
'''simple docstring'''





import collections
import inspect
import unittest

from transformers import DonutSwinConfig
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import is_torch_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch
 from torch import nn

 from transformers import DonutSwinModel
 from transformers.models.donut.modeling_donut_swin import DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST


class 	UpperCamelCase__					:






 def __init__(     self					:int		,			_A					:int		,			_A					:List[Any]=13		,			_A					:Any=32		,			_A					:List[str]=2		,			_A					:str=3		,			_A					:Optional[Any]=16		,			_A					:List[Any]=[1, 2, 1]		,			_A					:int=[2, 2, 4]		,			_A					:Optional[Any]=2		,			_A					:Optional[Any]=2.0		,			_A					:int=True		,			_A					:Any=0.0		,			_A					:Tuple=0.0		,			_A					:Union[str, Any]=0.1		,			_A					:str="gelu"		,			_A					:Tuple=False		,			_A					:List[str]=True		,			_A					:Union[str, Any]=0.02		,			_A					:List[str]=1E-5		,			_A					:List[Any]=True		,			_A					:int=None		,			_A					:Union[str, Any]=True		,			_A					:int=10		,			_A					:Optional[int]=8		,			)			->     Optional[int]:







       '''simple docstring'''



       __A    		=     parent
       __A    		=     batch_size
       __A    		=     image_size
       __A    		=     patch_size
       __A    		=     num_channels
       __A    		=     embed_dim
       __A    		=     depths
       __A    		=     num_heads
       __A    		=     window_size
       __A    		=     mlp_ratio
       __A    		=     qkv_bias
       __A    		=     hidden_dropout_prob
       __A    		=     attention_probs_dropout_prob
       __A    		=     drop_path_rate
       __A    		=     hidden_act
       __A    		=     use_absolute_embeddings
       __A    		=     patch_norm
       __A    		=     layer_norm_eps
       __A    		=     initializer_range
       __A    		=     is_training
       __A    		=     scope
       __A    		=     use_labels
       __A    		=     type_sequence_label_size
       __A    		=     encoder_stride






 def 		lowercase_  (     self					:Any     )			->     Optional[Any]:







       '''simple docstring'''



       __A    		=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

       __A    		=     None
       if self.use_labels:
             __A    		=     ids_tensor([self.batch_size]		,			self.type_sequence_label_size     )

       __A    		=     self.get_config()

       return config, pixel_values, labels






 def 		lowercase_  (     self					:List[str]     )			->     str:







       '''simple docstring'''



       return DonutSwinConfig(
           image_size=self.image_size		,			patch_size=self.patch_size		,			num_channels=self.num_channels		,			embed_dim=self.embed_dim		,			depths=self.depths		,			num_heads=self.num_heads		,			window_size=self.window_size		,			mlp_ratio=self.mlp_ratio		,			qkv_bias=self.qkv_bias		,			hidden_dropout_prob=self.hidden_dropout_prob		,			attention_probs_dropout_prob=self.attention_probs_dropout_prob		,			drop_path_rate=self.drop_path_rate		,			hidden_act=self.hidden_act		,			use_absolute_embeddings=self.use_absolute_embeddings		,			path_norm=self.patch_norm		,			layer_norm_eps=self.layer_norm_eps		,			initializer_range=self.initializer_range		,			encoder_stride=self.encoder_stride		,			)






 def 		lowercase_  (     self					:List[str]		,			_A					:Any		,			_A					:List[str]		,			_A					:List[str]     )			->     int:







       '''simple docstring'''



       __A    		=     DonutSwinModel(config=_A     )
       model.to(_A     )
       model.eval()
       __A    		=     model(_A     )

       __A    		=     ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths     ) - 1))
       __A    		=     int(config.embed_dim * 2 ** (len(config.depths     ) - 1)     )

       self.parent.assertEqual(result.last_hidden_state.shape		,			(self.batch_size, expected_seq_len, expected_dim)     )






 def 		lowercase_  (     self					:List[str]     )			->     List[str]:







       '''simple docstring'''



       __A    		=     self.prepare_config_and_inputs()
       (
           (
           __A
       )		, (
           __A
       )		, (
           __A
       )		, 
       )    		=     config_and_inputs
       __A    		=     {'pixel_values': pixel_values}
       return config, inputs_dict


@require_torch
class 	UpperCamelCase__					(      SCREAMING_SNAKE_CASE ,       SCREAMING_SNAKE_CASE ,       unittest.TestCase):
 UpperCAmelCase__      :      List[str]  			=   (DonutSwinModel,) if is_torch_available() else ()
 UpperCAmelCase__      :      Optional[Any]  			=   {'feature-extraction': DonutSwinModel} if is_torch_available() else {}
 UpperCAmelCase__      :      Dict  			=   True

 UpperCAmelCase__      :      int  			=   False
 UpperCAmelCase__      :      List[Any]  			=   False
 UpperCAmelCase__      :      Tuple  			=   False






 def 		lowercase_  (     self					:Union[str, Any]     )			->     int:







       '''simple docstring'''



       __A    		=     DonutSwinModelTester(self     )
       __A    		=     ConfigTester(self		,			config_class=_A		,			embed_dim=37     )






 def 		lowercase_  (     self					:Dict     )			->     Union[str, Any]:







       '''simple docstring'''



       self.create_and_test_config_common_properties()
       self.config_tester.create_and_test_config_to_json_string()
       self.config_tester.create_and_test_config_to_json_file()
       self.config_tester.create_and_test_config_from_and_save_pretrained()
       self.config_tester.create_and_test_config_with_num_labels()
       self.config_tester.check_config_can_be_init_without_params()
       self.config_tester.check_config_arguments_init()






 def 		lowercase_  (     self					:Tuple     )			->     str:







       '''simple docstring'''



       return






 def 		lowercase_  (     self					:List[Any]     )			->     Optional[Any]:







       '''simple docstring'''



       __A    		=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_A     )






 def 		lowercase_  (     self					:int     )			->     int:







       '''simple docstring'''



       pass






 def 		lowercase_  (     self					:Any     )			->     Tuple:







       '''simple docstring'''



       __A		, __A    		=     self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             __A    		=     model_class(_A     )
             self.assertIsInstance(model.get_input_embeddings()		,			(nn.Module)     )
             __A    		=     model.get_output_embeddings()
             self.assertTrue(x is None or isinstance(_A		,			nn.Linear     )     )






 def 		lowercase_  (     self					:Any     )			->     Dict:







       '''simple docstring'''



       __A		, __A    		=     self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             __A    		=     model_class(_A     )
             __A    		=     inspect.signature(model.forward     )
             # signature.parameters is an OrderedDict => so arg_names order is deterministic
             __A    		=     [*signature.parameters.keys()]

             __A    		=     ['pixel_values']
             self.assertListEqual(arg_names[:1]		,			_A     )






 def 		lowercase_  (     self					:List[str]     )			->     Optional[int]:







       '''simple docstring'''



       __A		, __A    		=     self.model_tester.prepare_config_and_inputs_for_common()
       __A    		=     True

       for model_class in self.all_model_classes:
             __A    		=     True
             __A    		=     False
             __A    		=     True
             __A    		=     model_class(_A     )
             model.to(_A     )
             model.eval()
             with torch.no_grad():
                   __A    		=     model(**self._prepare_for_class(_A		,			_A     )     )
             __A    		=     outputs.attentions
             __A    		=     len(self.model_tester.depths     )
             self.assertEqual(len(_A     )		,			_A     )

             # check that output_attentions also work using config
             del inputs_dict["output_attentions"]
             __A    		=     True
             __A    		=     config.window_size**2
             __A    		=     model_class(_A     )
             model.to(_A     )
             model.eval()
             with torch.no_grad():
                   __A    		=     model(**self._prepare_for_class(_A		,			_A     )     )
             __A    		=     outputs.attentions
             self.assertEqual(len(_A     )		,			_A     )

             self.assertListEqual(
                 list(attentions[0].shape[-3:]     )		,			[self.model_tester.num_heads[0], window_size_squared, window_size_squared]		,			)
             __A    		=     len(_A     )

             # Check attention is always last and order is fine
             __A    		=     True
             __A    		=     True
             __A    		=     model_class(_A     )
             model.to(_A     )
             model.eval()
             with torch.no_grad():
                   __A    		=     model(**self._prepare_for_class(_A		,			_A     )     )

             if hasattr(self.model_tester		,			'num_hidden_states_types'     ):
                   __A    		=     self.model_tester.num_hidden_states_types
             else:
                   # also another +1 for reshaped_hidden_states
                   __A    		=     2
             self.assertEqual(out_len + added_hidden_states		,			len(_A     )     )

             __A    		=     outputs.attentions

             self.assertEqual(len(_A     )		,			_A     )

             self.assertListEqual(
                 list(self_attentions[0].shape[-3:]     )		,			[self.model_tester.num_heads[0], window_size_squared, window_size_squared]		,			)






 def 		lowercase_  (     self					:Dict		,			_A					:Optional[int]		,			_A					:Tuple		,			_A					:List[Any]		,			_A					:List[Any]     )			->     Union[str, Any]:







       '''simple docstring'''



       __A    		=     model_class(_A     )
       model.to(_A     )
       model.eval()

       with torch.no_grad():
             __A    		=     model(**self._prepare_for_class(_A		,			_A     )     )

       __A    		=     outputs.hidden_states

       __A    		=     getattr(
           self.model_tester		,			'expected_num_hidden_layers'		,			len(self.model_tester.depths     ) + 1     )
       self.assertEqual(len(_A     )		,			_A     )

       # DonutSwin has a different seq_length
       __A    		=     (
           config.patch_size
           if isinstance(config.patch_size		,			collections.abc.Iterable     )
           else (config.patch_size, config.patch_size)
       )

       __A    		=     (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

       self.assertListEqual(
           list(hidden_states[0].shape[-2:]     )		,			[num_patches, self.model_tester.embed_dim]		,			)

       __A    		=     outputs.reshaped_hidden_states
       self.assertEqual(len(_A     )		,			_A     )

       __A		, __A		, __A		, __A    		=     reshaped_hidden_states[0].shape
       __A    		=     (
           reshaped_hidden_states[0].view(_A		,			_A		,			height * width     ).permute(0		,			2		,			1     )
       )
       self.assertListEqual(
           list(reshaped_hidden_states.shape[-2:]     )		,			[num_patches, self.model_tester.embed_dim]		,			)






 def 		lowercase_  (     self					:List[Any]     )			->     Any:







       '''simple docstring'''



       __A		, __A    		=     self.model_tester.prepare_config_and_inputs_for_common()

       __A    		=     (
           self.model_tester.image_size
           if isinstance(self.model_tester.image_size		,			collections.abc.Iterable     )
           else (self.model_tester.image_size, self.model_tester.image_size)
       )

       for model_class in self.all_model_classes:
             __A    		=     True
             self.check_hidden_states_output(_A		,			_A		,			_A		,			_A     )

             # check that output_hidden_states also work using config
             del inputs_dict["output_hidden_states"]
             __A    		=     True

             self.check_hidden_states_output(_A		,			_A		,			_A		,			_A     )






 def 		lowercase_  (     self					:Dict     )			->     Optional[int]:







       '''simple docstring'''



       __A		, __A    		=     self.model_tester.prepare_config_and_inputs_for_common()
       __A    		=     3

       __A    		=     (
           self.model_tester.image_size
           if isinstance(self.model_tester.image_size		,			collections.abc.Iterable     )
           else (self.model_tester.image_size, self.model_tester.image_size)
       )
       __A    		=     (
           config.patch_size
           if isinstance(config.patch_size		,			collections.abc.Iterable     )
           else (config.patch_size, config.patch_size)
       )

       __A    		=     image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
       __A    		=     image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

       for model_class in self.all_model_classes:
             __A    		=     True
             self.check_hidden_states_output(_A		,			_A		,			_A		,			(padded_height, padded_width)     )

             # check that output_hidden_states also work using config
             del inputs_dict["output_hidden_states"]
             __A    		=     True
             self.check_hidden_states_output(_A		,			_A		,			_A		,			(padded_height, padded_width)     )






 @slow
 def 		lowercase_  (     self					:Optional[int]     )			->     int:







       '''simple docstring'''



       for model_name in DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             __A    		=     DonutSwinModel.from_pretrained(_A     )
             self.assertIsNotNone(_A     )






 def 		lowercase_  (     self					:Tuple     )			->     Union[str, Any]:







       '''simple docstring'''



       __A		, __A    		=     self.model_tester.prepare_config_and_inputs_for_common()

       __A    		=     _config_zero_init(_A     )
       for model_class in self.all_model_classes:
             __A    		=     model_class(config=_A     )
             for name, param in model.named_parameters():
                   if "embeddings" not in name and param.requires_grad:
                         self.assertIn(
                             ((param.data.mean() * 1E9).round() / 1E9).item()		,			[0.0, 1.0]		,			msg=F'Parameter {name} of model {model_class} seems not properly initialized'		,			)








'''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)
a__						:				Optional[int]           =    1E-10
a__						:				str           =    "This is incredible! I love it, this is the best chicken I have ever had."
a__						:				List[Any]           =    1_0_0


class 	UpperCamelCase__					(      nn.Module):






 def __init__(     self					:List[Any]		,			_A					:List[str]		,			_A					:Optional[int]="gpt2-medium"		,			_A					:Tuple=False		,			_A					:List[str]="cpu"     )			->     int:







       '''simple docstring'''



       super().__init__()
       __A    		=     GPTaTokenizer.from_pretrained(_A     )
       __A    		=     GPTaLMHeadModel.from_pretrained(_A     )
       __A    		=     self.encoder.transformer.config.hidden_size
       __A    		=     ClassificationHead(class_size=_A		,			embed_size=self.embed_size     )
       __A    		=     cached_mode
       __A    		=     device






 def 		lowercase_  (     self					:Tuple     )			->     List[str]:







       '''simple docstring'''



       return self.classifier_head






 def 		lowercase_  (     self					:Optional[int]     )			->     Any:







       '''simple docstring'''



       for param in self.encoder.parameters():
             __A    		=     False
       self.classifier_head.train()






 def 		lowercase_  (     self					:List[Any]		,			_A					:Any     )			->     Tuple:







       '''simple docstring'''



       __A    		=     x.ne(0     ).unsqueeze(2     ).repeat(1		,			1		,			self.embed_size     ).float().to(self.device     ).detach()
       __A    		=     self.encoder.transformer(_A     )['last_hidden_state']
       __A    		=     hidden * mask
       __A    		=     torch.sum(_A		,			dim=1     ) / (torch.sum(_A		,			dim=1     ).detach() + EPSILON)
       return avg_hidden






 def 		lowercase_  (     self					:Optional[int]		,			_A					:Union[str, Any]     )			->     Dict:







       '''simple docstring'''



       if self.cached_mode:
             __A    		=     x.to(self.device     )
       else:
             __A    		=     self.avg_representation(x.to(self.device     )     )

       __A    		=     self.classifier_head(_A     )
       __A    		=     nn.functional.log_softmax(_A		,			dim=-1     )

       return probs


class 	UpperCamelCase__					(      data.Dataset):






 def __init__(     self					:Optional[Any]		,			_A					:str		,			_A					:Dict     )			->     Optional[int]:







       '''simple docstring'''



       __A    		=     X
       __A    		=     y






 def __len__(     self					:int     )			->     int:







       '''simple docstring'''



       return len(self.X     )






 def __getitem__(     self					:Union[str, Any]		,			_A					:str     )			->     List[str]:







       '''simple docstring'''



       __A    		=     {}
       __A    		=     self.X[index]
       __A    		=     self.y[index]
       return data





def     snake_case							(						UpperCAmelCase		)->     List[Any]:







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

            __A    		=     torch.zeros(len(UpperCAmelCase		) ,					max(UpperCAmelCase		)		).long()  # padding value = 0

            for i, seq in enumerate(UpperCAmelCase		):
                  __A    		=     lengths[i]
                  __A    		=     seq[:end]

            return padded_sequences, lengths

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

      __A		, __A    		=     pad_sequences(item_info['X']		)
      __A    		=     torch.tensor(item_info['y'] ,					dtype=torch.long		)

      return x_batch, y_batch





def     snake_case							(						UpperCAmelCase		)->     Dict:







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

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

      return x_batch, y_batch





def     snake_case							(						UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase=0 ,					UpperCAmelCase=1_0 ,					UpperCAmelCase="cpu"		)->     Optional[Any]:







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

            optimizer.zero_grad()

            __A    		=     discriminator(UpperCAmelCase		)
            __A    		=     nn.functional.nll_loss(UpperCAmelCase ,					UpperCAmelCase		)
            loss.backward(retain_graph=UpperCAmelCase		)
            optimizer.step()

            samples_so_far += len(UpperCAmelCase		)

            if batch_idx % log_interval == 0:
                  print(
                      'Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                          epoch + 1 ,					UpperCAmelCase ,					len(data_loader.dataset		) ,					1_0_0 * samples_so_far / len(data_loader.dataset		) ,					loss.item() ,					)		)





def     snake_case							(						UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase="cpu"		)->     Tuple:







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

      test_loss /= len(data_loader.dataset		)

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





def     snake_case							(						UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase=False ,					UpperCAmelCase="cpu"		)->     Tuple:







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

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





def     snake_case							(						UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase=False ,					UpperCAmelCase="cpu"		)->     Dict:







      """simple docstring"""
      __A    		=     torch.utils.data.DataLoader(dataset=UpperCAmelCase ,					batch_size=UpperCAmelCase ,					collate_fn=UpperCAmelCase		)

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

      __A    		=     torch.utils.data.DataLoader(
          dataset=Dataset(UpperCAmelCase ,					UpperCAmelCase		) ,					batch_size=UpperCAmelCase ,					shuffle=UpperCAmelCase ,					collate_fn=UpperCAmelCase		)

      return data_loader





def     snake_case							(						UpperCAmelCase ,					UpperCAmelCase=None ,					UpperCAmelCase="gpt2-medium" ,					UpperCAmelCase=1_0 ,					UpperCAmelCase=6_4 ,					UpperCAmelCase=1_0 ,					UpperCAmelCase=False ,					UpperCAmelCase=False ,					UpperCAmelCase=False ,					)->     Optional[int]:







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

      print('Preprocessing {} dataset...'.format(UpperCAmelCase		)		)
      __A    		=     time.time()

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

            __A    		=     Discriminator(
                class_size=len(UpperCAmelCase		) ,					pretrained_model=UpperCAmelCase ,					cached_mode=UpperCAmelCase ,					device=UpperCAmelCase		).to(UpperCAmelCase		)

            __A    		=     torchtext_data.Field()
            __A    		=     torchtext_data.Field(sequential=UpperCAmelCase		)
            __A		, __A		, __A    		=     datasets.SST.splits(
                UpperCAmelCase ,					UpperCAmelCase ,					fine_grained=UpperCAmelCase ,					train_subtrees=UpperCAmelCase ,					)

            __A    		=     []
            __A    		=     []
            for i in trange(len(UpperCAmelCase		) ,					ascii=UpperCAmelCase		):
                  __A    		=     TreebankWordDetokenizer().detokenize(vars(train_data[i]		)['text']		)
                  __A    		=     discriminator.tokenizer.encode(UpperCAmelCase		)
                  __A    		=     torch.tensor([5_0_2_5_6] + seq ,					device=UpperCAmelCase ,					dtype=torch.long		)
                  x.append(UpperCAmelCase		)
                  y.append(classaidx[vars(train_data[i]		)['label']]		)
            __A    		=     Dataset(UpperCAmelCase ,					UpperCAmelCase		)

            __A    		=     []
            __A    		=     []
            for i in trange(len(UpperCAmelCase		) ,					ascii=UpperCAmelCase		):
                  __A    		=     TreebankWordDetokenizer().detokenize(vars(test_data[i]		)['text']		)
                  __A    		=     discriminator.tokenizer.encode(UpperCAmelCase		)
                  __A    		=     torch.tensor([5_0_2_5_6] + seq ,					device=UpperCAmelCase ,					dtype=torch.long		)
                  test_x.append(UpperCAmelCase		)
                  test_y.append(classaidx[vars(test_data[i]		)['label']]		)
            __A    		=     Dataset(UpperCAmelCase ,					UpperCAmelCase		)

            __A    		=     {
                'class_size': len(UpperCAmelCase		),
                'embed_size': discriminator.embed_size,
                'pretrained_model': pretrained_model,
                'class_vocab': classaidx,
                'default_class': 2,
            }

      elif dataset == "clickbait":
            __A    		=     ['non_clickbait', 'clickbait']
            __A    		=     {c: i for i, c in enumerate(UpperCAmelCase		)}

            __A    		=     Discriminator(
                class_size=len(UpperCAmelCase		) ,					pretrained_model=UpperCAmelCase ,					cached_mode=UpperCAmelCase ,					device=UpperCAmelCase		).to(UpperCAmelCase		)

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

                              if len(UpperCAmelCase		) < max_length_seq:
                                    __A    		=     torch.tensor([5_0_2_5_6] + seq ,					device=UpperCAmelCase ,					dtype=torch.long		)
                              else:
                                    print('Line {} is longer than maximum length {}'.format(UpperCAmelCase ,					UpperCAmelCase		)		)
                                    continue
                              x.append(UpperCAmelCase		)
                              y.append(d['label']		)
                        except Exception:
                              print('Error evaluating / tokenizing line {}, skipping it'.format(UpperCAmelCase		)		)
                              pass

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

            __A    		=     {
                'class_size': len(UpperCAmelCase		),
                'embed_size': discriminator.embed_size,
                'pretrained_model': pretrained_model,
                'class_vocab': classaidx,
                'default_class': 1,
            }

      elif dataset == "toxic":
            __A    		=     ['non_toxic', 'toxic']
            __A    		=     {c: i for i, c in enumerate(UpperCAmelCase		)}

            __A    		=     Discriminator(
                class_size=len(UpperCAmelCase		) ,					pretrained_model=UpperCAmelCase ,					cached_mode=UpperCAmelCase ,					device=UpperCAmelCase		).to(UpperCAmelCase		)

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

                              if len(UpperCAmelCase		) < max_length_seq:
                                    __A    		=     torch.tensor([5_0_2_5_6] + seq ,					device=UpperCAmelCase ,					dtype=torch.long		)
                              else:
                                    print('Line {} is longer than maximum length {}'.format(UpperCAmelCase ,					UpperCAmelCase		)		)
                                    continue
                              x.append(UpperCAmelCase		)
                              y.append(int(np.sum(d['label']		) > 0		)		)
                        except Exception:
                              print('Error evaluating / tokenizing line {}, skipping it'.format(UpperCAmelCase		)		)
                              pass

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

            __A    		=     {
                'class_size': len(UpperCAmelCase		),
                '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.'		)

            __A    		=     set()
            with open(UpperCAmelCase		) as f:
                  __A    		=     csv.reader(UpperCAmelCase ,					delimiter='\t'		)
                  for row in tqdm(UpperCAmelCase ,					ascii=UpperCAmelCase		):
                        if row:
                              classes.add(row[0]		)

            __A    		=     sorted(UpperCAmelCase		)
            __A    		=     {c: i for i, c in enumerate(UpperCAmelCase		)}

            __A    		=     Discriminator(
                class_size=len(UpperCAmelCase		) ,					pretrained_model=UpperCAmelCase ,					cached_mode=UpperCAmelCase ,					device=UpperCAmelCase		).to(UpperCAmelCase		)

            __A    		=     []
            __A    		=     []
            with open(UpperCAmelCase		) as f:
                  __A    		=     csv.reader(UpperCAmelCase ,					delimiter='\t'		)
                  for i, row in enumerate(tqdm(UpperCAmelCase ,					ascii=UpperCAmelCase		)		):
                        if row:
                              __A    		=     row[0]
                              __A    		=     row[1]

                              try:
                                    __A    		=     discriminator.tokenizer.encode(UpperCAmelCase		)
                                    if len(UpperCAmelCase		) < max_length_seq:
                                          __A    		=     torch.tensor([5_0_2_5_6] + seq ,					device=UpperCAmelCase ,					dtype=torch.long		)

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

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

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

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

            __A    		=     {
                'class_size': len(UpperCAmelCase		),
                'embed_size': discriminator.embed_size,
                'pretrained_model': pretrained_model,
                'class_vocab': classaidx,
                'default_class': 0,
            }

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

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

            __A    		=     time.time()

            __A    		=     get_cached_data_loader(UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase ,					shuffle=UpperCAmelCase ,					device=UpperCAmelCase		)

            __A    		=     get_cached_data_loader(UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase ,					device=UpperCAmelCase		)

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

      else:
            __A    		=     torch.utils.data.DataLoader(
                dataset=UpperCAmelCase ,					batch_size=UpperCAmelCase ,					shuffle=UpperCAmelCase ,					collate_fn=UpperCAmelCase		)
            __A    		=     torch.utils.data.DataLoader(dataset=UpperCAmelCase ,					batch_size=UpperCAmelCase ,					collate_fn=UpperCAmelCase		)

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

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

      for epoch in range(UpperCAmelCase		):
            __A    		=     time.time()
            print('\nEpoch' ,					epoch + 1		)

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

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

            print('\nExample prediction'		)
            predict(UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase ,					cached=UpperCAmelCase ,					device=UpperCAmelCase		)

            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(UpperCAmelCase ,					epoch + 1		) ,					)


if __name__ == "__main__":
 a__						:				Union[str, Any]           =    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=1_0, metavar="N", help="Number of training epochs")
 parser.add_argument(
     "--batch_size", type=int, default=6_4, metavar="N", help="input batch size for training (default: 64)"
 )
 parser.add_argument(
     "--log_interval",
     type=int,
     default=1_0,
     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")
 a__						:				List[Any]           =    parser.parse_args()

 train_discriminator(**(vars(args)))






