

"""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					 =				logging.get_logger(__name__)




def     _A    (    UpperCamelCase_	:			Union[str, Any])      ->		List[str]:
   '''simple docstring'''


   __lowercase								=		tf.image.resize(UpperCamelCase_,     (346, 346))
   __lowercase								=		tf.image.crop_to_bounding_box(UpperCamelCase_,     (346 - 289) // 2,     (346 - 289) // 2,     289,     289)
   return image




def     _A    (    )      ->		Any:
   '''simple docstring'''


   __lowercase								=		EfficientNetConfig.from_pretrained("google/efficientnet-b7")
   __lowercase								=		289
   __lowercase								=		640
   __lowercase								=		{"0": "LABEL_0", "1": "LABEL_1"}
   __lowercase								=		{"LABEL_0": 0, "LABEL_1": 1}
   __lowercase								=		[]

   __lowercase								=		BertConfig()
   __lowercase								=		AlignConfig.from_text_vision_configs(
       text_config=UpperCamelCase_,     vision_config=UpperCamelCase_,     projection_dim=640)
   return config




def     _A    (    )      ->		Tuple:
   '''simple docstring'''


   __lowercase								=		"http://images.cocodataset.org/val2017/000000039769.jpg"
   __lowercase								=		Image.open(requests.get(UpperCamelCase_,     stream=UpperCamelCase_).raw)
   return im




def     _A    (    )      ->		Optional[Any]:
   '''simple docstring'''


   __lowercase								=		EfficientNetImageProcessor(
       do_center_crop=UpperCamelCase_,     rescale_factor=1 / 127.5,     rescale_offset=UpperCamelCase_,     do_normalize=UpperCamelCase_,     include_top=UpperCamelCase_,     resample=Image.BILINEAR,     )
   __lowercase								=		BertTokenizer.from_pretrained("bert-base-uncased")
   __lowercase								=		64
   __lowercase								=		AlignProcessor(image_processor=UpperCamelCase_,     tokenizer=UpperCamelCase_)
   return processor




def     _A    (    UpperCamelCase_	:			Optional[int])      ->		int:
   '''simple docstring'''


   __lowercase								=		[v.split("_")[0].split("block")[1] for v in original_param_names if v.startswith("block")]
   __lowercase								=		list(set(UpperCamelCase_))
   __lowercase								=		sorted(UpperCamelCase_)
   __lowercase								=		len(UpperCamelCase_)
   __lowercase								=		{b: str(UpperCamelCase_) for b, i in zip(UpperCamelCase_,     range(UpperCamelCase_))}

   __lowercase								=		[]
   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:
      __lowercase								=		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"""))

   __lowercase								=		{}
   for item in rename_keys:
      if item[0] in original_param_names:
         __lowercase								=		"vision_model." + item[1]

    # BERT text encoder
   __lowercase								=		[]
   __lowercase								=		"tf_bert_model/bert"
   __lowercase								=		"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:
         __lowercase								=		item[1]
   return key_mapping




def     _A    (    UpperCamelCase_	:			List[Any],     UpperCamelCase_	:			str,     UpperCamelCase_	:			Any)      ->		Tuple:
   '''simple docstring'''


   list(hf_params.keys())

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

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

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




@torch.no_grad()
def     _A    (    UpperCamelCase_	:			Union[str, Any],     UpperCamelCase_	:			List[Any],     UpperCamelCase_	:			Any,     UpperCamelCase_	:			List[str])      ->		Dict:
   '''simple docstring'''


   __lowercase								=		64
   __lowercase								=		Tokenizer(UpperCamelCase_)
   __lowercase								=		align.Align("efficientnet-b7",     "bert-base",     640,     UpperCamelCase_,     tok.get_vocab_size())
   original_model.compile()
   original_model.load_weights(UpperCamelCase_)

   __lowercase								=		original_model.trainable_variables
   __lowercase								=		original_model.non_trainable_variables
   __lowercase								=		{param.name: param.numpy() for param in tf_params}
   for param in tf_non_train_params:
      __lowercase								=		param.numpy()
   __lowercase								=		list(tf_params.keys())

   # Load HuggingFace model
   __lowercase								=		get_align_config()
   __lowercase								=		AlignModel(UpperCamelCase_).eval()
   __lowercase								=		hf_model.state_dict()

   # Create src-to-dst parameter name mapping dictionary
   print("Converting parameters...")
   __lowercase								=		rename_keys(UpperCamelCase_)
   replace_params(UpperCamelCase_,     UpperCamelCase_,     UpperCamelCase_)

   # Initialize processor
   __lowercase								=		get_processor()
   __lowercase								=		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():
      __lowercase								=		hf_model(**UpperCamelCase_)

   __lowercase								=		outputs.image_embeds.detach().numpy()
   __lowercase								=		outputs.text_embeds.detach().numpy()

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

   __lowercase								=		original_model.image_encoder(UpperCamelCase_,     training=UpperCamelCase_)
   __lowercase								=		original_model.text_encoder(UpperCamelCase_,     training=UpperCamelCase_)

   __lowercase								=		tf.nn.la_normalize(UpperCamelCase_,     axis=-1)
   __lowercase								=		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__":
   _a					 =				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					 =				parser.parse_args()
   convert_align_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)







"""simple docstring"""





def     _A    (    UpperCamelCase_	:			list)      ->		list:
   '''simple docstring'''


   for i, _ in enumerate(UpperCamelCase_):
      if (i % 2 == 1) == (nums[i - 1] > nums[i]):
         __lowercase						,__lowercase								=		nums[i], nums[i - 1]

   return nums


if __name__ == "__main__":
   print('Enter the array elements:')
   _a					 =				list(map(int, input().split()))
   print('The unsorted array is:')
   print(array)
   print('Array after Wiggle sort:')
   print(wiggle_sort(array))




