import inspect
import unittest

from transformers import YolosConfig
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
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch
							from torch import nn

							from transformers import YolosForObjectDetection, YolosModel
							from transformers.models.yolos.modeling_yolos import YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

							from transformers import AutoImageProcessor


class   lowercase      :




							def __init__(  self				:   Dict		,							_UpperCamelCase				:   Tuple		,							_UpperCamelCase				:   Any=13		,							_UpperCamelCase				:   Any=[30, 30]		,							_UpperCamelCase				:   Union[str, Any]=2		,							_UpperCamelCase				:   Tuple=3		,							_UpperCamelCase				:   Tuple=True		,							_UpperCamelCase				:   Union[str, Any]=True		,							_UpperCamelCase				:   int=32		,							_UpperCamelCase				:   List[str]=5		,							_UpperCamelCase				:   Union[str, Any]=4		,							_UpperCamelCase				:   Any=37		,							_UpperCamelCase				:   List[Any]="gelu"		,							_UpperCamelCase				:   List[str]=0.1		,							_UpperCamelCase				:   int=0.1		,							_UpperCamelCase				:   Optional[Any]=10		,							_UpperCamelCase				:   Dict=0.0_2		,							_UpperCamelCase				:   str=3		,							_UpperCamelCase				:   Optional[int]=None		,							_UpperCamelCase				:   Optional[Any]=8		,							_UpperCamelCase				:   List[Any]=10		,							)   ->			int:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    parent
												SCREAMING_SNAKE_CASE           =    batch_size
												SCREAMING_SNAKE_CASE           =    image_size
												SCREAMING_SNAKE_CASE           =    patch_size
												SCREAMING_SNAKE_CASE           =    num_channels
												SCREAMING_SNAKE_CASE           =    is_training
												SCREAMING_SNAKE_CASE           =    use_labels
												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           =    type_sequence_label_size
												SCREAMING_SNAKE_CASE           =    initializer_range
												SCREAMING_SNAKE_CASE           =    num_labels
												SCREAMING_SNAKE_CASE           =    scope
												SCREAMING_SNAKE_CASE           =    n_targets
												SCREAMING_SNAKE_CASE           =    num_detection_tokens
												# we set the expected sequence length (which is used in several tests)
												# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) + num_detection_tokens
												SCREAMING_SNAKE_CASE           =    (image_size[1] // patch_size) * (image_size[0] // patch_size)
												SCREAMING_SNAKE_CASE           =    num_patches + 1 + self.num_detection_tokens




							def 		__snake_case(  self				:   int )   ->			Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]] )

												SCREAMING_SNAKE_CASE           =    None
												if self.use_labels:
																	# labels is a list of Dict (each Dict being the labels for a given example in the batch)
																	SCREAMING_SNAKE_CASE           =    []
																	for i in range(self.batch_size ):
																						SCREAMING_SNAKE_CASE           =    {}
																						SCREAMING_SNAKE_CASE           =    torch.randint(
																						    high=self.num_labels		,							size=(self.n_targets,)		,							device=_UpperCamelCase )
																						SCREAMING_SNAKE_CASE           =    torch.rand(self.n_targets		,							4		,							device=_UpperCamelCase )
																						labels.append(_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    self.get_config()

												return config, pixel_values, labels




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



												'''simple docstring'''

												return YolosConfig(
												    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		,							num_detection_tokens=self.num_detection_tokens		,							num_labels=self.num_labels		,							)




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    YolosModel(config=_UpperCamelCase )
												model.to(_UpperCamelCase )
												model.eval()
												SCREAMING_SNAKE_CASE           =    model(_UpperCamelCase )
												self.parent.assertEqual(
												    result.last_hidden_state.shape		,							(self.batch_size, self.expected_seq_len, self.hidden_size) )




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    YolosForObjectDetection(_UpperCamelCase )
												model.to(_UpperCamelCase )
												model.eval()

												SCREAMING_SNAKE_CASE           =    model(pixel_values=_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    model(_UpperCamelCase )

												self.parent.assertEqual(result.logits.shape		,							(self.batch_size, self.num_detection_tokens, self.num_labels + 1) )
												self.parent.assertEqual(result.pred_boxes.shape		,							(self.batch_size, self.num_detection_tokens, 4) )

												SCREAMING_SNAKE_CASE           =    model(pixel_values=_UpperCamelCase		,							labels=_UpperCamelCase )

												self.parent.assertEqual(result.loss.shape		,							() )
												self.parent.assertEqual(result.logits.shape		,							(self.batch_size, self.num_detection_tokens, self.num_labels + 1) )
												self.parent.assertEqual(result.pred_boxes.shape		,							(self.batch_size, self.num_detection_tokens, 4) )





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



												'''simple docstring'''

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



@require_torch
class   lowercase      (       SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			,    unittest.TestCase       ):
							lowercase__  :    Union[str, Any]									=							(YolosModel, YolosForObjectDetection) if is_torch_available() else ()
							lowercase__  :    List[Any]									=							(
							    {"""feature-extraction""": YolosModel, """object-detection""": YolosForObjectDetection} if is_torch_available() else {}
							)

							lowercase__  :    List[Any]									=							False
							lowercase__  :    List[str]									=							False
							lowercase__  :    Tuple									=							False
							lowercase__  :    Union[str, Any]									=							False




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    super()._prepare_for_class(_UpperCamelCase		,							_UpperCamelCase		,							return_labels=_UpperCamelCase )

												if return_labels:
																	if model_class.__name__ == "YolosForObjectDetection":
																						SCREAMING_SNAKE_CASE           =    []
																						for i in range(self.model_tester.batch_size ):
																											SCREAMING_SNAKE_CASE           =    {}
																											SCREAMING_SNAKE_CASE           =    torch.ones(
																											    size=(self.model_tester.n_targets,)		,							device=_UpperCamelCase		,							dtype=torch.long )
																											SCREAMING_SNAKE_CASE           =    torch.ones(
																											    self.model_tester.n_targets		,							4		,							device=_UpperCamelCase		,							dtype=torch.float )
																											labels.append(_UpperCamelCase )
																						SCREAMING_SNAKE_CASE           =    labels

												return inputs_dict




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    YolosModelTester(self )
												SCREAMING_SNAKE_CASE           =    ConfigTester(self		,							config_class=_UpperCamelCase		,							has_text_modality=_UpperCamelCase		,							hidden_size=37 )




							def 		__snake_case(  self				:   Dict )   ->			Tuple:



												'''simple docstring'''

												self.config_tester.run_common_tests()




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



												'''simple docstring'''

												pass




							def 		__snake_case(  self				:   int )   ->			int:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.model_tester.prepare_config_and_inputs_for_common()

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




							def 		__snake_case(  self				:   Any )   ->			int:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.model_tester.prepare_config_and_inputs_for_common()

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

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




							def 		__snake_case(  self				:   Tuple )   ->			str:



												'''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[Any] )   ->			Optional[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.model_tester.prepare_config_and_inputs_for_common()
												SCREAMING_SNAKE_CASE           =    True

												# in YOLOS, the seq_len is different
												SCREAMING_SNAKE_CASE           =    self.model_tester.expected_seq_len
												for model_class in self.all_model_classes:
																	SCREAMING_SNAKE_CASE           =    True
																	SCREAMING_SNAKE_CASE           =    False
																	SCREAMING_SNAKE_CASE           =    True
																	SCREAMING_SNAKE_CASE           =    model_class(_UpperCamelCase )
																	model.to(_UpperCamelCase )
																	model.eval()
																	with torch.no_grad():
																						SCREAMING_SNAKE_CASE           =    model(**self._prepare_for_class(_UpperCamelCase		,							_UpperCamelCase ) )
																	SCREAMING_SNAKE_CASE           =    outputs.attentions
																	self.assertEqual(len(_UpperCamelCase )		,							self.model_tester.num_hidden_layers )

																	# check that output_attentions also work using config
																	del inputs_dict["output_attentions"]
																	SCREAMING_SNAKE_CASE           =    True
																	SCREAMING_SNAKE_CASE           =    model_class(_UpperCamelCase )
																	model.to(_UpperCamelCase )
																	model.eval()
																	with torch.no_grad():
																						SCREAMING_SNAKE_CASE           =    model(**self._prepare_for_class(_UpperCamelCase		,							_UpperCamelCase ) )
																	SCREAMING_SNAKE_CASE           =    outputs.attentions
																	self.assertEqual(len(_UpperCamelCase )		,							self.model_tester.num_hidden_layers )

																	self.assertListEqual(
																	    list(attentions[0].shape[-3:] )		,							[self.model_tester.num_attention_heads, seq_len, seq_len]		,							)
																	SCREAMING_SNAKE_CASE           =    len(_UpperCamelCase )

																	# Check attention is always last and order is fine
																	SCREAMING_SNAKE_CASE           =    True
																	SCREAMING_SNAKE_CASE           =    True
																	SCREAMING_SNAKE_CASE           =    model_class(_UpperCamelCase )
																	model.to(_UpperCamelCase )
																	model.eval()
																	with torch.no_grad():
																						SCREAMING_SNAKE_CASE           =    model(**self._prepare_for_class(_UpperCamelCase		,							_UpperCamelCase ) )

																	SCREAMING_SNAKE_CASE           =    1
																	self.assertEqual(out_len + added_hidden_states		,							len(_UpperCamelCase ) )

																	SCREAMING_SNAKE_CASE           =    outputs.attentions

																	self.assertEqual(len(_UpperCamelCase )		,							self.model_tester.num_hidden_layers )
																	self.assertListEqual(
																	    list(self_attentions[0].shape[-3:] )		,							[self.model_tester.num_attention_heads, seq_len, seq_len]		,							)




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



												'''simple docstring'''

												def check_hidden_states_output(_UpperCamelCase				:   List[str]		,							_UpperCamelCase				:   Any		,							_UpperCamelCase				:   List[str] ):
																	SCREAMING_SNAKE_CASE           =    model_class(_UpperCamelCase )
																	model.to(_UpperCamelCase )
																	model.eval()

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

																	SCREAMING_SNAKE_CASE           =    outputs.hidden_states

																	SCREAMING_SNAKE_CASE           =    getattr(
																	    self.model_tester		,							"expected_num_hidden_layers"		,							self.model_tester.num_hidden_layers + 1 )
																	self.assertEqual(len(_UpperCamelCase )		,							_UpperCamelCase )

																	# YOLOS has a different seq_length
																	SCREAMING_SNAKE_CASE           =    self.model_tester.expected_seq_len

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

												SCREAMING_SNAKE_CASE           =    self.model_tester.prepare_config_and_inputs_for_common()

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

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

																	check_hidden_states_output(_UpperCamelCase		,							_UpperCamelCase		,							_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_object_detection(*_UpperCamelCase )





							@slow
							def 		__snake_case(  self				:   Tuple )   ->			List[Any]:



												'''simple docstring'''

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





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


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




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



												'''simple docstring'''

												return AutoImageProcessor.from_pretrained("hustvl/yolos-small" ) if is_vision_available() else None





							@slow
							def 		__snake_case(  self				:   int )   ->			int:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    YolosForObjectDetection.from_pretrained("hustvl/yolos-small" ).to(_UpperCamelCase )

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

												# forward pass
												with torch.no_grad():
																	SCREAMING_SNAKE_CASE           =    model(inputs.pixel_values )

												# verify outputs
												SCREAMING_SNAKE_CASE           =    torch.Size((1, 100, 92) )
												self.assertEqual(outputs.logits.shape		,							_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    torch.tensor(
												    [[-2_4.0_2_4_8, -1_0.3_0_2_4, -1_4.8_2_9_0], [-4_2.0_3_9_2, -1_6.8_2_0_0, -2_7.4_3_3_4], [-2_7.2_7_4_3, -1_1.8_1_5_4, -1_8.7_1_4_8]]		,							device=_UpperCamelCase		,							)
												SCREAMING_SNAKE_CASE           =    torch.tensor(
												    [[0.2_5_5_9, 0.5_4_5_5, 0.4_7_0_6], [0.2_9_8_9, 0.7_2_7_9, 0.1_8_7_5], [0.7_7_3_2, 0.4_0_1_7, 0.4_4_6_2]]		,							device=_UpperCamelCase )
												self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]		,							_UpperCamelCase		,							atol=1e-4 ) )
												self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3]		,							_UpperCamelCase		,							atol=1e-4 ) )

												# verify postprocessing
												SCREAMING_SNAKE_CASE           =    image_processor.post_process_object_detection(
												    _UpperCamelCase		,							threshold=0.3		,							target_sizes=[image.size[::-1]] )[0]
												SCREAMING_SNAKE_CASE           =    torch.tensor([0.9_9_9_4, 0.9_7_9_0, 0.9_9_6_4, 0.9_9_7_2, 0.9_8_6_1] ).to(_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    [75, 75, 17, 63, 17]
												SCREAMING_SNAKE_CASE           =    torch.tensor([3_3_5.0_6_0_9, 7_9.3_8_4_8, 3_7_5.4_2_1_6, 1_8_7.2_4_9_5] ).to(_UpperCamelCase )

												self.assertEqual(len(results["scores"] )		,							5 )
												self.assertTrue(torch.allclose(results["scores"]		,							_UpperCamelCase		,							atol=1e-4 ) )
												self.assertSequenceEqual(results["labels"].tolist()		,							_UpperCamelCase )
												self.assertTrue(torch.allclose(results["boxes"][0, :]		,							_UpperCamelCase ) )





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




