




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







import operator







def        lowerCAmelCase_			(  snake_case_,snake_case_ = False,snake_case_ = None  ):
      _A							:  str    =		operator.lt if reverse else operator.gt
      _A							:  Optional[Any]    =		solution or []

      if not arr:
            return solution

      _A							:  Dict    =		[arr.pop(0  )]
      for i, item in enumerate(snake_case_  ):
            if _operator(snake_case_,sublist[-1]  ):
                  sublist.append(snake_case_  )
                  arr.pop(snake_case_  )

    #  merging sublist into solution list
      if not solution:
            solution.extend(snake_case_  )
      else:
            while sublist:
                  _A							:  Union[str, Any]    =		sublist.pop(0  )
                  for i, xx in enumerate(snake_case_  ):
                        if not _operator(snake_case_,snake_case_  ):
                              solution.insert(snake_case_,snake_case_  )
                              break
                  else:
                        solution.append(snake_case_  )

      strand_sort(snake_case_,snake_case_,snake_case_  )
      return solution


if __name__ == "__main__":
  assert strand_sort([4, 3, 5, 1, 2]) == [1, 2, 3, 4, 5]
  assert strand_sort([4, 3, 5, 1, 2], reverse=True) == [5, 4, 3, 2, 1]

