

'''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()
UpperCAmelCase					 =	logging.get_logger(__name__)

def 					__UpperCamelCase				(						lowercase__    :	Tuple,       lowercase__    :	int,       lowercase__    :	Union[str, Any]			):






     '''simple docstring'''





     __lowercase      		=os.path.abspath(lowercase__			)
     logger.info(F'''Converting TensorFlow checkpoint from {tf_path}'''			)
     # Load weights from TF model
     __lowercase      		=tf.train.list_variables(lowercase__			)
     __lowercase      		=[]
     __lowercase      		=[]
     __lowercase      		=[]
     for full_name, shape in init_vars:
          # logger.info(f"Loading TF weight {name} with shape {shape}")
          __lowercase      		=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'
               __lowercase      		=name[1:]
          # figure out how many levels deep the name is
          __lowercase      		=0
          for _name in name:
               if _name.startswith('layer_with_weights'			):
                    depth += 1
               else:
                    break
          layer_depth.append(lowercase__			)
          # read data
          __lowercase      		=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__			)			)})'''			)
     __lowercase      		=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__			):
          __lowercase      		=full_name.split('/'			)
          __lowercase      		=model
          __lowercase      		=[]
          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'			):
                    __lowercase      		=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']			)
                         __lowercase      		=getattr(lowercase__,       'embeddings'			)
                         __lowercase      		=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			)]			)
                         __lowercase      		=getattr(lowercase__,       'encoder'			)
                         __lowercase      		=getattr(lowercase__,       'layer'			)
                         __lowercase      		=pointer[layer_num - 4]
                    elif layer_num == config.num_hidden_layers + 4:
                         # pooler layer
                         trace.extend(['pooler', 'dense']			)
                         __lowercase      		=getattr(lowercase__,       'pooler'			)
                         __lowercase      		=getattr(lowercase__,       'dense'			)
               elif m_name == "embeddings":
                    trace.append('embeddings'			)
                    __lowercase      		=getattr(lowercase__,       'embeddings'			)
                    if layer_num == 0:
                         trace.append('word_embeddings'			)
                         __lowercase      		=getattr(lowercase__,       'word_embeddings'			)
                    elif layer_num == 1:
                         trace.append('position_embeddings'			)
                         __lowercase      		=getattr(lowercase__,       'position_embeddings'			)
                    elif layer_num == 2:
                         trace.append('token_type_embeddings'			)
                         __lowercase      		=getattr(lowercase__,       'token_type_embeddings'			)
                    else:
                         raise ValueError(F'''Unknown embedding layer with name {full_name}'''			)
                    trace.append('weight'			)
                    __lowercase      		=getattr(lowercase__,       'weight'			)
               elif m_name == "_attention_layer":
                    # self-attention layer
                    trace.extend(['attention', 'self']			)
                    __lowercase      		=getattr(lowercase__,       'attention'			)
                    __lowercase      		=getattr(lowercase__,       'self'			)
               elif m_name == "_attention_layer_norm":
                    # output attention norm
                    trace.extend(['attention', 'output', 'LayerNorm']			)
                    __lowercase      		=getattr(lowercase__,       'attention'			)
                    __lowercase      		=getattr(lowercase__,       'output'			)
                    __lowercase      		=getattr(lowercase__,       'LayerNorm'			)
               elif m_name == "_attention_output_dense":
                    # output attention dense
                    trace.extend(['attention', 'output', 'dense']			)
                    __lowercase      		=getattr(lowercase__,       'attention'			)
                    __lowercase      		=getattr(lowercase__,       'output'			)
                    __lowercase      		=getattr(lowercase__,       'dense'			)
               elif m_name == "_output_dense":
                    # output dense
                    trace.extend(['output', 'dense']			)
                    __lowercase      		=getattr(lowercase__,       'output'			)
                    __lowercase      		=getattr(lowercase__,       'dense'			)
               elif m_name == "_output_layer_norm":
                    # output dense
                    trace.extend(['output', 'LayerNorm']			)
                    __lowercase      		=getattr(lowercase__,       'output'			)
                    __lowercase      		=getattr(lowercase__,       'LayerNorm'			)
               elif m_name == "_key_dense":
                    # attention key
                    trace.append('key'			)
                    __lowercase      		=getattr(lowercase__,       'key'			)
               elif m_name == "_query_dense":
                    # attention query
                    trace.append('query'			)
                    __lowercase      		=getattr(lowercase__,       'query'			)
               elif m_name == "_value_dense":
                    # attention value
                    trace.append('value'			)
                    __lowercase      		=getattr(lowercase__,       'value'			)
               elif m_name == "_intermediate_dense":
                    # attention intermediate dense
                    trace.extend(['intermediate', 'dense']			)
                    __lowercase      		=getattr(lowercase__,       'intermediate'			)
                    __lowercase      		=getattr(lowercase__,       'dense'			)
               elif m_name == "_output_layer_norm":
                    # output layer norm
                    trace.append('output'			)
                    __lowercase      		=getattr(lowercase__,       'output'			)
               # weights & biases
               elif m_name in ["bias", "beta"]:
                    trace.append('bias'			)
                    __lowercase      		=getattr(lowercase__,       'bias'			)
               elif m_name in ["kernel", "gamma"]:
                    trace.append('weight'			)
                    __lowercase      		=getattr(lowercase__,       'weight'			)
               else:
                    logger.warning(F'''Ignored {m_name}'''			)
        # for certain layers reshape is necessary
          __lowercase      		='.'.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__			):
               __lowercase      		=array.reshape(pointer.data.shape			)
          if "kernel" in full_name:
               __lowercase      		=array.transpose()
          if pointer.shape == array.shape:
               __lowercase      		=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 					__UpperCamelCase				(						lowercase__    :	List[str],       lowercase__    :	Any,       lowercase__    :	Union[str, Any]			):






     '''simple docstring'''





     logger.info(F'''Loading model based on config from {config_path}...'''			)
     __lowercase      		=BertConfig.from_json_file(lowercase__			)
     __lowercase      		=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__":
       UpperCAmelCase					 =	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					 =	parser.parse_args()
       convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)






'''simple docstring'''






import sys

from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core


# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers

__UpperCAmelCase       							=				'''python tqdm regex requests packaging filelock numpy tokenizers'''.split()
if sys.version_info < (3, 7):
		pkgs_to_check_at_runtime.append('''dataclasses''')
if sys.version_info < (3, 8):
		pkgs_to_check_at_runtime.append('''importlib_metadata''')

for pkg in pkgs_to_check_at_runtime:
		if pkg in deps:
				if pkg == "tokenizers":
						# must be loaded here, or else tqdm check may fail
						from .utils import is_tokenizers_available

						if not is_tokenizers_available():
								continue  # not required, check version only if installed

				require_version_core(deps[pkg])
		else:
				raise ValueError(f"""can't find {pkg} in {deps.keys()}, check dependency_versions_table.py""")


def    _snake_case  (	A					,       A=None			)		->       Optional[Any]:
							require_version(deps[pkg]					,       A			)