


"""simple docstring"""
import unittest

from parameterized import parameterized

from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import OpenLlamaForCausalLM, OpenLlamaForSequenceClassification, OpenLlamaModel





class        __A       :





							'''simple docstring'''

							def __init__(			self							: str						,    UpperCAmelCase_							: Union[str, Any]						,    UpperCAmelCase_							: Union[str, Any]=13						,    UpperCAmelCase_							: List[str]=7						,    UpperCAmelCase_							: Optional[Any]=True						,    UpperCAmelCase_							: Union[str, Any]=True						,    UpperCAmelCase_							: int=False						,    UpperCAmelCase_							: Any=True						,    UpperCAmelCase_							: Any=99						,    UpperCAmelCase_							: str=32						,    UpperCAmelCase_							: Any=5						,    UpperCAmelCase_							: int=4						,    UpperCAmelCase_							: List[Any]=37						,    UpperCAmelCase_							: Any="gelu"						,    UpperCAmelCase_							: Optional[int]=0.1						,    UpperCAmelCase_							: Union[str, Any]=0.1						,    UpperCAmelCase_							: int=512						,    UpperCAmelCase_							: Optional[Any]=16						,    UpperCAmelCase_							: int=2						,    UpperCAmelCase_							: Dict=0.02						,    UpperCAmelCase_							: Optional[int]=3						,    UpperCAmelCase_							: Any=4						,    UpperCAmelCase_							: Tuple=None						,    )   ->int:



														"""simple docstring"""



														snake_case_          =						parent
														snake_case_          =						batch_size
														snake_case_          =						seq_length
														snake_case_          =						is_training
														snake_case_          =						use_input_mask
														snake_case_          =						use_token_type_ids
														snake_case_          =						use_labels
														snake_case_          =						vocab_size
														snake_case_          =						hidden_size
														snake_case_          =						num_hidden_layers
														snake_case_          =						num_attention_heads
														snake_case_          =						intermediate_size
														snake_case_          =						hidden_act
														snake_case_          =						hidden_dropout_prob
														snake_case_          =						attention_probs_dropout_prob
														snake_case_          =						max_position_embeddings
														snake_case_          =						type_vocab_size
														snake_case_          =						type_sequence_label_size
														snake_case_          =						initializer_range
														snake_case_          =						num_labels
														snake_case_          =						num_choices
														snake_case_          =						scope



							def 						lowerCAmelCase    (			self							: str    )   ->Any:



														"""simple docstring"""



														snake_case_          =						ids_tensor([self.batch_size, self.seq_length]						,    self.vocab_size    )

														snake_case_          =						None
														if self.use_input_mask:
																					snake_case_          =						random_attention_mask([self.batch_size, self.seq_length]    )

														snake_case_          =						None
														if self.use_token_type_ids:
																					snake_case_          =						ids_tensor([self.batch_size, self.seq_length]						,    self.type_vocab_size    )

														snake_case_          =						None
														snake_case_          =						None
														snake_case_          =						None
														if self.use_labels:
																					snake_case_          =						ids_tensor([self.batch_size]						,    self.type_sequence_label_size    )
																					snake_case_          =						ids_tensor([self.batch_size, self.seq_length]						,    self.num_labels    )
																					snake_case_          =						ids_tensor([self.batch_size]						,    self.num_choices    )

														snake_case_          =						self.get_config()

														return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels



							def 						lowerCAmelCase    (			self							: Any    )   ->Any:



														"""simple docstring"""



														return OpenLlamaConfig(
														    vocab_size=self.vocab_size						,    hidden_size=self.hidden_size						,    num_hidden_layers=self.num_hidden_layers						,    num_attention_heads=self.num_attention_heads						,    intermediate_size=self.intermediate_size						,    hidden_act=self.hidden_act						,    hidden_dropout_prob=self.hidden_dropout_prob						,    attention_probs_dropout_prob=self.attention_probs_dropout_prob						,    max_position_embeddings=self.max_position_embeddings						,    type_vocab_size=self.type_vocab_size						,    is_decoder=__SCREAMING_SNAKE_CASE						,    initializer_range=self.initializer_range						,    use_stable_embedding=__SCREAMING_SNAKE_CASE						,    )



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



														"""simple docstring"""



														snake_case_          =						OpenLlamaModel(config=__SCREAMING_SNAKE_CASE    )
														model.to(__SCREAMING_SNAKE_CASE    )
														model.eval()
														snake_case_          =						model(__SCREAMING_SNAKE_CASE						,    attention_mask=__SCREAMING_SNAKE_CASE    )
														snake_case_          =						model(__SCREAMING_SNAKE_CASE    )
														self.parent.assertEqual(result.last_hidden_state.shape						,    (self.batch_size, self.seq_length, self.hidden_size)    )



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



														"""simple docstring"""



														snake_case_          =						True
														snake_case_          =						OpenLlamaModel(__SCREAMING_SNAKE_CASE    )
														model.to(__SCREAMING_SNAKE_CASE    )
														model.eval()
														snake_case_          =						model(
														    __SCREAMING_SNAKE_CASE						,    attention_mask=__SCREAMING_SNAKE_CASE						,    encoder_hidden_states=__SCREAMING_SNAKE_CASE						,    encoder_attention_mask=__SCREAMING_SNAKE_CASE						,    )
														snake_case_          =						model(
														    __SCREAMING_SNAKE_CASE						,    attention_mask=__SCREAMING_SNAKE_CASE						,    encoder_hidden_states=__SCREAMING_SNAKE_CASE						,    )
														snake_case_          =						model(__SCREAMING_SNAKE_CASE						,    attention_mask=__SCREAMING_SNAKE_CASE    )
														self.parent.assertEqual(result.last_hidden_state.shape						,    (self.batch_size, self.seq_length, self.hidden_size)    )



							def 						lowerCAmelCase    (			self							: Any						,    UpperCAmelCase_							: Any						,    UpperCAmelCase_							: Union[str, Any]						,    UpperCAmelCase_							: Union[str, Any]						,    UpperCAmelCase_							: Optional[Any]						,    UpperCAmelCase_							: int						,    UpperCAmelCase_							: Tuple						,    UpperCAmelCase_							: List[str]						,    UpperCAmelCase_							: Any						,    UpperCAmelCase_							: Tuple						,    )   ->List[str]:



														"""simple docstring"""



														snake_case_          =						OpenLlamaForCausalLM(config=__SCREAMING_SNAKE_CASE    )
														model.to(__SCREAMING_SNAKE_CASE    )
														model.eval()
														snake_case_          =						model(__SCREAMING_SNAKE_CASE						,    attention_mask=__SCREAMING_SNAKE_CASE						,    labels=__SCREAMING_SNAKE_CASE    )
														self.parent.assertEqual(result.logits.shape						,    (self.batch_size, self.seq_length, self.vocab_size)    )



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



														"""simple docstring"""



														snake_case_          =						True
														snake_case_          =						True
														snake_case_          =						OpenLlamaForCausalLM(config=__SCREAMING_SNAKE_CASE    )
														model.to(__SCREAMING_SNAKE_CASE    )
														model.eval()

														# first forward pass
														snake_case_          =						model(
														    __SCREAMING_SNAKE_CASE						,    attention_mask=__SCREAMING_SNAKE_CASE						,    encoder_hidden_states=__SCREAMING_SNAKE_CASE						,    encoder_attention_mask=__SCREAMING_SNAKE_CASE						,    use_cache=__SCREAMING_SNAKE_CASE						,    )
														snake_case_          =						outputs.past_key_values

														# create hypothetical multiple next token and extent to next_input_ids
														snake_case_          =						ids_tensor((self.batch_size, 3)						,    config.vocab_size    )
														snake_case_          =						ids_tensor((self.batch_size, 3)						,    vocab_size=2    )

														# append to next input_ids and
														snake_case_          =						torch.cat([input_ids, next_tokens]						,    dim=-1    )
														snake_case_          =						torch.cat([input_mask, next_mask]						,    dim=-1    )

														snake_case_          =						model(
														    __SCREAMING_SNAKE_CASE						,    attention_mask=__SCREAMING_SNAKE_CASE						,    encoder_hidden_states=__SCREAMING_SNAKE_CASE						,    encoder_attention_mask=__SCREAMING_SNAKE_CASE						,    output_hidden_states=__SCREAMING_SNAKE_CASE						,    )["""hidden_states"""][0]
														snake_case_          =						model(
														    __SCREAMING_SNAKE_CASE						,    attention_mask=__SCREAMING_SNAKE_CASE						,    encoder_hidden_states=__SCREAMING_SNAKE_CASE						,    encoder_attention_mask=__SCREAMING_SNAKE_CASE						,    past_key_values=__SCREAMING_SNAKE_CASE						,    output_hidden_states=__SCREAMING_SNAKE_CASE						,    )["""hidden_states"""][0]

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

														self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]    )

														# test that outputs are equal for slice
														self.parent.assertTrue(torch.allclose(__SCREAMING_SNAKE_CASE						,    __SCREAMING_SNAKE_CASE						,    atol=1E-3    )    )



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



														"""simple docstring"""



														snake_case_          =						self.prepare_config_and_inputs()
														(
														    (
														    snake_case_	
														)    ,  (
														    snake_case_	
														)    ,  (
														    snake_case_	
														)    ,  (
														    snake_case_	
														)    ,  (
														    snake_case_	
														)    ,  (
														    snake_case_	
														)    ,  (
														    snake_case_	
														)    ,  	
														)          =						config_and_inputs
														snake_case_          =						{"""input_ids""": input_ids, """attention_mask""": input_mask}
														return config, inputs_dict





@require_torch
class        __A       (lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						unittest.TestCase):





							'''simple docstring'''

							__lowercase:						Union[str, Any]							 =      (
							    (OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
							)
							__lowercase:						Optional[Any]							 =      (OpenLlamaForCausalLM,) if is_torch_available() else ()
							__lowercase:						Dict							 =      (
							    {
							        """feature-extraction""": OpenLlamaModel,
							        """text-classification""": OpenLlamaForSequenceClassification,
							        """text-generation""": OpenLlamaForCausalLM,
							        """zero-shot""": OpenLlamaForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)
							__lowercase:						Dict							 =      False
							__lowercase:						List[Any]							 =      False



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



														"""simple docstring"""



														snake_case_          =						OpenLlamaModelTester(self    )
														snake_case_          =						ConfigTester(self						,    config_class=__SCREAMING_SNAKE_CASE						,    hidden_size=37    )



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



														"""simple docstring"""



														self.config_tester.run_common_tests()



							def 						lowerCAmelCase    (			self							: str    )   ->Dict:



														"""simple docstring"""



														snake_case_          =						self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE    )



							def 						lowerCAmelCase    (			self							: Any    )   ->Dict:



														"""simple docstring"""



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



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



														"""simple docstring"""



														snake_case_    ,  snake_case_          =						self.model_tester.prepare_config_and_inputs_for_common()
														snake_case_          =						3
														snake_case_          =						input_dict["""input_ids"""]
														snake_case_          =						input_ids.ne(1    ).to(__SCREAMING_SNAKE_CASE    )
														snake_case_          =						ids_tensor([self.model_tester.batch_size]						,    self.model_tester.type_sequence_label_size    )
														snake_case_          =						OpenLlamaForSequenceClassification(__SCREAMING_SNAKE_CASE    )
														model.to(__SCREAMING_SNAKE_CASE    )
														model.eval()
														snake_case_          =						model(__SCREAMING_SNAKE_CASE						,    attention_mask=__SCREAMING_SNAKE_CASE						,    labels=__SCREAMING_SNAKE_CASE    )
														self.assertEqual(result.logits.shape						,    (self.model_tester.batch_size, self.model_tester.num_labels)    )



							def 						lowerCAmelCase    (			self							: int    )   ->str:



														"""simple docstring"""



														snake_case_    ,  snake_case_          =						self.model_tester.prepare_config_and_inputs_for_common()
														snake_case_          =						3
														snake_case_          =						"""single_label_classification"""
														snake_case_          =						input_dict["""input_ids"""]
														snake_case_          =						input_ids.ne(1    ).to(__SCREAMING_SNAKE_CASE    )
														snake_case_          =						ids_tensor([self.model_tester.batch_size]						,    self.model_tester.type_sequence_label_size    )
														snake_case_          =						OpenLlamaForSequenceClassification(__SCREAMING_SNAKE_CASE    )
														model.to(__SCREAMING_SNAKE_CASE    )
														model.eval()
														snake_case_          =						model(__SCREAMING_SNAKE_CASE						,    attention_mask=__SCREAMING_SNAKE_CASE						,    labels=__SCREAMING_SNAKE_CASE    )
														self.assertEqual(result.logits.shape						,    (self.model_tester.batch_size, self.model_tester.num_labels)    )



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



														"""simple docstring"""



														snake_case_    ,  snake_case_          =						self.model_tester.prepare_config_and_inputs_for_common()
														snake_case_          =						3
														snake_case_          =						"""multi_label_classification"""
														snake_case_          =						input_dict["""input_ids"""]
														snake_case_          =						input_ids.ne(1    ).to(__SCREAMING_SNAKE_CASE    )
														snake_case_          =						ids_tensor(
														    [self.model_tester.batch_size, config.num_labels]						,    self.model_tester.type_sequence_label_size    ).to(torch.float    )
														snake_case_          =						OpenLlamaForSequenceClassification(__SCREAMING_SNAKE_CASE    )
														model.to(__SCREAMING_SNAKE_CASE    )
														model.eval()
														snake_case_          =						model(__SCREAMING_SNAKE_CASE						,    attention_mask=__SCREAMING_SNAKE_CASE						,    labels=__SCREAMING_SNAKE_CASE    )
														self.assertEqual(result.logits.shape						,    (self.model_tester.batch_size, self.model_tester.num_labels)    )



							@unittest.skip("""Open-Llama buffers include complex numbers, which breaks this test"""    )
							def 						lowerCAmelCase    (			self							: Union[str, Any]    )   ->Dict:



														"""simple docstring"""



														pass



							@parameterized.expand([("""linear""",), ("""dynamic""",)]    )
							def 						lowerCAmelCase    (			self							: List[str]						,    UpperCAmelCase_							: int    )   ->Tuple:



														"""simple docstring"""



														snake_case_    ,  snake_case_          =						self.model_tester.prepare_config_and_inputs_for_common()
														snake_case_          =						ids_tensor([1, 10]						,    config.vocab_size    )
														snake_case_          =						ids_tensor([1, int(config.max_position_embeddings * 1.5    )]						,    config.vocab_size    )

														set_seed(42    )  # Fixed seed at init time so the two models get the same random weights
														snake_case_          =						OpenLlamaModel(__SCREAMING_SNAKE_CASE    )
														original_model.to(__SCREAMING_SNAKE_CASE    )
														original_model.eval()
														snake_case_          =						original_model(__SCREAMING_SNAKE_CASE    ).last_hidden_state
														snake_case_          =						original_model(__SCREAMING_SNAKE_CASE    ).last_hidden_state

														set_seed(42    )  # Fixed seed at init time so the two models get the same random weights
														snake_case_          =						{"""type""": scaling_type, """factor""": 10.0}
														snake_case_          =						OpenLlamaModel(__SCREAMING_SNAKE_CASE    )
														scaled_model.to(__SCREAMING_SNAKE_CASE    )
														scaled_model.eval()
														snake_case_          =						scaled_model(__SCREAMING_SNAKE_CASE    ).last_hidden_state
														snake_case_          =						scaled_model(__SCREAMING_SNAKE_CASE    ).last_hidden_state

														# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
														# maximum sequence length, so the outputs for the short input should match.
														if scaling_type == "dynamic":
																					self.assertTrue(torch.allclose(__SCREAMING_SNAKE_CASE						,    __SCREAMING_SNAKE_CASE						,    atol=1E-5    )    )
														else:
																					self.assertFalse(torch.allclose(__SCREAMING_SNAKE_CASE						,    __SCREAMING_SNAKE_CASE						,    atol=1E-5    )    )

														# The output should be different for long inputs
														self.assertFalse(torch.allclose(__SCREAMING_SNAKE_CASE						,    __SCREAMING_SNAKE_CASE						,    atol=1E-5    )    )







"""simple docstring"""
import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin





class        __A       (snake_case__):





							'''simple docstring'''

							__lowercase:						Any							 =      ["""image_processor""", """tokenizer"""]
							__lowercase:						Dict							 =      """AutoImageProcessor"""
							__lowercase:						List[Any]							 =      """AutoTokenizer"""



							def __init__(			self							: Tuple						,    UpperCAmelCase_							: int=None						,    UpperCAmelCase_							: Tuple=None						,    **UpperCAmelCase_							: str    )   ->Optional[int]:



														"""simple docstring"""



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

														snake_case_          =						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__(UpperCAmelCase_						,    UpperCAmelCase_    )
														snake_case_          =						self.image_processor
														snake_case_          =						False



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



														"""simple docstring"""



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

														snake_case_          =						kwargs.pop("""images"""						,    UpperCAmelCase_    )
														snake_case_          =						kwargs.pop("""text"""						,    UpperCAmelCase_    )
														if len(UpperCAmelCase_    ) > 0:
																					snake_case_          =						args[0]
																					snake_case_          =						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:
																					snake_case_          =						self.image_processor(UpperCAmelCase_						,    *UpperCAmelCase_						,    **UpperCAmelCase_    )
														if text is not None:
																					snake_case_          =						self.tokenizer(UpperCAmelCase_						,    **UpperCAmelCase_    )

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



							def 						lowerCAmelCase    (			self							: Optional[Any]						,    *UpperCAmelCase_							: Optional[Any]						,    **UpperCAmelCase_							: Tuple    )   ->Optional[int]:



														"""simple docstring"""



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



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



														"""simple docstring"""



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



							@contextmanager
							def 						lowerCAmelCase    (			self							: List[str]    )   ->Dict:



														"""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."""    )
														snake_case_          =						True
														snake_case_          =						self.tokenizer
														yield
														snake_case_          =						self.image_processor
														snake_case_          =						False



							def 						lowerCAmelCase    (			self							: int						,    UpperCAmelCase_							: Union[str, Any]						,    UpperCAmelCase_							: str=False						,    UpperCAmelCase_							: str=None    )   ->Tuple:



														"""simple docstring"""



														if added_vocab is None:
																					snake_case_          =						self.tokenizer.get_added_vocab()

														snake_case_          =						{}

														while tokens:
																					snake_case_          =						re.search(R"""<s_(.*?)>"""						,    UpperCAmelCase_						,    re.IGNORECASE    )
																					if start_token is None:
																												break
																					snake_case_          =						start_token.group(1    )
																					snake_case_          =						re.search(RF"""</s_{key}>"""						,    UpperCAmelCase_						,    re.IGNORECASE    )
																					snake_case_          =						start_token.group()
																					if end_token is None:
																												snake_case_          =						tokens.replace(UpperCAmelCase_						,    """"""    )
																					else:
																												snake_case_          =						end_token.group()
																												snake_case_          =						re.escape(UpperCAmelCase_    )
																												snake_case_          =						re.escape(UpperCAmelCase_    )
																												snake_case_          =						re.search(F"""{start_token_escaped}(.*?){end_token_escaped}"""						,    UpperCAmelCase_						,    re.IGNORECASE    )
																												if content is not None:
																																			snake_case_          =						content.group(1    ).strip()
																																			if r"<s_" in content and r"</s_" in content:  # non-leaf node
																																										snake_case_          =						self.tokenajson(UpperCAmelCase_						,    is_inner_value=UpperCAmelCase_						,    added_vocab=UpperCAmelCase_    )
																																										if value:
																																																	if len(UpperCAmelCase_    ) == 1:
																																																								snake_case_          =						value[0]
																																																	snake_case_          =						value
																																			else:  # leaf nodes
																																										snake_case_          =						[]
																																										for leaf in content.split(R"""<sep/>"""    ):
																																																	snake_case_          =						leaf.strip()
																																																	if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
																																																								snake_case_          =						leaf[1:-2]  # for categorical special tokens
																																																	output[key].append(UpperCAmelCase_    )
																																										if len(output[key]    ) == 1:
																																																	snake_case_          =						output[key][0]

																												snake_case_          =						tokens[tokens.find(UpperCAmelCase_    ) + len(UpperCAmelCase_    ) :].strip()
																												if tokens[:6] == r"<sep/>":  # non-leaf nodes
																																			return [output] + self.tokenajson(tokens[6:]						,    is_inner_value=UpperCAmelCase_						,    added_vocab=UpperCAmelCase_    )

														if len(UpperCAmelCase_    ):
																					return [output] if is_inner_value else output
														else:
																					return [] if is_inner_value else {"text_sequence": tokens}



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



														"""simple docstring"""



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



							@property
							def 						lowerCAmelCase    (			self							: List[str]    )   ->str:



														"""simple docstring"""



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



