
											return self.cache[token]
								__lowercase 					=   tuple(snake_case_						)
								__lowercase 					=   get_pairs(snake_case_						)

								if not pairs:
											return token

								while True:
											__lowercase 					=   min(snake_case_ ,       key=lambda snake_case_						: self.bpe_ranks.get(snake_case_ ,       float('''inf'''						)						)						)
											if bigram not in self.bpe_ranks:
														break
											__lowercase					, __lowercase 					=   bigram
											__lowercase 					=   []
											__lowercase 					=   0
											while i < len(snake_case_						):
														try:
																	__lowercase 					=   word.index(snake_case_ ,       snake_case_						)
														except ValueError:
																	new_word.extend(word[i:]						)
																	break
														else:
																	new_word.extend(word[i:j]						)
																	__lowercase 					=   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
											__lowercase 					=   tuple(snake_case_						)
											__lowercase 					=   new_word
											if len(snake_case_						) == 1:
														break
											else:
														__lowercase 					=   get_pairs(snake_case_						)
								__lowercase 					=   ''' '''.join(snake_case_						)
								__lowercase 					=   word
								return word






					def A      (				self ,       snake_case_						)   ->			Tuple:







								'''simple docstring'''

								__lowercase 					=   []
								for token in re.findall(self.pat ,       snake_case_						):
											__lowercase 					=   ''''''.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 A      (				self ,       snake_case_						)   ->			List[Any]:







								'''simple docstring'''

								return self.encoder.get(snake_case_ ,       self.encoder.get(self.unk_token						)						)






					def A      (				self ,       snake_case_						)   ->			Tuple:







								'''simple docstring'''

								return self.decoder.get(snake_case_						)






					def A      (				self ,       snake_case_						)   ->			Any:







								'''simple docstring'''

								__lowercase 					=   ''''''.join(snake_case_						)
								__lowercase 					=   bytearray([self.byte_decoder[c] for c in text]						).decode('''utf-8''' ,       errors=self.errors						)
								return text






					def A      (				self ,       snake_case_ ,       snake_case_ = None						)   ->			Tuple[str]:







								'''simple docstring'''

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

								__lowercase 					=   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!'''						)
																	__lowercase 					=   token_index
														writer.write(''' '''.join(snake_case_						) + '''\n'''						)
														index += 1

								return vocab_file, merge_file






					def A      (				self ,       snake_case_ ,       snake_case_ = None						)   ->			List[int]:







								'''simple docstring'''

								if token_ids_a is None:
											return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
								__lowercase 					=   [self.cls_token_id]
								__lowercase 					=   [self.sep_token_id]
								return cls + token_ids_a + sep + sep + token_ids_a + sep






					def A      (				self ,       snake_case_ ,       snake_case_ = None ,       snake_case_ = False						)   ->			List[int]:







								'''simple docstring'''

								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 A      (				self ,       snake_case_ ,       snake_case_ = None						)   ->			List[int]:







								'''simple docstring'''

								__lowercase 					=   [self.sep_token_id]
								__lowercase 					=   [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 ,       snake_case_ ,       snake_case_=False ,       **snake_case_						)   ->			Any:







								'''simple docstring'''

								__lowercase 					=   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()):
											__lowercase 					=   ''' ''' + text
								return (text, kwargs)






					def A      (				self ,       snake_case_ ,       snake_case_ = None ,       snake_case_ = PaddingStrategy.DO_NOT_PAD ,       snake_case_ = None ,       snake_case_ = None ,       )   ->			dict:







								'''simple docstring'''

								__lowercase 					=   super()._pad(
								    encoded_inputs=snake_case_ ,       max_length=snake_case_ ,       padding_strategy=snake_case_ ,       pad_to_multiple_of=snake_case_ ,       return_attention_mask=snake_case_ ,       )

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

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

											if needs_to_be_padded:
														__lowercase 					=   len(snake_case_						) - 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`
																	__lowercase 					=   (
																	    encoded_inputs['''global_attention_mask'''] + [-1] * difference
																	)
														elif self.padding_side == "left":
																	__lowercase 					=   [-1] * difference + encoded_inputs[
																	    '''global_attention_mask'''
																	]
														else:
																	raise ValueError('''Invalid padding strategy:''' + str(self.padding_side						)						)

								return encoded_inputs


