

'''simple docstring'''


from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding



class 					lowercase						(  UpperCAmelCase_ ):
							_lowerCamelCase		:      List[str]=   "ClapFeatureExtractor"
							_lowerCamelCase		:      int=   ("RobertaTokenizer", "RobertaTokenizerFast")


							def __init__(			self    ,			_snake_case    ,			_snake_case)							->				Union[str, Any]:
								super().__init__(_lowercase    ,			_lowercase)


							def __call__(			self    ,			_snake_case=None    ,			_snake_case=None    ,			_snake_case=None    ,			**_snake_case)							->				Optional[int]:
								UpperCAmelCase_		:      int       	=					kwargs.pop('sampling_rate'    ,			_lowercase)

								if text is None and audios is None:
									raise ValueError('You have to specify either text or audios. Both cannot be none.')

								if text is not None:
									UpperCAmelCase_		:      Optional[int]       	=					self.tokenizer(_lowercase    ,			return_tensors=_lowercase    ,			**_lowercase)

								if audios is not None:
									UpperCAmelCase_		:      Union[str, Any]       	=					self.feature_extractor(
									    _lowercase    ,			sampling_rate=_lowercase    ,			return_tensors=_lowercase    ,			**_lowercase)

								if text is not None and audios is not None:
									UpperCAmelCase_		:      Optional[int]       	=					audio_features.input_features
									return encoding
								elif text is not None:
									return encoding
								else:
									return BatchEncoding(data=dict(**_lowercase)    ,			tensor_type=_lowercase)


							def 						_snake_case							(			self    ,			*_snake_case    ,			**_snake_case)							->				List[str]:
								return self.tokenizer.batch_decode(*_lowercase    ,			**_lowercase)


							def 						_snake_case							(			self    ,			*_snake_case    ,			**_snake_case)							->				Union[str, Any]:
								return self.tokenizer.decode(*_lowercase    ,			**_lowercase)





							@property
							def 						_snake_case							(			self)							->				int:
								UpperCAmelCase_		:      int       	=					self.tokenizer.model_input_names
								UpperCAmelCase_		:      List[str]       	=					self.feature_extractor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names))









'''simple docstring'''


from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCAmelCase__										= logging.get_logger(__name__)

lowerCAmelCase__										= {
    "facebook/xlm-roberta-xl": "https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json",
    "facebook/xlm-roberta-xxl": "https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json",
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}



class 					lowercase						(  a_ ):
							_lowerCamelCase		:      Any=   "xlm-roberta-xl"


							def __init__(			self    ,			_snake_case=25_0880    ,			_snake_case=2560    ,			_snake_case=36    ,			_snake_case=32    ,			_snake_case=1_0240    ,			_snake_case="gelu"    ,			_snake_case=0.1    ,			_snake_case=0.1    ,			_snake_case=514    ,			_snake_case=1    ,			_snake_case=0.02    ,			_snake_case=1e-05    ,			_snake_case=1    ,			_snake_case=0    ,			_snake_case=2    ,			_snake_case="absolute"    ,			_snake_case=True    ,			_snake_case=None    ,			**_snake_case    ,			)							->				Optional[Any]:
								super().__init__(pad_token_id=_snake_case    ,			bos_token_id=_snake_case    ,			eos_token_id=_snake_case    ,			**_snake_case)
								UpperCAmelCase_		:      List[str]       	=					vocab_size
								UpperCAmelCase_		:      int       	=					hidden_size
								UpperCAmelCase_		:      List[Any]       	=					num_hidden_layers
								UpperCAmelCase_		:      Optional[int]       	=					num_attention_heads
								UpperCAmelCase_		:      Union[str, Any]       	=					hidden_act
								UpperCAmelCase_		:      int       	=					intermediate_size
								UpperCAmelCase_		:      Tuple       	=					hidden_dropout_prob
								UpperCAmelCase_		:      List[str]       	=					attention_probs_dropout_prob
								UpperCAmelCase_		:      str       	=					max_position_embeddings
								UpperCAmelCase_		:      Union[str, Any]       	=					type_vocab_size
								UpperCAmelCase_		:      str       	=					initializer_range
								UpperCAmelCase_		:      List[str]       	=					layer_norm_eps
								UpperCAmelCase_		:      Dict       	=					position_embedding_type
								UpperCAmelCase_		:      int       	=					use_cache
								UpperCAmelCase_		:      List[Any]       	=					classifier_dropout



class 					lowercase						(  a_ ):


							@property
							def 						_snake_case							(			self)							->				Mapping[str, Mapping[int, str]]:
								if self.task == "multiple-choice":
									UpperCAmelCase_		:      Any       	=					{0: 'batch', 1: 'choice', 2: 'sequence'}
								else:
									UpperCAmelCase_		:      Optional[int]       	=					{0: 'batch', 1: 'sequence'}
								return OrderedDict(
								    [
								        ('input_ids', dynamic_axis),
								        ('attention_mask', dynamic_axis),
								    ])






