




import numpy





class 	lowerCamelCase_							:


   '''simple docstring'''







   def __init__(							self  :		Tuple    ,    _lowerCAmelCase  :		numpy.ndarray    ,    _lowerCAmelCase  :		numpy.ndarray						):
         SCREAMING_SNAKE_CASE_     						=      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.
         SCREAMING_SNAKE_CASE_     						=      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.
         SCREAMING_SNAKE_CASE_     						=      numpy.random.rand(
             4    ,    3						)

         # Random initial values for the second hidden layer.
         # Second hidden layer has 3 nodes.
         # Output layer has 1 node.
         SCREAMING_SNAKE_CASE_     						=      numpy.random.rand(3    ,    1						)

         # Real output values provided.
         SCREAMING_SNAKE_CASE_     						=      output_array

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



   def 				lowerCAmelCase_  (							self  :		Union[str, Any]						):
         SCREAMING_SNAKE_CASE_     						=      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.
         SCREAMING_SNAKE_CASE_     						=      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.
         SCREAMING_SNAKE_CASE_     						=      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  :		str						):
         SCREAMING_SNAKE_CASE_     						=      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						)    ,    )
         SCREAMING_SNAKE_CASE_     						=      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						)    ,    )
         SCREAMING_SNAKE_CASE_     						=      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  :		int    ,    _lowerCAmelCase  :		numpy.ndarray    ,    _lowerCAmelCase  :		int    ,    _lowerCAmelCase  :		bool						):
         for iteration in range(1    ,    iterations + 1						):
               SCREAMING_SNAKE_CASE_     						=      self.feedforward()
               self.back_propagation()
               if give_loss:
                     SCREAMING_SNAKE_CASE_     						=      numpy.mean(numpy.square(output - self.feedforward()						)						)
                     print(F"Iteration {iteration} Loss: {loss}"						)




   def 				lowerCAmelCase_  (							self  :		Any    ,    _lowerCAmelCase  :		numpy.ndarray						):
         SCREAMING_SNAKE_CASE_     						=      input_arr

         SCREAMING_SNAKE_CASE_     						=      sigmoid(
             numpy.dot(self.array    ,    self.input_layer_and_first_hidden_layer_weights						)						)

         SCREAMING_SNAKE_CASE_     						=      sigmoid(
             numpy.dot(
                 self.layer_between_input_and_first_hidden_layer    ,    self.first_hidden_layer_and_second_hidden_layer_weights    ,    )						)

         SCREAMING_SNAKE_CASE_     						=      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  UpperCAmelCase_							(				__UpperCAmelCase						:     numpy.ndarray							) ->			numpy.ndarray:
      return 1 / (1 + numpy.exp(-value							))

def  UpperCAmelCase_							(				__UpperCAmelCase						:     numpy.ndarray							) ->			numpy.ndarray:
      return (value) * (1 - (value))

def  UpperCAmelCase_							(				) ->			int:
      SCREAMING_SNAKE_CASE_     						=      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.
      SCREAMING_SNAKE_CASE_     						=      numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])      ,      dtype=numpy.floataa							)

      # Calling neural network class.
      SCREAMING_SNAKE_CASE_     						=      TwoHiddenLayerNeuralNetwork(
          input_array=__UpperCAmelCase      ,      output_array=__UpperCAmelCase							)

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

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


if __name__ == "__main__":
   example()





import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel

def  UpperCAmelCase_							(				__UpperCAmelCase						:     List[str]							) ->			List[Any]:
      # vision encoder
      if "img_encoder.pos_embed" in name:
            SCREAMING_SNAKE_CASE_     						=      name.replace('img_encoder.pos_embed'      ,      'vision_model.embeddings.position_embeddings'							)
      if "img_encoder.patch_embed.proj" in name:
            SCREAMING_SNAKE_CASE_     						=      name.replace('img_encoder.patch_embed.proj'      ,      'vision_model.embeddings.patch_embeddings.projection'							)
      if "img_encoder.patch_embed.norm" in name:
            SCREAMING_SNAKE_CASE_     						=      name.replace('img_encoder.patch_embed.norm'      ,      'vision_model.embeddings.layernorm'							)
      if "img_encoder.layers" in name:
            SCREAMING_SNAKE_CASE_     						=      name.replace('img_encoder.layers'      ,      'vision_model.encoder.stages'							)
      if "blocks" in name and "res" not in name:
            SCREAMING_SNAKE_CASE_     						=      name.replace('blocks'      ,      'layers'							)
      if "attn" in name and "pre_assign" not in name:
            SCREAMING_SNAKE_CASE_     						=      name.replace('attn'      ,      'self_attn'							)
      if "proj" in name and "self_attn" in name and "text" not in name:
            SCREAMING_SNAKE_CASE_     						=      name.replace('proj'      ,      'out_proj'							)
      if "pre_assign_attn.attn.proj" in name:
            SCREAMING_SNAKE_CASE_     						=      name.replace('pre_assign_attn.attn.proj'      ,      'pre_assign_attn.attn.out_proj'							)
      if "norm1" in name:
            SCREAMING_SNAKE_CASE_     						=      name.replace('norm1'      ,      'layer_norm1'							)
      if "norm2" in name and "pre_assign" not in name:
            SCREAMING_SNAKE_CASE_     						=      name.replace('norm2'      ,      'layer_norm2'							)
      if "img_encoder.norm" in name:
            SCREAMING_SNAKE_CASE_     						=      name.replace('img_encoder.norm'      ,      'vision_model.layernorm'							)
      # text encoder
      if "text_encoder.token_embedding" in name:
            SCREAMING_SNAKE_CASE_     						=      name.replace('text_encoder.token_embedding'      ,      'text_model.embeddings.token_embedding'							)
      if "text_encoder.positional_embedding" in name:
            SCREAMING_SNAKE_CASE_     						=      name.replace('text_encoder.positional_embedding'      ,      'text_model.embeddings.position_embedding.weight'							)
      if "text_encoder.transformer.resblocks." in name:
            SCREAMING_SNAKE_CASE_     						=      name.replace('text_encoder.transformer.resblocks.'      ,      'text_model.encoder.layers.'							)
      if "ln_1" in name:
            SCREAMING_SNAKE_CASE_     						=      name.replace('ln_1'      ,      'layer_norm1'							)
      if "ln_2" in name:
            SCREAMING_SNAKE_CASE_     						=      name.replace('ln_2'      ,      'layer_norm2'							)
      if "c_fc" in name:
            SCREAMING_SNAKE_CASE_     						=      name.replace('c_fc'      ,      'fc1'							)
      if "c_proj" in name:
            SCREAMING_SNAKE_CASE_     						=      name.replace('c_proj'      ,      'fc2'							)
      if "text_encoder" in name:
            SCREAMING_SNAKE_CASE_     						=      name.replace('text_encoder'      ,      'text_model'							)
      if "ln_final" in name:
            SCREAMING_SNAKE_CASE_     						=      name.replace('ln_final'      ,      'final_layer_norm'							)
      # projection layers
      if "img_projector.linear_hidden." in name:
            SCREAMING_SNAKE_CASE_     						=      name.replace('img_projector.linear_hidden.'      ,      'visual_projection.'							)
      if "img_projector.linear_out." in name:
            SCREAMING_SNAKE_CASE_     						=      name.replace('img_projector.linear_out.'      ,      'visual_projection.3.'							)
      if "text_projector.linear_hidden" in name:
            SCREAMING_SNAKE_CASE_     						=      name.replace('text_projector.linear_hidden'      ,      'text_projection'							)
      if "text_projector.linear_out" in name:
            SCREAMING_SNAKE_CASE_     						=      name.replace('text_projector.linear_out'      ,      'text_projection.3'							)

      return name

def  UpperCAmelCase_							(				__UpperCAmelCase						:     Union[str, Any]      ,      __UpperCAmelCase						:     int							) ->			List[str]:
      for key in orig_state_dict.copy().keys():
            SCREAMING_SNAKE_CASE_     						=      orig_state_dict.pop(__UpperCAmelCase							)

            if "qkv" in key:
                  # weights and biases of the key, value and query projections of vision encoder's attention layers require special treatment:
                  # we need to split them up into separate matrices/vectors
                  SCREAMING_SNAKE_CASE_     						=      key.split('.'							)
                  SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      int(key_split[2]							), int(key_split[4]							)
                  SCREAMING_SNAKE_CASE_     						=      config.vision_config.hidden_size
                  if "weight" in key:
                        SCREAMING_SNAKE_CASE_     						=      val[:dim, :]
                        SCREAMING_SNAKE_CASE_     						=      val[dim : dim * 2, :]
                        SCREAMING_SNAKE_CASE_     						=      val[-dim:, :]
                  else:
                        SCREAMING_SNAKE_CASE_     						=      val[:dim]
                        SCREAMING_SNAKE_CASE_     						=      val[dim : dim * 2]
                        SCREAMING_SNAKE_CASE_     						=      val[-dim:]
            elif "in_proj" in key:
                  # weights and biases of the key, value and query projections of text encoder's attention layers require special treatment:
                  # we need to split them up into separate matrices/vectors
                  SCREAMING_SNAKE_CASE_     						=      key.split('.'							)
                  SCREAMING_SNAKE_CASE_     						=      int(key_split[3]							)
                  SCREAMING_SNAKE_CASE_     						=      config.text_config.hidden_size
                  if "weight" in key:
                        SCREAMING_SNAKE_CASE_     						=      val[:dim, :]
                        SCREAMING_SNAKE_CASE_     						=      val[
                            dim : dim * 2, :
                        ]
                        SCREAMING_SNAKE_CASE_     						=      val[-dim:, :]
                  else:
                        SCREAMING_SNAKE_CASE_     						=      val[:dim]
                        SCREAMING_SNAKE_CASE_     						=      val[dim : dim * 2]
                        SCREAMING_SNAKE_CASE_     						=      val[-dim:]
            else:
                  SCREAMING_SNAKE_CASE_     						=      rename_key(__UpperCAmelCase							)
                  # squeeze if necessary
                  if (
                      "text_projection.0" in new_name
                      or "text_projection.3" in new_name
                      or "visual_projection.0" in new_name
                      or "visual_projection.3" in new_name
                  ):
                        SCREAMING_SNAKE_CASE_     						=      val.squeeze_()
                  else:
                        SCREAMING_SNAKE_CASE_     						=      val

      return orig_state_dict

def  UpperCAmelCase_							(				) ->			List[Any]:
      SCREAMING_SNAKE_CASE_     						=      'http://images.cocodataset.org/val2017/000000039769.jpg'
      SCREAMING_SNAKE_CASE_     						=      Image.open(requests.get(__UpperCAmelCase      ,      stream=__UpperCAmelCase							).raw							)
      return im

@torch.no_grad()
def  UpperCAmelCase_							(				__UpperCAmelCase						:     int      ,      __UpperCAmelCase						:     str      ,      __UpperCAmelCase						:     Dict="groupvit-gcc-yfcc"      ,      __UpperCAmelCase						:     Any=False							) ->			Optional[int]:
      SCREAMING_SNAKE_CASE_     						=      GroupViTConfig()
      SCREAMING_SNAKE_CASE_     						=      GroupViTModel(__UpperCAmelCase							).eval()

      SCREAMING_SNAKE_CASE_     						=      torch.load(__UpperCAmelCase      ,      map_location='cpu'							)['model']
      SCREAMING_SNAKE_CASE_     						=      convert_state_dict(__UpperCAmelCase      ,      __UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      model.load_state_dict(__UpperCAmelCase      ,      strict=__UpperCAmelCase							)
      assert missing_keys == ["text_model.embeddings.position_ids"]
      assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(__UpperCAmelCase							) == 0)

      # verify result
      SCREAMING_SNAKE_CASE_     						=      CLIPProcessor.from_pretrained('openai/clip-vit-base-patch32'							)
      SCREAMING_SNAKE_CASE_     						=      prepare_img()
      SCREAMING_SNAKE_CASE_     						=      processor(text=['a photo of a cat', 'a photo of a dog']      ,      images=__UpperCAmelCase      ,      padding=__UpperCAmelCase      ,      return_tensors='pt'							)

      with torch.no_grad():
            SCREAMING_SNAKE_CASE_     						=      model(**__UpperCAmelCase							)

      if model_name == "groupvit-gcc-yfcc":
            SCREAMING_SNAKE_CASE_     						=      torch.tensor([[1_3.3_5_2_3, 6.3_6_2_9]]							)
      elif model_name == "groupvit-gcc-redcaps":
            SCREAMING_SNAKE_CASE_     						=      torch.tensor([[1_6.1_8_7_3, 8.6_2_3_0]]							)
      else:
            raise ValueError(f"Model name {model_name} not supported."							)
      assert torch.allclose(outputs.logits_per_image      ,      __UpperCAmelCase      ,      atol=1E-3							)

      processor.save_pretrained(__UpperCAmelCase							)
      model.save_pretrained(__UpperCAmelCase							)
      print('Successfully saved processor and model to'      ,      __UpperCAmelCase							)

      if push_to_hub:
            print('Pushing to the hub...'							)
            processor.push_to_hub(__UpperCAmelCase      ,      organization='nielsr'							)
            model.push_to_hub(__UpperCAmelCase      ,      organization='nielsr'							)


if __name__ == "__main__":
   lowerCamelCase__			:       int         =							argparse.ArgumentParser()
   parser.add_argument(
       '--pytorch_dump_folder_path', default=None, type=str, help='Path to dump the processor and PyTorch model.'
   )
   parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to GroupViT checkpoint')
   parser.add_argument(
       '--model_name',
       default='groupvit-gccy-fcc',
       type=str,
       help='Name of the model. Expecting either \'groupvit-gcc-yfcc\' or \'groupvit-gcc-redcaps\'',
   )
   parser.add_argument(
       '--push_to_hub',
       action='store_true',
       help='Whether or not to push the converted model and processor to the 🤗 hub using the provided `model_name`.',
   )
   lowerCamelCase__			:       List[str]         =							parser.parse_args()

   convert_groupvit_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)