
'''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												=   logging.get_logger(__name__)



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	str							)       ->	Tuple:
 SCREAMING_SNAKE_CASE  :  str								=		tf.image.resize(snake_case_   ,     (346, 346)							)
 SCREAMING_SNAKE_CASE  :  List[str]								=		tf.image.crop_to_bounding_box(snake_case_   ,     (346 - 289) // 2   ,     (346 - 289) // 2   ,     289   ,     289							)
 return image



def        SCREAMING_SNAKE_CASE_  (	)       ->	int:
 SCREAMING_SNAKE_CASE  :  Any								=		EfficientNetConfig.from_pretrained('google/efficientnet-b7'							)
 SCREAMING_SNAKE_CASE  :  Any								=		289
 SCREAMING_SNAKE_CASE  :  List[Any]								=		640
 SCREAMING_SNAKE_CASE  :  List[Any]								=		{'0': 'LABEL_0', '1': 'LABEL_1'}
 SCREAMING_SNAKE_CASE  :  Tuple								=		{'LABEL_0': 0, 'LABEL_1': 1}
 SCREAMING_SNAKE_CASE  :  Optional[Any]								=		[]

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



def        SCREAMING_SNAKE_CASE_  (	)       ->	Optional[int]:
 SCREAMING_SNAKE_CASE  :  int								=		'http://images.cocodataset.org/val2017/000000039769.jpg'
 SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		Image.open(requests.get(snake_case_   ,     stream=snake_case_							).raw							)
 return im



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



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

 SCREAMING_SNAKE_CASE  :  Tuple								=		[]
 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:
  SCREAMING_SNAKE_CASE  :  int								=		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""")							)

 SCREAMING_SNAKE_CASE  :  List[Any]								=		{}
 for item in rename_keys:
  if item[0] in original_param_names:
   SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		'vision_model.' + item[1]

    # BERT text encoder
 SCREAMING_SNAKE_CASE  :  Any								=		[]
 SCREAMING_SNAKE_CASE  :  Dict								=		'tf_bert_model/bert'
 SCREAMING_SNAKE_CASE  :  int								=		'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:
   SCREAMING_SNAKE_CASE  :  Optional[Any]								=		item[1]
 return key_mapping



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	Union[str, Any]   ,     snake_case_				:	Optional[Any]   ,     snake_case_				:	Union[str, Any]							)       ->	Union[str, Any]:
 list(hf_params.keys()							)

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

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

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



@torch.no_grad()
def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	Union[str, Any]   ,     snake_case_				:	Optional[int]   ,     snake_case_				:	str   ,     snake_case_				:	Any							)       ->	List[Any]:
 SCREAMING_SNAKE_CASE  :  List[Any]								=		64
 SCREAMING_SNAKE_CASE  :  Optional[int]								=		Tokenizer(snake_case_							)
 SCREAMING_SNAKE_CASE  :  Optional[Any]								=		align.Align('efficientnet-b7'   ,     'bert-base'   ,     640   ,     snake_case_   ,     tok.get_vocab_size()							)
 original_model.compile()
 original_model.load_weights(snake_case_							)

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

 # Load HuggingFace model
 SCREAMING_SNAKE_CASE  :  List[Any]								=		get_align_config()
 SCREAMING_SNAKE_CASE  :  List[Any]								=		AlignModel(snake_case_							).eval()
 SCREAMING_SNAKE_CASE  :  Optional[int]								=		hf_model.state_dict()

 # Create src-to-dst parameter name mapping dictionary
 print('Converting parameters...'							)
 SCREAMING_SNAKE_CASE  :  int								=		rename_keys(snake_case_							)
 replace_params(snake_case_   ,     snake_case_   ,     snake_case_							)

 # Initialize processor
 SCREAMING_SNAKE_CASE  :  Optional[int]								=		get_processor()
 SCREAMING_SNAKE_CASE  :  str								=		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():
  SCREAMING_SNAKE_CASE  :  Any								=		hf_model(**snake_case_							)

 SCREAMING_SNAKE_CASE  :  List[str]								=		outputs.image_embeds.detach().numpy()
 SCREAMING_SNAKE_CASE  :  int								=		outputs.text_embeds.detach().numpy()

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

 SCREAMING_SNAKE_CASE  :  Optional[int]								=		original_model.image_encoder(snake_case_   ,     training=snake_case_							)
 SCREAMING_SNAKE_CASE  :  List[Any]								=		original_model.text_encoder(snake_case_   ,     training=snake_case_							)

 SCREAMING_SNAKE_CASE  :  Any								=		tf.nn.la_normalize(snake_case_   ,     axis=-1							)
 SCREAMING_SNAKE_CASE  :  List[str]								=		tf.nn.la_normalize(snake_case_   ,     axis=-1							)

 # Check whether original and HF model outputs match  -> np.allclose
 if not np.allclose(snake_case_   ,     snake_case_   ,     atol=1e-3							):
  raise ValueError('The predicted image features are not the same.'							)
 if not np.allclose(snake_case_   ,     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(snake_case_							):
   os.mkdir(snake_case_							)
  # Save converted model and image processor
  hf_model.save_pretrained(snake_case_							)
  processor.save_pretrained(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__":
   __UpperCAmelCase												=   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												=   parser.parse_args()
   convert_align_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)




'''simple docstring'''

import random
import sys

__UpperCAmelCase												=   'ABCDEFGHIJKLMNOPQRSTUVWXYZ'



def        SCREAMING_SNAKE_CASE_  (	)       ->	None:
 SCREAMING_SNAKE_CASE  :  Dict								=		input('Enter message: '							)
 SCREAMING_SNAKE_CASE  :  Dict								=		'LFWOAYUISVKMNXPBDCRJTQEGHZ'
 SCREAMING_SNAKE_CASE  :  Tuple								=		input('Encrypt/Decrypt [e/d]: '							)

 check_valid_key(snake_case_							)

 if resp.lower().startswith('e'							):
  SCREAMING_SNAKE_CASE  :  str								=		'encrypt'
  SCREAMING_SNAKE_CASE  :  Optional[Any]								=		encrypt_message(snake_case_   ,     snake_case_							)
 elif resp.lower().startswith('d'							):
  SCREAMING_SNAKE_CASE  :  List[Any]								=		'decrypt'
  SCREAMING_SNAKE_CASE  :  List[str]								=		decrypt_message(snake_case_   ,     snake_case_							)

 print(f"""\n{mode.title()}ion: \n{translated}"""							)



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	str							)       ->	None:
 SCREAMING_SNAKE_CASE  :  int								=		list(snake_case_							)
 SCREAMING_SNAKE_CASE  :  Optional[Any]								=		list(snake_case_							)
 key_list.sort()
 letters_list.sort()

 if key_list != letters_list:
  sys.exit('Error in the key or symbol set.'							)



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	str   ,     snake_case_				:	str							)       ->	str:
 return translate_message(snake_case_   ,     snake_case_   ,     'encrypt'							)



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	str   ,     snake_case_				:	str							)       ->	str:
 return translate_message(snake_case_   ,     snake_case_   ,     'decrypt'							)



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	str   ,     snake_case_				:	str   ,     snake_case_				:	str							)       ->	str:
 SCREAMING_SNAKE_CASE  :  Any								=		''
 SCREAMING_SNAKE_CASE  :  Optional[Any]								=		LETTERS
 SCREAMING_SNAKE_CASE  :  List[Any]								=		key

 if mode == "decrypt":
  SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE  :  List[Any]								=		chars_b, chars_a

 for symbol in message:
  if symbol.upper() in chars_a:
   SCREAMING_SNAKE_CASE  :  Optional[Any]								=		chars_a.find(symbol.upper()							)
   if symbol.isupper():
    translated += chars_b[sym_index].upper()
   else:
    translated += chars_b[sym_index].lower()
  else:
   translated += symbol

 return translated



def        SCREAMING_SNAKE_CASE_  (	)       ->	str:
 SCREAMING_SNAKE_CASE  :  Dict								=		list(snake_case_							)
 random.shuffle(snake_case_							)
 return "".join(snake_case_							)


if __name__ == "__main__":
   main()


