




'''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


_SCREAMING_SNAKE_CASE			=						logging.get_logger(__name__)
class   __UpperCAmelCase				(     lowerCAmelCase				):



					'''simple docstring'''





					def __init__(	self     :						Any      ,       _lowercase     :						int      ,       _lowercase     :						int      ,       _lowercase     :						float      ,       **_lowercase     :						List[str])    ->							List[str]:
								A_									= feature_size
								A_									= sampling_rate
								A_									= padding_value

								A_									= kwargs.pop('padding_side'      ,       'right')
								A_									= kwargs.pop('return_attention_mask'      ,       _lowercase)

								super().__init__(**_lowercase)



					def     __snake_case						(	self     :						Any      ,       _lowercase     :						Union[
					        BatchFeature,
					        List[BatchFeature],
					        Dict[str, BatchFeature],
					        Dict[str, List[BatchFeature]],
					        List[Dict[str, BatchFeature]],
					    ]      ,       _lowercase     :						Union[bool, str, PaddingStrategy] = True      ,       _lowercase     :						Optional[int] = None      ,       _lowercase     :						bool = False      ,       _lowercase     :						Optional[int] = None      ,       _lowercase     :						Optional[bool] = None      ,       _lowercase     :						Optional[Union[str, TensorType]] = None      ,       )    ->							BatchFeature:
								# 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(_lowercase      ,       (list, tuple)) and isinstance(processed_features[0]      ,       (dict, BatchFeature)):
											A_									= {
											    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())}')

								A_									= processed_features[self.model_input_names[0]]
								A_									= (
								    return_attention_mask if return_attention_mask is not None else self.return_attention_mask
								)

								if len(_lowercase) == 0:
											if return_attention_mask:
														A_									= []
											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

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

								if return_tensors is None:
											if is_tf_tensor(_lowercase):
														A_									= 'tf'
											elif is_torch_tensor(_lowercase):
														A_									= 'pt'
											elif isinstance(_lowercase      ,       (int, float, list, tuple, np.ndarray)):
														A_									= 'np'
											else:
														raise ValueError(
														    F'type of {first_element} unknown: {type(_lowercase)}. '
														    'Should be one of a python, numpy, pytorch or tensorflow object.')

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

        # Convert padding_strategy in PaddingStrategy
								A_									= self._get_padding_strategies(padding=_lowercase      ,       max_length=_lowercase)

								A_									= processed_features[self.model_input_names[0]]

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

								A_									= []
								for i in range(_lowercase):
											A_									= {k: v[i] for k, v in processed_features.items()}
											# truncation
											A_									= self._truncate(
											    _lowercase      ,       max_length=_lowercase      ,       pad_to_multiple_of=_lowercase      ,       truncation=_lowercase      ,       )
											truncated_inputs.append(_lowercase)

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

								A_									= {}
								for i in range(_lowercase):
											# padding
											A_									= self._pad(
											    truncated_inputs[i]      ,       max_length=_lowercase      ,       padding_strategy=_lowercase      ,       pad_to_multiple_of=_lowercase      ,       return_attention_mask=_lowercase      ,       )

											for key, value in outputs.items():
														if key not in batch_outputs:
																	A_									= []
														if value.dtype is np.dtype(np.floataa):
																	A_									= value.astype(np.floataa)
														batch_outputs[key].append(_lowercase)

								return BatchFeature(_lowercase      ,       tensor_type=_lowercase)



					def     __snake_case						(	self     :						List[str]      ,       _lowercase     :						Union[Dict[str, np.ndarray], BatchFeature]      ,       _lowercase     :						Optional[int] = None      ,       _lowercase     :						PaddingStrategy = PaddingStrategy.DO_NOT_PAD      ,       _lowercase     :						Optional[int] = None      ,       _lowercase     :						Optional[bool] = None      ,       )    ->							dict:
								A_									= processed_features[self.model_input_names[0]]

								if padding_strategy == PaddingStrategy.LONGEST:
											A_									= len(_lowercase)

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

								A_									= padding_strategy != PaddingStrategy.DO_NOT_PAD and len(_lowercase) < max_length

								if return_attention_mask and "attention_mask" not in processed_features:
											A_									= np.ones(len(_lowercase)      ,       dtype=np.intaa)

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

								return processed_features



					def     __snake_case						(	self     :						str      ,       _lowercase     :						Union[Dict[str, np.ndarray], BatchFeature]      ,       _lowercase     :						Optional[int] = None      ,       _lowercase     :						Optional[int] = None      ,       _lowercase     :						Optional[bool] = None      ,       )    ->							Optional[int]:
								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.')

								A_									= 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):
											A_									= ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

								A_									= len(_lowercase) > max_length

								if needs_to_be_truncated:
											A_									= processed_features[self.model_input_names[0]][:max_length]
											if "attention_mask" in processed_features:
														A_									= processed_features['attention_mask'][:max_length]

								return processed_features



					def     __snake_case						(	self     :						int      ,       _lowercase     :						Optional[int]=False      ,       _lowercase     :						Optional[int]=None)    ->							Optional[int]:

								# Get padding strategy
								if padding is not False:
											if padding is True:
														A_									= PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
											elif not isinstance(_lowercase      ,       _lowercase):
														A_									= PaddingStrategy(_lowercase)
											elif isinstance(_lowercase      ,       _lowercase):
														A_									= padding
								else:
											A_									= 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 pytest

from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict





@pytest.mark.parametrize(
    'split_dict'       ,[
        SplitDict(),
        SplitDict({'train': SplitInfo(name='train'       ,num_bytes=1337       ,num_examples=42       ,dataset_name='my_dataset'     )}     ),
        SplitDict({'train': SplitInfo(name='train'       ,num_bytes=1337       ,num_examples=42     )}     ),
        SplitDict({'train': SplitInfo()}     ),
    ]       ,)
def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_     )		->		List[str]:
			A_									= split_dict._to_yaml_list()
			assert len(SCREAMING_SNAKE_CASE_     ) == len(SCREAMING_SNAKE_CASE_     )
			A_									= SplitDict._from_yaml_list(SCREAMING_SNAKE_CASE_     )
			for split_name, split_info in split_dict.items():
						# dataset_name field is deprecated, and is therefore not part of the YAML dump
						A_									= None
						# the split name of split_dict takes over the name of the split info object
						A_									= split_name
			assert split_dict == reloaded






@pytest.mark.parametrize(
    'split_info'       ,[SplitInfo(), SplitInfo(dataset_name=SCREAMING_SNAKE_CASE_     ), SplitInfo(dataset_name='my_dataset'     )]     )
def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_     )		->		Optional[Any]:
			# For backward compatibility, we need asdict(split_dict) to return split info dictrionaries with the "dataset_name"
			# field even if it's deprecated. This way old versionso of `datasets` can still reload dataset_infos.json files
			A_									= asdict(SplitDict({'train': split_info}     )     )
			assert "dataset_name" in split_dict_asdict["train"]
			assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name




