





# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
			import torch






class       __A					(					TensorFormatter[Mapping, """torch.Tensor""", Mapping]				):

			'''simple docstring'''







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



				'''simple docstring'''







				super().__init__(features=__lowerCAmelCase							)
				lowerCamelCase__              =	torch_tensor_kwargs
				import torch  # noqa import torch at initialization





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase							):



				'''simple docstring'''







				import torch

				if isinstance(__lowerCAmelCase ,						__lowerCAmelCase							) and column:
					if all(
					    isinstance(__lowerCAmelCase ,						torch.Tensor							) and x.shape == column[0].shape and x.dtype == column[0].dtype
					    for x in column							):
						return torch.stack(__lowerCAmelCase							)
				return column





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase							):



				'''simple docstring'''







				import torch

				if isinstance(__lowerCAmelCase ,						(str, bytes, type(__lowerCAmelCase							))							):
					return value
				elif isinstance(__lowerCAmelCase ,						(np.character, np.ndarray)							) and np.issubdtype(value.dtype ,						np.character							):
					return value.tolist()

				lowerCamelCase__              =	{}

				if isinstance(__lowerCAmelCase ,						(np.number, np.ndarray)							) and np.issubdtype(value.dtype ,						np.integer							):
					lowerCamelCase__              =	{'''dtype''': torch.intaa}
				elif isinstance(__lowerCAmelCase ,						(np.number, np.ndarray)							) and np.issubdtype(value.dtype ,						np.floating							):
					lowerCamelCase__              =	{'''dtype''': torch.floataa}
				elif config.PIL_AVAILABLE and "PIL" in sys.modules:
					import PIL.Image

					if isinstance(__lowerCAmelCase ,						PIL.Image.Image							):
						lowerCamelCase__              =	np.asarray(__lowerCAmelCase							)
				return torch.tensor(__lowerCAmelCase ,						**{**default_dtype, **self.torch_tensor_kwargs}							)





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase							):



				'''simple docstring'''







				import torch

				# support for torch, tf, jax etc.
				if hasattr(__lowerCAmelCase ,						'''__array__'''							) and not isinstance(__lowerCAmelCase ,						torch.Tensor							):
					lowerCamelCase__              =	data_struct.__array__()
				# support for nested types like struct of list of struct
				if isinstance(__lowerCAmelCase ,						np.ndarray							):
					if data_struct.dtype == object:  # torch tensors cannot be instantied from an array of objects
						return self._consolidate([self.recursive_tensorize(__lowerCAmelCase							) for substruct in data_struct]							)
				elif isinstance(__lowerCAmelCase ,						(list, tuple)							):
					return self._consolidate([self.recursive_tensorize(__lowerCAmelCase							) for substruct in data_struct]							)
				return self._tensorize(__lowerCAmelCase							)





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase							):



				'''simple docstring'''







				return map_nested(self._recursive_tensorize ,						__lowerCAmelCase ,						map_list=__lowerCAmelCase							)





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__              =	self.numpy_arrow_extractor().extract_row(__lowerCAmelCase							)
				lowerCamelCase__              =	self.python_features_decoder.decode_row(__lowerCAmelCase							)
				return self.recursive_tensorize(__lowerCAmelCase							)





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__              =	self.numpy_arrow_extractor().extract_column(__lowerCAmelCase							)
				lowerCamelCase__              =	self.python_features_decoder.decode_column(__lowerCAmelCase ,						pa_table.column_names[0]							)
				lowerCamelCase__              =	self.recursive_tensorize(__lowerCAmelCase							)
				lowerCamelCase__              =	self._consolidate(__lowerCAmelCase							)
				return column





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__              =	self.numpy_arrow_extractor().extract_batch(__lowerCAmelCase							)
				lowerCamelCase__              =	self.python_features_decoder.decode_batch(__lowerCAmelCase							)
				lowerCamelCase__              =	self.recursive_tensorize(__lowerCAmelCase							)
				for column_name in batch:
					lowerCamelCase__              =	self._consolidate(batch[column_name]							)
				return batch










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							)							)



