




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


def 		_lowerCamelCase					(       UpperCamelCase				)      ->		Tuple:





  """simple docstring"""





  _lowercase:			Any      =     tf.image.resize(UpperCamelCase       ,  (346, 346)				)
  _lowercase:			Any      =     tf.image.crop_to_bounding_box(UpperCamelCase       ,  (346 - 289) // 2       ,  (346 - 289) // 2       ,  289       ,  289				)
  return image


def 		_lowerCamelCase					(       )      ->		Any:





  """simple docstring"""





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

  _lowercase:			Optional[Any]      =     BertConfig()
  _lowercase:			Tuple      =     AlignConfig.from_text_vision_configs(
      text_config=UpperCamelCase       ,  vision_config=UpperCamelCase       ,  projection_dim=640				)
  return config


def 		_lowerCamelCase					(       )      ->		Optional[Any]:





  """simple docstring"""





  _lowercase:			Any      =     'http://images.cocodataset.org/val2017/000000039769.jpg'
  _lowercase:			int      =     Image.open(requests.get(UpperCamelCase       ,  stream=UpperCamelCase				).raw				)
  return im


def 		_lowerCamelCase					(       )      ->		Optional[int]:





  """simple docstring"""





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


def 		_lowerCamelCase					(       UpperCamelCase				)      ->		str:





  """simple docstring"""





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

  _lowercase:			Dict      =     []
  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:
    _lowercase:			List[str]      =     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')				)

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

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


def 		_lowerCamelCase					(       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

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

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


@torch.no_grad()
def 		_lowerCamelCase					(       UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase				)      ->		Any:





  """simple docstring"""





  _lowercase:			int      =     64
  _lowercase:			List[Any]      =     Tokenizer(UpperCamelCase				)
  _lowercase:			Tuple      =     align.Align('efficientnet-b7'       ,  'bert-base'       ,  640       ,  UpperCamelCase       ,  tok.get_vocab_size()				)
  original_model.compile()
  original_model.load_weights(UpperCamelCase				)

  _lowercase:			str      =     original_model.trainable_variables
  _lowercase:			Union[str, Any]      =     original_model.non_trainable_variables
  _lowercase:			List[Any]      =     {param.name: param.numpy() for param in tf_params}
  for param in tf_non_train_params:
    _lowercase:			Dict      =     param.numpy()
  _lowercase:			List[str]      =     list(tf_params.keys()				)

  # Load HuggingFace model
  _lowercase:			Dict      =     get_align_config()
  _lowercase:			List[str]      =     AlignModel(UpperCamelCase				).eval()
  _lowercase:			Union[str, Any]      =     hf_model.state_dict()

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

  # Initialize processor
  _lowercase:			List[str]      =     get_processor()
  _lowercase:			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():
    _lowercase:			Any      =     hf_model(**UpperCamelCase				)

  _lowercase:			List[str]      =     outputs.image_embeds.detach().numpy()
  _lowercase:			List[Any]      =     outputs.text_embeds.detach().numpy()

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

  _lowercase:			Dict      =     original_model.image_encoder(UpperCamelCase       ,  training=UpperCamelCase				)
  _lowercase:			str      =     original_model.text_encoder(UpperCamelCase       ,  training=UpperCamelCase				)

  _lowercase:			Optional[int]      =     tf.nn.la_normalize(UpperCamelCase       ,  axis=-1				)
  _lowercase:			Any      =     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__":
   UpperCAmelCase__    :							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')

   UpperCAmelCase__    :							Optional[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 ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase       :     Any			=    logging.get_logger(__name__)

UpperCamelCase       :     Optional[int]			=    {
    """openbmb/cpm-ant-10b""": """https://huggingface.co/openbmb/cpm-ant-10b/blob/main/config.json"""
    # See all CPMAnt models at https://huggingface.co/models?filter=cpmant
}



class   UpperCamelCase					(       a_       ):







    """simple docstring"""





    A     :    List[str]				=			"cpmant"





    def __init__(					self     :						Tuple							,							UpperCAmelCase_     :						int = 3_0_7_2_0							,							UpperCAmelCase_     :						int = 4_0_9_6							,							UpperCAmelCase_     :						int = 3_2							,							UpperCAmelCase_     :						int = 1_2_8							,							UpperCAmelCase_     :						int = 1_0_2_4_0							,							UpperCAmelCase_     :						int = 4_8							,							UpperCAmelCase_     :						int = 0.0							,							UpperCAmelCase_     :						int = 5_1_2							,							UpperCAmelCase_     :						int = 2_0_4_8							,							UpperCAmelCase_     :						int = 1e-6							,							UpperCAmelCase_     :						float = 1.0							,							UpperCAmelCase_     :						int = 3_2							,							UpperCAmelCase_     :						int = 3_2							,							UpperCAmelCase_     :						int = 3_2							,							UpperCAmelCase_     :						bool = True							,							**UpperCAmelCase_     :						Tuple							,							):


         """simple docstring"""







         super().__init__(**UpperCAmelCase_)
         a					:							Union[str, Any]							= prompt_types
         a					:							List[str]							= prompt_length
         a					:							List[Any]							= segment_types
         a					:							Any							= hidden_size
         a					:							Optional[int]							= num_attention_heads
         a					:							Union[str, Any]							= dim_head
         a					:							int							= dim_ff
         a					:							Union[str, Any]							= num_hidden_layers
         a					:							List[Any]							= position_bias_num_buckets
         a					:							Optional[int]							= position_bias_max_distance
         a					:							str							= dropout_p
         a					:							List[str]							= eps
         a					:							Dict							= use_cache
         a					:							Tuple							= vocab_size
         a					:							Optional[Any]							= init_std

