



"""simple docstring"""





def   a__      ( lowerCAmelCase__      ,     lowerCAmelCase__ ):
      return x if y == 0 else greatest_common_divisor(lowerCAmelCase__      ,     x % y )
def   a__      ( lowerCAmelCase__      ,     lowerCAmelCase__ ):
      return (x * y) // greatest_common_divisor(lowerCAmelCase__      ,     lowerCAmelCase__ )
def   a__      ( lowerCAmelCase__ = 20 ):
      UpperCAmelCase_     					=				1
      for i in range(1      ,     n + 1 ):
            UpperCAmelCase_     					=				lcm(lowerCAmelCase__      ,     lowerCAmelCase__ )
      return g


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












from typing import Optional, Union

import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_mobilenet_va import MobileNetVaConfig


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


# General docstring
__a   :							List[str]						= "MobileNetV1Config"

# Base docstring
__a   :							int						= "google/mobilenet_v1_1.0_224"
__a   :							List[Any]						= [1, 1024, 7, 7]

# Image classification docstring
__a   :							Optional[int]						= "google/mobilenet_v1_1.0_224"
__a   :							List[Any]						= "tabby, tabby cat"


__a   :							Union[str, Any]						= [
    "google/mobilenet_v1_1.0_224",
    "google/mobilenet_v1_0.75_192",
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
]



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      List[str]      ,					__lowercase :      str      ,					__lowercase :      Union[str, Any]=None     )    ->   Any:





    """simple docstring"""

    __A       =						{}

    if isinstance(__lowercase      ,					__lowercase     ):
        __A       =						model.mobilenet_va
    else:
        __A       =						model

    __A       =						"""MobilenetV1/Conv2d_0/"""
    __A       =						backbone.conv_stem.convolution.weight
    __A       =						backbone.conv_stem.normalization.bias
    __A       =						backbone.conv_stem.normalization.weight
    __A       =						backbone.conv_stem.normalization.running_mean
    __A       =						backbone.conv_stem.normalization.running_var

    for i in range(1_3     ):
        __A       =						i + 1
        __A       =						i * 2

        __A       =						backbone.layer[pt_index]
        __A       =						f"MobilenetV1/Conv2d_{tf_index}_depthwise/"
        __A       =						pointer.convolution.weight
        __A       =						pointer.normalization.bias
        __A       =						pointer.normalization.weight
        __A       =						pointer.normalization.running_mean
        __A       =						pointer.normalization.running_var

        __A       =						backbone.layer[pt_index + 1]
        __A       =						f"MobilenetV1/Conv2d_{tf_index}_pointwise/"
        __A       =						pointer.convolution.weight
        __A       =						pointer.normalization.bias
        __A       =						pointer.normalization.weight
        __A       =						pointer.normalization.running_mean
        __A       =						pointer.normalization.running_var

    if isinstance(__lowercase      ,					__lowercase     ):
        __A       =						"""MobilenetV1/Logits/Conv2d_1c_1x1/"""
        __A       =						model.classifier.weight
        __A       =						model.classifier.bias

    return tf_to_pt_map



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      Optional[Any]      ,					__lowercase :      int      ,					__lowercase :      int     )    ->   str:





    """simple docstring"""

    try:
        import numpy as np
        import tensorflow as tf
    except ImportError:
        logger.error(
            """Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see """
            """https://www.tensorflow.org/install/ for installation instructions."""     )
        raise

    # Load weights from TF model
    __A       =						tf.train.list_variables(__lowercase     )
    __A       =						{}
    for name, shape in init_vars:
        logger.info(f"Loading TF weight {name} with shape {shape}"     )
        __A       =						tf.train.load_variable(__lowercase      ,					__lowercase     )
        __A       =						array

    # Build TF to PyTorch weights loading map
    __A       =						_build_tf_to_pytorch_map(__lowercase      ,					__lowercase      ,					__lowercase     )

    for name, pointer in tf_to_pt_map.items():
        logger.info(f"Importing {name}"     )
        if name not in tf_weights:
            logger.info(f"{name} not in tf pre-trained weights, skipping"     )
            continue

        __A       =						tf_weights[name]

        if "depthwise_weights" in name:
            logger.info("""Transposing depthwise"""     )
            __A       =						np.transpose(__lowercase      ,					(2, 3, 0, 1)     )
        elif "weights" in name:
            logger.info("""Transposing"""     )
            if len(pointer.shape     ) == 2:  # copying into linear layer
                __A       =						array.squeeze().transpose()
            else:
                __A       =						np.transpose(__lowercase      ,					(3, 2, 0, 1)     )

        if pointer.shape != array.shape:
            raise ValueError(f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched"     )

        logger.info(f"Initialize PyTorch weight {name} {array.shape}"     )
        __A       =						torch.from_numpy(__lowercase     )

        tf_weights.pop(__lowercase      ,					__lowercase     )
        tf_weights.pop(name + """/RMSProp"""      ,					__lowercase     )
        tf_weights.pop(name + """/RMSProp_1"""      ,					__lowercase     )
        tf_weights.pop(name + """/ExponentialMovingAverage"""      ,					__lowercase     )

    logger.info(f"Weights not copied to PyTorch model: {', '.join(tf_weights.keys()     )}"     )
    return model



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      torch.Tensor      ,					__lowercase :      nn.Convad     )    ->   torch.Tensor:





    """simple docstring"""

    __A     ,						__A       =						features.shape[-2:]
    __A     ,						__A       =						conv_layer.stride
    __A     ,						__A       =						conv_layer.kernel_size

    if in_height % stride_height == 0:
        __A       =						max(kernel_height - stride_height      ,					0     )
    else:
        __A       =						max(kernel_height - (in_height % stride_height)      ,					0     )

    if in_width % stride_width == 0:
        __A       =						max(kernel_width - stride_width      ,					0     )
    else:
        __A       =						max(kernel_width - (in_width % stride_width)      ,					0     )

    __A       =						pad_along_width // 2
    __A       =						pad_along_width - pad_left
    __A       =						pad_along_height // 2
    __A       =						pad_along_height - pad_top

    __A       =						(pad_left, pad_right, pad_top, pad_bottom)
    return nn.functional.pad(__lowercase      ,					__lowercase      ,					"""constant"""      ,					0.0     )


class    __lowercase   (						nn.Module	):







     '''simple docstring'''




     def __init__(					self							:      int			,						UpperCamelCase_							:      MobileNetVaConfig			,						UpperCamelCase_							:      int			,						UpperCamelCase_							:      int			,						UpperCamelCase_							:      int			,						UpperCamelCase_							:      Optional[int] = 1			,						UpperCamelCase_							:      Optional[int] = 1			,						UpperCamelCase_							:      bool = False			,						UpperCamelCase_							:      Optional[bool] = True			,						UpperCamelCase_							:      Optional[bool or str] = True			,						):


         """simple docstring"""



         super().__init__()
         __A       =						config

         if in_channels % groups != 0:
             raise ValueError(F"Input channels ({in_channels}) are not divisible by {groups} groups."					)
         if out_channels % groups != 0:
             raise ValueError(F"Output channels ({out_channels}) are not divisible by {groups} groups."					)

         __A       =						0 if config.tf_padding else int((kernel_size - 1) / 2					)

         __A       =						nn.Convad(
             in_channels=UpperCamelCase_			,						out_channels=UpperCamelCase_			,						kernel_size=UpperCamelCase_			,						stride=UpperCamelCase_			,						padding=UpperCamelCase_			,						groups=UpperCamelCase_			,						bias=UpperCamelCase_			,						padding_mode="""zeros"""			,						)

         if use_normalization:
             __A       =						nn.BatchNormad(
                 num_features=UpperCamelCase_			,						eps=config.layer_norm_eps			,						momentum=0.9997			,						affine=UpperCamelCase_			,						track_running_stats=UpperCamelCase_			,						)
         else:
             __A       =						None

         if use_activation:
             if isinstance(UpperCamelCase_			,						UpperCamelCase_					):
                 __A       =						ACTaFN[use_activation]
             elif isinstance(config.hidden_act			,						UpperCamelCase_					):
                 __A       =						ACTaFN[config.hidden_act]
             else:
                 __A       =						config.hidden_act
         else:
             __A       =						None





     def 		lowerCAmelCase_  (					self							:      Union[str, Any]			,						UpperCamelCase_							:      torch.Tensor					):


         """simple docstring"""



         if self.config.tf_padding:
             __A       =						apply_tf_padding(UpperCamelCase_			,						self.convolution					)
         __A       =						self.convolution(UpperCamelCase_					)
         if self.normalization is not None:
             __A       =						self.normalization(UpperCamelCase_					)
         if self.activation is not None:
             __A       =						self.activation(UpperCamelCase_					)
         return features


class    __lowercase   (						lowercase_	):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE     =					MobileNetVaConfig
     SCREAMING_SNAKE_CASE     =					load_tf_weights_in_mobilenet_va
     SCREAMING_SNAKE_CASE     =					"mobilenet_v1"
     SCREAMING_SNAKE_CASE     =					"pixel_values"
     SCREAMING_SNAKE_CASE     =					False




     def 		lowerCAmelCase_  (					self							:      Optional[Any]			,						UpperCamelCase_							:      Union[nn.Linear, nn.Convad]					):


         """simple docstring"""



         if isinstance(UpperCamelCase_			,						(nn.Linear, nn.Convad)					):
             module.weight.data.normal_(mean=0.0			,						std=self.config.initializer_range					)
             if module.bias is not None:
                 module.bias.data.zero_()
         elif isinstance(UpperCamelCase_			,						nn.BatchNormad					):
             module.bias.data.zero_()
             module.weight.data.fill_(1.0					)
__a   :							Tuple						= R"\n    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it\n    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n    behavior.\n\n    Parameters:\n        config ([`MobileNetV1Config`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n"

__a   :							int						= R"\n    Args:\n        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`MobileNetV1ImageProcessor.__call__`] for details.\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n            more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n"


@add_start_docstrings(
    "The bare MobileNetV1 model outputting raw hidden-states without any specific head on top."      ,   lowercase_      ,   )
class    __lowercase   (						lowercase_	):







     '''simple docstring'''




     def __init__(					self							:      int			,						UpperCamelCase_							:      MobileNetVaConfig			,						UpperCamelCase_							:      bool = True					):


         """simple docstring"""



         super().__init__(UpperCamelCase_					)
         __A       =						config

         __A       =						32
         __A       =						max(int(depth * config.depth_multiplier					)			,						config.min_depth					)

         __A       =						MobileNetVaConvLayer(
             UpperCamelCase_			,						in_channels=config.num_channels			,						out_channels=UpperCamelCase_			,						kernel_size=3			,						stride=2			,						)

         __A       =						[1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]

         __A       =						nn.ModuleList()
         for i in range(13					):
             __A       =						out_channels

             if strides[i] == 2 or i == 0:
                 depth *= 2
                 __A       =						max(int(depth * config.depth_multiplier					)			,						config.min_depth					)

             self.layer.append(
                 MobileNetVaConvLayer(
                     UpperCamelCase_			,						in_channels=UpperCamelCase_			,						out_channels=UpperCamelCase_			,						kernel_size=3			,						stride=strides[i]			,						groups=UpperCamelCase_			,						)					)

             self.layer.append(
                 MobileNetVaConvLayer(
                     UpperCamelCase_			,						in_channels=UpperCamelCase_			,						out_channels=UpperCamelCase_			,						kernel_size=1			,						)					)

         __A       =						nn.AdaptiveAvgPoolad((1, 1)					) if add_pooling_layer else None

         # Initialize weights and apply final processing
         self.post_init()




     def 		lowerCAmelCase_  (					self							:      Optional[int]			,						UpperCamelCase_							:      Optional[int]					):


         """simple docstring"""



         raise NotImplementedError





     @add_start_docstrings_to_model_forward(UpperCamelCase_					)
     @add_code_sample_docstrings(
         checkpoint=_CHECKPOINT_FOR_DOC			,						output_type=UpperCamelCase_			,						config_class=_CONFIG_FOR_DOC			,						modality="""vision"""			,						expected_output=_EXPECTED_OUTPUT_SHAPE			,						)
     def 		lowerCAmelCase_  (					self							:      Any			,						UpperCamelCase_							:      Optional[torch.Tensor] = None			,						UpperCamelCase_							:      Optional[bool] = None			,						UpperCamelCase_							:      Optional[bool] = None			,						):


         """simple docstring"""



         __A       =						(
             output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
         )
         __A       =						return_dict if return_dict is not None else self.config.use_return_dict

         if pixel_values is None:
             raise ValueError("""You have to specify pixel_values"""					)

         __A       =						self.conv_stem(UpperCamelCase_					)

         __A       =						() if output_hidden_states else None

         for i, layer_module in enumerate(self.layer					):
             __A       =						layer_module(UpperCamelCase_					)

             if output_hidden_states:
                 __A       =						all_hidden_states + (hidden_states,)

         __A       =						hidden_states

         if self.pooler is not None:
             __A       =						torch.flatten(self.pooler(UpperCamelCase_					)			,						start_dim=1					)
         else:
             __A       =						None

         if not return_dict:
             return tuple(v for v in [last_hidden_state, pooled_output, all_hidden_states] if v is not None					)

         return BaseModelOutputWithPoolingAndNoAttention(
             last_hidden_state=UpperCamelCase_			,						pooler_output=UpperCamelCase_			,						hidden_states=UpperCamelCase_			,						)


@add_start_docstrings(
    "\n    MobileNetV1 model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    "      ,   lowercase_      ,   )
class    __lowercase   (						lowercase_	):







     '''simple docstring'''




     def __init__(					self							:      Optional[int]			,						UpperCamelCase_							:      MobileNetVaConfig					):


         """simple docstring"""



         super().__init__(UpperCamelCase_					)

         __A       =						config.num_labels
         __A       =						MobileNetVaModel(UpperCamelCase_					)

         __A       =						self.mobilenet_va.layer[-1].convolution.out_channels

         # Classifier head
         __A       =						nn.Dropout(config.classifier_dropout_prob			,						inplace=UpperCamelCase_					)
         __A       =						nn.Linear(UpperCamelCase_			,						config.num_labels					) if config.num_labels > 0 else nn.Identity()

         # Initialize weights and apply final processing
         self.post_init()





     @add_start_docstrings_to_model_forward(UpperCamelCase_					)
     @add_code_sample_docstrings(
         checkpoint=_IMAGE_CLASS_CHECKPOINT			,						output_type=UpperCamelCase_			,						config_class=_CONFIG_FOR_DOC			,						expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT			,						)
     def 		lowerCAmelCase_  (					self							:      Tuple			,						UpperCamelCase_							:      Optional[torch.Tensor] = None			,						UpperCamelCase_							:      Optional[bool] = None			,						UpperCamelCase_							:      Optional[torch.Tensor] = None			,						UpperCamelCase_							:      Optional[bool] = None			,						):


         """simple docstring"""



         __A       =						return_dict if return_dict is not None else self.config.use_return_dict

         __A       =						self.mobilenet_va(UpperCamelCase_			,						output_hidden_states=UpperCamelCase_			,						return_dict=UpperCamelCase_					)

         __A       =						outputs.pooler_output if return_dict else outputs[1]

         __A       =						self.classifier(self.dropout(UpperCamelCase_					)					)

         __A       =						None
         if labels is not None:
             if self.config.problem_type is None:
                 if self.num_labels == 1:
                     __A       =						"""regression"""
                 elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
                     __A       =						"""single_label_classification"""
                 else:
                     __A       =						"""multi_label_classification"""

             if self.config.problem_type == "regression":
                 __A       =						MSELoss()
                 if self.num_labels == 1:
                     __A       =						loss_fct(logits.squeeze()			,						labels.squeeze()					)
                 else:
                     __A       =						loss_fct(UpperCamelCase_			,						UpperCamelCase_					)
             elif self.config.problem_type == "single_label_classification":
                 __A       =						CrossEntropyLoss()
                 __A       =						loss_fct(logits.view(-1			,						self.num_labels					)			,						labels.view(-1					)					)
             elif self.config.problem_type == "multi_label_classification":
                 __A       =						BCEWithLogitsLoss()
                 __A       =						loss_fct(UpperCamelCase_			,						UpperCamelCase_					)

         if not return_dict:
             __A       =						(logits,) + outputs[2:]
             return ((loss,) + output) if loss is not None else output

         return ImageClassifierOutputWithNoAttention(
             loss=UpperCamelCase_			,						logits=UpperCamelCase_			,						hidden_states=outputs.hidden_states			,						)




