"""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	:		Tuple   =   logging.get_logger(__name__)



def       A__    (	lowercase_     :	int						)  ->							Tuple:
   __lowerCamelCase    		=					tf.image.resize(lowercase_      , (346, 346)						)
   __lowerCamelCase    		=					tf.image.crop_to_bounding_box(lowercase_      , (346 - 289) // 2      , (346 - 289) // 2      , 289      , 289						)
   return image



def       A__    (	)  ->							Union[str, Any]:
   __lowerCamelCase    		=					EfficientNetConfig.from_pretrained('google/efficientnet-b7'						)
   __lowerCamelCase    		=					289
   __lowerCamelCase    		=					640
   __lowerCamelCase    		=					{'0': 'LABEL_0', '1': 'LABEL_1'}
   __lowerCamelCase    		=					{'LABEL_0': 0, 'LABEL_1': 1}
   __lowerCamelCase    		=					[]

   __lowerCamelCase    		=					BertConfig()
   __lowerCamelCase    		=					AlignConfig.from_text_vision_configs(
       text_config=lowercase_      , vision_config=lowercase_      , projection_dim=640						)
   return config



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



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



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

   __lowerCamelCase    		=					[]
   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:
      __lowerCamelCase    		=					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''')						)

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

    # BERT text encoder
   __lowerCamelCase    		=					[]
   __lowerCamelCase    		=					'tf_bert_model/bert'
   __lowerCamelCase    		=					'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:
         __lowerCamelCase    		=					item[1]
   return key_mapping



def       A__    (	lowercase_     :	Union[str, Any]      , lowercase_     :	Optional[int]      , lowercase_     :	Union[str, Any]						)  ->							List[str]:
   list(hf_params.keys()						)

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

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

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



@torch.no_grad()
def       A__    (	lowercase_     :	Any      , lowercase_     :	List[Any]      , lowercase_     :	List[str]      , lowercase_     :	Dict						)  ->							int:
   __lowerCamelCase    		=					64
   __lowerCamelCase    		=					Tokenizer(lowercase_						)
   __lowerCamelCase    		=					align.Align('efficientnet-b7'      , 'bert-base'      , 640      , lowercase_      , tok.get_vocab_size()						)
   original_model.compile()
   original_model.load_weights(lowercase_						)

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

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

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

   # Initialize processor
   __lowerCamelCase    		=					get_processor()
   __lowerCamelCase    		=					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():
      __lowerCamelCase    		=					hf_model(**lowercase_						)

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

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

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

   __lowerCamelCase    		=					tf.nn.la_normalize(lowercase_      , axis=-1						)
   __lowerCamelCase    		=					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	:		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''')

     _SCREAMING_SNAKE_CASE	:		Dict   =   parser.parse_args()
     convert_align_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)










from typing import TYPE_CHECKING

from ...utils import _LazyModule


lowerCamelCase		=						{'''tokenization_phobert''': ['''PhobertTokenizer''']}


if TYPE_CHECKING:
		from .tokenization_phobert import PhobertTokenizer

else:
		import sys

		lowerCamelCase		=						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




