





'''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()
lowercase__		:    Tuple =       logging.get_logger(__name__)







def       a__       (       lowercase      :  List[Any]   )	->	Union[str, Any]:





     """simple docstring"""

     _UpperCamelCase       					= tf.image.resize(lowercase,      (346, 346)   )
     _UpperCamelCase       					= tf.image.crop_to_bounding_box(lowercase,      (346 - 289) // 2,      (346 - 289) // 2,      289,      289   )
     return image







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





     """simple docstring"""

     _UpperCamelCase       					= EfficientNetConfig.from_pretrained('''google/efficientnet-b7'''   )
     _UpperCamelCase       					= 289
     _UpperCamelCase       					= 640
     _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=640   )
     return config







def       a__       (       )	->	List[Any]:





     """simple docstring"""

     _UpperCamelCase       					= '''http://images.cocodataset.org/val2017/000000039769.jpg'''
     _UpperCamelCase       					= Image.open(requests.get(lowercase,      stream=lowercase   ).raw   )
     return im







def       a__       (       )	->	int:





     """simple docstring"""

     _UpperCamelCase       					= EfficientNetImageProcessor(
         do_center_crop=lowercase,      rescale_factor=1 / 1_2_7.5,      rescale_offset=lowercase,      do_normalize=lowercase,      include_top=lowercase,      resample=Image.BILINEAR,      )
     _UpperCamelCase       					= BertTokenizer.from_pretrained('''bert-base-uncased'''   )
     _UpperCamelCase       					= 64
     _UpperCamelCase       					= AlignProcessor(image_processor=lowercase,      tokenizer=lowercase   )
     return processor







def       a__       (       lowercase      :  Union[str, Any]   )	->	List[Any]:





     """simple docstring"""

     _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(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       					= item[1]
     return key_mapping







def       a__       (       lowercase      :  Any,      lowercase      :  Dict,      lowercase      :  Tuple   )	->	Optional[Any]:





     """simple docstring"""

     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      :  Optional[Any],      lowercase      :  str,      lowercase      :  List[str],      lowercase      :  List[str]   )	->	List[str]:





     """simple docstring"""

     _UpperCamelCase       					= 64
     _UpperCamelCase       					= Tokenizer(lowercase   )
     _UpperCamelCase       					= align.Align('''efficientnet-b7''',      '''bert-base''',      640,      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=64,      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__":
   lowercase__		:    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')

   lowercase__		:    Optional[int] =       parser.parse_args()
   convert_align_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)










'''simple docstring'''

from __future__ import annotations







def       a__       (       lowercase      :  list[int | float]   )	->	int | float:





     """simple docstring"""

     if len(lowercase   ) == 0:
          raise ValueError('''find_min() arg is an empty sequence'''   )
     _UpperCamelCase       					= nums[0]
     for num in nums:
          _UpperCamelCase       					= min(lowercase,      lowercase   )
     return min_num


if __name__ == "__main__":
   import doctest

   doctest.testmod(verbose=True)



