
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           =  logging.get_logger(__name__)




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




from collections.abc import Sequence




def 	UpperCAmelCase   (						_lowerCamelCase = None    ):
			if nums is None or not nums:
						raise ValueError("Input sequence should not be empty"    )

			A							:					Dict              =						nums[0]
			for i in range(1 ,    len(_lowerCamelCase    )    ):
						A							:					Tuple              =						nums[i]
						A							:					List[Any]              =						max(_lowerCamelCase ,    ans + num ,    _lowerCamelCase    )

			return ans


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							# Try on a sample input from the user
							__SCREAMING_SNAKE_CASE           =  int(input("""Enter number of elements : """).strip())
							__SCREAMING_SNAKE_CASE           =  list(map(int, input("""\nEnter the numbers : """).strip().split()))[:n]
							print(max_subsequence_sum(array))