





import inspect
import math
import tempfile
import unittest

import numpy as np

from transformers import ViTMAEConfig
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 ViTMAEForPreTraining, ViTMAEModel
							from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

							from transformers import ViTImageProcessor

class 	lowerCamelCase_    :






						'''simple docstring'''







						def __init__(	self      :			List[str]	,	__lowerCamelCase      :			Any	,	__lowerCamelCase      :			List[Any]=13	,	__lowerCamelCase      :			List[str]=30	,	__lowerCamelCase      :			Dict=2	,	__lowerCamelCase      :			Optional[Any]=3	,	__lowerCamelCase      :			List[Any]=True	,	__lowerCamelCase      :			Tuple=True	,	__lowerCamelCase      :			Union[str, Any]=32	,	__lowerCamelCase      :			List[Any]=5	,	__lowerCamelCase      :			Any=4	,	__lowerCamelCase      :			List[str]=37	,	__lowerCamelCase      :			str="gelu"	,	__lowerCamelCase      :			List[Any]=0.1	,	__lowerCamelCase      :			Optional[Any]=0.1	,	__lowerCamelCase      :			Optional[int]=10	,	__lowerCamelCase      :			Any=0.02	,	__lowerCamelCase      :			Union[str, Any]=3	,	__lowerCamelCase      :			List[Any]=0.6	,	__lowerCamelCase      :			Any=None	,	)      ->				int:
									A							:					Dict              =						parent
									A							:					Optional[int]              =						batch_size
									A							:					List[str]              =						image_size
									A							:					Dict              =						patch_size
									A							:					Dict              =						num_channels
									A							:					Dict              =						is_training
									A							:					str              =						use_labels
									A							:					Optional[int]              =						hidden_size
									A							:					Tuple              =						num_hidden_layers
									A							:					Union[str, Any]              =						num_attention_heads
									A							:					Optional[Any]              =						intermediate_size
									A							:					Dict              =						hidden_act
									A							:					str              =						hidden_dropout_prob
									A							:					int              =						attention_probs_dropout_prob
									A							:					Dict              =						type_sequence_label_size
									A							:					List[Any]              =						initializer_range
									A							:					int              =						mask_ratio
									A							:					Dict              =						scope

									# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
									# (we add 1 for the [CLS] token)
									A							:					List[str]              =						(image_size // patch_size) ** 2
									A							:					List[str]              =						int(math.ceil((1 - mask_ratio) * (num_patches + 1) ) )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[Any] )      ->				Tuple:
									A							:					Any              =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

									A							:					Union[str, Any]              =						None
									if self.use_labels:
												A							:					int              =						ids_tensor([self.batch_size]	,	self.type_sequence_label_size )

									A							:					Dict              =						self.get_config()

									return config, pixel_values, labels







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str] )      ->				List[Any]:
									return ViTMAEConfig(
									    image_size=self.image_size	,	patch_size=self.patch_size	,	num_channels=self.num_channels	,	hidden_size=self.hidden_size	,	num_hidden_layers=self.num_hidden_layers	,	num_attention_heads=self.num_attention_heads	,	intermediate_size=self.intermediate_size	,	hidden_act=self.hidden_act	,	hidden_dropout_prob=self.hidden_dropout_prob	,	attention_probs_dropout_prob=self.attention_probs_dropout_prob	,	is_decoder=__UpperCamelCase	,	initializer_range=self.initializer_range	,	mask_ratio=self.mask_ratio	,	)







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Dict	,	__lowerCamelCase      :			Optional[Any]	,	__lowerCamelCase      :			Optional[Any]	,	__lowerCamelCase      :			Union[str, Any] )      ->				int:
									A							:					Optional[int]              =						ViTMAEModel(config=__UpperCamelCase )
									model.to(__UpperCamelCase )
									model.eval()
									A							:					Optional[Any]              =						model(__UpperCamelCase )
									self.parent.assertEqual(result.last_hidden_state.shape	,	(self.batch_size, self.seq_length, self.hidden_size) )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			str	,	__lowerCamelCase      :			Union[str, Any]	,	__lowerCamelCase      :			int	,	__lowerCamelCase      :			int )      ->				Optional[Any]:
									A							:					str              =						ViTMAEForPreTraining(__UpperCamelCase )
									model.to(__UpperCamelCase )
									model.eval()
									A							:					List[str]              =						model(__UpperCamelCase )
									A							:					List[Any]              =						(self.image_size // self.patch_size) ** 2
									A							:					Tuple              =						self.patch_size**2 * self.num_channels
									self.parent.assertEqual(result.logits.shape	,	(self.batch_size, num_patches, expected_num_channels) )

									# test greyscale images
									A							:					Union[str, Any]              =						1
									A							:					Union[str, Any]              =						ViTMAEForPreTraining(__UpperCamelCase )
									model.to(__UpperCamelCase )
									model.eval()
									A							:					Optional[int]              =						floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
									A							:					List[Any]              =						model(__UpperCamelCase )
									A							:					Union[str, Any]              =						self.patch_size**2
									self.parent.assertEqual(result.logits.shape	,	(self.batch_size, num_patches, expected_num_channels) )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str] )      ->				Any:
									A							:					Optional[int]              =						self.prepare_config_and_inputs()
									A       , A       , A							:					Optional[int]              =						config_and_inputs
									A							:					Union[str, Any]              =						{"pixel_values": pixel_values}
									return config, inputs_dict







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






						'''simple docstring'''
						a__   =  (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
						a__   =  {"feature-extraction": ViTMAEModel} if is_torch_available() else {}

						a__   =  False
						a__   =  False
						a__   =  False
						a__   =  False







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Tuple )      ->				List[Any]:
									A							:					str              =						ViTMAEModelTester(self )
									A							:					List[str]              =						ConfigTester(self	,	config_class=__UpperCamelCase	,	has_text_modality=__UpperCamelCase	,	hidden_size=37 )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[int] )      ->				Dict:
									self.config_tester.run_common_tests()







						@unittest.skip(reason="ViTMAE does not use inputs_embeds" )
						def 			SCREAMING_SNAKE_CASE__		(	self      :			str )      ->				Tuple:
									pass







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Dict )      ->				Tuple:
									A       , A							:					Tuple              =						self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
												A							:					Optional[int]              =						model_class(__UpperCamelCase )
												self.assertIsInstance(model.get_input_embeddings()	,	(nn.Module) )
												A							:					Any              =						model.get_output_embeddings()
												self.assertTrue(x is None or isinstance(__UpperCamelCase	,	nn.Linear ) )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			str )      ->				List[str]:
									A       , A							:					Union[str, Any]              =						self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
												A							:					int              =						model_class(__UpperCamelCase )
												A							:					Union[str, Any]              =						inspect.signature(model.forward )
												# signature.parameters is an OrderedDict => so arg_names order is deterministic
												A							:					Dict              =						[*signature.parameters.keys()]

												A							:					List[str]              =						["pixel_values"]
												self.assertListEqual(arg_names[:1]	,	__UpperCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[Any] )      ->				List[Any]:
									A							:					Dict              =						self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*__UpperCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Tuple )      ->				Any:
									A							:					List[Any]              =						self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_pretraining(*__UpperCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any]	,	__lowerCamelCase      :			Optional[int]	,	__lowerCamelCase      :			Tuple	,	__lowerCamelCase      :			Union[str, Any] )      ->				int:
									# make masks reproducible
									np.random.seed(2 )

									A							:					Optional[int]              =						int((pt_model.config.image_size // pt_model.config.patch_size) ** 2 )
									A							:					Optional[int]              =						np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
									A							:					Union[str, Any]              =						torch.from_numpy(__UpperCamelCase )

									# Add `noise` argument.
									# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
									A							:					Dict              =						pt_noise

									super().check_pt_tf_models(__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str] )      ->				Optional[int]:
									A       , A							:					List[Any]              =						self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
												A							:					int              =						model_class(__UpperCamelCase )
												model.to(__UpperCamelCase )
												model.eval()
												# make random mask reproducible
												torch.manual_seed(2 )
												with torch.no_grad():
															A							:					Any              =						model(**self._prepare_for_class(__UpperCamelCase	,	__UpperCamelCase ) )

												A							:					Optional[Any]              =						outputs[0].cpu().numpy()
												A							:					Tuple              =						0

												with tempfile.TemporaryDirectory() as tmpdirname:
															model.save_pretrained(__UpperCamelCase )
															A							:					int              =						model_class.from_pretrained(__UpperCamelCase )
															model.to(__UpperCamelCase )
															# make random mask reproducible
															torch.manual_seed(2 )
															with torch.no_grad():
																		A							:					List[Any]              =						model(**self._prepare_for_class(__UpperCamelCase	,	__UpperCamelCase ) )

															# Make sure we don't have nans
															A							:					Optional[int]              =						after_outputs[0].cpu().numpy()
															A							:					Dict              =						0
															A							:					Any              =						np.amax(np.abs(out_a - out_a ) )
															self.assertLessEqual(__UpperCamelCase	,	1e-5 )







						@unittest.skip(
						    reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results." )
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				List[str]:
									pass







						@unittest.skip(
						    reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results." )
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				Optional[int]:
									pass







						@unittest.skip(
						    reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results." )
						def 			SCREAMING_SNAKE_CASE__		(	self      :			str )      ->				Any:
									pass







						@unittest.skip(reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load" )
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[Any] )      ->				List[str]:
									pass







						@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
						def 			SCREAMING_SNAKE_CASE__		(	self      :			str )      ->				List[Any]:
									pass







						@slow
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				List[Any]:
									for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												A							:					int              =						ViTMAEModel.from_pretrained(__UpperCamelCase )
												self.assertIsNotNone(__UpperCamelCase )







def 	UpperCAmelCase   (						):
			A							:					Dict              =						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
			return image






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






						'''simple docstring'''







						@cached_property
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				Tuple:
									return ViTImageProcessor.from_pretrained("facebook/vit-mae-base" ) if is_vision_available() else None







						@slow
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[int] )      ->				Optional[int]:
									# make random mask reproducible across the PT and TF model
									np.random.seed(2 )

									A							:					Optional[Any]              =						ViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base" ).to(__UpperCamelCase )

									A							:					Union[str, Any]              =						self.default_image_processor
									A							:					int              =						prepare_img()
									A							:					Any              =						image_processor(images=__UpperCamelCase	,	return_tensors="pt" ).to(__UpperCamelCase )

									# prepare a noise vector that will be also used for testing the TF model
									# (this way we can ensure that the PT and TF models operate on the same inputs)
									A							:					Any              =						ViTMAEConfig()
									A							:					List[Any]              =						int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2 )
									A							:					Optional[Any]              =						np.random.uniform(size=(1, num_patches) )

									# forward pass
									with torch.no_grad():
												A							:					Optional[Any]              =						model(**__UpperCamelCase	,	noise=torch.from_numpy(__UpperCamelCase ).to(device=__UpperCamelCase ) )

									# verify the logits
									A							:					int              =						torch.Size((1, 1_96, 7_68) )
									self.assertEqual(outputs.logits.shape	,	__UpperCamelCase )

									A							:					Any              =						torch.tensor(
									    [[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]] )

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




import json
from typing import Dict, List, Optional, Tuple, Union

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer


__SCREAMING_SNAKE_CASE           =  logging.get_logger(__name__)


__SCREAMING_SNAKE_CASE           =  {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

__SCREAMING_SNAKE_CASE           =  {
    """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""",
    },
}

__SCREAMING_SNAKE_CASE           =  {
    """allenai/led-base-16384""": 16384,
}

class 	lowerCamelCase_    ( _A						):






						'''simple docstring'''
						a__   =  VOCAB_FILES_NAMES
						a__   =  PRETRAINED_VOCAB_FILES_MAP
						a__   =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						a__   =  LEDTokenizer
						a__   =  ["input_ids", "attention_mask"]







						def __init__(	self      :			int	,	__lowerCamelCase      :			List[str]=None	,	__lowerCamelCase      :			Optional[int]=None	,	__lowerCamelCase      :			Optional[int]=None	,	__lowerCamelCase      :			str="replace"	,	__lowerCamelCase      :			Optional[int]="<s>"	,	__lowerCamelCase      :			str="</s>"	,	__lowerCamelCase      :			Any="</s>"	,	__lowerCamelCase      :			Dict="<s>"	,	__lowerCamelCase      :			Optional[int]="<unk>"	,	__lowerCamelCase      :			Union[str, Any]="<pad>"	,	__lowerCamelCase      :			Dict="<mask>"	,	__lowerCamelCase      :			Union[str, Any]=False	,	__lowerCamelCase      :			str=True	,	**__lowerCamelCase      :			Union[str, Any]	,	)      ->				Optional[int]:
									super().__init__(
									    __lowerCamelCase	,	__lowerCamelCase	,	tokenizer_file=__lowerCamelCase	,	errors=__lowerCamelCase	,	bos_token=__lowerCamelCase	,	eos_token=__lowerCamelCase	,	sep_token=__lowerCamelCase	,	cls_token=__lowerCamelCase	,	unk_token=__lowerCamelCase	,	pad_token=__lowerCamelCase	,	mask_token=__lowerCamelCase	,	add_prefix_space=__lowerCamelCase	,	trim_offsets=__lowerCamelCase	,	**__lowerCamelCase	,	)

									A							:					Union[str, Any]              =						json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
									if pre_tok_state.get("add_prefix_space"	,	__lowerCamelCase ) != add_prefix_space:
												A							:					Any              =						getattr(__lowerCamelCase	,	pre_tok_state.pop("type" ) )
												A							:					Any              =						add_prefix_space
												A							:					Tuple              =						pre_tok_class(**__lowerCamelCase )

									A							:					str              =						add_prefix_space

									# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
									A							:					List[str]              =						"post_processor"
									A							:					Union[str, Any]              =						getattr(self.backend_tokenizer	,	__lowerCamelCase	,	__lowerCamelCase )
									if tokenizer_component_instance:
												A							:					Dict              =						json.loads(tokenizer_component_instance.__getstate__() )

												# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
												if "sep" in state:
															A							:					Union[str, Any]              =						tuple(state["sep"] )
												if "cls" in state:
															A							:					str              =						tuple(state["cls"] )

												A							:					int              =						False

												if state.get("add_prefix_space"	,	__lowerCamelCase ) != add_prefix_space:
															A							:					List[Any]              =						add_prefix_space
															A							:					Dict              =						True

												if state.get("trim_offsets"	,	__lowerCamelCase ) != trim_offsets:
															A							:					Dict              =						trim_offsets
															A							:					str              =						True

												if changes_to_apply:
															A							:					int              =						getattr(__lowerCamelCase	,	state.pop("type" ) )
															A							:					Dict              =						component_class(**__lowerCamelCase )
															setattr(self.backend_tokenizer	,	__lowerCamelCase	,	__lowerCamelCase )







						@property
						# Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[Any] )      ->				str:
									if self._mask_token is None:
												if self.verbose:
															logger.error("Using mask_token, but it is not set yet." )
												return None
									return str(self._mask_token )







						@mask_token.setter
						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str]	,	__lowerCamelCase      :			Any )      ->				Dict:
									A							:					Tuple              =						AddedToken(__lowerCamelCase	,	lstrip=__lowerCamelCase	,	rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase	,	__lowerCamelCase ) else value
									A							:					Tuple              =						value







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[Any]	,	*__lowerCamelCase      :			Optional[Any]	,	**__lowerCamelCase      :			List[str] )      ->				BatchEncoding:
									A							:					List[str]              =						kwargs.get("is_split_into_words"	,	__lowerCamelCase )

									if is_split_into_words and not self.add_prefix_space:
												raise ValueError(
												    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
												    "to use it with pretokenized inputs." )

									return super()._batch_encode_plus(*__lowerCamelCase	,	**__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Dict	,	*__lowerCamelCase      :			str	,	**__lowerCamelCase      :			Optional[Any] )      ->				BatchEncoding:
									A							:					List[str]              =						kwargs.get("is_split_into_words"	,	__lowerCamelCase )

									if is_split_into_words and not self.add_prefix_space:
												raise ValueError(
												    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
												    "to use it with pretokenized inputs." )

									return super()._encode_plus(*__lowerCamelCase	,	**__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str]	,	__lowerCamelCase      :			str	,	__lowerCamelCase      :			Optional[str] = None )      ->				Tuple[str]:
									A							:					Optional[Any]              =						self._tokenizer.model.save(__lowerCamelCase	,	name=__lowerCamelCase )
									return tuple(__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Dict	,	__lowerCamelCase      :			List[Any]	,	__lowerCamelCase      :			List[Any]=None )      ->				List[str]:
									A							:					Optional[int]              =						[self.bos_token_id] + token_ids_a + [self.eos_token_id]
									if token_ids_a is None:
												return output

									return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str]	,	__lowerCamelCase      :			List[int]	,	__lowerCamelCase      :			Optional[List[int]] = None )      ->				List[int]:
									A							:					str              =						[self.sep_token_id]
									A							:					int              =						[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 			SCREAMING_SNAKE_CASE__		(	self      :			str	,	__lowerCamelCase      :			Union[Dict[str, EncodedInput], BatchEncoding]	,	__lowerCamelCase      :			Optional[int] = None	,	__lowerCamelCase      :			PaddingStrategy = PaddingStrategy.DO_NOT_PAD	,	__lowerCamelCase      :			Optional[int] = None	,	__lowerCamelCase      :			Optional[bool] = None	,	)      ->				dict:
									A							:					Dict              =						super()._pad(
									    encoded_inputs=__lowerCamelCase	,	max_length=__lowerCamelCase	,	padding_strategy=__lowerCamelCase	,	pad_to_multiple_of=__lowerCamelCase	,	return_attention_mask=__lowerCamelCase	,	)

									# Load from model defaults
									if return_attention_mask is None:
												A							:					List[Any]              =						"attention_mask" in self.model_input_names

									if return_attention_mask and "global_attention_mask" in encoded_inputs:
												A							:					Optional[int]              =						encoded_inputs[self.model_input_names[0]]
												# `global_attention_mask` need to have the same length as other (sequential) inputs.
												A							:					Tuple              =						len(encoded_inputs["global_attention_mask"] ) != len(__lowerCamelCase )

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

									return encoded_inputs