code: 
  
  
  
  
  """simple docstring"""
  
  
  
  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 						_SCREAMING_SNAKE_CASE     (							lowercase_		)		->	str:
   A__ =       tf.image.resize(lowercase_    ,				(3_46, 3_46)		)
   A__ =       tf.image.crop_to_bounding_box(lowercase_    ,				(3_46 - 2_89) // 2    ,				(3_46 - 2_89) // 2    ,				2_89    ,				2_89		)
   return image
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							)		->	List[str]:
   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=lowercase_    ,				vision_config=lowercase_    ,				projection_dim=6_40		)
   return config
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							)		->	List[str]:
   A__ =       "http://images.cocodataset.org/val2017/000000039769.jpg"
   A__ =       Image.open(requests.get(lowercase_    ,				stream=lowercase_		).raw		)
   return im
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							)		->	Optional[int]:
   A__ =       EfficientNetImageProcessor(
       do_center_crop=lowercase_    ,				rescale_factor=1 / 1_2_7.5    ,				rescale_offset=lowercase_    ,				do_normalize=lowercase_    ,				include_top=lowercase_    ,				resample=Image.BILINEAR    ,				)
   A__ =       BertTokenizer.from_pretrained("bert-base-uncased"		)
   A__ =       64
   A__ =       AlignProcessor(image_processor=lowercase_    ,				tokenizer=lowercase_		)
   return processor
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_		)		->	List[str]:
   # EfficientNet image encoder
   A__ =       [v.split("_"		)[0].split("block"		)[1] for v in original_param_names if v.startswith("block"		)]
   A__ =       list(set(lowercase_		)		)
   A__ =       sorted(lowercase_		)
   A__ =       len(lowercase_		)
   A__ =       {b: str(lowercase_		) for b, i in zip(lowercase_    ,				range(lowercase_		)		)}
  
   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 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_		)		->	List[str]:
   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(lowercase_		).permute(3    ,				2    ,				0    ,				1		)
    elif "embeddings" in key:
     A__ =       torch.from_numpy(lowercase_		)
    elif "depthwise_kernel" in key:
     A__ =       torch.from_numpy(lowercase_		).permute(2    ,				3    ,				0    ,				1		)
    elif "kernel" in key:
     A__ =       torch.from_numpy(np.transpose(lowercase_		)		)
    elif "temperature" in key:
     A__ =       value
    elif "bn/gamma" or "bn/beta" in key:
     A__ =       torch.from_numpy(np.transpose(lowercase_		)		).squeeze()
    else:
     A__ =       torch.from_numpy(lowercase_		)
  
    # Replace HF parameters with original TF model parameters
    hf_params[hf_key].copy_(lowercase_		)
  
  
  
  
  
  
  
  @torch.no_grad()
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_    ,				lowercase_		)		->	str:
   A__ =       64
   A__ =       Tokenizer(lowercase_		)
   A__ =       align.Align("efficientnet-b7"    ,				"bert-base"    ,				6_40    ,				lowercase_    ,				tok.get_vocab_size()		)
   original_model.compile()
   original_model.load_weights(lowercase_		)
  
   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(lowercase_		).eval()
   A__ =       hf_model.state_dict()
  
   # Create src-to-dst parameter name mapping dictionary
   print("Converting parameters..."		)
   A__ =       rename_keys(lowercase_		)
   replace_params(lowercase_    ,				lowercase_    ,				lowercase_		)
  
   # 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(**lowercase_		)
  
   A__ =       outputs.image_embeds.detach().numpy()
   A__ =       outputs.text_embeds.detach().numpy()
  
   # Original model inference
   A__ =       False
   A__ =       EfficientNetImageProcessor(
       do_center_crop=lowercase_    ,				do_rescale=lowercase_    ,				do_normalize=lowercase_    ,				include_top=lowercase_    ,				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(lowercase_    ,				training=lowercase_		)
   A__ =       original_model.text_encoder(lowercase_    ,				training=lowercase_		)
  
   A__ =       tf.nn.la_normalize(lowercase_    ,				axis=-1		)
   A__ =       tf.nn.la_normalize(lowercase_    ,				axis=-1		)
  
   # Check whether original and HF model outputs match  -> np.allclose
   if not np.allclose(lowercase_    ,				lowercase_    ,				atol=1E-3		):
    raise ValueError("The predicted image features are not the same."		)
   if not np.allclose(lowercase_    ,				lowercase_    ,				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(lowercase_		):
     os.mkdir(lowercase_		)
    # Save converted model and image processor
    hf_model.save_pretrained(lowercase_		)
    processor.save_pretrained(lowercase_		)
  
   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: 
  
  
  
  
  """simple docstring"""
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_		)		->	str:
   return " ".join(sorted(set(sentence.split()		)		)		)
  
  
  if __name__ == "__main__":
        import doctest
  
        doctest.testmod()
  
  
  
  
  
  