



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()
__A   			=logging.get_logger(__name__)

def        a						(      _UpperCAmelCase							:					Optional[Any] ,   _UpperCAmelCase							:					List[Any] ,   _UpperCAmelCase							:					int						):







		'''simple docstring'''





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

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







		'''simple docstring'''





		logger.info(f'Loading model based on config from {config_path}...'						)
		__UpperCAmelCase    :     int													=	BertConfig.from_json_file(_UpperCAmelCase						)
		__UpperCAmelCase    :     List[str]													=	BertModel(_UpperCAmelCase						)

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

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


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






from collections import defaultdict




def       _snake_case	(   lowerCAmelCase							:   int	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		Any				      =    1
		SCREAMING_SNAKE_CASE_  :		Tuple				      =    True
		for v in tree[start]:
				if v not in visited:
						ret += dfs(lowerCAmelCase	)
		if ret % 2 == 0:
				cuts.append(lowerCAmelCase	)
		return ret




def       _snake_case	(   ):
		"""simple docstring"""







		dfs(1	)


if __name__ == "__main__":
		__lowerCamelCase   ,						__lowerCamelCase					:	Union[str, Any]       =  10, 9
		__lowerCamelCase					:	Optional[int]       =  defaultdict(list)
		__lowerCamelCase					:	dict[int, bool]       =  {}
		__lowerCamelCase					:	list[int]       =  []
		__lowerCamelCase					:	Optional[Any]       =  0
		__lowerCamelCase					:	Any       =  [(2, 1), (3, 1), (4, 3), (5, 2), (6, 1), (7, 2), (8, 6), (9, 8), (10, 8)]
		for u, v in edges:
				tree[u].append(v)
				tree[v].append(u)
		even_tree()
		print(len(cuts) - 1)
