


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()
UpperCAmelCase_				:			Any   =	logging.get_logger(__name__)







def 	SCREAMING_SNAKE_CASE_						(      __A			:      List[str]  ,   __A			:      Union[str, Any]  ,   __A			:      List[Any]    )     ->		int:

       """simple docstring"""

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

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

       """simple docstring"""

       logger.info(F"""Loading model based on config from {config_path}..."""    )
       a_ :		Optional[Any]							=  BertConfig.from_json_file(__A    )
       a_ :		List[str]							=  BertModel(__A    )

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

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


if __name__ == "__main__":
   UpperCAmelCase_				:			Optional[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).',
   )
   UpperCAmelCase_				:			Dict   =	parser.parse_args()
   convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)






def 	SCREAMING_SNAKE_CASE_						(      __A			:      int  ,   __A			:      int    )     ->		int:

       """simple docstring"""

       while b:
              a_							,		a_ :		int							=  b, a % b
       return a







def 	SCREAMING_SNAKE_CASE_						(      __A			:      int  ,   __A			:      int    )     ->		int:

       """simple docstring"""

       return a if b == 0 else euclidean_gcd_recursive(__A  ,   a % b    )







def 	SCREAMING_SNAKE_CASE_						(      )     ->		str:

       """simple docstring"""

       print(F"""euclidean_gcd(3, 5) = {euclidean_gcd(3  ,   5    )}"""    )
       print(F"""euclidean_gcd(5, 3) = {euclidean_gcd(5  ,   3    )}"""    )
       print(F"""euclidean_gcd(1, 3) = {euclidean_gcd(1  ,   3    )}"""    )
       print(F"""euclidean_gcd(3, 6) = {euclidean_gcd(3  ,   6    )}"""    )
       print(F"""euclidean_gcd(6, 3) = {euclidean_gcd(6  ,   3    )}"""    )

       print(F"""euclidean_gcd_recursive(3, 5) = {euclidean_gcd_recursive(3  ,   5    )}"""    )
       print(F"""euclidean_gcd_recursive(5, 3) = {euclidean_gcd_recursive(5  ,   3    )}"""    )
       print(F"""euclidean_gcd_recursive(1, 3) = {euclidean_gcd_recursive(1  ,   3    )}"""    )
       print(F"""euclidean_gcd_recursive(3, 6) = {euclidean_gcd_recursive(3  ,   6    )}"""    )
       print(F"""euclidean_gcd_recursive(6, 3) = {euclidean_gcd_recursive(6  ,   3    )}"""    )


if __name__ == "__main__":
   main()


