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







def     _a				(      lowercase__  :			List[str]     ,  lowercase__  :			Dict     ,  lowercase__  :			Union[str, Any] ):







					'''simple docstring'''


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







					'''simple docstring'''


					logger.info(f'''Loading model based on config from {config_path}...''' )
					SCREAMING_SNAKE_CASE__				:				Tuple			=		BertConfig.from_json_file(snake_case_ )
					SCREAMING_SNAKE_CASE__				:				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__":
						SCREAMING_SNAKE_CASE__		:					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).",
						)
						SCREAMING_SNAKE_CASE__		:					Optional[int]						=					parser.parse_args()
						convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)




def     _a				(      lowercase__  :			Optional[int] ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Optional[int]			=		[]
     SCREAMING_SNAKE_CASE__				:				List[Any]			=		set({'(', '[', '{'} )
     SCREAMING_SNAKE_CASE__				:				Optional[int]			=		set({')', ']', '}'} )
     SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		{'{': '}', '[': ']', '(': ')'}

     for i in range(len(lowercase__ ) ):
          if s[i] in open_brackets:
               stack.append(s[i] )

          elif s[i] in closed_brackets and (
              len(lowercase__ ) == 0 or (len(lowercase__ ) > 0 and open_to_closed[stack.pop()] != s[i])
          ):
               return False

     return len(lowercase__ ) == 0







def     _a				(      ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Tuple			=		input('Enter sequence of brackets: ' )
     if is_balanced(lowercase__ ):
          print(lowercase__     ,  'is balanced' )
     else:
          print(lowercase__     ,  'is not balanced' )


if __name__ == "__main__":
      main()



