




from dataclasses import dataclass
from typing import Any, Dict, Optional

import torch
import torch.nn.functional as F
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..models.embeddings import ImagePositionalEmbeddings
from ..utils import BaseOutput, deprecate
from .attention import BasicTransformerBlock
from .embeddings import PatchEmbed
from .modeling_utils import ModelMixin





@dataclass
class        lowerCamelCase__(   __lowerCamelCase):
      UpperCAmelCase__ :      torch.FloatTensor





class        lowerCamelCase__(   __lowerCamelCase					,	__lowerCamelCase):
      @register_to_config
      def __init__(							self:					Tuple       ,			UpperCamelCase_:					int = 16       ,			UpperCamelCase_:					int = 88       ,			UpperCamelCase_:					Optional[int] = None       ,			UpperCamelCase_:					Optional[int] = None       ,			UpperCamelCase_:					int = 1       ,			UpperCamelCase_:					float = 0.0       ,			UpperCamelCase_:					int = 32       ,			UpperCamelCase_:					Optional[int] = None       ,			UpperCamelCase_:					bool = False       ,			UpperCamelCase_:					Optional[int] = None       ,			UpperCamelCase_:					Optional[int] = None       ,			UpperCamelCase_:					Optional[int] = None       ,			UpperCamelCase_:					str = "geglu"       ,			UpperCamelCase_:					Optional[int] = None       ,			UpperCamelCase_:					bool = False       ,			UpperCamelCase_:					bool = False       ,			UpperCamelCase_:					bool = False       ,			UpperCamelCase_:					str = "layer_norm"       ,			UpperCamelCase_:					bool = True       ,			):
             super().__init__()
             __lowerCamelCase  =		use_linear_projection
             __lowerCamelCase  =		num_attention_heads
             __lowerCamelCase  =		attention_head_dim
             __lowerCamelCase  =		num_attention_heads * attention_head_dim

             # 1. Transformer2DModel can process both standard continuous images of shape `(batch_size, num_channels, width, height)` as well as quantized image embeddings of shape `(batch_size, num_image_vectors)`
             # Define whether input is continuous or discrete depending on configuration
             __lowerCamelCase  =		(in_channels is not None) and (patch_size is None)
             __lowerCamelCase  =		num_vector_embeds is not None
             __lowerCamelCase  =		in_channels is not None and patch_size is not None

             if norm_type == "layer_norm" and num_embeds_ada_norm is not None:
                    __lowerCamelCase  =		(
                        F'The configuration file of this model: {self.__class__} is outdated. `norm_type` is either not set or'
                        """ incorrectly set to `'layer_norm'`.Make sure to set `norm_type` to `'ada_norm'` in the config."""
                        """ Please make sure to update the config accordingly as leaving `norm_type` might led to incorrect"""
                        """ results in future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it"""
                        """ would be very nice if you could open a Pull request for the `transformer/config.json` file"""
                    )
                    deprecate("""norm_type!=num_embeds_ada_norm"""       ,			"""1.0.0"""       ,			UpperCamelCase_       ,			standard_warn=UpperCamelCase_  )
                    __lowerCamelCase  =		"""ada_norm"""

             if self.is_input_continuous and self.is_input_vectorized:
                    raise ValueError(
                        F'Cannot define both `in_channels`: {in_channels} and `num_vector_embeds`: {num_vector_embeds}. Make'
                        """ sure that either `in_channels` or `num_vector_embeds` is None."""  )
             elif self.is_input_vectorized and self.is_input_patches:
                    raise ValueError(
                        F'Cannot define both `num_vector_embeds`: {num_vector_embeds} and `patch_size`: {patch_size}. Make'
                        """ sure that either `num_vector_embeds` or `num_patches` is None."""  )
             elif not self.is_input_continuous and not self.is_input_vectorized and not self.is_input_patches:
                    raise ValueError(
                        F'Has to define `in_channels`: {in_channels}, `num_vector_embeds`: {num_vector_embeds}, or patch_size:'
                        F' {patch_size}. Make sure that `in_channels`, `num_vector_embeds` or `num_patches` is not None.'  )

             # 2. Define input layers
             if self.is_input_continuous:
                    __lowerCamelCase  =		in_channels

                    __lowerCamelCase  =		torch.nn.GroupNorm(num_groups=UpperCamelCase_       ,			num_channels=UpperCamelCase_       ,			eps=1E-6       ,			affine=UpperCamelCase_  )
                    if use_linear_projection:
                           __lowerCamelCase  =		nn.Linear(UpperCamelCase_       ,			UpperCamelCase_  )
                    else:
                           __lowerCamelCase  =		nn.Convad(UpperCamelCase_       ,			UpperCamelCase_       ,			kernel_size=1       ,			stride=1       ,			padding=0  )
             elif self.is_input_vectorized:
                    assert sample_size is not None, "Transformer2DModel over discrete input must provide sample_size"
                    assert num_vector_embeds is not None, "Transformer2DModel over discrete input must provide num_embed"

                    __lowerCamelCase  =		sample_size
                    __lowerCamelCase  =		sample_size
                    __lowerCamelCase  =		num_vector_embeds
                    __lowerCamelCase  =		self.height * self.width

                    __lowerCamelCase  =		ImagePositionalEmbeddings(
                        num_embed=UpperCamelCase_       ,			embed_dim=UpperCamelCase_       ,			height=self.height       ,			width=self.width  )
             elif self.is_input_patches:
                    assert sample_size is not None, "Transformer2DModel over patched input must provide sample_size"

                    __lowerCamelCase  =		sample_size
                    __lowerCamelCase  =		sample_size

                    __lowerCamelCase  =		patch_size
                    __lowerCamelCase  =		PatchEmbed(
                        height=UpperCamelCase_       ,			width=UpperCamelCase_       ,			patch_size=UpperCamelCase_       ,			in_channels=UpperCamelCase_       ,			embed_dim=UpperCamelCase_       ,			)

             # 3. Define transformers blocks
             __lowerCamelCase  =		nn.ModuleList(
                 [
                     BasicTransformerBlock(
                         UpperCamelCase_       ,			UpperCamelCase_       ,			UpperCamelCase_       ,			dropout=UpperCamelCase_       ,			cross_attention_dim=UpperCamelCase_       ,			activation_fn=UpperCamelCase_       ,			num_embeds_ada_norm=UpperCamelCase_       ,			attention_bias=UpperCamelCase_       ,			only_cross_attention=UpperCamelCase_       ,			upcast_attention=UpperCamelCase_       ,			norm_type=UpperCamelCase_       ,			norm_elementwise_affine=UpperCamelCase_       ,			)
                     for d in range(UpperCamelCase_  )
                 ]  )

             # 4. Define output layers
             __lowerCamelCase  =		in_channels if out_channels is None else out_channels
             if self.is_input_continuous:
                    # TODO: should use out_channels for continuous projections
                    if use_linear_projection:
                           __lowerCamelCase  =		nn.Linear(UpperCamelCase_       ,			UpperCamelCase_  )
                    else:
                           __lowerCamelCase  =		nn.Convad(UpperCamelCase_       ,			UpperCamelCase_       ,			kernel_size=1       ,			stride=1       ,			padding=0  )
             elif self.is_input_vectorized:
                    __lowerCamelCase  =		nn.LayerNorm(UpperCamelCase_  )
                    __lowerCamelCase  =		nn.Linear(UpperCamelCase_       ,			self.num_vector_embeds - 1  )
             elif self.is_input_patches:
                    __lowerCamelCase  =		nn.LayerNorm(UpperCamelCase_       ,			elementwise_affine=UpperCamelCase_       ,			eps=1E-6  )
                    __lowerCamelCase  =		nn.Linear(UpperCamelCase_       ,			2 * inner_dim  )
                    __lowerCamelCase  =		nn.Linear(UpperCamelCase_       ,			patch_size * patch_size * self.out_channels  )




      def      lowerCAmelCase__		(							self:					Tuple       ,			UpperCamelCase_:					torch.Tensor       ,			UpperCamelCase_:					Optional[torch.Tensor] = None       ,			UpperCamelCase_:					Optional[torch.LongTensor] = None       ,			UpperCamelCase_:					Optional[torch.LongTensor] = None       ,			UpperCamelCase_:					Dict[str, Any] = None       ,			UpperCamelCase_:					Optional[torch.Tensor] = None       ,			UpperCamelCase_:					Optional[torch.Tensor] = None       ,			UpperCamelCase_:					bool = True       ,			):
             # ensure attention_mask is a bias, and give it a singleton query_tokens dimension.
             #   we may have done this conversion already, e.g. if we came here via UNet2DConditionModel#forward.
             #   we can tell by counting dims; if ndim == 2: it's a mask rather than a bias.
             # expects mask of shape:
             #   [batch, key_tokens]
             # adds singleton query_tokens dimension:
             #   [batch,                    1, key_tokens]
             # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes:
             #   [batch,  heads, query_tokens, key_tokens] (e.g. torch sdp attn)
             #   [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn)
             if attention_mask is not None and attention_mask.ndim == 2:
                    # assume that mask is expressed as:
                    #   (1 = keep,      0 = discard)
                    # convert mask into a bias that can be added to attention scores:
                    #       (keep = +0,     discard = -10000.0)
                    __lowerCamelCase  =		(1 - attention_mask.to(hidden_states.dtype  )) * -1_0000.0
                    __lowerCamelCase  =		attention_mask.unsqueeze(1  )

             # convert encoder_attention_mask to a bias the same way we do for attention_mask
             if encoder_attention_mask is not None and encoder_attention_mask.ndim == 2:
                    __lowerCamelCase  =		(1 - encoder_attention_mask.to(hidden_states.dtype  )) * -1_0000.0
                    __lowerCamelCase  =		encoder_attention_mask.unsqueeze(1  )

             # 1. Input
             if self.is_input_continuous:
                    __lowerCamelCase,  __lowerCamelCase,  __lowerCamelCase,  __lowerCamelCase  =		hidden_states.shape
                    __lowerCamelCase  =		hidden_states

                    __lowerCamelCase  =		self.norm(UpperCamelCase_  )
                    if not self.use_linear_projection:
                           __lowerCamelCase  =		self.proj_in(UpperCamelCase_  )
                           __lowerCamelCase  =		hidden_states.shape[1]
                           __lowerCamelCase  =		hidden_states.permute(0       ,			2       ,			3       ,			1  ).reshape(UpperCamelCase_       ,			height * width       ,			UpperCamelCase_  )
                    else:
                           __lowerCamelCase  =		hidden_states.shape[1]
                           __lowerCamelCase  =		hidden_states.permute(0       ,			2       ,			3       ,			1  ).reshape(UpperCamelCase_       ,			height * width       ,			UpperCamelCase_  )
                           __lowerCamelCase  =		self.proj_in(UpperCamelCase_  )
             elif self.is_input_vectorized:
                    __lowerCamelCase  =		self.latent_image_embedding(UpperCamelCase_  )
             elif self.is_input_patches:
                    __lowerCamelCase  =		self.pos_embed(UpperCamelCase_  )

             # 2. Blocks
             for block in self.transformer_blocks:
                    __lowerCamelCase  =		block(
                        UpperCamelCase_       ,			attention_mask=UpperCamelCase_       ,			encoder_hidden_states=UpperCamelCase_       ,			encoder_attention_mask=UpperCamelCase_       ,			timestep=UpperCamelCase_       ,			cross_attention_kwargs=UpperCamelCase_       ,			class_labels=UpperCamelCase_       ,			)

             # 3. Output
             if self.is_input_continuous:
                    if not self.use_linear_projection:
                           __lowerCamelCase  =		hidden_states.reshape(UpperCamelCase_       ,			UpperCamelCase_       ,			UpperCamelCase_       ,			UpperCamelCase_  ).permute(0       ,			3       ,			1       ,			2  ).contiguous()
                           __lowerCamelCase  =		self.proj_out(UpperCamelCase_  )
                    else:
                           __lowerCamelCase  =		self.proj_out(UpperCamelCase_  )
                           __lowerCamelCase  =		hidden_states.reshape(UpperCamelCase_       ,			UpperCamelCase_       ,			UpperCamelCase_       ,			UpperCamelCase_  ).permute(0       ,			3       ,			1       ,			2  ).contiguous()

                    __lowerCamelCase  =		hidden_states + residual
             elif self.is_input_vectorized:
                    __lowerCamelCase  =		self.norm_out(UpperCamelCase_  )
                    __lowerCamelCase  =		self.out(UpperCamelCase_  )
                    # (batch, self.num_vector_embeds - 1, self.num_latent_pixels)
                    __lowerCamelCase  =		logits.permute(0       ,			2       ,			1  )

                    # log(p(x_0))
                    __lowerCamelCase  =		F.log_softmax(logits.double()       ,			dim=1  ).float()
             elif self.is_input_patches:
                    # TODO: cleanup!
                    __lowerCamelCase  =		self.transformer_blocks[0].norma.emb(
                        UpperCamelCase_       ,			UpperCamelCase_       ,			hidden_dtype=hidden_states.dtype  )
                    __lowerCamelCase,  __lowerCamelCase  =		self.proj_out_a(F.silu(UpperCamelCase_  )  ).chunk(2       ,			dim=1  )
                    __lowerCamelCase  =		self.norm_out(UpperCamelCase_  ) * (1 + scale[:, None]) + shift[:, None]
                    __lowerCamelCase  =		self.proj_out_a(UpperCamelCase_  )

                    # unpatchify
                    __lowerCamelCase  =		__lowerCamelCase  =		int(hidden_states.shape[1] ** 0.5  )
                    __lowerCamelCase  =		hidden_states.reshape(
                        shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels)  )
                    __lowerCamelCase  =		torch.einsum("""nhwpqc->nchpwq"""       ,			UpperCamelCase_  )
                    __lowerCamelCase  =		hidden_states.reshape(
                        shape=(-1, self.out_channels, height * self.patch_size, width * self.patch_size)  )

             if not return_dict:
                    return (output,)

             return TransformeraDModelOutput(sample=UpperCamelCase_  )







# 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)))

