
'''simple docstring'''



from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


UpperCamelCase__ :						List[str]    							=					logging.get_logger(__name__)

UpperCamelCase__ :						str    							=					{
    "google/bigbird-pegasus-large-arxiv": (
        "https://huggingface.co/google/bigbird-pegasus-large-arxiv/resolve/main/config.json"
    ),
    "google/bigbird-pegasus-large-pubmed": (
        "https://huggingface.co/google/bigbird-pegasus-large-pubmed/resolve/main/config.json"
    ),
    "google/bigbird-pegasus-large-bigpatent": (
        "https://huggingface.co/google/bigbird-pegasus-large-bigpatent/resolve/main/config.json"
    ),
    # See all BigBirdPegasus models at https://huggingface.co/models?filter=bigbird_pegasus
}

class 				_a			(_lowerCamelCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE =					'bigbird_pegasus'
       SCREAMING_SNAKE_CASE =					['past_key_values']
       SCREAMING_SNAKE_CASE =					{
           'num_attention_heads': 'encoder_attention_heads',
           'hidden_size': 'd_model',
           'attention_probs_dropout_prob': 'attention_dropout',
       }






       def __init__(							self    ,							A__=9_61_03    ,							A__=40_96    ,							A__=16    ,							A__=40_96    ,							A__=16    ,							A__=16    ,							A__=40_96    ,							A__=16    ,							A__=0.0    ,							A__=0.0    ,							A__=True    ,							A__=True    ,							A__="gelu_new"    ,							A__=10_24    ,							A__=0.1    ,							A__=0.0    ,							A__=0.0    ,							A__=0.02    ,							A__=2    ,							A__=0.0    ,							A__=True    ,							A__=0    ,							A__=2    ,							A__=1    ,							A__="block_sparse"    ,							A__=64    ,							A__=3    ,							A__=False    ,							**A__    ,							)      ->     Optional[int]:
          _SCREAMING_SNAKE_CASE       	=			vocab_size
          _SCREAMING_SNAKE_CASE       	=			max_position_embeddings
          _SCREAMING_SNAKE_CASE       	=			d_model
          _SCREAMING_SNAKE_CASE       	=			encoder_ffn_dim
          _SCREAMING_SNAKE_CASE       	=			encoder_layers
          _SCREAMING_SNAKE_CASE       	=			encoder_attention_heads
          _SCREAMING_SNAKE_CASE       	=			decoder_ffn_dim
          _SCREAMING_SNAKE_CASE       	=			decoder_layers
          _SCREAMING_SNAKE_CASE       	=			decoder_attention_heads
          _SCREAMING_SNAKE_CASE       	=			dropout
          _SCREAMING_SNAKE_CASE       	=			attention_dropout
          _SCREAMING_SNAKE_CASE       	=			activation_dropout
          _SCREAMING_SNAKE_CASE       	=			activation_function
          _SCREAMING_SNAKE_CASE       	=			init_std
          _SCREAMING_SNAKE_CASE       	=			encoder_layerdrop
          _SCREAMING_SNAKE_CASE       	=			decoder_layerdrop
          _SCREAMING_SNAKE_CASE       	=			classifier_dropout
          _SCREAMING_SNAKE_CASE       	=			use_cache
          _SCREAMING_SNAKE_CASE       	=			encoder_layers
          _SCREAMING_SNAKE_CASE       	=			scale_embedding  # scale factor will be sqrt(d_model) if True

          # extra config
          _SCREAMING_SNAKE_CASE       	=			attention_type
          _SCREAMING_SNAKE_CASE       	=			block_size
          _SCREAMING_SNAKE_CASE       	=			num_random_blocks
          _SCREAMING_SNAKE_CASE       	=			use_bias

          super().__init__(
              pad_token_id=A__    ,							bos_token_id=A__    ,							eos_token_id=A__    ,							is_encoder_decoder=A__    ,							decoder_start_token_id=A__    ,							**A__    ,							)






class 				_a			(_lowerCamelCase):





       """simple docstring"""




       @property
       def     UpperCamelCase			(							self )      ->     Mapping[str, Mapping[int, str]]:
          if self.task in ["default", "seq2seq-lm"]:
             _SCREAMING_SNAKE_CASE       	=			OrderedDict(
                 [
                     ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                     ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                 ] )

             if self.use_past:
                _SCREAMING_SNAKE_CASE       	=			{0: """batch"""}
                _SCREAMING_SNAKE_CASE       	=			{0: """batch""", 1: """past_decoder_sequence + sequence"""}
             else:
                _SCREAMING_SNAKE_CASE       	=			{0: """batch""", 1: """decoder_sequence"""}
                _SCREAMING_SNAKE_CASE       	=			{0: """batch""", 1: """decoder_sequence"""}

             if self.use_past:
                self.fill_with_past_key_values_(A__    ,							direction="""inputs""" )
          elif self.task == "causal-lm":
             # TODO: figure this case out.
             _SCREAMING_SNAKE_CASE       	=			OrderedDict(
                 [
                     ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                     ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                 ] )
             if self.use_past:
                _SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE       	=			self.num_layers
                for i in range(A__ ):
                   _SCREAMING_SNAKE_CASE       	=			{0: """batch""", 2: """past_sequence + sequence"""}
                   _SCREAMING_SNAKE_CASE       	=			{0: """batch""", 2: """past_sequence + sequence"""}
          else:
             _SCREAMING_SNAKE_CASE       	=			OrderedDict(
                 [
                     ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                     ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                     ("""decoder_input_ids""", {0: """batch""", 1: """decoder_sequence"""}),
                     ("""decoder_attention_mask""", {0: """batch""", 1: """decoder_sequence"""}),
                 ] )

          return common_inputs






       @property
       def     UpperCamelCase			(							self )      ->     Mapping[str, Mapping[int, str]]:
          if self.task in ["default", "seq2seq-lm"]:
             _SCREAMING_SNAKE_CASE       	=			super().outputs
          else:
             _SCREAMING_SNAKE_CASE       	=			super(A__    ,							self ).outputs
             if self.use_past:
                _SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE       	=			self.num_layers
                for i in range(A__ ):
                   _SCREAMING_SNAKE_CASE       	=			{0: """batch""", 2: """past_sequence + sequence"""}
                   _SCREAMING_SNAKE_CASE       	=			{0: """batch""", 2: """past_sequence + sequence"""}
          return common_outputs






       def     UpperCamelCase			(							self    ,							A__    ,							A__ = -1    ,							A__ = -1    ,							A__ = False    ,							A__ = None    ,							)      ->     Mapping[str, Any]:
          _SCREAMING_SNAKE_CASE       	=			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
              A__    ,							A__    ,							A__    ,							A__    ,							A__ )

          # Generate decoder inputs
          _SCREAMING_SNAKE_CASE       	=			seq_length if not self.use_past else 1
          _SCREAMING_SNAKE_CASE       	=			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
              A__    ,							A__    ,							A__    ,							A__    ,							A__ )
          _SCREAMING_SNAKE_CASE       	=			{F"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
          _SCREAMING_SNAKE_CASE       	=			dict(**A__    ,							**A__ )

          if self.use_past:
             if not is_torch_available():
                raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed.""" )
             else:
                import torch
             _SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE       	=			common_inputs["""input_ids"""].shape
             _SCREAMING_SNAKE_CASE       	=			common_inputs["""decoder_input_ids"""].shape[1]
             _SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE       	=			self.num_attention_heads
             _SCREAMING_SNAKE_CASE       	=			(
                 batch,
                 num_encoder_attention_heads,
                 encoder_seq_length,
                 self._config.hidden_size // num_encoder_attention_heads,
             )
             _SCREAMING_SNAKE_CASE       	=			decoder_seq_length + 3
             _SCREAMING_SNAKE_CASE       	=			(
                 batch,
                 num_decoder_attention_heads,
                 decoder_past_length,
                 self._config.hidden_size // num_decoder_attention_heads,
             )

             _SCREAMING_SNAKE_CASE       	=			torch.cat(
                 [common_inputs["""decoder_attention_mask"""], torch.ones(A__    ,							A__ )]    ,							dim=1 )

             _SCREAMING_SNAKE_CASE       	=			[]
             # If the number of encoder and decoder layers are present in the model configuration, both are considered
             _SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE       	=			self.num_layers
             _SCREAMING_SNAKE_CASE       	=			min(A__    ,							A__ )
             _SCREAMING_SNAKE_CASE       	=			max(A__    ,							A__ ) - min_num_layers
             _SCREAMING_SNAKE_CASE       	=			"""encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""

             for _ in range(A__ ):
                common_inputs["past_key_values"].append(
                    (
                        torch.zeros(A__ ),
                        torch.zeros(A__ ),
                        torch.zeros(A__ ),
                        torch.zeros(A__ ),
                    ) )
             # TODO: test this.
             _SCREAMING_SNAKE_CASE       	=			encoder_shape if remaining_side_name == """encoder""" else decoder_shape
             for _ in range(A__    ,							A__ ):
                common_inputs["past_key_values"].append((torch.zeros(A__ ), torch.zeros(A__ )) )
          return common_inputs






       def     UpperCamelCase			(							self    ,							A__    ,							A__ = -1    ,							A__ = -1    ,							A__ = False    ,							A__ = None    ,							)      ->     Mapping[str, Any]:
          _SCREAMING_SNAKE_CASE       	=			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
              A__    ,							A__    ,							A__    ,							A__    ,							A__ )

          if self.use_past:
             if not is_torch_available():
                raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed.""" )
             else:
                import torch
             _SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE       	=			common_inputs["""input_ids"""].shape
             # Not using the same length for past_key_values
             _SCREAMING_SNAKE_CASE       	=			seqlen + 2
             _SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE       	=			self.num_layers
             _SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE       	=			self.num_attention_heads
             _SCREAMING_SNAKE_CASE       	=			(
                 batch,
                 num_encoder_attention_heads,
                 past_key_values_length,
                 self._config.hidden_size // num_encoder_attention_heads,
             )

             _SCREAMING_SNAKE_CASE       	=			common_inputs["""attention_mask"""].dtype
             _SCREAMING_SNAKE_CASE       	=			torch.cat(
                 [common_inputs["""attention_mask"""], torch.ones(A__    ,							A__    ,							dtype=A__ )]    ,							dim=1 )
             _SCREAMING_SNAKE_CASE       	=			[
                 (torch.zeros(A__ ), torch.zeros(A__ )) for _ in range(A__ )
             ]
          return common_inputs






       def     UpperCamelCase			(							self    ,							A__    ,							A__ = -1    ,							A__ = -1    ,							A__ = False    ,							A__ = None    ,							)      ->     Mapping[str, Any]:
          # Copied from OnnxConfig.generate_dummy_inputs
          # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
          # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
          _SCREAMING_SNAKE_CASE       	=			compute_effective_axis_dimension(
              A__    ,							fixed_dimension=OnnxConfig.default_fixed_batch    ,							num_token_to_add=0 )

          # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
          _SCREAMING_SNAKE_CASE       	=			tokenizer.num_special_tokens_to_add(A__ )
          _SCREAMING_SNAKE_CASE       	=			compute_effective_axis_dimension(
              A__    ,							fixed_dimension=OnnxConfig.default_fixed_sequence    ,							num_token_to_add=A__ )

          # Generate dummy inputs according to compute batch and sequence
          _SCREAMING_SNAKE_CASE       	=			[""" """.join([tokenizer.unk_token] ) * seq_length] * batch_size
          _SCREAMING_SNAKE_CASE       	=			dict(tokenizer(A__    ,							return_tensors=A__ ) )
          return common_inputs






       def     UpperCamelCase			(							self    ,							A__    ,							A__ = -1    ,							A__ = -1    ,							A__ = False    ,							A__ = None    ,							)      ->     Mapping[str, Any]:
          if self.task in ["default", "seq2seq-lm"]:
             _SCREAMING_SNAKE_CASE       	=			self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                 A__    ,							batch_size=A__    ,							seq_length=A__    ,							is_pair=A__    ,							framework=A__ )

          elif self.task == "causal-lm":
             _SCREAMING_SNAKE_CASE       	=			self._generate_dummy_inputs_for_causal_lm(
                 A__    ,							batch_size=A__    ,							seq_length=A__    ,							is_pair=A__    ,							framework=A__ )
          else:
             _SCREAMING_SNAKE_CASE       	=			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                 A__    ,							batch_size=A__    ,							seq_length=A__    ,							is_pair=A__    ,							framework=A__ )

          return common_inputs






       def     UpperCamelCase			(							self    ,							A__    ,							A__    ,							A__    ,							A__ )      ->     Tuple:
          if self.task in ["default", "seq2seq-lm"]:
             _SCREAMING_SNAKE_CASE       	=			super()._flatten_past_key_values_(A__    ,							A__    ,							A__    ,							A__ )
          else:
             _SCREAMING_SNAKE_CASE       	=			super(A__    ,							self )._flatten_past_key_values_(
                 A__    ,							A__    ,							A__    ,							A__ )








'''simple docstring'''



# 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__ :						str    							=					1E-10
UpperCamelCase__ :						Any    							=					"This is incredible! I love it, this is the best chicken I have ever had."
UpperCamelCase__ :						Union[str, Any]    							=					100

class 				_a			(nn.Module):





       """simple docstring"""




       def __init__(							self    ,							A__    ,							A__="gpt2-medium"    ,							A__=False    ,							A__="cpu" )      ->     Any:
          super().__init__()
          _SCREAMING_SNAKE_CASE       	=			GPTaTokenizer.from_pretrained(A__ )
          _SCREAMING_SNAKE_CASE       	=			GPTaLMHeadModel.from_pretrained(A__ )
          _SCREAMING_SNAKE_CASE       	=			self.encoder.transformer.config.hidden_size
          _SCREAMING_SNAKE_CASE       	=			ClassificationHead(class_size=A__    ,							embed_size=self.embed_size )
          _SCREAMING_SNAKE_CASE       	=			cached_mode
          _SCREAMING_SNAKE_CASE       	=			device






       def     UpperCamelCase			(							self )      ->     str:
          return self.classifier_head






       def     UpperCamelCase			(							self )      ->     List[Any]:
          for param in self.encoder.parameters():
             _SCREAMING_SNAKE_CASE       	=			False
          self.classifier_head.train()






       def     UpperCamelCase			(							self    ,							A__ )      ->     List[Any]:
          _SCREAMING_SNAKE_CASE       	=			x.ne(0 ).unsqueeze(2 ).repeat(1    ,							1    ,							self.embed_size ).float().to(self.device ).detach()
          _SCREAMING_SNAKE_CASE       	=			self.encoder.transformer(A__ )["""last_hidden_state"""]
          _SCREAMING_SNAKE_CASE       	=			hidden * mask
          _SCREAMING_SNAKE_CASE       	=			torch.sum(A__    ,							dim=1 ) / (torch.sum(A__    ,							dim=1 ).detach() + EPSILON)
          return avg_hidden






       def     UpperCamelCase			(							self    ,							A__ )      ->     Any:
          if self.cached_mode:
             _SCREAMING_SNAKE_CASE       	=			x.to(self.device )
          else:
             _SCREAMING_SNAKE_CASE       	=			self.avg_representation(x.to(self.device ) )

          _SCREAMING_SNAKE_CASE       	=			self.classifier_head(A__ )
          _SCREAMING_SNAKE_CASE       	=			nn.functional.log_softmax(A__    ,							dim=-1 )

          return probs






class 				_a			(data.Dataset):





       """simple docstring"""




       def __init__(							self    ,							A__    ,							A__ )      ->     Tuple:
          _SCREAMING_SNAKE_CASE       	=			X
          _SCREAMING_SNAKE_CASE       	=			y






       def __len__(							self )      ->     Any:
          return len(self.X )






       def __getitem__(							self    ,							A__ )      ->     Dict:
          _SCREAMING_SNAKE_CASE       	=			{}
          _SCREAMING_SNAKE_CASE       	=			self.X[index]
          _SCREAMING_SNAKE_CASE       	=			self.y[index]
          return data






def 				lowerCAmelCase_		(			SCREAMING_SNAKE_CASE_  )       ->       Tuple:




   """simple docstring"""
   def pad_sequences(SCREAMING_SNAKE_CASE_  ):
      _SCREAMING_SNAKE_CASE       	=			[len(SCREAMING_SNAKE_CASE_  ) for seq in sequences]

      _SCREAMING_SNAKE_CASE       	=			torch.zeros(len(SCREAMING_SNAKE_CASE_  )      , max(SCREAMING_SNAKE_CASE_  )  ).long()  # padding value = 0

      for i, seq in enumerate(SCREAMING_SNAKE_CASE_  ):
         _SCREAMING_SNAKE_CASE       	=			lengths[i]
         _SCREAMING_SNAKE_CASE       	=			seq[:end]

      return padded_sequences, lengths

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

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

   return x_batch, y_batch
def 				lowerCAmelCase_		(			SCREAMING_SNAKE_CASE_  )       ->       Optional[int]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE       	=			{}
   for key in data[0].keys():
      _SCREAMING_SNAKE_CASE       	=			[d[key] for d in data]

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

   return x_batch, y_batch
def 				lowerCAmelCase_		(			SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_=0      , SCREAMING_SNAKE_CASE_=10      , SCREAMING_SNAKE_CASE_="cpu"  )       ->       Any:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE       	=			0
   discriminator.train_custom()
   for batch_idx, (input_t, target_t) in enumerate(SCREAMING_SNAKE_CASE_  ):
      _SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE       	=			input_t.to(SCREAMING_SNAKE_CASE_  ), target_t.to(SCREAMING_SNAKE_CASE_  )

      optimizer.zero_grad()

      _SCREAMING_SNAKE_CASE       	=			discriminator(SCREAMING_SNAKE_CASE_  )
      _SCREAMING_SNAKE_CASE       	=			nn.functional.nll_loss(SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_  )
      loss.backward(retain_graph=SCREAMING_SNAKE_CASE_  )
      optimizer.step()

      samples_so_far += len(SCREAMING_SNAKE_CASE_  )

      if batch_idx % log_interval == 0:
         print(
             """Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}""".format(
                 epoch + 1      , SCREAMING_SNAKE_CASE_      , len(data_loader.dataset  )      , 1_00 * samples_so_far / len(data_loader.dataset  )      , loss.item()      , )  )
def 				lowerCAmelCase_		(			SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_="cpu"  )       ->       Any:




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

   test_loss /= len(data_loader.dataset  )

   print(
       """Performance on test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)""".format(
           SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , len(data_loader.dataset  )      , 100.0 * correct / len(data_loader.dataset  )  )  )
def 				lowerCAmelCase_		(			SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_=False      , SCREAMING_SNAKE_CASE_="cpu"  )       ->       str:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE       	=			model.tokenizer.encode(SCREAMING_SNAKE_CASE_  )
   _SCREAMING_SNAKE_CASE       	=			torch.tensor([input_t]      , dtype=torch.long      , device=SCREAMING_SNAKE_CASE_  )
   if cached:
      _SCREAMING_SNAKE_CASE       	=			model.avg_representation(SCREAMING_SNAKE_CASE_  )

   _SCREAMING_SNAKE_CASE       	=			model(SCREAMING_SNAKE_CASE_  ).data.cpu().numpy().flatten().tolist()
   print("""Input sentence:"""      , SCREAMING_SNAKE_CASE_  )
   print(
       """Predictions:"""      , """, """.join("""{}: {:.4f}""".format(SCREAMING_SNAKE_CASE_      , math.exp(SCREAMING_SNAKE_CASE_  )  ) for c, log_prob in zip(SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_  )  )      , )
def 				lowerCAmelCase_		(			SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_=False      , SCREAMING_SNAKE_CASE_="cpu"  )       ->       List[str]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE       	=			torch.utils.data.DataLoader(dataset=SCREAMING_SNAKE_CASE_      , batch_size=SCREAMING_SNAKE_CASE_      , collate_fn=SCREAMING_SNAKE_CASE_  )

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

   _SCREAMING_SNAKE_CASE       	=			torch.utils.data.DataLoader(
       dataset=Dataset(SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_  )      , batch_size=SCREAMING_SNAKE_CASE_      , shuffle=SCREAMING_SNAKE_CASE_      , collate_fn=SCREAMING_SNAKE_CASE_  )

   return data_loader
def 				lowerCAmelCase_		(			SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_=None      , SCREAMING_SNAKE_CASE_="gpt2-medium"      , SCREAMING_SNAKE_CASE_=10      , SCREAMING_SNAKE_CASE_=64      , SCREAMING_SNAKE_CASE_=10      , SCREAMING_SNAKE_CASE_=False      , SCREAMING_SNAKE_CASE_=False      , SCREAMING_SNAKE_CASE_=False      , )       ->       Any:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE       	=			"""cuda""" if torch.cuda.is_available() and not no_cuda else """cpu"""

   print("""Preprocessing {} dataset...""".format(SCREAMING_SNAKE_CASE_  )  )
   _SCREAMING_SNAKE_CASE       	=			time.time()

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

      _SCREAMING_SNAKE_CASE       	=			Discriminator(
          class_size=len(SCREAMING_SNAKE_CASE_  )      , pretrained_model=SCREAMING_SNAKE_CASE_      , cached_mode=SCREAMING_SNAKE_CASE_      , device=SCREAMING_SNAKE_CASE_  ).to(SCREAMING_SNAKE_CASE_  )

      _SCREAMING_SNAKE_CASE       	=			torchtext_data.Field()
      _SCREAMING_SNAKE_CASE       	=			torchtext_data.Field(sequential=SCREAMING_SNAKE_CASE_  )
      _SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE       	=			datasets.SST.splits(
          SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , fine_grained=SCREAMING_SNAKE_CASE_      , train_subtrees=SCREAMING_SNAKE_CASE_      , )

      _SCREAMING_SNAKE_CASE       	=			[]
      _SCREAMING_SNAKE_CASE       	=			[]
      for i in trange(len(SCREAMING_SNAKE_CASE_  )      , ascii=SCREAMING_SNAKE_CASE_  ):
         _SCREAMING_SNAKE_CASE       	=			TreebankWordDetokenizer().detokenize(vars(train_data[i]  )["""text"""]  )
         _SCREAMING_SNAKE_CASE       	=			discriminator.tokenizer.encode(SCREAMING_SNAKE_CASE_  )
         _SCREAMING_SNAKE_CASE       	=			torch.tensor([5_02_56] + seq      , device=SCREAMING_SNAKE_CASE_      , dtype=torch.long  )
         x.append(SCREAMING_SNAKE_CASE_  )
         y.append(classaidx[vars(train_data[i]  )["""label"""]]  )
      _SCREAMING_SNAKE_CASE       	=			Dataset(SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_  )

      _SCREAMING_SNAKE_CASE       	=			[]
      _SCREAMING_SNAKE_CASE       	=			[]
      for i in trange(len(SCREAMING_SNAKE_CASE_  )      , ascii=SCREAMING_SNAKE_CASE_  ):
         _SCREAMING_SNAKE_CASE       	=			TreebankWordDetokenizer().detokenize(vars(test_data[i]  )["""text"""]  )
         _SCREAMING_SNAKE_CASE       	=			discriminator.tokenizer.encode(SCREAMING_SNAKE_CASE_  )
         _SCREAMING_SNAKE_CASE       	=			torch.tensor([5_02_56] + seq      , device=SCREAMING_SNAKE_CASE_      , dtype=torch.long  )
         test_x.append(SCREAMING_SNAKE_CASE_  )
         test_y.append(classaidx[vars(test_data[i]  )["""label"""]]  )
      _SCREAMING_SNAKE_CASE       	=			Dataset(SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_  )

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

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

      _SCREAMING_SNAKE_CASE       	=			Discriminator(
          class_size=len(SCREAMING_SNAKE_CASE_  )      , pretrained_model=SCREAMING_SNAKE_CASE_      , cached_mode=SCREAMING_SNAKE_CASE_      , device=SCREAMING_SNAKE_CASE_  ).to(SCREAMING_SNAKE_CASE_  )

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

               if len(SCREAMING_SNAKE_CASE_  ) < max_length_seq:
                  _SCREAMING_SNAKE_CASE       	=			torch.tensor([5_02_56] + seq      , device=SCREAMING_SNAKE_CASE_      , dtype=torch.long  )
               else:
                  print("""Line {} is longer than maximum length {}""".format(SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_  )  )
                  continue
               x.append(SCREAMING_SNAKE_CASE_  )
               y.append(d["""label"""]  )
            except Exception:
               print("""Error evaluating / tokenizing line {}, skipping it""".format(SCREAMING_SNAKE_CASE_  )  )
               pass

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

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

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

      _SCREAMING_SNAKE_CASE       	=			Discriminator(
          class_size=len(SCREAMING_SNAKE_CASE_  )      , pretrained_model=SCREAMING_SNAKE_CASE_      , cached_mode=SCREAMING_SNAKE_CASE_      , device=SCREAMING_SNAKE_CASE_  ).to(SCREAMING_SNAKE_CASE_  )

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

               if len(SCREAMING_SNAKE_CASE_  ) < max_length_seq:
                  _SCREAMING_SNAKE_CASE       	=			torch.tensor([5_02_56] + seq      , device=SCREAMING_SNAKE_CASE_      , dtype=torch.long  )
               else:
                  print("""Line {} is longer than maximum length {}""".format(SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_  )  )
                  continue
               x.append(SCREAMING_SNAKE_CASE_  )
               y.append(int(np.sum(d["""label"""]  ) > 0  )  )
            except Exception:
               print("""Error evaluating / tokenizing line {}, skipping it""".format(SCREAMING_SNAKE_CASE_  )  )
               pass

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

      _SCREAMING_SNAKE_CASE       	=			{
          """class_size""": len(SCREAMING_SNAKE_CASE_  ),
          """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."""  )

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

      _SCREAMING_SNAKE_CASE       	=			sorted(SCREAMING_SNAKE_CASE_  )
      _SCREAMING_SNAKE_CASE       	=			{c: i for i, c in enumerate(SCREAMING_SNAKE_CASE_  )}

      _SCREAMING_SNAKE_CASE       	=			Discriminator(
          class_size=len(SCREAMING_SNAKE_CASE_  )      , pretrained_model=SCREAMING_SNAKE_CASE_      , cached_mode=SCREAMING_SNAKE_CASE_      , device=SCREAMING_SNAKE_CASE_  ).to(SCREAMING_SNAKE_CASE_  )

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

               try:
                  _SCREAMING_SNAKE_CASE       	=			discriminator.tokenizer.encode(SCREAMING_SNAKE_CASE_  )
                  if len(SCREAMING_SNAKE_CASE_  ) < max_length_seq:
                     _SCREAMING_SNAKE_CASE       	=			torch.tensor([5_02_56] + seq      , device=SCREAMING_SNAKE_CASE_      , dtype=torch.long  )

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

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

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

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

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

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

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

      _SCREAMING_SNAKE_CASE       	=			time.time()

      _SCREAMING_SNAKE_CASE       	=			get_cached_data_loader(SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , shuffle=SCREAMING_SNAKE_CASE_      , device=SCREAMING_SNAKE_CASE_  )

      _SCREAMING_SNAKE_CASE       	=			get_cached_data_loader(SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , device=SCREAMING_SNAKE_CASE_  )

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

   else:
      _SCREAMING_SNAKE_CASE       	=			torch.utils.data.DataLoader(
          dataset=SCREAMING_SNAKE_CASE_      , batch_size=SCREAMING_SNAKE_CASE_      , shuffle=SCREAMING_SNAKE_CASE_      , collate_fn=SCREAMING_SNAKE_CASE_  )
      _SCREAMING_SNAKE_CASE       	=			torch.utils.data.DataLoader(dataset=SCREAMING_SNAKE_CASE_      , batch_size=SCREAMING_SNAKE_CASE_      , collate_fn=SCREAMING_SNAKE_CASE_  )

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

   _SCREAMING_SNAKE_CASE       	=			optim.Adam(discriminator.parameters()      , lr=0.0001  )

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

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

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

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

      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(SCREAMING_SNAKE_CASE_      , epoch + 1  )      , )


if __name__ == "__main__":
     UpperCamelCase__ :						int    							=					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__ :						List[Any]    							=					parser.parse_args()

     train_discriminator(**(vars(args)))






