code: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  def     a						(      _lowercase   :     List[str]       )     ->float:
  		return -num if num < 0 else num
  
  def     a						(      _lowercase   :     Dict       )     ->int:
  		if len(lowercase_       ) == 0:
  				raise ValueError("""abs_min() arg is an empty sequence"""       )
  		UpperCamelCase__         =	x[0]
  		for i in x:
  				if abs_val(lowercase_       ) < abs_val(lowercase_       ):
  						UpperCamelCase__         =	i
  		return j
  
  def     a						(      _lowercase   :     List[Any]       )     ->int:
  		if len(lowercase_       ) == 0:
  				raise ValueError("""abs_max() arg is an empty sequence"""       )
  		UpperCamelCase__         =	x[0]
  		for i in x:
  				if abs(lowercase_       ) > abs(lowercase_       ):
  						UpperCamelCase__         =	i
  		return j
  
  def     a						(      _lowercase   :     Dict       )     ->int:
  		if len(lowercase_       ) == 0:
  				raise ValueError("""abs_max_sort() arg is an empty sequence"""       )
  		return sorted(lowercase_ ,						key=lowercase_       )[-1]
  
  def     a						(      )     ->str:
  		assert abs_val(0       ) == 0
  		assert abs_val(3_4       ) == 3_4
  		assert abs_val(-1_0_0_0_0_0_0_0_0_0_0_0       ) == 1_0_0_0_0_0_0_0_0_0_0_0
  
  		UpperCamelCase__         =	[-3, -1, 2, -1_1]
  		assert abs_max(lowercase_       ) == -1_1
  		assert abs_max_sort(lowercase_       ) == -1_1
  		assert abs_min(lowercase_       ) == -1
  
  
  if __name__ == "__main__":
  				import doctest
  
  				doctest.testmod()
  
  				test_abs_val()
  				print(abs_val(-34))  # --> 34
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  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     =			logging.get_logger(__name__)
  
  def     a						(      _lowercase   :     Union[str, Any]       )     ->str:
  		UpperCamelCase__         =	tf.image.resize(_lowercase ,						(3_4_6, 3_4_6)       )
  		UpperCamelCase__         =	tf.image.crop_to_bounding_box(_lowercase ,						(3_4_6 - 2_8_9) // 2 ,						(3_4_6 - 2_8_9) // 2 ,						2_8_9 ,						2_8_9       )
  		return image
  
  def     a						(      )     ->Optional[int]:
  		UpperCamelCase__         =	EfficientNetConfig.from_pretrained("""google/efficientnet-b7"""       )
  		UpperCamelCase__         =	2_8_9
  		UpperCamelCase__         =	6_4_0
  		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=_lowercase ,						vision_config=_lowercase ,						projection_dim=6_4_0       )
  		return config
  
  def     a						(      )     ->Optional[int]:
  		UpperCamelCase__         =	"""http://images.cocodataset.org/val2017/000000039769.jpg"""
  		UpperCamelCase__         =	Image.open(requests.get(_lowercase ,						stream=_lowercase       ).raw       )
  		return im
  
  def     a						(      )     ->Optional[int]:
  		UpperCamelCase__         =	EfficientNetImageProcessor(
  		    do_center_crop=_lowercase ,						rescale_factor=1 / 127.5 ,						rescale_offset=_lowercase ,						do_normalize=_lowercase ,						include_top=_lowercase ,						resample=Image.BILINEAR ,						)
  		UpperCamelCase__         =	BertTokenizer.from_pretrained("""bert-base-uncased"""       )
  		UpperCamelCase__         =	6_4
  		UpperCamelCase__         =	AlignProcessor(image_processor=_lowercase ,						tokenizer=_lowercase       )
  		return processor
  
  def     a						(      _lowercase   :     str       )     ->int:
  		# EfficientNet image encoder
  		UpperCamelCase__         =	[v.split("""_"""       )[0].split("""block"""       )[1] for v in original_param_names if v.startswith("""block"""       )]
  		UpperCamelCase__         =	list(set(_lowercase       )       )
  		UpperCamelCase__         =	sorted(_lowercase       )
  		UpperCamelCase__         =	len(_lowercase       )
  		UpperCamelCase__         =	{b: str(_lowercase       ) for b, i in zip(_lowercase ,						range(_lowercase       )       )}
  
  		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(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:
  						UpperCamelCase__         =	item[1]
  		return key_mapping
  
  def     a						(      _lowercase   :     int ,						_lowercase   :     str ,						_lowercase   :     int       )     ->Optional[Any]:
  		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(_lowercase       ).permute(3 ,						2 ,						0 ,						1       )
  				elif "embeddings" in key:
  						UpperCamelCase__         =	torch.from_numpy(_lowercase       )
  				elif "depthwise_kernel" in key:
  						UpperCamelCase__         =	torch.from_numpy(_lowercase       ).permute(2 ,						3 ,						0 ,						1       )
  				elif "kernel" in key:
  						UpperCamelCase__         =	torch.from_numpy(np.transpose(_lowercase       )       )
  				elif "temperature" in key:
  						UpperCamelCase__         =	value
  				elif "bn/gamma" or "bn/beta" in key:
  						UpperCamelCase__         =	torch.from_numpy(np.transpose(_lowercase       )       ).squeeze()
  				else:
  						UpperCamelCase__         =	torch.from_numpy(_lowercase       )
  
  				# Replace HF parameters with original TF model parameters
  				hf_params[hf_key].copy_(_lowercase       )
  
  @torch.no_grad()
  def     a						(      _lowercase   :     List[str] ,						_lowercase   :     Any ,						_lowercase   :     Dict ,						_lowercase   :     Optional[Any]       )     ->str:
  		UpperCamelCase__         =	6_4
  		UpperCamelCase__         =	Tokenizer(_lowercase       )
  		UpperCamelCase__         =	align.Align("""efficientnet-b7""" ,						"""bert-base""" ,						6_4_0 ,						_lowercase ,						tok.get_vocab_size()       )
  		original_model.compile()
  		original_model.load_weights(_lowercase       )
  
  		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(_lowercase       ).eval()
  		UpperCamelCase__         =	hf_model.state_dict()
  
  		# Create src-to-dst parameter name mapping dictionary
  		print("""Converting parameters..."""       )
  		UpperCamelCase__         =	rename_keys(_lowercase       )
  		replace_params(_lowercase ,						_lowercase ,						_lowercase       )
  
  		# Initialize processor
  		UpperCamelCase__         =	get_processor()
  		UpperCamelCase__         =	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():
  				UpperCamelCase__         =	hf_model(**_lowercase       )
  
  		UpperCamelCase__         =	outputs.image_embeds.detach().numpy()
  		UpperCamelCase__         =	outputs.text_embeds.detach().numpy()
  
  		# Original model inference
  		UpperCamelCase__         =	False
  		UpperCamelCase__         =	EfficientNetImageProcessor(
  		    do_center_crop=_lowercase ,						do_rescale=_lowercase ,						do_normalize=_lowercase ,						include_top=_lowercase ,						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(_lowercase ,						training=_lowercase       )
  		UpperCamelCase__         =	original_model.text_encoder(_lowercase ,						training=_lowercase       )
  
  		UpperCamelCase__         =	tf.nn.la_normalize(_lowercase ,						axis=-1       )
  		UpperCamelCase__         =	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__":
  				__lowerCAmelCase     =			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     =			parser.parse_args()
  				convert_align_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)
  
  
  
  
  
  