
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
UpperCAmelCase__						:	Dict	     = 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     lowerCamelCase__				(   a						,						a						,						a=0 ) ->  List[str]:
   _A:    Union[str, Any]						     =			(size, size) if isinstance(a						,						a ) else size
   _A     ,    _A:    int						     =			img.size
   _A:    Optional[Any]						     =			size[1] - original_height if original_height < size[1] else 0
   _A:    Dict						     =			size[0] - original_width if original_width < size[0] else 0
   _A:    List[str]						     =			functional.pad(a						,						(0, 0, pad_width, pad_height)						,						fill=a )
   return img






class UpperCAmelCase  :





   '''simple docstring'''


   def __init__(   self	:				Tuple     ,     lowerCAmelCase_	:				str	):

      """simple docstring"""
      _A:    str						     =			transforms


   def __call__(   self	:				Optional[int]     ,     lowerCAmelCase_	:				Dict     ,     lowerCAmelCase_	:				List[Any]	):

      """simple docstring"""
      for t in self.transforms:
         _A     ,    _A:    Any						     =			t(lowerCAmelCase_     ,     lowerCAmelCase_	)
      return image, target






class UpperCAmelCase  :





   '''simple docstring'''


   def __init__(   self	:				Optional[int]	):

      """simple docstring"""
      pass


   def __call__(   self	:				Tuple     ,     lowerCAmelCase_	:				Tuple     ,     lowerCAmelCase_	:				Optional[Any]	):

      """simple docstring"""
      return image, target






class UpperCAmelCase  :





   '''simple docstring'''


   def __init__(   self	:				Union[str, Any]     ,     lowerCAmelCase_	:				str	):

      """simple docstring"""
      _A:    Tuple						     =			size


   def __call__(   self	:				Any     ,     lowerCAmelCase_	:				Tuple     ,     lowerCAmelCase_	:				int	):

      """simple docstring"""
      _A:    Union[str, Any]						     =			functional.resize(lowerCAmelCase_     ,     self.size	)
      _A:    Tuple						     =			functional.resize(lowerCAmelCase_     ,     self.size     ,     interpolation=transforms.InterpolationMode.NEAREST	)
      return image, target






class UpperCAmelCase  :





   '''simple docstring'''


   def __init__(   self	:				Union[str, Any]     ,     lowerCAmelCase_	:				Tuple     ,     lowerCAmelCase_	:				Optional[int]=None	):

      """simple docstring"""
      _A:    str						     =			min_size
      if max_size is None:
         _A:    Tuple						     =			min_size
      _A:    List[str]						     =			max_size


   def __call__(   self	:				Any     ,     lowerCAmelCase_	:				int     ,     lowerCAmelCase_	:				int	):

      """simple docstring"""
      _A:    List[str]						     =			random.randint(self.min_size     ,     self.max_size	)
      _A:    Tuple						     =			functional.resize(lowerCAmelCase_     ,     lowerCAmelCase_	)
      _A:    List[Any]						     =			functional.resize(lowerCAmelCase_     ,     lowerCAmelCase_     ,     interpolation=transforms.InterpolationMode.NEAREST	)
      return image, target






class UpperCAmelCase  :





   '''simple docstring'''


   def __init__(   self	:				Any     ,     lowerCAmelCase_	:				Optional[int]	):

      """simple docstring"""
      _A:    int						     =			size if isinstance(lowerCAmelCase_     ,     lowerCAmelCase_	) else (size, size)


   def __call__(   self	:				Optional[int]     ,     lowerCAmelCase_	:				str     ,     lowerCAmelCase_	:				Dict	):

      """simple docstring"""
      _A:    List[str]						     =			pad_if_smaller(lowerCAmelCase_     ,     self.size	)
      _A:    Dict						     =			pad_if_smaller(lowerCAmelCase_     ,     self.size     ,     fill=2_5_5	)
      _A:    Any						     =			transforms.RandomCrop.get_params(lowerCAmelCase_     ,     self.size	)
      _A:    List[Any]						     =			functional.crop(lowerCAmelCase_     ,     *lowerCAmelCase_	)
      _A:    Any						     =			functional.crop(lowerCAmelCase_     ,     *lowerCAmelCase_	)
      return image, target






class UpperCAmelCase  :





   '''simple docstring'''


   def __init__(   self	:				Dict     ,     lowerCAmelCase_	:				Tuple	):

      """simple docstring"""
      _A:    int						     =			flip_prob


   def __call__(   self	:				List[str]     ,     lowerCAmelCase_	:				Optional[int]     ,     lowerCAmelCase_	:				Optional[Any]	):

      """simple docstring"""
      if random.random() < self.flip_prob:
         _A:    Optional[int]						     =			functional.hflip(lowerCAmelCase_	)
         _A:    Optional[int]						     =			functional.hflip(lowerCAmelCase_	)
      return image, target






class UpperCAmelCase  :





   '''simple docstring'''


   def __call__(   self	:				Tuple     ,     lowerCAmelCase_	:				int     ,     lowerCAmelCase_	:				Tuple	):

      """simple docstring"""
      _A:    str						     =			functional.pil_to_tensor(lowerCAmelCase_	)
      _A:    Tuple						     =			torch.as_tensor(np.array(lowerCAmelCase_	)     ,     dtype=torch.intaa	)
      return image, target






class UpperCAmelCase  :





   '''simple docstring'''


   def __init__(   self	:				int     ,     lowerCAmelCase_	:				Any	):

      """simple docstring"""
      _A:    Dict						     =			dtype


   def __call__(   self	:				int     ,     lowerCAmelCase_	:				int     ,     lowerCAmelCase_	:				Optional[Any]	):

      """simple docstring"""
      _A:    List[str]						     =			functional.convert_image_dtype(lowerCAmelCase_     ,     self.dtype	)
      return image, target






class UpperCAmelCase  :





   '''simple docstring'''


   def __init__(   self	:				Union[str, Any]     ,     lowerCAmelCase_	:				str     ,     lowerCAmelCase_	:				Tuple	):

      """simple docstring"""
      _A:    Dict						     =			mean
      _A:    str						     =			std


   def __call__(   self	:				Optional[Any]     ,     lowerCAmelCase_	:				str     ,     lowerCAmelCase_	:				int	):

      """simple docstring"""
      _A:    List[str]						     =			functional.normalize(lowerCAmelCase_     ,     mean=self.mean     ,     std=self.std	)
      return image, target






class UpperCAmelCase  :





   '''simple docstring'''


   def __call__(   self	:				int     ,     lowerCAmelCase_	:				Any     ,     lowerCAmelCase_	:				Union[str, Any]	):

      """simple docstring"""
      if not isinstance(lowerCAmelCase_     ,     np.ndarray	):
         _A:    Optional[Any]						     =			np.array(lowerCAmelCase_	).astype(np.uinta	)
      # avoid using underflow conversion
      _A:    int						     =			2_5_5
      _A:    Dict						     =			target - 1
      _A:    List[str]						     =			2_5_5

      _A:    Optional[Any]						     =			Image.fromarray(lowerCAmelCase_	)
      return image, target






@dataclass
class UpperCAmelCase  :





   '''simple docstring'''
   __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=SCREAMING_SNAKE_CASE__				,				metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}  )
   __UpperCamelCase						:       Optional[float]												=       field(
       default=0.15				,				metadata={'''help''': '''Percent to split off of train for validation.'''}  )
   __UpperCamelCase						:       Optional[int]												=       field(
       default=SCREAMING_SNAKE_CASE__				,				metadata={
           '''help''': (
               '''For debugging purposes or quicker training, truncate the number of training examples to this '''
               '''value if set.'''
           )
       }				,				)
   __UpperCamelCase						:       Optional[int]												=       field(
       default=SCREAMING_SNAKE_CASE__				,				metadata={
           '''help''': (
               '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
               '''value if set.'''
           )
       }				,				)
   __UpperCamelCase						:       Optional[bool]												=       field(
       default=SCREAMING_SNAKE_CASE__				,				metadata={'''help''': '''Whether or not to reduce all labels by 1 and replace background by 255.'''}				,				)


   def 					__magic_name__							(   self	:				List[Any]	):

      """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  :





   '''simple docstring'''
   __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=SCREAMING_SNAKE_CASE__				,				metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}  )
   __UpperCamelCase						:       Optional[str]												=       field(
       default=SCREAMING_SNAKE_CASE__				,				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=SCREAMING_SNAKE_CASE__				,				metadata={'''help''': '''Name or path of preprocessor config.'''}  )
   __UpperCamelCase						:       bool												=       field(
       default=SCREAMING_SNAKE_CASE__				,				metadata={
           '''help''': (
               '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
               '''with private models).'''
           )
       }				,				)






def     lowerCamelCase__				(   ) ->  Optional[Any]:
   # See all possible arguments in src/transformers/training_args.py
   # or by passing the --help flag to this script.
   # We now keep distinct sets of args, for a cleaner separation of concerns.

   _A:    Any						     =			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:    Union[str, Any]						     =			parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
   else:
      _A     ,    _A     ,    _A:    int						     =			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'''						,						a						,						a )

   # 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:    Any						     =			training_args.get_process_log_level()
   logger.setLevel(a )
   transformers.utils.logging.set_verbosity(a )
   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:    Optional[Any]						     =			None
   if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
      _A:    List[str]						     =			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:    str						     =			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:    Dict						     =			dataset.rename_columns({'''pixel_values''': '''image'''} )
   if "annotation" in dataset["train"].column_names:
      _A:    Dict						     =			dataset.rename_columns({'''annotation''': '''label'''} )

   # If we don't have a validation split, split off a percentage of train as validation.
   _A:    List[Any]						     =			None if '''validation''' in dataset.keys() else data_args.train_val_split
   if isinstance(data_args.train_val_split						,						a ) and data_args.train_val_split > 0.0:
      _A:    int						     =			dataset['''train'''].train_test_split(data_args.train_val_split )
      _A:    Union[str, Any]						     =			split['''train''']
      _A:    Dict						     =			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:    Dict						     =			'''huggingface/label-files'''
      _A:    List[Any]						     =			'''ade20k-id2label.json'''
   else:
      _A:    List[Any]						     =			data_args.dataset_name
      _A:    List[str]						     =			'''id2label.json'''
   _A:    str						     =			json.load(open(hf_hub_download(a						,						a						,						repo_type='''dataset''' )						,						'''r''' ) )
   _A:    Optional[int]						     =			{int(a ): v for k, v in idalabel.items()}
   _A:    Tuple						     =			{v: str(a ) for k, v in idalabel.items()}

   # Load the mean IoU metric from the datasets package
   _A:    int						     =			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(a ):
      _A     ,    _A:    List[Any]						     =			eval_pred
      _A:    List[str]						     =			torch.from_numpy(a )
      # scale the logits to the size of the label
      _A:    Union[str, Any]						     =			nn.functional.interpolate(
          a						,						size=labels.shape[-2:]						,						mode='''bilinear'''						,						align_corners=a						,						).argmax(dim=1 )

      _A:    Union[str, Any]						     =			logits_tensor.detach().cpu().numpy()
      _A:    Dict						     =			metric.compute(
          predictions=a						,						references=a						,						num_labels=len(a )						,						ignore_index=0						,						reduce_labels=image_processor.do_reduce_labels						,						)
      # add per category metrics as individual key-value pairs
      _A:    int						     =			metrics.pop('''per_category_accuracy''' ).tolist()
      _A:    str						     =			metrics.pop('''per_category_iou''' ).tolist()

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

      return metrics

   _A:    int						     =			AutoConfig.from_pretrained(
       model_args.config_name or model_args.model_name_or_path						,						labelaid=a						,						idalabel=a						,						cache_dir=model_args.cache_dir						,						revision=model_args.model_revision						,						use_auth_token=True if model_args.use_auth_token else None						,						)
   _A:    List[Any]						     =			AutoModelForSemanticSegmentation.from_pretrained(
       model_args.model_name_or_path						,						from_tf=bool('''.ckpt''' in model_args.model_name_or_path )						,						config=a						,						cache_dir=model_args.cache_dir						,						revision=model_args.model_revision						,						use_auth_token=True if model_args.use_auth_token else None						,						)
   _A:    Any						     =			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:    Any						     =			(image_processor.size['''shortest_edge'''], image_processor.size['''shortest_edge'''])
   else:
      _A:    Any						     =			(image_processor.size['''height'''], image_processor.size['''width'''])
   _A:    Optional[int]						     =			Compose(
       [
           ReduceLabels() if data_args.reduce_labels else Identity(),
           RandomCrop(size=a ),
           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:    List[Any]						     =			Compose(
       [
           ReduceLabels() if data_args.reduce_labels else Identity(),
           Resize(size=a ),
           PILToTensor(),
           ConvertImageDtype(torch.float ),
           Normalize(mean=image_processor.image_mean						,						std=image_processor.image_std ),
       ] )

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

      _A:    List[str]						     =			{}
      _A:    List[Any]						     =			torch.stack(a )
      _A:    Tuple						     =			torch.stack(a )

      return encoding

   def preprocess_val(a ):
      _A:    int						     =			[]
      _A:    Any						     =			[]
      for image, target in zip(example_batch['''image''']						,						example_batch['''label'''] ):
         _A     ,    _A:    Union[str, Any]						     =			val_transforms(image.convert('''RGB''' )						,						a )
         pixel_values.append(a )
         labels.append(a )

      _A:    Optional[Any]						     =			{}
      _A:    int						     =			torch.stack(a )
      _A:    List[str]						     =			torch.stack(a )

      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:    List[str]						     =			(
             dataset['''train'''].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
         )
      # Set the training transforms
      dataset["train"].set_transform(a )

   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:    int						     =			(
             dataset['''validation'''].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
         )
      # Set the validation transforms
      dataset["validation"].set_transform(a )

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

   # Training
   if training_args.do_train:
      _A:    int						     =			None
      if training_args.resume_from_checkpoint is not None:
         _A:    List[str]						     =			training_args.resume_from_checkpoint
      elif last_checkpoint is not None:
         _A:    List[Any]						     =			last_checkpoint
      _A:    List[str]						     =			trainer.train(resume_from_checkpoint=a )
      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:    Dict						     =			trainer.evaluate()
      trainer.log_metrics('''eval'''						,						a )
      trainer.save_metrics('''eval'''						,						a )

   # Write model card and (optionally) push to hub
   _A:    Optional[int]						     =			{
       '''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(**a )
   else:
      trainer.create_model_card(**a )


if __name__ == "__main__":
   main()





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase__						:	List[str]	     = logging.get_logger(__name__)

UpperCAmelCase__						:	str	     = {
    'flaubert/flaubert_small_cased': 'https://huggingface.co/flaubert/flaubert_small_cased/resolve/main/config.json',
    'flaubert/flaubert_base_uncased': 'https://huggingface.co/flaubert/flaubert_base_uncased/resolve/main/config.json',
    'flaubert/flaubert_base_cased': 'https://huggingface.co/flaubert/flaubert_base_cased/resolve/main/config.json',
    'flaubert/flaubert_large_cased': 'https://huggingface.co/flaubert/flaubert_large_cased/resolve/main/config.json',
}






class UpperCAmelCase  (		SCREAMING_SNAKE_CASE__  ):





   '''simple docstring'''
   __UpperCamelCase						:       Tuple												=       '''flaubert'''
   __UpperCamelCase						:       str												=       {
       '''hidden_size''': '''emb_dim''',
       '''num_attention_heads''': '''n_heads''',
       '''num_hidden_layers''': '''n_layers''',
       '''n_words''': '''vocab_size''',  # For backward compatibility
   }


   def __init__(   self	:				Union[str, Any]     ,     lowerCAmelCase_	:				Union[str, Any]=False     ,     lowerCAmelCase_	:				List[str]=0.0     ,     lowerCAmelCase_	:				Dict=3_0_1_4_5     ,     lowerCAmelCase_	:				Tuple=2_0_4_8     ,     lowerCAmelCase_	:				List[Any]=1_2     ,     lowerCAmelCase_	:				str=1_6     ,     lowerCAmelCase_	:				Union[str, Any]=0.1     ,     lowerCAmelCase_	:				List[str]=0.1     ,     lowerCAmelCase_	:				str=True     ,     lowerCAmelCase_	:				Optional[int]=False     ,     lowerCAmelCase_	:				Any=False     ,     lowerCAmelCase_	:				int=False     ,     lowerCAmelCase_	:				int=1     ,     lowerCAmelCase_	:				List[str]=True     ,     lowerCAmelCase_	:				Dict=5_1_2     ,     lowerCAmelCase_	:				Any=2_0_4_8**-0.5     ,     lowerCAmelCase_	:				List[str]=1e-12     ,     lowerCAmelCase_	:				Any=0.02     ,     lowerCAmelCase_	:				Optional[Any]=0     ,     lowerCAmelCase_	:				Union[str, Any]=1     ,     lowerCAmelCase_	:				List[Any]=2     ,     lowerCAmelCase_	:				Dict=3     ,     lowerCAmelCase_	:				Optional[Any]=5     ,     lowerCAmelCase_	:				Any=True     ,     lowerCAmelCase_	:				Union[str, Any]="first"     ,     lowerCAmelCase_	:				List[Any]=True     ,     lowerCAmelCase_	:				Tuple=None     ,     lowerCAmelCase_	:				Optional[int]=True     ,     lowerCAmelCase_	:				int=0.1     ,     lowerCAmelCase_	:				Optional[int]=5     ,     lowerCAmelCase_	:				Union[str, Any]=5     ,     lowerCAmelCase_	:				List[Any]=0     ,     lowerCAmelCase_	:				Dict=0     ,     lowerCAmelCase_	:				Union[str, Any]=2     ,     lowerCAmelCase_	:				Optional[int]=0     ,     **lowerCAmelCase_	:				Any     ,     ):

      """simple docstring"""
      _A:    List[str]						     =			pre_norm
      _A:    List[Any]						     =			layerdrop
      _A:    Dict						     =			vocab_size
      _A:    List[Any]						     =			emb_dim
      _A:    Optional[Any]						     =			n_layers
      _A:    Union[str, Any]						     =			n_heads
      _A:    Optional[Any]						     =			dropout
      _A:    Optional[Any]						     =			attention_dropout
      _A:    Dict						     =			gelu_activation
      _A:    Tuple						     =			sinusoidal_embeddings
      _A:    Tuple						     =			causal
      _A:    List[Any]						     =			asm
      _A:    Dict						     =			n_langs
      _A:    Optional[int]						     =			use_lang_emb
      _A:    Any						     =			layer_norm_eps
      _A:    str						     =			bos_index
      _A:    Optional[int]						     =			eos_index
      _A:    int						     =			pad_index
      _A:    Tuple						     =			unk_index
      _A:    int						     =			mask_index
      _A:    int						     =			is_encoder
      _A:    Union[str, Any]						     =			max_position_embeddings
      _A:    List[str]						     =			embed_init_std
      _A:    List[str]						     =			init_std
      _A:    Tuple						     =			summary_type
      _A:    List[str]						     =			summary_use_proj
      _A:    Union[str, Any]						     =			summary_activation
      _A:    Union[str, Any]						     =			summary_proj_to_labels
      _A:    int						     =			summary_first_dropout
      _A:    Optional[Any]						     =			start_n_top
      _A:    Optional[Any]						     =			end_n_top
      _A:    Dict						     =			mask_token_id
      _A:    str						     =			lang_id

      if "n_words" in kwargs:
         _A:    int						     =			kwargs['''n_words''']

      super().__init__(pad_token_id=lowerCAmelCase_     ,     bos_token_id=lowerCAmelCase_     ,     **lowerCAmelCase_	)






class UpperCAmelCase  (		SCREAMING_SNAKE_CASE__  ):





   '''simple docstring'''


   @property
   def 					__magic_name__							(   self	:				Tuple	):

      """simple docstring"""
      if self.task == "multiple-choice":
         _A:    int						     =			{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
      else:
         _A:    Any						     =			{0: '''batch''', 1: '''sequence'''}
      return OrderedDict(
          [
              ('''input_ids''', dynamic_axis),
              ('''attention_mask''', dynamic_axis),
          ]	)



