"""simple docstring"""





from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


__A  =				logging.get_logger(__name__)
class  _snake_case     (					a__		):






						def __init__(       self      :			Union[str, Any]							,					UpperCAmelCase      :			int							,					UpperCAmelCase      :			int							,					UpperCAmelCase      :			float							,					**UpperCAmelCase      :			Optional[Any]    ):
													__lowerCamelCase					:		Optional[Any]		   =     feature_size
													__lowerCamelCase					:		Dict		   =     sampling_rate
													__lowerCamelCase					:		List[str]		   =     padding_value

													__lowerCamelCase					:		List[Any]		   =     kwargs.pop("padding_side"							,					"right"    )
													__lowerCamelCase					:		List[str]		   =     kwargs.pop("return_attention_mask"							,					UpperCAmelCase    )

													super().__init__(**UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			Optional[Any]							,					UpperCAmelCase      :			Union[
						        BatchFeature,
						        List[BatchFeature],
						        Dict[str, BatchFeature],
						        Dict[str, List[BatchFeature]],
						        List[Dict[str, BatchFeature]],
						    ]							,					UpperCAmelCase      :			Union[bool, str, PaddingStrategy] = True							,					UpperCAmelCase      :			Optional[int] = None							,					UpperCAmelCase      :			bool = False							,					UpperCAmelCase      :			Optional[int] = None							,					UpperCAmelCase      :			Optional[bool] = None							,					UpperCAmelCase      :			Optional[Union[str, TensorType]] = None							,					):
													# If we have a list of dicts, let's convert it in a dict of lists
													# We do this to allow using this method as a collate_fn function in PyTorch Dataloader
													if isinstance(UpperCAmelCase							,					(list, tuple)    ) and isinstance(processed_features[0]							,					(dict, BatchFeature)    ):
																				__lowerCamelCase					:		Tuple		   =     {
																				    key: [example[key] for example in processed_features] for key in processed_features[0].keys()
																				}

													# The model's main input name, usually `input_values`, has be passed for padding
													if self.model_input_names[0] not in processed_features:
																				raise ValueError(
																				    "You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`"
																				    F""" to this method that includes {self.model_input_names[0]}, but you provided"""
																				    F""" {list(processed_features.keys()    )}"""    )

													__lowerCamelCase					:		Optional[int]		   =     processed_features[self.model_input_names[0]]
													__lowerCamelCase					:		Dict		   =     (
													    return_attention_mask if return_attention_mask is not None else self.return_attention_mask
													)

													if len(UpperCAmelCase    ) == 0:
																				if return_attention_mask:
																											__lowerCamelCase					:		Union[str, Any]		   =     []
																				return processed_features

													# If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
													# and rebuild them afterwards if no return_tensors is specified
													# Note that we lose the specific device the tensor may be on for PyTorch

													__lowerCamelCase					:		Tuple		   =     required_input[0]
													if isinstance(UpperCAmelCase							,					(list, tuple)    ):
																				# first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
																				__lowerCamelCase					:		str		   =     0
																				while len(required_input[index]    ) == 0:
																											index += 1
																				if index < len(UpperCAmelCase    ):
																											__lowerCamelCase					:		Tuple		   =     required_input[index][0]

													if return_tensors is None:
																				if is_tf_tensor(UpperCAmelCase    ):
																											__lowerCamelCase					:		Dict		   =     "tf"
																				elif is_torch_tensor(UpperCAmelCase    ):
																											__lowerCamelCase					:		List[str]		   =     "pt"
																				elif isinstance(UpperCAmelCase							,					(int, float, list, tuple, np.ndarray)    ):
																											__lowerCamelCase					:		Optional[int]		   =     "np"
																				else:
																											raise ValueError(
																											    F"""type of {first_element} unknown: {type(UpperCAmelCase    )}. """
																											    "Should be one of a python, numpy, pytorch or tensorflow object."    )

													for key, value in processed_features.items():
																				if isinstance(value[0]							,					(int, float)    ):
																											__lowerCamelCase					:		str		   =     to_numpy(UpperCAmelCase    )
																				else:
																											__lowerCamelCase					:		str		   =     [to_numpy(UpperCAmelCase    ) for v in value]

        # Convert padding_strategy in PaddingStrategy
													__lowerCamelCase					:		List[str]		   =     self._get_padding_strategies(padding=UpperCAmelCase							,					max_length=UpperCAmelCase    )

													__lowerCamelCase					:		Union[str, Any]		   =     processed_features[self.model_input_names[0]]

													__lowerCamelCase					:		int		   =     len(UpperCAmelCase    )
													if not all(len(UpperCAmelCase    ) == batch_size for v in processed_features.values()    ):
																				raise ValueError("Some items in the output dictionary have a different batch size than others."    )

													__lowerCamelCase					:		str		   =     []
													for i in range(UpperCAmelCase    ):
																				__lowerCamelCase					:		int		   =     {k: v[i] for k, v in processed_features.items()}
																				# truncation
																				__lowerCamelCase					:		Dict		   =     self._truncate(
																				    UpperCAmelCase							,					max_length=UpperCAmelCase							,					pad_to_multiple_of=UpperCAmelCase							,					truncation=UpperCAmelCase							,					)
																				truncated_inputs.append(UpperCAmelCase    )

													if padding_strategy == PaddingStrategy.LONGEST:
																				# make sure that `max_length` cannot be longer than the longest truncated length
																				__lowerCamelCase					:		int		   =     max(len(input_slice[self.model_input_names[0]]    ) for input_slice in truncated_inputs    )
																				__lowerCamelCase					:		Dict		   =     PaddingStrategy.MAX_LENGTH

													__lowerCamelCase					:		str		   =     {}
													for i in range(UpperCAmelCase    ):
																				# padding
																				__lowerCamelCase					:		int		   =     self._pad(
																				    truncated_inputs[i]							,					max_length=UpperCAmelCase							,					padding_strategy=UpperCAmelCase							,					pad_to_multiple_of=UpperCAmelCase							,					return_attention_mask=UpperCAmelCase							,					)

																				for key, value in outputs.items():
																											if key not in batch_outputs:
																																		__lowerCamelCase					:		str		   =     []
																											if value.dtype is np.dtype(np.floataa    ):
																																		__lowerCamelCase					:		Tuple		   =     value.astype(np.floataa    )
																											batch_outputs[key].append(UpperCAmelCase    )

													return BatchFeature(UpperCAmelCase							,					tensor_type=UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			Optional[Any]							,					UpperCAmelCase      :			Union[Dict[str, np.ndarray], BatchFeature]							,					UpperCAmelCase      :			Optional[int] = None							,					UpperCAmelCase      :			PaddingStrategy = PaddingStrategy.DO_NOT_PAD							,					UpperCAmelCase      :			Optional[int] = None							,					UpperCAmelCase      :			Optional[bool] = None							,					):
													__lowerCamelCase					:		List[str]		   =     processed_features[self.model_input_names[0]]

													if padding_strategy == PaddingStrategy.LONGEST:
																				__lowerCamelCase					:		Optional[Any]		   =     len(UpperCAmelCase    )

													if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
																				__lowerCamelCase					:		int		   =     ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

													__lowerCamelCase					:		Optional[int]		   =     padding_strategy != PaddingStrategy.DO_NOT_PAD and len(UpperCAmelCase    ) < max_length

													if return_attention_mask and "attention_mask" not in processed_features:
																				__lowerCamelCase					:		List[str]		   =     np.ones(len(UpperCAmelCase    )							,					dtype=np.intaa    )

													if needs_to_be_padded:
																				__lowerCamelCase					:		Optional[Any]		   =     max_length - len(UpperCAmelCase    )
																				if self.padding_side == "right":
																											if return_attention_mask:
																																		__lowerCamelCase					:		int		   =     np.pad(
																																		    processed_features["attention_mask"]							,					(0, difference)    )
																											__lowerCamelCase					:		str		   =     ((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
																											__lowerCamelCase					:		Optional[Any]		   =     np.pad(
																											    UpperCAmelCase							,					UpperCAmelCase							,					"constant"							,					constant_values=self.padding_value    )
																				elif self.padding_side == "left":
																											if return_attention_mask:
																																		__lowerCamelCase					:		int		   =     np.pad(
																																		    processed_features["attention_mask"]							,					(difference, 0)    )
																											__lowerCamelCase					:		List[str]		   =     ((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
																											__lowerCamelCase					:		Union[str, Any]		   =     np.pad(
																											    UpperCAmelCase							,					UpperCAmelCase							,					"constant"							,					constant_values=self.padding_value    )
																				else:
																											raise ValueError("Invalid padding strategy:" + str(self.padding_side    )    )

													return processed_features






						def       lowerCamelCase__     (       self      :			Dict							,					UpperCAmelCase      :			Union[Dict[str, np.ndarray], BatchFeature]							,					UpperCAmelCase      :			Optional[int] = None							,					UpperCAmelCase      :			Optional[int] = None							,					UpperCAmelCase      :			Optional[bool] = None							,					):
													if not truncation:
																				return processed_features
													elif truncation and max_length is None:
																				raise ValueError("When setting ``truncation=True``, make sure that ``max_length`` is defined."    )

													__lowerCamelCase					:		Dict		   =     processed_features[self.model_input_names[0]]

													# find `max_length` that fits `pad_to_multiple_of`
													if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
																				__lowerCamelCase					:		List[Any]		   =     ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

													__lowerCamelCase					:		Union[str, Any]		   =     len(UpperCAmelCase    ) > max_length

													if needs_to_be_truncated:
																				__lowerCamelCase					:		Dict		   =     processed_features[self.model_input_names[0]][:max_length]
																				if "attention_mask" in processed_features:
																											__lowerCamelCase					:		List[Any]		   =     processed_features["attention_mask"][:max_length]

													return processed_features







						def       lowerCamelCase__     (       self      :			str							,					UpperCAmelCase      :			Tuple=False							,					UpperCAmelCase      :			List[Any]=None    ):

													# Get padding strategy
													if padding is not False:
																				if padding is True:
																											__lowerCamelCase					:		int		   =     PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
																				elif not isinstance(UpperCAmelCase							,					UpperCAmelCase    ):
																											__lowerCamelCase					:		Any		   =     PaddingStrategy(UpperCAmelCase    )
																				elif isinstance(UpperCAmelCase							,					UpperCAmelCase    ):
																											__lowerCamelCase					:		Optional[Any]		   =     padding
													else:
																				__lowerCamelCase					:		Optional[int]		   =     PaddingStrategy.DO_NOT_PAD

													# Set max length if needed
													if max_length is None:
																				if padding_strategy == PaddingStrategy.MAX_LENGTH:
																											raise ValueError(
																											    F"""When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined"""    )

        # Test if we have a padding value
													if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
																				raise ValueError(
																				    "Asking to pad but the feature_extractor does not have a padding value. Please select a value to use"
																				    " as `padding_value`. For example: `feature_extractor.padding_value = 0.0`."    )

													return padding_strategy
"""simple docstring"""





import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class  _snake_case     :
						snake_case__			  =			None
						snake_case__			  =			False
						snake_case__			  =			False
						snake_case__			  =			False
						snake_case__			  =			None
						snake_case__			  =			None
						snake_case__			  =			False
						snake_case__			  =			False
						snake_case__			  =			False
						snake_case__			  =			True
						snake_case__			  =			None
						snake_case__			  =			1
						snake_case__			  =			None
						snake_case__			  =			False
						snake_case__			  =			None
						snake_case__			  =			None






						def       lowerCamelCase__     (       self      :			Any    ):
													return self.__class__(**{k: copy.deepcopy(UpperCAmelCase    ) for k, v in self.__dict__.items()}    )