
'''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__ :		Optional[int]					=logging.get_logger(__name__)







def 							A_  (    __SCREAMING_SNAKE_CASE    :	int			)					->							Optional[int]:





  """simple docstring"""

  __A		:  Optional[int]       				=		tf.image.resize(__lowerCAmelCase      ,  (346, 346)			)
  __A		:  List[str]       				=		tf.image.crop_to_bounding_box(__lowerCAmelCase      ,  (346 - 289) // 2      ,  (346 - 289) // 2      ,  289      ,  289			)
  return image







def 							A_  (    )					->							Tuple:





  """simple docstring"""

  __A		:  Optional[int]       				=		EfficientNetConfig.from_pretrained("""google/efficientnet-b7"""			)
  __A		:  Any       				=		289
  __A		:  List[Any]       				=		640
  __A		:  Union[str, Any]       				=		{"0": "LABEL_0", "1": "LABEL_1"}
  __A		:  int       				=		{"LABEL_0": 0, "LABEL_1": 1}
  __A		:  Optional[Any]       				=		[]

  __A		:  int       				=		BertConfig()
  __A		:  Tuple       				=		AlignConfig.from_text_vision_configs(
      text_config=__lowerCAmelCase      ,  vision_config=__lowerCAmelCase      ,  projection_dim=640			)
  return config







def 							A_  (    )					->							Union[str, Any]:





  """simple docstring"""

  __A		:  List[str]       				=		"http://images.cocodataset.org/val2017/000000039769.jpg"
  __A		:  Any       				=		Image.open(requests.get(__lowerCAmelCase      ,  stream=__lowerCAmelCase			).raw			)
  return im







def 							A_  (    )					->							List[str]:





  """simple docstring"""

  __A		:  List[str]       				=		EfficientNetImageProcessor(
      do_center_crop=__lowerCAmelCase      ,  rescale_factor=1 / 127.5      ,  rescale_offset=__lowerCAmelCase      ,  do_normalize=__lowerCAmelCase      ,  include_top=__lowerCAmelCase      ,  resample=Image.BILINEAR      ,  )
  __A		:  List[Any]       				=		BertTokenizer.from_pretrained("""bert-base-uncased"""			)
  __A		:  Optional[int]       				=		64
  __A		:  List[Any]       				=		AlignProcessor(image_processor=__lowerCAmelCase      ,  tokenizer=__lowerCAmelCase			)
  return processor







def 							A_  (    __SCREAMING_SNAKE_CASE    :	Optional[int]			)					->							Optional[int]:





  """simple docstring"""

  __A		:  Optional[int]       				=		[v.split("""_"""			)[0].split("""block"""			)[1] for v in original_param_names if v.startswith("""block"""			)]
  __A		:  Optional[int]       				=		list(set(__lowerCAmelCase			)			)
  __A		:  List[str]       				=		sorted(__lowerCAmelCase			)
  __A		:  Optional[Any]       				=		len(__lowerCAmelCase			)
  __A		:  Union[str, Any]       				=		{b: str(__lowerCAmelCase			) for b, i in zip(__lowerCAmelCase      ,  range(__lowerCAmelCase			)			)}

  __A		:  Optional[int]       				=		[]
  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		:  List[Any]       				=		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		:  int       				=		{}
  for item in rename_keys:
    if item[0] in original_param_names:
      __A		:  Tuple       				=		"vision_model." + item[1]

       # BERT text encoder
  __A		:  Dict       				=		[]
  __A		:  Optional[int]       				=		"tf_bert_model/bert"
  __A		:  Any       				=		"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		:  Optional[int]       				=		item[1]
  return key_mapping







def 							A_  (    __SCREAMING_SNAKE_CASE    :	Optional[Any]      ,  __SCREAMING_SNAKE_CASE    :	Tuple      ,  __SCREAMING_SNAKE_CASE    :	Optional[int]			)					->							Union[str, Any]:





  """simple docstring"""

  list(hf_params.keys()			)

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

    __A		:  List[Any]       				=		key_mapping[key]
    if "_conv" in key and "kernel" in key:
      __A		:  Dict       				=		torch.from_numpy(__lowerCAmelCase			).permute(3      ,  2      ,  0      ,  1			)
    elif "embeddings" in key:
      __A		:  Tuple       				=		torch.from_numpy(__lowerCAmelCase			)
    elif "depthwise_kernel" in key:
      __A		:  Tuple       				=		torch.from_numpy(__lowerCAmelCase			).permute(2      ,  3      ,  0      ,  1			)
    elif "kernel" in key:
      __A		:  Dict       				=		torch.from_numpy(np.transpose(__lowerCAmelCase			)			)
    elif "temperature" in key:
      __A		:  Union[str, Any]       				=		value
    elif "bn/gamma" or "bn/beta" in key:
      __A		:  str       				=		torch.from_numpy(np.transpose(__lowerCAmelCase			)			).squeeze()
    else:
      __A		:  str       				=		torch.from_numpy(__lowerCAmelCase			)

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







@torch.no_grad()
def 							A_  (    __SCREAMING_SNAKE_CASE    :	int      ,  __SCREAMING_SNAKE_CASE    :	int      ,  __SCREAMING_SNAKE_CASE    :	Optional[int]      ,  __SCREAMING_SNAKE_CASE    :	str			)					->							Tuple:





  """simple docstring"""

  __A		:  Dict       				=		64
  __A		:  Tuple       				=		Tokenizer(__lowerCAmelCase			)
  __A		:  Tuple       				=		align.Align("""efficientnet-b7"""      ,  """bert-base"""      ,  640      ,  __lowerCAmelCase      ,  tok.get_vocab_size()			)
  original_model.compile()
  original_model.load_weights(__lowerCAmelCase			)

  __A		:  Optional[Any]       				=		original_model.trainable_variables
  __A		:  Any       				=		original_model.non_trainable_variables
  __A		:  List[Any]       				=		{param.name: param.numpy() for param in tf_params}
  for param in tf_non_train_params:
    __A		:  Optional[Any]       				=		param.numpy()
  __A		:  Tuple       				=		list(tf_params.keys()			)

  # Load HuggingFace model
  __A		:  Union[str, Any]       				=		get_align_config()
  __A		:  Optional[int]       				=		AlignModel(__lowerCAmelCase			).eval()
  __A		:  Dict       				=		hf_model.state_dict()

  # Create src-to-dst parameter name mapping dictionary
  print("""Converting parameters..."""			)
  __A		:  Optional[int]       				=		rename_keys(__lowerCAmelCase			)
  replace_params(__lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase			)

  # Initialize processor
  __A		:  Union[str, Any]       				=		get_processor()
  __A		:  Dict       				=		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		:  Union[str, Any]       				=		hf_model(**__lowerCAmelCase			)

  __A		:  Union[str, Any]       				=		outputs.image_embeds.detach().numpy()
  __A		:  int       				=		outputs.text_embeds.detach().numpy()

  # Original model inference
  __A		:  Tuple       				=		False
  __A		:  Dict       				=		EfficientNetImageProcessor(
      do_center_crop=__lowerCAmelCase      ,  do_rescale=__lowerCAmelCase      ,  do_normalize=__lowerCAmelCase      ,  include_top=__lowerCAmelCase      ,  resample=Image.BILINEAR      ,  )
  __A		:  Optional[int]       				=		tf_image_processor(images=prepare_img()      ,  return_tensors="""tf"""      ,  data_format="""channels_last"""			)["pixel_values"]
  __A		:  Union[str, Any]       				=		tok(tf.constant(["""A picture of a cat"""]			)			)

  __A		:  Any       				=		original_model.image_encoder(__lowerCAmelCase      ,  training=__lowerCAmelCase			)
  __A		:  Dict       				=		original_model.text_encoder(__lowerCAmelCase      ,  training=__lowerCAmelCase			)

  __A		:  Union[str, Any]       				=		tf.nn.la_normalize(__lowerCAmelCase      ,  axis=-1			)
  __A		:  List[Any]       				=		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[Any]					=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__ :		int					=parser.parse_args()
     convert_align_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)









"""simple docstring"""
import numpy as np
from numpy import floataa
from numpy.typing import NDArray






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		NDArray[floataa]  ,	__lowerCAmelCase			:		NDArray[floataa]						)    ->       NDArray[floataa]:
 _UpperCamelCase      ,	_UpperCamelCase      :	Optional[Any]					      =  np.shape(__lowerCAmelCase						)

 _UpperCamelCase      :	NDArray[floataa]					      =  np.zeros((rows, 1)  ,	dtype=__lowerCAmelCase						)
 for row in reversed(range(__lowerCAmelCase						)						):
  _UpperCamelCase      :	List[str]					      =  0
  for col in range(row + 1  ,	__lowerCAmelCase						):
   total += coefficients[row, col] * x[col]

  _UpperCamelCase      :	Dict					      =  (vector[row] - total) / coefficients[row, row]

 return x






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		NDArray[floataa]  ,	__lowerCAmelCase			:		NDArray[floataa]						)    ->       NDArray[floataa]:
 _UpperCamelCase      ,	_UpperCamelCase      :	Optional[Any]					      =  np.shape(__lowerCAmelCase						)
 if rows != columns:
  return np.array(()  ,	dtype=__lowerCAmelCase						)

 # augmented matrix
 _UpperCamelCase      :	NDArray[floataa]					      =  np.concatenate((coefficients, vector)  ,	axis=1						)
 _UpperCamelCase      :	List[str]					      =  augmented_mat.astype("float64"						)

 # scale the matrix leaving it triangular
 for row in range(rows - 1						):
  _UpperCamelCase      :	Optional[Any]					      =  augmented_mat[row, row]
  for col in range(row + 1  ,	__lowerCAmelCase						):
   _UpperCamelCase      :	Union[str, Any]					      =  augmented_mat[col, row] / pivot
   augmented_mat[col, :] -= factor * augmented_mat[row, :]

 _UpperCamelCase      :	Any					      =  retroactive_resolution(
     augmented_mat[:, 0:columns]  ,	augmented_mat[:, columns : columns + 1]						)

 return x


if __name__ == "__main__":
    import doctest

    doctest.testmod()



