from typing import Tuple, Union

from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig


if is_timm_available():
				import timm


if is_torch_available():
				from torch import Tensor



class        lowerCamelCase__				(   snake_case_       ,snake_case_				):
			UpperCamelCase__    							="pixel_values"
			UpperCamelCase__    							=False
			UpperCamelCase__    							=TimmBackboneConfig





			def __init__(       self	:	Optional[int]  ,  lowercase__	:	Optional[Any]  ,  **lowercase__	:	Any						):
										requires_backends(self  ,  'timm'						)
										super().__init__(lowercase__						)
										_lowerCAmelCase							=							config

										if config.backbone is None:
																	raise ValueError('backbone is not set in the config. Please set it to a timm model name.'						)

										if config.backbone not in timm.list_models():
																	raise ValueError(f'backbone {config.backbone} is not supported by timm.'						)

										if hasattr(lowercase__  ,  'out_features'						) and config.out_features is not None:
																	raise ValueError('out_features is not supported by TimmBackbone. Please use out_indices instead.'						)

										_lowerCAmelCase							=							getattr(lowercase__  ,  'use_pretrained_backbone'  ,  lowercase__						)
										if pretrained is None:
																	raise ValueError('use_pretrained_backbone is not set in the config. Please set it to True or False.'						)

										# We just take the final layer by default. This matches the default for the transformers models.
										_lowerCAmelCase							=							config.out_indices if getattr(lowercase__  ,  'out_indices'  ,  lowercase__						) is not None else (-1,)

										_lowerCAmelCase							=							timm.create_model(
										    config.backbone  ,  pretrained=lowercase__  ,  features_only=config.features_only  ,  in_chans=config.num_channels  ,  out_indices=lowercase__  ,  **lowercase__  ,  )
										# These are used to control the output of the model when called. If output_hidden_states is True, then
										# return_layers is modified to include all layers.
										_lowerCAmelCase							=							self._backbone.return_layers
										_lowerCAmelCase							=							{layer['module']: str(lowercase__						) for i, layer in enumerate(self._backbone.feature_info.info						)}
										super()._init_backbone(lowercase__						)





			@classmethod
			def   SCREAMING_SNAKE_CASE__     (       cls	:	List[Any]  ,  lowercase__	:	Any  ,  *lowercase__	:	Dict  ,  **lowercase__	:	List[str]						):
										requires_backends(cls  ,  ['vision', 'timm']						)
										from ...models.timm_backbone import TimmBackboneConfig

										_lowerCAmelCase							=							kwargs.pop('config'  ,  TimmBackboneConfig()						)

										_lowerCAmelCase							=							kwargs.pop('use_timm_backbone'  ,  lowercase__						)
										if not use_timm:
																	raise ValueError('use_timm_backbone must be True for timm backbones'						)

										_lowerCAmelCase							=							kwargs.pop('num_channels'  ,  config.num_channels						)
										_lowerCAmelCase							=							kwargs.pop('features_only'  ,  config.features_only						)
										_lowerCAmelCase							=							kwargs.pop('use_pretrained_backbone'  ,  config.use_pretrained_backbone						)
										_lowerCAmelCase							=							kwargs.pop('out_indices'  ,  config.out_indices						)
										_lowerCAmelCase							=							TimmBackboneConfig(
										    backbone=lowercase__  ,  num_channels=lowercase__  ,  features_only=lowercase__  ,  use_pretrained_backbone=lowercase__  ,  out_indices=lowercase__  ,  )
										return super()._from_config(lowercase__  ,  **lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	str  ,  lowercase__	:	str						):
										pass





			def   SCREAMING_SNAKE_CASE__     (       self	:	List[Any]  ,  lowercase__	:	Dict  ,  lowercase__	:	Optional[Any]=None  ,  lowercase__	:	str=None  ,  lowercase__	:	List[Any]=None  ,  **lowercase__	:	Any						):
										_lowerCAmelCase							=							return_dict if return_dict is not None else self.config.use_return_dict
										_lowerCAmelCase							=							(
										    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
										)
										_lowerCAmelCase							=							output_attentions if output_attentions is not None else self.config.output_attentions

										if output_attentions:
																	raise ValueError('Cannot output attentions for timm backbones at the moment'						)

										if output_hidden_states:
																	# We modify the return layers to include all the stages of the backbone
																	_lowerCAmelCase							=							self._all_layers
																	_lowerCAmelCase							=							self._backbone(lowercase__  ,  **lowercase__						)
																	_lowerCAmelCase							=							self._return_layers
																	_lowerCAmelCase							=							tuple(hidden_states[i] for i in self.out_indices						)
										else:
																	_lowerCAmelCase							=							self._backbone(lowercase__  ,  **lowercase__						)
																	_lowerCAmelCase							=							None

										_lowerCAmelCase							=							tuple(lowercase__						)
										_lowerCAmelCase							=							tuple(lowercase__						) if hidden_states is not None else None

										if not return_dict:
																	_lowerCAmelCase							=							(feature_maps,)
																	if output_hidden_states:
																								_lowerCAmelCase							=							output + (hidden_states,)
																	return output

										return BackboneOutput(feature_maps=lowercase__  ,  hidden_states=lowercase__  ,  attentions=lowercase__						)








from math import isqrt





def      lowerCamelCase_					(							lowerCamelCase__       ):
				lowerCamelCase_         =     [True] * max_number
				for i in range(2      ,					isqrt(max_number - 1       ) + 1       ):
								if is_prime[i]:
												for j in range(i**2      ,					lowerCamelCase__      ,					lowerCamelCase__       ):
																lowerCamelCase_         =     False

				return [i for i in range(2      ,					lowerCamelCase__       ) if is_prime[i]]





def      lowerCamelCase_					(							lowerCamelCase__ = 1_0**8       ):
				lowerCamelCase_         =     calculate_prime_numbers(max_number // 2       )

				lowerCamelCase_         =     0
				lowerCamelCase_         =     0
				lowerCamelCase_         =     len(lowerCamelCase__       ) - 1
				while left <= right:
								while prime_numbers[left] * prime_numbers[right] >= max_number:
												right -= 1
								semiprimes_count += right - left + 1
								left += 1

				return semiprimes_count


if __name__ == "__main__":
			print(F"""{solution() = }""")


