




"""simple docstring"""





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 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)			->			Dict:


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


			"""simple docstring"""
			# Instantiate model
			logger.info(f"Loading model based on config from {config_path}..."							)
			__UpperCAmelCase	: Dict		=					BertConfig.from_json_file(_snake_case							)
			__UpperCAmelCase	: Dict		=					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__":
					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)








"""simple docstring"""





def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)			->			Tuple:


			"""simple docstring"""
			global f  # a global dp table for knapsack
			if f[i][j] < 0:
						if j < wt[i - 1]:
									__UpperCAmelCase	: Optional[int]		=					mf_knapsack(i - 1				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)
						else:
									__UpperCAmelCase	: Any		=					max(
									    mf_knapsack(i - 1				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)				,       mf_knapsack(i - 1				,       UpperCamelCase				,       UpperCamelCase				,       j - wt[i - 1]							) + val[i - 1]				,       )
						__UpperCAmelCase	: str		=					val
			return f[i][j]

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)			->			List[Any]:


			"""simple docstring"""
			__UpperCAmelCase	: List[Any]		=					[[0] * (w + 1) for _ in range(n + 1							)]

			for i in range(1				,       n + 1							):
						for w_ in range(1				,       w + 1							):
									if wt[i - 1] <= w_:
												__UpperCAmelCase	: Tuple		=					max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]]				,       dp[i - 1][w_]							)
									else:
												__UpperCAmelCase	: int		=					dp[i - 1][w_]

			return dp[n][w_], dp

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)			->			int:


			"""simple docstring"""
			if not (isinstance(UpperCamelCase				,       (list, tuple)							) and isinstance(UpperCamelCase				,       (list, tuple)							)):
						raise ValueError(
						    "Both the weights and values vectors must be either lists or tuples"							)

			__UpperCAmelCase	: List[Any]		=					len(UpperCamelCase							)
			if num_items != len(UpperCamelCase							):
						__UpperCAmelCase	: int		=					(
						    "The number of weights must be the same as the number of values.\n"
						    f"But got {num_items} weights and {len(UpperCamelCase							)} values"
						)
						raise ValueError(UpperCamelCase							)
			for i in range(UpperCamelCase							):
						if not isinstance(wt[i]				,       UpperCamelCase							):
									__UpperCAmelCase	: Tuple		=					(
									    "All weights must be integers but got weight of "
									    f"type {type(wt[i]							)} at index {i}"
									)
									raise TypeError(UpperCamelCase							)

			__UpperCAmelCase	,     __UpperCAmelCase	: Tuple		=					knapsack(UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)
			__UpperCAmelCase	: set		=					set()
			_construct_solution(UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)

			return optimal_val, example_optional_set

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)			->			Dict:


			"""simple docstring"""
			# for the current item i at a maximum weight j to be part of an optimal subset,
			# the optimal value at (i, j) must be greater than the optimal value at (i-1, j).
			# where i - 1 means considering only the previous items at the given maximum weight
			if i > 0 and j > 0:
						if dp[i - 1][j] == dp[i][j]:
									_construct_solution(UpperCamelCase				,       UpperCamelCase				,       i - 1				,       UpperCamelCase				,       UpperCamelCase							)
						else:
									optimal_set.add(UpperCamelCase							)
									_construct_solution(UpperCamelCase				,       UpperCamelCase				,       i - 1				,       j - wt[i - 1]				,       UpperCamelCase							)


if __name__ == "__main__":
					A   =       [3, 2, 4, 4]
					A   =       [4, 3, 2, 3]
					A   =       4
					A   =       6
					A   =       [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
					A ,						A   =       knapsack(w, wt, val, n)
					print(optimal_solution)
					print(mf_knapsack(n, wt, val, w))  # switched the n and w

					# testing the dynamic programming problem with example
					# the optimal subset for the above example are items 3 and 4
					A ,						A   =       knapsack_with_example_solution(w, wt, val)
					assert optimal_solution == 8
					assert optimal_subset == {3, 4}
					print("""optimal_value = """, optimal_solution)
					print("""An optimal subset corresponding to the optimal value""", optimal_subset)


