

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCamelCase			       =			logging.get_logger(__name__)

_UpperCamelCase			       =			{
    # pcqm4mv1 now deprecated
    '''graphormer-base''': '''https://huggingface.co/clefourrier/graphormer-base-pcqm4mv2/resolve/main/config.json''',
    # See all Graphormer models at https://huggingface.co/models?filter=graphormer
}



class     lowercase						(       _UpperCamelCase					):



 '''simple docstring'''


 __SCREAMING_SNAKE_CASE												=				"""graphormer"""
 __SCREAMING_SNAKE_CASE												=				["""past_key_values"""]




 def __init__(self       ,       __a = 1       ,       __a = 512 * 9       ,       __a = 512 * 3       ,       __a = 512       ,       __a = 512       ,       __a = 512       ,       __a = 128       ,       __a = 5       ,       __a = 1024       ,       __a = "multi_hop"       ,       __a = 512       ,       __a = False       ,       __a = 12       ,       __a = 768       ,       __a = 768       ,       __a = 32       ,       __a = 0.1       ,       __a = 0.1       ,       __a = 0.0       ,       __a = False       ,       __a = False       ,       __a = False       ,       __a = "gelu"       ,       __a = None       ,       __a = False       ,       __a = 0       ,       __a = False       ,       __a = 0.0       ,       __a = 8       ,       __a = None       ,       __a = None       ,       __a = True       ,       __a = True       ,       __a=0       ,       __a=1       ,       __a=2       ,       **__a       ,       ) ->					List[str]:






  """simple docstring"""





  UpperCAmelCase__							=				num_classes
  UpperCAmelCase__							=				num_atoms
  UpperCAmelCase__							=				num_in_degree
  UpperCAmelCase__							=				num_out_degree
  UpperCAmelCase__							=				num_edges
  UpperCAmelCase__							=				num_spatial
  UpperCAmelCase__							=				num_edge_dis
  UpperCAmelCase__							=				edge_type
  UpperCAmelCase__							=				multi_hop_max_dist
  UpperCAmelCase__							=				spatial_pos_max
  UpperCAmelCase__							=				max_nodes
  UpperCAmelCase__							=				num_hidden_layers
  UpperCAmelCase__							=				embedding_dim
  UpperCAmelCase__							=				embedding_dim
  UpperCAmelCase__							=				ffn_embedding_dim
  UpperCAmelCase__							=				num_attention_heads
  UpperCAmelCase__							=				dropout
  UpperCAmelCase__							=				attention_dropout
  UpperCAmelCase__							=				layerdrop
  UpperCAmelCase__							=				encoder_normalize_before
  UpperCAmelCase__							=				pre_layernorm
  UpperCAmelCase__							=				apply_graphormer_init
  UpperCAmelCase__							=				activation_fn
  UpperCAmelCase__							=				embed_scale
  UpperCAmelCase__							=				freeze_embeddings
  UpperCAmelCase__							=				num_trans_layers_to_freeze
  UpperCAmelCase__							=				share_input_output_embed
  UpperCAmelCase__							=				traceable
  UpperCAmelCase__							=				q_noise
  UpperCAmelCase__							=				qn_block_size

  # These parameters are here for future extensions
  # atm, the model only supports self attention
  UpperCAmelCase__							=				kdim
  UpperCAmelCase__							=				vdim
  UpperCAmelCase__							=				self_attention
  UpperCAmelCase__							=				bias

  super().__init__(
      pad_token_id=__a       ,       bos_token_id=__a       ,       eos_token_id=__a       ,       **__a       ,       )










"""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()
__UpperCamelCase			:		Union[str, Any]		    =   logging.get_logger(__name__)

def 						_SCREAMING_SNAKE_CASE       (_UpperCAmelCase :  Optional[int]       ):
 lowerCAmelCase  							=      tf.image.resize(_UpperCAmelCase   ,			(346, 346)       )
 lowerCAmelCase  							=      tf.image.crop_to_bounding_box(_UpperCAmelCase   ,			(346 - 289) // 2   ,			(346 - 289) // 2   ,			289   ,			289       )
 return image

def 						_SCREAMING_SNAKE_CASE       ():
 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=_UpperCAmelCase   ,			vision_config=_UpperCAmelCase   ,			projection_dim=640       )
 return config

def 						_SCREAMING_SNAKE_CASE       ():
 lowerCAmelCase  							=      'http://images.cocodataset.org/val2017/000000039769.jpg'
 lowerCAmelCase  							=      Image.open(requests.get(_UpperCAmelCase   ,			stream=_UpperCAmelCase       ).raw       )
 return im

def 						_SCREAMING_SNAKE_CASE       ():
 lowerCAmelCase  							=      EfficientNetImageProcessor(
     do_center_crop=_UpperCAmelCase   ,			rescale_factor=1 / 127.5   ,			rescale_offset=_UpperCAmelCase   ,			do_normalize=_UpperCAmelCase   ,			include_top=_UpperCAmelCase   ,			resample=Image.BILINEAR   ,			)
 lowerCAmelCase  							=      BertTokenizer.from_pretrained('bert-base-uncased'       )
 lowerCAmelCase  							=      64
 lowerCAmelCase  							=      AlignProcessor(image_processor=_UpperCAmelCase   ,			tokenizer=_UpperCAmelCase       )
 return processor

def 						_SCREAMING_SNAKE_CASE       (_UpperCAmelCase :  List[str]       ):
 # EfficientNet image encoder
 lowerCAmelCase  							=      [v.split('_'       )[0].split('block'       )[1] for v in original_param_names if v.startswith('block'       )]
 lowerCAmelCase  							=      list(set(_UpperCAmelCase       )       )
 lowerCAmelCase  							=      sorted(_UpperCAmelCase       )
 lowerCAmelCase  							=      len(_UpperCAmelCase       )
 lowerCAmelCase  							=      {b: str(_UpperCAmelCase       ) for b, i in zip(_UpperCAmelCase   ,			range(_UpperCAmelCase       )       )}

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

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

@torch.no_grad()
def 						_SCREAMING_SNAKE_CASE       (_UpperCAmelCase :  str   ,			_UpperCAmelCase :  Tuple   ,			_UpperCAmelCase :  Any   ,			_UpperCAmelCase :  Any       ):
 lowerCAmelCase  							=      64
 lowerCAmelCase  							=      Tokenizer(_UpperCAmelCase       )
 lowerCAmelCase  							=      align.Align('efficientnet-b7'   ,			'bert-base'   ,			640   ,			_UpperCAmelCase   ,			tok.get_vocab_size()       )
 original_model.compile()
 original_model.load_weights(_UpperCAmelCase       )

 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(_UpperCAmelCase       ).eval()
 lowerCAmelCase  							=      hf_model.state_dict()

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

 # 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(**_UpperCAmelCase       )

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

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

 lowerCAmelCase  							=      tf.nn.la_normalize(_UpperCAmelCase   ,			axis=-1       )
 lowerCAmelCase  							=      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			:		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''')

       __UpperCamelCase			:		int		    =   parser.parse_args()
       convert_align_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)





