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()
  a     :			List[Any]					    =						logging.get_logger(__name__)
  def 		snake_case__  (		lowercase				):
     lowerCAmelCase_:       Tuple         =  tf.image.resize(lowercase       ,							(346, 346)				)
     lowerCAmelCase_:       List[Any]         =  tf.image.crop_to_bounding_box(lowercase       ,							(346 - 289) // 2       ,							(346 - 289) // 2       ,							289       ,							289				)
     return image
  def 		snake_case__  (		):
     lowerCAmelCase_:       Any         =  EfficientNetConfig.from_pretrained("google/efficientnet-b7"				)
     lowerCAmelCase_:       int         =  289
     lowerCAmelCase_:       Tuple         =  640
     lowerCAmelCase_:       Union[str, Any]         =  {"0": "LABEL_0", "1": "LABEL_1"}
     lowerCAmelCase_:       Any         =  {"LABEL_0": 0, "LABEL_1": 1}
     lowerCAmelCase_:       List[str]         =  []
  
     lowerCAmelCase_:       List[str]         =  BertConfig()
     lowerCAmelCase_:       Any         =  AlignConfig.from_text_vision_configs(
         text_config=lowercase       ,							vision_config=lowercase       ,							projection_dim=640				)
     return config
  def 		snake_case__  (		):
     lowerCAmelCase_:       List[str]         =  "http://images.cocodataset.org/val2017/000000039769.jpg"
     lowerCAmelCase_:       List[Any]         =  Image.open(requests.get(lowercase       ,							stream=lowercase				).raw				)
     return im
  def 		snake_case__  (		):
     lowerCAmelCase_:       Any         =  EfficientNetImageProcessor(
         do_center_crop=lowercase       ,							rescale_factor=1 / 127.5       ,							rescale_offset=lowercase       ,							do_normalize=lowercase       ,							include_top=lowercase       ,							resample=Image.BILINEAR       ,							)
     lowerCAmelCase_:       Tuple         =  BertTokenizer.from_pretrained("bert-base-uncased"				)
     lowerCAmelCase_:       int         =  64
     lowerCAmelCase_:       Dict         =  AlignProcessor(image_processor=lowercase       ,							tokenizer=lowercase				)
     return processor
  def 		snake_case__  (		lowercase				):
     # EfficientNet image encoder
     lowerCAmelCase_:       List[Any]         =  [v.split("_"				)[0].split("block"				)[1] for v in original_param_names if v.startswith("block"				)]
     lowerCAmelCase_:       Dict         =  list(set(lowercase				)				)
     lowerCAmelCase_:       Optional[int]         =  sorted(lowercase				)
     lowerCAmelCase_:       List[Any]         =  len(lowercase				)
     lowerCAmelCase_:       Optional[int]         =  {b: str(lowercase				) for b, i in zip(lowercase       ,							range(lowercase				)				)}
  
     lowerCAmelCase_:       str         =  []
     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:
        lowerCAmelCase_:       List[str]         =  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''')				)
  
     lowerCAmelCase_:       str         =  {}
     for item in rename_keys:
        if item[0] in original_param_names:
           lowerCAmelCase_:       Optional[Any]         =  "vision_model." + item[1]
  
      # BERT text encoder
     lowerCAmelCase_:       List[Any]         =  []
     lowerCAmelCase_:       Union[str, Any]         =  "tf_bert_model/bert"
     lowerCAmelCase_:       Dict         =  "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:
           lowerCAmelCase_:       Dict         =  item[1]
     return key_mapping
  def 		snake_case__  (		lowercase       ,							lowercase       ,							lowercase				):
     list(hf_params.keys()				)
  
     for key, value in tf_params.items():
        if key not in key_mapping:
           continue
  
        lowerCAmelCase_:       Tuple         =  key_mapping[key]
        if "_conv" in key and "kernel" in key:
           lowerCAmelCase_:       int         =  torch.from_numpy(lowercase				).permute(3       ,							2       ,							0       ,							1				)
        elif "embeddings" in key:
           lowerCAmelCase_:       Any         =  torch.from_numpy(lowercase				)
        elif "depthwise_kernel" in key:
           lowerCAmelCase_:       Union[str, Any]         =  torch.from_numpy(lowercase				).permute(2       ,							3       ,							0       ,							1				)
        elif "kernel" in key:
           lowerCAmelCase_:       str         =  torch.from_numpy(np.transpose(lowercase				)				)
        elif "temperature" in key:
           lowerCAmelCase_:       Tuple         =  value
        elif "bn/gamma" or "bn/beta" in key:
           lowerCAmelCase_:       Optional[int]         =  torch.from_numpy(np.transpose(lowercase				)				).squeeze()
        else:
           lowerCAmelCase_:       Any         =  torch.from_numpy(lowercase				)
  
        # Replace HF parameters with original TF model parameters
        hf_params[hf_key].copy_(lowercase				)
  @torch.no_grad()
  def 		snake_case__  (		lowercase       ,							lowercase       ,							lowercase       ,							lowercase				):
     lowerCAmelCase_:       Tuple         =  64
     lowerCAmelCase_:       Tuple         =  Tokenizer(lowercase				)
     lowerCAmelCase_:       List[Any]         =  align.Align("efficientnet-b7"       ,							"bert-base"       ,							640       ,							lowercase       ,							tok.get_vocab_size()				)
     original_model.compile()
     original_model.load_weights(lowercase				)
  
     lowerCAmelCase_:       List[str]         =  original_model.trainable_variables
     lowerCAmelCase_:       str         =  original_model.non_trainable_variables
     lowerCAmelCase_:       Union[str, Any]         =  {param.name: param.numpy() for param in tf_params}
     for param in tf_non_train_params:
        lowerCAmelCase_:       Dict         =  param.numpy()
     lowerCAmelCase_:       int         =  list(tf_params.keys()				)
  
     # Load HuggingFace model
     lowerCAmelCase_:       Optional[Any]         =  get_align_config()
     lowerCAmelCase_:       str         =  AlignModel(lowercase				).eval()
     lowerCAmelCase_:       str         =  hf_model.state_dict()
  
     # Create src-to-dst parameter name mapping dictionary
     print("Converting parameters..."				)
     lowerCAmelCase_:       Any         =  rename_keys(lowercase				)
     replace_params(lowercase       ,							lowercase       ,							lowercase				)
  
     # Initialize processor
     lowerCAmelCase_:       Optional[Any]         =  get_processor()
     lowerCAmelCase_:       Dict         =  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():
        lowerCAmelCase_:       Optional[Any]         =  hf_model(**lowercase				)
  
     lowerCAmelCase_:       Dict         =  outputs.image_embeds.detach().numpy()
     lowerCAmelCase_:       List[Any]         =  outputs.text_embeds.detach().numpy()
  
     # Original model inference
     lowerCAmelCase_:       str         =  False
     lowerCAmelCase_:       Tuple         =  EfficientNetImageProcessor(
         do_center_crop=lowercase       ,							do_rescale=lowercase       ,							do_normalize=lowercase       ,							include_top=lowercase       ,							resample=Image.BILINEAR       ,							)
     lowerCAmelCase_:       List[Any]         =  tf_image_processor(images=prepare_img()       ,							return_tensors="tf"       ,							data_format="channels_last"				)["pixel_values"]
     lowerCAmelCase_:       Union[str, Any]         =  tok(tf.constant(["A picture of a cat"]				)				)
  
     lowerCAmelCase_:       int         =  original_model.image_encoder(lowercase       ,							training=lowercase				)
     lowerCAmelCase_:       Dict         =  original_model.text_encoder(lowercase       ,							training=lowercase				)
  
     lowerCAmelCase_:       str         =  tf.nn.la_normalize(lowercase       ,							axis=-1				)
     lowerCAmelCase_:       Dict         =  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__":
   a     :			Any					    =						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""")
  
   a     :			List[str]					    =						parser.parse_args()
   convert_align_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)
style_context: from ctypes import c_float, sizeof
  from enum import Enum
  from typing import TYPE_CHECKING, Optional, Union
  
  
  if TYPE_CHECKING:
   from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore
  
  class 				_lowercase						(    UpperCAmelCase__ ):
  
  
  
  
  
  
      '''simple docstring'''
      SCREAMING_SNAKE_CASE:							Union[str, Any]        =						c_float
  
  
  
  
      @property
      def     _a						(						self   ):
         return sizeof(self.value   )
  
  
  
  
  
  def 		snake_case__  (		lowercase       ,							lowercase       ,							lowercase = 0				):
     # < 0 is possible if using a dynamic axis
     if dimension <= 0:
        lowerCAmelCase_:       int         =  fixed_dimension
  
     dimension -= num_token_to_add
     return dimension
  def 		snake_case__  (		lowercase       ,							lowercase				):
     return num_parameters * dtype.size
  
  
  
  
  
  
  
  def 		snake_case__  (		lowercase				):
     from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore
  
     try:
        return AutoProcessor.from_pretrained(lowercase				)
     except (ValueError, OSError, KeyError):
        lowerCAmelCase_     ,  lowerCAmelCase_:       int         =  None, None
        try:
           lowerCAmelCase_:       Dict         =  AutoTokenizer.from_pretrained(lowercase				)
        except (OSError, KeyError):
           pass
        try:
           lowerCAmelCase_:       Optional[int]         =  AutoFeatureExtractor.from_pretrained(lowercase				)
        except (OSError, KeyError):
           pass
  
        if tokenizer is not None and feature_extractor is not None:
           raise ValueError(
               F'''Couldn\'t auto-detect preprocessor for {model_name}. Found both a tokenizer and a feature extractor.'''				)
        elif tokenizer is None and feature_extractor is None:
           return None
        elif tokenizer is not None:
           return tokenizer
        else:
           return feature_extractor