



"""simple docstring"""


from itertools import product

from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						Optional[int]					,     _SCREAMING_SNAKE_CASE   :						int				):






				'''simple docstring'''



				_UpperCAmelCase					=    k_size // 2
				_UpperCAmelCase		,						_UpperCAmelCase					=    mgrid[0 - center : k_size - center, 0 - center : k_size - center]
				_UpperCAmelCase					=    1 / (2 * pi * sigma) * exp(-(square(_SCREAMING_SNAKE_CASE				) + square(_SCREAMING_SNAKE_CASE				)) / (2 * square(_SCREAMING_SNAKE_CASE				))				)
				return g







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						List[Any]					,     _SCREAMING_SNAKE_CASE   :						Optional[Any]					,     _SCREAMING_SNAKE_CASE   :						Union[str, Any]				):






				'''simple docstring'''



				_UpperCAmelCase		,						_UpperCAmelCase					=    image.shape[0], image.shape[1]
				# dst image height and width
				_UpperCAmelCase					=    height - k_size + 1
				_UpperCAmelCase					=    width - k_size + 1

				# im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
				_UpperCAmelCase					=    zeros((dst_height * dst_width, k_size * k_size)				)
				_UpperCAmelCase					=    0
				for i, j in product(range(_SCREAMING_SNAKE_CASE				)					,     range(_SCREAMING_SNAKE_CASE				)				):
								_UpperCAmelCase					=    ravel(image[i : i + k_size, j : j + k_size]				)
								_UpperCAmelCase					=    window
								row += 1

				#  turn the kernel into shape(k*k, 1)
				_UpperCAmelCase					=    gen_gaussian_kernel(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)
				_UpperCAmelCase					=    ravel(_SCREAMING_SNAKE_CASE				)

				# reshape and get the dst image
				_UpperCAmelCase					=    dot(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				).reshape(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				).astype(_SCREAMING_SNAKE_CASE				)

				return dst


if __name__ == "__main__":
					# read original image
					__A   :							Tuple										=   imread(r"../image_data/lena.jpg")
					# turn image in gray scale value
					__A   :							List[str]										=   cvtColor(img, COLOR_BGR2GRAY)

					# get values with two different mask size
					__A   :							str										=   gaussian_filter(gray, 3, sigma=1)
					__A   :							Union[str, Any]										=   gaussian_filter(gray, 5, sigma=0.8)

					# show result images
					imshow("gaussian filter with 3x3 mask", gaussianaxa)
					imshow("gaussian filter with 5x5 mask", gaussianaxa)
					waitKey()








"""simple docstring"""


from collections import UserDict
from typing import Union

import numpy as np
import requests

from ..utils import (
    add_end_docstrings,
    logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline


__A   :							List[str]										=   logging.get_logger(__name__)
@add_end_docstrings(lowerCAmelCase)
class 			_a (					lowerCAmelCase):
			"""simple docstring"""



			def __init__(		self     :    Union[str, Any]				,   **__UpperCamelCase     :    Optional[Any]   )->List[Any]:
							super().__init__(**__UpperCamelCase   )

							if self.framework != "pt":
											raise ValueError(F'The {self.__class__} is only available in PyTorch.'   )
							# No specific FOR_XXX available yet



			def __call__(		self     :    Optional[int]				,   __UpperCamelCase     :    Union[np.ndarray, bytes, str]				,   **__UpperCamelCase     :    Tuple   )->List[str]:
							return super().__call__(__UpperCamelCase				,   **__UpperCamelCase   )



			def  lowercase__						(		self     :    Any				,   **__UpperCamelCase     :    Any   )->Union[str, Any]:
							_UpperCAmelCase					=    {}
							if "candidate_labels" in kwargs:
											_UpperCAmelCase					=    kwargs['''candidate_labels''']
							if "hypothesis_template" in kwargs:
											_UpperCAmelCase					=    kwargs['''hypothesis_template''']

							return preprocess_params, {}, {}



			def  lowercase__						(		self     :    Optional[int]				,   __UpperCamelCase     :    Union[str, Any]				,   __UpperCamelCase     :    List[Any]=None				,   __UpperCamelCase     :    Tuple="This is a sound of {}."   )->int:
							if isinstance(__UpperCamelCase				,   __UpperCamelCase   ):
											if audio.startswith('''http://'''   ) or audio.startswith('''https://'''   ):
															# We need to actually check for a real protocol, otherwise it's impossible to use a local file
															# like http_huggingface_co.png
															_UpperCAmelCase					=    requests.get(__UpperCamelCase   ).content
											else:
															with open(__UpperCamelCase				,   '''rb'''   ) as f:
																			_UpperCAmelCase					=    f.read()

							if isinstance(__UpperCamelCase				,   __UpperCamelCase   ):
											_UpperCAmelCase					=    ffmpeg_read(__UpperCamelCase				,   self.feature_extractor.sampling_rate   )

							if not isinstance(__UpperCamelCase				,   np.ndarray   ):
											raise ValueError('''We expect a numpy ndarray as input'''   )
							if len(audio.shape   ) != 1:
											raise ValueError('''We expect a single channel audio input for ZeroShotAudioClassificationPipeline'''   )

							_UpperCAmelCase					=    self.feature_extractor(
							    [audio]				,   sampling_rate=self.feature_extractor.sampling_rate				,   return_tensors='''pt'''   )
							_UpperCAmelCase					=    candidate_labels
							_UpperCAmelCase					=    [hypothesis_template.format(__UpperCamelCase   ) for x in candidate_labels]
							_UpperCAmelCase					=    self.tokenizer(__UpperCamelCase				,   return_tensors=self.framework				,   padding=__UpperCamelCase   )
							_UpperCAmelCase					=    [text_inputs]
							return inputs



			def  lowercase__						(		self     :    Union[str, Any]				,   __UpperCamelCase     :    Optional[int]   )->Any:
							_UpperCAmelCase					=    model_inputs.pop('''candidate_labels'''   )
							_UpperCAmelCase					=    model_inputs.pop('''text_inputs'''   )
							if isinstance(text_inputs[0]				,   __UpperCamelCase   ):
											_UpperCAmelCase					=    text_inputs[0]
							else:
											# Batching case.
											_UpperCAmelCase					=    text_inputs[0][0]

							_UpperCAmelCase					=    self.model(**__UpperCamelCase				,   **__UpperCamelCase   )

							_UpperCAmelCase					=    {
							    '''candidate_labels''': candidate_labels,
							    '''logits''': outputs.logits_per_audio,
							}
							return model_outputs



			def  lowercase__						(		self     :    List[str]				,   __UpperCamelCase     :    Any   )->List[Any]:
							_UpperCAmelCase					=    model_outputs.pop('''candidate_labels'''   )
							_UpperCAmelCase					=    model_outputs['''logits'''][0]

							if self.framework == "pt":
											_UpperCAmelCase					=    logits.softmax(dim=0   )
											_UpperCAmelCase					=    probs.tolist()
							else:
											raise ValueError('''`tf` framework not supported.'''   )

							_UpperCAmelCase					=    [
							    {'''score''': score, '''label''': candidate_label}
							    for score, candidate_label in sorted(zip(__UpperCamelCase				,   __UpperCamelCase   )				,   key=lambda __UpperCamelCase   : -x[0]   )
							]
							return result



