"""simple docstring"""





from collections import OrderedDict
from typing import Mapping

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


_UpperCamelCase   :						List[Any]      =    logging.get_logger(__name__)

_UpperCamelCase   :						str      =    {
    "facebook/xmod-base": "https://huggingface.co/facebook/xmod-base/resolve/main/config.json",
    "facebook/xmod-large-prenorm": "https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json",
    "facebook/xmod-base-13-125k": "https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json",
    "facebook/xmod-base-30-125k": "https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json",
    "facebook/xmod-base-30-195k": "https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json",
    "facebook/xmod-base-60-125k": "https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json",
    "facebook/xmod-base-60-265k": "https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json",
    "facebook/xmod-base-75-125k": "https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json",
    "facebook/xmod-base-75-269k": "https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json",
}


class      UpperCAmelCase_	(    _a):
      lowerCamelCase__		:    Dict   =  "xmod"


      def __init__(   self		,			a=3_0_5_2_2		,			a=7_6_8		,			a=1_2		,			a=1_2		,			a=3_0_7_2		,			a="gelu"		,			a=0.1		,			a=0.1		,			a=5_1_2		,			a=2		,			a=0.02		,			a=1e-12		,			a=1		,			a=0		,			a=2		,			a="absolute"		,			a=True		,			a=None		,			a=False		,			a=2		,			a=False		,			a=True		,			a=True		,			a=("en_XX",)		,			a=None		,			**a		,			)       ->					int:
             super().__init__(pad_token_id=a		,			bos_token_id=a		,			eos_token_id=a		,			**a							)

             lowercase__			:		Any       =	vocab_size
             lowercase__			:		str       =	hidden_size
             lowercase__			:		int       =	num_hidden_layers
             lowercase__			:		List[Any]       =	num_attention_heads
             lowercase__			:		Any       =	hidden_act
             lowercase__			:		Optional[int]       =	intermediate_size
             lowercase__			:		str       =	hidden_dropout_prob
             lowercase__			:		Dict       =	attention_probs_dropout_prob
             lowercase__			:		List[str]       =	max_position_embeddings
             lowercase__			:		int       =	type_vocab_size
             lowercase__			:		List[Any]       =	initializer_range
             lowercase__			:		int       =	layer_norm_eps
             lowercase__			:		List[str]       =	position_embedding_type
             lowercase__			:		str       =	use_cache
             lowercase__			:		str       =	classifier_dropout
             lowercase__			:		int       =	pre_norm
             lowercase__			:		Optional[int]       =	adapter_reduction_factor
             lowercase__			:		str       =	adapter_layer_norm
             lowercase__			:		Optional[Any]       =	adapter_reuse_layer_norm
             lowercase__			:		str       =	ln_before_adapter
             lowercase__			:		Tuple       =	list(a							)
             lowercase__			:		str       =	default_language





class      UpperCAmelCase_	(    _a):


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







from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy







class     __lowerCAmelCase (       __magic_name__ ):

			"""simple docstring"""







			snake_case_   		=       '''EncodecFeatureExtractor'''
			snake_case_   		=       ('''T5Tokenizer''', '''T5TokenizerFast''')


			def __init__(      self			,						lowerCamelCase__			,						lowerCamelCase__							)    ->      int:



							'''simple docstring'''
							super().__init__(lowerCamelCase__			,						lowerCamelCase__							)
							__lowerCamelCase		       =    self.feature_extractor
							__lowerCamelCase		       =    False


			def       lowercase_    (      self			,						lowerCamelCase__=None			,						lowerCamelCase__=None			,						lowerCamelCase__=True							)    ->      List[Any]:



							'''simple docstring'''
							return self.tokenizer.get_decoder_prompt_ids(task=lowerCamelCase__			,						language=lowerCamelCase__			,						no_timestamps=lowerCamelCase__							)


			def __call__(      self			,						*lowerCamelCase__			,						**lowerCamelCase__							)    ->      Dict:



							'''simple docstring'''
							# For backward compatibility
							if self._in_target_context_manager:
											return self.current_processor(*lowerCamelCase__			,						**lowerCamelCase__							)

							__lowerCamelCase		       =    kwargs.pop('audio'			,						lowerCamelCase__							)
							__lowerCamelCase		       =    kwargs.pop('sampling_rate'			,						lowerCamelCase__							)
							__lowerCamelCase		       =    kwargs.pop('text'			,						lowerCamelCase__							)
							if len(lowerCamelCase__							) > 0:
											__lowerCamelCase		       =    args[0]
											__lowerCamelCase		       =    args[1:]

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

							if text is not None:
											__lowerCamelCase		       =    self.tokenizer(lowerCamelCase__			,						**lowerCamelCase__							)

							if audio is not None:
											__lowerCamelCase		       =    self.feature_extractor(lowerCamelCase__			,						*lowerCamelCase__			,						sampling_rate=lowerCamelCase__			,						**lowerCamelCase__							)

							if audio is None:
											return inputs

							elif text is None:
											return audio_inputs

							else:
											__lowerCamelCase		       =    audio_inputs['input_values']
											if "padding_mask" in audio_inputs:
															__lowerCamelCase		       =    audio_inputs['padding_mask']
											return inputs


			def       lowercase_    (      self			,						*lowerCamelCase__			,						**lowerCamelCase__							)    ->      Union[str, Any]:



							'''simple docstring'''
							__lowerCamelCase		       =    kwargs.pop('audio'			,						lowerCamelCase__							)
							__lowerCamelCase		       =    kwargs.pop('padding_mask'			,						lowerCamelCase__							)

							if len(lowerCamelCase__							) > 0:
											__lowerCamelCase		       =    args[0]
											__lowerCamelCase		       =    args[1:]

							if audio_values is not None:
											return self._decode_audio(lowerCamelCase__			,						padding_mask=lowerCamelCase__							)
							else:
											return self.tokenizer.batch_decode(*lowerCamelCase__			,						**lowerCamelCase__							)


			def       lowercase_    (      self			,						*lowerCamelCase__			,						**lowerCamelCase__							)    ->      List[Any]:



							'''simple docstring'''
							return self.tokenizer.decode(*lowerCamelCase__			,						**lowerCamelCase__							)






			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__ = None							)    ->      List[np.ndarray]:



							'''simple docstring'''
							__lowerCamelCase		       =    to_numpy(lowerCamelCase__							)
							__lowerCamelCase       ,						__lowerCamelCase       ,						__lowerCamelCase		       =    audio_values.shape

							if padding_mask is None:
											return list(lowerCamelCase__							)

							__lowerCamelCase		       =    to_numpy(lowerCamelCase__							)

							# match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
							# token (so that the generated audio values are **not** treated as padded tokens)
							__lowerCamelCase		       =    seq_len - padding_mask.shape[-1]
							__lowerCamelCase		       =    1 - self.feature_extractor.padding_value
							__lowerCamelCase		       =    np.pad(lowerCamelCase__			,						((0, 0), (0, difference))			,						'constant'			,						constant_values=lowerCamelCase__							)

							__lowerCamelCase		       =    audio_values.tolist()
							for i in range(lowerCamelCase__							):
											__lowerCamelCase		       =    np.asarray(audio_values[i]							)[
											    padding_mask[i][None, :] != self.feature_extractor.padding_value
											]
											__lowerCamelCase		       =    sliced_audio.reshape(lowerCamelCase__			,						-1							)

							return audio_values


