


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			:			int												=					logging.get_logger(__name__)
class 						lowerCamelCase_							(					lowerCAmelCase__					):



					'''simple docstring'''







					def __init__(					self			,							snake_case_			,							snake_case_			,							snake_case_			,							**snake_case_    ) ->      Any:







								'''simple docstring'''

								__lowercase			       = feature_size
								__lowercase			       = sampling_rate
								__lowercase			       = padding_value

								__lowercase			       = kwargs.pop('''padding_side'''			,							'''right'''    )
								__lowercase			       = kwargs.pop('''return_attention_mask'''			,							snake_case_    )

								super().__init__(**snake_case_    )






					def A   (					self			,							snake_case_			,							snake_case_ = True			,							snake_case_ = None			,							snake_case_ = False			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							) ->      BatchFeature:







								'''simple docstring'''

								if isinstance(snake_case_			,							(list, tuple)    ) and isinstance(processed_features[0]			,							(dict, BatchFeature)    ):
											__lowercase			       = {
											    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()    )}'    )

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

								if len(snake_case_    ) == 0:
											if return_attention_mask:
														__lowercase			       = []
											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

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

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

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

        # Convert padding_strategy in PaddingStrategy
								__lowercase			       = self._get_padding_strategies(padding=snake_case_			,							max_length=snake_case_    )

								__lowercase			       = processed_features[self.model_input_names[0]]

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

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

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

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

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

								return BatchFeature(snake_case_			,							tensor_type=snake_case_    )






					def A   (					self			,							snake_case_			,							snake_case_ = None			,							snake_case_ = PaddingStrategy.DO_NOT_PAD			,							snake_case_ = None			,							snake_case_ = None			,							) ->      dict:







								'''simple docstring'''

								__lowercase			       = processed_features[self.model_input_names[0]]

								if padding_strategy == PaddingStrategy.LONGEST:
											__lowercase			       = len(snake_case_    )

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

								__lowercase			       = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(snake_case_    ) < max_length

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

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

								return processed_features






					def A   (					self			,							snake_case_			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							) ->      str:







								'''simple docstring'''

								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.'''    )

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

								__lowercase			       = len(snake_case_    ) > max_length

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

								return processed_features






					def A   (					self			,							snake_case_=False			,							snake_case_=None    ) ->      Any:







								'''simple docstring'''

								if padding is not False:
											if padding is True:
														__lowercase			       = PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
											elif not isinstance(snake_case_			,							snake_case_    ):
														__lowercase			       = PaddingStrategy(snake_case_    )
											elif isinstance(snake_case_			,							snake_case_    ):
														__lowercase			       = padding
								else:
											__lowercase			       = 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






a			:			Any												=					[sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(100000)]


def 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







			__lowercase			       = 0
			while number:
						# Increased Speed Slightly by checking every 5 digits together.
						sum_of_digits_squared += DIGITS_SQUARED[number % 10_00_00]
						number //= 10_00_00

			return sum_of_digits_squared


# There are 2 Chains made,
# One ends with 89 with the chain member 58 being the one which when declared first,
# there will be the least number of iterations for all the members to be checked.

# The other one ends with 1 and has only one element 1.

# So 58 and 1 are chosen to be declared at the starting.

# Changed dictionary to an array to quicken the solution
a			:			list[bool | None]												=					[None] * 10000000
a			:			int												=					True
a			:			Any												=					False


def 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







			if CHAINS[number - 1] is not None:
						return CHAINS[number - 1]  # type: ignore

			__lowercase			       = chain(next_number(_UpperCamelCase	)	)
			__lowercase			       = number_chain

			while number < 10_00_00_00:
						__lowercase			       = number_chain
						number *= 10

			return number_chain


def 		lowercase_	(  _UpperCamelCase = 10_00_00_00	):


			'''simple docstring'''







			for i in range(1							,      _UpperCamelCase	):
						if CHAINS[i] is None:
									chain(i + 1	)

			return CHAINS[:number].count(_UpperCamelCase	)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	print(f'''{solution() = }''')


