
'''simple docstring'''





import json
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional

import evaluate
import numpy as np
import torch
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from PIL import Image
from torch import nn
from torchvision import transforms
from torchvision.transforms import functional

import transformers
from transformers import (
    AutoConfig,
    AutoImageProcessor,
    AutoModelForSemanticSegmentation,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
a__						:				Optional[int]           =    logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.31.0")

require_version("datasets>=2.0.0", "To fix: pip install -r examples/pytorch/semantic-segmentation/requirements.txt")





def     snake_case							(						UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase=0		)->     List[Any]:







      """simple docstring"""
      __A    		=     (size, size) if isinstance(UpperCAmelCase ,					UpperCAmelCase		) else size
      __A		, __A    		=     img.size
      __A    		=     size[1] - original_height if original_height < size[1] else 0
      __A    		=     size[0] - original_width if original_width < size[0] else 0
      __A    		=     functional.pad(UpperCAmelCase ,					(0, 0, pad_width, pad_height) ,					fill=UpperCAmelCase		)
      return img


class 	UpperCamelCase__					:






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







       '''simple docstring'''



       __A    		=     transforms






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







       '''simple docstring'''



       for t in self.transforms:
             __A		, __A    		=     t(_A		,			_A     )
       return image, target


class 	UpperCamelCase__					:






 def __init__(     self					:str     )			->     Dict:







       '''simple docstring'''



       pass






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







       '''simple docstring'''



       return image, target


class 	UpperCamelCase__					:






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







       '''simple docstring'''



       __A    		=     size






 def __call__(     self					:Tuple		,			_A					:Dict		,			_A					:Optional[Any]     )			->     str:







       '''simple docstring'''



       __A    		=     functional.resize(_A		,			self.size     )
       __A    		=     functional.resize(_A		,			self.size		,			interpolation=transforms.InterpolationMode.NEAREST     )
       return image, target


class 	UpperCamelCase__					:






 def __init__(     self					:Dict		,			_A					:int		,			_A					:Optional[Any]=None     )			->     List[str]:







       '''simple docstring'''



       __A    		=     min_size
       if max_size is None:
             __A    		=     min_size
       __A    		=     max_size






 def __call__(     self					:Optional[int]		,			_A					:Optional[int]		,			_A					:Any     )			->     Any:







       '''simple docstring'''



       __A    		=     random.randint(self.min_size		,			self.max_size     )
       __A    		=     functional.resize(_A		,			_A     )
       __A    		=     functional.resize(_A		,			_A		,			interpolation=transforms.InterpolationMode.NEAREST     )
       return image, target


class 	UpperCamelCase__					:






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







       '''simple docstring'''



       __A    		=     size if isinstance(_A		,			_A     ) else (size, size)






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







       '''simple docstring'''



       __A    		=     pad_if_smaller(_A		,			self.size     )
       __A    		=     pad_if_smaller(_A		,			self.size		,			fill=255     )
       __A    		=     transforms.RandomCrop.get_params(_A		,			self.size     )
       __A    		=     functional.crop(_A		,			*_A     )
       __A    		=     functional.crop(_A		,			*_A     )
       return image, target


class 	UpperCamelCase__					:






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







       '''simple docstring'''



       __A    		=     flip_prob






 def __call__(     self					:List[Any]		,			_A					:Optional[Any]		,			_A					:Optional[Any]     )			->     Dict:







       '''simple docstring'''



       if random.random() < self.flip_prob:
             __A    		=     functional.hflip(_A     )
             __A    		=     functional.hflip(_A     )
       return image, target


class 	UpperCamelCase__					:






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







       '''simple docstring'''



       __A    		=     functional.pil_to_tensor(_A     )
       __A    		=     torch.as_tensor(np.array(_A     )		,			dtype=torch.intaa     )
       return image, target


class 	UpperCamelCase__					:






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







       '''simple docstring'''



       __A    		=     dtype






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







       '''simple docstring'''



       __A    		=     functional.convert_image_dtype(_A		,			self.dtype     )
       return image, target


class 	UpperCamelCase__					:






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







       '''simple docstring'''



       __A    		=     mean
       __A    		=     std






 def __call__(     self					:Optional[Any]		,			_A					:Dict		,			_A					:List[str]     )			->     Dict:







       '''simple docstring'''



       __A    		=     functional.normalize(_A		,			mean=self.mean		,			std=self.std     )
       return image, target


class 	UpperCamelCase__					:






 def __call__(     self					:Dict		,			_A					:Any		,			_A					:Optional[int]     )			->     List[Any]:







       '''simple docstring'''



       if not isinstance(_A		,			np.ndarray     ):
             __A    		=     np.array(_A     ).astype(np.uinta     )
       # avoid using underflow conversion
       __A    		=     255
       __A    		=     target - 1
       __A    		=     255

       __A    		=     Image.fromarray(_A     )
       return image, target


@dataclass
class 	UpperCamelCase__					:
 UpperCAmelCase__      :      Optional[str]  			=   field(
     default='segments/sidewalk-semantic' ,       metadata={
         'help': 'Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub).'
     } ,       )
 UpperCAmelCase__      :      Optional[str]  			=   field(
     default=lowercase_ ,       metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'})
 UpperCAmelCase__      :      Optional[float]  			=   field(
     default=0.1_5 ,       metadata={'help': 'Percent to split off of train for validation.'})
 UpperCAmelCase__      :      Optional[int]  			=   field(
     default=lowercase_ ,       metadata={
         'help': (
             'For debugging purposes or quicker training, truncate the number of training examples to this '
             'value if set.'
         )
     } ,       )
 UpperCAmelCase__      :      Optional[int]  			=   field(
     default=lowercase_ ,       metadata={
         'help': (
             'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
             'value if set.'
         )
     } ,       )
 UpperCAmelCase__      :      Optional[bool]  			=   field(
     default=lowercase_ ,       metadata={'help': 'Whether or not to reduce all labels by 1 and replace background by 255.'} ,       )






 def 		lowercase_  (     self					:Any     )			->     str:







       '''simple docstring'''



       if self.dataset_name is None and (self.train_dir is None and self.validation_dir is None):
             raise ValueError(
                 'You must specify either a dataset name from the hub or a train and/or validation directory.'     )


@dataclass
class 	UpperCamelCase__					:
 UpperCAmelCase__      :      str  			=   field(
     default='nvidia/mit-b0' ,       metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} ,       )
 UpperCAmelCase__      :      Optional[str]  			=   field(
     default=lowercase_ ,       metadata={'help': 'Pretrained config name or path if not the same as model_name'})
 UpperCAmelCase__      :      Optional[str]  			=   field(
     default=lowercase_ ,       metadata={'help': 'Where do you want to store the pretrained models downloaded from s3'})
 UpperCAmelCase__      :      str  			=   field(
     default='main' ,       metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'} ,       )
 UpperCAmelCase__      :      str  			=   field(default=lowercase_ ,       metadata={'help': 'Name or path of preprocessor config.'})
 UpperCAmelCase__      :      bool  			=   field(
     default=lowercase_ ,       metadata={
         'help': (
             'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
             'with private models).'
         )
     } ,       )





def     snake_case							(						)->     Tuple:







      """simple docstring"""
      __A    		=     HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)		)
      if len(sys.argv		) == 2 and sys.argv[1].endswith('.json'		):
            # If we pass only one argument to the script and it's the path to a json file,
            # let's parse it to get our arguments.
            __A		, __A		, __A    		=     parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]		)		)
      else:
            __A		, __A		, __A    		=     parser.parse_args_into_dataclasses()

      # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
      # information sent is the one passed as arguments along with your Python/PyTorch versions.
      send_example_telemetry('run_semantic_segmentation' ,					UpperCAmelCase ,					UpperCAmelCase		)

      # Setup logging
      logging.basicConfig(
          format='%(asctime)s - %(levelname)s - %(name)s - %(message)s' ,					datefmt='%m/%d/%Y %H:%M:%S' ,					handlers=[logging.StreamHandler(sys.stdout		)] ,					)

      if training_args.should_log:
            # The default of training_args.log_level is passive, so we set log level at info here to have that default.
            transformers.utils.logging.set_verbosity_info()

      __A    		=     training_args.get_process_log_level()
      logger.setLevel(UpperCAmelCase		)
      transformers.utils.logging.set_verbosity(UpperCAmelCase		)
      transformers.utils.logging.enable_default_handler()
      transformers.utils.logging.enable_explicit_format()

      # Log on each process the small summary:
      logger.warning(
          f'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
          + f'distributed training: {bool(training_args.local_rank != -1		)}, 16-bits training: {training_args.fpaa}'		)
      logger.info(f'Training/evaluation parameters {training_args}'		)

      # Detecting last checkpoint.
      __A    		=     None
      if os.path.isdir(training_args.output_dir		) and training_args.do_train and not training_args.overwrite_output_dir:
            __A    		=     get_last_checkpoint(training_args.output_dir		)
            if last_checkpoint is None and len(os.listdir(training_args.output_dir		)		) > 0:
                  raise ValueError(
                      f'Output directory ({training_args.output_dir}) already exists and is not empty. '
                      'Use --overwrite_output_dir to overcome.'		)
            elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
                  logger.info(
                      f'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
                      'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'		)

    # Load dataset
    # In distributed training, the load_dataset function guarantees that only one local process can concurrently
    # download the dataset.
    # TODO support datasets from local folders
      __A    		=     load_dataset(data_args.dataset_name ,					cache_dir=model_args.cache_dir		)

      # Rename column names to standardized names (only "image" and "label" need to be present)
      if "pixel_values" in dataset["train"].column_names:
            __A    		=     dataset.rename_columns({'pixel_values': 'image'}		)
      if "annotation" in dataset["train"].column_names:
            __A    		=     dataset.rename_columns({'annotation': 'label'}		)

      # If we don't have a validation split, split off a percentage of train as validation.
      __A    		=     None if 'validation' in dataset.keys() else data_args.train_val_split
      if isinstance(data_args.train_val_split ,					UpperCAmelCase		) and data_args.train_val_split > 0.0:
            __A    		=     dataset['train'].train_test_split(data_args.train_val_split		)
            __A    		=     split['train']
            __A    		=     split['test']

      # Prepare label mappings.
      # We'll include these in the model's config to get human readable labels in the Inference API.
      if data_args.dataset_name == "scene_parse_150":
            __A    		=     'huggingface/label-files'
            __A    		=     'ade20k-id2label.json'
      else:
            __A    		=     data_args.dataset_name
            __A    		=     'id2label.json'
      __A    		=     json.load(open(hf_hub_download(UpperCAmelCase ,					UpperCAmelCase ,					repo_type='dataset'		) ,					'r'		)		)
      __A    		=     {int(UpperCAmelCase		): v for k, v in idalabel.items()}
      __A    		=     {v: str(UpperCAmelCase		) for k, v in idalabel.items()}

      # Load the mean IoU metric from the datasets package
      __A    		=     evaluate.load('mean_iou'		)

      # Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
      # predictions and label_ids field) and has to return a dictionary string to float.
      @torch.no_grad()
      def compute_metrics(UpperCAmelCase		):
            __A		, __A    		=     eval_pred
            __A    		=     torch.from_numpy(UpperCAmelCase		)
            # scale the logits to the size of the label
            __A    		=     nn.functional.interpolate(
                UpperCAmelCase ,					size=labels.shape[-2:] ,					mode='bilinear' ,					align_corners=UpperCAmelCase ,					).argmax(dim=1		)

            __A    		=     logits_tensor.detach().cpu().numpy()
            __A    		=     metric.compute(
                predictions=UpperCAmelCase ,					references=UpperCAmelCase ,					num_labels=len(UpperCAmelCase		) ,					ignore_index=0 ,					reduce_labels=image_processor.do_reduce_labels ,					)
            # add per category metrics as individual key-value pairs
            __A    		=     metrics.pop('per_category_accuracy'		).tolist()
            __A    		=     metrics.pop('per_category_iou'		).tolist()

            metrics.update({f'accuracy_{idalabel[i]}': v for i, v in enumerate(UpperCAmelCase		)}		)
            metrics.update({f'iou_{idalabel[i]}': v for i, v in enumerate(UpperCAmelCase		)}		)

            return metrics

      __A    		=     AutoConfig.from_pretrained(
          model_args.config_name or model_args.model_name_or_path ,					labelaid=UpperCAmelCase ,					idalabel=UpperCAmelCase ,					cache_dir=model_args.cache_dir ,					revision=model_args.model_revision ,					use_auth_token=True if model_args.use_auth_token else None ,					)
      __A    		=     AutoModelForSemanticSegmentation.from_pretrained(
          model_args.model_name_or_path ,					from_tf=bool('.ckpt' in model_args.model_name_or_path		) ,					config=UpperCAmelCase ,					cache_dir=model_args.cache_dir ,					revision=model_args.model_revision ,					use_auth_token=True if model_args.use_auth_token else None ,					)
      __A    		=     AutoImageProcessor.from_pretrained(
          model_args.image_processor_name or model_args.model_name_or_path ,					cache_dir=model_args.cache_dir ,					revision=model_args.model_revision ,					use_auth_token=True if model_args.use_auth_token else None ,					)

      # Define torchvision transforms to be applied to each image + target.
      # Not that straightforward in torchvision: https://github.com/pytorch/vision/issues/9
      # Currently based on official torchvision references: https://github.com/pytorch/vision/blob/main/references/segmentation/transforms.py
      if "shortest_edge" in image_processor.size:
            # We instead set the target size as (shortest_edge, shortest_edge) to here to ensure all images are batchable.
            __A    		=     (image_processor.size['shortest_edge'], image_processor.size['shortest_edge'])
      else:
            __A    		=     (image_processor.size['height'], image_processor.size['width'])
      __A    		=     Compose(
          [
              ReduceLabels() if data_args.reduce_labels else Identity(),
              RandomCrop(size=UpperCAmelCase		),
              RandomHorizontalFlip(flip_prob=0.5		),
              PILToTensor(),
              ConvertImageDtype(torch.float		),
              Normalize(mean=image_processor.image_mean ,					std=image_processor.image_std		),
          ]		)
      # Define torchvision transform to be applied to each image.
      # jitter = ColorJitter(brightness=0.25, contrast=0.25, saturation=0.25, hue=0.1)
      __A    		=     Compose(
          [
              ReduceLabels() if data_args.reduce_labels else Identity(),
              Resize(size=UpperCAmelCase		),
              PILToTensor(),
              ConvertImageDtype(torch.float		),
              Normalize(mean=image_processor.image_mean ,					std=image_processor.image_std		),
          ]		)

      def preprocess_train(UpperCAmelCase		):
            __A    		=     []
            __A    		=     []
            for image, target in zip(example_batch['image'] ,					example_batch['label']		):
                  __A		, __A    		=     train_transforms(image.convert('RGB'		) ,					UpperCAmelCase		)
                  pixel_values.append(UpperCAmelCase		)
                  labels.append(UpperCAmelCase		)

            __A    		=     {}
            __A    		=     torch.stack(UpperCAmelCase		)
            __A    		=     torch.stack(UpperCAmelCase		)

            return encoding

      def preprocess_val(UpperCAmelCase		):
            __A    		=     []
            __A    		=     []
            for image, target in zip(example_batch['image'] ,					example_batch['label']		):
                  __A		, __A    		=     val_transforms(image.convert('RGB'		) ,					UpperCAmelCase		)
                  pixel_values.append(UpperCAmelCase		)
                  labels.append(UpperCAmelCase		)

            __A    		=     {}
            __A    		=     torch.stack(UpperCAmelCase		)
            __A    		=     torch.stack(UpperCAmelCase		)

            return encoding

      if training_args.do_train:
            if "train" not in dataset:
                  raise ValueError('--do_train requires a train dataset'		)
            if data_args.max_train_samples is not None:
                  __A    		=     (
                      dataset['train'].shuffle(seed=training_args.seed		).select(range(data_args.max_train_samples		)		)
                  )
            # Set the training transforms
            dataset["train"].set_transform(UpperCAmelCase		)

      if training_args.do_eval:
            if "validation" not in dataset:
                  raise ValueError('--do_eval requires a validation dataset'		)
            if data_args.max_eval_samples is not None:
                  __A    		=     (
                      dataset['validation'].shuffle(seed=training_args.seed		).select(range(data_args.max_eval_samples		)		)
                  )
            # Set the validation transforms
            dataset["validation"].set_transform(UpperCAmelCase		)

      # Initalize our trainer
      __A    		=     Trainer(
          model=UpperCAmelCase ,					args=UpperCAmelCase ,					train_dataset=dataset['train'] if training_args.do_train else None ,					eval_dataset=dataset['validation'] if training_args.do_eval else None ,					compute_metrics=UpperCAmelCase ,					tokenizer=UpperCAmelCase ,					data_collator=UpperCAmelCase ,					)

      # Training
      if training_args.do_train:
            __A    		=     None
            if training_args.resume_from_checkpoint is not None:
                  __A    		=     training_args.resume_from_checkpoint
            elif last_checkpoint is not None:
                  __A    		=     last_checkpoint
            __A    		=     trainer.train(resume_from_checkpoint=UpperCAmelCase		)
            trainer.save_model()
            trainer.log_metrics('train' ,					train_result.metrics		)
            trainer.save_metrics('train' ,					train_result.metrics		)
            trainer.save_state()

      # Evaluation
      if training_args.do_eval:
            __A    		=     trainer.evaluate()
            trainer.log_metrics('eval' ,					UpperCAmelCase		)
            trainer.save_metrics('eval' ,					UpperCAmelCase		)

      # Write model card and (optionally) push to hub
      __A    		=     {
          'finetuned_from': model_args.model_name_or_path,
          'dataset': data_args.dataset_name,
          'tags': ['image-segmentation', 'vision'],
      }
      if training_args.push_to_hub:
            trainer.push_to_hub(**UpperCAmelCase		)
      else:
            trainer.create_model_card(**UpperCAmelCase		)


if __name__ == "__main__":
 main()








'''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)))






