

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase_							:					Any		 =   {
    '''configuration_roberta''': ['''ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RobertaConfig''', '''RobertaOnnxConfig'''],
    '''tokenization_roberta''': ['''RobertaTokenizer'''],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCAmelCase_							:					Union[str, Any]		 =   ['''RobertaTokenizerFast''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCAmelCase_							:					int		 =   [
			    '''ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''RobertaForCausalLM''',
			    '''RobertaForMaskedLM''',
			    '''RobertaForMultipleChoice''',
			    '''RobertaForQuestionAnswering''',
			    '''RobertaForSequenceClassification''',
			    '''RobertaForTokenClassification''',
			    '''RobertaModel''',
			    '''RobertaPreTrainedModel''',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCAmelCase_							:					int		 =   [
			    '''TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''TFRobertaForCausalLM''',
			    '''TFRobertaForMaskedLM''',
			    '''TFRobertaForMultipleChoice''',
			    '''TFRobertaForQuestionAnswering''',
			    '''TFRobertaForSequenceClassification''',
			    '''TFRobertaForTokenClassification''',
			    '''TFRobertaMainLayer''',
			    '''TFRobertaModel''',
			    '''TFRobertaPreTrainedModel''',
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCAmelCase_							:					List[Any]		 =   [
			    '''FlaxRobertaForCausalLM''',
			    '''FlaxRobertaForMaskedLM''',
			    '''FlaxRobertaForMultipleChoice''',
			    '''FlaxRobertaForQuestionAnswering''',
			    '''FlaxRobertaForSequenceClassification''',
			    '''FlaxRobertaForTokenClassification''',
			    '''FlaxRobertaModel''',
			    '''FlaxRobertaPreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
			from .tokenization_roberta import RobertaTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_roberta_fast import RobertaTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_roberta import (
						    ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
						    RobertaForCausalLM,
						    RobertaForMaskedLM,
						    RobertaForMultipleChoice,
						    RobertaForQuestionAnswering,
						    RobertaForSequenceClassification,
						    RobertaForTokenClassification,
						    RobertaModel,
						    RobertaPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_roberta import (
						    TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFRobertaForCausalLM,
						    TFRobertaForMaskedLM,
						    TFRobertaForMultipleChoice,
						    TFRobertaForQuestionAnswering,
						    TFRobertaForSequenceClassification,
						    TFRobertaForTokenClassification,
						    TFRobertaMainLayer,
						    TFRobertaModel,
						    TFRobertaPreTrainedModel,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_roberta import (
						    FlaxRobertaForCausalLM,
						    FlaxRobertaForMaskedLM,
						    FlaxRobertaForMultipleChoice,
						    FlaxRobertaForQuestionAnswering,
						    FlaxRobertaForSequenceClassification,
						    FlaxRobertaForTokenClassification,
						    FlaxRobertaModel,
						    FlaxRobertaPreTrainedModel,
						)

else:
			import sys

			UpperCAmelCase_							:					List[str]		 =   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)








from typing import List, Optional

import numpy as np

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






class      lowerCamelCase_      (	_lowercase	):
	_lowercase		:   Union[str, Any]     					=				'''EncodecFeatureExtractor'''
	_lowercase		:   Any     					=				('''T5Tokenizer''', '''T5TokenizerFast''')




	def __init__(     self						:     List[Any]					,      __A						:     Any					,      __A						:     Tuple							):
		super().__init__(__A					,      __A							)
		__A :			Dict		    =			self.feature_extractor
		__A :			List[str]		    =			False




	def        lowerCAmelCase_       (     self						:     Union[str, Any]					,      __A						:     str=None					,      __A						:     Tuple=None					,      __A						:     Dict=True							):
		return self.tokenizer.get_decoder_prompt_ids(task=__A					,      language=__A					,      no_timestamps=__A							)




	def __call__(     self						:     Optional[Any]					,      *__A						:     Tuple					,      **__A						:     Tuple							):
		# For backward compatibility
		if self._in_target_context_manager:
			return self.current_processor(*__A					,      **__A							)

		__A :			str		    =			kwargs.pop("""audio"""					,      __A							)
		__A :			Optional[Any]		    =			kwargs.pop("""sampling_rate"""					,      __A							)
		__A :			int		    =			kwargs.pop("""text"""					,      __A							)
		if len(__A							) > 0:
			__A :			int		    =			args[0]
			__A :			Dict		    =			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:
			__A :			Dict		    =			self.tokenizer(__A					,      **__A							)

		if audio is not None:
			__A :			Optional[int]		    =			self.feature_extractor(__A					,      *__A					,      sampling_rate=__A					,      **__A							)

		if audio is None:
			return inputs

		elif text is None:
			return audio_inputs

		else:
			__A :			List[Any]		    =			audio_inputs["""input_values"""]
			if "padding_mask" in audio_inputs:
				__A :			int		    =			audio_inputs["""padding_mask"""]
			return inputs




	def        lowerCAmelCase_       (     self						:     List[str]					,      *__A						:     int					,      **__A						:     Tuple							):
		__A :			Optional[int]		    =			kwargs.pop("""audio"""					,      __A							)
		__A :			List[str]		    =			kwargs.pop("""padding_mask"""					,      __A							)

		if len(__A							) > 0:
			__A :			Dict		    =			args[0]
			__A :			Optional[int]		    =			args[1:]

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




	def        lowerCAmelCase_       (     self						:     Optional[Any]					,      *__A						:     Dict					,      **__A						:     Any							):
		return self.tokenizer.decode(*__A					,      **__A							)




	def        lowerCAmelCase_       (     self						:     Tuple					,      __A						:     Union[str, Any]					,      __A						:     Optional = None							):
		__A :			List[str]		    =			to_numpy(__A							)
		__A    ,				__A    ,				__A :			Tuple		    =			audio_values.shape

		if padding_mask is None:
			return list(__A							)

		__A :			Union[str, Any]		    =			to_numpy(__A							)

		# 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)
		__A :			List[str]		    =			seq_len - padding_mask.shape[-1]
		__A :			Tuple		    =			1 - self.feature_extractor.padding_value
		__A :			Optional[int]		    =			np.pad(__A					,      ((0, 0), (0, difference))					,      """constant"""					,      constant_values=__A							)

		__A :			int		    =			audio_values.tolist()
		for i in range(__A							):
			__A :			str		    =			np.asarray(audio_values[i]							)[
			    padding_mask[i][None, :] != self.feature_extractor.padding_value
			]
			__A :			List[Any]		    =			sliced_audio.reshape(__A					,      -1							)

		return audio_values





