




import numpy as np
from matplotlib import pyplot as plt

def 			lowerCAmelCase_			(  lowerCamelCase:       Optional[int]						)					->			List[Any]:


      '''simple docstring'''




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






class 			A			:
     """simple docstring"""

     def __init__(    self  :   List[Any]	,  A__  :   Optional[Any]	,  A__  :   List[str]=None	,  A__  :   Optional[Any]=None	,  A__  :   Optional[Any]=False			)				->       Optional[Any]:
           lowerCamelCase__        =				units
           lowerCamelCase__        =				None
           lowerCamelCase__        =				None
           lowerCamelCase__        =				activation
           if learning_rate is None:
                 lowerCamelCase__        =				0.3
           lowerCamelCase__        =				learning_rate
           lowerCamelCase__        =				is_input_layer




     def  __snake_case   (    self  :   Any	,  A__  :   int			)				->       Dict:
           lowerCamelCase__        =				np.asmatrix(np.random.normal(0	,  0.5	,  (self.units, back_units)			)			)
           lowerCamelCase__        =				np.asmatrix(np.random.normal(0	,  0.5	,  self.units			)			).T
           if self.activation is None:
                 lowerCamelCase__        =				sigmoid




     def  __snake_case   (    self  :   Tuple			)				->       Optional[int]:
           # activation function may be sigmoid or linear
           if self.activation == sigmoid:
                 lowerCamelCase__        =				np.dot(self.output	,  (1 - self.output).T			)
                 lowerCamelCase__        =				np.diag(np.diag(A__			)			)
           else:
                 lowerCamelCase__        =				1
           return gradient_activation




     def  __snake_case   (    self  :   Tuple	,  A__  :   str			)				->       Any:
           lowerCamelCase__        =				xdata
           if self.is_input_layer:
                 # input layer
                 lowerCamelCase__        =				xdata
                 lowerCamelCase__        =				xdata
                 return xdata
           else:
                 lowerCamelCase__        =				np.dot(self.weight	,  self.xdata			) - self.bias
                 lowerCamelCase__        =				self.activation(self.wx_plus_b			)
                 return self.output




     def  __snake_case   (    self  :   List[str]	,  A__  :   Optional[Any]			)				->       int:
           lowerCamelCase__        =				self.cal_gradient()  # i * i 维
           lowerCamelCase__        =				np.asmatrix(np.dot(gradient.T	,  A__			)			)

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

           lowerCamelCase__        =				np.dot(gradient.T	,  self._gradient_weight.T			)
           lowerCamelCase__        =				gradient * self._gradient_bias
           lowerCamelCase__        =				np.dot(A__	,  self._gradient_x			).T
           # upgrade: the Negative gradient direction
           lowerCamelCase__        =				self.weight - self.learn_rate * self.gradient_weight
           lowerCamelCase__        =				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 			A			:
     """simple docstring"""

     def __init__(    self  :   str			)				->       List[str]:
           lowerCamelCase__        =				[]
           lowerCamelCase__        =				[]
           lowerCamelCase__        =				plt.figure()
           lowerCamelCase__        =				self.fig_loss.add_subplot(1	,  1	,  1			)




     def  __snake_case   (    self  :   List[Any]	,  A__  :   Dict			)				->       Optional[int]:
           self.layers.append(A__			)




     def  __snake_case   (    self  :   Union[str, Any]			)				->       Optional[int]:
           for i, layer in enumerate(self.layers[:]			):
                 if i < 1:
                       lowerCamelCase__        =				True
                 else:
                       layer.initializer(self.layers[i - 1].units			)




     def  __snake_case   (    self  :   str			)				->       int:
           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  __snake_case   (    self  :   Any	,  A__  :   Dict	,  A__  :   Union[str, Any]	,  A__  :   Dict	,  A__  :   Any			)				->       Dict:
           lowerCamelCase__        =				train_round
           lowerCamelCase__        =				accuracy

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

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

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

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

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

                 lowerCamelCase__        =				all_loss / x_shape[0]
                 self.train_mse.append(A__			)

                 self.plot_loss()

                 if mse < self.accuracy:
                       print('----达到精度----'			)
                       return mse
           return None




     def  __snake_case   (    self  :   Optional[int]	,  A__  :   int	,  A__  :   int			)				->       Optional[Any]:
           lowerCamelCase__        =				np.sum(np.power((ydata - ydata_)	,  2			)			)
           lowerCamelCase__        =				2 * (ydata_ - ydata)
           # vector (shape is the same as _ydata.shape)
           return self.loss, self.loss_gradient




     def  __snake_case   (    self  :   Union[str, Any]			)				->       Tuple:
           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 			lowerCAmelCase_			(  )					->			List[str]:


      '''simple docstring'''




      lowerCamelCase__        =				np.random.randn(10     ,				10						)
      lowerCamelCase__        =				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],
          ]						)
      lowerCamelCase__        =				BPNN()
      for i in (10, 20, 30, 2):
            model.add_layer(DenseLayer(lowerCamelCase						)						)
      model.build()
      model.summary()
      model.train(xdata=lowerCamelCase     ,				ydata=lowerCamelCase     ,				train_round=1_00     ,				accuracy=0.01						)


if __name__ == "__main__":
      example()





import getopt
import json
import os

# import numpy as np
import sys
from collections import OrderedDict

import datasets
import numpy as np
import torch
from modeling_frcnn import GeneralizedRCNN
from processing_image import Preprocess

from utils import Config
__SCREAMING_SNAKE_CASE    :   Optional[Any]       =False
__SCREAMING_SNAKE_CASE    :   int       =Config.from_pretrained("""unc-nlp/frcnn-vg-finetuned""")
__SCREAMING_SNAKE_CASE    :   Optional[Any]       =datasets.Features(
    OrderedDict(
        {
            """attr_ids""": datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value("""float32""")),
            """attr_probs""": datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value("""float32""")),
            """boxes""": datasets.ArrayaD((CONFIG.MAX_DETECTIONS, 4), dtype="""float32"""),
            """img_id""": datasets.Value("""int32"""),
            """obj_ids""": datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value("""float32""")),
            """obj_probs""": datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value("""float32""")),
            """roi_features""": datasets.ArrayaD((CONFIG.MAX_DETECTIONS, 2_048), dtype="""float32"""),
            """sizes""": datasets.Sequence(length=2, feature=datasets.Value("""float32""")),
            """preds_per_image""": datasets.Value(dtype="""int32"""),
        }
    )
)






class 			A			:
     """simple docstring"""

     def __init__(    self  :   List[str]	,  A__  :   int=sys.argv[1:]			)				->       Optional[Any]:
           lowerCamelCase__        =				None
           lowerCamelCase__        =				None
           lowerCamelCase__        =				None
           lowerCamelCase__        =				1
           lowerCamelCase__  ,		lowerCamelCase__        =				getopt.getopt(A__	,  'i:o:b:s'	,  ['inputdir=', 'outfile=', 'batch_size=', 'subset_list=']			)
           for opt, arg in opts:
                 if opt in ("-i", "--inputdir"):
                       lowerCamelCase__        =				arg
                 elif opt in ("-o", "--outfile"):
                       lowerCamelCase__        =				arg
                 elif opt in ("-b", "--batch_size"):
                       lowerCamelCase__        =				int(A__			)
                 elif opt in ("-s", "--subset_list"):
                       lowerCamelCase__        =				arg

           assert inputdir is not None  # and os.path.isdir(inputdir), f"{inputdir}"
           assert outputfile is not None and not os.path.isfile(A__			), F"""{outputfile}"""
           if subset_list is not None:
                 with open(os.path.realpath(A__			)			) as f:
                       lowerCamelCase__        =				{self._vqa_file_split()[0] for x in tryload(A__			)}
           else:
                 lowerCamelCase__        =				None

           lowerCamelCase__        =				CONFIG
           if torch.cuda.is_available():
                 lowerCamelCase__        =				'cuda'
           lowerCamelCase__        =				os.path.realpath(A__			)
           lowerCamelCase__        =				os.path.realpath(A__			)
           lowerCamelCase__        =				Preprocess(self.config			)
           lowerCamelCase__        =				GeneralizedRCNN.from_pretrained('unc-nlp/frcnn-vg-finetuned'	,  config=self.config			)
           lowerCamelCase__        =				batch_size if batch_size != 0 else 1
           lowerCamelCase__        =				DEFAULT_SCHEMA




     def  __snake_case   (    self  :   Dict	,  A__  :   Any			)				->       Union[str, Any]:
           lowerCamelCase__        =				int(file.split('.'			)[0].split('_'			)[-1]			)
           lowerCamelCase__        =				os.path.join(self.inputdir	,  A__			)
           return (img_id, filepath)




     @property
     def  __snake_case   (    self  :   int			)				->       Optional[Any]:
           lowerCamelCase__        =				[]
           for i, file in enumerate(os.listdir(self.inputdir			)			):
                 if self.subset_list is not None and i not in self.subset_list:
                       continue
                 batch.append(self._vqa_file_split(A__			)			)
                 if len(A__			) == self.batch:
                       lowerCamelCase__        =				batch
                       lowerCamelCase__        =				[]
                       yield list(map(A__	,  zip(*A__			)			)			)

           for i in range(1			):
                 yield list(map(A__	,  zip(*A__			)			)			)




     def __call__(    self  :   List[Any]			)				->       Optional[Any]:
           # make writer
           if not TEST:
                 lowerCamelCase__        =				datasets.ArrowWriter(features=self.schema	,  path=self.outputfile			)
           # do file generator
           for i, (img_ids, filepaths) in enumerate(self.file_generator			):
                 lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__        =				self.preprocess(A__			)
                 lowerCamelCase__        =				self.model(
                     A__	,  A__	,  scales_yx=A__	,  padding='max_detections'	,  max_detections=self.config.MAX_DETECTIONS	,  pad_value=0	,  return_tensors='np'	,  location='cpu'	,  )
                 lowerCamelCase__        =				output_dict.pop('normalized_boxes'			)
                 if not TEST:
                       lowerCamelCase__        =				np.array(A__			)
                       lowerCamelCase__        =				self.schema.encode_batch(A__			)
                       writer.write_batch(A__			)
                 if TEST:
                       break
                 # finalizer the writer
           if not TEST:
                 lowerCamelCase__  ,		lowerCamelCase__        =				writer.finalize()
                 print(F"""Success! You wrote {num_examples} entry(s) and {num_bytes >> 2_0} mb"""			)

def 			lowerCAmelCase_			(  lowerCamelCase:       Tuple						)					->			Optional[Any]:


      '''simple docstring'''




      try:
            lowerCamelCase__        =				json.load(lowerCamelCase						)
            try:
                  lowerCamelCase__        =				list(data.keys()						)
            except Exception:
                  lowerCamelCase__        =				[d['img_id'] for d in data]
      except Exception:
            try:
                  lowerCamelCase__        =				eval(stream.read()						)
            except Exception:
                  lowerCamelCase__        =				stream.read().split('\n'						)
      return data


if __name__ == "__main__":
      __SCREAMING_SNAKE_CASE    :   Tuple       =Extract(sys.argv[1:])
      extract()
      if not TEST:
            __SCREAMING_SNAKE_CASE    :   Any       =datasets.Dataset.from_file(extract.outputfile)
            # wala!
            # print(np.array(dataset[0:2]["roi_features"]).shape)