"""simple docstring"""


from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


a							=logging.get_logger(__name__)


if is_vision_available():
	import PIL






class    __UpperCAmelCase						(	__lowerCAmelCase	):
				A__      :   Union[str, Any]  							=       ['''pixel_values''']

				def __init__(      self	, _lowerCamelCase = True	, _lowerCamelCase = None	, _lowerCamelCase = PILImageResampling.BICUBIC	, _lowerCamelCase = True	, _lowerCamelCase = None	, _lowerCamelCase = True	, _lowerCamelCase = 1 / 255	, _lowerCamelCase = True	, _lowerCamelCase = None	, _lowerCamelCase = None	, _lowerCamelCase = True	, **_lowerCamelCase	, ):
										super().__init__(**_lowerCamelCase      )
										lowerCamelCase__					=size if size is not None else {"shortest_edge": 224}
										lowerCamelCase__					=get_size_dict(_lowerCamelCase	, default_to_square=_lowerCamelCase      )
										lowerCamelCase__					=crop_size if crop_size is not None else {"height": 224, "width": 224}
										lowerCamelCase__					=get_size_dict(_lowerCamelCase      )

										lowerCamelCase__					=do_resize
										lowerCamelCase__					=size
										lowerCamelCase__					=resample
										lowerCamelCase__					=do_center_crop
										lowerCamelCase__					=crop_size
										lowerCamelCase__					=do_rescale
										lowerCamelCase__					=rescale_factor
										lowerCamelCase__					=do_normalize
										lowerCamelCase__					=image_mean if image_mean is not None else OPENAI_CLIP_MEAN
										lowerCamelCase__					=image_std if image_std is not None else OPENAI_CLIP_STD
										lowerCamelCase__					=do_convert_rgb

				def 		_a	(      self	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase = PILImageResampling.BICUBIC	, _lowerCamelCase = None	, **_lowerCamelCase	, ):
										lowerCamelCase__					=get_size_dict(_lowerCamelCase	, default_to_square=_lowerCamelCase      )
										lowerCamelCase__					=get_resize_output_image_size(
										    _lowerCamelCase	, size=(size["height"], size["width"])	, default_to_square=_lowerCamelCase      )
										return resize(_lowerCamelCase	, size=_lowerCamelCase	, resample=_lowerCamelCase	, data_format=_lowerCamelCase	, **_lowerCamelCase      )

				def 		_a	(      self	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase = None	, **_lowerCamelCase	, ):
										lowerCamelCase__					=get_size_dict(_lowerCamelCase      )
										return center_crop(_lowerCamelCase	, size=(size["height"], size["width"])	, data_format=_lowerCamelCase	, **_lowerCamelCase      )

				def 		_a	(      self	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase = None	, **_lowerCamelCase	, ):
										return rescale(_lowerCamelCase	, scale=_lowerCamelCase	, data_format=_lowerCamelCase	, **_lowerCamelCase      )

				def 		_a	(      self	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase = None	, **_lowerCamelCase	, ):
										return normalize(_lowerCamelCase	, mean=_lowerCamelCase	, std=_lowerCamelCase	, data_format=_lowerCamelCase	, **_lowerCamelCase      )






				def 		_a	(      self	, _lowerCamelCase	, _lowerCamelCase = None	, _lowerCamelCase = None	, _lowerCamelCase = None	, _lowerCamelCase = None	, _lowerCamelCase = None	, _lowerCamelCase = None	, _lowerCamelCase = None	, _lowerCamelCase = None	, _lowerCamelCase = None	, _lowerCamelCase = None	, _lowerCamelCase = None	, _lowerCamelCase = None	, _lowerCamelCase = ChannelDimension.FIRST	, **_lowerCamelCase	, ):
										lowerCamelCase__					=do_resize if do_resize is not None else self.do_resize
										lowerCamelCase__					=size if size is not None else self.size
										lowerCamelCase__					=get_size_dict(_lowerCamelCase	, default_to_square=_lowerCamelCase      )
										lowerCamelCase__					=resample if resample is not None else self.resample
										lowerCamelCase__					=do_center_crop if do_center_crop is not None else self.do_center_crop
										lowerCamelCase__					=crop_size if crop_size is not None else self.crop_size
										lowerCamelCase__					=get_size_dict(_lowerCamelCase      )
										lowerCamelCase__					=do_rescale if do_rescale is not None else self.do_rescale
										lowerCamelCase__					=rescale_factor if rescale_factor is not None else self.rescale_factor
										lowerCamelCase__					=do_normalize if do_normalize is not None else self.do_normalize
										lowerCamelCase__					=image_mean if image_mean is not None else self.image_mean
										lowerCamelCase__					=image_std if image_std is not None else self.image_std
										lowerCamelCase__					=do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

										lowerCamelCase__					=make_list_of_images(_lowerCamelCase      )

										if not valid_images(_lowerCamelCase      ):
																raise ValueError(
																    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
																    "torch.Tensor, tf.Tensor or jax.ndarray."      )

										if do_resize and size is None:
																raise ValueError("Size must be specified if do_resize is True."      )

										if do_center_crop and crop_size is None:
																raise ValueError("Crop size must be specified if do_center_crop is True."      )

										if do_rescale and rescale_factor is None:
																raise ValueError("Rescale factor must be specified if do_rescale is True."      )

										if do_normalize and (image_mean is None or image_std is None):
																raise ValueError("Image mean and std must be specified if do_normalize is True."      )

										# PIL RGBA images are converted to RGB
										if do_convert_rgb:
																lowerCamelCase__					=[convert_to_rgb(_lowerCamelCase      ) for image in images]

										# All transformations expect numpy arrays.
										lowerCamelCase__					=[to_numpy_array(_lowerCamelCase      ) for image in images]

										if do_resize:
																lowerCamelCase__					=[self.resize(image=_lowerCamelCase	, size=_lowerCamelCase	, resample=_lowerCamelCase      ) for image in images]

										if do_center_crop:
																lowerCamelCase__					=[self.center_crop(image=_lowerCamelCase	, size=_lowerCamelCase      ) for image in images]

										if do_rescale:
																lowerCamelCase__					=[self.rescale(image=_lowerCamelCase	, scale=_lowerCamelCase      ) for image in images]

										if do_normalize:
																lowerCamelCase__					=[self.normalize(image=_lowerCamelCase	, mean=_lowerCamelCase	, std=_lowerCamelCase      ) for image in images]

										lowerCamelCase__					=[to_channel_dimension_format(_lowerCamelCase	, _lowerCamelCase      ) for image in images]

										lowerCamelCase__					={"pixel_values": images}
										return BatchFeature(data=_lowerCamelCase	, tensor_type=_lowerCamelCase      )









"""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()
__UpperCamelCase			:		Union[str, Any]		    =   logging.get_logger(__name__)

def 						_SCREAMING_SNAKE_CASE       (_UpperCAmelCase :  Optional[int]       ):
 lowerCAmelCase  							=      tf.image.resize(_UpperCAmelCase   ,			(346, 346)       )
 lowerCAmelCase  							=      tf.image.crop_to_bounding_box(_UpperCAmelCase   ,			(346 - 289) // 2   ,			(346 - 289) // 2   ,			289   ,			289       )
 return image

def 						_SCREAMING_SNAKE_CASE       ():
 lowerCAmelCase  							=      EfficientNetConfig.from_pretrained('google/efficientnet-b7'       )
 lowerCAmelCase  							=      289
 lowerCAmelCase  							=      640
 lowerCAmelCase  							=      {'0': 'LABEL_0', '1': 'LABEL_1'}
 lowerCAmelCase  							=      {'LABEL_0': 0, 'LABEL_1': 1}
 lowerCAmelCase  							=      []

 lowerCAmelCase  							=      BertConfig()
 lowerCAmelCase  							=      AlignConfig.from_text_vision_configs(
     text_config=_UpperCAmelCase   ,			vision_config=_UpperCAmelCase   ,			projection_dim=640       )
 return config

def 						_SCREAMING_SNAKE_CASE       ():
 lowerCAmelCase  							=      'http://images.cocodataset.org/val2017/000000039769.jpg'
 lowerCAmelCase  							=      Image.open(requests.get(_UpperCAmelCase   ,			stream=_UpperCAmelCase       ).raw       )
 return im

def 						_SCREAMING_SNAKE_CASE       ():
 lowerCAmelCase  							=      EfficientNetImageProcessor(
     do_center_crop=_UpperCAmelCase   ,			rescale_factor=1 / 127.5   ,			rescale_offset=_UpperCAmelCase   ,			do_normalize=_UpperCAmelCase   ,			include_top=_UpperCAmelCase   ,			resample=Image.BILINEAR   ,			)
 lowerCAmelCase  							=      BertTokenizer.from_pretrained('bert-base-uncased'       )
 lowerCAmelCase  							=      64
 lowerCAmelCase  							=      AlignProcessor(image_processor=_UpperCAmelCase   ,			tokenizer=_UpperCAmelCase       )
 return processor

def 						_SCREAMING_SNAKE_CASE       (_UpperCAmelCase :  List[str]       ):
 # EfficientNet image encoder
 lowerCAmelCase  							=      [v.split('_'       )[0].split('block'       )[1] for v in original_param_names if v.startswith('block'       )]
 lowerCAmelCase  							=      list(set(_UpperCAmelCase       )       )
 lowerCAmelCase  							=      sorted(_UpperCAmelCase       )
 lowerCAmelCase  							=      len(_UpperCAmelCase       )
 lowerCAmelCase  							=      {b: str(_UpperCAmelCase       ) for b, i in zip(_UpperCAmelCase   ,			range(_UpperCAmelCase       )       )}

 lowerCAmelCase  							=      []
 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  							=      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  							=      {}
 for item in rename_keys:
  if item[0] in original_param_names:
   lowerCAmelCase  							=      'vision_model.' + item[1]

    # BERT text encoder
 lowerCAmelCase  							=      []
 lowerCAmelCase  							=      'tf_bert_model/bert'
 lowerCAmelCase  							=      '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  							=      item[1]
 return key_mapping

def 						_SCREAMING_SNAKE_CASE       (_UpperCAmelCase :  List[Any]   ,			_UpperCAmelCase :  List[str]   ,			_UpperCAmelCase :  Any       ):
 list(hf_params.keys()       )

 for key, value in tf_params.items():
  if key not in key_mapping:
   continue

  lowerCAmelCase  							=      key_mapping[key]
  if "_conv" in key and "kernel" in key:
   lowerCAmelCase  							=      torch.from_numpy(_UpperCAmelCase       ).permute(3   ,			2   ,			0   ,			1       )
  elif "embeddings" in key:
   lowerCAmelCase  							=      torch.from_numpy(_UpperCAmelCase       )
  elif "depthwise_kernel" in key:
   lowerCAmelCase  							=      torch.from_numpy(_UpperCAmelCase       ).permute(2   ,			3   ,			0   ,			1       )
  elif "kernel" in key:
   lowerCAmelCase  							=      torch.from_numpy(np.transpose(_UpperCAmelCase       )       )
  elif "temperature" in key:
   lowerCAmelCase  							=      value
  elif "bn/gamma" or "bn/beta" in key:
   lowerCAmelCase  							=      torch.from_numpy(np.transpose(_UpperCAmelCase       )       ).squeeze()
  else:
   lowerCAmelCase  							=      torch.from_numpy(_UpperCAmelCase       )

  # Replace HF parameters with original TF model parameters
  hf_params[hf_key].copy_(_UpperCAmelCase       )

@torch.no_grad()
def 						_SCREAMING_SNAKE_CASE       (_UpperCAmelCase :  str   ,			_UpperCAmelCase :  Tuple   ,			_UpperCAmelCase :  Any   ,			_UpperCAmelCase :  Any       ):
 lowerCAmelCase  							=      64
 lowerCAmelCase  							=      Tokenizer(_UpperCAmelCase       )
 lowerCAmelCase  							=      align.Align('efficientnet-b7'   ,			'bert-base'   ,			640   ,			_UpperCAmelCase   ,			tok.get_vocab_size()       )
 original_model.compile()
 original_model.load_weights(_UpperCAmelCase       )

 lowerCAmelCase  							=      original_model.trainable_variables
 lowerCAmelCase  							=      original_model.non_trainable_variables
 lowerCAmelCase  							=      {param.name: param.numpy() for param in tf_params}
 for param in tf_non_train_params:
  lowerCAmelCase  							=      param.numpy()
 lowerCAmelCase  							=      list(tf_params.keys()       )

 # Load HuggingFace model
 lowerCAmelCase  							=      get_align_config()
 lowerCAmelCase  							=      AlignModel(_UpperCAmelCase       ).eval()
 lowerCAmelCase  							=      hf_model.state_dict()

 # Create src-to-dst parameter name mapping dictionary
 print('Converting parameters...'       )
 lowerCAmelCase  							=      rename_keys(_UpperCAmelCase       )
 replace_params(_UpperCAmelCase   ,			_UpperCAmelCase   ,			_UpperCAmelCase       )

 # Initialize processor
 lowerCAmelCase  							=      get_processor()
 lowerCAmelCase  							=      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  							=      hf_model(**_UpperCAmelCase       )

 lowerCAmelCase  							=      outputs.image_embeds.detach().numpy()
 lowerCAmelCase  							=      outputs.text_embeds.detach().numpy()

 # Original model inference
 lowerCAmelCase  							=      False
 lowerCAmelCase  							=      EfficientNetImageProcessor(
     do_center_crop=_UpperCAmelCase   ,			do_rescale=_UpperCAmelCase   ,			do_normalize=_UpperCAmelCase   ,			include_top=_UpperCAmelCase   ,			resample=Image.BILINEAR   ,			)
 lowerCAmelCase  							=      tf_image_processor(images=prepare_img()   ,			return_tensors='tf'   ,			data_format='channels_last'       )['pixel_values']
 lowerCAmelCase  							=      tok(tf.constant(['A picture of a cat']       )       )

 lowerCAmelCase  							=      original_model.image_encoder(_UpperCAmelCase   ,			training=_UpperCAmelCase       )
 lowerCAmelCase  							=      original_model.text_encoder(_UpperCAmelCase   ,			training=_UpperCAmelCase       )

 lowerCAmelCase  							=      tf.nn.la_normalize(_UpperCAmelCase   ,			axis=-1       )
 lowerCAmelCase  							=      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__":
       __UpperCamelCase			:		str		    =   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''')

       __UpperCamelCase			:		int		    =   parser.parse_args()
       convert_align_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)





