






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     lowerCamelCase__				(   __lowerCamelCase      :					Optional[int]		):
  __UpperCAmelCase							:     List[str]									=  tf.image.resize(__lowerCamelCase  ,		(346, 346)		)
  __UpperCAmelCase							:     Tuple									=  tf.image.crop_to_bounding_box(__lowerCamelCase  ,		(346 - 289) // 2  ,		(346 - 289) // 2  ,		289  ,		289		)
  return image






def     lowerCamelCase__				(   ):
  __UpperCAmelCase							:     Any									=  EfficientNetConfig.from_pretrained("""google/efficientnet-b7"""		)
  __UpperCAmelCase							:     int									=  289
  __UpperCAmelCase							:     Union[str, Any]									=  640
  __UpperCAmelCase							:     Dict									=  {"""0""": """LABEL_0""", """1""": """LABEL_1"""}
  __UpperCAmelCase							:     Dict									=  {"""LABEL_0""": 0, """LABEL_1""": 1}
  __UpperCAmelCase							:     Tuple									=  []

  __UpperCAmelCase							:     str									=  BertConfig()
  __UpperCAmelCase							:     Tuple									=  AlignConfig.from_text_vision_configs(
      text_config=__lowerCamelCase  ,		vision_config=__lowerCamelCase  ,		projection_dim=640		)
  return config






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






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






def     lowerCamelCase__				(   __lowerCamelCase      :					Union[str, Any]		):
  # EfficientNet image encoder
  __UpperCAmelCase							:     Optional[Any]									=  [v.split("""_"""		)[0].split("""block"""		)[1] for v in original_param_names if v.startswith("""block"""		)]
  __UpperCAmelCase							:     List[Any]									=  list(set(__lowerCamelCase		)		)
  __UpperCAmelCase							:     Optional[int]									=  sorted(__lowerCamelCase		)
  __UpperCAmelCase							:     List[Any]									=  len(__lowerCamelCase		)
  __UpperCAmelCase							:     Dict									=  {b: str(__lowerCamelCase		) for b, i in zip(__lowerCamelCase  ,		range(__lowerCamelCase		)		)}

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

    # BERT text encoder
  __UpperCAmelCase							:     Tuple									=  []
  __UpperCAmelCase							:     List[Any]									=  """tf_bert_model/bert"""
  __UpperCAmelCase							:     List[str]									=  """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							:     str									=  item[1]
  return key_mapping






def     lowerCamelCase__				(   __lowerCamelCase      :					Optional[Any]  ,		__lowerCamelCase      :					Optional[int]  ,		__lowerCamelCase      :					Any		):
  list(hf_params.keys()		)

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

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

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






@torch.no_grad()
def     lowerCamelCase__				(   __lowerCamelCase      :					List[str]  ,		__lowerCamelCase      :					Optional[Any]  ,		__lowerCamelCase      :					List[Any]  ,		__lowerCamelCase      :					int		):
  __UpperCAmelCase							:     str									=  64
  __UpperCAmelCase							:     Optional[Any]									=  Tokenizer(__lowerCamelCase		)
  __UpperCAmelCase							:     str									=  align.Align("""efficientnet-b7"""  ,		"""bert-base"""  ,		640  ,		__lowerCamelCase  ,		tok.get_vocab_size()		)
  original_model.compile()
  original_model.load_weights(__lowerCamelCase		)

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

  # Load HuggingFace model
  __UpperCAmelCase							:     int									=  get_align_config()
  __UpperCAmelCase							:     str									=  AlignModel(__lowerCamelCase		).eval()
  __UpperCAmelCase							:     Union[str, Any]									=  hf_model.state_dict()

  # Create src-to-dst parameter name mapping dictionary
  print("""Converting parameters..."""		)
  __UpperCAmelCase							:     List[Any]									=  rename_keys(__lowerCamelCase		)
  replace_params(__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase		)

  # Initialize processor
  __UpperCAmelCase							:     Optional[Any]									=  get_processor()
  __UpperCAmelCase							:     Any									=  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							:     List[str]									=  hf_model(**__lowerCamelCase		)

  __UpperCAmelCase							:     Optional[Any]									=  outputs.image_embeds.detach().numpy()
  __UpperCAmelCase							:     Dict									=  outputs.text_embeds.detach().numpy()

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

  __UpperCAmelCase							:     Union[str, Any]									=  original_model.image_encoder(__lowerCamelCase  ,		training=__lowerCamelCase		)
  __UpperCAmelCase							:     Tuple									=  original_model.text_encoder(__lowerCamelCase  ,		training=__lowerCamelCase		)

  __UpperCAmelCase							:     Tuple									=  tf.nn.la_normalize(__lowerCamelCase  ,		axis=-1		)
  __UpperCAmelCase							:     Optional[int]									=  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		:	List[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		:	List[Any]      					=			parser.parse_args()
       convert_align_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)










from sklearn.neural_network import MLPClassifier

a		:	int      					=			[[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
a		:	List[str]      					=			[0, 1, 0, 0]


a		:	str      					=			MLPClassifier(
    solver="lbfgs", alpha=1e-5, hidden_layer_sizes=(5, 2), random_state=1
)

clf.fit(X, y)


a		:	Union[str, Any]      					=			[[0.0, 0.0], [0.0, 1.0], [1.0, 1.0]]
a		:	Optional[int]      					=			clf.predict(test)






def     lowerCamelCase__				(   __lowerCamelCase      :					Dict		):
  return list(__lowerCamelCase		)


if __name__ == "__main__":
       import doctest

       doctest.testmod()


