


"""simple docstring"""






import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase    :     Union[str, Any]   						=		logging.get_logger(__name__)

UpperCAmelCase    :     Dict   						=		{
    'microsoft/unispeech-large-1500h-cv': (
        'https://huggingface.co/microsoft/unispeech-large-1500h-cv/resolve/main/config.json'
    ),
    # See all UniSpeech models at https://huggingface.co/models?filter=unispeech
}





class      lowerCamelCase__   (     A				):







					"""simple docstring"""


					__a	       =							"""unispeech"""






					def __init__(       self    : int ,       UpperCamelCase    : List[Any]=32 ,       UpperCamelCase    : List[str]=768 ,       UpperCamelCase    : str=12 ,       UpperCamelCase    : Any=12 ,       UpperCamelCase    : Dict=3_072 ,       UpperCamelCase    : Tuple="gelu" ,       UpperCamelCase    : Optional[Any]=0.1 ,       UpperCamelCase    : Dict=0.1 ,       UpperCamelCase    : Dict=0.1 ,       UpperCamelCase    : str=0.0 ,       UpperCamelCase    : Tuple=0.0 ,       UpperCamelCase    : Union[str, Any]=0.1 ,       UpperCamelCase    : Dict=0.1 ,       UpperCamelCase    : Optional[Any]=0.02 ,       UpperCamelCase    : Tuple=1e-5 ,       UpperCamelCase    : Union[str, Any]="group" ,       UpperCamelCase    : str="gelu" ,       UpperCamelCase    : Any=(512, 512, 512, 512, 512, 512, 512) ,       UpperCamelCase    : Union[str, Any]=(5, 2, 2, 2, 2, 2, 2) ,       UpperCamelCase    : Optional[int]=(10, 3, 3, 3, 3, 2, 2) ,       UpperCamelCase    : int=False ,       UpperCamelCase    : int=128 ,       UpperCamelCase    : List[str]=16 ,       UpperCamelCase    : Optional[Any]=False ,       UpperCamelCase    : Any=True ,       UpperCamelCase    : Union[str, Any]=0.05 ,       UpperCamelCase    : Optional[Any]=10 ,       UpperCamelCase    : List[str]=2 ,       UpperCamelCase    : List[str]=0.0 ,       UpperCamelCase    : Tuple=10 ,       UpperCamelCase    : str=0 ,       UpperCamelCase    : Dict=320 ,       UpperCamelCase    : Tuple=2 ,       UpperCamelCase    : Optional[Any]=0.1 ,       UpperCamelCase    : Union[str, Any]=100 ,       UpperCamelCase    : List[Any]=256 ,       UpperCamelCase    : Union[str, Any]=256 ,       UpperCamelCase    : Union[str, Any]=0.1 ,       UpperCamelCase    : int="mean" ,       UpperCamelCase    : Tuple=False ,       UpperCamelCase    : List[str]=False ,       UpperCamelCase    : List[str]=256 ,       UpperCamelCase    : int=80 ,       UpperCamelCase    : Union[str, Any]=0 ,       UpperCamelCase    : Any=1 ,       UpperCamelCase    : int=2 ,       UpperCamelCase    : Tuple=0.5 ,       **UpperCamelCase    : Optional[int] ,       ):
												'''simple docstring'''






												super().__init__(**UpperCamelCase ,       pad_token_id=UpperCamelCase ,       bos_token_id=UpperCamelCase ,       eos_token_id=UpperCamelCase  )
												__UpperCAmelCase   :						Any      							=					hidden_size
												__UpperCAmelCase   :						List[Any]      							=					feat_extract_norm
												__UpperCAmelCase   :						int      							=					feat_extract_activation
												__UpperCAmelCase   :						Tuple      							=					list(UpperCamelCase  )
												__UpperCAmelCase   :						Any      							=					list(UpperCamelCase  )
												__UpperCAmelCase   :						List[str]      							=					list(UpperCamelCase  )
												__UpperCAmelCase   :						Optional[int]      							=					conv_bias
												__UpperCAmelCase   :						Any      							=					num_conv_pos_embeddings
												__UpperCAmelCase   :						int      							=					num_conv_pos_embedding_groups
												__UpperCAmelCase   :						List[Any]      							=					len(self.conv_dim  )
												__UpperCAmelCase   :						List[Any]      							=					num_hidden_layers
												__UpperCAmelCase   :						Optional[int]      							=					intermediate_size
												__UpperCAmelCase   :						Tuple      							=					hidden_act
												__UpperCAmelCase   :						List[str]      							=					num_attention_heads
												__UpperCAmelCase   :						Tuple      							=					hidden_dropout
												__UpperCAmelCase   :						Dict      							=					attention_dropout
												__UpperCAmelCase   :						Any      							=					activation_dropout
												__UpperCAmelCase   :						Tuple      							=					feat_proj_dropout
												__UpperCAmelCase   :						Any      							=					final_dropout
												__UpperCAmelCase   :						str      							=					layerdrop
												__UpperCAmelCase   :						Tuple      							=					layer_norm_eps
												__UpperCAmelCase   :						Tuple      							=					initializer_range
												__UpperCAmelCase   :						Optional[Any]      							=					num_ctc_classes
												__UpperCAmelCase   :						Union[str, Any]      							=					vocab_size
												__UpperCAmelCase   :						Tuple      							=					do_stable_layer_norm
												__UpperCAmelCase   :						List[Any]      							=					use_weighted_layer_sum
												__UpperCAmelCase   :						Optional[int]      							=					classifier_proj_size

												if (
												    (len(self.conv_stride  ) != self.num_feat_extract_layers)
												    or (len(self.conv_kernel  ) != self.num_feat_extract_layers)
												    or (len(self.conv_dim  ) != self.num_feat_extract_layers)
												):
																			raise ValueError(
																			    """Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="""
																			    """ `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="""
																			    f''' {len(self.conv_dim  )}`, `len(config.conv_stride) = {len(self.conv_stride  )}`,'''
																			    f''' `len(config.conv_kernel) = {len(self.conv_kernel  )}`.'''  )

												# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
												__UpperCAmelCase   :						Union[str, Any]      							=					apply_spec_augment
												__UpperCAmelCase   :						Any      							=					mask_time_prob
												__UpperCAmelCase   :						List[str]      							=					mask_time_length
												__UpperCAmelCase   :						Dict      							=					mask_time_min_masks
												__UpperCAmelCase   :						Dict      							=					mask_feature_prob
												__UpperCAmelCase   :						Optional[Any]      							=					mask_feature_length
												__UpperCAmelCase   :						Tuple      							=					mask_feature_min_masks

												# parameters for pretraining with codevector quantized representations
												__UpperCAmelCase   :						List[Any]      							=					num_codevectors_per_group
												__UpperCAmelCase   :						List[str]      							=					num_codevector_groups
												__UpperCAmelCase   :						Any      							=					contrastive_logits_temperature
												__UpperCAmelCase   :						Optional[int]      							=					feat_quantizer_dropout
												__UpperCAmelCase   :						str      							=					num_negatives
												__UpperCAmelCase   :						str      							=					codevector_dim
												__UpperCAmelCase   :						Dict      							=					proj_codevector_dim
												__UpperCAmelCase   :						int      							=					diversity_loss_weight

												# ctc loss
												__UpperCAmelCase   :						Optional[int]      							=					ctc_loss_reduction
												__UpperCAmelCase   :						Union[str, Any]      							=					ctc_zero_infinity

												# pretraining loss
												__UpperCAmelCase   :						str      							=					replace_prob






					@property
					def lowerCamelCase__							(       self    : Optional[Any]  ):
												'''simple docstring'''






												return functools.reduce(operator.mul ,       self.conv_stride ,       1  )




"""simple docstring"""






import math
def      lowerCamelCase       (     _UpperCamelCase		:      int							)		->       list[int]:

							'''simple docstring'''



							__UpperCAmelCase   :						List[Any]      							=					[]
							__UpperCAmelCase   :						Dict      							=					2
							__UpperCAmelCase   :						Union[str, Any]      							=					int(math.sqrt(_UpperCamelCase							)							)  # Size of every segment
							__UpperCAmelCase   :						Tuple      							=					[True] * (end + 1)
							__UpperCAmelCase   :						int      							=					[]

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

							__UpperCAmelCase   :						Optional[int]      							=					end + 1
							__UpperCAmelCase   :						Dict      							=					min(2 * end       ,			_UpperCamelCase							)

							while low <= n:
														__UpperCAmelCase   :						Union[str, Any]      							=					[True] * (high - low + 1)
														for each in in_prime:
																					__UpperCAmelCase   :						Dict      							=					math.floor(low / each							) * each
																					if t < low:
																												t += each

																					for j in range(_UpperCamelCase       ,			high + 1       ,			_UpperCamelCase							):
																												__UpperCAmelCase   :						Tuple      							=					False

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

														__UpperCAmelCase   :						Tuple      							=					high + 1
														__UpperCAmelCase   :						Optional[int]      							=					min(high + end       ,			_UpperCamelCase							)

							return prime
print(sieve(10**6))
