


'''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


__UpperCAmelCase :    Tuple       =							logging.get_logger(__name__)




class 		a__    (							__SCREAMING_SNAKE_CASE  ):





							'''simple docstring'''






							def __init__(		self ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_ ,   **lowerCamelCase_       )	->							Optional[int]:
														lowerCAmelCase__	       =     feature_size
														lowerCAmelCase__	       =     sampling_rate
														lowerCAmelCase__	       =     padding_value

														lowerCAmelCase__	       =     kwargs.pop('''padding_side''' ,   '''right'''       )
														lowerCAmelCase__	       =     kwargs.pop('''return_attention_mask''' ,   UpperCamelCase__       )

														super().__init__(**UpperCamelCase__       )







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_ = True ,   lowerCamelCase_ = None ,   lowerCamelCase_ = False ,   lowerCamelCase_ = None ,   lowerCamelCase_ = None ,   lowerCamelCase_ = None ,   )	->							BatchFeature:







														if isinstance(UpperCamelCase__ ,   (list, tuple)       ) and isinstance(processed_features[0] ,   (dict, BatchFeature)       ):
																					lowerCAmelCase__	       =     {
																					    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__	       =     processed_features[self.model_input_names[0]]
														lowerCAmelCase__	       =     (
														    return_attention_mask if return_attention_mask is not None else self.return_attention_mask
														)

														if len(UpperCamelCase__       ) == 0:
																					if return_attention_mask:
																												lowerCAmelCase__	       =     []
																					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__	       =     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__	       =     0
																					while len(required_input[index]       ) == 0:
																												index += 1
																					if index < len(UpperCamelCase__       ):
																												lowerCAmelCase__	       =     required_input[index][0]

														if return_tensors is None:
																					if is_tf_tensor(UpperCamelCase__       ):
																												lowerCAmelCase__	       =     '''tf'''
																					elif is_torch_tensor(UpperCamelCase__       ):
																												lowerCAmelCase__	       =     '''pt'''
																					elif isinstance(UpperCamelCase__ ,   (int, float, list, tuple, np.ndarray)       ):
																												lowerCAmelCase__	       =     '''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__	       =     to_numpy(UpperCamelCase__       )
																					else:
																												lowerCAmelCase__	       =     [to_numpy(UpperCamelCase__       ) for v in value]

        # Convert padding_strategy in PaddingStrategy
														lowerCAmelCase__	       =     self._get_padding_strategies(padding=UpperCamelCase__ ,   max_length=UpperCamelCase__       )

														lowerCAmelCase__	       =     processed_features[self.model_input_names[0]]

														lowerCAmelCase__	       =     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__	       =     []
														for i in range(UpperCamelCase__       ):
																					lowerCAmelCase__	       =     {k: v[i] for k, v in processed_features.items()}
																					# truncation
																					lowerCAmelCase__	       =     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__	       =     max(len(input_slice[self.model_input_names[0]]       ) for input_slice in truncated_inputs       )
																					lowerCAmelCase__	       =     PaddingStrategy.MAX_LENGTH

														lowerCAmelCase__	       =     {}
														for i in range(UpperCamelCase__       ):
																					# padding
																					lowerCAmelCase__	       =     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__	       =     []
																												if value.dtype is np.dtype(np.floataa       ):
																																			lowerCAmelCase__	       =     value.astype(np.floataa       )
																												batch_outputs[key].append(UpperCamelCase__       )

														return BatchFeature(UpperCamelCase__ ,   tensor_type=UpperCamelCase__       )







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_ = None ,   lowerCamelCase_ = PaddingStrategy.DO_NOT_PAD ,   lowerCamelCase_ = None ,   lowerCamelCase_ = None ,   )	->							dict:
														lowerCAmelCase__	       =     processed_features[self.model_input_names[0]]

														if padding_strategy == PaddingStrategy.LONGEST:
																					lowerCAmelCase__	       =     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__	       =     ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

														lowerCAmelCase__	       =     padding_strategy != PaddingStrategy.DO_NOT_PAD and len(UpperCamelCase__       ) < max_length

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

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

														return processed_features







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_ = None ,   lowerCamelCase_ = None ,   lowerCamelCase_ = None ,   )	->							List[Any]:







														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__	       =     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__	       =     ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

														lowerCAmelCase__	       =     len(UpperCamelCase__       ) > max_length

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

														return processed_features







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_=False ,   lowerCamelCase_=None       )	->							Dict:







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






from __future__ import annotations

import math
from collections.abc import Callable


def  _snake_case       (					A			,   A			,   A			,   A = 100			,   )						->   float:
							lowerCAmelCase__	       =     x_start
							lowerCAmelCase__	       =     fnc(A	)
							lowerCAmelCase__	       =     0.0

							for _ in range(A	):
														# Approximates curve as a sequence of linear lines and sums their length
														lowerCAmelCase__	       =     (x_end - x_start) / steps + xa
														lowerCAmelCase__	       =     fnc(A	)
														length += math.hypot(xa - xa			,   fxa - fxa	)

														# Increment step
														lowerCAmelCase__	       =     xa
														lowerCAmelCase__	       =     fxa

							return length


if __name__ == "__main__":


		def  _snake_case       (					A	)						->   List[Any]:
									return math.sin(10 * x	)

		print('''f(x) = sin(10 * x)''')
		print('''The length of the curve from x = -10 to x = 10 is:''')
		__UpperCAmelCase       =							10
		while i <= 100_000:
				print(f"""With {i} steps: {line_length(f, -10, 10, i)}""")
				i *= 10