





from __future__ import annotations

import random
from collections.abc import Iterable







class     lowerCamelCase:
     '''simple docstring'''


     def __init__(						self							,    snake_case_       ):
         _A    =       {literal: None for literal in literals}




     def __str__(						self       ):
         return "{" + " , ".join(self.literals       ) + "}"




     def __len__(						self       ):
         return len(self.literals       )




     def 					lowerCAmelCase__		(						self							,    snake_case_       ):
         for literal in self.literals:
             _A    =       literal[:2]
             if symbol in model:
                 _A    =       model[symbol]
             else:
                 continue
             if value is not None:
                 # Complement assignment if literal is in complemented form
                 if literal.endswith('\''       ):
                     _A    =       not value
             _A    =       value




     def 					lowerCAmelCase__		(						self							,    snake_case_       ):
         for literal in self.literals:
             _A    =       literal.rstrip('\''       ) if literal.endswith('\''       ) else literal + '\''
             if symbol in self.literals:
                 return True

         self.assign(snake_case_       )
         for value in self.literals.values():
             if value in (True, None):
                 return value
         return any(self.literals.values()       )







class     lowerCamelCase:
     '''simple docstring'''


     def __init__(						self							,    snake_case_       ):
         _A    =       list(snake_case_       )




     def __str__(						self       ):
         return "{" + " , ".join(str(snake_case_       ) for clause in self.clauses       ) + "}"





def 							__lowerCAmelCase(					)       ->   Clause:




    """simple docstring"""




    _A    =       []
    _A    =       random.randint(1       ,    5							)
    _A    =       'A'
    _A    =       0
    while i < no_of_literals:
        _A    =       random.randint(1       ,    5							)
        _A    =       base_var + str(_SCREAMING_SNAKE_CASE							)
        _A    =       random.randint(0       ,    1							)
        if var_complement == 1:
            var_name += "'"
        if var_name in literals:
            i -= 1
        else:
            literals.append(_SCREAMING_SNAKE_CASE							)
        i += 1
    return Clause(_SCREAMING_SNAKE_CASE							)





def 							__lowerCAmelCase(					)       ->   Formula:




    """simple docstring"""




    _A    =       set()
    _A    =       random.randint(1       ,    10							)
    while len(_SCREAMING_SNAKE_CASE							) < no_of_clauses:
        clauses.add(generate_clause()							)
    return Formula(_SCREAMING_SNAKE_CASE							)





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE							)       ->   tuple[list[Clause], list[str]]:




    """simple docstring"""




    _A    =       formula.clauses
    _A    =       []
    for clause in formula.clauses:
        for literal in clause.literals:
            _A    =       literal[:2]
            if symbol not in symbols_set:
                symbols_set.append(_SCREAMING_SNAKE_CASE							)
    return clauses, symbols_set





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)       ->   tuple[list[str], dict[str, bool | None]]:




    """simple docstring"""




    _A    =       []
    _A    =       {}
    _A    =       []

    for clause in clauses:
        if clause.evaluate(_SCREAMING_SNAKE_CASE							):
            continue
        for literal in clause.literals:
            literals.append(_SCREAMING_SNAKE_CASE							)

    for s in symbols:
        _A    =       s + '\''
        if (s in literals and sym not in literals) or (
            s not in literals and sym in literals
        ):
            pure_symbols.append(_SCREAMING_SNAKE_CASE							)
    for p in pure_symbols:
        _A    =       None
    for s in pure_symbols:
        _A    =       s + '\''
        if s in literals:
            _A    =       True
        elif sym in literals:
            _A    =       False
    return pure_symbols, assignment





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)       ->   tuple[list[str], dict[str, bool | None]]:




    """simple docstring"""




    _A    =       []
    for clause in clauses:
        if len(_SCREAMING_SNAKE_CASE							) == 1:
            unit_symbols.append(list(clause.literals.keys()							)[0]							)
        else:
            _A, _A    =       0, 0
            for literal, value in clause.literals.items():
                if value is False:
                    f_count += 1
                elif value is None:
                    _A    =       literal
                    n_count += 1
            if f_count == len(_SCREAMING_SNAKE_CASE							) - 1 and n_count == 1:
                unit_symbols.append(_SCREAMING_SNAKE_CASE							)
    _A    =       {}
    for i in unit_symbols:
        _A    =       i[:2]
        _A    =       len(_SCREAMING_SNAKE_CASE							) == 2
    _A    =       [i[:2] for i in unit_symbols]

    return unit_symbols, assignment





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)       ->   tuple[bool | None, dict[str, bool | None] | None]:




    """simple docstring"""




    _A    =       True
    for clause in clauses:
        _A    =       clause.evaluate(_SCREAMING_SNAKE_CASE							)
        if clause_check is False:
            return False, None
        elif clause_check is None:
            _A    =       False
            continue

    if check_clause_all_true:
        return True, model

    try:
        _A, _A    =       find_pure_symbols(_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)
    except RecursionError:
        print('raises a RecursionError and is'							)
        return None, {}
    _A    =       None
    if len(_SCREAMING_SNAKE_CASE							) > 0:
        _A, _A    =       pure_symbols[0], assignment[pure_symbols[0]]

    if p:
        _A    =       model
        _A    =       value
        _A    =       list(_SCREAMING_SNAKE_CASE							)
        if p in tmp_symbols:
            tmp_symbols.remove(_SCREAMING_SNAKE_CASE							)
        return dpll_algorithm(_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)

    _A, _A    =       find_unit_clauses(_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)
    _A    =       None
    if len(_SCREAMING_SNAKE_CASE							) > 0:
        _A, _A    =       unit_symbols[0], assignment[unit_symbols[0]]
    if p:
        _A    =       model
        _A    =       value
        _A    =       list(_SCREAMING_SNAKE_CASE							)
        if p in tmp_symbols:
            tmp_symbols.remove(_SCREAMING_SNAKE_CASE							)
        return dpll_algorithm(_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)
    _A    =       symbols[0]
    _A    =       symbols[1:]
    _A, _A    =       model, model
    _A, _A    =       True, False

    return dpll_algorithm(_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							) or dpll_algorithm(_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 __A       : Union[str, Any]						    = generate_formula()
 print(f"The formula {formula} is", end=" ")

 __A    ,	__A       : int						    = generate_parameters(formula)
 __A    ,	__A       : Optional[int]						    = dpll_algorithm(clauses, symbols, {})

 if solution:
  print(f"satisfiable with the assignment {model}.")
 else:
  print("not satisfiable.")












import argparse

import torch
from flax.training.checkpoints import restore_checkpoint

from transformers import FNetConfig, FNetForPreTraining
from transformers.utils import logging


logging.set_verbosity_info()





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)       ->   List[str]:




    """simple docstring"""




    _A    =       FNetConfig.from_json_file(_SCREAMING_SNAKE_CASE							)
    print(F"Building PyTorch model from configuration: {config}"							)
    _A    =       FNetForPreTraining(_SCREAMING_SNAKE_CASE							)

    _A    =       restore_checkpoint(_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)
    _A    =       checkpoint_dict['target']

    # Embeddings
    # Position IDs
    _A    =       fnet_pretraining_model.state_dict()

    _A    =       state_dict['fnet.embeddings.position_ids']
    _A    =       {'fnet.embeddings.position_ids': position_ids}
    # Embedding Layers
    _A    =       torch.tensor(
        pretrained_model_params['encoder']['embedder']['word']['embedding']							)
    _A    =       torch.tensor(
        pretrained_model_params['encoder']['embedder']['position']['embedding'][0]							)
    _A    =       torch.tensor(
        pretrained_model_params['encoder']['embedder']['type']['embedding']							)
    _A    =       torch.tensor(
        pretrained_model_params['encoder']['embedder']['hidden_mapping_in']['kernel']							).T
    _A    =       torch.tensor(
        pretrained_model_params['encoder']['embedder']['hidden_mapping_in']['bias']							)
    _A    =       torch.tensor(
        pretrained_model_params['encoder']['embedder']['layer_norm']['scale']							)
    _A    =       torch.tensor(
        pretrained_model_params['encoder']['embedder']['layer_norm']['bias']							)

    # Encoder Layers
    for layer in range(config.num_hidden_layers							):
        _A    =       torch.tensor(
            pretrained_model_params['encoder'][F"encoder_{layer}"]['mixing_layer_norm']['scale']							)
        _A    =       torch.tensor(
            pretrained_model_params['encoder'][F"encoder_{layer}"]['mixing_layer_norm']['bias']							)

        _A    =       torch.tensor(
            pretrained_model_params['encoder'][F"feed_forward_{layer}"]['intermediate']['kernel']							).T
        _A    =       torch.tensor(
            pretrained_model_params['encoder'][F"feed_forward_{layer}"]['intermediate']['bias']							)

        _A    =       torch.tensor(
            pretrained_model_params['encoder'][F"feed_forward_{layer}"]['output']['kernel']							).T
        _A    =       torch.tensor(
            pretrained_model_params['encoder'][F"feed_forward_{layer}"]['output']['bias']							)

        _A    =       torch.tensor(
            pretrained_model_params['encoder'][F"encoder_{layer}"]['output_layer_norm']['scale']							)
        _A    =       torch.tensor(
            pretrained_model_params['encoder'][F"encoder_{layer}"]['output_layer_norm']['bias']							)

    # Pooler Layers
    _A    =       torch.tensor(pretrained_model_params['encoder']['pooler']['kernel']							).T
    _A    =       torch.tensor(pretrained_model_params['encoder']['pooler']['bias']							)

    # Masked LM Layers
    _A    =       torch.tensor(
        pretrained_model_params['predictions_dense']['kernel']							).T
    _A    =       torch.tensor(
        pretrained_model_params['predictions_dense']['bias']							)
    _A    =       torch.tensor(
        pretrained_model_params['predictions_layer_norm']['scale']							)
    _A    =       torch.tensor(
        pretrained_model_params['predictions_layer_norm']['bias']							)
    _A    =       torch.tensor(
        pretrained_model_params['encoder']['embedder']['word']['embedding']							)
    _A    =       torch.tensor(
        pretrained_model_params['predictions_output']['output_bias']							)
    _A    =       torch.tensor(pretrained_model_params['predictions_output']['output_bias']							)

    # Seq Relationship Layers
    _A    =       torch.tensor(
        pretrained_model_params['classification']['output_kernel']							)
    _A    =       torch.tensor(
        pretrained_model_params['classification']['output_bias']							)

    # Load State Dict
    fnet_pretraining_model.load_state_dict(_SCREAMING_SNAKE_CASE							)

    # Save PreTrained
    print(F"Saving pretrained model to {save_path}"							)
    fnet_pretraining_model.save_pretrained(_SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
 __A       : str						    = argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--flax_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
 )
 parser.add_argument(
     "--fnet_config_file",
     default=None,
     type=str,
     required=True,
     help=(
         "The config json file corresponding to the pre-trained FNet model. \n"
         "This specifies the model architecture."
     ),
 )
 parser.add_argument("--save_path", default=None, type=str, required=True, help="Path to the output model.")
 __A       : Any						    = parser.parse_args()
 convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)





