import copy
import unittest

from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, 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, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import (
							    MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
							    MODEL_FOR_QUESTION_ANSWERING_MAPPING,
							    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
							    MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
							    LayoutLMvaConfig,
							    LayoutLMvaForQuestionAnswering,
							    LayoutLMvaForSequenceClassification,
							    LayoutLMvaForTokenClassification,
							    LayoutLMvaModel,
							)
							from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
							from PIL import Image

							from transformers import LayoutLMvaImageProcessor


class   lowercase      :




							def __init__(  self				:   List[str]		,							_UpperCamelCase				:   List[str]		,							_UpperCamelCase				:   List[Any]=2		,							_UpperCamelCase				:   Optional[Any]=3		,							_UpperCamelCase				:   Tuple=4		,							_UpperCamelCase				:   Union[str, Any]=2		,							_UpperCamelCase				:   int=7		,							_UpperCamelCase				:   Optional[int]=True		,							_UpperCamelCase				:   Tuple=True		,							_UpperCamelCase				:   List[str]=True		,							_UpperCamelCase				:   Union[str, Any]=True		,							_UpperCamelCase				:   int=99		,							_UpperCamelCase				:   List[Any]=36		,							_UpperCamelCase				:   str=3		,							_UpperCamelCase				:   Optional[Any]=4		,							_UpperCamelCase				:   Optional[Any]=37		,							_UpperCamelCase				:   Tuple="gelu"		,							_UpperCamelCase				:   str=0.1		,							_UpperCamelCase				:   List[str]=0.1		,							_UpperCamelCase				:   List[str]=512		,							_UpperCamelCase				:   int=16		,							_UpperCamelCase				:   Union[str, Any]=2		,							_UpperCamelCase				:   Optional[int]=0.0_2		,							_UpperCamelCase				:   Optional[Any]=6		,							_UpperCamelCase				:   Optional[Any]=6		,							_UpperCamelCase				:   str=3		,							_UpperCamelCase				:   List[str]=4		,							_UpperCamelCase				:   str=None		,							_UpperCamelCase				:   Tuple=1_000		,							)   ->			List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    parent
												SCREAMING_SNAKE_CASE           =    batch_size
												SCREAMING_SNAKE_CASE           =    num_channels
												SCREAMING_SNAKE_CASE           =    image_size
												SCREAMING_SNAKE_CASE           =    patch_size
												SCREAMING_SNAKE_CASE           =    text_seq_length
												SCREAMING_SNAKE_CASE           =    is_training
												SCREAMING_SNAKE_CASE           =    use_input_mask
												SCREAMING_SNAKE_CASE           =    use_token_type_ids
												SCREAMING_SNAKE_CASE           =    use_labels
												SCREAMING_SNAKE_CASE           =    vocab_size
												SCREAMING_SNAKE_CASE           =    hidden_size
												SCREAMING_SNAKE_CASE           =    num_hidden_layers
												SCREAMING_SNAKE_CASE           =    num_attention_heads
												SCREAMING_SNAKE_CASE           =    intermediate_size
												SCREAMING_SNAKE_CASE           =    hidden_act
												SCREAMING_SNAKE_CASE           =    hidden_dropout_prob
												SCREAMING_SNAKE_CASE           =    attention_probs_dropout_prob
												SCREAMING_SNAKE_CASE           =    max_position_embeddings
												SCREAMING_SNAKE_CASE           =    type_vocab_size
												SCREAMING_SNAKE_CASE           =    type_sequence_label_size
												SCREAMING_SNAKE_CASE           =    initializer_range
												SCREAMING_SNAKE_CASE           =    coordinate_size
												SCREAMING_SNAKE_CASE           =    shape_size
												SCREAMING_SNAKE_CASE           =    num_labels
												SCREAMING_SNAKE_CASE           =    num_choices
												SCREAMING_SNAKE_CASE           =    scope
												SCREAMING_SNAKE_CASE           =    range_bbox

												# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
												SCREAMING_SNAKE_CASE           =    text_seq_length
												SCREAMING_SNAKE_CASE           =    (image_size // patch_size) ** 2 + 1
												SCREAMING_SNAKE_CASE           =    self.text_seq_length + self.image_seq_length




							def 		__snake_case(  self				:   Tuple )   ->			Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    ids_tensor([self.batch_size, self.text_seq_length]		,							self.vocab_size )

												SCREAMING_SNAKE_CASE           =    ids_tensor([self.batch_size, self.text_seq_length, 4]		,							self.range_bbox )
												# Ensure that bbox is legal
												for i in range(bbox.shape[0] ):
																	for j in range(bbox.shape[1] ):
																						if bbox[i, j, 3] < bbox[i, j, 1]:
																											SCREAMING_SNAKE_CASE           =    bbox[i, j, 3]
																											SCREAMING_SNAKE_CASE           =    bbox[i, j, 1]
																											SCREAMING_SNAKE_CASE           =    t
																						if bbox[i, j, 2] < bbox[i, j, 0]:
																											SCREAMING_SNAKE_CASE           =    bbox[i, j, 2]
																											SCREAMING_SNAKE_CASE           =    bbox[i, j, 0]
																											SCREAMING_SNAKE_CASE           =    t

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

												SCREAMING_SNAKE_CASE           =    None
												if self.use_input_mask:
																	SCREAMING_SNAKE_CASE           =    random_attention_mask([self.batch_size, self.text_seq_length] )

												SCREAMING_SNAKE_CASE           =    None
												if self.use_token_type_ids:
																	SCREAMING_SNAKE_CASE           =    ids_tensor([self.batch_size, self.text_seq_length]		,							self.type_vocab_size )

												SCREAMING_SNAKE_CASE           =    None
												SCREAMING_SNAKE_CASE           =    None
												if self.use_labels:
																	SCREAMING_SNAKE_CASE           =    ids_tensor([self.batch_size]		,							self.type_sequence_label_size )
																	SCREAMING_SNAKE_CASE           =    ids_tensor([self.batch_size, self.text_seq_length]		,							self.num_labels )

												SCREAMING_SNAKE_CASE           =    LayoutLMvaConfig(
												    vocab_size=self.vocab_size		,							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		,							max_position_embeddings=self.max_position_embeddings		,							type_vocab_size=self.type_vocab_size		,							initializer_range=self.initializer_range		,							coordinate_size=self.coordinate_size		,							shape_size=self.shape_size		,							input_size=self.image_size		,							patch_size=self.patch_size		,							)

												return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels




							def 		__snake_case(  self				:   int		,							_UpperCamelCase				:   Any		,							_UpperCamelCase				:   Dict		,							_UpperCamelCase				:   Union[str, Any]		,							_UpperCamelCase				:   Dict		,							_UpperCamelCase				:   Union[str, Any]		,							_UpperCamelCase				:   Any		,							_UpperCamelCase				:   Any		,							_UpperCamelCase				:   Dict )   ->			Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    LayoutLMvaModel(config=_UpperCamelCase )
												model.to(_UpperCamelCase )
												model.eval()

												# text + image
												SCREAMING_SNAKE_CASE           =    model(_UpperCamelCase		,							pixel_values=_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    model(
												    _UpperCamelCase		,							bbox=_UpperCamelCase		,							pixel_values=_UpperCamelCase		,							attention_mask=_UpperCamelCase		,							token_type_ids=_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    model(_UpperCamelCase		,							bbox=_UpperCamelCase		,							pixel_values=_UpperCamelCase		,							token_type_ids=_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    model(_UpperCamelCase		,							bbox=_UpperCamelCase		,							pixel_values=_UpperCamelCase )

												self.parent.assertEqual(result.last_hidden_state.shape		,							(self.batch_size, self.seq_length, self.hidden_size) )

												# text only
												SCREAMING_SNAKE_CASE           =    model(_UpperCamelCase )
												self.parent.assertEqual(
												    result.last_hidden_state.shape		,							(self.batch_size, self.text_seq_length, self.hidden_size) )

												# image only
												SCREAMING_SNAKE_CASE           =    model(pixel_values=_UpperCamelCase )
												self.parent.assertEqual(
												    result.last_hidden_state.shape		,							(self.batch_size, self.image_seq_length, self.hidden_size) )




							def 		__snake_case(  self				:   Any		,							_UpperCamelCase				:   Dict		,							_UpperCamelCase				:   List[str]		,							_UpperCamelCase				:   List[Any]		,							_UpperCamelCase				:   int		,							_UpperCamelCase				:   Optional[Any]		,							_UpperCamelCase				:   Optional[int]		,							_UpperCamelCase				:   Tuple		,							_UpperCamelCase				:   Union[str, Any] )   ->			Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.num_labels
												SCREAMING_SNAKE_CASE           =    LayoutLMvaForSequenceClassification(_UpperCamelCase )
												model.to(_UpperCamelCase )
												model.eval()
												SCREAMING_SNAKE_CASE           =    model(
												    _UpperCamelCase		,							bbox=_UpperCamelCase		,							pixel_values=_UpperCamelCase		,							attention_mask=_UpperCamelCase		,							token_type_ids=_UpperCamelCase		,							labels=_UpperCamelCase		,							)
												self.parent.assertEqual(result.logits.shape		,							(self.batch_size, self.num_labels) )




							def 		__snake_case(  self				:   str		,							_UpperCamelCase				:   Any		,							_UpperCamelCase				:   Tuple		,							_UpperCamelCase				:   Optional[int]		,							_UpperCamelCase				:   Union[str, Any]		,							_UpperCamelCase				:   int		,							_UpperCamelCase				:   Dict		,							_UpperCamelCase				:   int		,							_UpperCamelCase				:   Optional[Any] )   ->			Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.num_labels
												SCREAMING_SNAKE_CASE           =    LayoutLMvaForTokenClassification(config=_UpperCamelCase )
												model.to(_UpperCamelCase )
												model.eval()
												SCREAMING_SNAKE_CASE           =    model(
												    _UpperCamelCase		,							bbox=_UpperCamelCase		,							pixel_values=_UpperCamelCase		,							attention_mask=_UpperCamelCase		,							token_type_ids=_UpperCamelCase		,							labels=_UpperCamelCase		,							)
												self.parent.assertEqual(result.logits.shape		,							(self.batch_size, self.text_seq_length, self.num_labels) )




							def 		__snake_case(  self				:   List[str]		,							_UpperCamelCase				:   str		,							_UpperCamelCase				:   Dict		,							_UpperCamelCase				:   Tuple		,							_UpperCamelCase				:   Tuple		,							_UpperCamelCase				:   Dict		,							_UpperCamelCase				:   Dict		,							_UpperCamelCase				:   int		,							_UpperCamelCase				:   Optional[Any] )   ->			Optional[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    LayoutLMvaForQuestionAnswering(config=_UpperCamelCase )
												model.to(_UpperCamelCase )
												model.eval()
												SCREAMING_SNAKE_CASE           =    model(
												    _UpperCamelCase		,							bbox=_UpperCamelCase		,							pixel_values=_UpperCamelCase		,							attention_mask=_UpperCamelCase		,							token_type_ids=_UpperCamelCase		,							start_positions=_UpperCamelCase		,							end_positions=_UpperCamelCase		,							)
												self.parent.assertEqual(result.start_logits.shape		,							(self.batch_size, self.seq_length) )
												self.parent.assertEqual(result.end_logits.shape		,							(self.batch_size, self.seq_length) )





							def 		__snake_case(  self				:   List[Any] )   ->			Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.prepare_config_and_inputs()
												(
												    (
												    SCREAMING_SNAKE_CASE
												)     ,				(
												    SCREAMING_SNAKE_CASE
												)     ,				(
												    SCREAMING_SNAKE_CASE
												)     ,				(
												    SCREAMING_SNAKE_CASE
												)     ,				(
												    SCREAMING_SNAKE_CASE
												)     ,				(
												    SCREAMING_SNAKE_CASE
												)     ,				(
												    SCREAMING_SNAKE_CASE
												)     ,				(
												    SCREAMING_SNAKE_CASE
												)     ,				
												)           =    config_and_inputs
												SCREAMING_SNAKE_CASE           =    {
												    "input_ids": input_ids,
												    "bbox": bbox,
												    "pixel_values": pixel_values,
												    "token_type_ids": token_type_ids,
												    "attention_mask": input_mask,
												}
												return config, inputs_dict



@require_torch
class   lowercase      (       a			,    a			,    unittest.TestCase       ):
							lowercase__  :    Union[str, Any]									=							False
							lowercase__  :    List[Any]									=							False
							lowercase__  :    str									=							False

							lowercase__  :    int									=							(
							    (
							        LayoutLMvaModel,
							        LayoutLMvaForSequenceClassification,
							        LayoutLMvaForTokenClassification,
							        LayoutLMvaForQuestionAnswering,
							    )
							    if is_torch_available()
							    else ()
							)
							lowercase__  :    List[Any]									=							(
							    {"""document-question-answering""": LayoutLMvaForQuestionAnswering, """feature-extraction""": LayoutLMvaModel}
							    if is_torch_available()
							    else {}
							)




							def 		__snake_case(  self				:   Optional[Any]		,							_UpperCamelCase				:   int		,							_UpperCamelCase				:   int		,							_UpperCamelCase				:   List[str]		,							_UpperCamelCase				:   Dict		,							_UpperCamelCase				:   List[str] )   ->			Any:



												'''simple docstring'''

												return True




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    LayoutLMvaModelTester(self )
												SCREAMING_SNAKE_CASE           =    ConfigTester(self		,							config_class=_UpperCamelCase		,							hidden_size=37 )




							def 		__snake_case(  self				:   Optional[int]		,							_UpperCamelCase				:   Optional[int]		,							_UpperCamelCase				:   Union[str, Any]		,							_UpperCamelCase				:   Union[str, Any]=False )   ->			List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    copy.deepcopy(_UpperCamelCase )
												if model_class in get_values(_UpperCamelCase ):
																	SCREAMING_SNAKE_CASE           =    {
																	    k: v.unsqueeze(1 ).expand(-1		,							self.model_tester.num_choices		,							-1 ).contiguous()
																	    if isinstance(_UpperCamelCase		,							torch.Tensor ) and v.ndim > 1
																	    else v
																	    for k, v in inputs_dict.items()
																	}
												if return_labels:
																	if model_class in get_values(_UpperCamelCase ):
																						SCREAMING_SNAKE_CASE           =    torch.ones(self.model_tester.batch_size		,							dtype=torch.long		,							device=_UpperCamelCase )
																	elif model_class in get_values(_UpperCamelCase ):
																						SCREAMING_SNAKE_CASE           =    torch.zeros(
																						    self.model_tester.batch_size		,							dtype=torch.long		,							device=_UpperCamelCase )
																						SCREAMING_SNAKE_CASE           =    torch.zeros(
																						    self.model_tester.batch_size		,							dtype=torch.long		,							device=_UpperCamelCase )
																	elif model_class in [
																	    *get_values(_UpperCamelCase ),
																	]:
																						SCREAMING_SNAKE_CASE           =    torch.zeros(
																						    self.model_tester.batch_size		,							dtype=torch.long		,							device=_UpperCamelCase )
																	elif model_class in [
																	    *get_values(_UpperCamelCase ),
																	]:
																						SCREAMING_SNAKE_CASE           =    torch.zeros(
																						    (self.model_tester.batch_size, self.model_tester.text_seq_length)		,							dtype=torch.long		,							device=_UpperCamelCase		,							)

												return inputs_dict




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



												'''simple docstring'''

												self.config_tester.run_common_tests()




							def 		__snake_case(  self				:   str )   ->			Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*_UpperCamelCase )




							def 		__snake_case(  self				:   Optional[int] )   ->			List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.model_tester.prepare_config_and_inputs()
												for type in ["absolute", "relative_key", "relative_key_query"]:
																	SCREAMING_SNAKE_CASE           =    type
																	self.model_tester.create_and_check_model(*_UpperCamelCase )




							def 		__snake_case(  self				:   int )   ->			Any:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_sequence_classification(*_UpperCamelCase )




							def 		__snake_case(  self				:   int )   ->			str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_token_classification(*_UpperCamelCase )




							def 		__snake_case(  self				:   Dict )   ->			List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_question_answering(*_UpperCamelCase )





							@slow
							def 		__snake_case(  self				:   Union[str, Any] )   ->			List[str]:



												'''simple docstring'''

												for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	SCREAMING_SNAKE_CASE           =    LayoutLMvaModel.from_pretrained(_UpperCamelCase )
																	self.assertIsNotNone(_UpperCamelCase )





def 		__lowerCamelCase							():
					SCREAMING_SNAKE_CASE           =    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"					)
					return image


@require_torch
class   lowercase      (       unittest.TestCase       ):




							@cached_property
							def 		__snake_case(  self				:   Optional[int] )   ->			int:



												'''simple docstring'''

												return LayoutLMvaImageProcessor(apply_ocr=_UpperCamelCase ) if is_vision_available() else None





							@slow
							def 		__snake_case(  self				:   Dict )   ->			Union[str, Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    LayoutLMvaModel.from_pretrained("microsoft/layoutlmv3-base" ).to(_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    self.default_image_processor
												SCREAMING_SNAKE_CASE           =    prepare_img()
												SCREAMING_SNAKE_CASE           =    image_processor(images=_UpperCamelCase		,							return_tensors="pt" ).pixel_values.to(_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    torch.tensor([[1, 2]] )
												SCREAMING_SNAKE_CASE           =    torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]] ).unsqueeze(0 )

												# forward pass
												SCREAMING_SNAKE_CASE           =    model(
												    input_ids=input_ids.to(_UpperCamelCase )		,							bbox=bbox.to(_UpperCamelCase )		,							pixel_values=pixel_values.to(_UpperCamelCase )		,							)

												# verify the logits
												SCREAMING_SNAKE_CASE           =    torch.Size((1, 199, 768) )
												self.assertEqual(outputs.last_hidden_state.shape		,							_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    torch.tensor(
												    [[-0.0_5_2_9, 0.3_6_1_8, 0.1_6_3_2], [-0.1_5_8_7, -0.1_6_6_7, -0.0_4_0_0], [-0.1_5_5_7, -0.1_6_7_1, -0.0_5_0_5]] ).to(_UpperCamelCase )

												self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]		,							_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


_lowerCamelCase   :				Union[str, Any]     			=						logging.get_logger(__name__)


_lowerCamelCase   :				str     			=						{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

_lowerCamelCase   :				int     			=						{
    '''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''',
    },
}

_lowerCamelCase   :				List[Any]     			=						{
    '''allenai/led-base-16384''': 1_63_84,
}


class   lowercase      (       a       ):
							lowercase__  :    Tuple									=							VOCAB_FILES_NAMES
							lowercase__  :    Any									=							PRETRAINED_VOCAB_FILES_MAP
							lowercase__  :    Optional[Any]									=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase__  :    Optional[Any]									=							LEDTokenizer
							lowercase__  :    str									=							["""input_ids""", """attention_mask"""]




							def __init__(  self				:   Union[str, Any]		,							_UpperCamelCase				:   Tuple=None		,							_UpperCamelCase				:   str=None		,							_UpperCamelCase				:   Tuple=None		,							_UpperCamelCase				:   List[str]="replace"		,							_UpperCamelCase				:   str="<s>"		,							_UpperCamelCase				:   List[Any]="</s>"		,							_UpperCamelCase				:   List[Any]="</s>"		,							_UpperCamelCase				:   List[str]="<s>"		,							_UpperCamelCase				:   Tuple="<unk>"		,							_UpperCamelCase				:   List[Any]="<pad>"		,							_UpperCamelCase				:   Tuple="<mask>"		,							_UpperCamelCase				:   List[str]=False		,							_UpperCamelCase				:   List[Any]=True		,							**_UpperCamelCase				:   Optional[Any]		,							)   ->			Tuple:



												'''simple docstring'''

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

												SCREAMING_SNAKE_CASE           =    json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
												if pre_tok_state.get("add_prefix_space"		,							_UpperCamelCase ) != add_prefix_space:
																	SCREAMING_SNAKE_CASE           =    getattr(_UpperCamelCase		,							pre_tok_state.pop("type" ) )
																	SCREAMING_SNAKE_CASE           =    add_prefix_space
																	SCREAMING_SNAKE_CASE           =    pre_tok_class(**_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    add_prefix_space

												# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
												SCREAMING_SNAKE_CASE           =    "post_processor"
												SCREAMING_SNAKE_CASE           =    getattr(self.backend_tokenizer		,							_UpperCamelCase		,							_UpperCamelCase )
												if tokenizer_component_instance:
																	SCREAMING_SNAKE_CASE           =    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:
																						SCREAMING_SNAKE_CASE           =    tuple(state["sep"] )
																	if "cls" in state:
																						SCREAMING_SNAKE_CASE           =    tuple(state["cls"] )

																	SCREAMING_SNAKE_CASE           =    False

																	if state.get("add_prefix_space"		,							_UpperCamelCase ) != add_prefix_space:
																						SCREAMING_SNAKE_CASE           =    add_prefix_space
																						SCREAMING_SNAKE_CASE           =    True

																	if state.get("trim_offsets"		,							_UpperCamelCase ) != trim_offsets:
																						SCREAMING_SNAKE_CASE           =    trim_offsets
																						SCREAMING_SNAKE_CASE           =    True

																	if changes_to_apply:
																						SCREAMING_SNAKE_CASE           =    getattr(_UpperCamelCase		,							state.pop("type" ) )
																						SCREAMING_SNAKE_CASE           =    component_class(**_UpperCamelCase )
																						setattr(self.backend_tokenizer		,							_UpperCamelCase		,							_UpperCamelCase )




							@property
							# Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
							def 		__snake_case(  self				:   int )   ->			str:



												'''simple docstring'''

												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 		__snake_case(  self				:   Optional[int]		,							_UpperCamelCase				:   Optional[int] )   ->			Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    AddedToken(_UpperCamelCase		,							lstrip=_UpperCamelCase		,							rstrip=_UpperCamelCase ) if isinstance(_UpperCamelCase		,							_UpperCamelCase ) else value
												SCREAMING_SNAKE_CASE           =    value




							def 		__snake_case(  self				:   List[Any]		,							*_UpperCamelCase				:   Optional[int]		,							**_UpperCamelCase				:   Union[str, Any] )   ->			BatchEncoding:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    kwargs.get("is_split_into_words"		,							_UpperCamelCase )

												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(*_UpperCamelCase		,							**_UpperCamelCase )




							def 		__snake_case(  self				:   int		,							*_UpperCamelCase				:   Dict		,							**_UpperCamelCase				:   Tuple )   ->			BatchEncoding:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    kwargs.get("is_split_into_words"		,							_UpperCamelCase )

												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(*_UpperCamelCase		,							**_UpperCamelCase )




							def 		__snake_case(  self				:   List[Any]		,							_UpperCamelCase				:   str		,							_UpperCamelCase				:   Optional[str] = None )   ->			Tuple[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self._tokenizer.model.save(_UpperCamelCase		,							name=_UpperCamelCase )
												return tuple(_UpperCamelCase )




							def 		__snake_case(  self				:   List[Any]		,							_UpperCamelCase				:   List[Any]		,							_UpperCamelCase				:   int=None )   ->			List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    [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 		__snake_case(  self				:   Dict		,							_UpperCamelCase				:   List[int]		,							_UpperCamelCase				:   Optional[List[int]] = None )   ->			List[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    [self.sep_token_id]
												SCREAMING_SNAKE_CASE           =    [self.cls_token_id]

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





							def 		__snake_case(  self				:   Optional[Any]		,							_UpperCamelCase				:   Union[Dict[str, EncodedInput], BatchEncoding]		,							_UpperCamelCase				:   Optional[int] = None		,							_UpperCamelCase				:   PaddingStrategy = PaddingStrategy.DO_NOT_PAD		,							_UpperCamelCase				:   Optional[int] = None		,							_UpperCamelCase				:   Optional[bool] = None		,							)   ->			dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    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:
																	SCREAMING_SNAKE_CASE           =    "attention_mask" in self.model_input_names

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

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

												return encoded_inputs




