
'''simple docstring'''



import argparse
import json

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.models import create_model

from transformers import (
    BeitImageProcessor,
    DataaVecVisionConfig,
    DataaVecVisionForImageClassification,
    DataaVecVisionModel,
)
def 				lowerCAmelCase_		(			SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_=False      , SCREAMING_SNAKE_CASE_=False      , SCREAMING_SNAKE_CASE_="data2vec."  )       ->       List[Any]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE       	=			"""backbone.""" if is_semantic else """"""

   _SCREAMING_SNAKE_CASE       	=			[]
   for i in range(config.num_hidden_layers  ):
      # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
      rename_keys.append(
          (F"{prefix}blocks.{i}.norm1.weight", F"{hf_prefix}encoder.layer.{i}.layernorm_before.weight")  )
      rename_keys.append((F"{prefix}blocks.{i}.norm1.bias", F"{hf_prefix}encoder.layer.{i}.layernorm_before.bias")  )
      rename_keys.append(
          (F"{prefix}blocks.{i}.attn.proj.weight", F"{hf_prefix}encoder.layer.{i}.attention.output.dense.weight")  )
      rename_keys.append(
          (F"{prefix}blocks.{i}.attn.proj.bias", F"{hf_prefix}encoder.layer.{i}.attention.output.dense.bias")  )
      rename_keys.append(
          (F"{prefix}blocks.{i}.norm2.weight", F"{hf_prefix}encoder.layer.{i}.layernorm_after.weight")  )
      rename_keys.append((F"{prefix}blocks.{i}.norm2.bias", F"{hf_prefix}encoder.layer.{i}.layernorm_after.bias")  )
      rename_keys.append(
          (F"{prefix}blocks.{i}.mlp.fc1.weight", F"{hf_prefix}encoder.layer.{i}.intermediate.dense.weight")  )
      rename_keys.append(
          (F"{prefix}blocks.{i}.mlp.fc1.bias", F"{hf_prefix}encoder.layer.{i}.intermediate.dense.bias")  )
      rename_keys.append((F"{prefix}blocks.{i}.mlp.fc2.weight", F"{hf_prefix}encoder.layer.{i}.output.dense.weight")  )
      rename_keys.append((F"{prefix}blocks.{i}.mlp.fc2.bias", F"{hf_prefix}encoder.layer.{i}.output.dense.bias")  )

   # projection layer + position embeddings
   rename_keys.extend(
       [
           (F"{prefix}cls_token", F"{hf_prefix}embeddings.cls_token"),
           (F"{prefix}patch_embed.proj.weight", F"{hf_prefix}embeddings.patch_embeddings.projection.weight"),
           (F"{prefix}patch_embed.proj.bias", F"{hf_prefix}embeddings.patch_embeddings.projection.bias"),
       ]  )

   if has_lm_head:
      # mask token + shared relative position bias + layernorm
      rename_keys.extend(
          [
              ("""mask_token""", F"{hf_prefix}embeddings.mask_token"),
              (
                  """rel_pos_bias.relative_position_bias_table""",
                  F"{hf_prefix}encoder.relative_position_bias.relative_position_bias_table",
              ),
              (
                  """rel_pos_bias.relative_position_index""",
                  F"{hf_prefix}encoder.relative_position_bias.relative_position_index",
              ),
              ("""norm.weight""", """layernorm.weight"""),
              ("""norm.bias""", """layernorm.bias"""),
          ]  )
   elif is_semantic:
      # semantic segmentation classification heads
      rename_keys.extend(
          [
              ("""decode_head.conv_seg.weight""", """decode_head.classifier.weight"""),
              ("""decode_head.conv_seg.bias""", """decode_head.classifier.bias"""),
              ("""auxiliary_head.conv_seg.weight""", """auxiliary_head.classifier.weight"""),
              ("""auxiliary_head.conv_seg.bias""", """auxiliary_head.classifier.bias"""),
          ]  )
   else:
      # layernorm + classification head
      rename_keys.extend(
          [
              ("""fc_norm.weight""", F"{hf_prefix}pooler.layernorm.weight"),
              ("""fc_norm.bias""", F"{hf_prefix}pooler.layernorm.bias"),
              ("""head.weight""", """classifier.weight"""),
              ("""head.bias""", """classifier.bias"""),
          ]  )

   return rename_keys
def 				lowerCAmelCase_		(			SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_=False      , SCREAMING_SNAKE_CASE_=False      , SCREAMING_SNAKE_CASE_="data2vec_vision."  )       ->       str:




   """simple docstring"""
   for i in range(config.num_hidden_layers  ):
      _SCREAMING_SNAKE_CASE       	=			"""backbone.""" if is_semantic else """"""
      # queries, keys and values
      _SCREAMING_SNAKE_CASE       	=			state_dict.pop(F"{prefix}blocks.{i}.attn.qkv.weight"  )
      _SCREAMING_SNAKE_CASE       	=			state_dict.pop(F"{prefix}blocks.{i}.attn.q_bias"  )
      _SCREAMING_SNAKE_CASE       	=			state_dict.pop(F"{prefix}blocks.{i}.attn.v_bias"  )

      _SCREAMING_SNAKE_CASE       	=			in_proj_weight[
          : config.hidden_size, :
      ]
      _SCREAMING_SNAKE_CASE       	=			q_bias
      _SCREAMING_SNAKE_CASE       	=			in_proj_weight[
          config.hidden_size : config.hidden_size * 2, :
      ]
      _SCREAMING_SNAKE_CASE       	=			in_proj_weight[
          -config.hidden_size :, :
      ]
      _SCREAMING_SNAKE_CASE       	=			v_bias

      # gamma_1 and gamma_2
      # we call them lambda because otherwise they are renamed when using .from_pretrained
      _SCREAMING_SNAKE_CASE       	=			state_dict.pop(F"{prefix}blocks.{i}.gamma_1"  )
      _SCREAMING_SNAKE_CASE       	=			state_dict.pop(F"{prefix}blocks.{i}.gamma_2"  )

      _SCREAMING_SNAKE_CASE       	=			gamma_a
      _SCREAMING_SNAKE_CASE       	=			gamma_a

      # relative_position bias table + index
      if not has_lm_head:
         # each layer has its own relative position bias
         _SCREAMING_SNAKE_CASE       	=			state_dict.pop(F"{prefix}blocks.{i}.attn.relative_position_bias_table"  )
         _SCREAMING_SNAKE_CASE       	=			state_dict.pop(F"{prefix}blocks.{i}.attn.relative_position_index"  )

         _SCREAMING_SNAKE_CASE       	=			table
         _SCREAMING_SNAKE_CASE       	=			index
def 				lowerCAmelCase_		(			)       ->       List[str]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE       	=			argparse.ArgumentParser(
       """Convert Data2VecVision to HF for image classification and pretraining"""      , add_help=SCREAMING_SNAKE_CASE_  )
   parser.add_argument("""--hf_checkpoint_name"""      , type=SCREAMING_SNAKE_CASE_  )
   parser.add_argument("""--input_size"""      , default=2_24      , type=SCREAMING_SNAKE_CASE_      , help="""images input size"""  )
   parser.add_argument("""--beit_checkpoint"""      , default=""""""      , help="""beit checkpoint"""  )

   return parser.parse_args()
def 				lowerCAmelCase_		(			SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_  )       ->       str:




   """simple docstring"""
   def load_state_dict(SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_=""      , SCREAMING_SNAKE_CASE_="relative_position_index"  ):
      _SCREAMING_SNAKE_CASE       	=			[]
      _SCREAMING_SNAKE_CASE       	=			[]
      _SCREAMING_SNAKE_CASE       	=			[]
      # copy state_dict so _load_from_state_dict can modify it
      _SCREAMING_SNAKE_CASE       	=			getattr(SCREAMING_SNAKE_CASE_      , """_metadata"""      , SCREAMING_SNAKE_CASE_  )
      _SCREAMING_SNAKE_CASE       	=			state_dict.copy()
      if metadata is not None:
         _SCREAMING_SNAKE_CASE       	=			metadata

      def load(SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_=""  ):
         _SCREAMING_SNAKE_CASE       	=			{} if metadata is None else metadata.get(prefix[:-1]      , {}  )
         module._load_from_state_dict(
             SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_  )
         for name, child in module._modules.items():
            if child is not None:
               load(SCREAMING_SNAKE_CASE_      , prefix + name + """."""  )

      load(SCREAMING_SNAKE_CASE_      , prefix=SCREAMING_SNAKE_CASE_  )

      _SCREAMING_SNAKE_CASE       	=			[]
      _SCREAMING_SNAKE_CASE       	=			[]
      for key in missing_keys:
         _SCREAMING_SNAKE_CASE       	=			True
         for ignore_key in ignore_missing.split("""|"""  ):
            if ignore_key in key:
               _SCREAMING_SNAKE_CASE       	=			False
               break
         if keep_flag:
            warn_missing_keys.append(SCREAMING_SNAKE_CASE_  )
         else:
            ignore_missing_keys.append(SCREAMING_SNAKE_CASE_  )

      _SCREAMING_SNAKE_CASE       	=			warn_missing_keys

      if len(SCREAMING_SNAKE_CASE_  ) > 0:
         print(
             """Weights of {} not initialized from pretrained model: {}""".format(
                 model.__class__.__name__      , SCREAMING_SNAKE_CASE_  )  )
      if len(SCREAMING_SNAKE_CASE_  ) > 0:
         print("""Weights from pretrained model not used in {}: {}""".format(model.__class__.__name__      , SCREAMING_SNAKE_CASE_  )  )
      if len(SCREAMING_SNAKE_CASE_  ) > 0:
         print(
             """Ignored weights of {} not initialized from pretrained model: {}""".format(
                 model.__class__.__name__      , SCREAMING_SNAKE_CASE_  )  )
      if len(SCREAMING_SNAKE_CASE_  ) > 0:
         print("""\n""".join(SCREAMING_SNAKE_CASE_  )  )

   _SCREAMING_SNAKE_CASE       	=			{
       """pretrained""": False,
       """use_shared_rel_pos_bias""": True,
       """use_abs_pos_emb""": False,
       """init_values""": 0.1,
   }

   if is_finetuned:
      model_kwargs.update(
          {
              """num_classes""": 10_00,
              """use_mean_pooling""": True,
              """init_scale""": 0.001,
              """use_rel_pos_bias""": True,
          }  )

   _SCREAMING_SNAKE_CASE       	=			create_model(
       """beit_large_patch16_224""" if is_large else """beit_base_patch16_224"""      , **SCREAMING_SNAKE_CASE_      , )
   _SCREAMING_SNAKE_CASE       	=			model.patch_embed.patch_size
   _SCREAMING_SNAKE_CASE       	=			(args.input_size // patch_size[0], args.input_size // patch_size[1])
   _SCREAMING_SNAKE_CASE       	=			torch.load(args.beit_checkpoint      , map_location="""cpu"""  )

   print(F"Load ckpt from {args.beit_checkpoint}"  )
   _SCREAMING_SNAKE_CASE       	=			None
   for model_key in ("model", "module"):
      if model_key in checkpoint:
         _SCREAMING_SNAKE_CASE       	=			checkpoint[model_key]
         print(F"Load state_dict by model_key = {model_key}"  )
         break

   _SCREAMING_SNAKE_CASE       	=			list(checkpoint_model.keys()  )
   for key in all_keys:
      if "relative_position_index" in key:
         checkpoint_model.pop(SCREAMING_SNAKE_CASE_  )

      if "relative_position_bias_table" in key:
         _SCREAMING_SNAKE_CASE       	=			checkpoint_model[key]
         _SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE       	=			rel_pos_bias.size()
         _SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE       	=			model.state_dict()[key].size()
         _SCREAMING_SNAKE_CASE       	=			model.patch_embed.patch_shape
         if dst_patch_shape[0] != dst_patch_shape[1]:
            raise NotImplementedError()

   load_state_dict(SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , prefix=""""""  )

   return model
def 				lowerCAmelCase_		(			)       ->       Optional[int]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE       	=			get_args()

   _SCREAMING_SNAKE_CASE       	=			"""ft1k""" in args.hf_checkpoint_name
   _SCREAMING_SNAKE_CASE       	=			"""large""" in args.hf_checkpoint_name

   if is_finetuned:
      # To convert Beit's data2vec_vision to HF you need to copy
      # https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_finetune.py
      # into this folder.
      import modeling_finetune  # noqa: F401
   else:
      # To convert Beit's data2vec_vision to HF you need to copy
      # https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_cyclical.py
      # into this folder
      # IMPORTANT: Note that for now we've only converted the down-stream
      # model and not the full pretrained model. This means for the integration
      # test you need to add a `return x` after the following line:
      # https://github.com/facebookresearch/data2vec_vision/blob/af9a36349aaed59ae66e69b5dabeef2d62fdc5da/beit/modeling_cyclical.py#L197
      # to make the integration test pass.
      import modeling_cyclical  # noqa: F401

   # 1. Create model config
   _SCREAMING_SNAKE_CASE       	=			DataaVecVisionConfig()
   if is_finetuned:
      _SCREAMING_SNAKE_CASE       	=			True
      _SCREAMING_SNAKE_CASE       	=			False
      _SCREAMING_SNAKE_CASE       	=			True
      _SCREAMING_SNAKE_CASE       	=			10_00

      _SCREAMING_SNAKE_CASE       	=			"""huggingface/label-files"""
      _SCREAMING_SNAKE_CASE       	=			"""imagenet-1k-id2label.json"""
      _SCREAMING_SNAKE_CASE       	=			json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , repo_type="""dataset"""  )      , """r"""  )  )
      _SCREAMING_SNAKE_CASE       	=			{int(SCREAMING_SNAKE_CASE_  ): v for k, v in idalabel.items()}
      _SCREAMING_SNAKE_CASE       	=			idalabel
      _SCREAMING_SNAKE_CASE       	=			{v: k for k, v in idalabel.items()}
   else:
      _SCREAMING_SNAKE_CASE       	=			False
      _SCREAMING_SNAKE_CASE       	=			True
      _SCREAMING_SNAKE_CASE       	=			False

   if is_large:
      _SCREAMING_SNAKE_CASE       	=			10_24
      _SCREAMING_SNAKE_CASE       	=			40_96
      _SCREAMING_SNAKE_CASE       	=			24
      _SCREAMING_SNAKE_CASE       	=			16

   # 2. Load Beit model
   _SCREAMING_SNAKE_CASE       	=			load_beit_model(SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_  )
   orig_model.eval()

   # 3. Forward Beit model
   _SCREAMING_SNAKE_CASE       	=			BeitImageProcessor(size=config.image_size      , do_center_crop=SCREAMING_SNAKE_CASE_  )
   _SCREAMING_SNAKE_CASE       	=			Image.open("""../../../../tests/fixtures/tests_samples/COCO/000000039769.png"""  )
   _SCREAMING_SNAKE_CASE       	=			image_processor(images=SCREAMING_SNAKE_CASE_      , return_tensors="""pt"""  )
   _SCREAMING_SNAKE_CASE       	=			encoding["""pixel_values"""]

   _SCREAMING_SNAKE_CASE       	=			(pixel_values,) if is_finetuned else (pixel_values, None)
   with torch.no_grad():
      _SCREAMING_SNAKE_CASE       	=			orig_model(*SCREAMING_SNAKE_CASE_  )

   # 4. Load HF Data2VecVision model
   if is_finetuned:
      _SCREAMING_SNAKE_CASE       	=			DataaVecVisionForImageClassification(SCREAMING_SNAKE_CASE_  )
      hf_model.eval()
      _SCREAMING_SNAKE_CASE       	=			False
      _SCREAMING_SNAKE_CASE       	=			"""data2vec_vision."""
   else:
      _SCREAMING_SNAKE_CASE       	=			DataaVecVisionModel(SCREAMING_SNAKE_CASE_  )
      hf_model.eval()
      _SCREAMING_SNAKE_CASE       	=			True
      _SCREAMING_SNAKE_CASE       	=			""""""

   _SCREAMING_SNAKE_CASE       	=			create_rename_keys(SCREAMING_SNAKE_CASE_      , hf_prefix=SCREAMING_SNAKE_CASE_      , has_lm_head=SCREAMING_SNAKE_CASE_  )
   _SCREAMING_SNAKE_CASE       	=			orig_model.state_dict()
   for src, dest in rename_keys:
      _SCREAMING_SNAKE_CASE       	=			state_dict.pop(SCREAMING_SNAKE_CASE_  )
      _SCREAMING_SNAKE_CASE       	=			val

   read_in_q_k_v(SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , hf_prefix=SCREAMING_SNAKE_CASE_      , has_lm_head=SCREAMING_SNAKE_CASE_  )
   _SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE       	=			hf_model.load_state_dict(SCREAMING_SNAKE_CASE_      , strict=SCREAMING_SNAKE_CASE_  )
   print("""HF missing"""      , SCREAMING_SNAKE_CASE_  )
   print("""HF unexpected_keys"""      , SCREAMING_SNAKE_CASE_  )

   # 5. Forward HF Data2VecVision model
   with torch.no_grad():
      _SCREAMING_SNAKE_CASE       	=			hf_model(SCREAMING_SNAKE_CASE_  )

   _SCREAMING_SNAKE_CASE       	=			hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

   # 6. Compare
   _SCREAMING_SNAKE_CASE       	=			torch.max(torch.abs(hf_output - orig_model_output  )  ).item()

   print(F"max_absolute_diff = {max_absolute_diff}"  )
   _SCREAMING_SNAKE_CASE       	=			torch.allclose(SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , atol=1e-3  )
   print("""Do both models output the same tensors?"""      , """🔥""" if success else """💩"""  )
   if not success:
      raise Exception("""Something went wRoNg"""  )

   # 7. Save
   print(F"Saving to {args.hf_checkpoint_name}"  )
   hf_model.save_pretrained(args.hf_checkpoint_name  )
   image_processor.save_pretrained(args.hf_checkpoint_name  )


if __name__ == "__main__":
     main()
     # Run the following to convert checkpoints
     #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
     #          --beit_checkpoint ./pretrained_base.pt \
     #          --hf_checkpoint_name "./data2vec-vision-base"
     #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
     #          --beit_checkpoint ./finetuned_base.pt \
     #          --hf_checkpoint_name "./data2vec-vision-base-ft1k"
     #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
     #          --beit_checkpoint ./pretrained_large.pt \
     #          --hf_checkpoint_name "./data2vec-vision-large"
     #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
     #          --beit_checkpoint ./finetuned_large.pt \
     #          --hf_checkpoint_name "./data2vec-vision-large-ft1k"








'''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)))






