

'''simple docstring'''




from copy import deepcopy
from typing import Optional, Union

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available


if is_torch_available():
						import torch

if is_tf_available():
						import tensorflow as tf





class 					__magic_name__		(					_UpperCAmelCase):
			UpperCamelCase__       = ['''image_processor''']
			UpperCamelCase__       = '''SamImageProcessor'''







			def __init__(			self       :						Any       ,    lowercase_       :						Optional[int]					):
										super().__init__(__A					)
										lowercase_      :    Union[str, Any]       		= self.image_processor
										lowercase_      :    Optional[int]       		= -10
										lowercase_      :    List[str]       		= self.image_processor.size["longest_edge"]







			def __call__(			self       :						Any       ,    lowercase_       :						Tuple=None       ,    lowercase_       :						Any=None       ,    lowercase_       :						Optional[Any]=None       ,    lowercase_       :						Dict=None       ,    lowercase_       :						Optional[Union[str, TensorType]] = None       ,    **lowercase_       :						str       ,    ):
										lowercase_      :    str       		= self.image_processor(
										    __A       ,    return_tensors=__A       ,    **__A       ,    )

										# pop arguments that are not used in the foward but used nevertheless
										lowercase_      :    List[Any]       		= encoding_image_processor["original_sizes"]

										if hasattr(__A       ,    """numpy"""					):  # Checks if Torch or TF tensor
																	lowercase_      :    Dict       		= original_sizes.numpy()

										lowercase_      :    Union[str, Any]       		= self._check_and_preprocess_points(
										    input_points=__A       ,    input_labels=__A       ,    input_boxes=__A       ,    )

										lowercase_      :    Union[str, Any]       		= self._normalize_and_convert(
										    __A       ,    __A       ,    input_points=__A       ,    input_labels=__A       ,    input_boxes=__A       ,    return_tensors=__A       ,    )

										return encoding_image_processor







			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[str]       ,    lowercase_       :						List[str]       ,    lowercase_       :						Union[str, Any]       ,    lowercase_       :						List[str]=None       ,    lowercase_       :						int=None       ,    lowercase_       :						Any=None       ,    lowercase_       :						int="pt"       ,    ):
										if input_points is not None:
																	if len(__A					) != len(__A					):
																								lowercase_      :    Tuple       		= [
																								    self._normalize_coordinates(self.target_size       ,    __A       ,    original_sizes[0]					) for point in input_points
																								]
																	else:
																								lowercase_      :    Dict       		= [
																								    self._normalize_coordinates(self.target_size       ,    __A       ,    __A					)
																								    for point, original_size in zip(__A       ,    __A					)
																								]
																	# check that all arrays have the same shape
																	if not all(point.shape == input_points[0].shape for point in input_points					):
																								if input_labels is not None:
																															lowercase_      :    Dict       		= self._pad_points_and_labels(__A       ,    __A					)

																	lowercase_      :    Optional[int]       		= np.array(__A					)

										if input_labels is not None:
																	lowercase_      :    List[Any]       		= np.array(__A					)

										if input_boxes is not None:
																	if len(__A					) != len(__A					):
																								lowercase_      :    Optional[Any]       		= [
																								    self._normalize_coordinates(self.target_size       ,    __A       ,    original_sizes[0]       ,    is_bounding_box=__A					)
																								    for box in input_boxes
																								]
																	else:
																								lowercase_      :    List[str]       		= [
																								    self._normalize_coordinates(self.target_size       ,    __A       ,    __A       ,    is_bounding_box=__A					)
																								    for box, original_size in zip(__A       ,    __A					)
																								]
																	lowercase_      :    Dict       		= np.array(__A					)

										if input_boxes is not None:
																	if return_tensors == "pt":
																								lowercase_      :    str       		= torch.from_numpy(__A					)
																								# boxes batch size of 1 by default
																								lowercase_      :    int       		= input_boxes.unsqueeze(1					) if len(input_boxes.shape					) != 3 else input_boxes
																	elif return_tensors == "tf":
																								lowercase_      :    str       		= tf.convert_to_tensor(__A					)
																								# boxes batch size of 1 by default
																								lowercase_      :    Optional[Any]       		= tf.expand_dims(__A       ,    1					) if len(input_boxes.shape					) != 3 else input_boxes
																	encoding_image_processor.update({"""input_boxes""": input_boxes}					)
										if input_points is not None:
																	if return_tensors == "pt":
																								lowercase_      :    Dict       		= torch.from_numpy(__A					)
																								# point batch size of 1 by default
																								lowercase_      :    int       		= input_points.unsqueeze(1					) if len(input_points.shape					) != 4 else input_points
																	elif return_tensors == "tf":
																								lowercase_      :    Any       		= tf.convert_to_tensor(__A					)
																								# point batch size of 1 by default
																								lowercase_      :    Any       		= tf.expand_dims(__A       ,    1					) if len(input_points.shape					) != 4 else input_points
																	encoding_image_processor.update({"""input_points""": input_points}					)
										if input_labels is not None:
																	if return_tensors == "pt":
																								lowercase_      :    Optional[int]       		= torch.from_numpy(__A					)
																								# point batch size of 1 by default
																								lowercase_      :    List[str]       		= input_labels.unsqueeze(1					) if len(input_labels.shape					) != 3 else input_labels
																	elif return_tensors == "tf":
																								lowercase_      :    Dict       		= tf.convert_to_tensor(__A					)
																								# point batch size of 1 by default
																								lowercase_      :    Tuple       		= tf.expand_dims(__A       ,    1					) if len(input_labels.shape					) != 3 else input_labels
																	encoding_image_processor.update({"""input_labels""": input_labels}					)

										return encoding_image_processor







			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[str]       ,    lowercase_       :						Dict       ,    lowercase_       :						str					):
										lowercase_      :    Optional[Any]       		= max([point.shape[0] for point in input_points]					)
										lowercase_      :    Dict       		= []
										for i, point in enumerate(__A					):
																	if point.shape[0] != expected_nb_points:
																								lowercase_      :    Tuple       		= np.concatenate(
																								    [point, np.zeros((expected_nb_points - point.shape[0], 2)					) + self.point_pad_value]       ,    axis=0					)
																								lowercase_      :    Dict       		= np.append(input_labels[i]       ,    [self.point_pad_value]					)
																	processed_input_points.append(__A					)
										lowercase_      :    Tuple       		= processed_input_points
										return input_points, input_labels







			def 			SCREAMING_SNAKE_CASE_  (			self       :						int       ,    lowercase_       :						int       ,    lowercase_       :						np.ndarray       ,    lowercase_       :						Union[str, Any]       ,    lowercase_       :						Optional[int]=False					):
										lowercase_      :    List[str]       		= original_size
										lowercase_      :    Union[str, Any]       		= self.image_processor._get_preprocess_shape(__A       ,    longest_edge=__A					)
										lowercase_      :    str       		= deepcopy(__A					).astype(__A					)

										if is_bounding_box:
																	lowercase_      :    Dict       		= coords.reshape(-1       ,    2       ,    2					)

										lowercase_      :    int       		= coords[..., 0] * (new_w / old_w)
										lowercase_      :    Dict       		= coords[..., 1] * (new_h / old_h)

										if is_bounding_box:
																	lowercase_      :    List[Any]       		= coords.reshape(-1       ,    4					)

										return coords







			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[str]       ,    lowercase_       :						str=None       ,    lowercase_       :						Tuple=None       ,    lowercase_       :						List[Any]=None       ,    ):
										if input_points is not None:
																	if hasattr(__A       ,    """numpy"""					):  # Checks for TF or Torch tensor
																								lowercase_      :    Optional[int]       		= input_points.numpy().tolist()

																	if not isinstance(__A       ,    __A					) or not isinstance(input_points[0]       ,    __A					):
																								raise ValueError("""Input points must be a list of list of floating points."""					)
																	lowercase_      :    str       		= [np.array(__A					) for input_point in input_points]
										else:
																	lowercase_      :    List[str]       		= None

										if input_labels is not None:
																	if hasattr(__A       ,    """numpy"""					):
																								lowercase_      :    Any       		= input_labels.numpy().tolist()

																	if not isinstance(__A       ,    __A					) or not isinstance(input_labels[0]       ,    __A					):
																								raise ValueError("""Input labels must be a list of list integers."""					)
																	lowercase_      :    str       		= [np.array(__A					) for label in input_labels]
										else:
																	lowercase_      :    int       		= None

										if input_boxes is not None:
																	if hasattr(__A       ,    """numpy"""					):
																								lowercase_      :    str       		= input_boxes.numpy().tolist()

																	if (
																	    not isinstance(__A       ,    __A					)
																	    or not isinstance(input_boxes[0]       ,    __A					)
																	    or not isinstance(input_boxes[0][0]       ,    __A					)
																	):
																								raise ValueError("""Input boxes must be a list of list of list of floating points."""					)
																	lowercase_      :    str       		= [np.array(__A					).astype(np.floataa					) for box in input_boxes]
										else:
																	lowercase_      :    str       		= None

										return input_points, input_labels, input_boxes







			@property
			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[int]					):
										lowercase_      :    List[Any]       		= self.image_processor.model_input_names
										return list(dict.fromkeys(__A					)					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[str]       ,    *lowercase_       :						int       ,    **lowercase_       :						Union[str, Any]					):
										return self.image_processor.post_process_masks(*__A       ,    **__A					)






'''simple docstring'''




from math import cos, sin, sqrt, tau

from audio_filters.iir_filter import IIRFilter




def 							lowerCamelCase			(   UpperCAmelCase__ :      int   ,   UpperCAmelCase__ :      int   ,   UpperCAmelCase__ :      float = 1 / sqrt(2				)				)    ->   IIRFilter:
							lowercase_      :    str       		= tau * frequency / samplerate
							lowercase_      :    Tuple       		= sin(UpperCAmelCase__				)
							lowercase_      :    int       		= cos(UpperCAmelCase__				)
							lowercase_      :    Any       		= _sin / (2 * q_factor)

							lowercase_      :    Dict       		= (1 - _cos) / 2
							lowercase_      :    Optional[int]       		= 1 - _cos

							lowercase_      :    Dict       		= 1 + alpha
							lowercase_      :    List[Any]       		= -2 * _cos
							lowercase_      :    Union[str, Any]       		= 1 - alpha

							lowercase_      :    List[Any]       		= IIRFilter(2				)
							filt.set_coefficients([aa, aa, aa]   ,   [ba, ba, ba]				)
							return filt




def 							lowerCamelCase			(   UpperCAmelCase__ :      int   ,   UpperCAmelCase__ :      int   ,   UpperCAmelCase__ :      float = 1 / sqrt(2				)				)    ->   IIRFilter:
							lowercase_      :    str       		= tau * frequency / samplerate
							lowercase_      :    Optional[int]       		= sin(UpperCAmelCase__				)
							lowercase_      :    Dict       		= cos(UpperCAmelCase__				)
							lowercase_      :    Optional[int]       		= _sin / (2 * q_factor)

							lowercase_      :    Dict       		= (1 + _cos) / 2
							lowercase_      :    str       		= -1 - _cos

							lowercase_      :    Dict       		= 1 + alpha
							lowercase_      :    Optional[Any]       		= -2 * _cos
							lowercase_      :    List[Any]       		= 1 - alpha

							lowercase_      :    Union[str, Any]       		= IIRFilter(2				)
							filt.set_coefficients([aa, aa, aa]   ,   [ba, ba, ba]				)
							return filt




def 							lowerCamelCase			(   UpperCAmelCase__ :      int   ,   UpperCAmelCase__ :      int   ,   UpperCAmelCase__ :      float = 1 / sqrt(2				)				)    ->   IIRFilter:
							lowercase_      :    int       		= tau * frequency / samplerate
							lowercase_      :    int       		= sin(UpperCAmelCase__				)
							lowercase_      :    Union[str, Any]       		= cos(UpperCAmelCase__				)
							lowercase_      :    str       		= _sin / (2 * q_factor)

							lowercase_      :    str       		= _sin / 2
							lowercase_      :    Any       		= 0
							lowercase_      :    Optional[Any]       		= -ba

							lowercase_      :    Dict       		= 1 + alpha
							lowercase_      :    Union[str, Any]       		= -2 * _cos
							lowercase_      :    Union[str, Any]       		= 1 - alpha

							lowercase_      :    Tuple       		= IIRFilter(2				)
							filt.set_coefficients([aa, aa, aa]   ,   [ba, ba, ba]				)
							return filt




def 							lowerCamelCase			(   UpperCAmelCase__ :      int   ,   UpperCAmelCase__ :      int   ,   UpperCAmelCase__ :      float = 1 / sqrt(2				)				)    ->   IIRFilter:
							lowercase_      :    List[str]       		= tau * frequency / samplerate
							lowercase_      :    Any       		= sin(UpperCAmelCase__				)
							lowercase_      :    List[Any]       		= cos(UpperCAmelCase__				)
							lowercase_      :    Optional[Any]       		= _sin / (2 * q_factor)

							lowercase_      :    Any       		= 1 - alpha
							lowercase_      :    Optional[Any]       		= -2 * _cos
							lowercase_      :    Optional[int]       		= 1 + alpha

							lowercase_      :    Dict       		= IIRFilter(2				)
							filt.set_coefficients([ba, ba, ba]   ,   [ba, ba, ba]				)
							return filt




def 							lowerCamelCase			(   UpperCAmelCase__ :      int   ,   UpperCAmelCase__ :      int   ,   UpperCAmelCase__ :      float   ,   UpperCAmelCase__ :      float = 1 / sqrt(2				)   ,   )    ->   IIRFilter:
							lowercase_      :    Dict       		= tau * frequency / samplerate
							lowercase_      :    Tuple       		= sin(UpperCAmelCase__				)
							lowercase_      :    List[Any]       		= cos(UpperCAmelCase__				)
							lowercase_      :    List[Any]       		= _sin / (2 * q_factor)
							lowercase_      :    Any       		= 10 ** (gain_db / 40)

							lowercase_      :    List[str]       		= 1 + alpha * big_a
							lowercase_      :    List[Any]       		= -2 * _cos
							lowercase_      :    Dict       		= 1 - alpha * big_a
							lowercase_      :    str       		= 1 + alpha / big_a
							lowercase_      :    List[str]       		= -2 * _cos
							lowercase_      :    Tuple       		= 1 - alpha / big_a

							lowercase_      :    Any       		= IIRFilter(2				)
							filt.set_coefficients([aa, aa, aa]   ,   [ba, ba, ba]				)
							return filt




def 							lowerCamelCase			(   UpperCAmelCase__ :      int   ,   UpperCAmelCase__ :      int   ,   UpperCAmelCase__ :      float   ,   UpperCAmelCase__ :      float = 1 / sqrt(2				)   ,   )    ->   IIRFilter:
							lowercase_      :    Dict       		= tau * frequency / samplerate
							lowercase_      :    Union[str, Any]       		= sin(UpperCAmelCase__				)
							lowercase_      :    Any       		= cos(UpperCAmelCase__				)
							lowercase_      :    Any       		= _sin / (2 * q_factor)
							lowercase_      :    Any       		= 10 ** (gain_db / 40)
							lowercase_      :    Any       		= (big_a + 1) - (big_a - 1) * _cos
							lowercase_      :    int       		= (big_a + 1) + (big_a - 1) * _cos
							lowercase_      :    Tuple       		= (big_a - 1) - (big_a + 1) * _cos
							lowercase_      :    Optional[Any]       		= (big_a - 1) + (big_a + 1) * _cos
							lowercase_      :    int       		= 2 * sqrt(UpperCAmelCase__				) * alpha

							lowercase_      :    Tuple       		= big_a * (pmc + aaa)
							lowercase_      :    List[str]       		= 2 * big_a * mpc
							lowercase_      :    Union[str, Any]       		= big_a * (pmc - aaa)
							lowercase_      :    Optional[int]       		= ppmc + aaa
							lowercase_      :    Optional[int]       		= -2 * pmpc
							lowercase_      :    Any       		= ppmc - aaa

							lowercase_      :    Optional[int]       		= IIRFilter(2				)
							filt.set_coefficients([aa, aa, aa]   ,   [ba, ba, ba]				)
							return filt




def 							lowerCamelCase			(   UpperCAmelCase__ :      int   ,   UpperCAmelCase__ :      int   ,   UpperCAmelCase__ :      float   ,   UpperCAmelCase__ :      float = 1 / sqrt(2				)   ,   )    ->   IIRFilter:
							lowercase_      :    str       		= tau * frequency / samplerate
							lowercase_      :    int       		= sin(UpperCAmelCase__				)
							lowercase_      :    int       		= cos(UpperCAmelCase__				)
							lowercase_      :    Dict       		= _sin / (2 * q_factor)
							lowercase_      :    Union[str, Any]       		= 10 ** (gain_db / 40)
							lowercase_      :    Union[str, Any]       		= (big_a + 1) - (big_a - 1) * _cos
							lowercase_      :    Optional[int]       		= (big_a + 1) + (big_a - 1) * _cos
							lowercase_      :    Any       		= (big_a - 1) - (big_a + 1) * _cos
							lowercase_      :    str       		= (big_a - 1) + (big_a + 1) * _cos
							lowercase_      :    Optional[int]       		= 2 * sqrt(UpperCAmelCase__				) * alpha

							lowercase_      :    Tuple       		= big_a * (ppmc + aaa)
							lowercase_      :    List[Any]       		= -2 * big_a * pmpc
							lowercase_      :    Optional[Any]       		= big_a * (ppmc - aaa)
							lowercase_      :    Optional[Any]       		= pmc + aaa
							lowercase_      :    int       		= 2 * mpc
							lowercase_      :    Tuple       		= pmc - aaa

							lowercase_      :    Union[str, Any]       		= IIRFilter(2				)
							filt.set_coefficients([aa, aa, aa]   ,   [ba, ba, ba]				)
							return filt





