




import inspect
import unittest

from datasets import load_dataset
from packaging import version

from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, 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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch
							from torch import nn

							from transformers import (
							    MODEL_MAPPING,
							    BeitForImageClassification,
							    BeitForMaskedImageModeling,
							    BeitForSemanticSegmentation,
							    BeitModel,
							)
							from transformers.models.beit.modeling_beit import BEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							import PIL
							from PIL import Image

							from transformers import BeitImageProcessor




class 		__A   :

						def __init__(		self :List[str]     ,			__snake_case :List[Any]     ,			__snake_case :Any=1_00     ,			__snake_case :List[Any]=13     ,			__snake_case :List[str]=30     ,			__snake_case :List[Any]=2     ,			__snake_case :str=3     ,			__snake_case :Optional[int]=True     ,			__snake_case :List[str]=True     ,			__snake_case :Tuple=32     ,			__snake_case :Optional[Any]=4     ,			__snake_case :str=4     ,			__snake_case :Optional[int]=37     ,			__snake_case :Any="gelu"     ,			__snake_case :Any=0.1     ,			__snake_case :int=0.1     ,			__snake_case :Optional[Any]=10     ,			__snake_case :Dict=0.02     ,			__snake_case :List[Any]=3     ,			__snake_case :Union[str, Any]=None     ,			__snake_case :Any=[0, 1, 2, 3]     ,			):
										'''simple docstring'''

										__magic_name__						:						Any										=parent
										__magic_name__						:						List[str]										=1_00
										__magic_name__						:						str										=batch_size
										__magic_name__						:						str										=image_size
										__magic_name__						:						Any										=patch_size
										__magic_name__						:						int										=num_channels
										__magic_name__						:						Union[str, Any]										=is_training
										__magic_name__						:						List[str]										=use_labels
										__magic_name__						:						Union[str, Any]										=hidden_size
										__magic_name__						:						Dict										=num_hidden_layers
										__magic_name__						:						str										=num_attention_heads
										__magic_name__						:						int										=intermediate_size
										__magic_name__						:						Any										=hidden_act
										__magic_name__						:						Optional[int]										=hidden_dropout_prob
										__magic_name__						:						Any										=attention_probs_dropout_prob
										__magic_name__						:						Tuple										=type_sequence_label_size
										__magic_name__						:						Dict										=initializer_range
										__magic_name__						:						int										=scope
										__magic_name__						:						Union[str, Any]										=out_indices
										__magic_name__						:						int										=num_labels

										# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
										__magic_name__						:						Union[str, Any]										=(image_size // patch_size) ** 2
										__magic_name__						:						Any										=num_patches + 1

						def      A__    (		self :int			):
										'''simple docstring'''

										__magic_name__						:						List[str]										=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

										__magic_name__						:						Optional[Any]										=None
										__magic_name__						:						List[Any]										=None
										if self.use_labels:
														__magic_name__						:						Any										=ids_tensor([self.batch_size]     ,			self.type_sequence_label_size			)
														__magic_name__						:						int										=ids_tensor([self.batch_size, self.image_size, self.image_size]     ,			self.num_labels			)

										__magic_name__						:						Optional[int]										=self.get_config()

										return config, pixel_values, labels, pixel_labels

						def      A__    (		self :List[str]			):
										'''simple docstring'''

										return BeitConfig(
										    vocab_size=self.vocab_size     ,			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=__snake_case     ,			initializer_range=self.initializer_range     ,			out_indices=self.out_indices     ,			)

						def      A__    (		self :Optional[int]     ,			__snake_case :Tuple     ,			__snake_case :Optional[Any]     ,			__snake_case :Dict     ,			__snake_case :List[str]			):
										'''simple docstring'''

										__magic_name__						:						Tuple										=BeitModel(config=__snake_case			)
										model.to(__snake_case			)
										model.eval()
										__magic_name__						:						Optional[Any]										=model(__snake_case			)
										self.parent.assertEqual(result.last_hidden_state.shape     ,			(self.batch_size, self.seq_length, self.hidden_size)			)

						def      A__    (		self :Union[str, Any]     ,			__snake_case :Any     ,			__snake_case :List[Any]     ,			__snake_case :Optional[int]     ,			__snake_case :Optional[Any]			):
										'''simple docstring'''

										__magic_name__						:						str										=BeitForMaskedImageModeling(config=__snake_case			)
										model.to(__snake_case			)
										model.eval()
										__magic_name__						:						Dict										=model(__snake_case			)
										self.parent.assertEqual(result.logits.shape     ,			(self.batch_size, self.seq_length - 1, self.vocab_size)			)

						def      A__    (		self :str     ,			__snake_case :List[Any]     ,			__snake_case :Optional[int]     ,			__snake_case :List[Any]     ,			__snake_case :Union[str, Any]			):
										'''simple docstring'''

										__magic_name__						:						Tuple										=self.type_sequence_label_size
										__magic_name__						:						Optional[int]										=BeitForImageClassification(__snake_case			)
										model.to(__snake_case			)
										model.eval()
										__magic_name__						:						List[Any]										=model(__snake_case     ,			labels=__snake_case			)
										self.parent.assertEqual(result.logits.shape     ,			(self.batch_size, self.type_sequence_label_size)			)

										# test greyscale images
										__magic_name__						:						Optional[Any]										=1
										__magic_name__						:						List[Any]										=BeitForImageClassification(__snake_case			)
										model.to(__snake_case			)
										model.eval()

										__magic_name__						:						List[Any]										=floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
										__magic_name__						:						Dict										=model(__snake_case     ,			labels=__snake_case			)
										self.parent.assertEqual(result.logits.shape     ,			(self.batch_size, self.type_sequence_label_size)			)

						def      A__    (		self :List[str]     ,			__snake_case :Optional[Any]     ,			__snake_case :List[str]     ,			__snake_case :List[str]     ,			__snake_case :Union[str, Any]			):
										'''simple docstring'''

										__magic_name__						:						Union[str, Any]										=self.num_labels
										__magic_name__						:						str										=BeitForSemanticSegmentation(__snake_case			)
										model.to(__snake_case			)
										model.eval()
										__magic_name__						:						str										=model(__snake_case			)
										self.parent.assertEqual(
										    result.logits.shape     ,			(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)			)
										__magic_name__						:						Optional[int]										=model(__snake_case     ,			labels=__snake_case			)
										self.parent.assertEqual(
										    result.logits.shape     ,			(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)			)


						def      A__    (		self :Optional[Any]			):
										'''simple docstring'''

										__magic_name__						:						Union[str, Any]										=self.prepare_config_and_inputs()
										__magic_name__					,			__magic_name__					,			__magic_name__					,			__magic_name__						:						int										=config_and_inputs
										__magic_name__						:						int										={"""pixel_values""": pixel_values}
										return config, inputs_dict




@require_torch
class 		__A   (      UpperCamelCase__       ,    UpperCamelCase__       ,    unittest.TestCase      ):
						UpperCamelCase      =       (
						    (BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
						    if is_torch_available()
						    else ()
						)
						UpperCamelCase      =       (
						    {
						        """feature-extraction""": BeitModel,
						        """image-classification""": BeitForImageClassification,
						        """image-segmentation""": BeitForSemanticSegmentation,
						    }
						    if is_torch_available()
						    else {}
						)

						UpperCamelCase      =       False
						UpperCamelCase      =       False
						UpperCamelCase      =       False

						def      A__    (		self :str			):
										'''simple docstring'''

										__magic_name__						:						List[Any]										=BeitModelTester(self			)
										__magic_name__						:						Optional[Any]										=ConfigTester(self     ,			config_class=__snake_case     ,			has_text_modality=__snake_case     ,			hidden_size=37			)

						def      A__    (		self :Optional[Any]			):
										'''simple docstring'''

										self.config_tester.run_common_tests()

						@unittest.skip(reason="""BEiT does not use inputs_embeds"""			)
						def      A__    (		self :Tuple			):
										'''simple docstring'''

										pass

						@require_torch_multi_gpu
						@unittest.skip(reason="""BEiT has some layers using `add_module` which doesn't work well with `nn.DataParallel`"""			)
						def      A__    (		self :str			):
										'''simple docstring'''

										pass

						def      A__    (		self :Tuple			):
										'''simple docstring'''

										__magic_name__					,			__magic_name__						:						int										=self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
														__magic_name__						:						Dict										=model_class(__snake_case			)
														self.assertIsInstance(model.get_input_embeddings()     ,			(nn.Module)			)
														__magic_name__						:						Union[str, Any]										=model.get_output_embeddings()
														self.assertTrue(x is None or isinstance(__snake_case     ,			nn.Linear			)			)

						def      A__    (		self :Optional[Any]			):
										'''simple docstring'''

										__magic_name__					,			__magic_name__						:						int										=self.model_tester.prepare_config_and_inputs_for_common()

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

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

						def      A__    (		self :Optional[int]			):
										'''simple docstring'''

										__magic_name__						:						Optional[int]										=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*__snake_case			)

						def      A__    (		self :int			):
										'''simple docstring'''

										__magic_name__						:						Optional[Any]										=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_lm(*__snake_case			)

						def      A__    (		self :int			):
										'''simple docstring'''

										__magic_name__						:						Optional[Any]										=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*__snake_case			)

						def      A__    (		self :Dict			):
										'''simple docstring'''

										__magic_name__						:						Optional[Any]										=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_semantic_segmentation(*__snake_case			)

						def      A__    (		self :List[str]			):
										'''simple docstring'''

										if not self.model_tester.is_training:
														return

										__magic_name__					,			__magic_name__						:						int										=self.model_tester.prepare_config_and_inputs_for_common()
										__magic_name__						:						int										=True

										for model_class in self.all_model_classes:
														# we don't test BeitForMaskedImageModeling
														if model_class in [*get_values(__snake_case			), BeitForMaskedImageModeling]:
																		continue

														__magic_name__						:						Tuple										=model_class(__snake_case			)
														model.to(__snake_case			)
														model.train()
														__magic_name__						:						int										=self._prepare_for_class(__snake_case     ,			__snake_case     ,			return_labels=__snake_case			)
														__magic_name__						:						Any										=model(**__snake_case			).loss
														loss.backward()

						def      A__    (		self :List[Any]			):
										'''simple docstring'''

										__magic_name__					,			__magic_name__						:						List[Any]										=self.model_tester.prepare_config_and_inputs_for_common()
										if not self.model_tester.is_training:
														return

										__magic_name__						:						Optional[int]										=False
										__magic_name__						:						str										=True

										for model_class in self.all_model_classes:
														# we don't test BeitForMaskedImageModeling
														if (
														    model_class in [*get_values(__snake_case			), BeitForMaskedImageModeling]
														    or not model_class.supports_gradient_checkpointing
														):
																		continue

														__magic_name__						:						List[Any]										=model_class(__snake_case			)
														model.gradient_checkpointing_enable()
														model.to(__snake_case			)
														model.train()
														__magic_name__						:						str										=self._prepare_for_class(__snake_case     ,			__snake_case     ,			return_labels=__snake_case			)
														__magic_name__						:						str										=model(**__snake_case			).loss
														loss.backward()

						def      A__    (		self :int			):
										'''simple docstring'''

										__magic_name__					,			__magic_name__						:						Dict										=self.model_tester.prepare_config_and_inputs_for_common()

										__magic_name__						:						str										=_config_zero_init(__snake_case			)
										for model_class in self.all_model_classes:
														__magic_name__						:						str										=model_class(config=__snake_case			)
														for name, param in model.named_parameters():
																		# we skip lambda parameters as these require special initial values
																		# determined by config.layer_scale_init_value
																		if "lambda" in name:
																						continue
																		if param.requires_grad:
																						self.assertIn(
																						    ((param.data.mean() * 1E9).round() / 1E9).item()     ,			[0.0, 1.0]     ,			msg=f"Parameter {name} of model {model_class} seems not properly initialized"     ,			)


						@slow
						def      A__    (		self :List[str]			):
										'''simple docstring'''

										for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														__magic_name__						:						Dict										=BeitModel.from_pretrained(__snake_case			)
														self.assertIsNotNone(__snake_case			)




def 		lowerCAmelCase_ (						):
				__magic_name__						:						List[str]										=Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
				return image




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

						@cached_property
						def      A__    (		self :Any			):
										'''simple docstring'''

										return BeitImageProcessor.from_pretrained("""microsoft/beit-base-patch16-224"""			) if is_vision_available() else None

						@slow
						def      A__    (		self :Dict			):
										'''simple docstring'''

										__magic_name__						:						Union[str, Any]										=BeitForMaskedImageModeling.from_pretrained("""microsoft/beit-base-patch16-224-pt22k"""			).to(__snake_case			)

										__magic_name__						:						List[Any]										=self.default_image_processor
										__magic_name__						:						Union[str, Any]										=prepare_img()
										__magic_name__						:						Dict										=image_processor(images=__snake_case     ,			return_tensors="""pt"""			).pixel_values.to(__snake_case			)

										# prepare bool_masked_pos
										__magic_name__						:						Any										=torch.ones((1, 1_96)     ,			dtype=torch.bool			).to(__snake_case			)

										# forward pass
										with torch.no_grad():
														__magic_name__						:						str										=model(pixel_values=__snake_case     ,			bool_masked_pos=__snake_case			)
										__magic_name__						:						Any										=outputs.logits

										# verify the logits
										__magic_name__						:						Dict										=torch.Size((1, 1_96, 81_92)			)
										self.assertEqual(logits.shape     ,			__snake_case			)

										__magic_name__						:						int										=torch.tensor(
										    [[-3.2437, 0.5072, -13.9174], [-3.2456, 0.4948, -13.9401], [-3.2033, 0.5121, -13.8550]]			).to(__snake_case			)

										self.assertTrue(torch.allclose(logits[bool_masked_pos][:3, :3]     ,			__snake_case     ,			atol=1E-2			)			)

						@slow
						def      A__    (		self :Tuple			):
										'''simple docstring'''

										__magic_name__						:						Optional[int]										=BeitForImageClassification.from_pretrained("""microsoft/beit-base-patch16-224"""			).to(__snake_case			)

										__magic_name__						:						str										=self.default_image_processor
										__magic_name__						:						Optional[Any]										=prepare_img()
										__magic_name__						:						Tuple										=image_processor(images=__snake_case     ,			return_tensors="""pt"""			).to(__snake_case			)

										# forward pass
										with torch.no_grad():
														__magic_name__						:						str										=model(**__snake_case			)
										__magic_name__						:						Optional[int]										=outputs.logits

										# verify the logits
										__magic_name__						:						Any										=torch.Size((1, 10_00)			)
										self.assertEqual(logits.shape     ,			__snake_case			)

										__magic_name__						:						List[Any]										=torch.tensor([-1.2385, -1.0987, -1.0108]			).to(__snake_case			)

										self.assertTrue(torch.allclose(logits[0, :3]     ,			__snake_case     ,			atol=1E-4			)			)

										__magic_name__						:						str										=2_81
										self.assertEqual(logits.argmax(-1			).item()     ,			__snake_case			)

						@slow
						def      A__    (		self :Optional[int]			):
										'''simple docstring'''

										__magic_name__						:						Any										=BeitForImageClassification.from_pretrained("""microsoft/beit-large-patch16-224-pt22k-ft22k"""			).to(
										    __snake_case			)

										__magic_name__						:						str										=self.default_image_processor
										__magic_name__						:						str										=prepare_img()
										__magic_name__						:						Any										=image_processor(images=__snake_case     ,			return_tensors="""pt"""			).to(__snake_case			)

										# forward pass
										with torch.no_grad():
														__magic_name__						:						int										=model(**__snake_case			)
										__magic_name__						:						str										=outputs.logits

										# verify the logits
										__magic_name__						:						int										=torch.Size((1, 2_18_41)			)
										self.assertEqual(logits.shape     ,			__snake_case			)

										__magic_name__						:						str										=torch.tensor([1.6881, -0.2787, 0.5901]			).to(__snake_case			)

										self.assertTrue(torch.allclose(logits[0, :3]     ,			__snake_case     ,			atol=1E-4			)			)

										__magic_name__						:						str										=23_96
										self.assertEqual(logits.argmax(-1			).item()     ,			__snake_case			)

						@slow
						def      A__    (		self :List[Any]			):
										'''simple docstring'''

										__magic_name__						:						Union[str, Any]										=BeitForSemanticSegmentation.from_pretrained("""microsoft/beit-base-finetuned-ade-640-640"""			)
										__magic_name__						:						List[Any]										=model.to(__snake_case			)

										__magic_name__						:						Dict										=BeitImageProcessor(do_resize=__snake_case     ,			size=6_40     ,			do_center_crop=__snake_case			)

										__magic_name__						:						Optional[int]										=load_dataset("""hf-internal-testing/fixtures_ade20k"""     ,			split="""test"""			)
										__magic_name__						:						Optional[int]										=Image.open(ds[0]["""file"""]			)
										__magic_name__						:						Dict										=image_processor(images=__snake_case     ,			return_tensors="""pt"""			).to(__snake_case			)

										# forward pass
										with torch.no_grad():
														__magic_name__						:						Optional[Any]										=model(**__snake_case			)
										__magic_name__						:						Tuple										=outputs.logits

										# verify the logits
										__magic_name__						:						Union[str, Any]										=torch.Size((1, 1_50, 1_60, 1_60)			)
										self.assertEqual(logits.shape     ,			__snake_case			)

										__magic_name__						:						Dict										=version.parse(PIL.__version__			) < version.parse("""9.0.0"""			)

										if is_pillow_less_than_a:
														__magic_name__						:						Optional[int]										=torch.tensor(
														    [
														        [[-4.9225, -2.3954, -3.0522], [-2.8822, -1.0046, -1.7561], [-2.9549, -1.3228, -2.1347]],
														        [[-5.8168, -3.4129, -4.0778], [-3.8651, -2.2214, -3.0277], [-3.8356, -2.4643, -3.3535]],
														        [[-0.0078, 3.9952, 4.0754], [2.9856, 4.6944, 5.0035], [3.2413, 4.7813, 4.9969]],
														    ]     ,			device=__snake_case     ,			)
										else:
														__magic_name__						:						Tuple										=torch.tensor(
														    [
														        [[-4.8960, -2.3688, -3.0355], [-2.8478, -0.9836, -1.7418], [-2.9449, -1.3332, -2.1456]],
														        [[-5.8081, -3.4124, -4.1006], [-3.8561, -2.2081, -3.0323], [-3.8365, -2.4601, -3.3669]],
														        [[-0.0309, 3.9868, 4.0540], [2.9640, 4.6877, 4.9976], [3.2081, 4.7690, 4.9942]],
														    ]     ,			device=__snake_case     ,			)

										self.assertTrue(torch.allclose(logits[0, :3, :3, :3]     ,			__snake_case     ,			atol=1E-4			)			)


						@slow
						def      A__    (		self :Optional[Any]			):
										'''simple docstring'''

										__magic_name__						:						List[Any]										=BeitForSemanticSegmentation.from_pretrained("""microsoft/beit-base-finetuned-ade-640-640"""			)
										__magic_name__						:						Union[str, Any]										=model.to(__snake_case			)

										__magic_name__						:						int										=BeitImageProcessor(do_resize=__snake_case     ,			size=6_40     ,			do_center_crop=__snake_case			)

										__magic_name__						:						Optional[int]										=load_dataset("""hf-internal-testing/fixtures_ade20k"""     ,			split="""test"""			)
										__magic_name__						:						Dict										=Image.open(ds[0]["""file"""]			)
										__magic_name__						:						int										=image_processor(images=__snake_case     ,			return_tensors="""pt"""			).to(__snake_case			)

										# forward pass
										with torch.no_grad():
														__magic_name__						:						Any										=model(**__snake_case			)

										__magic_name__						:						List[str]										=outputs.logits.detach().cpu()

										__magic_name__						:						Optional[int]										=image_processor.post_process_semantic_segmentation(outputs=__snake_case     ,			target_sizes=[(5_00, 3_00)]			)
										__magic_name__						:						Optional[Any]										=torch.Size((5_00, 3_00)			)
										self.assertEqual(segmentation[0].shape     ,			__snake_case			)

										__magic_name__						:						List[str]										=image_processor.post_process_semantic_segmentation(outputs=__snake_case			)
										__magic_name__						:						Union[str, Any]										=torch.Size((1_60, 1_60)			)
										self.assertEqual(segmentation[0].shape     ,			__snake_case			)










import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer




class 		__A   (      tf.keras.layers.Layer      ):

						def __init__(		self :Optional[int]     ,			__snake_case :Dict[str, int]     ,			__snake_case :List[str]     ,			__snake_case :int = None     ,			__snake_case :int = None			):
										'''simple docstring'''

										super().__init__()
										__magic_name__						:						Optional[int]										=pad_token_id
										__magic_name__						:						List[Any]										=max_length
										__magic_name__						:						Dict										=vocab
										__magic_name__						:						int										=merges
										__magic_name__						:						Optional[int]										=BytePairTokenizer(__snake_case     ,			__snake_case     ,			sequence_length=__snake_case			)

						@classmethod
						def      A__    (		cls :List[Any]     ,			__snake_case :GPTaTokenizer     ,			*__snake_case :int     ,			**__snake_case :Any			):
										'''simple docstring'''

										__magic_name__						:						List[Any]										=[""" """.join(__snake_case			) for m in tokenizer.bpe_ranks.keys()]
										__magic_name__						:						str										=tokenizer.get_vocab()
										return cls(__snake_case     ,			__snake_case     ,			*__snake_case     ,			**__snake_case			)

						@classmethod
						def      A__    (		cls :Dict     ,			__snake_case :Union[str, os.PathLike]     ,			*__snake_case :Union[str, Any]     ,			**__snake_case :int			):
										'''simple docstring'''

										__magic_name__						:						Dict										=GPTaTokenizer.from_pretrained(__snake_case     ,			*__snake_case     ,			**__snake_case			)
										return cls.from_tokenizer(__snake_case     ,			*__snake_case     ,			**__snake_case			)

						@classmethod
						def      A__    (		cls :Optional[Any]     ,			__snake_case :List[Any]			):
										'''simple docstring'''

										return cls(**__snake_case			)

						def      A__    (		self :Union[str, Any]			):
										'''simple docstring'''

										return {
										    "vocab": self.vocab,
										    "merges": self.merges,
										    "max_length": self.max_length,
										    "pad_token_id": self.pad_token_id,
										}


						def      A__    (		self :List[Any]     ,			__snake_case :Dict     ,			__snake_case :int = None			):
										'''simple docstring'''

										__magic_name__						:						Optional[Any]										=self.tf_tokenizer(__snake_case			)
										__magic_name__						:						Tuple										=tf.ones_like(__snake_case			)

										if self.pad_token_id is not None:
														# pad the tokens up to max length
														__magic_name__						:						Tuple										=max_length if max_length is not None else self.max_length

														if max_length is not None:
																		__magic_name__					,			__magic_name__						:						Tuple										=pad_model_inputs(
																		    __snake_case     ,			max_seq_length=__snake_case     ,			pad_value=self.pad_token_id			)

										return {"attention_mask": attention_mask, "input_ids": input_ids}




