


'''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()
lowerCAmelCase			:   List[str] 		=  logging.get_logger(__name__)






def 	lowercase (_A    ):







       """simple docstring"""







       _lowerCAmelCase			:							Optional[Any]      	=				tf.image.resize(UpperCamelCase_      ,  (3_4_6, 3_4_6)    )
       _lowerCAmelCase			:							List[str]      	=				tf.image.crop_to_bounding_box(UpperCamelCase_      ,  (3_4_6 - 2_8_9) // 2      ,  (3_4_6 - 2_8_9) // 2      ,  2_8_9      ,  2_8_9    )
       return image






def 	lowercase ():







       """simple docstring"""







       _lowerCAmelCase			:							str      	=				EfficientNetConfig.from_pretrained('google/efficientnet-b7'    )
       _lowerCAmelCase			:							List[str]      	=				2_8_9
       _lowerCAmelCase			:							Tuple      	=				6_4_0
       _lowerCAmelCase			:							List[Any]      	=				{'0': 'LABEL_0', '1': 'LABEL_1'}
       _lowerCAmelCase			:							Union[str, Any]      	=				{'LABEL_0': 0, 'LABEL_1': 1}
       _lowerCAmelCase			:							List[Any]      	=				[]

       _lowerCAmelCase			:							Tuple      	=				BertConfig()
       _lowerCAmelCase			:							Optional[int]      	=				AlignConfig.from_text_vision_configs(
           text_config=UpperCamelCase_      ,  vision_config=UpperCamelCase_      ,  projection_dim=6_4_0    )
       return config






def 	lowercase ():







       """simple docstring"""







       _lowerCAmelCase			:							Optional[Any]      	=				'http://images.cocodataset.org/val2017/000000039769.jpg'
       _lowerCAmelCase			:							Optional[Any]      	=				Image.open(requests.get(UpperCamelCase_      ,  stream=UpperCamelCase_    ).raw    )
       return im






def 	lowercase ():







       """simple docstring"""







       _lowerCAmelCase			:							List[str]      	=				EfficientNetImageProcessor(
           do_center_crop=UpperCamelCase_      ,  rescale_factor=1 / 1_2_7.5      ,  rescale_offset=UpperCamelCase_      ,  do_normalize=UpperCamelCase_      ,  include_top=UpperCamelCase_      ,  resample=Image.BILINEAR      ,  )
       _lowerCAmelCase			:							Union[str, Any]      	=				BertTokenizer.from_pretrained('bert-base-uncased'    )
       _lowerCAmelCase			:							List[str]      	=				6_4
       _lowerCAmelCase			:							List[str]      	=				AlignProcessor(image_processor=UpperCamelCase_      ,  tokenizer=UpperCamelCase_    )
       return processor






def 	lowercase (_A    ):







       """simple docstring"""







       _lowerCAmelCase			:							Optional[Any]      	=				[v.split('_'    )[0].split('block'    )[1] for v in original_param_names if v.startswith('block'    )]
       _lowerCAmelCase			:							List[Any]      	=				list(set(UpperCamelCase_    )    )
       _lowerCAmelCase			:							List[Any]      	=				sorted(UpperCamelCase_    )
       _lowerCAmelCase			:							int      	=				len(UpperCamelCase_    )
       _lowerCAmelCase			:							Tuple      	=				{b: str(UpperCamelCase_    ) for b, i in zip(UpperCamelCase_      ,  range(UpperCamelCase_    )    )}

       _lowerCAmelCase			:							Any      	=				[]
       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[Any]      	=				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			:							Tuple      	=				{}
       for item in rename_keys:
              if item[0] in original_param_names:
                     _lowerCAmelCase			:							List[Any]      	=				'vision_model.' + item[1]

    # BERT text encoder
       _lowerCAmelCase			:							List[str]      	=				[]
       _lowerCAmelCase			:							Any      	=				'tf_bert_model/bert'
       _lowerCAmelCase			:							Tuple      	=				'text_model'
       for i in range(1_2    ):
              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			:							Tuple      	=				item[1]
       return key_mapping






def 	lowercase (_A      ,  _A      ,  _A    ):







       """simple docstring"""







       list(hf_params.keys()    )

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

              _lowerCAmelCase			:							Any      	=				key_mapping[key]
              if "_conv" in key and "kernel" in key:
                     _lowerCAmelCase			:							Union[str, Any]      	=				torch.from_numpy(UpperCamelCase_    ).permute(3      ,  2      ,  0      ,  1    )
              elif "embeddings" in key:
                     _lowerCAmelCase			:							List[str]      	=				torch.from_numpy(UpperCamelCase_    )
              elif "depthwise_kernel" in key:
                     _lowerCAmelCase			:							int      	=				torch.from_numpy(UpperCamelCase_    ).permute(2      ,  3      ,  0      ,  1    )
              elif "kernel" in key:
                     _lowerCAmelCase			:							List[Any]      	=				torch.from_numpy(np.transpose(UpperCamelCase_    )    )
              elif "temperature" in key:
                     _lowerCAmelCase			:							Optional[Any]      	=				value
              elif "bn/gamma" or "bn/beta" in key:
                     _lowerCAmelCase			:							List[Any]      	=				torch.from_numpy(np.transpose(UpperCamelCase_    )    ).squeeze()
              else:
                     _lowerCAmelCase			:							Any      	=				torch.from_numpy(UpperCamelCase_    )

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






@torch.no_grad()
def 	lowercase (_A      ,  _A      ,  _A      ,  _A    ):







       """simple docstring"""







       _lowerCAmelCase			:							Optional[Any]      	=				6_4
       _lowerCAmelCase			:							str      	=				Tokenizer(UpperCamelCase_    )
       _lowerCAmelCase			:							Tuple      	=				align.Align('efficientnet-b7'      ,  'bert-base'      ,  6_4_0      ,  UpperCamelCase_      ,  tok.get_vocab_size()    )
       original_model.compile()
       original_model.load_weights(UpperCamelCase_    )

       _lowerCAmelCase			:							str      	=				original_model.trainable_variables
       _lowerCAmelCase			:							List[Any]      	=				original_model.non_trainable_variables
       _lowerCAmelCase			:							List[str]      	=				{param.name: param.numpy() for param in tf_params}
       for param in tf_non_train_params:
              _lowerCAmelCase			:							int      	=				param.numpy()
       _lowerCAmelCase			:							Optional[int]      	=				list(tf_params.keys()    )

       # Load HuggingFace model
       _lowerCAmelCase			:							Optional[Any]      	=				get_align_config()
       _lowerCAmelCase			:							Optional[Any]      	=				AlignModel(UpperCamelCase_    ).eval()
       _lowerCAmelCase			:							List[Any]      	=				hf_model.state_dict()

       # Create src-to-dst parameter name mapping dictionary
       print('Converting parameters...'    )
       _lowerCAmelCase			:							Optional[Any]      	=				rename_keys(UpperCamelCase_    )
       replace_params(UpperCamelCase_      ,  UpperCamelCase_      ,  UpperCamelCase_    )

       # Initialize processor
       _lowerCAmelCase			:							List[Any]      	=				get_processor()
       _lowerCAmelCase			:							Optional[Any]      	=				processor(
           images=prepare_img()      ,  text='A picture of a cat'      ,  padding='max_length'      ,  max_length=6_4      ,  return_tensors='pt'    )

       # HF model inference
       hf_model.eval()
       with torch.no_grad():
              _lowerCAmelCase			:							int      	=				hf_model(**UpperCamelCase_    )

       _lowerCAmelCase			:							List[Any]      	=				outputs.image_embeds.detach().numpy()
       _lowerCAmelCase			:							List[Any]      	=				outputs.text_embeds.detach().numpy()

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

       _lowerCAmelCase			:							Optional[int]      	=				original_model.image_encoder(UpperCamelCase_      ,  training=UpperCamelCase_    )
       _lowerCAmelCase			:							Optional[int]      	=				original_model.text_encoder(UpperCamelCase_      ,  training=UpperCamelCase_    )

       _lowerCAmelCase			:							Tuple      	=				tf.nn.la_normalize(UpperCamelCase_      ,  axis=-1    )
       _lowerCAmelCase			:							Dict      	=				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__":
       lowerCAmelCase			:   Union[str, 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""")

       lowerCAmelCase			:   Optional[int] 		=  parser.parse_args()
       convert_align_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)








import math




def        lowerCAmelCase_       (							UpperCamelCase_     )   ->						bool:

       if 1 < number < 4:
              # 2 and 3 are primes
              return True
       elif number < 2 or number % 2 == 0 or number % 3 == 0:
              # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
              return False

       # All primes number are in format of 6k +/- 1
       for i in range(5    ,						int(math.sqrt(UpperCamelCase_     ) + 1     )    ,						6     ):
              if number % i == 0 or number % (i + 2) == 0:
                     return False
       return True




def        lowerCAmelCase_       (							UpperCamelCase_ = 600851475143     )   ->						int:

       try:
              UpperCamelCase_      				=     int(UpperCamelCase_     )
       except (TypeError, ValueError):
              raise TypeError("Parameter n must be int or castable to int."     )
       if n <= 0:
              raise ValueError("Parameter n must be greater than or equal to one."     )
       UpperCamelCase_      				=     0
       if is_prime(UpperCamelCase_     ):
              return n
       while n % 2 == 0:
              n //= 2
       if is_prime(UpperCamelCase_     ):
              return n
       for i in range(3    ,						int(math.sqrt(UpperCamelCase_     )     ) + 1    ,						2     ):
              if n % i == 0:
                     if is_prime(n // i     ):
                            UpperCamelCase_      				=     n // i
                            break
                     elif is_prime(UpperCamelCase_     ):
                            UpperCamelCase_      				=     i
       return max_number


if __name__ == "__main__":
     print(f'''{solution() = }''')

