"""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[Any]       =logging.get_logger(__name__)




def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				Dict						)    ->     Optional[Any]:







     '''simple docstring'''


     __A					:					Optional[Any]								=		tf.image.resize(_SCREAMING_SNAKE_CASE  ,       (346, 346)						)
     __A					:					Tuple								=		tf.image.crop_to_bounding_box(_SCREAMING_SNAKE_CASE  ,       (346 - 289) // 2  ,       (346 - 289) // 2  ,       289  ,       289						)
     return image




def 						_lowercase				(  )    ->     List[Any]:







     '''simple docstring'''


     __A					:					Any								=		EfficientNetConfig.from_pretrained('google/efficientnet-b7'						)
     __A					:					List[Any]								=		289
     __A					:					Optional[int]								=		640
     __A					:					Dict								=		{'0': 'LABEL_0', '1': 'LABEL_1'}
     __A					:					Optional[int]								=		{'LABEL_0': 0, 'LABEL_1': 1}
     __A					:					List[Any]								=		[]

     __A					:					Union[str, Any]								=		BertConfig()
     __A					:					Any								=		AlignConfig.from_text_vision_configs(
         text_config=_SCREAMING_SNAKE_CASE  ,       vision_config=_SCREAMING_SNAKE_CASE  ,       projection_dim=640						)
     return config




def 						_lowercase				(  )    ->     int:







     '''simple docstring'''


     __A					:					str								=		'http://images.cocodataset.org/val2017/000000039769.jpg'
     __A					:					Union[str, Any]								=		Image.open(requests.get(_SCREAMING_SNAKE_CASE  ,       stream=_SCREAMING_SNAKE_CASE						).raw						)
     return im




def 						_lowercase				(  )    ->     Optional[Any]:







     '''simple docstring'''


     __A					:					int								=		EfficientNetImageProcessor(
         do_center_crop=_SCREAMING_SNAKE_CASE  ,       rescale_factor=1 / 1_27.5  ,       rescale_offset=_SCREAMING_SNAKE_CASE  ,       do_normalize=_SCREAMING_SNAKE_CASE  ,       include_top=_SCREAMING_SNAKE_CASE  ,       resample=Image.BILINEAR  ,       )
     __A					:					int								=		BertTokenizer.from_pretrained('bert-base-uncased'						)
     __A					:					Optional[Any]								=		64
     __A					:					Optional[int]								=		AlignProcessor(image_processor=_SCREAMING_SNAKE_CASE  ,       tokenizer=_SCREAMING_SNAKE_CASE						)
     return processor




def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				str						)    ->     int:







     '''simple docstring'''


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

     __A					:					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:
          __A					:					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')						)

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

    # BERT text encoder
     __A					:					Any								=		[]
     __A					:					Tuple								=		'tf_bert_model/bert'
     __A					:					List[Any]								=		'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					:					List[Any]								=		item[1]
     return key_mapping




def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				Union[str, Any]  ,       _SCREAMING_SNAKE_CASE				:				Optional[Any]  ,       _SCREAMING_SNAKE_CASE				:				Tuple						)    ->     Optional[int]:







     '''simple docstring'''


     list(hf_params.keys()						)

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

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

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




@torch.no_grad()
def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				List[str]  ,       _SCREAMING_SNAKE_CASE				:				Tuple  ,       _SCREAMING_SNAKE_CASE				:				Dict  ,       _SCREAMING_SNAKE_CASE				:				Optional[Any]						)    ->     int:







     '''simple docstring'''


     __A					:					Any								=		64
     __A					:					List[Any]								=		Tokenizer(_SCREAMING_SNAKE_CASE						)
     __A					:					Any								=		align.Align('efficientnet-b7'  ,       'bert-base'  ,       640  ,       _SCREAMING_SNAKE_CASE  ,       tok.get_vocab_size()						)
     original_model.compile()
     original_model.load_weights(_SCREAMING_SNAKE_CASE						)

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

     # Load HuggingFace model
     __A					:					Union[str, Any]								=		get_align_config()
     __A					:					Tuple								=		AlignModel(_SCREAMING_SNAKE_CASE						).eval()
     __A					:					Optional[Any]								=		hf_model.state_dict()

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

     # Initialize processor
     __A					:					List[Any]								=		get_processor()
     __A					:					Tuple								=		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					:					Any								=		hf_model(**_SCREAMING_SNAKE_CASE						)

     __A					:					List[str]								=		outputs.image_embeds.detach().numpy()
     __A					:					Dict								=		outputs.text_embeds.detach().numpy()

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

     __A					:					Union[str, Any]								=		original_model.image_encoder(_SCREAMING_SNAKE_CASE  ,       training=_SCREAMING_SNAKE_CASE						)
     __A					:					List[Any]								=		original_model.text_encoder(_SCREAMING_SNAKE_CASE  ,       training=_SCREAMING_SNAKE_CASE						)

     __A					:					int								=		tf.nn.la_normalize(_SCREAMING_SNAKE_CASE  ,       axis=-1						)
     __A					:					Optional[int]								=		tf.nn.la_normalize(_SCREAMING_SNAKE_CASE  ,       axis=-1						)

     # Check whether original and HF model outputs match  -> np.allclose
     if not np.allclose(_SCREAMING_SNAKE_CASE  ,       _SCREAMING_SNAKE_CASE  ,       atol=1E-3						):
          raise ValueError('The predicted image features are not the same.'						)
     if not np.allclose(_SCREAMING_SNAKE_CASE  ,       _SCREAMING_SNAKE_CASE  ,       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(_SCREAMING_SNAKE_CASE						):
               os.mkdir(_SCREAMING_SNAKE_CASE						)
          # Save converted model and image processor
          hf_model.save_pretrained(_SCREAMING_SNAKE_CASE						)
          processor.save_pretrained(_SCREAMING_SNAKE_CASE						)

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




"""simple docstring"""







def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				list[int]						)    ->     int:







     '''simple docstring'''


     if not isinstance(_SCREAMING_SNAKE_CASE  ,       _SCREAMING_SNAKE_CASE						) or not all(
         isinstance(_SCREAMING_SNAKE_CASE  ,       _SCREAMING_SNAKE_CASE						) and item >= 0 for item in citations						):
          raise ValueError('The citations should be a list of non negative integers.'						)

     citations.sort()
     __A					:					Any								=		len(_SCREAMING_SNAKE_CASE						)

     for i in range(_SCREAMING_SNAKE_CASE						):
          if citations[len_citations - 1 - i] <= i:
               return i

     return len_citations


if __name__ == "__main__":
       import doctest

       doctest.testmod()



