





from collections import OrderedDict
from typing import Mapping

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


_a						=   logging.get_logger(__name__)

_a						=   {
    "xlm-mlm-en-2048": "https://huggingface.co/xlm-mlm-en-2048/resolve/main/config.json",
    "xlm-mlm-ende-1024": "https://huggingface.co/xlm-mlm-ende-1024/resolve/main/config.json",
    "xlm-mlm-enfr-1024": "https://huggingface.co/xlm-mlm-enfr-1024/resolve/main/config.json",
    "xlm-mlm-enro-1024": "https://huggingface.co/xlm-mlm-enro-1024/resolve/main/config.json",
    "xlm-mlm-tlm-xnli15-1024": "https://huggingface.co/xlm-mlm-tlm-xnli15-1024/resolve/main/config.json",
    "xlm-mlm-xnli15-1024": "https://huggingface.co/xlm-mlm-xnli15-1024/resolve/main/config.json",
    "xlm-clm-enfr-1024": "https://huggingface.co/xlm-clm-enfr-1024/resolve/main/config.json",
    "xlm-clm-ende-1024": "https://huggingface.co/xlm-clm-ende-1024/resolve/main/config.json",
    "xlm-mlm-17-1280": "https://huggingface.co/xlm-mlm-17-1280/resolve/main/config.json",
    "xlm-mlm-100-1280": "https://huggingface.co/xlm-mlm-100-1280/resolve/main/config.json",
}






class       __A					(					lowerCAmelCase				):

			'''simple docstring'''







			lowerCAmelCase_							  =		"""xlm"""
			lowerCAmelCase_							  =		{
			    """hidden_size""": """emb_dim""",
			    """num_attention_heads""": """n_heads""",
			    """num_hidden_layers""": """n_layers""",
			    """n_words""": """vocab_size""",  # For backward compatibility
			}





			def __init__(					self ,						__lowerCAmelCase=3_0_1_4_5 ,						__lowerCAmelCase=2_0_4_8 ,						__lowerCAmelCase=1_2 ,						__lowerCAmelCase=1_6 ,						__lowerCAmelCase=0.1 ,						__lowerCAmelCase=0.1 ,						__lowerCAmelCase=True ,						__lowerCAmelCase=False ,						__lowerCAmelCase=False ,						__lowerCAmelCase=False ,						__lowerCAmelCase=1 ,						__lowerCAmelCase=True ,						__lowerCAmelCase=5_1_2 ,						__lowerCAmelCase=2_0_4_8**-0.5 ,						__lowerCAmelCase=1E-12 ,						__lowerCAmelCase=0.02 ,						__lowerCAmelCase=0 ,						__lowerCAmelCase=1 ,						__lowerCAmelCase=2 ,						__lowerCAmelCase=3 ,						__lowerCAmelCase=5 ,						__lowerCAmelCase=True ,						__lowerCAmelCase="first" ,						__lowerCAmelCase=True ,						__lowerCAmelCase=None ,						__lowerCAmelCase=True ,						__lowerCAmelCase=0.1 ,						__lowerCAmelCase=5 ,						__lowerCAmelCase=5 ,						__lowerCAmelCase=0 ,						__lowerCAmelCase=0 ,						__lowerCAmelCase=2 ,						__lowerCAmelCase=0 ,						**__lowerCAmelCase ,						):



				'''simple docstring'''







				lowerCamelCase__              =	vocab_size
				lowerCamelCase__              =	emb_dim
				lowerCamelCase__              =	n_layers
				lowerCamelCase__              =	n_heads
				lowerCamelCase__              =	dropout
				lowerCamelCase__              =	attention_dropout
				lowerCamelCase__              =	gelu_activation
				lowerCamelCase__              =	sinusoidal_embeddings
				lowerCamelCase__              =	causal
				lowerCamelCase__              =	asm
				lowerCamelCase__              =	n_langs
				lowerCamelCase__              =	use_lang_emb
				lowerCamelCase__              =	layer_norm_eps
				lowerCamelCase__              =	bos_index
				lowerCamelCase__              =	eos_index
				lowerCamelCase__              =	pad_index
				lowerCamelCase__              =	unk_index
				lowerCamelCase__              =	mask_index
				lowerCamelCase__              =	is_encoder
				lowerCamelCase__              =	max_position_embeddings
				lowerCamelCase__              =	embed_init_std
				lowerCamelCase__              =	init_std
				lowerCamelCase__              =	summary_type
				lowerCamelCase__              =	summary_use_proj
				lowerCamelCase__              =	summary_activation
				lowerCamelCase__              =	summary_proj_to_labels
				lowerCamelCase__              =	summary_first_dropout
				lowerCamelCase__              =	start_n_top
				lowerCamelCase__              =	end_n_top
				lowerCamelCase__              =	mask_token_id
				lowerCamelCase__              =	lang_id

				if "n_words" in kwargs:
					lowerCamelCase__              =	kwargs['''n_words''']

				super().__init__(pad_token_id=__lowerCAmelCase ,						bos_token_id=__lowerCAmelCase ,						**__lowerCAmelCase							)






class       __A					(					lowerCAmelCase				):

			'''simple docstring'''







			@property
			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				if self.task == "multiple-choice":
					lowerCamelCase__              =	{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
				else:
					lowerCamelCase__              =	{0: '''batch''', 1: '''sequence'''}
				return OrderedDict(
				    [
				        ('''input_ids''', dynamic_axis),
				        ('''attention_mask''', dynamic_axis),
				        ('''token_type_ids''', dynamic_axis),
				    ]							)










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






class       __A					(					lowerCAmelCase				):

			'''simple docstring'''







			lowerCAmelCase_							  =		"""ClapFeatureExtractor"""
			lowerCAmelCase_							  =		("""RobertaTokenizer""", """RobertaTokenizerFast""")





			def __init__(					self ,						__lowerCAmelCase ,						__lowerCAmelCase							):



				'''simple docstring'''







				super().__init__(__lowerCAmelCase ,						__lowerCAmelCase							)





			def __call__(					self ,						__lowerCAmelCase=None ,						__lowerCAmelCase=None ,						__lowerCAmelCase=None ,						**__lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__              =	kwargs.pop('''sampling_rate''' ,						__lowerCAmelCase							)

				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:
					lowerCamelCase__              =	self.tokenizer(__lowerCAmelCase ,						return_tensors=__lowerCAmelCase ,						**__lowerCAmelCase							)

				if audios is not None:
					lowerCamelCase__              =	self.feature_extractor(
					    __lowerCAmelCase ,						sampling_rate=__lowerCAmelCase ,						return_tensors=__lowerCAmelCase ,						**__lowerCAmelCase							)

				if text is not None and audios is not None:
					lowerCamelCase__              =	audio_features.input_features
					return encoding
				elif text is not None:
					return encoding
				else:
					return BatchEncoding(data=dict(**__lowerCAmelCase							) ,						tensor_type=__lowerCAmelCase							)





			def 	__lowerCamelCase       (					self ,						*__lowerCAmelCase ,						**__lowerCAmelCase							):



				'''simple docstring'''







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





			def 	__lowerCamelCase       (					self ,						*__lowerCAmelCase ,						**__lowerCAmelCase							):



				'''simple docstring'''







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





			@property
			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	self.tokenizer.model_input_names
				lowerCamelCase__              =	self.feature_extractor.model_input_names
				return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names							)							)



