code: import argparse
  import os
  
  import align
  import numpy as np
  import requests
  import tensorflow as tf
  import torch
  from PIL import Image
  from tokenizer import Tokenizer
  
  from transformers import (
      AlignConfig,
      AlignModel,
      AlignProcessor,
      BertConfig,
      BertTokenizer,
      EfficientNetConfig,
      EfficientNetImageProcessor,
  )
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  _SCREAMING_SNAKE_CASE   						=    logging.get_logger(__name__)
  def 				__lowercase	(						lowerCamelCase__					)				->str:
  
    '''simple docstring'''
  
  
  
  
    a__     		=     tf.image.resize(lowerCamelCase__ ,				(3_46, 3_46)					)
    a__     		=     tf.image.crop_to_bounding_box(lowerCamelCase__ ,				(3_46 - 2_89) // 2 ,				(3_46 - 2_89) // 2 ,				2_89 ,				2_89					)
    return image
  def 				__lowercase	(						)				->Union[str, Any]:
  
    '''simple docstring'''
  
  
  
  
    a__     		=     EfficientNetConfig.from_pretrained("google/efficientnet-b7"					)
    a__     		=     2_89
    a__     		=     6_40
    a__     		=     {"0": "LABEL_0", "1": "LABEL_1"}
    a__     		=     {"LABEL_0": 0, "LABEL_1": 1}
    a__     		=     []
  
    a__     		=     BertConfig()
    a__     		=     AlignConfig.from_text_vision_configs(
        text_config=lowerCamelCase__ ,				vision_config=lowerCamelCase__ ,				projection_dim=6_40					)
    return config
  def 				__lowercase	(						)				->Optional[Any]:
  
    '''simple docstring'''
  
  
  
  
    a__     		=     "http://images.cocodataset.org/val2017/000000039769.jpg"
    a__     		=     Image.open(requests.get(lowerCamelCase__ ,				stream=lowerCamelCase__					).raw					)
    return im
  def 				__lowercase	(						)				->List[Any]:
  
    '''simple docstring'''
  
  
  
  
    a__     		=     EfficientNetImageProcessor(
        do_center_crop=lowerCamelCase__ ,				rescale_factor=1 / 127.5 ,				rescale_offset=lowerCamelCase__ ,				do_normalize=lowerCamelCase__ ,				include_top=lowerCamelCase__ ,				resample=Image.BILINEAR ,				)
    a__     		=     BertTokenizer.from_pretrained("bert-base-uncased"					)
    a__     		=     64
    a__     		=     AlignProcessor(image_processor=lowerCamelCase__ ,				tokenizer=lowerCamelCase__					)
    return processor
  def 				__lowercase	(						lowerCamelCase__					)				->Union[str, Any]:
  
    '''simple docstring'''
  
  
  
  
    a__     		=     [v.split("_"					)[0].split("block"					)[1] for v in original_param_names if v.startswith("block"					)]
    a__     		=     list(set(lowerCamelCase__					)					)
    a__     		=     sorted(lowerCamelCase__					)
    a__     		=     len(lowerCamelCase__					)
    a__     		=     {b: str(lowerCamelCase__					) for b, i in zip(lowerCamelCase__ ,				range(lowerCamelCase__					)					)}
  
    a__     		=     []
    rename_keys.append(("stem_conv/kernel:0", "embeddings.convolution.weight")					)
    rename_keys.append(("stem_bn/gamma:0", "embeddings.batchnorm.weight")					)
    rename_keys.append(("stem_bn/beta:0", "embeddings.batchnorm.bias")					)
    rename_keys.append(("stem_bn/moving_mean:0", "embeddings.batchnorm.running_mean")					)
    rename_keys.append(("stem_bn/moving_variance:0", "embeddings.batchnorm.running_var")					)
  
    for b in block_names:
      a__     		=     block_name_mapping[b]
      rename_keys.append((f"block{b}_expand_conv/kernel:0", f"encoder.blocks.{hf_b}.expansion.expand_conv.weight")					)
      rename_keys.append((f"block{b}_expand_bn/gamma:0", f"encoder.blocks.{hf_b}.expansion.expand_bn.weight")					)
      rename_keys.append((f"block{b}_expand_bn/beta:0", f"encoder.blocks.{hf_b}.expansion.expand_bn.bias")					)
      rename_keys.append(
          (f"block{b}_expand_bn/moving_mean:0", f"encoder.blocks.{hf_b}.expansion.expand_bn.running_mean")					)
      rename_keys.append(
          (f"block{b}_expand_bn/moving_variance:0", f"encoder.blocks.{hf_b}.expansion.expand_bn.running_var")					)
      rename_keys.append(
          (f"block{b}_dwconv/depthwise_kernel:0", f"encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight")					)
      rename_keys.append((f"block{b}_bn/gamma:0", f"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight")					)
      rename_keys.append((f"block{b}_bn/beta:0", f"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias")					)
      rename_keys.append(
          (f"block{b}_bn/moving_mean:0", f"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean")					)
      rename_keys.append(
          (f"block{b}_bn/moving_variance:0", f"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var")					)
  
      rename_keys.append((f"block{b}_se_reduce/kernel:0", f"encoder.blocks.{hf_b}.squeeze_excite.reduce.weight")					)
      rename_keys.append((f"block{b}_se_reduce/bias:0", f"encoder.blocks.{hf_b}.squeeze_excite.reduce.bias")					)
      rename_keys.append((f"block{b}_se_expand/kernel:0", f"encoder.blocks.{hf_b}.squeeze_excite.expand.weight")					)
      rename_keys.append((f"block{b}_se_expand/bias:0", f"encoder.blocks.{hf_b}.squeeze_excite.expand.bias")					)
      rename_keys.append(
          (f"block{b}_project_conv/kernel:0", f"encoder.blocks.{hf_b}.projection.project_conv.weight")					)
      rename_keys.append((f"block{b}_project_bn/gamma:0", f"encoder.blocks.{hf_b}.projection.project_bn.weight")					)
      rename_keys.append((f"block{b}_project_bn/beta:0", f"encoder.blocks.{hf_b}.projection.project_bn.bias")					)
      rename_keys.append(
          (f"block{b}_project_bn/moving_mean:0", f"encoder.blocks.{hf_b}.projection.project_bn.running_mean")					)
      rename_keys.append(
          (f"block{b}_project_bn/moving_variance:0", f"encoder.blocks.{hf_b}.projection.project_bn.running_var")					)
  
    a__     		=     {}
    for item in rename_keys:
      if item[0] in original_param_names:
        a__     		=     "vision_model." + item[1]
  
      # BERT text encoder
    a__     		=     []
    a__     		=     "tf_bert_model/bert"
    a__     		=     "text_model"
    for i in range(12					):
      rename_keys.append(
          (
              f"{old}/encoder/layer_._{i}/attention/self/query/kernel:0",
              f"{new}.encoder.layer.{i}.attention.self.query.weight",
          )					)
      rename_keys.append(
          (
              f"{old}/encoder/layer_._{i}/attention/self/query/bias:0",
              f"{new}.encoder.layer.{i}.attention.self.query.bias",
          )					)
      rename_keys.append(
          (
              f"{old}/encoder/layer_._{i}/attention/self/key/kernel:0",
              f"{new}.encoder.layer.{i}.attention.self.key.weight",
          )					)
      rename_keys.append(
          (
              f"{old}/encoder/layer_._{i}/attention/self/key/bias:0",
              f"{new}.encoder.layer.{i}.attention.self.key.bias",
          )					)
      rename_keys.append(
          (
              f"{old}/encoder/layer_._{i}/attention/self/value/kernel:0",
              f"{new}.encoder.layer.{i}.attention.self.value.weight",
          )					)
      rename_keys.append(
          (
              f"{old}/encoder/layer_._{i}/attention/self/value/bias:0",
              f"{new}.encoder.layer.{i}.attention.self.value.bias",
          )					)
      rename_keys.append(
          (
              f"{old}/encoder/layer_._{i}/attention/output/dense/kernel:0",
              f"{new}.encoder.layer.{i}.attention.output.dense.weight",
          )					)
      rename_keys.append(
          (
              f"{old}/encoder/layer_._{i}/attention/output/dense/bias:0",
              f"{new}.encoder.layer.{i}.attention.output.dense.bias",
          )					)
      rename_keys.append(
          (
              f"{old}/encoder/layer_._{i}/attention/output/LayerNorm/gamma:0",
              f"{new}.encoder.layer.{i}.attention.output.LayerNorm.weight",
          )					)
      rename_keys.append(
          (
              f"{old}/encoder/layer_._{i}/attention/output/LayerNorm/beta:0",
              f"{new}.encoder.layer.{i}.attention.output.LayerNorm.bias",
          )					)
      rename_keys.append(
          (
              f"{old}/encoder/layer_._{i}/intermediate/dense/kernel:0",
              f"{new}.encoder.layer.{i}.intermediate.dense.weight",
          )					)
      rename_keys.append(
          (
              f"{old}/encoder/layer_._{i}/intermediate/dense/bias:0",
              f"{new}.encoder.layer.{i}.intermediate.dense.bias",
          )					)
      rename_keys.append(
          (f"{old}/encoder/layer_._{i}/output/dense/kernel:0", f"{new}.encoder.layer.{i}.output.dense.weight")					)
      rename_keys.append(
          (f"{old}/encoder/layer_._{i}/output/dense/bias:0", f"{new}.encoder.layer.{i}.output.dense.bias")					)
      rename_keys.append(
          (f"{old}/encoder/layer_._{i}/output/LayerNorm/gamma:0", f"{new}.encoder.layer.{i}.output.LayerNorm.weight")					)
      rename_keys.append(
          (f"{old}/encoder/layer_._{i}/output/LayerNorm/beta:0", f"{new}.encoder.layer.{i}.output.LayerNorm.bias")					)
  
    rename_keys.append((f"{old}/embeddings/word_embeddings/weight:0", f"{new}.embeddings.word_embeddings.weight")					)
    rename_keys.append(
        (f"{old}/embeddings/position_embeddings/embeddings:0", f"{new}.embeddings.position_embeddings.weight")					)
    rename_keys.append(
        (f"{old}/embeddings/token_type_embeddings/embeddings:0", f"{new}.embeddings.token_type_embeddings.weight")					)
    rename_keys.append((f"{old}/embeddings/LayerNorm/gamma:0", f"{new}.embeddings.LayerNorm.weight")					)
    rename_keys.append((f"{old}/embeddings/LayerNorm/beta:0", f"{new}.embeddings.LayerNorm.bias")					)
  
    rename_keys.append((f"{old}/pooler/dense/kernel:0", f"{new}.pooler.dense.weight")					)
    rename_keys.append((f"{old}/pooler/dense/bias:0", f"{new}.pooler.dense.bias")					)
    rename_keys.append(("dense/kernel:0", "text_projection.weight")					)
    rename_keys.append(("dense/bias:0", "text_projection.bias")					)
    rename_keys.append(("dense/bias:0", "text_projection.bias")					)
    rename_keys.append(("temperature:0", "temperature")					)
  
    for item in rename_keys:
      if item[0] in original_param_names:
        a__     		=     item[1]
    return key_mapping
  def 				__lowercase	(						lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__					)				->Optional[Any]:
  
    '''simple docstring'''
  
  
  
  
    list(hf_params.keys()					)
  
    for key, value in tf_params.items():
      if key not in key_mapping:
        continue
  
      a__     		=     key_mapping[key]
      if "_conv" in key and "kernel" in key:
        a__     		=     torch.from_numpy(lowerCamelCase__					).permute(3 ,				2 ,				0 ,				1					)
      elif "embeddings" in key:
        a__     		=     torch.from_numpy(lowerCamelCase__					)
      elif "depthwise_kernel" in key:
        a__     		=     torch.from_numpy(lowerCamelCase__					).permute(2 ,				3 ,				0 ,				1					)
      elif "kernel" in key:
        a__     		=     torch.from_numpy(np.transpose(lowerCamelCase__					)					)
      elif "temperature" in key:
        a__     		=     value
      elif "bn/gamma" or "bn/beta" in key:
        a__     		=     torch.from_numpy(np.transpose(lowerCamelCase__					)					).squeeze()
      else:
        a__     		=     torch.from_numpy(lowerCamelCase__					)
  
      # Replace HF parameters with original TF model parameters
      hf_params[hf_key].copy_(lowerCamelCase__					)
  @torch.no_grad()
  def 				__lowercase	(						lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__					)				->Optional[int]:
  
    '''simple docstring'''
  
  
  
  
    a__     		=     64
    a__     		=     Tokenizer(lowerCamelCase__					)
    a__     		=     align.Align("efficientnet-b7" ,				"bert-base" ,				6_40 ,				lowerCamelCase__ ,				tok.get_vocab_size()					)
    original_model.compile()
    original_model.load_weights(lowerCamelCase__					)
  
    a__     		=     original_model.trainable_variables
    a__     		=     original_model.non_trainable_variables
    a__     		=     {param.name: param.numpy() for param in tf_params}
    for param in tf_non_train_params:
      a__     		=     param.numpy()
    a__     		=     list(tf_params.keys()					)
  
    # Load HuggingFace model
    a__     		=     get_align_config()
    a__     		=     AlignModel(lowerCamelCase__					).eval()
    a__     		=     hf_model.state_dict()
  
    # Create src-to-dst parameter name mapping dictionary
    print("Converting parameters..."					)
    a__     		=     rename_keys(lowerCamelCase__					)
    replace_params(lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__					)
  
    # Initialize processor
    a__     		=     get_processor()
    a__     		=     processor(
        images=prepare_img() ,				text="A picture of a cat" ,				padding="max_length" ,				max_length=64 ,				return_tensors="pt"					)
  
    # HF model inference
    hf_model.eval()
    with torch.no_grad():
      a__     		=     hf_model(**lowerCamelCase__					)
  
    a__     		=     outputs.image_embeds.detach().numpy()
    a__     		=     outputs.text_embeds.detach().numpy()
  
    # Original model inference
    a__     		=     False
    a__     		=     EfficientNetImageProcessor(
        do_center_crop=lowerCamelCase__ ,				do_rescale=lowerCamelCase__ ,				do_normalize=lowerCamelCase__ ,				include_top=lowerCamelCase__ ,				resample=Image.BILINEAR ,				)
    a__     		=     tf_image_processor(images=prepare_img() ,				return_tensors="tf" ,				data_format="channels_last"					)["pixel_values"]
    a__     		=     tok(tf.constant(["A picture of a cat"]					)					)
  
    a__     		=     original_model.image_encoder(lowerCamelCase__ ,				training=lowerCamelCase__					)
    a__     		=     original_model.text_encoder(lowerCamelCase__ ,				training=lowerCamelCase__					)
  
    a__     		=     tf.nn.la_normalize(lowerCamelCase__ ,				axis=-1					)
    a__     		=     tf.nn.la_normalize(lowerCamelCase__ ,				axis=-1					)
  
    # Check whether original and HF model outputs match  -> np.allclose
    if not np.allclose(lowerCamelCase__ ,				lowerCamelCase__ ,				atol=1E-3					):
      raise ValueError("The predicted image features are not the same."					)
    if not np.allclose(lowerCamelCase__ ,				lowerCamelCase__ ,				atol=1E-3					):
      raise ValueError("The predicted text features are not the same."					)
    print("Model outputs match!"					)
  
    if save_model:
      # Create folder to save model
      if not os.path.isdir(lowerCamelCase__					):
        os.mkdir(lowerCamelCase__					)
      # Save converted model and image processor
      hf_model.save_pretrained(lowerCamelCase__					)
      processor.save_pretrained(lowerCamelCase__					)
  
    if push_to_hub:
      # Push model and image processor to hub
      print("Pushing converted ALIGN to the hub..."					)
      processor.push_to_hub("align-base"					)
      hf_model.push_to_hub("align-base"					)
  
  
  if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE   						=    argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           """--checkpoint_path""",
           default="""./weights/model-weights""",
           type=str,
           help="""Path to the pretrained TF ALIGN checkpoint.""",
       )
       parser.add_argument(
           """--pytorch_dump_folder_path""",
           default="""hf_model""",
           type=str,
           help="""Path to the output PyTorch model directory.""",
       )
       parser.add_argument("""--save_model""", action="""store_true""", help="""Save model to local""")
       parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Push model and image processor to the hub""")
  
       _SCREAMING_SNAKE_CASE   						=    parser.parse_args()
       convert_align_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)
  
  
style_context: import random
  
  
  
  
  
  
  
  class 					UpperCAmelCase_				:
  
  
  
  
      def __init__(					self  :   Optional[int]      ,      lowerCAmelCase  :   list[list[float]]      ,      lowerCAmelCase  :   list[int]      ,      lowerCAmelCase  :   float = 0.01      ,      lowerCAmelCase  :   int = 1000      ,      lowerCAmelCase  :   float = -1      ,      )	-> None:
        a__     		=     sample
        if len(self.sample   ) == 0:
          raise ValueError("Sample data can not be empty"   )
        a__     		=     target
        if len(self.target   ) == 0:
          raise ValueError("Target data can not be empty"   )
        if len(self.sample   ) != len(self.target   ):
          raise ValueError("Sample data and Target data do not have matching lengths"   )
        a__     		=     learning_rate
        a__     		=     epoch_number
        a__     		=     bias
        a__     		=     len(lowerCAmelCase   )
        a__     		=     len(sample[0]   )  # number of columns in dataset
        a__     		=     []
  
  
  
  
      def 						__lowerCamelCase (					self  :   Optional[Any]   )	-> None:
        for sample in self.sample:
          sample.insert(0      ,      self.bias   )
  
        for _ in range(self.col_sample   ):
          self.weight.append(random.random()   )
  
        self.weight.insert(0      ,      self.bias   )
  
        a__     		=     0
  
        while True:
          a__     		=     False
          for i in range(self.number_sample   ):
            a__     		=     0
            for j in range(self.col_sample + 1   ):
              a__     		=     u + self.weight[j] * self.sample[i][j]
            a__     		=     self.sign(lowerCAmelCase   )
            if y != self.target[i]:
              for j in range(self.col_sample + 1   ):
                a__     		=     (
                    self.weight[j]
                    + self.learning_rate
                    * (self.target[i] - y)
                    * self.sample[i][j]
                )
              a__     		=     True
              # print('Epoch: \n',epoch_count)
          a__     		=     epoch_count + 1
          # if you want control the epoch or just by error
          if not has_misclassified:
            print(("\nEpoch:\n", epoch_count)   )
            print("------------------------\n"   )
            # if epoch_count > self.epoch_number or not error:
            break
  
  
  
  
      def 						__lowerCamelCase (					self  :   List[str]      ,      lowerCAmelCase  :   list[float]   )	-> None:
        if len(self.sample   ) == 0:
          raise ValueError("Sample data can not be empty"   )
        sample.insert(0      ,      self.bias   )
        a__     		=     0
        for i in range(self.col_sample + 1   ):
          a__     		=     u + self.weight[i] * sample[i]
  
        a__     		=     self.sign(lowerCAmelCase   )
  
        if y == -1:
          print(("Sample: ", sample)   )
          print("classification: P1"   )
        else:
          print(("Sample: ", sample)   )
          print("classification: P2"   )
  
  
  
  
      def 						__lowerCamelCase (					self  :   int      ,      lowerCAmelCase  :   float   )	-> int:
        return 1 if u >= 0 else -1
  
  _SCREAMING_SNAKE_CASE   						=    [
      [-0.6508, 0.1097, 4.0009],
      [-1.4492, 0.8896, 4.4005],
      [2.0850, 0.6876, 12.0710],
      [0.2626, 1.1476, 7.7985],
      [0.6418, 1.0234, 7.0427],
      [0.2569, 0.6730, 8.3265],
      [1.1155, 0.6043, 7.4446],
      [0.0914, 0.3399, 7.0677],
      [0.0121, 0.5256, 4.6316],
      [-0.0429, 0.4660, 5.4323],
      [0.4340, 0.6870, 8.2287],
      [0.2735, 1.0287, 7.1934],
      [0.4839, 0.4851, 7.4850],
      [0.4089, -0.1267, 5.5019],
      [1.4391, 0.1614, 8.5843],
      [-0.9115, -0.1973, 2.1962],
      [0.3654, 1.0475, 7.4858],
      [0.2144, 0.7515, 7.1699],
      [0.2013, 1.0014, 6.5489],
      [0.6483, 0.2183, 5.8991],
      [-0.1147, 0.2242, 7.2435],
      [-0.7970, 0.8795, 3.8762],
      [-1.0625, 0.6366, 2.4707],
      [0.5307, 0.1285, 5.6883],
      [-1.2200, 0.7777, 1.7252],
      [0.3957, 0.1076, 5.6623],
      [-0.1013, 0.5989, 7.1812],
      [2.4482, 0.9455, 11.2095],
      [2.0149, 0.6192, 10.9263],
      [0.2012, 0.2611, 5.4631],
  ]
  
  _SCREAMING_SNAKE_CASE   						=    [
      -1,
      -1,
      -1,
      1,
      1,
      -1,
      1,
      -1,
      1,
      1,
      -1,
      1,
      -1,
      -1,
      -1,
      -1,
      1,
      1,
      1,
      1,
      -1,
      1,
      1,
      1,
      1,
      -1,
      -1,
      1,
      -1,
      1,
  ]
  
  
  if __name__ == "__main__":
       import doctest
  
       doctest.testmod()
  
       _SCREAMING_SNAKE_CASE   						=    Perceptron(
           sample=samples, target=target, learning_rate=0.01, epoch_number=1000, bias=-1
       )
       network.training()
       print("""Finished training perceptron""")
       print("""Enter values to predict or q to exit""")
       while True:
            _SCREAMING_SNAKE_CASE   						=    []
            for i in range(len(samples[0])):
                 _SCREAMING_SNAKE_CASE   						=    input("""value: """).strip()
                 if user_input == "q":
                      break
                 _SCREAMING_SNAKE_CASE   						=    float(user_input)
                 sample.insert(i, observation)
            network.sort(sample)
  
  