



from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy



class 	lowerCamelCase      (						lowercase__  ):







					'''simple docstring'''







					lowerCAmelCase_				:    str									=  'EncodecFeatureExtractor'
					lowerCAmelCase_				:    str									=  ('T5Tokenizer', 'T5TokenizerFast')



					def __init__(			self    ,	lowerCAmelCase    ,	lowerCAmelCase     ):
							super().__init__(lowerCAmelCase    ,	lowerCAmelCase     )
							UpperCAmelCase_						=				self.feature_extractor
							UpperCAmelCase_						=				False



					def    A__		(			self    ,	lowerCAmelCase=None    ,	lowerCAmelCase=None    ,	lowerCAmelCase=True     ):
							return self.tokenizer.get_decoder_prompt_ids(task=lowerCAmelCase    ,	language=lowerCAmelCase    ,	no_timestamps=lowerCAmelCase     )



					def __call__(			self    ,	*lowerCAmelCase    ,	**lowerCAmelCase     ):
							# For backward compatibility
							if self._in_target_context_manager:
									return self.current_processor(*lowerCAmelCase    ,	**lowerCAmelCase     )

							UpperCAmelCase_						=				kwargs.pop("audio"    ,	lowerCAmelCase     )
							UpperCAmelCase_						=				kwargs.pop("sampling_rate"    ,	lowerCAmelCase     )
							UpperCAmelCase_						=				kwargs.pop("text"    ,	lowerCAmelCase     )
							if len(lowerCAmelCase     ) > 0:
									UpperCAmelCase_						=				args[0]
									UpperCAmelCase_						=				args[1:]

							if audio is None and text is None:
									raise ValueError("You need to specify either an `audio` or `text` input to process."     )

							if text is not None:
									UpperCAmelCase_						=				self.tokenizer(lowerCAmelCase    ,	**lowerCAmelCase     )

							if audio is not None:
									UpperCAmelCase_						=				self.feature_extractor(lowerCAmelCase    ,	*lowerCAmelCase    ,	sampling_rate=lowerCAmelCase    ,	**lowerCAmelCase     )

							if audio is None:
									return inputs

							elif text is None:
									return audio_inputs

							else:
									UpperCAmelCase_						=				audio_inputs["input_values"]
									if "padding_mask" in audio_inputs:
											UpperCAmelCase_						=				audio_inputs["padding_mask"]
									return inputs



					def    A__		(			self    ,	*lowerCAmelCase    ,	**lowerCAmelCase     ):
							UpperCAmelCase_						=				kwargs.pop("audio"    ,	lowerCAmelCase     )
							UpperCAmelCase_						=				kwargs.pop("padding_mask"    ,	lowerCAmelCase     )

							if len(lowerCAmelCase     ) > 0:
									UpperCAmelCase_						=				args[0]
									UpperCAmelCase_						=				args[1:]

							if audio_values is not None:
									return self._decode_audio(lowerCAmelCase    ,	padding_mask=lowerCAmelCase     )
							else:
									return self.tokenizer.batch_decode(*lowerCAmelCase    ,	**lowerCAmelCase     )



					def    A__		(			self    ,	*lowerCAmelCase    ,	**lowerCAmelCase     ):
							return self.tokenizer.decode(*lowerCAmelCase    ,	**lowerCAmelCase     )



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase = None     ):
							UpperCAmelCase_						=				to_numpy(lowerCAmelCase     )
							UpperCAmelCase_					,						UpperCAmelCase_					,						UpperCAmelCase_						=				audio_values.shape

							if padding_mask is None:
									return list(lowerCAmelCase     )

							UpperCAmelCase_						=				to_numpy(lowerCAmelCase     )

							# match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
							# token (so that the generated audio values are **not** treated as padded tokens)
							UpperCAmelCase_						=				seq_len - padding_mask.shape[-1]
							UpperCAmelCase_						=				1 - self.feature_extractor.padding_value
							UpperCAmelCase_						=				np.pad(lowerCAmelCase    ,	((0, 0), (0, difference))    ,	"constant"    ,	constant_values=lowerCAmelCase     )

							UpperCAmelCase_						=				audio_values.tolist()
							for i in range(lowerCAmelCase     ):
									UpperCAmelCase_						=				np.asarray(audio_values[i]     )[
									    padding_mask[i][None, :] != self.feature_extractor.padding_value
									]
									UpperCAmelCase_						=				sliced_audio.reshape(lowerCAmelCase    ,	-1     )

							return audio_values






import math







def snake_case__  (			__SCREAMING_SNAKE_CASE )		->							list[int]:
		UpperCAmelCase_						=				[]
		UpperCAmelCase_						=				2
		UpperCAmelCase_						=				int(math.sqrt(__SCREAMING_SNAKE_CASE ) )  # Size of every segment
		UpperCAmelCase_						=				[True] * (end + 1)
		UpperCAmelCase_						=				[]

		while start <= end:
				if temp[start] is True:
						in_prime.append(__SCREAMING_SNAKE_CASE )
						for i in range(start * start			,     end + 1			,     __SCREAMING_SNAKE_CASE ):
								UpperCAmelCase_						=				False
				start += 1
		prime += in_prime

		UpperCAmelCase_						=				end + 1
		UpperCAmelCase_						=				min(2 * end			,     __SCREAMING_SNAKE_CASE )

		while low <= n:
				UpperCAmelCase_						=				[True] * (high - low + 1)
				for each in in_prime:
						UpperCAmelCase_						=				math.floor(low / each ) * each
						if t < low:
								t += each

						for j in range(__SCREAMING_SNAKE_CASE			,     high + 1			,     __SCREAMING_SNAKE_CASE ):
								UpperCAmelCase_						=				False

				for j in range(len(__SCREAMING_SNAKE_CASE ) ):
						if temp[j] is True:
								prime.append(j + low )

				UpperCAmelCase_						=				high + 1
				UpperCAmelCase_						=				min(high + end			,     __SCREAMING_SNAKE_CASE )

		return prime

print(sieve(10**6))

