<commit_before># model.py
# Authors: <NAME> <<EMAIL>>

"""
This holds the primary model object, which allows you to build
new graphs, load them from prototxts, train them, or use them
to do prediction.
"""

import subprocess
#import caffe

from .layers import *
from .solvers import *

class Param( object ):
	"""A parameter, for ease of use."""

	name = "param"

	def __init__( self, **kwargs ):
		self.kwargs = kwargs

	def to_prototxt( self ):
		"""Convert the output to the string format for the prototxt file."""

		prototxt = "  param {\n"
		for key, value in self.kwargs.items():
			prototxt += "    {}: {}\n".format( key, value )
		prototxt += "  }\n"

		return prototxt

class Output( object ):
	"""The output of a network."""

	name = "Output"

	def __init__( self, type, name, bottom=None ):
		self.type = type
		self.name = name
		self.bottom = bottom 

	def to_prototxt( self ):
		"""Convert the output to the string format for the prototxt file."""

		return 'layer {\n' + \
		       '  name: "{}"\n'.format( self.name ) + \
		       '  type: "{}"\n'.format( self.type ) + \
		       '  bottom: "{}"\n'.format( self.bottom ) + \
		       '  top: "{}"\n'.format( self.name ) + \
		       '}\n'

class Layer( object ):
	"""A single node, containing a layer, a name, and a pointer to
	nodes above and below."""

	name = "Layer"

	def __init__( self, layer, name, bottom=None, top=None, param=None, phase=None ):
		self.layer = layer 
		self.name = name
		self.bottom = bottom
		self.top = top
		self.param = param
		self.phase = phase

	def to_prototxt( self ):
		"""Convert the node to the string format for the prototxt file."""

		prototxt = 'layer {\n' + \
		           '  name: "{}"\n'.format( self.name ) + \
		           '  type: "{}"\n'.format( self.layer.type ) + \
		           '  top: "{}"\n'.format( self.name )

		if self.top is not None:
			prototxt += '  top: "{}"\n'.format( self.top )

		if isinstance( self.bottom, str ):
			prototxt += '  bottom: "{}"\n'.format( self.bottom )
		elif isinstance( self.bottom, list ):
			for node in self.bottom:
				prototxt += '  bottom: "{}"\n'.format( node )

		if self.phase is not None:
			prototxt += '  include {\n' + \
			            '    phase: {}\n'.format( self.phase ) + \
			            '  }\n'

		if isinstance( self.param, dict ):
			prototxt += '  param {\n'
			for key, val in param.items():
				prototxt += '    {}: {}\n'.format( key, val )
			prototxt += '  }\n'
		elif isinstance( self.param, Param ):
			prototxt += self.param.to_prototxt()
		elif isinstance( self.param, list ):
			for param in self.param:
				if isinstance( param, Param ):
					prototxt += param.to_prototxt()
				elif isinstance( param, dict ):
					prototxt += '  param {\n'
					for key, val in param.items():
						prototxt += '    {}: {}\n'.format( key, val )
					prototxt += '  }\n'					

		prototxt += '  {}\n'.format( self.layer.to_prototxt() )
		prototxt += '}\n'

		if hasattr( self.layer, 'activation' ) and self.layer.activation != 'linear':
			prototxt += '\nlayer {\n' + \
			            '  name: "{}"\n'.format( self.name + '_relu' ) + \
			            '  type: "{}"\n'.format( self.layer.activation ) + \
			            '  bottom: "{}"\n'.format( self.name ) + \
			            '  top: "{}"\n'.format( self.name ) + \
			            '}\n'

		return prototxt


class Model( object ):
	"""Wrapper for the caffe model, exposes a scikit-learn API."""

	name = 'Model'

	def __init__( self, name='caffe-model', policy_name=None ):
		self.name = name
		self.policy_name = policy_name or name + '_policy'
		self.ordered_nodes = []
		self.input_names = []
		self.nodes = {}

	def add_node( self, layer, name, input, params=None, concat_dim=1 ):
		"""Add a node to the graph that is this model."""

		if isinstance( input, list ) and not isinstance( layer, Concat ):
			node = Layer( Concat( concat_dim ), name="pre_{}_concat".format( name ), bottom=input )
			self.nodes["pre_{}_concat".format( name )] = node
			self.ordered_nodes.append( node )

			input = "pre_{}_concat".format( name )

		node = Layer( layer, name, bottom=input )
		self.nodes[name] = node
		self.ordered_nodes.append( node )

	def add_input( self, layer, name, label='label' ):
		"""Add a node which represents an input to the model."""

		node = Layer( layer, name, top=label )
		self.input_names.append( name )
		self.nodes[name] = node
		self.ordered_nodes.append( node )

	def add_output( self, type, name, input ):
		"""Add a node which represents an output of the model."""

		node = Output( type, name, bottom=input )
		self.nodes[name] = node
		self.ordered_nodes.append( node )

	def compile( self, solver, **kwargs ):
		"""Compile the graph, added all of the 'top' linkers."""

		labels = []
		for name in self.input_names:
			node = self.nodes[name]
			if node.top in labels:
				node.top = None
			else:<commit_msg>FIX output labels<commit_after># model.py
# Authors: <NAME> <<EMAIL>>

"""
This holds the primary model object, which allows you to build
new graphs, load them from prototxts, train them, or use them
to do prediction.
"""

import subprocess
#import caffe

from .layers import *
from .solvers import *

class Param( object ):
	"""A parameter, for ease of use."""

	name = "param"

	def __init__( self, **kwargs ):
		self.kwargs = kwargs

	def to_prototxt( self ):
		"""Convert the output to the string format for the prototxt file."""

		prototxt = "  param {\n"
		for key, value in self.kwargs.items():
			prototxt += "    {}: {}\n".format( key, value )
		prototxt += "  }\n"

		return prototxt

class Output( object ):
	"""The output of a network."""

	name = "Output"

	def __init__( self, type, name, bottom ):
		self.type = type
		self.name = name
		self.bottom = bottom 

	def to_prototxt( self ):
		"""Convert the output to the string format for the prototxt file."""

		prototxt = 'layer {\n' + \
		           '  name: "{}"\n'.format( self.name ) + \
		           '  type: "{}"\n'.format( self.type ) + \
		           '  top: "{}"\n'.format( self.name )

		if isinstance( self.bottom, list ):
			for bottom in self.bottom:
				prototxt += '  bottom: "{}"\n'.format( bottom )
		else:
			prototxt += '  bottom: "{}"\n'.format( bottom )

		return prototxt

class Layer( object ):
	"""A single node, containing a layer, a name, and a pointer to
	nodes above and below."""

	name = "Layer"

	def __init__( self, layer, name, bottom=None, top=None, param=None, phase=None ):
		self.layer = layer 
		self.name = name
		self.bottom = bottom
		self.top = top
		self.param = param
		self.phase = phase

	def to_prototxt( self ):
		"""Convert the node to the string format for the prototxt file."""

		prototxt = 'layer {\n' + \
		           '  name: "{}"\n'.format( self.name ) + \
		           '  type: "{}"\n'.format( self.layer.type ) + \
		           '  top: "{}"\n'.format( self.name )

		if self.top is not None:
			prototxt += '  top: "{}"\n'.format( self.top )

		if isinstance( self.bottom, str ):
			prototxt += '  bottom: "{}"\n'.format( self.bottom )
		elif isinstance( self.bottom, list ):
			for node in self.bottom:
				prototxt += '  bottom: "{}"\n'.format( node )

		if self.phase is not None:
			prototxt += '  include {\n' + \
			            '    phase: {}\n'.format( self.phase ) + \
			            '  }\n'

		if isinstance( self.param, dict ):
			prototxt += '  param {\n'
			for key, val in param.items():
				prototxt += '    {}: {}\n'.format( key, val )
			prototxt += '  }\n'
		elif isinstance( self.param, Param ):
			prototxt += self.param.to_prototxt()
		elif isinstance( self.param, list ):
			for param in self.param:
				if isinstance( param, Param ):
					prototxt += param.to_prototxt()
				elif isinstance( param, dict ):
					prototxt += '  param {\n'
					for key, val in param.items():
						prototxt += '    {}: {}\n'.format( key, val )
					prototxt += '  }\n'					

		prototxt += '  {}\n'.format( self.layer.to_prototxt() )
		prototxt += '}\n'

		if hasattr( self.layer, 'activation' ) and self.layer.activation != 'linear':
			prototxt += '\nlayer {\n' + \
			            '  name: "{}"\n'.format( self.name + '_relu' ) + \
			            '  type: "{}"\n'.format( self.layer.activation ) + \
			            '  bottom: "{}"\n'.format( self.name ) + \
			            '  top: "{}"\n'.format( self.name ) + \
			            '}\n'

		return prototxt


class Model( object ):
	"""Wrapper for the caffe model, exposes a scikit-learn API."""

	name = 'Model'

	def __init__( self, name='caffe-model', policy_name=None ):
		self.name = name
		self.policy_name = policy_name or name + '_policy'
		self.ordered_nodes = []
		self.input_names = []
		self.nodes = {}

	def add_node( self, layer, name, input, params=None, concat_dim=1 ):
		"""Add a node to the graph that is this model."""

		if isinstance( input, list ) and not isinstance( layer, Concat ):
			node = Layer( Concat( concat_dim ), name="pre_{}_concat".format( name ), bottom=input )
			self.nodes["pre_{}_concat".format( name )] = node
			self.ordered_nodes.append( node )

			input = "pre_{}_concat".format( name )

		node = Layer( layer, name, bottom=input )
		self.nodes[name] = node
		self.ordered_nodes.append( node )

	def add_input( self, layer, name, label='label' ):
		"""Add a node which represents an input to the model."""

		node = Layer( layer, name, top=label )
		self.input_names.append( name )
		self.nodes[name] = node
		self.ordered_nodes.append( node )

	def add_output( self, type, name, input, label='label' ):
		"""Add a node which represents an output of the model."""

		if isinstance( input, list ):
			input += [label]
		else:
			input = [input, label]

		node = Output( type, name, bottom=input )
		self.nodes[name] = node
		self.ordered_nodes.append( node )

	def compile( self, solver, **kwargs ):
		"""Compile the graph, added all of the 'top' linkers."""

		labels = []
		for name in self.input_names:
			node = self.nodes[name]
			if node.top in labels:
				node.top = None
			else:
{'fraction_non_alphanumeric': 0.16183405237283033, 'fraction_numerical': 0.00020066218521119696, 'mean_word_length': 2.678228782287823, 'pattern_counts': {'":': 0, '<': 9, '<?xml version=': 0, '>': 9, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '2801351', 'n_tokens_mistral': 3746, 'n_tokens_neox': 3309, 'n_words': 1017}