
'''simple docstring'''



import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import OPTConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
     import os

     # The slow tests are often failing with OOM error on GPU
     # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
     # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
     UpperCamelCase__ :						Optional[Any]    							=					'''platform'''

     import jax
     import jax.numpy as jnp

     from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer
def 				lowerCAmelCase_		(			SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_=None      , SCREAMING_SNAKE_CASE_=None  )       ->       Optional[Any]:




   """simple docstring"""
   if attention_mask is None:
      _SCREAMING_SNAKE_CASE       	=			np.where(input_ids != config.pad_token_id      , 1      , 0  )
   return {
       "input_ids": input_ids,
       "attention_mask": attention_mask,
   }

@require_flax
class 				_a			:





       """simple docstring"""




       def __init__(							self    ,							A__    ,							A__=13    ,							A__=7    ,							A__=True    ,							A__=False    ,							A__=99    ,							A__=16    ,							A__=2    ,							A__=4    ,							A__=4    ,							A__="gelu"    ,							A__=0.1    ,							A__=0.1    ,							A__=20    ,							A__=2    ,							A__=1    ,							A__=0    ,							A__=16    ,							A__=16    ,							A__=0.02    ,							)      ->     int:
          _SCREAMING_SNAKE_CASE       	=			parent
          _SCREAMING_SNAKE_CASE       	=			batch_size
          _SCREAMING_SNAKE_CASE       	=			seq_length
          _SCREAMING_SNAKE_CASE       	=			is_training
          _SCREAMING_SNAKE_CASE       	=			use_labels
          _SCREAMING_SNAKE_CASE       	=			vocab_size
          _SCREAMING_SNAKE_CASE       	=			hidden_size
          _SCREAMING_SNAKE_CASE       	=			num_hidden_layers
          _SCREAMING_SNAKE_CASE       	=			num_attention_heads
          _SCREAMING_SNAKE_CASE       	=			intermediate_size
          _SCREAMING_SNAKE_CASE       	=			hidden_act
          _SCREAMING_SNAKE_CASE       	=			hidden_dropout_prob
          _SCREAMING_SNAKE_CASE       	=			attention_probs_dropout_prob
          _SCREAMING_SNAKE_CASE       	=			max_position_embeddings
          _SCREAMING_SNAKE_CASE       	=			eos_token_id
          _SCREAMING_SNAKE_CASE       	=			pad_token_id
          _SCREAMING_SNAKE_CASE       	=			bos_token_id
          _SCREAMING_SNAKE_CASE       	=			embed_dim
          _SCREAMING_SNAKE_CASE       	=			word_embed_proj_dim
          _SCREAMING_SNAKE_CASE       	=			initializer_range
          _SCREAMING_SNAKE_CASE       	=			False






       def     UpperCamelCase			(							self )      ->     str:
          _SCREAMING_SNAKE_CASE       	=			np.clip(ids_tensor([self.batch_size, self.seq_length - 1]    ,							self.vocab_size )    ,							3    ,							self.vocab_size )
          _SCREAMING_SNAKE_CASE       	=			np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)    ,							dtype=np.intaa ))    ,							-1 )

          _SCREAMING_SNAKE_CASE       	=			OPTConfig(
              vocab_size=self.vocab_size    ,							hidden_size=self.hidden_size    ,							num_hidden_layers=self.num_hidden_layers    ,							num_attention_heads=self.num_attention_heads    ,							ffn_dim=self.intermediate_size    ,							dropout=self.hidden_dropout_prob    ,							attention_dropout=self.attention_probs_dropout_prob    ,							max_position_embeddings=self.max_position_embeddings    ,							eos_token_id=self.eos_token_id    ,							bos_token_id=self.bos_token_id    ,							pad_token_id=self.pad_token_id    ,							embed_dim=self.embed_dim    ,							is_encoder_decoder=_A    ,							word_embed_proj_dim=self.word_embed_proj_dim    ,							initializer_range=self.initializer_range    ,							use_cache=_A    ,							)
          _SCREAMING_SNAKE_CASE       	=			prepare_opt_inputs_dict(_A    ,							_A )
          return config, inputs_dict






       def     UpperCamelCase			(							self )      ->     int:
          _SCREAMING_SNAKE_CASE       	=			self.prepare_config_and_inputs()
          return config, inputs_dict






       def     UpperCamelCase			(							self    ,							A__    ,							A__    ,							A__ )      ->     Optional[Any]:
          _SCREAMING_SNAKE_CASE       	=			20
          _SCREAMING_SNAKE_CASE       	=			model_class_name(_A )

          _SCREAMING_SNAKE_CASE       	=			inputs_dict['input_ids']
          _SCREAMING_SNAKE_CASE       	=			inputs_dict['attention_mask']

          _SCREAMING_SNAKE_CASE       	=			model.init_cache(input_ids.shape[0]    ,							_A )
          _SCREAMING_SNAKE_CASE       	=			jnp.ones((input_ids.shape[0], max_length)    ,							dtype="""i4""" )

          _SCREAMING_SNAKE_CASE       	=			jnp.broadcast_to(
              jnp.arange(input_ids.shape[-1] - 1 )[None, :]    ,							(input_ids.shape[0], input_ids.shape[-1] - 1)    ,							)
          _SCREAMING_SNAKE_CASE       	=			model(
              input_ids[:, :-1]    ,							attention_mask=_A    ,							past_key_values=_A    ,							position_ids=_A    ,							)

          _SCREAMING_SNAKE_CASE       	=			jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]    ,							dtype="""i4""" )
          _SCREAMING_SNAKE_CASE       	=			model(
              input_ids[:, -1:]    ,							attention_mask=_A    ,							past_key_values=outputs_cache.past_key_values    ,							position_ids=_A    ,							)

          _SCREAMING_SNAKE_CASE       	=			model(_A )

          _SCREAMING_SNAKE_CASE       	=			np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
          self.parent.assertTrue(diff < 1E-3    ,							msg=F"Max diff is {diff}" )






       def     UpperCamelCase			(							self    ,							A__    ,							A__    ,							A__ )      ->     int:
          _SCREAMING_SNAKE_CASE       	=			20
          _SCREAMING_SNAKE_CASE       	=			model_class_name(_A )

          _SCREAMING_SNAKE_CASE       	=			(
              inputs_dict['input_ids'],
              inputs_dict['attention_mask'],
          )

          _SCREAMING_SNAKE_CASE       	=			jnp.concatenate(
              [
                  attention_mask,
                  jnp.zeros((attention_mask.shape[0], max_length - attention_mask.shape[1]) ),
              ]    ,							axis=-1    ,							)

          _SCREAMING_SNAKE_CASE       	=			model.init_cache(input_ids.shape[0]    ,							_A )
          _SCREAMING_SNAKE_CASE       	=			jnp.broadcast_to(
              jnp.arange(input_ids.shape[-1] - 1 )[None, :]    ,							(input_ids.shape[0], input_ids.shape[-1] - 1)    ,							)

          _SCREAMING_SNAKE_CASE       	=			model(
              input_ids[:, :-1]    ,							attention_mask=_A    ,							past_key_values=_A    ,							position_ids=_A    ,							)
          _SCREAMING_SNAKE_CASE       	=			jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]    ,							dtype="""i4""" )
          _SCREAMING_SNAKE_CASE       	=			model(
              input_ids[:, -1:]    ,							past_key_values=outputs_cache.past_key_values    ,							attention_mask=_A    ,							position_ids=_A    ,							)

          _SCREAMING_SNAKE_CASE       	=			model(_A    ,							attention_mask=_A )

          _SCREAMING_SNAKE_CASE       	=			np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
          self.parent.assertTrue(diff < 1E-3    ,							msg=F"Max diff is {diff}" )






@require_flax
class 				_a			(snake_case__					,   unittest.TestCase					,   snake_case__):





       """simple docstring"""




       SCREAMING_SNAKE_CASE =					(FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
       SCREAMING_SNAKE_CASE =					() if is_flax_available() else ()






       def     UpperCamelCase			(							self )      ->     Union[str, Any]:
          _SCREAMING_SNAKE_CASE       	=			FlaxOPTModelTester(self )






       def     UpperCamelCase			(							self )      ->     Optional[int]:
          _SCREAMING_SNAKE_CASE       	=			self.model_tester.prepare_config_and_inputs()
          for model_class in self.all_model_classes:
             self.model_tester.check_use_cache_forward(_A    ,							_A    ,							_A )






       def     UpperCamelCase			(							self )      ->     List[str]:
          _SCREAMING_SNAKE_CASE       	=			self.model_tester.prepare_config_and_inputs()
          for model_class in self.all_model_classes:
             self.model_tester.check_use_cache_forward_with_attn_mask(_A    ,							_A    ,							_A )






       @slow
       def     UpperCamelCase			(							self )      ->     str:
          for model_class_name in self.all_model_classes:
             _SCREAMING_SNAKE_CASE       	=			model_class_name.from_pretrained("""facebook/opt-125m""" )
             _SCREAMING_SNAKE_CASE       	=			np.ones((1, 1) ) * model.config.eos_token_id
             _SCREAMING_SNAKE_CASE       	=			model(_A )
             self.assertIsNotNone(_A )






@require_sentencepiece
@require_flax
class 				_a			(unittest.TestCase):





       """simple docstring"""




       @slow
       def     UpperCamelCase			(							self )      ->     Tuple:
          _SCREAMING_SNAKE_CASE       	=			FlaxOPTModel.from_pretrained("""facebook/opt-350m""" )
          _SCREAMING_SNAKE_CASE       	=			jnp.array([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]] )
          _SCREAMING_SNAKE_CASE       	=			model(input_ids=_A ).last_hidden_state
          _SCREAMING_SNAKE_CASE       	=			(1, 11, 5_12)
          self.assertEqual(output.shape    ,							_A )
          _SCREAMING_SNAKE_CASE       	=			jnp.array(
              [[-0.2867, -1.9256, -0.3062], [-1.2711, -0.1337, -0.1897], [0.4109, 0.1187, -1.3142]] )
          self.assertTrue(jnp.allclose(output[:, :3, :3]    ,							_A    ,							atol=4E-2 ) )






@require_flax
@slow
class 				_a			(unittest.TestCase):





       """simple docstring"""




       def     UpperCamelCase			(							self )      ->     int:
          super().setUp()
          _SCREAMING_SNAKE_CASE       	=			'facebook/opt-350m'






       def     UpperCamelCase			(							self )      ->     Dict:
          _SCREAMING_SNAKE_CASE       	=			FlaxOPTForCausalLM.from_pretrained(self.path_model )
          _SCREAMING_SNAKE_CASE       	=			GPTaTokenizer.from_pretrained(self.path_model )

          _SCREAMING_SNAKE_CASE       	=			[
              'Today is a beautiful day and I want to',
              'In the city of',
              'Paris is the capital of France and',
              'Computers and mobile phones have taken',
          ]
          # verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
          _SCREAMING_SNAKE_CASE       	=			tokenizer(_A    ,							return_tensors="""jax"""    ,							padding=_A    ,							add_special_tokens=_A )
          _SCREAMING_SNAKE_CASE       	=			model(inputs.input_ids    ,							attention_mask=inputs.attention_mask )[0].mean(axis=-1 )
          _SCREAMING_SNAKE_CASE       	=			jnp.array(
              [
                  [1.3851, -13.89_23, -10.52_29, -10.75_33, -0.2309, -10.23_84, -0.5365, -9.0947, -5.1670],
                  [-4.7073, -10.62_76, -3.9415, -21.52_42, -0.2822, -0.2822, -0.2822, -0.2822, -0.2822],
                  [0.6247, -3.4229, -8.9179, -1.4297, -14.16_50, 1.4146, -9.0218, -0.2703, -0.2703],
                  [6.4783, -1.9913, -10.79_26, -2.3336, 1.5092, -0.9974, -6.8213, 1.3477, 1.3477],
              ] )
          self.assertTrue(jnp.allclose(_A    ,							_A    ,							atol=4E-2 ) )

          _SCREAMING_SNAKE_CASE       	=			jax.jit(_A )
          _SCREAMING_SNAKE_CASE       	=			model(inputs.input_ids    ,							attention_mask=inputs.attention_mask )[0].mean(axis=-1 )
          self.assertTrue(jnp.allclose(_A    ,							_A    ,							atol=4E-2 ) )






@require_flax
@slow
class 				_a			(unittest.TestCase):





       """simple docstring"""




       @property
       def     UpperCamelCase			(							self )      ->     Union[str, Any]:
          return [
              "Today is a beautiful day and I want",
              "In the city of",
              "Paris is the capital of France and",
              "Computers and mobile phones have taken",
          ]






       def     UpperCamelCase			(							self )      ->     Tuple:
          _SCREAMING_SNAKE_CASE       	=			'facebook/opt-125m'

          _SCREAMING_SNAKE_CASE       	=			[
              'Today is a beautiful day and I want to',
              'In the city of New York, the city',
              'Paris is the capital of France and the capital',
              'Computers and mobile phones have taken over the',
          ]

          _SCREAMING_SNAKE_CASE       	=			[]

          _SCREAMING_SNAKE_CASE       	=			FlaxOPTForCausalLM.from_pretrained(_A )
          _SCREAMING_SNAKE_CASE       	=			GPTaTokenizer.from_pretrained(_A )

          for prompt in self.prompts:
             _SCREAMING_SNAKE_CASE       	=			tokenizer(_A    ,							return_tensors="""jax""" ).input_ids

             _SCREAMING_SNAKE_CASE       	=			model.generate(_A    ,							max_length=10 )
             _SCREAMING_SNAKE_CASE       	=			generated_ids[0]

             _SCREAMING_SNAKE_CASE       	=			tokenizer.batch_decode(_A    ,							skip_special_tokens=_A )
             predicted_outputs += generated_string

          self.assertListEqual(_A    ,							_A )






       def     UpperCamelCase			(							self )      ->     Union[str, Any]:
          _SCREAMING_SNAKE_CASE       	=			'facebook/opt-350m'

          _SCREAMING_SNAKE_CASE       	=			[
              'Today is a beautiful day and I want to',
              'In the city of San Francisco, the city',
              'Paris is the capital of France and the capital',
              'Computers and mobile phones have taken over the',
          ]

          _SCREAMING_SNAKE_CASE       	=			[]
          _SCREAMING_SNAKE_CASE       	=			FlaxOPTForCausalLM.from_pretrained(_A )
          _SCREAMING_SNAKE_CASE       	=			GPTaTokenizer.from_pretrained(_A )

          for prompt in self.prompts:
             _SCREAMING_SNAKE_CASE       	=			tokenizer(_A    ,							return_tensors="""jax""" ).input_ids

             _SCREAMING_SNAKE_CASE       	=			model.generate(_A    ,							max_length=10 )
             _SCREAMING_SNAKE_CASE       	=			generated_ids[0]

             _SCREAMING_SNAKE_CASE       	=			tokenizer.batch_decode(_A    ,							skip_special_tokens=_A )
             predicted_outputs += generated_string

          self.assertListEqual(_A    ,							_A )






       def     UpperCamelCase			(							self )      ->     Tuple:
          _SCREAMING_SNAKE_CASE       	=			'facebook/opt-125m'
          _SCREAMING_SNAKE_CASE       	=			[
              'Today is a beautiful day and I want to thank',
              'In the city of Rome Canaver Canaver Canaver Canaver',
          ]
          _SCREAMING_SNAKE_CASE       	=			FlaxOPTForCausalLM.from_pretrained(_A )
          _SCREAMING_SNAKE_CASE       	=			GPTaTokenizer.from_pretrained(_A )
          _SCREAMING_SNAKE_CASE       	=			tokenizer(
              [
                  """Today is a beautiful day and I want to""",
                  """In the city of""",
              ]    ,							return_tensors="""jax"""    ,							padding=_A    ,							)

          _SCREAMING_SNAKE_CASE       	=			jax.jit(model.generate )

          _SCREAMING_SNAKE_CASE       	=			jit_generate(inputs["""input_ids"""]    ,							attention_mask=inputs["""attention_mask"""] ).sequences

          _SCREAMING_SNAKE_CASE       	=			tokenizer.batch_decode(_A    ,							skip_special_tokens=_A )

          self.assertIsNotNone(_A    ,							_A )






       def     UpperCamelCase			(							self )      ->     Dict:
          _SCREAMING_SNAKE_CASE       	=			'facebook/opt-350m'

          _SCREAMING_SNAKE_CASE       	=			GPTaTokenizer.from_pretrained(_A )
          _SCREAMING_SNAKE_CASE       	=			FlaxOPTForCausalLM.from_pretrained(_A )

          _SCREAMING_SNAKE_CASE       	=			'left'

          # use different length sentences to test batching
          _SCREAMING_SNAKE_CASE       	=			[
              'Hello, my dog is a little',
              'Today, I',
          ]

          _SCREAMING_SNAKE_CASE       	=			tokenizer(_A    ,							return_tensors="""jax"""    ,							padding=_A )
          _SCREAMING_SNAKE_CASE       	=			inputs['input_ids']

          _SCREAMING_SNAKE_CASE       	=			model.generate(input_ids=_A    ,							attention_mask=inputs["""attention_mask"""]    ,							trace=_A )

          _SCREAMING_SNAKE_CASE       	=			tokenizer(sentences[0]    ,							return_tensors="""jax""" ).input_ids
          _SCREAMING_SNAKE_CASE       	=			model.generate(input_ids=_A )

          _SCREAMING_SNAKE_CASE       	=			inputs_non_padded.shape[-1] - inputs['attention_mask'][-1].sum()
          _SCREAMING_SNAKE_CASE       	=			tokenizer(sentences[1]    ,							return_tensors="""jax""" ).input_ids
          _SCREAMING_SNAKE_CASE       	=			model.generate(input_ids=_A    ,							max_length=model.config.max_length - num_paddings )

          _SCREAMING_SNAKE_CASE       	=			tokenizer.batch_decode(outputs[0]    ,							skip_special_tokens=_A )
          _SCREAMING_SNAKE_CASE       	=			tokenizer.decode(output_non_padded[0][0]    ,							skip_special_tokens=_A )
          _SCREAMING_SNAKE_CASE       	=			tokenizer.decode(output_padded[0][0]    ,							skip_special_tokens=_A )

          _SCREAMING_SNAKE_CASE       	=			[
              'Hello, my dog is a little bit of a dork.\nI\'m a little bit',
              'Today, I was in the middle of a conversation with a friend about the',
          ]
          self.assertListEqual(_A    ,							_A )
          self.assertListEqual(_A    ,							[non_padded_sentence, padded_sentence] )








'''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)))






