code: 
  
  
  import cva
  import numpy as np
  
  from digital_image_processing.filters.convolve import img_convolve
  from digital_image_processing.filters.sobel_filter import sobel_filter
  
  __a   				=      1_80
  
  
  
  
  def 			__lowercase	(      _UpperCamelCase,    _UpperCamelCase     )    ->Dict:
  
  
  
  
  
  
  
        """simple docstring"""
  
        lowercase			:   int					   =		k_size // 2
        lowercase			:   Tuple					   =		np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
        lowercase			:   Union[str, Any]					   =		(
            1
            / (2 * np.pi * sigma)
            * np.exp(-(np.square(UpperCamelCase__     ) + np.square(UpperCamelCase__     )) / (2 * np.square(UpperCamelCase__     ))     )
        )
        return g
  
  
  
  
  def 			__lowercase	(      _UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase     )    ->Union[str, Any]:
  
  
  
  
  
  
  
        """simple docstring"""
  
        lowercase			:   Optional[int]					   =		np.zeros(UpperCamelCase__     )
  
        for row in range(1,    image_shape[0] - 1     ):
              for col in range(1,    image_shape[1] - 1     ):
                    lowercase			:   Dict					   =		gradient_direction[row, col]
  
                    if (
                        0 <= direction < PI / 8
                        or 15 * PI / 8 <= direction <= 2 * PI
                        or 7 * PI / 8 <= direction <= 9 * PI / 8
                    ):
                          lowercase			:   Dict					   =		sobel_grad[row, col - 1]
                          lowercase			:   Any					   =		sobel_grad[row, col + 1]
                          if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
                                lowercase			:   List[Any]					   =		sobel_grad[row, col]
  
                    elif (
                        PI / 8 <= direction < 3 * PI / 8
                        or 9 * PI / 8 <= direction < 11 * PI / 8
                    ):
                          lowercase			:   List[str]					   =		sobel_grad[row + 1, col - 1]
                          lowercase			:   str					   =		sobel_grad[row - 1, col + 1]
                          if sobel_grad[row, col] >= sw and sobel_grad[row, col] >= ne:
                                lowercase			:   List[Any]					   =		sobel_grad[row, col]
  
                    elif (
                        3 * PI / 8 <= direction < 5 * PI / 8
                        or 11 * PI / 8 <= direction < 13 * PI / 8
                    ):
                          lowercase			:   Any					   =		sobel_grad[row - 1, col]
                          lowercase			:   int					   =		sobel_grad[row + 1, col]
                          if sobel_grad[row, col] >= n and sobel_grad[row, col] >= s:
                                lowercase			:   Any					   =		sobel_grad[row, col]
  
                    elif (
                        5 * PI / 8 <= direction < 7 * PI / 8
                        or 13 * PI / 8 <= direction < 15 * PI / 8
                    ):
                          lowercase			:   List[str]					   =		sobel_grad[row - 1, col - 1]
                          lowercase			:   Optional[Any]					   =		sobel_grad[row + 1, col + 1]
                          if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
                                lowercase			:   List[Any]					   =		sobel_grad[row, col]
  
        return destination
  
  
  
  
  def 			__lowercase	(      _UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase     )    ->List[str]:
  
  
  
  
  
  
  
        """simple docstring"""
  
        for row in range(1,    image_shape[0] - 1     ):
              for col in range(1,    image_shape[1] - 1     ):
                    if destination[row, col] >= threshold_high:
                          lowercase			:   Optional[int]					   =		strong
                    elif destination[row, col] <= threshold_low:
                          lowercase			:   Optional[int]					   =		0
                    else:
                          lowercase			:   Union[str, Any]					   =		weak
  
  
  
  
  def 			__lowercase	(      _UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase     )    ->Dict:
  
  
  
  
  
  
  
        """simple docstring"""
  
        for row in range(1,    image_shape[0]     ):
              for col in range(1,    image_shape[1]     ):
                    if destination[row, col] == weak:
                          if 255 in (
                              destination[row, col + 1],
                              destination[row, col - 1],
                              destination[row - 1, col],
                              destination[row + 1, col],
                              destination[row - 1, col - 1],
                              destination[row + 1, col - 1],
                              destination[row - 1, col + 1],
                              destination[row + 1, col + 1],
                          ):
                                lowercase			:   str					   =		strong
                          else:
                                lowercase			:   Any					   =		0
  
  
  
  
  def 			__lowercase	(      _UpperCamelCase,    _UpperCamelCase=15,    _UpperCamelCase=30,    _UpperCamelCase=128,    _UpperCamelCase=255     )    ->str:
  
  
  
  
  
  
  
        """simple docstring"""
  
        lowercase			:   Union[str, Any]					   =		img_convolve(UpperCamelCase__,    gen_gaussian_kernel(9,    sigma=1.4     )     )
        # get the gradient and degree by sobel_filter
        lowercase			:   Dict					   =		sobel_filter(UpperCamelCase__     )
        lowercase			:   Tuple					   =		PI + np.radadeg(UpperCamelCase__     )
  
        lowercase			:   Optional[int]					   =		suppress_non_maximum(image.shape,    UpperCamelCase__,    UpperCamelCase__     )
  
        detect_high_low_threshold(
            image.shape,    UpperCamelCase__,    UpperCamelCase__,    UpperCamelCase__,    UpperCamelCase__,    UpperCamelCase__     )
  
        track_edge(image.shape,    UpperCamelCase__,    UpperCamelCase__,    UpperCamelCase__     )
  
        return destination
  
  
  if __name__ == "__main__":
    # read original image in gray mode
    __a   				=      cva.imread(r'''../image_data/lena.jpg''', 0)
    # canny edge detection
    __a   				=      canny(lena)
    cva.imshow('''canny''', canny_destination)
    cva.waitKey(0)
  
  
style_context: 
  
  
  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   				=      logging.get_logger(__name__)
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase     )    ->Tuple:
  
  
  
  
  
  
  
        """simple docstring"""
  
        lowercase			:   Optional[int]					   =		tf.image.resize(_UpperCamelCase,    (346, 346)     )
        lowercase			:   Any					   =		tf.image.crop_to_bounding_box(_UpperCamelCase,    (346 - 289) // 2,    (346 - 289) // 2,    289,    289     )
        return image
  
  
  
  
  def 			__lowercase	(						)    ->List[str]:
  
  
  
  
  
  
  
        """simple docstring"""
  
        lowercase			:   Tuple					   =		EfficientNetConfig.from_pretrained('''google/efficientnet-b7'''     )
        lowercase			:   Optional[int]					   =		289
        lowercase			:   Optional[Any]					   =		640
        lowercase			:   Any					   =		{'''0''': '''LABEL_0''', '''1''': '''LABEL_1'''}
        lowercase			:   Optional[Any]					   =		{'''LABEL_0''': 0, '''LABEL_1''': 1}
        lowercase			:   Union[str, Any]					   =		[]
  
        lowercase			:   Union[str, Any]					   =		BertConfig()
        lowercase			:   Any					   =		AlignConfig.from_text_vision_configs(
            text_config=_UpperCamelCase,    vision_config=_UpperCamelCase,    projection_dim=640     )
        return config
  
  
  
  
  def 			__lowercase	(						)    ->Union[str, Any]:
  
  
  
  
  
  
  
        """simple docstring"""
  
        lowercase			:   int					   =		'''http://images.cocodataset.org/val2017/000000039769.jpg'''
        lowercase			:   int					   =		Image.open(requests.get(_UpperCamelCase,    stream=_UpperCamelCase     ).raw     )
        return im
  
  
  
  
  def 			__lowercase	(						)    ->Optional[int]:
  
  
  
  
  
  
  
        """simple docstring"""
  
        lowercase			:   Any					   =		EfficientNetImageProcessor(
            do_center_crop=_UpperCamelCase,    rescale_factor=1 / 1_2_7.5,    rescale_offset=_UpperCamelCase,    do_normalize=_UpperCamelCase,    include_top=_UpperCamelCase,    resample=Image.BILINEAR,    )
        lowercase			:   Any					   =		BertTokenizer.from_pretrained('''bert-base-uncased'''     )
        lowercase			:   int					   =		64
        lowercase			:   List[Any]					   =		AlignProcessor(image_processor=_UpperCamelCase,    tokenizer=_UpperCamelCase     )
        return processor
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase     )    ->int:
  
  
  
  
  
  
  
        """simple docstring"""
  
        lowercase			:   str					   =		[v.split('''_'''     )[0].split('''block'''     )[1] for v in original_param_names if v.startswith('''block'''     )]
        lowercase			:   str					   =		list(set(_UpperCamelCase     )     )
        lowercase			:   List[str]					   =		sorted(_UpperCamelCase     )
        lowercase			:   str					   =		len(_UpperCamelCase     )
        lowercase			:   Dict					   =		{b: str(_UpperCamelCase     ) for b, i in zip(_UpperCamelCase,    range(_UpperCamelCase     )     )}
  
        lowercase			:   int					   =		[]
        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:
              lowercase			:   Dict					   =		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""")     )
  
        lowercase			:   Union[str, Any]					   =		{}
        for item in rename_keys:
              if item[0] in original_param_names:
                    lowercase			:   Dict					   =		'''vision_model.''' + item[1]
  
      # BERT text encoder
        lowercase			:   str					   =		[]
        lowercase			:   Optional[Any]					   =		'''tf_bert_model/bert'''
        lowercase			:   str					   =		'''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:
                    lowercase			:   Tuple					   =		item[1]
        return key_mapping
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase     )    ->Any:
  
  
  
  
  
  
  
        """simple docstring"""
  
        list(hf_params.keys()     )
  
        for key, value in tf_params.items():
              if key not in key_mapping:
                    continue
  
              lowercase			:   List[str]					   =		key_mapping[key]
              if "_conv" in key and "kernel" in key:
                    lowercase			:   List[Any]					   =		torch.from_numpy(_UpperCamelCase     ).permute(3,    2,    0,    1     )
              elif "embeddings" in key:
                    lowercase			:   int					   =		torch.from_numpy(_UpperCamelCase     )
              elif "depthwise_kernel" in key:
                    lowercase			:   Union[str, Any]					   =		torch.from_numpy(_UpperCamelCase     ).permute(2,    3,    0,    1     )
              elif "kernel" in key:
                    lowercase			:   int					   =		torch.from_numpy(np.transpose(_UpperCamelCase     )     )
              elif "temperature" in key:
                    lowercase			:   Optional[int]					   =		value
              elif "bn/gamma" or "bn/beta" in key:
                    lowercase			:   Dict					   =		torch.from_numpy(np.transpose(_UpperCamelCase     )     ).squeeze()
              else:
                    lowercase			:   Union[str, Any]					   =		torch.from_numpy(_UpperCamelCase     )
  
              # Replace HF parameters with original TF model parameters
              hf_params[hf_key].copy_(_UpperCamelCase     )
  
  
  
  
  @torch.no_grad()
  def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase     )    ->Dict:
  
  
  
  
  
  
  
        """simple docstring"""
  
        lowercase			:   str					   =		64
        lowercase			:   str					   =		Tokenizer(_UpperCamelCase     )
        lowercase			:   Optional[int]					   =		align.Align('''efficientnet-b7''',    '''bert-base''',    640,    _UpperCamelCase,    tok.get_vocab_size()     )
        original_model.compile()
        original_model.load_weights(_UpperCamelCase     )
  
        lowercase			:   Optional[Any]					   =		original_model.trainable_variables
        lowercase			:   Tuple					   =		original_model.non_trainable_variables
        lowercase			:   Optional[int]					   =		{param.name: param.numpy() for param in tf_params}
        for param in tf_non_train_params:
              lowercase			:   List[str]					   =		param.numpy()
        lowercase			:   str					   =		list(tf_params.keys()     )
  
        # Load HuggingFace model
        lowercase			:   Tuple					   =		get_align_config()
        lowercase			:   List[Any]					   =		AlignModel(_UpperCamelCase     ).eval()
        lowercase			:   Union[str, Any]					   =		hf_model.state_dict()
  
        # Create src-to-dst parameter name mapping dictionary
        print('''Converting parameters...'''     )
        lowercase			:   List[str]					   =		rename_keys(_UpperCamelCase     )
        replace_params(_UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase     )
  
        # Initialize processor
        lowercase			:   Union[str, Any]					   =		get_processor()
        lowercase			:   Optional[Any]					   =		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():
              lowercase			:   Dict					   =		hf_model(**_UpperCamelCase     )
  
        lowercase			:   int					   =		outputs.image_embeds.detach().numpy()
        lowercase			:   Any					   =		outputs.text_embeds.detach().numpy()
  
        # Original model inference
        lowercase			:   Any					   =		False
        lowercase			:   Tuple					   =		EfficientNetImageProcessor(
            do_center_crop=_UpperCamelCase,    do_rescale=_UpperCamelCase,    do_normalize=_UpperCamelCase,    include_top=_UpperCamelCase,    resample=Image.BILINEAR,    )
        lowercase			:   str					   =		tf_image_processor(images=prepare_img(),    return_tensors='''tf''',    data_format='''channels_last'''     )['''pixel_values''']
        lowercase			:   int					   =		tok(tf.constant(['''A picture of a cat''']     )     )
  
        lowercase			:   Optional[int]					   =		original_model.image_encoder(_UpperCamelCase,    training=_UpperCamelCase     )
        lowercase			:   Optional[int]					   =		original_model.text_encoder(_UpperCamelCase,    training=_UpperCamelCase     )
  
        lowercase			:   Union[str, Any]					   =		tf.nn.la_normalize(_UpperCamelCase,    axis=-1     )
        lowercase			:   List[str]					   =		tf.nn.la_normalize(_UpperCamelCase,    axis=-1     )
  
        # Check whether original and HF model outputs match  -> np.allclose
        if not np.allclose(_UpperCamelCase,    _UpperCamelCase,    atol=1e-3     ):
              raise ValueError('''The predicted image features are not the same.'''     )
        if not np.allclose(_UpperCamelCase,    _UpperCamelCase,    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(_UpperCamelCase     ):
                    os.mkdir(_UpperCamelCase     )
              # Save converted model and image processor
              hf_model.save_pretrained(_UpperCamelCase     )
              processor.save_pretrained(_UpperCamelCase     )
  
        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   				=      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   				=      parser.parse_args()
    convert_align_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)
  
  