





import argparse
import os
import re

import tensorflow as tf
import torch

from transformers import BertConfig, BertModel
from transformers.utils import logging


logging.set_verbosity_info()
snake_case_           =      logging.get_logger(__name__)






def lowerCamelCase__   (							snake_case_     :  List[Any]	,				snake_case_     :  Dict	,				snake_case_     :  List[Any]	)				->    List[str]:
   __snake_case								=       os.path.abspath(snake_case_	)
   logger.info(f"""Converting TensorFlow checkpoint from {tf_path}"""	)
   # Load weights from TF model
   __snake_case								=       tf.train.list_variables(snake_case_	)
   __snake_case								=       []
   __snake_case								=       []
   __snake_case								=       []
   for full_name, shape in init_vars:
      # logger.info(f"Loading TF weight {name} with shape {shape}")
      __snake_case								=       full_name.split('''/'''	)
      if full_name == "_CHECKPOINTABLE_OBJECT_GRAPH" or name[0] in ["global_step", "save_counter"]:
         logger.info(f"""Skipping non-model layer {full_name}"""	)
         continue
      if "optimizer" in full_name:
         logger.info(f"""Skipping optimization layer {full_name}"""	)
         continue
      if name[0] == "model":
         # ignore initial 'model'
         __snake_case								=       name[1:]
      # figure out how many levels deep the name is
      __snake_case								=       0
      for _name in name:
         if _name.startswith('''layer_with_weights'''	):
            depth += 1
         else:
            break
      layer_depth.append(snake_case_	)
      # read data
      __snake_case								=       tf.train.load_variable(snake_case_	,				snake_case_	)
      names.append('''/'''.join(snake_case_	)	)
      arrays.append(snake_case_	)
   logger.info(f"""Read a total of {len(snake_case_	):,} layers"""	)

   # Sanity check
   if len(set(snake_case_	)	) != 1:
      raise ValueError(f"""Found layer names with different depths (layer depth {list(set(snake_case_	)	)})"""	)
   __snake_case								=       list(set(snake_case_	)	)[0]
   if layer_depth != 1:
      raise ValueError(
          '''The model contains more than just the embedding/encoder layers. This script does not handle MLM/NSP'''
          ''' heads.'''	)

   # convert layers
   logger.info('''Converting weights...'''	)
   for full_name, array in zip(snake_case_	,				snake_case_	):
      __snake_case								=       full_name.split('''/'''	)
      __snake_case								=       model
      __snake_case								=       []
      for i, m_name in enumerate(snake_case_	):
         if m_name == ".ATTRIBUTES":
            # variable names end with .ATTRIBUTES/VARIABLE_VALUE
            break
         if m_name.startswith('''layer_with_weights'''	):
            __snake_case								=       int(m_name.split('''-'''	)[-1]	)
            if layer_num <= 2:
               # embedding layers
               # layer_num 0: word_embeddings
               # layer_num 1: position_embeddings
               # layer_num 2: token_type_embeddings
               continue
            elif layer_num == 3:
               # embedding LayerNorm
               trace.extend(['''embeddings''', '''LayerNorm''']	)
               __snake_case								=       getattr(snake_case_	,				'''embeddings'''	)
               __snake_case								=       getattr(snake_case_	,				'''LayerNorm'''	)
            elif layer_num > 3 and layer_num < config.num_hidden_layers + 4:
               # encoder layers
               trace.extend(['''encoder''', '''layer''', str(layer_num - 4	)]	)
               __snake_case								=       getattr(snake_case_	,				'''encoder'''	)
               __snake_case								=       getattr(snake_case_	,				'''layer'''	)
               __snake_case								=       pointer[layer_num - 4]
            elif layer_num == config.num_hidden_layers + 4:
               # pooler layer
               trace.extend(['''pooler''', '''dense''']	)
               __snake_case								=       getattr(snake_case_	,				'''pooler'''	)
               __snake_case								=       getattr(snake_case_	,				'''dense'''	)
         elif m_name == "embeddings":
            trace.append('''embeddings'''	)
            __snake_case								=       getattr(snake_case_	,				'''embeddings'''	)
            if layer_num == 0:
               trace.append('''word_embeddings'''	)
               __snake_case								=       getattr(snake_case_	,				'''word_embeddings'''	)
            elif layer_num == 1:
               trace.append('''position_embeddings'''	)
               __snake_case								=       getattr(snake_case_	,				'''position_embeddings'''	)
            elif layer_num == 2:
               trace.append('''token_type_embeddings'''	)
               __snake_case								=       getattr(snake_case_	,				'''token_type_embeddings'''	)
            else:
               raise ValueError(f"""Unknown embedding layer with name {full_name}"""	)
            trace.append('''weight'''	)
            __snake_case								=       getattr(snake_case_	,				'''weight'''	)
         elif m_name == "_attention_layer":
            # self-attention layer
            trace.extend(['''attention''', '''self''']	)
            __snake_case								=       getattr(snake_case_	,				'''attention'''	)
            __snake_case								=       getattr(snake_case_	,				'''self'''	)
         elif m_name == "_attention_layer_norm":
            # output attention norm
            trace.extend(['''attention''', '''output''', '''LayerNorm''']	)
            __snake_case								=       getattr(snake_case_	,				'''attention'''	)
            __snake_case								=       getattr(snake_case_	,				'''output'''	)
            __snake_case								=       getattr(snake_case_	,				'''LayerNorm'''	)
         elif m_name == "_attention_output_dense":
            # output attention dense
            trace.extend(['''attention''', '''output''', '''dense''']	)
            __snake_case								=       getattr(snake_case_	,				'''attention'''	)
            __snake_case								=       getattr(snake_case_	,				'''output'''	)
            __snake_case								=       getattr(snake_case_	,				'''dense'''	)
         elif m_name == "_output_dense":
            # output dense
            trace.extend(['''output''', '''dense''']	)
            __snake_case								=       getattr(snake_case_	,				'''output'''	)
            __snake_case								=       getattr(snake_case_	,				'''dense'''	)
         elif m_name == "_output_layer_norm":
            # output dense
            trace.extend(['''output''', '''LayerNorm''']	)
            __snake_case								=       getattr(snake_case_	,				'''output'''	)
            __snake_case								=       getattr(snake_case_	,				'''LayerNorm'''	)
         elif m_name == "_key_dense":
            # attention key
            trace.append('''key'''	)
            __snake_case								=       getattr(snake_case_	,				'''key'''	)
         elif m_name == "_query_dense":
            # attention query
            trace.append('''query'''	)
            __snake_case								=       getattr(snake_case_	,				'''query'''	)
         elif m_name == "_value_dense":
            # attention value
            trace.append('''value'''	)
            __snake_case								=       getattr(snake_case_	,				'''value'''	)
         elif m_name == "_intermediate_dense":
            # attention intermediate dense
            trace.extend(['''intermediate''', '''dense''']	)
            __snake_case								=       getattr(snake_case_	,				'''intermediate'''	)
            __snake_case								=       getattr(snake_case_	,				'''dense'''	)
         elif m_name == "_output_layer_norm":
            # output layer norm
            trace.append('''output'''	)
            __snake_case								=       getattr(snake_case_	,				'''output'''	)
         # weights & biases
         elif m_name in ["bias", "beta"]:
            trace.append('''bias'''	)
            __snake_case								=       getattr(snake_case_	,				'''bias'''	)
         elif m_name in ["kernel", "gamma"]:
            trace.append('''weight'''	)
            __snake_case								=       getattr(snake_case_	,				'''weight'''	)
         else:
            logger.warning(f"""Ignored {m_name}"""	)
        # for certain layers reshape is necessary
      __snake_case								=       '''.'''.join(snake_case_	)
      if re.match(R'''(\S+)\.attention\.self\.(key|value|query)\.(bias|weight)'''	,				snake_case_	) or re.match(
          R'''(\S+)\.attention\.output\.dense\.weight'''	,				snake_case_	):
         __snake_case								=       array.reshape(pointer.data.shape	)
      if "kernel" in full_name:
         __snake_case								=       array.transpose()
      if pointer.shape == array.shape:
         __snake_case								=       torch.from_numpy(snake_case_	)
      else:
         raise ValueError(
             f"""Shape mismatch in layer {full_name}: Model expects shape {pointer.shape} but layer contains shape:"""
             f""" {array.shape}"""	)
      logger.info(f"""Successfully set variable {full_name} to PyTorch layer {trace}"""	)
   return model






def lowerCamelCase__   (							snake_case_     :  Any	,				snake_case_     :  Optional[int]	,				snake_case_     :  Optional[Any]	)				->    Tuple:
   # Instantiate model
   logger.info(f"""Loading model based on config from {config_path}..."""	)
   __snake_case								=       BertConfig.from_json_file(snake_case_	)
   __snake_case								=       BertModel(snake_case_	)

   # Load weights from checkpoint
   logger.info(f"""Loading weights from checkpoint {tf_checkpoint_path}..."""	)
   load_tfa_weights_in_bert(snake_case_	,				snake_case_	,				snake_case_	)

   # Save pytorch-model
   logger.info(f"""Saving PyTorch model to {pytorch_dump_path}..."""	)
   torch.save(model.state_dict()	,				snake_case_	)


if __name__ == "__main__":
    snake_case_           =      argparse.ArgumentParser()
    parser.add_argument(
        '--tf_checkpoint_path', type=str, required=True, help='Path to the TensorFlow 2.x checkpoint path.'
    )
    parser.add_argument(
        '--bert_config_file',
        type=str,
        required=True,
        help='The config json file corresponding to the BERT model. This specifies the model architecture.',
    )
    parser.add_argument(
        '--pytorch_dump_path',
        type=str,
        required=True,
        help='Path to the output PyTorch model (must include filename).',
    )
    snake_case_           =      parser.parse_args()
    convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)








from __future__ import annotations

import collections
import pprint
from pathlib import Path






def lowerCamelCase__   (							snake_case_     :  str	)				->    str:
   return "".join(sorted(snake_case_	)	)






def lowerCamelCase__   (							snake_case_     :  str	)				->    list[str]:
   return word_by_signature[signature(snake_case_	)]


snake_case_           =      Path(__file__).parent.joinpath('words.txt').read_text(encoding='utf-8')
snake_case_           =      sorted({word.strip().lower() for word in data.splitlines()})

snake_case_           =      collections.defaultdict(list)
for word in word_list:
    word_by_signature[signature(word)].append(word)

if __name__ == "__main__":
    snake_case_           =      {word: anagram(word) for word in word_list if len(anagram(word)) > 1}

    with open('anagrams.txt', 'w') as file:
        file.write('all_anagrams = \n ')
        file.write(pprint.pformat(all_anagrams))

