


import numpy





class   SCREAMING_SNAKE_CASE_			:




       '''simple docstring'''



       def __init__(						self				:				int		,				__a				:				numpy.ndarray		,				__a				:				numpy.ndarray							)					->None:
          lowerCamelCase_						:   Optional[int]		= input_array

          # Random initial weights are assigned where first argument is the
          # number of nodes in previous layer and second argument is the
          # number of nodes in the next layer.

          # Random initial weights are assigned.
          # self.input_array.shape[1] is used to represent number of nodes in input layer.
          # First hidden layer consists of 4 nodes.
          lowerCamelCase_						:   Dict		= numpy.random.rand(
              self.input_array.shape[1]		,				4							)

          # Random initial values for the first hidden layer.
          # First hidden layer has 4 nodes.
          # Second hidden layer has 3 nodes.
          lowerCamelCase_						:   Tuple		= numpy.random.rand(
              4		,				3							)

          # Random initial values for the second hidden layer.
          # Second hidden layer has 3 nodes.
          # Output layer has 1 node.
          lowerCamelCase_						:   List[str]		= numpy.random.rand(3		,				1							)

          # Real output values provided.
          lowerCamelCase_						:   Tuple		= output_array

          # Predicted output values by the neural network.
          # Predicted_output array initially consists of zeroes.
          lowerCamelCase_						:   Dict		= numpy.zeros(output_array.shape							)



       def 				_lowerCAmelCase							(						self				:				Optional[int]							)					->numpy.ndarray:
          lowerCamelCase_						:   Optional[Any]		= sigmoid(
              numpy.dot(self.input_array		,				self.input_layer_and_first_hidden_layer_weights							)							)

          # layer_between_first_hidden_layer_and_second_hidden_layer is the layer
          # connecting the first hidden set of nodes with the second hidden set of nodes.
          lowerCamelCase_						:   str		= sigmoid(
              numpy.dot(
                  self.layer_between_input_and_first_hidden_layer		,				self.first_hidden_layer_and_second_hidden_layer_weights		,				)							)

          # layer_between_second_hidden_layer_and_output is the layer connecting
          # second hidden layer with the output node.
          lowerCamelCase_						:   Optional[int]		= sigmoid(
              numpy.dot(
                  self.layer_between_first_hidden_layer_and_second_hidden_layer		,				self.second_hidden_layer_and_output_layer_weights		,				)							)

          return self.layer_between_second_hidden_layer_and_output



       def 				_lowerCAmelCase							(						self				:				Optional[Any]							)					->None:
          lowerCamelCase_						:   List[Any]		= numpy.dot(
              self.layer_between_first_hidden_layer_and_second_hidden_layer.T		,				2
              * (self.output_array - self.predicted_output)
              * sigmoid_derivative(self.predicted_output							)		,				)
          lowerCamelCase_						:   Tuple		= numpy.dot(
              self.layer_between_input_and_first_hidden_layer.T		,				numpy.dot(
                  2
                  * (self.output_array - self.predicted_output)
                  * sigmoid_derivative(self.predicted_output							)		,				self.second_hidden_layer_and_output_layer_weights.T		,				)
              * sigmoid_derivative(
                  self.layer_between_first_hidden_layer_and_second_hidden_layer							)		,				)
          lowerCamelCase_						:   Tuple		= numpy.dot(
              self.input_array.T		,				numpy.dot(
                  numpy.dot(
                      2
                      * (self.output_array - self.predicted_output)
                      * sigmoid_derivative(self.predicted_output							)		,				self.second_hidden_layer_and_output_layer_weights.T		,				)
                  * sigmoid_derivative(
                      self.layer_between_first_hidden_layer_and_second_hidden_layer							)		,				self.first_hidden_layer_and_second_hidden_layer_weights.T		,				)
              * sigmoid_derivative(self.layer_between_input_and_first_hidden_layer							)		,				)

          self.input_layer_and_first_hidden_layer_weights += (
              updated_input_layer_and_first_hidden_layer_weights
          )
          self.first_hidden_layer_and_second_hidden_layer_weights += (
              updated_first_hidden_layer_and_second_hidden_layer_weights
          )
          self.second_hidden_layer_and_output_layer_weights += (
              updated_second_hidden_layer_and_output_layer_weights
          )



       def 				_lowerCAmelCase							(						self				:				List[Any]		,				__a				:				numpy.ndarray		,				__a				:				int		,				__a				:				bool							)					->None:
          for iteration in range(1		,				iterations + 1							):
             lowerCamelCase_						:   int		= self.feedforward()
             self.back_propagation()
             if give_loss:
                lowerCamelCase_						:   Dict		= numpy.mean(numpy.square(output - self.feedforward()							)							)
                print(F'''Iteration {iteration} Loss: {loss}'''							)




       def 				_lowerCAmelCase							(						self				:				List[Any]		,				__a				:				numpy.ndarray							)					->int:
          lowerCamelCase_						:   Union[str, Any]		= input_arr

          lowerCamelCase_						:   Optional[Any]		= sigmoid(
              numpy.dot(self.array		,				self.input_layer_and_first_hidden_layer_weights							)							)

          lowerCamelCase_						:   Optional[Any]		= sigmoid(
              numpy.dot(
                  self.layer_between_input_and_first_hidden_layer		,				self.first_hidden_layer_and_second_hidden_layer_weights		,				)							)

          lowerCamelCase_						:   int		= sigmoid(
              numpy.dot(
                  self.layer_between_first_hidden_layer_and_second_hidden_layer		,				self.second_hidden_layer_and_output_layer_weights		,				)							)

          return int(self.layer_between_second_hidden_layer_and_output > 0.6							)







def    __lowerCamelCase   (      A__						: numpy.ndarray					)     ->    numpy.ndarray:
   return 1 / (1 + numpy.exp(-value					))


def    __lowerCamelCase   (      A__						: numpy.ndarray					)     ->    numpy.ndarray:
   return (value) * (1 - (value))


def    __lowerCamelCase   (      )     ->    int:
   lowerCamelCase_						:   Tuple		= numpy.array(
       (
           [0, 0, 0],
           [0, 0, 1],
           [0, 1, 0],
           [0, 1, 1],
           [1, 0, 0],
           [1, 0, 1],
           [1, 1, 0],
           [1, 1, 1],
       )						,						dtype=numpy.floataa						,						)

   # True output values for the given input values.
   lowerCamelCase_						:   str		= numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])						,						dtype=numpy.floataa					)

   # Calling neural network class.
   lowerCamelCase_						:   Union[str, Any]		= TwoHiddenLayerNeuralNetwork(
       input_array=A__						,						output_array=A__					)

   # Calling training function.
   # Set give_loss to True if you want to see loss in every iteration.
   neural_network.train(output=A__						,						iterations=10						,						give_loss=A__					)

   return neural_network.predict(numpy.array(([1, 1, 1])						,						dtype=numpy.floataa					)					)


if __name__ == "__main__":
      example()









import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
snake_case__				:	Dict					       =					logging.get_logger(__name__)


def    __lowerCamelCase   (      A__						: Optional[Any]					)     ->    List[str]:
   lowerCamelCase_						:   int		= """huggingface/label-files"""
   lowerCamelCase_						:   Dict		= """imagenet-1k-id2label.json"""
   lowerCamelCase_						:   Optional[Any]		= json.load(open(hf_hub_download(A__						,						A__						,						repo_type="""dataset"""					)						,						"""r"""					)					)
   lowerCamelCase_						:   str		= {int(A__					): v for k, v in idalabel.items()}
   lowerCamelCase_						:   List[Any]		= {v: k for k, v in idalabel.items()}

   lowerCamelCase_						:   str		= """std_conv""" if """bit""" in model_name else False

   # note that when using BiT as backbone for ViT-hybrid checkpoints,
   # one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
   # config.conv_layer = "std_conv_same"
   lowerCamelCase_						:   Optional[Any]		= BitConfig(
       conv_layer=A__						,						num_labels=1000						,						idalabel=A__						,						labelaid=A__						,						)

   return config


def    __lowerCamelCase   (      A__						: str					)     ->    Any:
   if "stem.conv" in name:
      lowerCamelCase_						:   Any		= name.replace("""stem.conv"""						,						"""bit.embedder.convolution"""					)
   if "blocks" in name:
      lowerCamelCase_						:   Dict		= name.replace("""blocks"""						,						"""layers"""					)
   if "head.fc" in name:
      lowerCamelCase_						:   Optional[Any]		= name.replace("""head.fc"""						,						"""classifier.1"""					)
   if name.startswith("""norm"""					):
      lowerCamelCase_						:   int		= """bit.""" + name
   if "bit" not in name and "classifier" not in name:
      lowerCamelCase_						:   str		= """bit.encoder.""" + name

   return name


def    __lowerCamelCase   (      )     ->    List[Any]:
   lowerCamelCase_						:   List[Any]		= """http://images.cocodataset.org/val2017/000000039769.jpg"""
   lowerCamelCase_						:   Optional[Any]		= Image.open(requests.get(A__						,						stream=A__					).raw					)
   return im


@torch.no_grad()
def    __lowerCamelCase   (      A__						: List[Any]						,						A__						: List[str]						,						A__						: Tuple=False					)     ->    List[str]:
   lowerCamelCase_						:   Optional[Any]		= get_config(A__					)

   # load original model from timm
   lowerCamelCase_						:   Optional[Any]		= create_model(A__						,						pretrained=A__					)
   timm_model.eval()

   # load state_dict of original model
   lowerCamelCase_						:   Optional[int]		= timm_model.state_dict()
   for key in state_dict.copy().keys():
      lowerCamelCase_						:   int		= state_dict.pop(A__					)
      lowerCamelCase_						:   Union[str, Any]		= val.squeeze() if """head""" in key else val

   # load HuggingFace model
   lowerCamelCase_						:   Tuple		= BitForImageClassification(A__					)
   model.eval()
   model.load_state_dict(A__					)

   # create image processor
   lowerCamelCase_						:   Optional[Any]		= create_transform(**resolve_data_config({}						,						model=A__					)					)
   lowerCamelCase_						:   List[Any]		= transform.transforms

   lowerCamelCase_						:   List[Any]		= {
       """bilinear""": PILImageResampling.BILINEAR,
       """bicubic""": PILImageResampling.BICUBIC,
       """nearest""": PILImageResampling.NEAREST,
   }

   lowerCamelCase_						:   List[str]		= BitImageProcessor(
       do_resize=A__						,						size={"""shortest_edge""": timm_transforms[0].size}						,						resample=pillow_resamplings[timm_transforms[0].interpolation.value]						,						do_center_crop=A__						,						crop_size={"""height""": timm_transforms[1].size[0], """width""": timm_transforms[1].size[1]}						,						do_normalize=A__						,						image_mean=timm_transforms[-1].mean.tolist()						,						image_std=timm_transforms[-1].std.tolist()						,						)

   lowerCamelCase_						:   int		= prepare_img()
   lowerCamelCase_						:   int		= transform(A__					).unsqueeze(0					)
   lowerCamelCase_						:   List[str]		= processor(A__						,						return_tensors="""pt"""					).pixel_values

   # verify pixel values
   assert torch.allclose(A__						,						A__					)

   # verify logits
   with torch.no_grad():
      lowerCamelCase_						:   str		= model(A__					)
      lowerCamelCase_						:   int		= outputs.logits

   print("""Logits:"""						,						logits[0, :3]					)
   print("""Predicted class:"""						,						model.config.idalabel[logits.argmax(-1					).item()]					)
   lowerCamelCase_						:   List[Any]		= timm_model(A__					)
   assert timm_logits.shape == outputs.logits.shape
   assert torch.allclose(A__						,						outputs.logits						,						atol=1e-3					)
   print("""Looks ok!"""					)

   if pytorch_dump_folder_path is not None:
      Path(A__					).mkdir(exist_ok=A__					)
      print(f'''Saving model {model_name} and processor to {pytorch_dump_folder_path}'''					)
      model.save_pretrained(A__					)
      processor.save_pretrained(A__					)

   if push_to_hub:
      print(f'''Pushing model {model_name} and processor to the hub'''					)
      model.push_to_hub(f'''ybelkada/{model_name}'''					)
      processor.push_to_hub(f'''ybelkada/{model_name}'''					)


if __name__ == "__main__":
      snake_case__				:	Dict					       =					argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '--model_name',
          default='resnetv2_50x1_bitm',
          type=str,
          help='Name of the BiT timm model you\'d like to convert.',
      )
      parser.add_argument(
          '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
      )
      parser.add_argument(
          '--push_to_hub',
          action='store_true',
          help='Whether to push the model to the hub.',
      )

      snake_case__				:	int					       =					parser.parse_args()
      convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





