code: 
  '''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()
  lowercase__						=			logging.get_logger(__name__)
  
  
  
  
  def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__ )     ->       Optional[Any]:
  
  
  		'''simple docstring'''
  
  
  		snake_case     :					Union[str, Any]     =	tf.image.resize(SCREAMING_SNAKE_CASE__   ,					(346, 346) )
  		snake_case     :					List[str]     =	tf.image.crop_to_bounding_box(SCREAMING_SNAKE_CASE__   ,					(346 - 289) // 2   ,					(346 - 289) // 2   ,					289   ,					289 )
  		return image
  
  
  
  
  def       _UpperCamelCase	(					)     ->       int:
  
  
  		'''simple docstring'''
  
  
  		snake_case     :					List[str]     =	EfficientNetConfig.from_pretrained('''google/efficientnet-b7''' )
  		snake_case     :					Tuple     =	289
  		snake_case     :					Optional[int]     =	640
  		snake_case     :					Optional[Any]     =	{'''0''': '''LABEL_0''', '''1''': '''LABEL_1'''}
  		snake_case     :					Any     =	{'''LABEL_0''': 0, '''LABEL_1''': 1}
  		snake_case     :					List[str]     =	[]
  
  		snake_case     :					Any     =	BertConfig()
  		snake_case     :					Optional[int]     =	AlignConfig.from_text_vision_configs(
  		    text_config=SCREAMING_SNAKE_CASE__   ,					vision_config=SCREAMING_SNAKE_CASE__   ,					projection_dim=640 )
  		return config
  
  
  
  
  def       _UpperCamelCase	(					)     ->       List[Any]:
  
  
  		'''simple docstring'''
  
  
  		snake_case     :					Optional[Any]     =	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
  		snake_case     :					Any     =	Image.open(requests.get(SCREAMING_SNAKE_CASE__   ,					stream=SCREAMING_SNAKE_CASE__ ).raw )
  		return im
  
  
  
  
  def       _UpperCamelCase	(					)     ->       int:
  
  
  		'''simple docstring'''
  
  
  		snake_case     :					Tuple     =	EfficientNetImageProcessor(
  		    do_center_crop=SCREAMING_SNAKE_CASE__   ,					rescale_factor=1 / 127.5   ,					rescale_offset=SCREAMING_SNAKE_CASE__   ,					do_normalize=SCREAMING_SNAKE_CASE__   ,					include_top=SCREAMING_SNAKE_CASE__   ,					resample=Image.BILINEAR   ,					)
  		snake_case     :					Any     =	BertTokenizer.from_pretrained('''bert-base-uncased''' )
  		snake_case     :					Union[str, Any]     =	64
  		snake_case     :					Tuple     =	AlignProcessor(image_processor=SCREAMING_SNAKE_CASE__   ,					tokenizer=SCREAMING_SNAKE_CASE__ )
  		return processor
  
  
  
  
  def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__ )     ->       List[Any]:
  
  
  		'''simple docstring'''
  
  
  		snake_case     :					Union[str, Any]     =	[v.split('''_''' )[0].split('''block''' )[1] for v in original_param_names if v.startswith('''block''' )]
  		snake_case     :					Any     =	list(set(SCREAMING_SNAKE_CASE__ ) )
  		snake_case     :					List[Any]     =	sorted(SCREAMING_SNAKE_CASE__ )
  		snake_case     :					Optional[Any]     =	len(SCREAMING_SNAKE_CASE__ )
  		snake_case     :					List[Any]     =	{b: str(SCREAMING_SNAKE_CASE__ ) for b, i in zip(SCREAMING_SNAKE_CASE__   ,					range(SCREAMING_SNAKE_CASE__ ) )}
  
  		snake_case     :					Optional[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:
  				snake_case     :					int     =	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') )
  
  		snake_case     :					List[str]     =	{}
  		for item in rename_keys:
  				if item[0] in original_param_names:
  						snake_case     :					Any     =	'''vision_model.''' + item[1]
  
      # BERT text encoder
  		snake_case     :					Union[str, Any]     =	[]
  		snake_case     :					int     =	'''tf_bert_model/bert'''
  		snake_case     :					Optional[int]     =	'''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:
  						snake_case     :					List[Any]     =	item[1]
  		return key_mapping
  
  
  
  
  def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )     ->       Optional[int]:
  
  
  		'''simple docstring'''
  
  
  		list(hf_params.keys() )
  
  		for key, value in tf_params.items():
  				if key not in key_mapping:
  						continue
  
  				snake_case     :					List[str]     =	key_mapping[key]
  				if "_conv" in key and "kernel" in key:
  						snake_case     :					List[str]     =	torch.from_numpy(SCREAMING_SNAKE_CASE__ ).permute(3   ,					2   ,					0   ,					1 )
  				elif "embeddings" in key:
  						snake_case     :					Any     =	torch.from_numpy(SCREAMING_SNAKE_CASE__ )
  				elif "depthwise_kernel" in key:
  						snake_case     :					Dict     =	torch.from_numpy(SCREAMING_SNAKE_CASE__ ).permute(2   ,					3   ,					0   ,					1 )
  				elif "kernel" in key:
  						snake_case     :					Tuple     =	torch.from_numpy(np.transpose(SCREAMING_SNAKE_CASE__ ) )
  				elif "temperature" in key:
  						snake_case     :					List[str]     =	value
  				elif "bn/gamma" or "bn/beta" in key:
  						snake_case     :					int     =	torch.from_numpy(np.transpose(SCREAMING_SNAKE_CASE__ ) ).squeeze()
  				else:
  						snake_case     :					Dict     =	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       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )     ->       int:
  
  
  		'''simple docstring'''
  
  
  		snake_case     :					Tuple     =	64
  		snake_case     :					int     =	Tokenizer(SCREAMING_SNAKE_CASE__ )
  		snake_case     :					List[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__ )
  
  		snake_case     :					List[str]     =	original_model.trainable_variables
  		snake_case     :					str     =	original_model.non_trainable_variables
  		snake_case     :					Dict     =	{param.name: param.numpy() for param in tf_params}
  		for param in tf_non_train_params:
  				snake_case     :					Optional[int]     =	param.numpy()
  		snake_case     :					List[str]     =	list(tf_params.keys() )
  
  		# Load HuggingFace model
  		snake_case     :					str     =	get_align_config()
  		snake_case     :					str     =	AlignModel(SCREAMING_SNAKE_CASE__ ).eval()
  		snake_case     :					Union[str, Any]     =	hf_model.state_dict()
  
  		# Create src-to-dst parameter name mapping dictionary
  		print('''Converting parameters...''' )
  		snake_case     :					Tuple     =	rename_keys(SCREAMING_SNAKE_CASE__ )
  		replace_params(SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )
  
  		# Initialize processor
  		snake_case     :					Union[str, Any]     =	get_processor()
  		snake_case     :					List[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():
  				snake_case     :					Tuple     =	hf_model(**SCREAMING_SNAKE_CASE__ )
  
  		snake_case     :					List[Any]     =	outputs.image_embeds.detach().numpy()
  		snake_case     :					Dict     =	outputs.text_embeds.detach().numpy()
  
  		# Original model inference
  		snake_case     :					Dict     =	False
  		snake_case     :					Optional[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   ,					)
  		snake_case     :					str     =	tf_image_processor(images=prepare_img()   ,					return_tensors='''tf'''   ,					data_format='''channels_last''' )['''pixel_values''']
  		snake_case     :					Tuple     =	tok(tf.constant(['''A picture of a cat'''] ) )
  
  		snake_case     :					Any     =	original_model.image_encoder(SCREAMING_SNAKE_CASE__   ,					training=SCREAMING_SNAKE_CASE__ )
  		snake_case     :					str     =	original_model.text_encoder(SCREAMING_SNAKE_CASE__   ,					training=SCREAMING_SNAKE_CASE__ )
  
  		snake_case     :					Union[str, Any]     =	tf.nn.la_normalize(SCREAMING_SNAKE_CASE__   ,					axis=-1 )
  		snake_case     :					List[Any]     =	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__":
  						lowercase__						=			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")
  
  						lowercase__						=			parser.parse_args()
  						convert_align_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)
  
  
  
style_context: 
  '''simple docstring'''
  def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )     ->       int:
  
  
  		'''simple docstring'''
  
  
  		snake_case     :					Optional[Any]     =	0
  		for ele in arr:
  				total_sum += product_sum(SCREAMING_SNAKE_CASE__   ,					depth + 1 ) if isinstance(SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ ) else ele
  		return total_sum * depth
  
  
  
  
  def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__ )     ->       int:
  
  
  		'''simple docstring'''
  
  
  		return product_sum(SCREAMING_SNAKE_CASE__   ,					1 )
  
  
  if __name__ == "__main__":
  						import doctest
  
  						doctest.testmod()
  
  
  