

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

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

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

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


if __name__ == "__main__":
			UpperCAmelCase_							:					Optional[Any]		 =   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).''',
			)
			UpperCAmelCase_							:					Union[str, Any]		 =   parser.parse_args()
			convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)








class      lowerCamelCase_      :




	def __init__(     self						:     Dict					,      __A						:     int					,      __A						:     Tuple					,      __A						:     List[Any]							):
		__A :			Optional[int]		    =			None
		__A :			Any		    =			None
		__A :			int		    =			graph

		self._normalize_graph(__A					,      __A							)
		__A :			str		    =			len(__A							)
		__A :			Optional[int]		    =			None




	def        lowerCAmelCase_       (     self						:     int					,      __A						:     Any					,      __A						:     Optional[Any]							):
		if sources is int:
			__A :			Dict		    =			[sources]
		if sinks is int:
			__A :			Optional[int]		    =			[sinks]

		if len(__A							) == 0 or len(__A							) == 0:
			return

		__A :			str		    =			sources[0]
		__A :			Dict		    =			sinks[0]

		# make fake vertex if there are more
		# than one source or sink
		if len(__A							) > 1 or len(__A							) > 1:
			__A :			Optional[Any]		    =			0
			for i in sources:
				max_input_flow += sum(self.graph[i]							)

			__A :			List[Any]		    =			len(self.graph							) + 1
			for room in self.graph:
				room.insert(0					,      0							)
			self.graph.insert(0					,      [0] * size							)
			for i in sources:
				__A :			str		    =			max_input_flow
			__A :			Union[str, Any]		    =			0

			__A :			Any		    =			len(self.graph							) + 1
			for room in self.graph:
				room.append(0							)
			self.graph.append([0] * size							)
			for i in sinks:
				__A :			int		    =			max_input_flow
			__A :			Optional[Any]		    =			size - 1




	def        lowerCAmelCase_       (     self						:     Optional[Any]							):
		if self.maximum_flow_algorithm is None:
			raise Exception("""You need to set maximum flow algorithm before."""							)
		if self.source_index is None or self.sink_index is None:
			return 0

		self.maximum_flow_algorithm.execute()
		return self.maximum_flow_algorithm.getMaximumFlow()




	def        lowerCAmelCase_       (     self						:     Optional[Any]					,      __A						:     Dict							):
		__A :			Dict		    =			algorithm(self							)






class      lowerCamelCase_      :




	def __init__(     self						:     Union[str, Any]					,      __A						:     str							):
		__A :			Any		    =			flow_network
		__A :			int		    =			flow_network.verticesCount
		__A :			List[Any]		    =			flow_network.sourceIndex
		__A :			Union[str, Any]		    =			flow_network.sinkIndex
		# it's just a reference, so you shouldn't change
		# it in your algorithms, use deep copy before doing that
		__A :			Optional[int]		    =			flow_network.graph
		__A :			str		    =			False




	def        lowerCAmelCase_       (     self						:     List[Any]							):
		if not self.executed:
			self._algorithm()
			__A :			Any		    =			True




	def        lowerCAmelCase_       (     self						:     List[str]							):
		pass






class      lowerCamelCase_      (	_lowercase	):




	def __init__(     self						:     Any					,      __A						:     List[str]							):
		super().__init__(__A							)
		# use this to save your result
		__A :			str		    =			-1




	def        lowerCAmelCase_       (     self						:     Any							):
		if not self.executed:
			raise Exception("""You should execute algorithm before using its result!"""							)

		return self.maximum_flow






class      lowerCamelCase_      (	_lowercase	):




	def __init__(     self						:     List[Any]					,      __A						:     Dict							):
		super().__init__(__A							)

		__A :			Tuple		    =			[[0] * self.verticies_count for i in range(self.verticies_count							)]

		__A :			Optional[Any]		    =			[0] * self.verticies_count
		__A :			Union[str, Any]		    =			[0] * self.verticies_count




	def        lowerCAmelCase_       (     self						:     int							):
		__A :			Optional[int]		    =			self.verticies_count

		# push some substance to graph
		for nextvertex_index, bandwidth in enumerate(self.graph[self.source_index]							):
			self.preflow[self.source_index][nextvertex_index] += bandwidth
			self.preflow[nextvertex_index][self.source_index] -= bandwidth
			self.excesses[nextvertex_index] += bandwidth

		# Relabel-to-front selection rule
		__A :			List[str]		    =			[
		    i
		    for i in range(self.verticies_count							)
		    if i != self.source_index and i != self.sink_index
		]

		# move through list
		__A :			Dict		    =			0
		while i < len(__A							):
			__A :			List[Any]		    =			vertices_list[i]
			__A :			Optional[Any]		    =			self.heights[vertex_index]
			self.process_vertex(__A							)
			if self.heights[vertex_index] > previous_height:
				# if it was relabeled, swap elements
				# and start from 0 index
				vertices_list.insert(0					,      vertices_list.pop(__A							)							)
				__A :			Any		    =			0
			else:
				i += 1

		__A :			Optional[int]		    =			sum(self.preflow[self.source_index]							)




	def        lowerCAmelCase_       (     self						:     Optional[Any]					,      __A						:     str							):
		while self.excesses[vertex_index] > 0:
			for neighbour_index in range(self.verticies_count							):
				# if it's neighbour and current vertex is higher
				if (
				    self.graph[vertex_index][neighbour_index]
				    - self.preflow[vertex_index][neighbour_index]
				    > 0
				    and self.heights[vertex_index] > self.heights[neighbour_index]
				):
					self.push(__A					,      __A							)

			self.relabel(__A							)




	def        lowerCAmelCase_       (     self						:     Dict					,      __A						:     List[str]					,      __A						:     Optional[Any]							):
		__A :			Union[str, Any]		    =			min(
		    self.excesses[from_index]					,      self.graph[from_index][to_index] - self.preflow[from_index][to_index]					,      )
		self.preflow[from_index][to_index] += preflow_delta
		self.preflow[to_index][from_index] -= preflow_delta
		self.excesses[from_index] -= preflow_delta
		self.excesses[to_index] += preflow_delta




	def        lowerCAmelCase_       (     self						:     Optional[Any]					,      __A						:     Tuple							):
		__A :			Tuple		    =			None
		for to_index in range(self.verticies_count							):
			if (
			    self.graph[vertex_index][to_index]
			    - self.preflow[vertex_index][to_index]
			    > 0
			) and (min_height is None or self.heights[to_index] < min_height):
				__A :			Dict		    =			self.heights[to_index]

		if min_height is not None:
			__A :			Optional[int]		    =			min_height + 1


if __name__ == "__main__":
			UpperCAmelCase_							:					Union[str, Any]		 =   [0]
			UpperCAmelCase_							:					Dict		 =   [3]
			# graph = [
			#     [0, 0, 4, 6, 0, 0],
			#     [0, 0, 5, 2, 0, 0],
			#     [0, 0, 0, 0, 4, 4],
			#     [0, 0, 0, 0, 6, 6],
			#     [0, 0, 0, 0, 0, 0],
			#     [0, 0, 0, 0, 0, 0],
			# ]
			UpperCAmelCase_							:					int		 =   [[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]

			# prepare our network
			UpperCAmelCase_							:					str		 =   FlowNetwork(graph, entrances, exits)
			# set algorithm
			flow_network.set_maximum_flow_algorithm(PushRelabelExecutor)
			# and calculate
			UpperCAmelCase_							:					int		 =   flow_network.find_maximum_flow()

			print(f"""maximum flow is {maximum_flow}""")





