




"""simple docstring"""



import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import TvltImageProcessor







def     _SCREAMING_SNAKE_CASE  (				lowercase_     ,					lowercase_=10     ,					lowercase_=10     ,					lowercase_=False     ,					lowercase_=False		)		->						Tuple:
 A__			=							[]
 for i in range(image_processor_tester.num_frames		):
  video.append(np.random.randint(2_55     ,					size=(image_processor_tester.num_channels, width, height)     ,					dtype=np.uinta		)		)

 if not numpify and not torchify:
  # PIL expects the channel dimension as last dimension
  A__			=							[Image.fromarray(np.moveaxis(lowercase_     ,					0     ,					-1		)		) for frame in video]

 if torchify:
  A__			=							[torch.from_numpy(lowercase_		) for frame in video]

 return video







def     _SCREAMING_SNAKE_CASE  (				lowercase_     ,					lowercase_=False     ,					lowercase_=False     ,					lowercase_=False		)		->						Optional[int]:
 assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

 A__			=							[]
 for i in range(image_processor_tester.batch_size		):
  if equal_resolution:
   A__			=							A__			=							image_processor_tester.max_resolution
  else:
   A__, A__			=							np.random.choice(
       np.arange(image_processor_tester.min_resolution     ,					image_processor_tester.max_resolution		)     ,					2		)
   A__			=							prepare_video(
       image_processor_tester=lowercase_     ,					width=lowercase_     ,					height=lowercase_     ,					numpify=lowercase_     ,					torchify=lowercase_     ,					)
  video_inputs.append(lowercase_		)

 return video_inputs




class       UpperCAmelCase_    (		unittest.TestCase				):




    def __init__(       self		:			Union[str, Any]      ,					snake_case_		:			Dict      ,					snake_case_		:			Dict=7      ,					snake_case_		:			int=3      ,					snake_case_		:			int=4      ,					snake_case_		:			List[Any]=18      ,					snake_case_		:			Any=30      ,					snake_case_		:			Dict=400      ,					snake_case_		:			int=True      ,					snake_case_		:			List[str]=None      ,					snake_case_		:			Dict=True      ,					snake_case_		:			Optional[int]=[0.5, 0.5, 0.5]      ,					snake_case_		:			int=[0.5, 0.5, 0.5]      ,					snake_case_		:			str=True      ,					snake_case_		:			Any=None      ,					) ->				List[str]:




     '''simple docstring'''

     A__			=							size if size is not None else {"shortest_edge": 18}
     A__			=							crop_size if crop_size is not None else {"height": 18, "width": 18}

     A__			=							parent
     A__			=							batch_size
     A__			=							num_channels
     A__			=							num_frames
     A__			=							image_size
     A__			=							min_resolution
     A__			=							max_resolution
     A__			=							do_resize
     A__			=							size
     A__			=							do_normalize
     A__			=							image_mean
     A__			=							image_std
     A__			=							do_center_crop
     A__			=							crop_size




    def 				__magic_name__				(       self		:			str ) ->				int:




     '''simple docstring'''

     return {
         "image_mean": self.image_mean,
         "image_std": self.image_std,
         "do_normalize": self.do_normalize,
         "do_resize": self.do_resize,
         "size": self.size,
         "do_center_crop": self.do_center_crop,
         "crop_size": self.crop_size,
     }






@require_torch
@require_vision
class       UpperCAmelCase_    (		A_,  unittest.TestCase				):
    lowercase__		   = TvltImageProcessor if is_vision_available() else None




    def 				__magic_name__				(       self		:			Dict ) ->				List[Any]:




     '''simple docstring'''

     A__			=							TvltImageProcessorTester(self )




    @property
    def 				__magic_name__				(       self		:			List[Any] ) ->				int:




     '''simple docstring'''

     return self.image_processor_tester.prepare_image_processor_dict()




    def 				__magic_name__				(       self		:			List[Any] ) ->				Dict:




     '''simple docstring'''

     A__			=							self.image_processing_class(**self.image_processor_dict )
     self.assertTrue(hasattr(snake_case_      ,					"image_mean" ) )
     self.assertTrue(hasattr(snake_case_      ,					"image_std" ) )
     self.assertTrue(hasattr(snake_case_      ,					"do_normalize" ) )
     self.assertTrue(hasattr(snake_case_      ,					"do_resize" ) )
     self.assertTrue(hasattr(snake_case_      ,					"do_center_crop" ) )
     self.assertTrue(hasattr(snake_case_      ,					"size" ) )




    def 				__magic_name__				(       self		:			Optional[int] ) ->				str:




     '''simple docstring'''

     A__			=							self.image_processing_class(**self.image_processor_dict )
     # create random PIL videos
     A__			=							prepare_video_inputs(self.image_processor_tester      ,					equal_resolution=snake_case_ )
     for video in video_inputs:
      self.assertIsInstance(snake_case_      ,					snake_case_ )
      self.assertIsInstance(video[0]      ,					Image.Image )

     # Test not batched input
     A__			=							image_processor(video_inputs[0]      ,					return_tensors="pt" ).pixel_values
     self.assertEqual(
         encoded_videos.shape      ,					(
             1,
             self.image_processor_tester.num_frames,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size["height"],
             self.image_processor_tester.crop_size["width"],
         )      ,					)

     # Test batched
     A__			=							image_processor(snake_case_      ,					return_tensors="pt" ).pixel_values
     self.assertEqual(
         encoded_videos.shape      ,					(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_frames,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size["height"],
             self.image_processor_tester.crop_size["width"],
         )      ,					)




    def 				__magic_name__				(       self		:			List[str] ) ->				Tuple:




     '''simple docstring'''

     A__			=							self.image_processing_class(**self.image_processor_dict )
     # create random numpy tensors
     A__			=							prepare_video_inputs(self.image_processor_tester      ,					equal_resolution=snake_case_      ,					numpify=snake_case_ )
     for video in video_inputs:
      self.assertIsInstance(snake_case_      ,					snake_case_ )
      self.assertIsInstance(video[0]      ,					np.ndarray )

     # Test not batched input
     A__			=							image_processor(video_inputs[0]      ,					return_tensors="pt" ).pixel_values
     self.assertEqual(
         encoded_videos.shape      ,					(
             1,
             self.image_processor_tester.num_frames,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size["height"],
             self.image_processor_tester.crop_size["width"],
         )      ,					)

     # Test batched
     A__			=							image_processor(snake_case_      ,					return_tensors="pt" ).pixel_values
     self.assertEqual(
         encoded_videos.shape      ,					(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_frames,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size["height"],
             self.image_processor_tester.crop_size["width"],
         )      ,					)




    def 				__magic_name__				(       self		:			int ) ->				Optional[int]:




     '''simple docstring'''

     A__			=							self.image_processing_class(**self.image_processor_dict )
     # create random PyTorch tensors
     A__			=							prepare_video_inputs(self.image_processor_tester      ,					equal_resolution=snake_case_      ,					torchify=snake_case_ )
     for video in video_inputs:
      self.assertIsInstance(snake_case_      ,					snake_case_ )
      self.assertIsInstance(video[0]      ,					torch.Tensor )

     # Test not batched input
     A__			=							image_processor(video_inputs[0]      ,					return_tensors="pt" ).pixel_values
     self.assertEqual(
         encoded_videos.shape      ,					(
             1,
             self.image_processor_tester.num_frames,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size["height"],
             self.image_processor_tester.crop_size["width"],
         )      ,					)

     # Test batched
     A__			=							image_processor(snake_case_      ,					return_tensors="pt" ).pixel_values
     self.assertEqual(
         encoded_videos.shape      ,					(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_frames,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size["height"],
             self.image_processor_tester.crop_size["width"],
         )      ,					)











"""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  (				)		->						Dict:
 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  (				)		->						int:
 A__			=							"http://images.cocodataset.org/val2017/000000039769.jpg"
 A__			=							Image.open(requests.get(lowercase_     ,					stream=lowercase_		).raw		)
 return im







def     _SCREAMING_SNAKE_CASE  (				)		->						List[str]:
 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_		)		->						int:
 # 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_		)		->						Optional[Any]:
 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_		)		->						int:
 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)





