





from __future__ import annotations

import unittest

import numpy as np

from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
						import tensorflow as tf

						from transformers import GPTaTokenizer, TFOPTForCausalLM, TFOPTModel


def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a				,	__a=None				,	__a=None				):
	if attention_mask is None:
		snake_case_						:     List[Any]     =		tf.cast(tf.math.not_equal(__a				,	config.pad_token_id				)				,	tf.inta				)
	return {"input_ids": input_ids, "attention_mask": attention_mask}
@require_tf
class SCREAMING_SNAKE_CASE_  :
						__magic_name__:     List[str]	 =    OPTConfig
						__magic_name__:     Optional[Any]	 =    {}
						__magic_name__:     Union[str, Any]	 =    "gelu"






						def __init__( self : Dict						,				_A : Union[str, Any]						,				_A : Union[str, Any]=13						,				_A : List[Any]=7						,				_A : Optional[Any]=True						,				_A : Optional[Any]=False						,				_A : List[str]=99						,				_A : Tuple=16						,				_A : int=2						,				_A : List[str]=4						,				_A : int=4						,				_A : List[str]="gelu"						,				_A : Dict=0.1						,				_A : List[Any]=0.1						,				_A : int=20						,				_A : Tuple=2						,				_A : Optional[Any]=1						,				_A : Optional[int]=0						,				_A : Optional[Any]=16						,				_A : Dict=16						,				)							->      List[str]:







							"""simple docstring"""


							snake_case_						:     int     =		parent
							snake_case_						:     List[str]     =		batch_size
							snake_case_						:     str     =		seq_length
							snake_case_						:     Any     =		is_training
							snake_case_						:     List[str]     =		use_labels
							snake_case_						:     Any     =		vocab_size
							snake_case_						:     str     =		hidden_size
							snake_case_						:     Union[str, Any]     =		num_hidden_layers
							snake_case_						:     Optional[int]     =		num_attention_heads
							snake_case_						:     Tuple     =		intermediate_size
							snake_case_						:     str     =		hidden_act
							snake_case_						:     Optional[Any]     =		hidden_dropout_prob
							snake_case_						:     Tuple     =		attention_probs_dropout_prob
							snake_case_						:     Any     =		max_position_embeddings
							snake_case_						:     Optional[int]     =		eos_token_id
							snake_case_						:     List[Any]     =		pad_token_id
							snake_case_						:     int     =		bos_token_id
							snake_case_						:     List[Any]     =		embed_dim
							snake_case_						:     int     =		word_embed_proj_dim
							snake_case_						:     List[str]     =		False






						def UpperCAmelCase_			( self : List[Any]		)							->      List[str]:







							"""simple docstring"""


							snake_case_						:     int     =		ids_tensor([self.batch_size, self.seq_length - 1]						,				self.vocab_size		)
							snake_case_						:     Optional[Any]     =		tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size		)						,				1		)
							snake_case_						:     List[str]     =		tf.concat([input_ids, eos_tensor]						,				axis=1		)

							snake_case_						:     Optional[Any]     =		self.config_cls(
							    vocab_size=self.vocab_size						,				hidden_size=self.hidden_size						,				num_hidden_layers=self.num_hidden_layers						,				num_attention_heads=self.num_attention_heads						,				ffn_dim=self.intermediate_size						,				dropout=self.hidden_dropout_prob						,				attention_dropout=self.attention_probs_dropout_prob						,				max_position_embeddings=self.max_position_embeddings						,				eos_token_id=self.eos_token_id						,				bos_token_id=self.bos_token_id						,				pad_token_id=self.pad_token_id						,				embed_dim=self.embed_dim						,				word_embed_proj_dim=self.word_embed_proj_dim						,				is_encoder_decoder=_A						,				**self.config_updates						,				)
							snake_case_						:     List[str]     =		prepare_opt_inputs_dict(_A						,				_A		)
							return config, inputs_dict






						def UpperCAmelCase_			( self : str						,				_A : List[Any]						,				_A : List[str]		)							->      Optional[Any]:







							"""simple docstring"""


							snake_case_						:     str     =		TFOPTModel(config=_A		)
							snake_case_						:     Any     =		inputs_dict['input_ids']

							snake_case_						:     Tuple     =		input_ids[:1, :]
							snake_case_						:     List[Any]     =		inputs_dict['attention_mask'][:1, :]
							snake_case_						:     str     =		1

							# first forward pass
							snake_case_						:     Optional[int]     =		model(_A						,				attention_mask=_A						,				use_cache=_A		)

							snake_case_  ,snake_case_						:     int     =		outputs.to_tuple()

							# create hypothetical next token and extent to next_input_ids
							snake_case_						:     Tuple     =		ids_tensor((self.batch_size, 3)						,				config.vocab_size		)
							snake_case_						:     int     =		tf.cast(ids_tensor((self.batch_size, 3)						,				2		)						,				tf.inta		)

							# append to next input_ids and
							snake_case_						:     List[str]     =		tf.concat([input_ids, next_tokens]						,				axis=-1		)
							snake_case_						:     Dict     =		tf.concat([attention_mask, next_attn_mask]						,				axis=-1		)

							snake_case_						:     Dict     =		model(_A						,				attention_mask=_A		)[0]
							snake_case_						:     Dict     =		model(_A						,				attention_mask=_A						,				past_key_values=_A		)[0]

							self.parent.assertEqual(next_tokens.shape[1]						,				output_from_past.shape[1]		)

							# select random slice
							snake_case_						:     str     =		int(ids_tensor((1,)						,				output_from_past.shape[-1]		)		)
							snake_case_						:     int     =		output_from_no_past[:, -3:, random_slice_idx]
							snake_case_						:     List[str]     =		output_from_past[:, :, random_slice_idx]

							# test that outputs are equal for slice
							tf.debugging.assert_near(_A						,				_A						,				rtol=1E-3		)
@require_tf
class SCREAMING_SNAKE_CASE_  (   snake_case_    ,			snake_case_    ,			unittest.TestCase   ):
						__magic_name__:     List[str]	 =    (TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
						__magic_name__:     Optional[Any]	 =    (TFOPTForCausalLM,) if is_tf_available() else ()
						__magic_name__:     List[str]	 =    (
						    {"feature-extraction": TFOPTModel, "text-generation": TFOPTForCausalLM} if is_tf_available() else {}
						)
						__magic_name__:     List[str]	 =    False
						__magic_name__:     List[Any]	 =    False
						__magic_name__:     List[Any]	 =    False
						__magic_name__:     Optional[Any]	 =    10






						def UpperCAmelCase_			( self : Optional[Any]		)							->      str:







							"""simple docstring"""


							snake_case_						:     int     =		TFOPTModelTester(self		)
							snake_case_						:     Any     =		ConfigTester(self						,				config_class=_A		)






						def UpperCAmelCase_			( self : str		)							->      Tuple:







							"""simple docstring"""


							self.config_tester.run_common_tests()






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







							"""simple docstring"""


							snake_case_						:     List[Any]     =		self.model_tester.prepare_config_and_inputs_for_common()
							self.model_tester.check_decoder_model_past_large_inputs(*_A		)






						def UpperCAmelCase_			( self : Tuple		)							->      List[str]:







							"""simple docstring"""


							snake_case_  ,snake_case_						:     str     =		self.model_tester.prepare_config_and_inputs_for_common()

							def _get_word_embedding_weight(_A : List[str]						,				_A : List[Any]		):
								if hasattr(_A						,				'weight'		):
									return embedding_layer.weight
								else:
									# Here we build the word embeddings weights if not exists.
									# And then we retry to get the attribute once built.
									model.build()
									if hasattr(_A						,				'weight'		):
										return embedding_layer.weight
									else:
										return None

							for model_class in self.all_model_classes:
								for size in [config.vocab_size - 10, config.vocab_size + 10]:
									# build the embeddings
									snake_case_						:     Union[str, Any]     =		model_class(config=_A		)
									snake_case_						:     Tuple     =		_get_word_embedding_weight(_A						,				model.get_input_embeddings()		)
									snake_case_						:     Optional[int]     =		_get_word_embedding_weight(_A						,				model.get_output_embeddings()		)

									# reshape the embeddings
									model.resize_token_embeddings(_A		)
									snake_case_						:     Optional[int]     =		_get_word_embedding_weight(_A						,				model.get_input_embeddings()		)
									snake_case_						:     int     =		_get_word_embedding_weight(_A						,				model.get_output_embeddings()		)

									# check that the resized embeddings size matches the desired size.
									snake_case_						:     List[Any]     =		size if size is not None else config.vocab_size

									self.assertEqual(new_input_embeddings.shape[0]						,				_A		)

									# check that weights remain the same after resizing
									snake_case_						:     str     =		True
									for pa, pa in zip(old_input_embeddings.value()						,				new_input_embeddings.value()		):
										if tf.math.reduce_sum(tf.math.abs(pa - pa		)		) > 0:
											snake_case_						:     Optional[Any]     =		False
									self.assertTrue(_A		)

									if old_output_embeddings is not None and new_output_embeddings is not None:
										self.assertEqual(new_output_embeddings.shape[0]						,				_A		)

										snake_case_						:     Optional[Any]     =		True
										for pa, pa in zip(old_output_embeddings.value()						,				new_output_embeddings.value()		):
											if tf.math.reduce_sum(tf.math.abs(pa - pa		)		) > 0:
												snake_case_						:     List[str]     =		False
										self.assertTrue(_A		)


def 							SCREAMING_SNAKE_CASE__  (      __a				):
	return tf.constant(__a				,	dtype=tf.intaa				)
@require_tf
class SCREAMING_SNAKE_CASE_  (   unittest.TestCase   ):
						__magic_name__:     List[str]	 =    99






						def UpperCAmelCase_			( self : List[str]		)							->      int:







							"""simple docstring"""


							snake_case_						:     List[str]     =		tf.ones((4, 1)						,				dtype=tf.intaa		) * 2
							snake_case_						:     Dict     =		tf.concat([ids_tensor((4, 6)						,				self.vocab_size - 3		) + 3, eos_column_vector]						,				axis=1		)
							snake_case_						:     Any     =		input_ids.shape[0]
							snake_case_						:     Dict     =		OPTConfig(
							    vocab_size=self.vocab_size						,				hidden_size=24						,				num_hidden_layers=2						,				num_attention_heads=2						,				ffn_dim=32						,				max_position_embeddings=48						,				eos_token_id=2						,				pad_token_id=1						,				bos_token_id=0						,				)
							return config, input_ids, batch_size
@require_sentencepiece
@require_tf
class SCREAMING_SNAKE_CASE_  (   unittest.TestCase   ):






						@slow
						def UpperCAmelCase_			( self : List[str]		)							->      str:







							"""simple docstring"""


							snake_case_						:     Any     =		TFOPTModel.from_pretrained('facebook/opt-350m'		)
							snake_case_						:     int     =		_long_tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]		)
							snake_case_						:     Optional[Any]     =		tf.not_equal(_A						,				model.config.pad_token_id		)
							with tf.GradientTape():
								snake_case_						:     List[Any]     =		model(input_ids=_A						,				attention_mask=_A		).last_hidden_state
							snake_case_						:     int     =		(1, 11, 512)
							self.assertEqual(output.shape						,				_A		)
							snake_case_						:     Optional[int]     =		tf.constant(
							    [[-0.2_8_7_3, -1.9_2_1_8, -0.3_0_3_3], [-1.2_7_1_0, -0.1_3_3_8, -0.1_9_0_2], [0.4_0_9_5, 0.1_2_1_4, -1.3_1_2_1]]		)
							self.assertTrue(np.allclose(output[:, :3, :3]						,				_A						,				atol=4E-3		)		)

							snake_case_						:     Dict     =		tf.function(_A						,				jit_compile=_A		)
							snake_case_						:     Dict     =		xla_generate(_A						,				_A		)[0]
							self.assertTrue(np.allclose(output[:, :3, :3]						,				_A						,				atol=4E-2		)		)
@require_tf
@slow
class SCREAMING_SNAKE_CASE_  (   unittest.TestCase   ):






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







							"""simple docstring"""


							super().setUp()
							snake_case_						:     str     =		'facebook/opt-350m'






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







							"""simple docstring"""


							snake_case_						:     Any     =		TFOPTForCausalLM.from_pretrained(self.path_model		)
							snake_case_						:     Dict     =		GPTaTokenizer.from_pretrained(self.path_model		)

							snake_case_						:     List[Any]     =		[
							    'Today is a beautiful day and I want to',
							    'In the city of',
							    'Paris is the capital of France and',
							    'Computers and mobile phones have taken',
							]
							# verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
							snake_case_						:     int     =		tokenizer(_A						,				return_tensors='tf'						,				padding=_A						,				add_special_tokens=_A		)
							snake_case_						:     List[str]     =		tf.math.reduce_mean(model(inputs.input_ids						,				attention_mask=inputs.attention_mask		)[0]						,				axis=-1		)
							snake_case_						:     Optional[Any]     =		tf.constant(
							    [
							        [1.3_8_5_1, -1_3.8_9_2_3, -1_0.5_2_2_9, -1_0.7_5_3_3, -0.2_3_0_9, -1_0.2_3_8_4, -0.5_3_6_5, -9.0_9_4_7, -5.1_6_7_0],
							        [-4.7_0_7_3, -1_0.6_2_7_6, -3.9_4_1_5, -2_1.5_2_4_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2],
							        [0.6_2_4_7, -3.4_2_2_9, -8.9_1_7_9, -1.4_2_9_7, -1_4.1_6_5_0, 1.4_1_4_6, -9.0_2_1_8, -0.2_7_0_3, -0.2_7_0_3],
							        [6.4_7_8_3, -1.9_9_1_3, -1_0.7_9_2_6, -2.3_3_3_6, 1.5_0_9_2, -0.9_9_7_4, -6.8_2_1_3, 1.3_4_7_7, 1.3_4_7_7],
							    ]		)
							self.assertTrue(np.allclose(_A						,				_A						,				atol=1E-4		)		)

							snake_case_						:     Dict     =		tf.function(_A						,				jit_compile=_A		)
							snake_case_						:     Union[str, Any]     =		tf.math.reduce_mean(xla_generate(inputs.input_ids						,				attention_mask=inputs.attention_mask		)[0]						,				axis=-1		)
							self.assertTrue(np.allclose(_A						,				_A						,				atol=1E-4		)		)
@require_tf
@slow
class SCREAMING_SNAKE_CASE_  (   unittest.TestCase   ):






						@property
						def UpperCAmelCase_			( self : Optional[int]		)							->      Tuple:







							"""simple docstring"""


							return [
							    "Today is a beautiful day and I want",
							    "In the city of",
							    "Paris is the capital of France and",
							    "Computers and mobile phones have taken",
							]






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







							"""simple docstring"""


							snake_case_						:     Optional[int]     =		'facebook/opt-125m'

							snake_case_						:     List[str]     =		[
							    'Today is a beautiful day and I want to',
							    'In the city of New York, the city',
							    'Paris is the capital of France and the capital',
							    'Computers and mobile phones have taken over the',
							]

							snake_case_						:     Dict     =		[]
							snake_case_						:     str     =		GPTaTokenizer.from_pretrained(_A		)
							snake_case_						:     Union[str, Any]     =		TFOPTForCausalLM.from_pretrained(_A		)

							for prompt in self.prompts:
								snake_case_						:     Optional[int]     =		tokenizer(_A						,				return_tensors='tf'		).input_ids

								snake_case_						:     Any     =		model.generate(_A						,				max_length=10		)

								snake_case_						:     List[str]     =		tokenizer.batch_decode(_A						,				skip_special_tokens=_A		)
								predicted_outputs += generated_string

							self.assertListEqual(_A						,				_A		)






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







							"""simple docstring"""


							snake_case_						:     Dict     =		'facebook/opt-350m'

							snake_case_						:     Tuple     =		GPTaTokenizer.from_pretrained(_A		)
							snake_case_						:     Optional[Any]     =		TFOPTForCausalLM.from_pretrained(_A		)

							snake_case_						:     Optional[int]     =		'left'

							# use different length sentences to test batching
							snake_case_						:     Optional[int]     =		[
							    'Hello, my dog is a little',
							    'Today, I',
							]

							snake_case_						:     Tuple     =		tokenizer(_A						,				return_tensors='tf'						,				padding=_A		)
							snake_case_						:     Tuple     =		inputs['input_ids']

							snake_case_						:     int     =		model.generate(input_ids=_A						,				attention_mask=inputs['attention_mask']		)

							snake_case_						:     int     =		tokenizer(sentences[0]						,				return_tensors='tf'		).input_ids
							snake_case_						:     Optional[int]     =		model.generate(input_ids=_A		)

							snake_case_						:     Tuple     =		inputs_non_padded.shape[-1] - tf.math.reduce_sum(
							    tf.cast(inputs['attention_mask'][-1]						,				tf.intaa		)		)
							snake_case_						:     str     =		tokenizer(sentences[1]						,				return_tensors='tf'		).input_ids
							snake_case_						:     Optional[int]     =		model.generate(input_ids=_A						,				max_length=model.config.max_length - num_paddings		)

							snake_case_						:     int     =		tokenizer.batch_decode(_A						,				skip_special_tokens=_A		)
							snake_case_						:     List[str]     =		tokenizer.decode(output_non_padded[0]						,				skip_special_tokens=_A		)
							snake_case_						:     Optional[int]     =		tokenizer.decode(output_padded[0]						,				skip_special_tokens=_A		)

							snake_case_						:     Dict     =		[
							    'Hello, my dog is a little bit of a dork.\nI\'m a little bit',
							    'Today, I was in the middle of a conversation with a friend about the',
							]
							self.assertListEqual(_A						,				_A		)
							self.assertListEqual(_A						,				[non_padded_sentence, padded_sentence]		)






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







							"""simple docstring"""


							snake_case_						:     int     =		'facebook/opt-350m'

							snake_case_						:     List[str]     =		[
							    'Today is a beautiful day and I want to',
							    'In the city of San Francisco, the city',
							    'Paris is the capital of France and the capital',
							    'Computers and mobile phones have taken over the',
							]

							snake_case_						:     List[str]     =		[]
							snake_case_						:     Any     =		GPTaTokenizer.from_pretrained(_A		)
							snake_case_						:     Optional[int]     =		TFOPTForCausalLM.from_pretrained(_A		)

							for prompt in self.prompts:
								snake_case_						:     int     =		tokenizer(_A						,				return_tensors='tf'		).input_ids

								snake_case_						:     Tuple     =		model.generate(_A						,				max_length=10		)

								snake_case_						:     Optional[int]     =		tokenizer.batch_decode(_A						,				skip_special_tokens=_A		)
								predicted_outputs += generated_string

							self.assertListEqual(_A						,				_A		)








import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class SCREAMING_SNAKE_CASE_  (   snake_case_   ):
						__magic_name__:     Optional[Any]	 =    ["image_processor", "tokenizer"]
						__magic_name__:     Optional[Any]	 =    "LayoutLMv3ImageProcessor"
						__magic_name__:     str	 =    ("LayoutLMv3Tokenizer", "LayoutLMv3TokenizerFast")






						def __init__( self : int						,				_A : List[str]=None						,				_A : Dict=None						,				**_A : int		)							->      List[str]:







							"""simple docstring"""


							snake_case_						:     Any     =		None
							if "feature_extractor" in kwargs:
								warnings.warn(
								    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
								    ' instead.'						,				_A						,				)
								snake_case_						:     Any     =		kwargs.pop('feature_extractor'		)

							snake_case_						:     Optional[int]     =		image_processor if image_processor is not None else feature_extractor
							if image_processor is None:
								raise ValueError('You need to specify an `image_processor`.'		)
							if tokenizer is None:
								raise ValueError('You need to specify a `tokenizer`.'		)

							super().__init__(_A						,				_A		)






						def __call__( self : List[str]						,				_A : Optional[Any]						,				_A : Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None						,				_A : Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None						,				_A : Union[List[List[int]], List[List[List[int]]]] = None						,				_A : Optional[Union[List[int], List[List[int]]]] = None						,				_A : bool = True						,				_A : Union[bool, str, PaddingStrategy] = False						,				_A : Union[bool, str, TruncationStrategy] = None						,				_A : Optional[int] = None						,				_A : int = 0						,				_A : Optional[int] = None						,				_A : Optional[bool] = None						,				_A : Optional[bool] = None						,				_A : bool = False						,				_A : bool = False						,				_A : bool = False						,				_A : bool = False						,				_A : bool = True						,				_A : Optional[Union[str, TensorType]] = None						,				**_A : str						,				)							->      BatchEncoding:







							"""simple docstring"""


							if self.image_processor.apply_ocr and (boxes is not None):
								raise ValueError(
								    'You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True.'		)

							if self.image_processor.apply_ocr and (word_labels is not None):
								raise ValueError(
								    'You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'		)

							# first, apply the image processor
							snake_case_						:     str     =		self.image_processor(images=_A						,				return_tensors=_A		)

							# second, apply the tokenizer
							if text is not None and self.image_processor.apply_ocr and text_pair is None:
								if isinstance(_A						,				_A		):
									snake_case_						:     List[Any]     =		[text]  # add batch dimension (as the image processor always adds a batch dimension)
								snake_case_						:     str     =		features['words']

							snake_case_						:     Optional[int]     =		self.tokenizer(
							    text=text if text is not None else features['words']						,				text_pair=text_pair if text_pair is not None else None						,				boxes=boxes if boxes is not None else features['boxes']						,				word_labels=_A						,				add_special_tokens=_A						,				padding=_A						,				truncation=_A						,				max_length=_A						,				stride=_A						,				pad_to_multiple_of=_A						,				return_token_type_ids=_A						,				return_attention_mask=_A						,				return_overflowing_tokens=_A						,				return_special_tokens_mask=_A						,				return_offsets_mapping=_A						,				return_length=_A						,				verbose=_A						,				return_tensors=_A						,				**_A						,				)

							# add pixel values
							snake_case_						:     List[str]     =		features.pop('pixel_values'		)
							if return_overflowing_tokens is True:
								snake_case_						:     Dict     =		self.get_overflowing_images(_A						,				encoded_inputs['overflow_to_sample_mapping']		)
							snake_case_						:     Optional[Any]     =		images

							return encoded_inputs






						def UpperCAmelCase_			( self : Dict						,				_A : Tuple						,				_A : Dict		)							->      Union[str, Any]:







							"""simple docstring"""


							snake_case_						:     List[str]     =		[]
							for sample_idx in overflow_to_sample_mapping:
								images_with_overflow.append(images[sample_idx]		)

							if len(_A		) != len(_A		):
								raise ValueError(
								    'Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'
								    F""" {len(_A		)} and {len(_A		)}"""		)

							return images_with_overflow






						def UpperCAmelCase_			( self : Optional[Any]						,				*_A : Optional[Any]						,				**_A : List[Any]		)							->      List[str]:







							"""simple docstring"""


							return self.tokenizer.batch_decode(*_A						,				**_A		)






						def UpperCAmelCase_			( self : Union[str, Any]						,				*_A : Dict						,				**_A : str		)							->      Any:







							"""simple docstring"""


							return self.tokenizer.decode(*_A						,				**_A		)






						@property
						def UpperCAmelCase_			( self : Optional[int]		)							->      int:







							"""simple docstring"""


							return ["input_ids", "bbox", "attention_mask", "pixel_values"]






						@property
						def UpperCAmelCase_			( self : Any		)							->      Any:







							"""simple docstring"""


							warnings.warn(
							    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'						,				_A						,				)
							return self.image_processor_class






						@property
						def UpperCAmelCase_			( self : List[Any]		)							->      int:







							"""simple docstring"""


							warnings.warn(
							    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'						,				_A						,				)
							return self.image_processor

