


"""simple docstring"""







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()
lowerCamelCase_       :       Any      						=  logging.get_logger(__name__)
def _A						(						lowercase	,   lowercase	,   lowercase		):







    """simple docstring"""
    a					=os.path.abspath(lowercase		)
    logger.info(f'''Converting TensorFlow checkpoint from {tf_path}'''		)
    # Load weights from TF model
    a					=tf.train.list_variables(lowercase		)
    a					=[]
    a					=[]
    a					=[]
    for full_name, shape in init_vars:
        # logger.info(f"Loading TF weight {name} with shape {shape}")
        a					=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'
            a					=name[1:]
        # figure out how many levels deep the name is
        a					=0
        for _name in name:
            if _name.startswith('''layer_with_weights'''		):
                depth += 1
            else:
                break
        layer_depth.append(lowercase		)
        # read data
        a					=tf.train.load_variable(lowercase	,   lowercase		)
        names.append('''/'''.join(lowercase		)		)
        arrays.append(lowercase		)
    logger.info(f'''Read a total of {len(lowercase		):,} layers'''		)

    # Sanity check
    if len(set(lowercase		)		) != 1:
        raise ValueError(f'''Found layer names with different depths (layer depth {list(set(lowercase		)		)})'''		)
    a					=list(set(lowercase		)		)[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(lowercase	,   lowercase		):
        a					=full_name.split('''/'''		)
        a					=model
        a					=[]
        for i, m_name in enumerate(lowercase		):
            if m_name == ".ATTRIBUTES":
                # variable names end with .ATTRIBUTES/VARIABLE_VALUE
                break
            if m_name.startswith('''layer_with_weights'''		):
                a					=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''']		)
                    a					=getattr(lowercase	,   '''embeddings'''		)
                    a					=getattr(lowercase	,   '''LayerNorm'''		)
                elif layer_num > 3 and layer_num < config.num_hidden_layers + 4:
                    # encoder layers
                    trace.extend(['''encoder''', '''layer''', str(layer_num - 4		)]		)
                    a					=getattr(lowercase	,   '''encoder'''		)
                    a					=getattr(lowercase	,   '''layer'''		)
                    a					=pointer[layer_num - 4]
                elif layer_num == config.num_hidden_layers + 4:
                    # pooler layer
                    trace.extend(['''pooler''', '''dense''']		)
                    a					=getattr(lowercase	,   '''pooler'''		)
                    a					=getattr(lowercase	,   '''dense'''		)
            elif m_name == "embeddings":
                trace.append('''embeddings'''		)
                a					=getattr(lowercase	,   '''embeddings'''		)
                if layer_num == 0:
                    trace.append('''word_embeddings'''		)
                    a					=getattr(lowercase	,   '''word_embeddings'''		)
                elif layer_num == 1:
                    trace.append('''position_embeddings'''		)
                    a					=getattr(lowercase	,   '''position_embeddings'''		)
                elif layer_num == 2:
                    trace.append('''token_type_embeddings'''		)
                    a					=getattr(lowercase	,   '''token_type_embeddings'''		)
                else:
                    raise ValueError(f'''Unknown embedding layer with name {full_name}'''		)
                trace.append('''weight'''		)
                a					=getattr(lowercase	,   '''weight'''		)
            elif m_name == "_attention_layer":
                # self-attention layer
                trace.extend(['''attention''', '''self''']		)
                a					=getattr(lowercase	,   '''attention'''		)
                a					=getattr(lowercase	,   '''self'''		)
            elif m_name == "_attention_layer_norm":
                # output attention norm
                trace.extend(['''attention''', '''output''', '''LayerNorm''']		)
                a					=getattr(lowercase	,   '''attention'''		)
                a					=getattr(lowercase	,   '''output'''		)
                a					=getattr(lowercase	,   '''LayerNorm'''		)
            elif m_name == "_attention_output_dense":
                # output attention dense
                trace.extend(['''attention''', '''output''', '''dense''']		)
                a					=getattr(lowercase	,   '''attention'''		)
                a					=getattr(lowercase	,   '''output'''		)
                a					=getattr(lowercase	,   '''dense'''		)
            elif m_name == "_output_dense":
                # output dense
                trace.extend(['''output''', '''dense''']		)
                a					=getattr(lowercase	,   '''output'''		)
                a					=getattr(lowercase	,   '''dense'''		)
            elif m_name == "_output_layer_norm":
                # output dense
                trace.extend(['''output''', '''LayerNorm''']		)
                a					=getattr(lowercase	,   '''output'''		)
                a					=getattr(lowercase	,   '''LayerNorm'''		)
            elif m_name == "_key_dense":
                # attention key
                trace.append('''key'''		)
                a					=getattr(lowercase	,   '''key'''		)
            elif m_name == "_query_dense":
                # attention query
                trace.append('''query'''		)
                a					=getattr(lowercase	,   '''query'''		)
            elif m_name == "_value_dense":
                # attention value
                trace.append('''value'''		)
                a					=getattr(lowercase	,   '''value'''		)
            elif m_name == "_intermediate_dense":
                # attention intermediate dense
                trace.extend(['''intermediate''', '''dense''']		)
                a					=getattr(lowercase	,   '''intermediate'''		)
                a					=getattr(lowercase	,   '''dense'''		)
            elif m_name == "_output_layer_norm":
                # output layer norm
                trace.append('''output'''		)
                a					=getattr(lowercase	,   '''output'''		)
            # weights & biases
            elif m_name in ["bias", "beta"]:
                trace.append('''bias'''		)
                a					=getattr(lowercase	,   '''bias'''		)
            elif m_name in ["kernel", "gamma"]:
                trace.append('''weight'''		)
                a					=getattr(lowercase	,   '''weight'''		)
            else:
                logger.warning(f'''Ignored {m_name}'''		)
        # for certain layers reshape is necessary
        a					='''.'''.join(lowercase		)
        if re.match(R'''(\S+)\.attention\.self\.(key|value|query)\.(bias|weight)'''	,   lowercase		) or re.match(
            R'''(\S+)\.attention\.output\.dense\.weight'''	,   lowercase		):
            a					=array.reshape(pointer.data.shape		)
        if "kernel" in full_name:
            a					=array.transpose()
        if pointer.shape == array.shape:
            a					=torch.from_numpy(lowercase		)
        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 _A						(						lowercase	,   lowercase	,   lowercase		):







    """simple docstring"""
    # Instantiate model
    logger.info(f'''Loading model based on config from {config_path}...'''		)
    a					=BertConfig.from_json_file(lowercase		)
    a					=BertModel(lowercase		)

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

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


if __name__ == "__main__":
       lowerCamelCase_       :       int      						=  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).""",
       )
       lowerCamelCase_       :       Dict      						=  parser.parse_args()
       convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)



"""simple docstring"""







from __future__ import annotations






class 				__A    :






 """simple docstring"""




 def __init__(		self			,   __A = 0						) ->					Dict:
     a					=key


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A						) ->					list[str]:
     assert isinstance(__A			,   __A						) and isinstance(__A			,   __A						)

     a					=key or self.__key or 1

     # make sure key is an appropriate size
     key %= 255

     return [chr(ord(__A						) ^ key						) for ch in content]


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A						) ->					list[str]:
     assert isinstance(__A			,   __A						) and isinstance(__A			,   __A						)

     a					=key or self.__key or 1

     # make sure key is an appropriate size
     key %= 255

     return [chr(ord(__A						) ^ key						) for ch in content]


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A = 0						) ->					str:
     assert isinstance(__A			,   __A						) and isinstance(__A			,   __A						)

     a					=key or self.__key or 1

     # make sure key can be any size
     while key > 255:
         key -= 255

     # This will be returned
     a					=''''''

     for ch in content:
         ans += chr(ord(__A						) ^ key						)

     return ans


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A = 0						) ->					str:
     assert isinstance(__A			,   __A						) and isinstance(__A			,   __A						)

     a					=key or self.__key or 1

     # make sure key can be any size
     while key > 255:
         key -= 255

     # This will be returned
     a					=''''''

     for ch in content:
         ans += chr(ord(__A						) ^ key						)

     return ans


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A = 0						) ->					bool:
     assert isinstance(__A			,   __A						) and isinstance(__A			,   __A						)

     try:
         with open(__A						) as fin, open('''encrypt.out'''			,   '''w+'''						) as fout:
             # actual encrypt-process
             for line in fin:
                 fout.write(self.encrypt_string(__A			,   __A						)						)

     except OSError:
         return False

     return True





 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A						) ->					bool:
     assert isinstance(__A			,   __A						) and isinstance(__A			,   __A						)

     try:
         with open(__A						) as fin, open('''decrypt.out'''			,   '''w+'''						) as fout:
             # actual encrypt-process
             for line in fin:
                 fout.write(self.decrypt_string(__A			,   __A						)						)

     except OSError:
         return False

     return True


# Tests
# crypt = XORCipher()
# key = 67

# # test encrypt
# print(crypt.encrypt("hallo welt",key))
# # test decrypt
# print(crypt.decrypt(crypt.encrypt("hallo welt",key), key))

# # test encrypt_string
# print(crypt.encrypt_string("hallo welt",key))

# # test decrypt_string
# print(crypt.decrypt_string(crypt.encrypt_string("hallo welt",key),key))

# if (crypt.encrypt_file("test.txt",key)):
#       print("encrypt successful")
# else:
#       print("encrypt unsuccessful")

# if (crypt.decrypt_file("encrypt.out",key)):
#       print("decrypt successful")
# else:
#       print("decrypt unsuccessful")