






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


SCREAMING_SNAKE_CASE__				     =			logging.get_logger(__name__)






@add_end_docstrings(__UpperCamelCase		)
class 			_UpperCamelCase(       __UpperCamelCase		):

		def __init__(					self		:    Optional[Any]    ,       **SCREAMING_SNAKE_CASE__		:    List[Any]    ):



					'''simple docstring'''



					super().__init__(**snake_case__    )

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

		def __call__(					self		:    Union[str, Any]    ,       SCREAMING_SNAKE_CASE__		:    Union[np.ndarray, bytes, str]    ,       **SCREAMING_SNAKE_CASE__		:    int    ):



					'''simple docstring'''



					return super().__call__(snake_case__    ,       **snake_case__    )

		def    __lowerCAmelCase  (					self		:    Any    ,       **SCREAMING_SNAKE_CASE__		:    Any    ):



					'''simple docstring'''



					__a						:  Union[str, Any]									=     {}
					if "candidate_labels" in kwargs:
								__a						:  Optional[Any]									=     kwargs['candidate_labels']
					if "hypothesis_template" in kwargs:
								__a						:  str									=     kwargs['hypothesis_template']

					return preprocess_params, {}, {}

		def    __lowerCAmelCase  (					self		:    Optional[Any]    ,       SCREAMING_SNAKE_CASE__		:    str    ,       SCREAMING_SNAKE_CASE__		:    int=None    ,       SCREAMING_SNAKE_CASE__		:    List[str]="This is a sound of {}."    ):



					'''simple docstring'''



					if isinstance(snake_case__    ,       snake_case__    ):
								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
											__a						:  Optional[int]									=     requests.get(snake_case__    ).content
								else:
											with open(snake_case__    ,       'rb'    ) as f:
														__a						:  Tuple									=     f.read()

					if isinstance(snake_case__    ,       snake_case__    ):
								__a						:  List[Any]									=     ffmpeg_read(snake_case__    ,       self.feature_extractor.sampling_rate    )

					if not isinstance(snake_case__    ,       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'    )

					__a						:  str									=     self.feature_extractor(
					    [audio]    ,       sampling_rate=self.feature_extractor.sampling_rate    ,       return_tensors='pt'    )
					__a						:  Optional[int]									=     candidate_labels
					__a						:  str									=     [hypothesis_template.format(snake_case__    ) for x in candidate_labels]
					__a						:  List[str]									=     self.tokenizer(snake_case__    ,       return_tensors=self.framework    ,       padding=snake_case__    )
					__a						:  Dict									=     [text_inputs]
					return inputs

		def    __lowerCAmelCase  (					self		:    Union[str, Any]    ,       SCREAMING_SNAKE_CASE__		:    int    ):



					'''simple docstring'''



					__a						:  Optional[Any]									=     model_inputs.pop('candidate_labels'    )
					__a						:  Tuple									=     model_inputs.pop('text_inputs'    )
					if isinstance(text_inputs[0]    ,       snake_case__    ):
								__a						:  Union[str, Any]									=     text_inputs[0]
					else:
								# Batching case.
								__a						:  List[Any]									=     text_inputs[0][0]

					__a						:  Optional[int]									=     self.model(**snake_case__    ,       **snake_case__    )

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



		def    __lowerCAmelCase  (					self		:    int    ,       SCREAMING_SNAKE_CASE__		:    str    ):



					'''simple docstring'''



					__a						:  Tuple									=     model_outputs.pop('candidate_labels'    )
					__a						:  Tuple									=     model_outputs['logits'][0]

					if self.framework == "pt":
								__a						:  Dict									=     logits.softmax(dim=0    )
								__a						:  Optional[int]									=     probs.tolist()
					else:
								raise ValueError('`tf` framework not supported.'    )

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






"""simple docstring"""




import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		if (ksize % 2) == 0:
				lowerCAmelCase__   = ksize + 1
		lowerCAmelCase__   = np.zeros((ksize, ksize)      ,	dtype=np.floataa  )

		# each value
		for y in range(lowerCamelCase__  ):
				for x in range(lowerCamelCase__  ):
						# distance from center
						lowerCAmelCase__   = x - ksize // 2
						lowerCAmelCase__   = y - ksize // 2

						# degree to radiant
						lowerCAmelCase__   = theta / 180 * np.pi
						lowerCAmelCase__   = np.cos(_theta  )
						lowerCAmelCase__   = np.sin(_theta  )

						# get kernel x
						lowerCAmelCase__   = cos_theta * px + sin_theta * py

						# get kernel y
						lowerCAmelCase__   = -sin_theta * px + cos_theta * py

						# fill kernel
						lowerCAmelCase__   = np.exp(
						    -(_x**2 + gamma**2 * _y**2) / (2 * sigma**2)  ) * np.cos(2 * np.pi * _x / lambd + psi  )

		return gabor


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			# read original image
			__lowerCAmelCase     :				Tuple    				=    imread("../image_data/lena.jpg")
			# turn image in gray scale value
			__lowerCAmelCase     :				List[str]    				=    cvtColor(img, COLOR_BGR2GRAY)

			# Apply multiple Kernel to detect edges
			__lowerCAmelCase     :				Union[str, Any]    				=    np.zeros(gray.shape[:2])
			for theta in [0, 30, 60, 90, 1_20, 1_50]:
						__lowerCAmelCase     :				Union[str, Any]    				=    gabor_filter_kernel(10, 8, theta, 10, 0, 0)
						out += filteraD(gray, CV_8UC3, kernel_aa)
			__lowerCAmelCase     :				Optional[int]    				=    out / out.max() * 2_55
			__lowerCAmelCase     :				Tuple    				=    out.astype(np.uinta)

			imshow("Original", gray)
			imshow("Gabor filter with 20x20 mask and 6 directions", out)

			waitKey(0)




