





"""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


__magic_name__       :					Dict						=			logging.get_logger(__name__)






@add_end_docstrings(__SCREAMING_SNAKE_CASE	)
class 							lowercase__			(   __SCREAMING_SNAKE_CASE	):

			"""simple docstring"""







			def __init__(      self						,   **_A    ):






									'''simple docstring'''







									super().__init__(**_A    )

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







			def __call__(      self						,   _A						,   **_A    ):






									'''simple docstring'''







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







			def   _a      (      self						,   **_A    ):






									'''simple docstring'''







									UpperCamelCase				:		str				   =			{}
									if "candidate_labels" in kwargs:
															UpperCamelCase				:		Optional[int]				   =			kwargs["""candidate_labels"""]
									if "hypothesis_template" in kwargs:
															UpperCamelCase				:		Optional[int]				   =			kwargs["""hypothesis_template"""]

									return preprocess_params, {}, {}







			def   _a      (      self						,   _A						,   _A=None						,   _A="This is a sound of {}."    ):






									'''simple docstring'''







									if isinstance(_A						,   _A    ):
															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				:		Optional[Any]				   =			requests.get(_A    ).content
															else:
																					with open(_A						,   """rb"""    ) as f:
																											UpperCamelCase				:		Any				   =			f.read()

									if isinstance(_A						,   _A    ):
															UpperCamelCase				:		Any				   =			ffmpeg_read(_A						,   self.feature_extractor.sampling_rate    )

									if not isinstance(_A						,   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				:		Any				   =			self.feature_extractor(
									    [audio]						,   sampling_rate=self.feature_extractor.sampling_rate						,   return_tensors="""pt"""    )
									UpperCamelCase				:		List[Any]				   =			candidate_labels
									UpperCamelCase				:		Optional[int]				   =			[hypothesis_template.format(_A    ) for x in candidate_labels]
									UpperCamelCase				:		Optional[int]				   =			self.tokenizer(_A						,   return_tensors=self.framework						,   padding=_A    )
									UpperCamelCase				:		Optional[Any]				   =			[text_inputs]
									return inputs







			def   _a      (      self						,   _A    ):






									'''simple docstring'''







									UpperCamelCase				:		Optional[int]				   =			model_inputs.pop("""candidate_labels"""    )
									UpperCamelCase				:		Optional[Any]				   =			model_inputs.pop("""text_inputs"""    )
									if isinstance(text_inputs[0]						,   _A    ):
															UpperCamelCase				:		str				   =			text_inputs[0]
									else:
															# Batching case.
															UpperCamelCase				:		Tuple				   =			text_inputs[0][0]

									UpperCamelCase				:		Optional[Any]				   =			self.model(**_A						,   **_A    )

									UpperCamelCase				:		Tuple				   =			{
									    """candidate_labels""": candidate_labels,
									    """logits""": outputs.logits_per_audio,
									}
									return model_outputs







			def   _a      (      self						,   _A    ):






									'''simple docstring'''







									UpperCamelCase				:		Tuple				   =			model_outputs.pop("""candidate_labels"""    )
									UpperCamelCase				:		Optional[int]				   =			model_outputs["""logits"""][0]

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

									UpperCamelCase				:		int				   =			[
									    {"""score""": score, """label""": candidate_label}
									    for score, candidate_label in sorted(zip(_A						,   _A    )						,   key=lambda _A    : -x[0]    )
									]
									return result










"""simple docstring"""


import colorsys

from PIL import Image  # type: ignore

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		Optional[int]				   =			x
						UpperCamelCase				:		str				   =			y
						for step in range(SCREAMING_SNAKE_CASE     ):  # noqa: B007
												UpperCamelCase				:		str				   =			a * a - b * b + x
												UpperCamelCase				:		Any				   =			2 * a * b + y
												UpperCamelCase				:		List[str]				   =			a_new

												# divergence happens for all complex number with an absolute value
												# greater than 4
												if a * a + b * b > 4:
																		break
						return step / (max_step - 1)

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						if distance == 1:
												return (0, 0, 0)
						else:
												return (255, 255, 255)

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						if distance == 1:
												return (0, 0, 0)
						else:
												return tuple(round(i * 255     ) for i in colorsys.hsv_to_rgb(SCREAMING_SNAKE_CASE							,     1							,     1     )     )

def      UpperCamelCase							(SCREAMING_SNAKE_CASE = 800							,     SCREAMING_SNAKE_CASE = 600							,     SCREAMING_SNAKE_CASE = -0.6							,     SCREAMING_SNAKE_CASE = 0							,     SCREAMING_SNAKE_CASE = 3.2							,     SCREAMING_SNAKE_CASE = 50							,     SCREAMING_SNAKE_CASE = True							,     ):
						UpperCamelCase				:		Tuple				   =			Image.new("""RGB"""							,     (image_width, image_height)     )
						UpperCamelCase				:		Dict				   =			img.load()

						# loop through the image-coordinates
						for image_x in range(SCREAMING_SNAKE_CASE     ):
												for image_y in range(SCREAMING_SNAKE_CASE     ):
																		# determine the figure-coordinates based on the image-coordinates
																		UpperCamelCase				:		Dict				   =			figure_width / image_width * image_height
																		UpperCamelCase				:		Tuple				   =			figure_center_x + (image_x / image_width - 0.5) * figure_width
																		UpperCamelCase				:		Dict				   =			figure_center_y + (image_y / image_height - 0.5) * figure_height

																		UpperCamelCase				:		Union[str, Any]				   =			get_distance(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )

																		# color the corresponding pixel based on the selected coloring-function
																		if use_distance_color_coding:
																								UpperCamelCase				:		int				   =			get_color_coded_rgb(SCREAMING_SNAKE_CASE     )
																		else:
																								UpperCamelCase				:		Optional[int]				   =			get_black_and_white_rgb(SCREAMING_SNAKE_CASE     )

						return img


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		# colored version, full figure
		__magic_name__       :					int						=			get_image()

		# uncomment for colored version, different section, zoomed in
		# img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
		# figure_width = 0.8)

		# uncomment for black and white version, full figure
		# img = get_image(use_distance_color_coding = False)

		# uncomment to save the image
		# img.save("mandelbrot.png")

		img.show()



