
'''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 							snake_case				(		UpperCAmelCase						)->      Union[str, Any]:







      """simple docstring"""
      __A													=      tf.image.resize(UpperCAmelCase   ,	(3_4_6, 3_4_6)						)
      __A													=      tf.image.crop_to_bounding_box(UpperCAmelCase   ,	(3_4_6 - 2_8_9) // 2   ,	(3_4_6 - 2_8_9) // 2   ,	2_8_9   ,	2_8_9						)
      return image





def 							snake_case				(		)->      List[Any]:







      """simple docstring"""
      __A													=      EfficientNetConfig.from_pretrained('google/efficientnet-b7'						)
      __A													=      2_8_9
      __A													=      6_4_0
      __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=UpperCAmelCase   ,	vision_config=UpperCAmelCase   ,	projection_dim=6_4_0						)
      return config





def 							snake_case				(		)->      Any:







      """simple docstring"""
      __A													=      'http://images.cocodataset.org/val2017/000000039769.jpg'
      __A													=      Image.open(requests.get(UpperCAmelCase   ,	stream=UpperCAmelCase						).raw						)
      return im





def 							snake_case				(		)->      List[Any]:







      """simple docstring"""
      __A													=      EfficientNetImageProcessor(
          do_center_crop=UpperCAmelCase   ,	rescale_factor=1 / 127.5   ,	rescale_offset=UpperCAmelCase   ,	do_normalize=UpperCAmelCase   ,	include_top=UpperCAmelCase   ,	resample=Image.BILINEAR   ,	)
      __A													=      BertTokenizer.from_pretrained('bert-base-uncased'						)
      __A													=      6_4
      __A													=      AlignProcessor(image_processor=UpperCAmelCase   ,	tokenizer=UpperCAmelCase						)
      return processor





def 							snake_case				(		UpperCAmelCase						)->      Any:







      """simple docstring"""
      # EfficientNet image encoder
      __A													=      [v.split('_'						)[0].split('block'						)[1] for v in original_param_names if v.startswith('block'						)]
      __A													=      list(set(UpperCAmelCase						)						)
      __A													=      sorted(UpperCAmelCase						)
      __A													=      len(UpperCAmelCase						)
      __A													=      {b: str(UpperCAmelCase						) for b, i in zip(UpperCAmelCase   ,	range(UpperCAmelCase						)						)}

      __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(1_2						):
            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 							snake_case				(		UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase						)->      Union[str, Any]:







      """simple docstring"""
      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(UpperCAmelCase						).permute(3   ,	2   ,	0   ,	1						)
            elif "embeddings" in key:
                  __A													=      torch.from_numpy(UpperCAmelCase						)
            elif "depthwise_kernel" in key:
                  __A													=      torch.from_numpy(UpperCAmelCase						).permute(2   ,	3   ,	0   ,	1						)
            elif "kernel" in key:
                  __A													=      torch.from_numpy(np.transpose(UpperCAmelCase						)						)
            elif "temperature" in key:
                  __A													=      value
            elif "bn/gamma" or "bn/beta" in key:
                  __A													=      torch.from_numpy(np.transpose(UpperCAmelCase						)						).squeeze()
            else:
                  __A													=      torch.from_numpy(UpperCAmelCase						)

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





@torch.no_grad()
def 							snake_case				(		UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase						)->      Any:







      """simple docstring"""
      __A													=      6_4
      __A													=      Tokenizer(UpperCAmelCase						)
      __A													=      align.Align('efficientnet-b7'   ,	'bert-base'   ,	6_4_0   ,	UpperCAmelCase   ,	tok.get_vocab_size()						)
      original_model.compile()
      original_model.load_weights(UpperCAmelCase						)

      __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(UpperCAmelCase						).eval()
      __A													=      hf_model.state_dict()

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

      # Initialize processor
      __A													=      get_processor()
      __A													=      processor(
          images=prepare_img()   ,	text='A picture of a cat'   ,	padding='max_length'   ,	max_length=6_4   ,	return_tensors='pt'						)

      # HF model inference
      hf_model.eval()
      with torch.no_grad():
            __A													=      hf_model(**UpperCAmelCase						)

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

      # Original model inference
      __A													=      False
      __A													=      EfficientNetImageProcessor(
          do_center_crop=UpperCAmelCase   ,	do_rescale=UpperCAmelCase   ,	do_normalize=UpperCAmelCase   ,	include_top=UpperCAmelCase   ,	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(UpperCAmelCase   ,	training=UpperCAmelCase						)
      __A													=      original_model.text_encoder(UpperCAmelCase   ,	training=UpperCAmelCase						)

      __A													=      tf.nn.la_normalize(UpperCAmelCase   ,	axis=-1						)
      __A													=      tf.nn.la_normalize(UpperCAmelCase   ,	axis=-1						)

      # Check whether original and HF model outputs match  -> np.allclose
      if not np.allclose(UpperCAmelCase   ,	UpperCAmelCase   ,	atol=1e-3						):
            raise ValueError('The predicted image features are not the same.'						)
      if not np.allclose(UpperCAmelCase   ,	UpperCAmelCase   ,	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(UpperCAmelCase						):
                  os.mkdir(UpperCAmelCase						)
            # Save converted model and image processor
            hf_model.save_pretrained(UpperCAmelCase						)
            processor.save_pretrained(UpperCAmelCase						)

      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__		: str  							=							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)








'''simple docstring'''





from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
 if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.27.0")):
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 from ...utils.dummy_torch_and_transformers_objects import (
     AudioLDMPipeline,
 )
else:
 from .pipeline_audioldm import AudioLDMPipeline






