


'''simple docstring'''




import numpy as np
from matplotlib import pyplot as plt





def      _a		(lowercase__ :      Union[str, Any]	) -> Any:






     """simple docstring"""
     return 1 / (1 + np.exp(-1 * x	))




class  _lowercase   :
   def __init__(	self					:			Optional[int]					,      SCREAMING_SNAKE_CASE_					:			List[Any]					,      SCREAMING_SNAKE_CASE_					:			int=None					,      SCREAMING_SNAKE_CASE_					:			List[Any]=None					,      SCREAMING_SNAKE_CASE_					:			Union[str, Any]=False			)   ->       Optional[Any]:
        __snake_case    			=	units
        __snake_case    			=	None
        __snake_case    			=	None
        __snake_case    			=	activation
        if learning_rate is None:
             __snake_case    			=	0.3
        __snake_case    			=	learning_rate
        __snake_case    			=	is_input_layer
   def a     (	self					:			Union[str, Any]					,      SCREAMING_SNAKE_CASE_					:			Optional[int]			)   ->       str:
        __snake_case    			=	np.asmatrix(np.random.normal(0					,      0.5					,      (self.units, back_units)			)			)
        __snake_case    			=	np.asmatrix(np.random.normal(0					,      0.5					,      self.units			)			).T
        if self.activation is None:
             __snake_case    			=	sigmoid
   def a     (	self					:			Any			)   ->       Union[str, Any]:
        # activation function may be sigmoid or linear
        if self.activation == sigmoid:
             __snake_case    			=	np.dot(self.output					,      (1 - self.output).T			)
             __snake_case    			=	np.diag(np.diag(SCREAMING_SNAKE_CASE_			)			)
        else:
             __snake_case    			=	1
        return gradient_activation
   def a     (	self					:			Union[str, Any]					,      SCREAMING_SNAKE_CASE_					:			Optional[Any]			)   ->       Tuple:
        __snake_case    			=	xdata
        if self.is_input_layer:
             # input layer
             __snake_case    			=	xdata
             __snake_case    			=	xdata
             return xdata
        else:
             __snake_case    			=	np.dot(self.weight					,      self.xdata			) - self.bias
             __snake_case    			=	self.activation(self.wx_plus_b			)
             return self.output





   def a     (	self					:			str					,      SCREAMING_SNAKE_CASE_					:			str			)   ->       Tuple:
        __snake_case    			=	self.cal_gradient()  # i * i 维
        __snake_case    			=	np.asmatrix(np.dot(gradient.T					,      SCREAMING_SNAKE_CASE_			)			)

        __snake_case    			=	np.asmatrix(self.xdata			)
        __snake_case    			=	-1
        __snake_case    			=	self.weight

        __snake_case    			=	np.dot(gradient.T					,      self._gradient_weight.T			)
        __snake_case    			=	gradient * self._gradient_bias
        __snake_case    			=	np.dot(SCREAMING_SNAKE_CASE_					,      self._gradient_x			).T
        # upgrade: the Negative gradient direction
        __snake_case    			=	self.weight - self.learn_rate * self.gradient_weight
        __snake_case    			=	self.bias - self.learn_rate * self.gradient_bias.T
        # updates the weights and bias according to learning rate (0.3 if undefined)
        return self.gradient




class  _lowercase   :
   def __init__(	self					:			Optional[Any]			)   ->       Union[str, Any]:
        __snake_case    			=	[]
        __snake_case    			=	[]
        __snake_case    			=	plt.figure()
        __snake_case    			=	self.fig_loss.add_subplot(1					,      1					,      1			)
   def a     (	self					:			List[str]					,      SCREAMING_SNAKE_CASE_					:			str			)   ->       Optional[int]:
        self.layers.append(SCREAMING_SNAKE_CASE_			)
   def a     (	self					:			List[Any]			)   ->       Dict:
        for i, layer in enumerate(self.layers[:]			):
             if i < 1:
                  __snake_case    			=	True
             else:
                  layer.initializer(self.layers[i - 1].units			)
   def a     (	self					:			str			)   ->       Any:
        for i, layer in enumerate(self.layers[:]			):
             print(f'------- layer {i} -------'			)
             print('weight.shape '					,      np.shape(layer.weight			)			)
             print('bias.shape '					,      np.shape(layer.bias			)			)
   def a     (	self					:			int					,      SCREAMING_SNAKE_CASE_					:			int					,      SCREAMING_SNAKE_CASE_					:			Tuple					,      SCREAMING_SNAKE_CASE_					:			int					,      SCREAMING_SNAKE_CASE_					:			Any			)   ->       List[Any]:
        __snake_case    			=	train_round
        __snake_case    			=	accuracy

        self.ax_loss.hlines(self.accuracy					,      0					,      self.train_round * 1.1			)

        __snake_case    			=	np.shape(SCREAMING_SNAKE_CASE_			)
        for _ in range(SCREAMING_SNAKE_CASE_			):
             __snake_case    			=	0
             for row in range(x_shape[0]			):
                  __snake_case    			=	np.asmatrix(xdata[row, :]			).T
                  __snake_case    			=	np.asmatrix(ydata[row, :]			).T

                  # forward propagation
                  for layer in self.layers:
                       __snake_case    			=	layer.forward_propagation(_xdata			)

                  __snake_case			,					__snake_case    			=	self.cal_loss(_ydata					,      _xdata			)
                  __snake_case    			=	all_loss + loss

                  # back propagation: the input_layer does not upgrade
                  for layer in self.layers[:0:-1]:
                       __snake_case    			=	layer.back_propagation(SCREAMING_SNAKE_CASE_			)

             __snake_case    			=	all_loss / x_shape[0]
             self.train_mse.append(SCREAMING_SNAKE_CASE_			)

             self.plot_loss()

             if mse < self.accuracy:
                  print('----达到精度----'			)
                  return mse
        return None
   def a     (	self					:			List[str]					,      SCREAMING_SNAKE_CASE_					:			int					,      SCREAMING_SNAKE_CASE_					:			List[Any]			)   ->       Union[str, Any]:
        __snake_case    			=	np.sum(np.power((ydata - ydata_)					,      2			)			)
        __snake_case    			=	2 * (ydata_ - ydata)
        # vector (shape is the same as _ydata.shape)
        return self.loss, self.loss_gradient





   def a     (	self					:			Union[str, Any]			)   ->       int:
        if self.ax_loss.lines:
             self.ax_loss.lines.remove(self.ax_loss.lines[0]			)
        self.ax_loss.plot(self.train_mse					,      'r-'			)
        plt.ion()
        plt.xlabel('step'			)
        plt.ylabel('loss'			)
        plt.show()
        plt.pause(0.1			)





def      _a		() -> Optional[int]:






     """simple docstring"""
     __snake_case    			=	np.random.randn(1_0       ,      1_0	)
     __snake_case    			=	np.asarray(
         [
             [0.8, 0.4],
             [0.4, 0.3],
             [0.34, 0.45],
             [0.67, 0.32],
             [0.88, 0.67],
             [0.78, 0.77],
             [0.55, 0.66],
             [0.55, 0.43],
             [0.54, 0.1],
             [0.1, 0.5],
         ]	)
     __snake_case    			=	BPNN()
     for i in (1_0, 2_0, 3_0, 2):
          model.add_layer(DenseLayer(lowercase__	)	)
     model.build()
     model.summary()
     model.train(xdata=lowercase__       ,      ydata=lowercase__       ,      train_round=1_0_0       ,      accuracy=0.01	)


if __name__ == "__main__":
       example()








'''simple docstring'''




import argparse
import json
import re
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileNetVaConfig,
    MobileNetVaForImageClassification,
    MobileNetVaForSemanticSegmentation,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
_a      :      int       				=      logging.get_logger(__name__)





def      _a		(lowercase__ :      int	) -> Union[str, Any]:






     """simple docstring"""
     __snake_case    			=	MobileNetVaConfig(layer_norm_eps=0.0_01	)

     if "quant" in model_name:
          raise ValueError('Quantized models are not supported.'	)

     __snake_case    			=	re.match(r'^.*mobilenet_v2_([^_]*)_([^_]*)$'       ,      lowercase__	)
     if matches:
          __snake_case    			=	float(matches[1]	)
          __snake_case    			=	int(matches[2]	)

     if model_name.startswith('deeplabv3_'	):
          __snake_case    			=	8
          __snake_case    			=	2_1
          __snake_case    			=	'pascal-voc-id2label.json'
     else:
          # The TensorFlow version of MobileNetV2 predicts 1001 classes instead
          # of the usual 1000. The first class (index 0) is "background".
          __snake_case    			=	1_0_0_1
          __snake_case    			=	'imagenet-1k-id2label.json'

     __snake_case    			=	'huggingface/label-files'
     __snake_case    			=	json.load(open(hf_hub_download(lowercase__       ,      lowercase__       ,      repo_type='dataset'	)       ,      'r'	)	)

     if config.num_labels == 1_0_0_1:
          __snake_case    			=	{int(lowercase__	) + 1: v for k, v in idalabel.items()}
          __snake_case    			=	'background'
     else:
          __snake_case    			=	{int(lowercase__	): v for k, v in idalabel.items()}

     __snake_case    			=	idalabel
     __snake_case    			=	{v: k for k, v in idalabel.items()}

     return config





def      _a		() -> Dict:






     """simple docstring"""
     __snake_case    			=	'http://images.cocodataset.org/val2017/000000039769.jpg'
     __snake_case    			=	Image.open(requests.get(lowercase__       ,      stream=lowercase__	).raw	)
     return im





@torch.no_grad()
def      _a		(lowercase__ :      str       ,      lowercase__ :      Optional[int]       ,      lowercase__ :      Optional[Any]       ,      lowercase__ :      Dict=False	) -> Any:






     """simple docstring"""
     __snake_case    			=	get_mobilenet_va_config(lowercase__	)

     # Load 🤗 model
     if model_name.startswith('deeplabv3_'	):
          __snake_case    			=	MobileNetVaForSemanticSegmentation(lowercase__	).eval()
     else:
          __snake_case    			=	MobileNetVaForImageClassification(lowercase__	).eval()

     # Load weights from TensorFlow checkpoint
     load_tf_weights_in_mobilenet_va(lowercase__       ,      lowercase__       ,      lowercase__	)

     # Check outputs on an image, prepared by MobileNetV2ImageProcessor
     __snake_case    			=	MobileNetVaImageProcessor(
         crop_size={'width': config.image_size, 'height': config.image_size}       ,      size={'shortest_edge': config.image_size + 3_2}       ,      )
     __snake_case    			=	image_processor(images=prepare_img()       ,      return_tensors='pt'	)
     __snake_case    			=	model(**lowercase__	)
     __snake_case    			=	outputs.logits

     if model_name.startswith('deeplabv3_'	):
          assert logits.shape == (1, 2_1, 6_5, 6_5)

          if model_name == "deeplabv3_mobilenet_v2_1.0_513":
               __snake_case    			=	torch.tensor(
                   [
                       [[17.57_90, 17.75_81, 18.33_55], [18.32_57, 18.42_30, 18.89_73], [18.61_69, 18.86_50, 19.21_87]],
                       [[-2.15_95, -2.09_77, -2.37_41], [-2.42_26, -2.30_28, -2.68_35], [-2.78_19, -2.59_91, -2.77_06]],
                       [[4.20_58, 4.83_17, 4.76_38], [4.41_36, 5.03_61, 4.93_83], [4.50_28, 4.96_44, 4.87_34]],
                   ]	)

          else:
               raise ValueError(f'Unknown model name: {model_name}'	)

          assert torch.allclose(logits[0, :3, :3, :3]       ,      lowercase__       ,      atol=1e-4	)
     else:
          assert logits.shape == (1, 1_0_0_1)

          if model_name == "mobilenet_v2_1.4_224":
               __snake_case    			=	torch.tensor([0.01_81, -1.00_15, 0.46_88]	)
          elif model_name == "mobilenet_v2_1.0_224":
               __snake_case    			=	torch.tensor([0.24_45, -1.19_93, 0.19_05]	)
          elif model_name == "mobilenet_v2_0.75_160":
               __snake_case    			=	torch.tensor([0.24_82, 0.41_36, 0.66_69]	)
          elif model_name == "mobilenet_v2_0.35_96":
               __snake_case    			=	torch.tensor([0.14_51, -0.46_24, 0.71_92]	)
          else:
               __snake_case    			=	None

          if expected_logits is not None:
               assert torch.allclose(logits[0, :3]       ,      lowercase__       ,      atol=1e-4	)

     Path(lowercase__	).mkdir(exist_ok=lowercase__	)
     print(f'Saving model {model_name} to {pytorch_dump_folder_path}'	)
     model.save_pretrained(lowercase__	)
     print(f'Saving image processor to {pytorch_dump_folder_path}'	)
     image_processor.save_pretrained(lowercase__	)

     if push_to_hub:
          print('Pushing to the hub...'	)
          __snake_case    			=	'google/' + model_name
          image_processor.push_to_hub(lowercase__	)
          model.push_to_hub(lowercase__	)


if __name__ == "__main__":
       _a      :      List[str]       				=      argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--model_name",
           default="mobilenet_v2_1.0_224",
           type=str,
           help="Name of the MobileNetV2 model you'd like to convert. Should in the form 'mobilenet_v2_<depth>_<size>'.",
       )
       parser.add_argument(
           "--checkpoint_path", required=True, type=str, help="Path to the original TensorFlow checkpoint (.ckpt file)."
       )
       parser.add_argument(
           "--pytorch_dump_folder_path", required=True, type=str, help="Path to the output PyTorch model directory."
       )
       parser.add_argument(
           "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
       )

       _a      :      str       				=      parser.parse_args()
       convert_movilevit_checkpoint(
           args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
       )




