from __future__ import annotations

import sys

def 	__lowerCamelCase							(				__a    : Optional[Any]      ,							__a    : Optional[int]		)      ->       tuple[int, int]:
	# base cases
	if abs(lowercase_		) == 1:
		return a, 0
	elif abs(lowercase_		) == 1:
		return 0, b

	_lowercase   ,  _lowercase   =a, b
	_lowercase   ,  _lowercase   =1, 0
	_lowercase   ,  _lowercase   =0, 1

	while remainder != 0:
		_lowercase   =old_remainder // remainder
		_lowercase   ,  _lowercase   =remainder, old_remainder - quotient * remainder
		_lowercase   ,  _lowercase   =coeff_a, old_coeff_a - quotient * coeff_a
		_lowercase   ,  _lowercase   =coeff_b, old_coeff_b - quotient * coeff_b

	# sign correction for negative numbers
	if a < 0:
		_lowercase   =-old_coeff_a
	if b < 0:
		_lowercase   =-old_coeff_b

	return old_coeff_a, old_coeff_b

def 	__lowerCamelCase							(				)      ->       List[str]:
	if len(sys.argv		) < 3:
		print("2 integer arguments required"		)
		return 1
	_lowercase   =int(sys.argv[1]		)
	_lowercase   =int(sys.argv[2]		)
	print(extended_euclidean_algorithm(lowercase_      ,							lowercase_		)		)
	return 0


if __name__ == "__main__":
						raise SystemExit(main())










"""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()
SCREAMING_SNAKE_CASE							=			logging.get_logger(__name__)







def     _SCREAMING_SNAKE_CASE  (				lowercase_		)		->						str:
 A__			=							tf.image.resize(lowercase_     ,					(3_46, 3_46)		)
 A__			=							tf.image.crop_to_bounding_box(lowercase_     ,					(3_46 - 2_89) // 2     ,					(3_46 - 2_89) // 2     ,					2_89     ,					2_89		)
 return image







def     _SCREAMING_SNAKE_CASE  (				)		->						Dict:
 A__			=							EfficientNetConfig.from_pretrained("google/efficientnet-b7"		)
 A__			=							2_89
 A__			=							6_40
 A__			=							{"0": "LABEL_0", "1": "LABEL_1"}
 A__			=							{"LABEL_0": 0, "LABEL_1": 1}
 A__			=							[]

 A__			=							BertConfig()
 A__			=							AlignConfig.from_text_vision_configs(
     text_config=lowercase_     ,					vision_config=lowercase_     ,					projection_dim=6_40		)
 return config







def     _SCREAMING_SNAKE_CASE  (				)		->						int:
 A__			=							"http://images.cocodataset.org/val2017/000000039769.jpg"
 A__			=							Image.open(requests.get(lowercase_     ,					stream=lowercase_		).raw		)
 return im







def     _SCREAMING_SNAKE_CASE  (				)		->						List[str]:
 A__			=							EfficientNetImageProcessor(
     do_center_crop=lowercase_     ,					rescale_factor=1 / 1_2_7.5     ,					rescale_offset=lowercase_     ,					do_normalize=lowercase_     ,					include_top=lowercase_     ,					resample=Image.BILINEAR     ,					)
 A__			=							BertTokenizer.from_pretrained("bert-base-uncased"		)
 A__			=							64
 A__			=							AlignProcessor(image_processor=lowercase_     ,					tokenizer=lowercase_		)
 return processor







def     _SCREAMING_SNAKE_CASE  (				lowercase_		)		->						int:
 # EfficientNet image encoder
 A__			=							[v.split("_"		)[0].split("block"		)[1] for v in original_param_names if v.startswith("block"		)]
 A__			=							list(set(lowercase_		)		)
 A__			=							sorted(lowercase_		)
 A__			=							len(lowercase_		)
 A__			=							{b: str(lowercase_		) for b, i in zip(lowercase_     ,					range(lowercase_		)		)}

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

    # BERT text encoder
 A__			=							[]
 A__			=							"tf_bert_model/bert"
 A__			=							"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__			=							item[1]
 return key_mapping







def     _SCREAMING_SNAKE_CASE  (				lowercase_     ,					lowercase_     ,					lowercase_		)		->						Optional[Any]:
 list(hf_params.keys()		)

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

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

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







@torch.no_grad()
def     _SCREAMING_SNAKE_CASE  (				lowercase_     ,					lowercase_     ,					lowercase_     ,					lowercase_		)		->						int:
 A__			=							64
 A__			=							Tokenizer(lowercase_		)
 A__			=							align.Align("efficientnet-b7"     ,					"bert-base"     ,					6_40     ,					lowercase_     ,					tok.get_vocab_size()		)
 original_model.compile()
 original_model.load_weights(lowercase_		)

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

 # Load HuggingFace model
 A__			=							get_align_config()
 A__			=							AlignModel(lowercase_		).eval()
 A__			=							hf_model.state_dict()

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

 # Initialize processor
 A__			=							get_processor()
 A__			=							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__			=							hf_model(**lowercase_		)

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

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

 A__			=							original_model.image_encoder(lowercase_     ,					training=lowercase_		)
 A__			=							original_model.text_encoder(lowercase_     ,					training=lowercase_		)

 A__			=							tf.nn.la_normalize(lowercase_     ,					axis=-1		)
 A__			=							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__":
      SCREAMING_SNAKE_CASE							=			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")

      SCREAMING_SNAKE_CASE							=			parser.parse_args()
      convert_align_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)





