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()
  a__						:			str										=			logging.get_logger(__name__)
  
  
  
  
  
  
  
  def _lowercase   (  __A       ):
  
  
  
  	'''simple docstring'''
  
  
  
  	__UpperCamelCase				      =  tf.image.resize(lowerCamelCase__							,(346, 346)       )
  	__UpperCamelCase				      =  tf.image.crop_to_bounding_box(lowerCamelCase__							,(346 - 289) // 2							,(346 - 289) // 2							,289							,289       )
  	return image
  
  
  
  
  
  
  
  def _lowercase   (  ):
  
  
  
  	'''simple docstring'''
  
  
  
  	__UpperCamelCase				      =  EfficientNetConfig.from_pretrained("""google/efficientnet-b7"""       )
  	__UpperCamelCase				      =  289
  	__UpperCamelCase				      =  640
  	__UpperCamelCase				      =  {"""0""": """LABEL_0""", """1""": """LABEL_1"""}
  	__UpperCamelCase				      =  {"""LABEL_0""": 0, """LABEL_1""": 1}
  	__UpperCamelCase				      =  []
  
  	__UpperCamelCase				      =  BertConfig()
  	__UpperCamelCase				      =  AlignConfig.from_text_vision_configs(
  	    text_config=lowerCamelCase__							,vision_config=lowerCamelCase__							,projection_dim=640       )
  	return config
  
  
  
  
  
  
  
  def _lowercase   (  ):
  
  
  
  	'''simple docstring'''
  
  
  
  	__UpperCamelCase				      =  """http://images.cocodataset.org/val2017/000000039769.jpg"""
  	__UpperCamelCase				      =  Image.open(requests.get(lowerCamelCase__							,stream=lowerCamelCase__       ).raw       )
  	return im
  
  
  
  
  
  
  
  def _lowercase   (  ):
  
  
  
  	'''simple docstring'''
  
  
  
  	__UpperCamelCase				      =  EfficientNetImageProcessor(
  	    do_center_crop=lowerCamelCase__							,rescale_factor=1 / 127.5							,rescale_offset=lowerCamelCase__							,do_normalize=lowerCamelCase__							,include_top=lowerCamelCase__							,resample=Image.BILINEAR							,)
  	__UpperCamelCase				      =  BertTokenizer.from_pretrained("""bert-base-uncased"""       )
  	__UpperCamelCase				      =  64
  	__UpperCamelCase				      =  AlignProcessor(image_processor=lowerCamelCase__							,tokenizer=lowerCamelCase__       )
  	return processor
  
  
  
  
  
  
  
  def _lowercase   (  __A       ):
  
  
  
  	'''simple docstring'''
  
  
  
  	__UpperCamelCase				      =  [v.split("""_"""       )[0].split("""block"""       )[1] for v in original_param_names if v.startswith("""block"""       )]
  	__UpperCamelCase				      =  list(set(lowerCamelCase__       )       )
  	__UpperCamelCase				      =  sorted(lowerCamelCase__       )
  	__UpperCamelCase				      =  len(lowerCamelCase__       )
  	__UpperCamelCase				      =  {b: str(lowerCamelCase__       ) for b, i in zip(lowerCamelCase__							,range(lowerCamelCase__       )       )}
  
  	__UpperCamelCase				      =  []
  	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:
  		__UpperCamelCase				      =  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")       )
  
  	__UpperCamelCase				      =  {}
  	for item in rename_keys:
  		if item[0] in original_param_names:
  			__UpperCamelCase				      =  """vision_model.""" + item[1]
  
  		# BERT text encoder
  	__UpperCamelCase				      =  []
  	__UpperCamelCase				      =  """tf_bert_model/bert"""
  	__UpperCamelCase				      =  """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:
  			__UpperCamelCase				      =  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
  
  		__UpperCamelCase				      =  key_mapping[key]
  		if "_conv" in key and "kernel" in key:
  			__UpperCamelCase				      =  torch.from_numpy(lowerCamelCase__       ).permute(3							,2							,0							,1       )
  		elif "embeddings" in key:
  			__UpperCamelCase				      =  torch.from_numpy(lowerCamelCase__       )
  		elif "depthwise_kernel" in key:
  			__UpperCamelCase				      =  torch.from_numpy(lowerCamelCase__       ).permute(2							,3							,0							,1       )
  		elif "kernel" in key:
  			__UpperCamelCase				      =  torch.from_numpy(np.transpose(lowerCamelCase__       )       )
  		elif "temperature" in key:
  			__UpperCamelCase				      =  value
  		elif "bn/gamma" or "bn/beta" in key:
  			__UpperCamelCase				      =  torch.from_numpy(np.transpose(lowerCamelCase__       )       ).squeeze()
  		else:
  			__UpperCamelCase				      =  torch.from_numpy(lowerCamelCase__       )
  
  		# Replace HF parameters with original TF model parameters
  		hf_params[hf_key].copy_(lowerCamelCase__       )
  
  
  
  
  
  
  
  @torch.no_grad()
  def _lowercase   (  __A							,__A							,__A							,__A       ):
  
  
  
  	'''simple docstring'''
  
  
  
  	__UpperCamelCase				      =  64
  	__UpperCamelCase				      =  Tokenizer(lowerCamelCase__       )
  	__UpperCamelCase				      =  align.Align("""efficientnet-b7"""							,"""bert-base"""							,640							,lowerCamelCase__							,tok.get_vocab_size()       )
  	original_model.compile()
  	original_model.load_weights(lowerCamelCase__       )
  
  	__UpperCamelCase				      =  original_model.trainable_variables
  	__UpperCamelCase				      =  original_model.non_trainable_variables
  	__UpperCamelCase				      =  {param.name: param.numpy() for param in tf_params}
  	for param in tf_non_train_params:
  		__UpperCamelCase				      =  param.numpy()
  	__UpperCamelCase				      =  list(tf_params.keys()       )
  
  	# Load HuggingFace model
  	__UpperCamelCase				      =  get_align_config()
  	__UpperCamelCase				      =  AlignModel(lowerCamelCase__       ).eval()
  	__UpperCamelCase				      =  hf_model.state_dict()
  
  	# Create src-to-dst parameter name mapping dictionary
  	print("""Converting parameters..."""       )
  	__UpperCamelCase				      =  rename_keys(lowerCamelCase__       )
  	replace_params(lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__       )
  
  	# Initialize processor
  	__UpperCamelCase				      =  get_processor()
  	__UpperCamelCase				      =  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():
  		__UpperCamelCase				      =  hf_model(**lowerCamelCase__       )
  
  	__UpperCamelCase				      =  outputs.image_embeds.detach().numpy()
  	__UpperCamelCase				      =  outputs.text_embeds.detach().numpy()
  
  	# Original model inference
  	__UpperCamelCase				      =  False
  	__UpperCamelCase				      =  EfficientNetImageProcessor(
  	    do_center_crop=lowerCamelCase__							,do_rescale=lowerCamelCase__							,do_normalize=lowerCamelCase__							,include_top=lowerCamelCase__							,resample=Image.BILINEAR							,)
  	__UpperCamelCase				      =  tf_image_processor(images=prepare_img()							,return_tensors="""tf"""							,data_format="""channels_last"""       )["""pixel_values"""]
  	__UpperCamelCase				      =  tok(tf.constant(["""A picture of a cat"""]       )       )
  
  	__UpperCamelCase				      =  original_model.image_encoder(lowerCamelCase__							,training=lowerCamelCase__       )
  	__UpperCamelCase				      =  original_model.text_encoder(lowerCamelCase__							,training=lowerCamelCase__       )
  
  	__UpperCamelCase				      =  tf.nn.la_normalize(lowerCamelCase__							,axis=-1       )
  	__UpperCamelCase				      =  tf.nn.la_normalize(lowerCamelCase__							,axis=-1       )
  
  	# Check whether original and HF model outputs match  -> np.allclose
  	if not np.allclose(lowerCamelCase__							,lowerCamelCase__							,atol=1E-3       ):
  		raise ValueError("""The predicted image features are not the same."""       )
  	if not np.allclose(lowerCamelCase__							,lowerCamelCase__							,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(lowerCamelCase__       ):
  			os.mkdir(lowerCamelCase__       )
  		# Save converted model and image processor
  		hf_model.save_pretrained(lowerCamelCase__       )
  		processor.save_pretrained(lowerCamelCase__       )
  
  	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__":
  	a__						:			Optional[int]										=			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')
  
  	a__						:			List[str]										=			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'''
  
  
  
  from .stack import Stack
  
  
  
  
  
  
  
  def _lowercase   (  __A       ):
  
  
  
   '''simple docstring'''
  
  
  
   __UpperCamelCase				      =  Stack()
   __UpperCamelCase				      =  {"""(""": """)""", """[""": """]""", """{""": """}"""}
   for bracket in parentheses:
    if bracket in bracket_pairs:
     stack.push(__A       )
    elif bracket in (")", "]", "}"):
     if stack.is_empty() or bracket_pairs[stack.pop()] != bracket:
      return False
   return stack.is_empty()
  
  
  if __name__ == "__main__":
   from doctest import testmod
  
   testmod()
  
   a__						:			List[Any]										=			['((()))', '((())', '(()))']
   print('Balanced parentheses demonstration:\n')
   for example in examples:
    a__						:			int										=			'' if balanced_parentheses(example) else 'not '
    print(f'''{example} is {not_str}balanced''')
  
  
  
  
  
  