






"""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__		(   _SCREAMING_SNAKE_CASE			)    ->List[Any]:
 UpperCAmelCase__ 						=				tf.image.resize(_SCREAMING_SNAKE_CASE       ,	(3_4_6, 3_4_6)			)
 UpperCAmelCase__ 						=				tf.image.crop_to_bounding_box(_SCREAMING_SNAKE_CASE       ,	(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]:
 UpperCAmelCase__ 						=				EfficientNetConfig.from_pretrained("""google/efficientnet-b7"""			)
 UpperCAmelCase__ 						=				2_8_9
 UpperCAmelCase__ 						=				6_4_0
 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=_SCREAMING_SNAKE_CASE       ,	vision_config=_SCREAMING_SNAKE_CASE       ,	projection_dim=6_4_0			)
 return config




def       snake_case__		(   )    ->Dict:
 UpperCAmelCase__ 						=				"""http://images.cocodataset.org/val2017/000000039769.jpg"""
 UpperCAmelCase__ 						=				Image.open(requests.get(_SCREAMING_SNAKE_CASE       ,	stream=_SCREAMING_SNAKE_CASE			).raw			)
 return im




def       snake_case__		(   )    ->Optional[int]:
 UpperCAmelCase__ 						=				EfficientNetImageProcessor(
     do_center_crop=_SCREAMING_SNAKE_CASE       ,	rescale_factor=1 / 127.5       ,	rescale_offset=_SCREAMING_SNAKE_CASE       ,	do_normalize=_SCREAMING_SNAKE_CASE       ,	include_top=_SCREAMING_SNAKE_CASE       ,	resample=Image.BILINEAR       ,	)
 UpperCAmelCase__ 						=				BertTokenizer.from_pretrained("""bert-base-uncased"""			)
 UpperCAmelCase__ 						=				6_4
 UpperCAmelCase__ 						=				AlignProcessor(image_processor=_SCREAMING_SNAKE_CASE       ,	tokenizer=_SCREAMING_SNAKE_CASE			)
 return processor




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

 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(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:
   UpperCAmelCase__ 						=				item[1]
 return key_mapping




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

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




@torch.no_grad()
def       snake_case__		(   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			)    ->int:
 UpperCAmelCase__ 						=				6_4
 UpperCAmelCase__ 						=				Tokenizer(_SCREAMING_SNAKE_CASE			)
 UpperCAmelCase__ 						=				align.Align("""efficientnet-b7"""       ,	"""bert-base"""       ,	6_4_0       ,	_SCREAMING_SNAKE_CASE       ,	tok.get_vocab_size()			)
 original_model.compile()
 original_model.load_weights(_SCREAMING_SNAKE_CASE			)

 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(_SCREAMING_SNAKE_CASE			).eval()
 UpperCAmelCase__ 						=				hf_model.state_dict()

 # Create src-to-dst parameter name mapping dictionary
 print("""Converting parameters..."""			)
 UpperCAmelCase__ 						=				rename_keys(_SCREAMING_SNAKE_CASE			)
 replace_params(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			)

 # Initialize processor
 UpperCAmelCase__ 						=				get_processor()
 UpperCAmelCase__ 						=				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():
  UpperCAmelCase__ 						=				hf_model(**_SCREAMING_SNAKE_CASE			)

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

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

 UpperCAmelCase__ 						=				tf.nn.la_normalize(_SCREAMING_SNAKE_CASE       ,	axis=-1			)
 UpperCAmelCase__ 						=				tf.nn.la_normalize(_SCREAMING_SNAKE_CASE       ,	axis=-1			)

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

 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  :						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''')

 a  :						List[str]					       =							parser.parse_args()
 convert_align_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)











"""simple docstring"""



import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mpnet import MPNetTokenizer


a  :						List[Any]					       =							logging.get_logger(__name__)

a  :						List[Any]					       =							{'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

a  :						Any					       =							{
    '''vocab_file''': {
        '''microsoft/mpnet-base''': '''https://huggingface.co/microsoft/mpnet-base/resolve/main/vocab.txt''',
    },
    '''tokenizer_file''': {
        '''microsoft/mpnet-base''': '''https://huggingface.co/microsoft/mpnet-base/resolve/main/tokenizer.json''',
    },
}

a  :						str					       =							{
    '''microsoft/mpnet-base''': 512,
}

a  :						Union[str, Any]					       =							{
    '''microsoft/mpnet-base''': {'''do_lower_case''': True},
}


class 	_UpperCamelCase ( __UpperCamelCase							):



    '''simple docstring'''


    __lowercase    :   Optional[Any]											=						VOCAB_FILES_NAMES
    __lowercase    :   Optional[int]											=						PRETRAINED_VOCAB_FILES_MAP
    __lowercase    :   Dict											=						PRETRAINED_INIT_CONFIGURATION
    __lowercase    :   Optional[Any]											=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __lowercase    :   Dict											=						MPNetTokenizer
    __lowercase    :   Dict											=						['input_ids', 'attention_mask']


    def __init__(      self   ,			__lowercase=None   ,			__lowercase=None   ,			__lowercase=True   ,			__lowercase="<s>"   ,			__lowercase="</s>"   ,			__lowercase="</s>"   ,			__lowercase="<s>"   ,			__lowercase="[UNK]"   ,			__lowercase="<pad>"   ,			__lowercase="<mask>"   ,			__lowercase=True   ,			__lowercase=None   ,			**__lowercase   ,			):
     super().__init__(
         __lowercase   ,			tokenizer_file=__lowercase   ,			do_lower_case=__lowercase   ,			bos_token=__lowercase   ,			eos_token=__lowercase   ,			sep_token=__lowercase   ,			cls_token=__lowercase   ,			unk_token=__lowercase   ,			pad_token=__lowercase   ,			mask_token=__lowercase   ,			tokenize_chinese_chars=__lowercase   ,			strip_accents=__lowercase   ,			**__lowercase   ,			)

     UpperCAmelCase__ 						=				json.loads(self.backend_tokenizer.normalizer.__getstate__()			)
     if (
         pre_tok_state.get("""lowercase"""   ,			__lowercase			) != do_lower_case
         or pre_tok_state.get("""strip_accents"""   ,			__lowercase			) != strip_accents
     ):
      UpperCAmelCase__ 						=				getattr(__lowercase   ,			pre_tok_state.pop("""type"""			)			)
      UpperCAmelCase__ 						=				do_lower_case
      UpperCAmelCase__ 						=				strip_accents
      UpperCAmelCase__ 						=				pre_tok_class(**__lowercase			)

     UpperCAmelCase__ 						=				do_lower_case


    @property
    def 		A__      (      self			):
     if self._mask_token is None:
      if self.verbose:
       logger.error("""Using mask_token, but it is not set yet."""			)
      return None
     return str(self._mask_token			)


    @mask_token.setter
    def 		A__      (      self   ,			__lowercase			):
     UpperCAmelCase__ 						=				AddedToken(__lowercase   ,			lstrip=__lowercase   ,			rstrip=__lowercase			) if isinstance(__lowercase   ,			__lowercase			) else value
     UpperCAmelCase__ 						=				value


    def 		A__      (      self   ,			__lowercase   ,			__lowercase=None			):
     UpperCAmelCase__ 						=				[self.bos_token_id] + token_ids_a + [self.eos_token_id]
     if token_ids_a is None:
      return output

     return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]


    def 		A__      (      self   ,			__lowercase   ,			__lowercase = None			):
     UpperCAmelCase__ 						=				[self.sep_token_id]
     UpperCAmelCase__ 						=				[self.cls_token_id]

     if token_ids_a is None:
      return len(cls + token_ids_a + sep			) * [0]
     return len(cls + token_ids_a + sep + sep + token_ids_a + sep			) * [0]






    def 		A__      (      self   ,			__lowercase   ,			__lowercase = None			):
     UpperCAmelCase__ 						=				self._tokenizer.model.save(__lowercase   ,			name=__lowercase			)
     return tuple(__lowercase			)



