



__UpperCAmelCase						  =     [0.33, 0.44, 0.55, 0.44, 0.33]
__UpperCAmelCase						  =     3
__UpperCAmelCase						  =     5

# Buffer Space (with Parameters Space)
__UpperCAmelCase						  =     []
__UpperCAmelCase						  =     []

# Machine Time
__UpperCAmelCase						  =     0





def 						__lowerCamelCase       (      __magic_name__	:  List[Any]						):
       global buffer_space, params_space, machine_time, K, m, t

       # Choosing Dynamical Systems (All)
       for key, value in enumerate(__magic_name__						):
              # Evolution Parameter
              a__:       Any											=float(seed / value						)

              # Control Theory: Orbit Change
              a__:       List[str]											=(buffer_space[(key + 1) % m] + e) % 1

              # Control Theory: Trajectory Change
              a__:       Union[str, Any]											=(params_space[key] + e) % 1 + 3

              # Modification (Transition Function) - Jumps
              a__:       int											=round(float(r * value * (1 - value)						)     , 10						)
              a__:       List[str]											=r  # Saving to Parameters Space

       # Logistic Map
       assert max(__magic_name__						) < 1
       assert max(__magic_name__						) < 4

       # Machine Time
       machine_time += 1





def 						__lowerCamelCase       (      ):
       global buffer_space, params_space, machine_time, K, m, t

       # PRNG (Xorshift by George Marsaglia)
       def xorshift(__magic_name__	:  Dict     , __magic_name__	:  List[Any]						):
              x ^= y >> 13
              y ^= x << 17
              x ^= y >> 5
              return x

       # Choosing Dynamical Systems (Increment)
       a__:       List[Any]											=machine_time % m

       # Evolution (Time Length)
       for _ in range(0     , __magic_name__						):
              # Variables (Position + Parameters)
              a__:       Tuple											=params_space[key]
              a__:       Dict											=buffer_space[key]

              # Modification (Transition Function) - Flow
              a__:       Any											=round(float(r * value * (1 - value)						)     , 10						)
              a__:       int											=(machine_time * 0.01 + r * 1.01) % 1 + 3

       # Choosing Chaotic Data
       a__:       str											=int(buffer_space[(key + 2) % m] * (10**10)						)
       a__:       List[Any]											=int(buffer_space[(key - 2) % m] * (10**10)						)

       # Machine Time
       machine_time += 1

       return xorshift(__magic_name__     , __magic_name__						) % 0xFF_FF_FF_FF





def 						__lowerCamelCase       (      ):
       global buffer_space, params_space, machine_time, K, m, t

       a__:       Optional[Any]											=K
       a__:       Dict											=[0] * m
       a__:       Union[str, Any]											=0


if __name__ == "__main__":
  # Initialization
  reset()

  # Pushing Data (Input)
  import random

  __UpperCAmelCase						  =     random.sample(range(0xF_F_F_F_F_F_F_F), 1_00)
  for chunk in message:
    push(chunk)

  # for controlling
  __UpperCAmelCase						  =     ''''''

  # Pulling Data (Output)
  while inp in ("e", "E"):
    print(f"""{format(pull(), '#04x')}""")
    print(buffer_space)
    print(params_space)
    __UpperCAmelCase						  =     input('''(e)exit? ''').strip()










import subprocess
from typing import Union

import numpy as np
import requests

from ..utils import add_end_docstrings, is_torch_available, is_torchaudio_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_torch_available():
		from ..models.auto.modeling_auto import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING

__UpperCAmelCase						  =     logging.get_logger(__name__)





def 						__lowerCamelCase       (      __magic_name__	:  bytes     , __magic_name__	:  int						):
							a__:       Optional[int]											=F"{sampling_rate}"
							a__:       Union[str, Any]											="1"
							a__:       List[str]											="f32le"
							a__:       Dict											=[
							    "ffmpeg",
							    "-i",
							    "pipe:0",
							    "-ac",
							    ac,
							    "-ar",
							    ar,
							    "-f",
							    format_for_conversion,
							    "-hide_banner",
							    "-loglevel",
							    "quiet",
							    "pipe:1",
							]

							try:
														a__:       List[Any]											=subprocess.Popen(__magic_name__     , stdin=subprocess.PIPE     , stdout=subprocess.PIPE						)
							except FileNotFoundError:
														raise ValueError("ffmpeg was not found but is required to load audio files from filename"						)
							a__:       int											=ffmpeg_process.communicate(__magic_name__						)
							a__:       List[str]											=output_stream[0]

							a__:       str											=np.frombuffer(__magic_name__     , np.floataa						)
							if audio.shape[0] == 0:
														raise ValueError("Malformed soundfile"						)
							return audio







@add_end_docstrings(_a   )
class 	lowerCamelCase__ (		_a   ):


						def __init__( self						:  Union[str, Any]      ,       *_a						:  Dict      ,       **_a						:  Tuple			):
													# Default, might be overriden by the model.config.
													a__:       List[Any]											=5
													super().__init__(*_a      ,       **_a			)

													if self.framework != "pt":
																				raise ValueError(F"The {self.__class__} is only available in PyTorch."			)

													self.check_model_type(_a			)


						def __call__( self						:  Tuple      ,       _a						:  Union[np.ndarray, bytes, str]      ,       **_a						:  Union[str, Any]      ,       ):
													return super().__call__(_a      ,       **_a			)


						def   _lowerCamelCase			( self						:  int      ,       _a						:  Tuple=None      ,       **_a						:  int			):
													# No parameters on this pipeline right now
													a__:       Optional[int]											={}
													if top_k is not None:
																				if top_k > self.model.config.num_labels:
																											a__:       Union[str, Any]											=self.model.config.num_labels
																				a__:       Union[str, Any]											=top_k
													return {}, {}, postprocess_params


						def   _lowerCamelCase			( self						:  Any      ,       _a						:  Union[str, Any]			):
													if isinstance(_a      ,       _a			):
																				if inputs.startswith("http://"			) or inputs.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__:       List[str]											=requests.get(_a			).content
																				else:
																											with open(_a      ,       "rb"			) as f:
																																		a__:       Dict											=f.read()

													if isinstance(_a      ,       _a			):
																				a__:       Tuple											=ffmpeg_read(_a      ,       self.feature_extractor.sampling_rate			)

													if isinstance(_a      ,       _a			):
																				# Accepting `"array"` which is the key defined in `datasets` for
																				# better integration
																				if not ("sampling_rate" in inputs and ("raw" in inputs or "array" in inputs)):
																											raise ValueError(
																											    "When passing a dictionary to AudioClassificationPipeline, the dict needs to contain a "
																											    "\"raw\" key containing the numpy array representing the audio and a \"sampling_rate\" key, "
																											    "containing the sampling_rate associated with that array"			)

																				a__:       Tuple											=inputs.pop("raw"      ,       _a			)
																				if _inputs is None:
																											# Remove path which will not be used from `datasets`.
																											inputs.pop("path"      ,       _a			)
																											a__:       int											=inputs.pop("array"      ,       _a			)
																				a__:       Optional[Any]											=inputs.pop("sampling_rate"			)
																				a__:       Optional[int]											=_inputs
																				if in_sampling_rate != self.feature_extractor.sampling_rate:
																											import torch

																											if is_torchaudio_available():
																																		from torchaudio import functional as F
																											else:
																																		raise ImportError(
																																		    "torchaudio is required to resample audio samples in AudioClassificationPipeline. "
																																		    "The torchaudio package can be installed through: `pip install torchaudio`."			)

																											a__:       Union[str, Any]											=F.resample(
																											    torch.from_numpy(_a			)      ,       _a      ,       self.feature_extractor.sampling_rate			).numpy()

													if not isinstance(_a      ,       np.ndarray			):
																				raise ValueError("We expect a numpy ndarray as input"			)
													if len(inputs.shape			) != 1:
																				raise ValueError("We expect a single channel audio input for AudioClassificationPipeline"			)

													a__:       Union[str, Any]											=self.feature_extractor(
													    _a      ,       sampling_rate=self.feature_extractor.sampling_rate      ,       return_tensors="pt"			)
													return processed


						def   _lowerCamelCase			( self						:  Dict      ,       _a						:  Tuple			):
													a__:       str											=self.model(**_a			)
													return model_outputs







						def   _lowerCamelCase			( self						:  Optional[Any]      ,       _a						:  Tuple      ,       _a						:  Tuple=5			):
													a__:       Tuple											=model_outputs.logits[0].softmax(-1			)
													a__							,			a__:       Any											=probs.topk(_a			)

													a__:       Union[str, Any]											=scores.tolist()
													a__:       Dict											=ids.tolist()

													a__:       Optional[int]											=[{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(_a      ,       _a			)]

													return labels





