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()
__lowerCamelCase					:	List[Any]       =  logging.get_logger(__name__)




def       _snake_case	(   lowerCAmelCase							:   Optional[Any]   , lowerCAmelCase							:   List[Any]   , lowerCAmelCase							:   int	):
		"""simple docstring"""







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

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







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

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

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


if __name__ == "__main__":
		__lowerCamelCase					:	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).''',
		)
		__lowerCamelCase					:	List[Any]       =  parser.parse_args()
		convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)

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







		SCREAMING_SNAKE_CASE_  :		Dict				      =    len(lowerCAmelCase	)
		for i in range(1   , lowerCAmelCase	):
				SCREAMING_SNAKE_CASE_  :		int				      =    collection[i]
				SCREAMING_SNAKE_CASE_  :		Any				      =    0
				SCREAMING_SNAKE_CASE_  :		Tuple				      =    i - 1

				while low <= high:
						SCREAMING_SNAKE_CASE_  :		int				      =    (low + high) // 2
						if val < collection[mid]:
								SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    mid - 1
						else:
								SCREAMING_SNAKE_CASE_  :		Tuple				      =    mid + 1
				for j in range(lowerCAmelCase   , lowerCAmelCase   , -1	):
						SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    collection[j - 1]
				SCREAMING_SNAKE_CASE_  :		int				      =    val
		return collection


if __name__ == "__main__":
		__lowerCamelCase					:	Dict       =  input('''Enter numbers separated by a comma:\n''').strip()
		__lowerCamelCase					:	List[str]       =  [int(item) for item in user_input.split(''',''')]
		print(binary_insertion_sort(unsorted))
