


import math
import sys

class 			lowerCamelCase_					:





							def __init__(							self    ):
								a_     			=   0
								a_     			=   []
								a_     			=   {}  # To store the pos of node in array





							def   __magic_name__      (							self    ):
								return self.cur_size == 0





							def   __magic_name__      (							self			,       _SCREAMING_SNAKE_CASE    ):
								a_     			=   self.left(UpperCamelCase_    )
								a_     			=   self.right(UpperCamelCase_    )
								if lc < self.cur_size and self.array(UpperCamelCase_    )[0] < self.array(UpperCamelCase_    )[0]:
									a_     			=   lc
								else:
									a_     			=   idx
								if rc < self.cur_size and self.array(UpperCamelCase_    )[0] < self.array(UpperCamelCase_    )[0]:
									a_     			=   rc
								if smallest != idx:
									self.swap(UpperCamelCase_			,       UpperCamelCase_    )
									self.min_heapify(UpperCamelCase_    )





							def   __magic_name__      (							self			,       _SCREAMING_SNAKE_CASE    ):
								# Inserts a node into the Priority Queue
								a_     			=   self.cur_size
								self.cur_size += 1
								self.array.append((sys.maxsize, tup[1])    )
								self.decrease_key((sys.maxsize, tup[1])			,       tup[0]    )





							def   __magic_name__      (							self    ):
								# Removes and returns the min element at top of priority queue
								a_     			=   self.array[0][1]
								a_     			=   self.array[self.cur_size - 1]
								self.cur_size -= 1
								self.min_heapify(1    )
								del self.pos[min_node]
								return min_node





							def   __magic_name__      (							self			,       _SCREAMING_SNAKE_CASE    ):
								# returns the index of left child
								return 2 * i + 1





							def   __magic_name__      (							self			,       _SCREAMING_SNAKE_CASE    ):
								# returns the index of right child
								return 2 * i + 2





							def   __magic_name__      (							self			,       _SCREAMING_SNAKE_CASE    ):
								# returns the index of parent
								return math.floor(i / 2    )





							def   __magic_name__      (							self			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE    ):
								# swaps array elements at indices i and j
								# update the pos{}
								a_     			=   j
								a_     			=   i
								a_     			=   self.array[i]
								a_     			=   self.array[j]
								a_     			=   temp





							def   __magic_name__      (							self			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE    ):
								a_     			=   self.pos[tup[1]]
								# assuming the new_d is atmost old_d
								a_     			=   (new_d, tup[1])
								while idx > 0 and self.array[self.par(UpperCamelCase_    )][0] > self.array[idx][0]:
									self.swap(UpperCamelCase_			,       self.par(UpperCamelCase_    )    )
									a_     			=   self.par(UpperCamelCase_    )







class 			lowerCamelCase_					:





							def __init__(							self			,       _SCREAMING_SNAKE_CASE    ):
								a_     			=   {}  # To store graph: u -> (v,w)
								a_     			=   num  # Number of nodes in graph
								# To store the distance from source vertex
								a_     			=   [0] * self.num_nodes
								a_     			=   [-1] * self.num_nodes  # To store the path





							def   __magic_name__      (							self			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE    ):
								#  Edge going from node u to v and v to u with weight w
								# u (w)-> v, v (w) -> u
								# Check if u already in graph
								if u in self.adjList:
									self.adjList[u].append((v, w)    )
								else:
									a_     			=   [(v, w)]

								# Assuming undirected graph
								if v in self.adjList:
									self.adjList[v].append((u, w)    )
								else:
									a_     			=   [(u, w)]





							def   __magic_name__      (							self    ):
								# u -> v(w)
								for u in self.adjList:
									print(UpperCamelCase_			,       """->"""			,       """ -> """.join(str(f"""{v}({w})"""    ) for v, w in self.adjList[u]    )    )





							def   __magic_name__      (							self			,       _SCREAMING_SNAKE_CASE    ):
								# Flush old junk values in par[]
								a_     			=   [-1] * self.num_nodes
								# src is the source node
								a_     			=   0
								a_     			=   PriorityQueue()
								q.insert((0, src)    )  # (dist from src, node)
								for u in self.adjList:
									if u != src:
										a_     			=   sys.maxsize  # Infinity
										a_     			=   -1

								while not q.is_empty():
									a_     			=   q.extract_min()  # Returns node with the min dist from source
									# Update the distance of all the neighbours of u and
									# if their prev dist was INFINITY then push them in Q
									for v, w in self.adjList[u]:
										a_     			=   self.dist[u] + w
										if self.dist[v] > new_dist:
											if self.dist[v] == sys.maxsize:
												q.insert((new_dist, v)    )
											else:
												q.decrease_key((self.dist[v], v)			,       UpperCamelCase_    )
											a_     			=   new_dist
											a_     			=   u

        # Show the shortest distances from src
								self.show_distances(UpperCamelCase_    )





							def   __magic_name__      (							self			,       _SCREAMING_SNAKE_CASE    ):
								print(f"""Distance from node: {src}"""    )
								for u in range(self.num_nodes    ):
									print(f"""Node {u} has distance: {self.dist[u]}"""    )





							def   __magic_name__      (							self			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE    ):
								# To show the shortest path from src to dest
								# WARNING: Use it *after* calling dijkstra
								a_     			=   []
								a_     			=   0
								a_     			=   dest
								# Backtracking from dest to src
								while self.par[temp] != -1:
									path.append(UpperCamelCase_    )
									if temp != src:
										for v, w in self.adjList[temp]:
											if v == self.par[temp]:
												cost += w
												break
									a_     			=   self.par[temp]
								path.append(UpperCamelCase_    )
								path.reverse()

								print(f"""----Path to reach {dest} from {src}----"""    )
								for u in path:
									print(f"""{u}"""			,       end=""" """    )
									if u != dest:
										print("""-> """			,       end=""""""    )

								print("""\nTotal cost of path: """			,       UpperCamelCase_    )


if __name__ == "__main__":
			_A							     =       Graph(9)
			graph.add_edge(0, 1, 4)
			graph.add_edge(0, 7, 8)
			graph.add_edge(1, 2, 8)
			graph.add_edge(1, 7, 11)
			graph.add_edge(2, 3, 7)
			graph.add_edge(2, 8, 2)
			graph.add_edge(2, 5, 4)
			graph.add_edge(3, 4, 9)
			graph.add_edge(3, 5, 14)
			graph.add_edge(4, 5, 10)
			graph.add_edge(5, 6, 2)
			graph.add_edge(6, 7, 1)
			graph.add_edge(6, 8, 6)
			graph.add_edge(7, 8, 7)
			graph.show_graph()
			graph.dijkstra(0)
			graph.show_path(0, 4)

# OUTPUT
# 0 -> 1(4) -> 7(8)
# 1 -> 0(4) -> 2(8) -> 7(11)
# 7 -> 0(8) -> 1(11) -> 6(1) -> 8(7)
# 2 -> 1(8) -> 3(7) -> 8(2) -> 5(4)
# 3 -> 2(7) -> 4(9) -> 5(14)
# 8 -> 2(2) -> 6(6) -> 7(7)
# 5 -> 2(4) -> 3(14) -> 4(10) -> 6(2)
# 4 -> 3(9) -> 5(10)
# 6 -> 5(2) -> 7(1) -> 8(6)
# Distance from node: 0
# Node 0 has distance: 0
# Node 1 has distance: 4
# Node 2 has distance: 12
# Node 3 has distance: 19
# Node 4 has distance: 21
# Node 5 has distance: 11
# Node 6 has distance: 9
# Node 7 has distance: 8
# Node 8 has distance: 14
# ----Path to reach 4 from 0----
# 0 -> 7 -> 6 -> 5 -> 4
# Total cost of path:  21





# Copyright (c) 2019 Uber Technologies, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import csv
import json
import math
import time

import numpy as np
import torch
import torch.optim as optim
import torch.utils.data as data
from nltk.tokenize.treebank import TreebankWordDetokenizer
from pplm_classification_head import ClassificationHead
from torch import nn
from torchtext import data as torchtext_data
from torchtext import datasets
from tqdm import tqdm, trange

from transformers import GPTaLMHeadModel, GPTaTokenizer


torch.manual_seed(0)
np.random.seed(0)
UpperCAmelCase_   							=      1E-10
UpperCAmelCase_   							=      'This is incredible! I love it, this is the best chicken I have ever had.'
UpperCAmelCase_   							=      100





class        lowerCamelCase__(   nn.Module):
      def __init__(							self:					Optional[Any]       ,			UpperCamelCase_:					int       ,			UpperCamelCase_:					Any="gpt2-medium"       ,			UpperCamelCase_:					Any=False       ,			UpperCamelCase_:					List[Any]="cpu"  ):
             super().__init__()
             __lowerCamelCase  =		GPTaTokenizer.from_pretrained(UpperCamelCase_  )
             __lowerCamelCase  =		GPTaLMHeadModel.from_pretrained(UpperCamelCase_  )
             __lowerCamelCase  =		self.encoder.transformer.config.hidden_size
             __lowerCamelCase  =		ClassificationHead(class_size=UpperCamelCase_       ,			embed_size=self.embed_size  )
             __lowerCamelCase  =		cached_mode
             __lowerCamelCase  =		device
      def      lowerCAmelCase__		(							self:					List[Any]  ):
             return self.classifier_head
      def      lowerCAmelCase__		(							self:					int  ):
             for param in self.encoder.parameters():
                    __lowerCamelCase  =		False
             self.classifier_head.train()
      def      lowerCAmelCase__		(							self:					Optional[Any]       ,			UpperCamelCase_:					Optional[int]  ):
             __lowerCamelCase  =		x.ne(0  ).unsqueeze(2  ).repeat(1       ,			1       ,			self.embed_size  ).float().to(self.device  ).detach()
             __lowerCamelCase  =		self.encoder.transformer(UpperCamelCase_  )["""last_hidden_state"""]
             __lowerCamelCase  =		hidden * mask
             __lowerCamelCase  =		torch.sum(UpperCamelCase_       ,			dim=1  ) / (torch.sum(UpperCamelCase_       ,			dim=1  ).detach() + EPSILON)
             return avg_hidden




      def      lowerCAmelCase__		(							self:					Dict       ,			UpperCamelCase_:					Optional[int]  ):
             if self.cached_mode:
                    __lowerCamelCase  =		x.to(self.device  )
             else:
                    __lowerCamelCase  =		self.avg_representation(x.to(self.device  )  )

             __lowerCamelCase  =		self.classifier_head(UpperCamelCase_  )
             __lowerCamelCase  =		nn.functional.log_softmax(UpperCamelCase_       ,			dim=-1  )

             return probs





class        lowerCamelCase__(   data.Dataset):
      def __init__(							self:					List[Any]       ,			UpperCamelCase_:					Optional[Any]       ,			UpperCamelCase_:					Optional[int]  ):
             __lowerCamelCase  =		X
             __lowerCamelCase  =		y
      def __len__(							self:					Dict  ):
             return len(self.X  )




      def __getitem__(							self:					Dict       ,			UpperCamelCase_:					Dict  ):
             __lowerCamelCase  =		{}
             __lowerCamelCase  =		self.X[index]
             __lowerCamelCase  =		self.y[index]
             return data




def    lowerCamelCase__			(		A__			:   List[Any]      ):





       '''simple docstring'''



       def pad_sequences(A__			:   List[str]      ):
              __lowerCamelCase  =		[len(A__      ) for seq in sequences]

              __lowerCamelCase  =		torch.zeros(len(A__      )    ,     max(A__      )      ).long()  # padding value = 0

              for i, seq in enumerate(A__      ):
                     __lowerCamelCase  =		lengths[i]
                     __lowerCamelCase  =		seq[:end]

              return padded_sequences, lengths

       __lowerCamelCase  =		{}
       for key in data[0].keys():
              __lowerCamelCase  =		[d[key] for d in data]

       __lowerCamelCase,  __lowerCamelCase  =		pad_sequences(item_info["""X"""]      )
       __lowerCamelCase  =		torch.tensor(item_info["""y"""]    ,     dtype=torch.long      )

       return x_batch, y_batch




def    lowerCamelCase__			(		A__			:   Optional[int]      ):





       '''simple docstring'''



       __lowerCamelCase  =		{}
       for key in data[0].keys():
              __lowerCamelCase  =		[d[key] for d in data]

       __lowerCamelCase  =		torch.cat(item_info["""X"""]    ,     0      )
       __lowerCamelCase  =		torch.tensor(item_info["""y"""]    ,     dtype=torch.long      )

       return x_batch, y_batch




def    lowerCamelCase__			(		A__			:   List[Any]    ,     A__			:   Optional[Any]    ,     A__			:   List[str]    ,     A__			:   int=0    ,     A__			:   Optional[Any]=10    ,     A__			:   List[str]="cpu"      ):





       '''simple docstring'''



       __lowerCamelCase  =		0
       discriminator.train_custom()
       for batch_idx, (input_t, target_t) in enumerate(A__      ):
              __lowerCamelCase,  __lowerCamelCase  =		input_t.to(A__      ), target_t.to(A__      )

              optimizer.zero_grad()

              __lowerCamelCase  =		discriminator(A__      )
              __lowerCamelCase  =		nn.functional.nll_loss(A__    ,     A__      )
              loss.backward(retain_graph=A__      )
              optimizer.step()

              samples_so_far += len(A__      )

              if batch_idx % log_interval == 0:
                     print(
                         """Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}""".format(
                             epoch + 1    ,     A__    ,     len(data_loader.dataset      )    ,     100 * samples_so_far / len(data_loader.dataset      )    ,     loss.item()    ,     )      )




def    lowerCamelCase__			(		A__			:   Tuple    ,     A__			:   str    ,     A__			:   int="cpu"      ):





       '''simple docstring'''



       discriminator.eval()
       __lowerCamelCase  =		0
       __lowerCamelCase  =		0
       with torch.no_grad():
              for input_t, target_t in data_loader:
                     __lowerCamelCase,  __lowerCamelCase  =		input_t.to(A__      ), target_t.to(A__      )
                     __lowerCamelCase  =		discriminator(A__      )
                     # sum up batch loss
                     test_loss += nn.functional.nll_loss(A__    ,     A__    ,     reduction="""sum"""      ).item()
                     # get the index of the max log-probability
                     __lowerCamelCase  =		output_t.argmax(dim=1    ,     keepdim=A__      )
                     correct += pred_t.eq(target_t.view_as(A__      )      ).sum().item()

       test_loss /= len(data_loader.dataset      )

       print(
           """Performance on test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)""".format(
               A__    ,     A__    ,     len(data_loader.dataset      )    ,     100.0 * correct / len(data_loader.dataset      )      )      )




def    lowerCamelCase__			(		A__			:   Tuple    ,     A__			:   str    ,     A__			:   str    ,     A__			:   List[str]=False    ,     A__			:   Optional[Any]="cpu"      ):





       '''simple docstring'''



       __lowerCamelCase  =		model.tokenizer.encode(A__      )
       __lowerCamelCase  =		torch.tensor([input_t]    ,     dtype=torch.long    ,     device=A__      )
       if cached:
              __lowerCamelCase  =		model.avg_representation(A__      )

       __lowerCamelCase  =		model(A__      ).data.cpu().numpy().flatten().tolist()
       print("""Input sentence:"""    ,     A__      )
       print(
           """Predictions:"""    ,     """, """.join("""{}: {:.4f}""".format(A__    ,     math.exp(A__      )      ) for c, log_prob in zip(A__    ,     A__      )      )    ,     )




def    lowerCamelCase__			(		A__			:   Tuple    ,     A__			:   Any    ,     A__			:   Tuple    ,     A__			:   List[str]=False    ,     A__			:   Union[str, Any]="cpu"      ):





       '''simple docstring'''



       __lowerCamelCase  =		torch.utils.data.DataLoader(dataset=A__    ,     batch_size=A__    ,     collate_fn=A__      )

       __lowerCamelCase  =		[]
       __lowerCamelCase  =		[]
       for batch_idx, (x, y) in enumerate(tqdm(A__    ,     ascii=A__      )      ):
              with torch.no_grad():
                     __lowerCamelCase  =		x.to(A__      )
                     __lowerCamelCase  =		discriminator.avg_representation(A__      ).cpu().detach()
                     __lowerCamelCase  =		torch.unbind(avg_rep.unsqueeze(1      )      )
                     xs += avg_rep_list
                     ys += y.cpu().numpy().tolist()

       __lowerCamelCase  =		torch.utils.data.DataLoader(
           dataset=Dataset(A__    ,     A__      )    ,     batch_size=A__    ,     shuffle=A__    ,     collate_fn=A__      )

       return data_loader




def    lowerCamelCase__			(		A__			:   List[Any]    ,     A__			:   Union[str, Any]=None    ,     A__			:   List[str]="gpt2-medium"    ,     A__			:   Union[str, Any]=10    ,     A__			:   str=64    ,     A__			:   str=10    ,     A__			:   Tuple=False    ,     A__			:   Optional[int]=False    ,     A__			:   str=False    ,     ):





       '''simple docstring'''



       __lowerCamelCase  =		"""cuda""" if torch.cuda.is_available() and not no_cuda else """cpu"""

       print("""Preprocessing {} dataset...""".format(A__      )      )
       __lowerCamelCase  =		time.time()

       if dataset == "SST":
              __lowerCamelCase  =		["""positive""", """negative""", """very positive""", """very negative""", """neutral"""]
              __lowerCamelCase  =		{c: i for i, c in enumerate(A__      )}

              __lowerCamelCase  =		Discriminator(
                  class_size=len(A__      )    ,     pretrained_model=A__    ,     cached_mode=A__    ,     device=A__      ).to(A__      )

              __lowerCamelCase  =		torchtext_data.Field()
              __lowerCamelCase  =		torchtext_data.Field(sequential=A__      )
              __lowerCamelCase,  __lowerCamelCase,  __lowerCamelCase  =		datasets.SST.splits(
                  A__    ,     A__    ,     fine_grained=A__    ,     train_subtrees=A__    ,     )

              __lowerCamelCase  =		[]
              __lowerCamelCase  =		[]
              for i in trange(len(A__      )    ,     ascii=A__      ):
                     __lowerCamelCase  =		TreebankWordDetokenizer().detokenize(vars(train_data[i]      )["""text"""]      )
                     __lowerCamelCase  =		discriminator.tokenizer.encode(A__      )
                     __lowerCamelCase  =		torch.tensor([50256] + seq    ,     device=A__    ,     dtype=torch.long      )
                     x.append(A__      )
                     y.append(classaidx[vars(train_data[i]      )["""label"""]]      )
              __lowerCamelCase  =		Dataset(A__    ,     A__      )

              __lowerCamelCase  =		[]
              __lowerCamelCase  =		[]
              for i in trange(len(A__      )    ,     ascii=A__      ):
                     __lowerCamelCase  =		TreebankWordDetokenizer().detokenize(vars(test_data[i]      )["""text"""]      )
                     __lowerCamelCase  =		discriminator.tokenizer.encode(A__      )
                     __lowerCamelCase  =		torch.tensor([50256] + seq    ,     device=A__    ,     dtype=torch.long      )
                     test_x.append(A__      )
                     test_y.append(classaidx[vars(test_data[i]      )["""label"""]]      )
              __lowerCamelCase  =		Dataset(A__    ,     A__      )

              __lowerCamelCase  =		{
                  """class_size""": len(A__      ),
                  """embed_size""": discriminator.embed_size,
                  """pretrained_model""": pretrained_model,
                  """class_vocab""": classaidx,
                  """default_class""": 2,
              }

       elif dataset == "clickbait":
              __lowerCamelCase  =		["""non_clickbait""", """clickbait"""]
              __lowerCamelCase  =		{c: i for i, c in enumerate(A__      )}

              __lowerCamelCase  =		Discriminator(
                  class_size=len(A__      )    ,     pretrained_model=A__    ,     cached_mode=A__    ,     device=A__      ).to(A__      )

              with open("""datasets/clickbait/clickbait_train_prefix.txt"""      ) as f:
                     __lowerCamelCase  =		[]
                     for i, line in enumerate(A__      ):
                            try:
                                   data.append(eval(A__      )      )
                            except Exception:
                                   print("""Error evaluating line {}: {}""".format(A__    ,     A__      )      )
                                   continue
              __lowerCamelCase  =		[]
              __lowerCamelCase  =		[]
              with open("""datasets/clickbait/clickbait_train_prefix.txt"""      ) as f:
                     for i, line in enumerate(tqdm(A__    ,     ascii=A__      )      ):
                            try:
                                   __lowerCamelCase  =		eval(A__      )
                                   __lowerCamelCase  =		discriminator.tokenizer.encode(d["""text"""]      )

                                   if len(A__      ) < max_length_seq:
                                          __lowerCamelCase  =		torch.tensor([50256] + seq    ,     device=A__    ,     dtype=torch.long      )
                                   else:
                                          print("""Line {} is longer than maximum length {}""".format(A__    ,     A__      )      )
                                          continue
                                   x.append(A__      )
                                   y.append(d["""label"""]      )
                            except Exception:
                                   print("""Error evaluating / tokenizing line {}, skipping it""".format(A__      )      )
                                   pass

              __lowerCamelCase  =		Dataset(A__    ,     A__      )
              __lowerCamelCase  =		int(0.9 * len(A__      )      )
              __lowerCamelCase  =		len(A__      ) - train_size
              __lowerCamelCase,  __lowerCamelCase  =		torch.utils.data.random_split(A__    ,     [train_size, test_size]      )

              __lowerCamelCase  =		{
                  """class_size""": len(A__      ),
                  """embed_size""": discriminator.embed_size,
                  """pretrained_model""": pretrained_model,
                  """class_vocab""": classaidx,
                  """default_class""": 1,
              }

       elif dataset == "toxic":
              __lowerCamelCase  =		["""non_toxic""", """toxic"""]
              __lowerCamelCase  =		{c: i for i, c in enumerate(A__      )}

              __lowerCamelCase  =		Discriminator(
                  class_size=len(A__      )    ,     pretrained_model=A__    ,     cached_mode=A__    ,     device=A__      ).to(A__      )

              __lowerCamelCase  =		[]
              __lowerCamelCase  =		[]
              with open("""datasets/toxic/toxic_train.txt"""      ) as f:
                     for i, line in enumerate(tqdm(A__    ,     ascii=A__      )      ):
                            try:
                                   __lowerCamelCase  =		eval(A__      )
                                   __lowerCamelCase  =		discriminator.tokenizer.encode(d["""text"""]      )

                                   if len(A__      ) < max_length_seq:
                                          __lowerCamelCase  =		torch.tensor([50256] + seq    ,     device=A__    ,     dtype=torch.long      )
                                   else:
                                          print("""Line {} is longer than maximum length {}""".format(A__    ,     A__      )      )
                                          continue
                                   x.append(A__      )
                                   y.append(int(np.sum(d["""label"""]      ) > 0      )      )
                            except Exception:
                                   print("""Error evaluating / tokenizing line {}, skipping it""".format(A__      )      )
                                   pass

              __lowerCamelCase  =		Dataset(A__    ,     A__      )
              __lowerCamelCase  =		int(0.9 * len(A__      )      )
              __lowerCamelCase  =		len(A__      ) - train_size
              __lowerCamelCase,  __lowerCamelCase  =		torch.utils.data.random_split(A__    ,     [train_size, test_size]      )

              __lowerCamelCase  =		{
                  """class_size""": len(A__      ),
                  """embed_size""": discriminator.embed_size,
                  """pretrained_model""": pretrained_model,
                  """class_vocab""": classaidx,
                  """default_class""": 0,
              }

       else:  # if dataset == "generic":
              # This assumes the input dataset is a TSV with the following structure:
              # class \t text

              if dataset_fp is None:
                     raise ValueError("""When generic dataset is selected, dataset_fp needs to be specified aswell."""      )

              __lowerCamelCase  =		set()
              with open(A__      ) as f:
                     __lowerCamelCase  =		csv.reader(A__    ,     delimiter="""\t"""      )
                     for row in tqdm(A__    ,     ascii=A__      ):
                            if row:
                                   classes.add(row[0]      )

              __lowerCamelCase  =		sorted(A__      )
              __lowerCamelCase  =		{c: i for i, c in enumerate(A__      )}

              __lowerCamelCase  =		Discriminator(
                  class_size=len(A__      )    ,     pretrained_model=A__    ,     cached_mode=A__    ,     device=A__      ).to(A__      )

              __lowerCamelCase  =		[]
              __lowerCamelCase  =		[]
              with open(A__      ) as f:
                     __lowerCamelCase  =		csv.reader(A__    ,     delimiter="""\t"""      )
                     for i, row in enumerate(tqdm(A__    ,     ascii=A__      )      ):
                            if row:
                                   __lowerCamelCase  =		row[0]
                                   __lowerCamelCase  =		row[1]

                                   try:
                                          __lowerCamelCase  =		discriminator.tokenizer.encode(A__      )
                                          if len(A__      ) < max_length_seq:
                                                 __lowerCamelCase  =		torch.tensor([50256] + seq    ,     device=A__    ,     dtype=torch.long      )

                                          else:
                                                 print("""Line {} is longer than maximum length {}""".format(A__    ,     A__      )      )
                                                 continue

                                          x.append(A__      )
                                          y.append(classaidx[label]      )

                                   except Exception:
                                          print("""Error tokenizing line {}, skipping it""".format(A__      )      )
                                          pass

              __lowerCamelCase  =		Dataset(A__    ,     A__      )
              __lowerCamelCase  =		int(0.9 * len(A__      )      )
              __lowerCamelCase  =		len(A__      ) - train_size
              __lowerCamelCase,  __lowerCamelCase  =		torch.utils.data.random_split(A__    ,     [train_size, test_size]      )

              __lowerCamelCase  =		{
                  """class_size""": len(A__      ),
                  """embed_size""": discriminator.embed_size,
                  """pretrained_model""": pretrained_model,
                  """class_vocab""": classaidx,
                  """default_class""": 0,
              }

       __lowerCamelCase  =		time.time()
       print("""Preprocessed {} data points""".format(len(A__      ) + len(A__      )      )      )
       print("""Data preprocessing took: {:.3f}s""".format(end - start      )      )

       if cached:
              print("""Building representation cache..."""      )

              __lowerCamelCase  =		time.time()

              __lowerCamelCase  =		get_cached_data_loader(A__    ,     A__    ,     A__    ,     shuffle=A__    ,     device=A__      )

              __lowerCamelCase  =		get_cached_data_loader(A__    ,     A__    ,     A__    ,     device=A__      )

              __lowerCamelCase  =		time.time()
              print("""Building representation cache took: {:.3f}s""".format(end - start      )      )

       else:
              __lowerCamelCase  =		torch.utils.data.DataLoader(
                  dataset=A__    ,     batch_size=A__    ,     shuffle=A__    ,     collate_fn=A__      )
              __lowerCamelCase  =		torch.utils.data.DataLoader(dataset=A__    ,     batch_size=A__    ,     collate_fn=A__      )

       if save_model:
              with open("""{}_classifier_head_meta.json""".format(A__      )    ,     """w"""      ) as meta_file:
                     json.dump(A__    ,     A__      )

       __lowerCamelCase  =		optim.Adam(discriminator.parameters()    ,     lr=0.0_001      )

       for epoch in range(A__      ):
              __lowerCamelCase  =		time.time()
              print("""\nEpoch"""    ,     epoch + 1      )

              train_epoch(
                  discriminator=A__    ,     data_loader=A__    ,     optimizer=A__    ,     epoch=A__    ,     log_interval=A__    ,     device=A__    ,     )
              evaluate_performance(data_loader=A__    ,     discriminator=A__    ,     device=A__      )

              __lowerCamelCase  =		time.time()
              print("""Epoch took: {:.3f}s""".format(end - start      )      )

              print("""\nExample prediction"""      )
              predict(A__    ,     A__    ,     A__    ,     cached=A__    ,     device=A__      )

              if save_model:
                     # torch.save(discriminator.state_dict(),
                     #           "{}_discriminator_{}.pt".format(
                     #               args.dataset, epoch + 1
                     #               ))
                     torch.save(
                         discriminator.get_classifier().state_dict()    ,     """{}_classifier_head_epoch_{}.pt""".format(A__    ,     epoch + 1      )    ,     )


if __name__ == "__main__":
       UpperCAmelCase_   							=      argparse.ArgumentParser(description='Train a discriminator on top of GPT-2 representations')
       parser.add_argument(
           '--dataset',
           type=str,
           default='SST',
           choices=('SST', 'clickbait', 'toxic', 'generic'),
           help=(
               'dataset to train the discriminator on.'
               'In case of generic, the dataset is expected'
               'to be a TSBV file with structure: class \\t text'
           ),
       )
       parser.add_argument(
           '--dataset_fp',
           type=str,
           default='',
           help='File path of the dataset to use. Needed only in case of generic datadset',
       )
       parser.add_argument(
           '--pretrained_model', type=str, default='gpt2-medium', help='Pretrained model to use as encoder'
       )
       parser.add_argument('--epochs', type=int, default=10, metavar='N', help='Number of training epochs')
       parser.add_argument(
           '--batch_size', type=int, default=64, metavar='N', help='input batch size for training (default: 64)'
       )
       parser.add_argument(
           '--log_interval',
           type=int,
           default=10,
           metavar='N',
           help='how many batches to wait before logging training status',
       )
       parser.add_argument('--save_model', action='store_true', help='whether to save the model')
       parser.add_argument('--cached', action='store_true', help='whether to cache the input representations')
       parser.add_argument('--no_cuda', action='store_true', help='use to turn off cuda')
       UpperCAmelCase_   							=      parser.parse_args()

       train_discriminator(**(vars(args)))

