



'''simple docstring'''




import tempfile
import unittest

import transformers
from transformers import XGLMConfig, XGLMTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, require_sentencepiece, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
						import jax
						import jax.numpy as jnp
						import numpy as np

						from transformers.modeling_flax_pytorch_utils import (
						    convert_pytorch_state_dict_to_flax,
						    load_flax_weights_in_pytorch_model,
						)
						from transformers.models.xglm.modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel


if is_torch_available():
						import torch






@require_flax
class 						_UpperCamelCase  :


			'''simple docstring'''

			def __init__(							self					:			str							,					lowerCAmelCase__					:			Optional[int]							,					lowerCAmelCase__					:			Any=1_4							,					lowerCAmelCase__					:			int=7							,					lowerCAmelCase__					:			Tuple=True							,					lowerCAmelCase__					:			List[str]=True							,					lowerCAmelCase__					:			Optional[Any]=True							,					lowerCAmelCase__					:			Dict=9_9							,					lowerCAmelCase__					:			Dict=3_2							,					lowerCAmelCase__					:			Optional[Any]=5							,					lowerCAmelCase__					:			int=4							,					lowerCAmelCase__					:			Optional[Any]=3_7							,					lowerCAmelCase__					:			Optional[Any]="gelu"							,					lowerCAmelCase__					:			Optional[Any]=0.1							,					lowerCAmelCase__					:			Dict=0.1							,					lowerCAmelCase__					:			Tuple=5_1_2							,					lowerCAmelCase__					:			Optional[Any]=0.02							,					lowerCAmelCase__					:			Optional[Any]=None							,					):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Any   	=		parent
					__SCREAMING_SNAKE_CASE	:		Dict   	=		batch_size
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		seq_length
					__SCREAMING_SNAKE_CASE	:		Dict   	=		is_training
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		use_input_mask
					__SCREAMING_SNAKE_CASE	:		str   	=		use_labels
					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		vocab_size
					__SCREAMING_SNAKE_CASE	:		str   	=		d_model
					__SCREAMING_SNAKE_CASE	:		int   	=		num_hidden_layers
					__SCREAMING_SNAKE_CASE	:		Tuple   	=		num_attention_heads
					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		ffn_dim
					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		activation_function
					__SCREAMING_SNAKE_CASE	:		int   	=		activation_dropout
					__SCREAMING_SNAKE_CASE	:		int   	=		attention_dropout
					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		max_position_embeddings
					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		initializer_range
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		None
					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		0
					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		2
					__SCREAMING_SNAKE_CASE	:		Dict   	=		1

			def UpperCamelCase__ (							self					:			Tuple						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		np.clip(ids_tensor([self.batch_size, self.seq_length]							,					self.vocab_size						)							,					3							,					self.vocab_size						)

					__SCREAMING_SNAKE_CASE	:		int   	=		None
					if self.use_input_mask:
							__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		random_attention_mask([self.batch_size, self.seq_length]						)

					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		XGLMConfig(
					    vocab_size=self.vocab_size							,					d_model=self.hidden_size							,					num_layers=self.num_hidden_layers							,					attention_heads=self.num_attention_heads							,					ffn_dim=self.ffn_dim							,					activation_function=self.activation_function							,					activation_dropout=self.activation_dropout							,					attention_dropout=self.attention_dropout							,					max_position_embeddings=self.max_position_embeddings							,					initializer_range=self.initializer_range							,					use_cache=lowerCAmelCase__							,					bos_token_id=self.bos_token_id							,					eos_token_id=self.eos_token_id							,					pad_token_id=self.pad_token_id							,					)

					return (config, input_ids, input_mask)

			def UpperCamelCase__ (							self					:			Optional[int]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		self.prepare_config_and_inputs()
					__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		List[str]   	=		config_and_inputs
					__SCREAMING_SNAKE_CASE	:		Any   	=		{"""input_ids""": input_ids, """attention_mask""": attention_mask}
					return config, inputs_dict

			def UpperCamelCase__ (							self					:			str						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		Tuple   	=		self.prepare_config_and_inputs()

					__SCREAMING_SNAKE_CASE	:		List[str]   	=		floats_tensor([self.batch_size, self.seq_length, self.hidden_size]						)
					__SCREAMING_SNAKE_CASE	:		int   	=		ids_tensor([self.batch_size, self.seq_length]							,					vocab_size=2						)

					return (
					    config,
					    input_ids,
					    attention_mask,
					    encoder_hidden_states,
					    encoder_attention_mask,
					)

			def UpperCamelCase__ (							self					:			str							,					lowerCAmelCase__					:			List[Any]							,					lowerCAmelCase__					:			Optional[Any]							,					lowerCAmelCase__					:			Optional[int]							,					lowerCAmelCase__					:			Optional[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Tuple   	=		2_0
					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		model_class_name(lowerCAmelCase__						)

					__SCREAMING_SNAKE_CASE	:		List[str]   	=		model.init_cache(input_ids.shape[0]							,					lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE	:		str   	=		jnp.ones((input_ids.shape[0], max_decoder_length)							,					dtype="""i4"""						)

					__SCREAMING_SNAKE_CASE	:		int   	=		jnp.broadcast_to(
					    jnp.arange(input_ids.shape[-1] - 1						)[None, :]							,					(input_ids.shape[0], input_ids.shape[-1] - 1)						)
					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		model(
					    input_ids[:, :-1]							,					attention_mask=lowerCAmelCase__							,					past_key_values=lowerCAmelCase__							,					position_ids=lowerCAmelCase__							,					)

					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]							,					dtype="""i4"""						)
					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		model(
					    input_ids[:, -1:]							,					attention_mask=lowerCAmelCase__							,					past_key_values=outputs_cache.past_key_values							,					position_ids=lowerCAmelCase__							,					)

					__SCREAMING_SNAKE_CASE	:		Dict   	=		model(lowerCAmelCase__						)

					__SCREAMING_SNAKE_CASE	:		str   	=		np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])						)						)
					self.parent.assertTrue(diff < 1E-3							,					msg=F"Max diff is {diff}"						)



			def UpperCamelCase__ (							self					:			List[Any]							,					lowerCAmelCase__					:			Union[str, Any]							,					lowerCAmelCase__					:			int							,					lowerCAmelCase__					:			Dict							,					lowerCAmelCase__					:			Tuple						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Tuple   	=		2_0
					__SCREAMING_SNAKE_CASE	:		str   	=		model_class_name(lowerCAmelCase__						)

					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		jnp.concatenate(
					    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])						)]							,					axis=-1							,					)

					__SCREAMING_SNAKE_CASE	:		Dict   	=		model.init_cache(input_ids.shape[0]							,					lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		jnp.broadcast_to(
					    jnp.arange(input_ids.shape[-1] - 1						)[None, :]							,					(input_ids.shape[0], input_ids.shape[-1] - 1)						)

					__SCREAMING_SNAKE_CASE	:		Any   	=		model(
					    input_ids[:, :-1]							,					attention_mask=lowerCAmelCase__							,					past_key_values=lowerCAmelCase__							,					position_ids=lowerCAmelCase__							,					)
					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]							,					dtype="""i4"""						)
					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		model(
					    input_ids[:, -1:]							,					past_key_values=outputs_cache.past_key_values							,					attention_mask=lowerCAmelCase__							,					position_ids=lowerCAmelCase__							,					)

					__SCREAMING_SNAKE_CASE	:		str   	=		model(lowerCAmelCase__							,					attention_mask=lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])						)						)
					self.parent.assertTrue(diff < 1E-3							,					msg=F"Max diff is {diff}"						)






@require_sentencepiece
@require_flax
class 						_UpperCamelCase  (							lowerCamelCase__   , lowerCamelCase__   , unittest.TestCase   ):


			'''simple docstring'''

			_A				:		Optional[int]         =		(FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
			_A				:		Tuple         =		(FlaxXGLMForCausalLM,) if is_flax_available() else ()

			def UpperCamelCase__ (							self					:			Optional[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		int   	=		FlaxXGLMModelTester(self						)

			def UpperCamelCase__ (							self					:			Tuple						):





					"""simple docstring"""






					for model_class_name in self.all_model_classes:
							__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		str   	=		self.model_tester.prepare_config_and_inputs()
							self.model_tester.check_use_cache_forward(lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__						)

			def UpperCamelCase__ (							self					:			Union[str, Any]						):





					"""simple docstring"""






					for model_class_name in self.all_model_classes:
							__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		List[str]   	=		self.model_tester.prepare_config_and_inputs()
							self.model_tester.check_use_cache_forward_with_attn_mask(
							    lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__						)

			@slow
			def UpperCamelCase__ (							self					:			Union[str, Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		List[str]   	=		XGLMTokenizer.from_pretrained("""XGLM"""							,					padding_side="""left"""						)
					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		tokenizer(["""Hello this is a long string""", """Hey"""]							,					return_tensors="""np"""							,					padding=lowerCAmelCase__							,					truncation=lowerCAmelCase__						)

					__SCREAMING_SNAKE_CASE	:		Tuple   	=		FlaxXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""						)
					__SCREAMING_SNAKE_CASE	:		int   	=		1
					__SCREAMING_SNAKE_CASE	:		Any   	=		False

					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		jax.jit(model.generate						)

					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		jit_generate(inputs["""input_ids"""]							,					attention_mask=inputs["""attention_mask"""]						).sequences

					__SCREAMING_SNAKE_CASE	:		Any   	=		tokenizer.batch_decode(lowerCAmelCase__							,					skip_special_tokens=lowerCAmelCase__						)

					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		[
					    """Hello this is a long string of questions, but I'm not sure if I'm""",
					    """Hey, I'm a newbie to the forum and I'""",
					]

					self.assertListEqual(lowerCAmelCase__							,					lowerCAmelCase__						)

			@is_pt_flax_cross_test
			def UpperCamelCase__ (							self					:			Union[str, Any]						):





					"""simple docstring"""






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

					for model_class in self.all_model_classes:
							with self.subTest(model_class.__name__						):
									# prepare inputs
									__SCREAMING_SNAKE_CASE	:		str   	=		self._prepare_for_class(lowerCAmelCase__							,					lowerCAmelCase__						)
									__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		{k: torch.tensor(v.tolist()						) for k, v in prepared_inputs_dict.items()}

									# load corresponding PyTorch class
									__SCREAMING_SNAKE_CASE	:		Dict   	=		model_class.__name__[4:]  # Skip the "Flax" at the beginning
									__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		getattr(lowerCAmelCase__							,					lowerCAmelCase__						)

									__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		pt_inputs["""input_ids"""].shape
									__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		np.random.randint(0							,					seq_length - 1							,					size=(batch_size,)						)
									for batch_idx, start_index in enumerate(lowerCAmelCase__						):
											__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		0
											__SCREAMING_SNAKE_CASE	:		List[str]   	=		1
											__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		0
											__SCREAMING_SNAKE_CASE	:		str   	=		1
									__SCREAMING_SNAKE_CASE	:		Any   	=		pt_model_class(lowerCAmelCase__						).eval()
									# Flax models don't use the `use_cache` option and cache is not returned as a default.
									# So we disable `use_cache` here for PyTorch model.
									__SCREAMING_SNAKE_CASE	:		Tuple   	=		False
									__SCREAMING_SNAKE_CASE	:		str   	=		model_class(lowerCAmelCase__							,					dtype=jnp.floataa						)
									__SCREAMING_SNAKE_CASE	:		Tuple   	=		convert_pytorch_state_dict_to_flax(pt_model.state_dict()							,					lowerCAmelCase__						)
									__SCREAMING_SNAKE_CASE	:		Dict   	=		fx_state

									with torch.no_grad():
											__SCREAMING_SNAKE_CASE	:		List[str]   	=		pt_model(**lowerCAmelCase__						).to_tuple()

									__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		fx_model(**lowerCAmelCase__						).to_tuple()
									self.assertEqual(len(lowerCAmelCase__						)							,					len(lowerCAmelCase__						)							,					"""Output lengths differ between Flax and PyTorch"""						)
									for fx_output, pt_output in zip(lowerCAmelCase__							,					lowerCAmelCase__						):
											self.assert_almost_equals(fx_output[:, -1]							,					pt_output[:, -1].numpy()							,					4E-2						)

									with tempfile.TemporaryDirectory() as tmpdirname:
											pt_model.save_pretrained(lowerCAmelCase__						)
											__SCREAMING_SNAKE_CASE	:		List[str]   	=		model_class.from_pretrained(lowerCAmelCase__							,					from_pt=lowerCAmelCase__						)

									__SCREAMING_SNAKE_CASE	:		Any   	=		fx_model_loaded(**lowerCAmelCase__						).to_tuple()
									self.assertEqual(
									    len(lowerCAmelCase__						)							,					len(lowerCAmelCase__						)							,					"""Output lengths differ between Flax and PyTorch"""						)
									for fx_output_loaded, pt_output in zip(lowerCAmelCase__							,					lowerCAmelCase__						):
											self.assert_almost_equals(fx_output_loaded[:, -1]							,					pt_output[:, -1].numpy()							,					4E-2						)

			@is_pt_flax_cross_test
			def UpperCamelCase__ (							self					:			List[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		Any   	=		self.model_tester.prepare_config_and_inputs_for_common()
					for model_class in self.all_model_classes:
							with self.subTest(model_class.__name__						):
									# prepare inputs
									__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		self._prepare_for_class(lowerCAmelCase__							,					lowerCAmelCase__						)
									__SCREAMING_SNAKE_CASE	:		Any   	=		{k: torch.tensor(v.tolist()						) for k, v in prepared_inputs_dict.items()}

									# load corresponding PyTorch class
									__SCREAMING_SNAKE_CASE	:		Tuple   	=		model_class.__name__[4:]  # Skip the "Flax" at the beginning
									__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		getattr(lowerCAmelCase__							,					lowerCAmelCase__						)

									__SCREAMING_SNAKE_CASE	:		List[str]   	=		pt_model_class(lowerCAmelCase__						).eval()
									__SCREAMING_SNAKE_CASE	:		int   	=		False
									__SCREAMING_SNAKE_CASE	:		List[Any]   	=		model_class(lowerCAmelCase__							,					dtype=jnp.floataa						)

									__SCREAMING_SNAKE_CASE	:		Dict   	=		load_flax_weights_in_pytorch_model(lowerCAmelCase__							,					fx_model.params						)
									__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		Any   	=		pt_inputs["""input_ids"""].shape
									__SCREAMING_SNAKE_CASE	:		Any   	=		np.random.randint(0							,					seq_length - 1							,					size=(batch_size,)						)
									for batch_idx, start_index in enumerate(lowerCAmelCase__						):
											__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		0
											__SCREAMING_SNAKE_CASE	:		int   	=		1
											__SCREAMING_SNAKE_CASE	:		Any   	=		0
											__SCREAMING_SNAKE_CASE	:		Tuple   	=		1

									# make sure weights are tied in PyTorch
									pt_model.tie_weights()

									with torch.no_grad():
											__SCREAMING_SNAKE_CASE	:		Any   	=		pt_model(**lowerCAmelCase__						).to_tuple()

									__SCREAMING_SNAKE_CASE	:		int   	=		fx_model(**lowerCAmelCase__						).to_tuple()
									self.assertEqual(len(lowerCAmelCase__						)							,					len(lowerCAmelCase__						)							,					"""Output lengths differ between Flax and PyTorch"""						)
									for fx_output, pt_output in zip(lowerCAmelCase__							,					lowerCAmelCase__						):
											self.assert_almost_equals(fx_output[:, -1]							,					pt_output[:, -1].numpy()							,					4E-2						)

									with tempfile.TemporaryDirectory() as tmpdirname:
											fx_model.save_pretrained(lowerCAmelCase__						)
											__SCREAMING_SNAKE_CASE	:		Dict   	=		pt_model_class.from_pretrained(lowerCAmelCase__							,					from_flax=lowerCAmelCase__						)

									with torch.no_grad():
											__SCREAMING_SNAKE_CASE	:		int   	=		pt_model_loaded(**lowerCAmelCase__						).to_tuple()

									self.assertEqual(
									    len(lowerCAmelCase__						)							,					len(lowerCAmelCase__						)							,					"""Output lengths differ between Flax and PyTorch"""						)
									for fx_output, pt_output in zip(lowerCAmelCase__							,					lowerCAmelCase__						):
											self.assert_almost_equals(fx_output[:, -1]							,					pt_output[:, -1].numpy()							,					4E-2						)



			@slow
			def UpperCamelCase__ (							self					:			Optional[int]						):





					"""simple docstring"""






					for model_class_name in self.all_model_classes:
							__SCREAMING_SNAKE_CASE	:		List[Any]   	=		model_class_name.from_pretrained("""facebook/xglm-564M"""						)
							__SCREAMING_SNAKE_CASE	:		Dict   	=		model(np.ones((1, 1)						)						)
							self.assertIsNotNone(lowerCAmelCase__						)




'''simple docstring'''




import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin






class 						_UpperCamelCase  (							lowerCamelCase__   ):


			'''simple docstring'''

			_A				:		int         =		['''image_processor''', '''tokenizer''']
			_A				:		Union[str, Any]         =		'''AutoImageProcessor'''
			_A				:		Optional[Any]         =		'''AutoTokenizer'''

			def __init__(							self					:			Tuple							,					lowerCAmelCase__					:			Tuple=None							,					lowerCAmelCase__					:			Union[str, Any]=None							,					**lowerCAmelCase__					:			Any						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Tuple   	=		None
					if "feature_extractor" in kwargs:
							warnings.warn(
							    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
							    """ instead."""							,					lowerCAmelCase__							,					)
							__SCREAMING_SNAKE_CASE	:		str   	=		kwargs.pop("""feature_extractor"""						)

					__SCREAMING_SNAKE_CASE	:		Any   	=		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__(lowerCAmelCase__							,					lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		self.image_processor
					__SCREAMING_SNAKE_CASE	:		Dict   	=		False

			def __call__(							self					:			List[str]							,					*lowerCAmelCase__					:			List[str]							,					**lowerCAmelCase__					:			Dict						):





					"""simple docstring"""






					if self._in_target_context_manager:
							return self.current_processor(*lowerCAmelCase__							,					**lowerCAmelCase__						)

					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		kwargs.pop("""images"""							,					lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		kwargs.pop("""text"""							,					lowerCAmelCase__						)
					if len(lowerCAmelCase__						) > 0:
							__SCREAMING_SNAKE_CASE	:		Tuple   	=		args[0]
							__SCREAMING_SNAKE_CASE	:		int   	=		args[1:]

					if images is None and text is None:
							raise ValueError("""You need to specify either an `images` or `text` input to process."""						)

					if images is not None:
							__SCREAMING_SNAKE_CASE	:		List[str]   	=		self.image_processor(lowerCAmelCase__							,					*lowerCAmelCase__							,					**lowerCAmelCase__						)
					if text is not None:
							__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		self.tokenizer(lowerCAmelCase__							,					**lowerCAmelCase__						)

					if text is None:
							return inputs
					elif images is None:
							return encodings
					else:
							__SCREAMING_SNAKE_CASE	:		int   	=		encodings["""input_ids"""]
							return inputs

			def UpperCamelCase__ (							self					:			Any							,					*lowerCAmelCase__					:			Any							,					**lowerCAmelCase__					:			List[str]						):





					"""simple docstring"""






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

			def UpperCamelCase__ (							self					:			int							,					*lowerCAmelCase__					:			str							,					**lowerCAmelCase__					:			Dict						):





					"""simple docstring"""






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

			@contextmanager
			def UpperCamelCase__ (							self					:			Union[str, Any]						):





					"""simple docstring"""






					warnings.warn(
					    """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
					    """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
					    """your images inputs, or in a separate call."""						)
					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		True
					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		self.tokenizer
					yield
					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		self.image_processor
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		False

			@property
			def UpperCamelCase__ (							self					:			Optional[Any]						):





					"""simple docstring"""






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



			@property
			def UpperCamelCase__ (							self					:			List[Any]						):





					"""simple docstring"""






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