

'''simple docstring'''

import logging

import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader

from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set_seed





class 							_snake_case (       _a						):
   def __init__( self			:      Union[str, Any]     ,SCREAMING_SNAKE_CASE__			:      Any     ,SCREAMING_SNAKE_CASE__			:      Optional[Any]     ,SCREAMING_SNAKE_CASE__			:      Union[str, Any]=1_024     ,SCREAMING_SNAKE_CASE__			:      int=1_024     ,SCREAMING_SNAKE_CASE__			:      List[Any]=3.6							):
     SCREAMING_SNAKE_CASE:List[str]						      =					tokenizer
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					tokenizer.bos_token_id
     SCREAMING_SNAKE_CASE:Dict						      =					dataset
     SCREAMING_SNAKE_CASE:Any						      =					seq_length
     SCREAMING_SNAKE_CASE:Dict						      =					seq_length * chars_per_token * num_of_sequences







   def __iter__( self			:      Union[str, Any]							):
     SCREAMING_SNAKE_CASE:List[Any]						      =					iter(self.dataset							)
     SCREAMING_SNAKE_CASE:Any						      =					True
     while more_examples:
       SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:int						      =					[], 0
       while True:
         if buffer_len >= self.input_characters:
           break
         try:
           buffer.append(next(SCREAMING_SNAKE_CASE__							)["content"]							)
           buffer_len += len(buffer[-1]							)
         except StopIteration:
           SCREAMING_SNAKE_CASE:List[Any]						      =					False
           break
       SCREAMING_SNAKE_CASE:Tuple						      =					tokenizer(SCREAMING_SNAKE_CASE__     ,truncation=SCREAMING_SNAKE_CASE__							)["input_ids"]
       SCREAMING_SNAKE_CASE:str						      =					[]
       for tokenized_input in tokenized_inputs:
         all_token_ids.extend(tokenized_input + [self.concat_token_id]							)
       for i in range(0     ,len(SCREAMING_SNAKE_CASE__							)     ,self.seq_length							):
         SCREAMING_SNAKE_CASE:int						      =					all_token_ids[i : i + self.seq_length]
         if len(SCREAMING_SNAKE_CASE__							) == self.seq_length:
           yield torch.tensor(SCREAMING_SNAKE_CASE__							)





def    A_  ( snake_case ):
  SCREAMING_SNAKE_CASE:Optional[int]						      =					{"streaming": True}
  SCREAMING_SNAKE_CASE:Union[str, Any]						      =					load_dataset(args.dataset_name      ,       split="train"      ,       **snake_case )
  SCREAMING_SNAKE_CASE:List[Any]						      =					ConstantLengthDataset(snake_case      ,       snake_case      ,       seq_length=args.seq_length )
  SCREAMING_SNAKE_CASE:List[Any]						      =					DataLoader(snake_case      ,       batch_size=args.batch_size )
  return eval_dataloader





def    A_  ( snake_case ):
  model.eval()
  SCREAMING_SNAKE_CASE:Any						      =					[]
  for step, batch in enumerate(snake_case ):
    with torch.no_grad():
      SCREAMING_SNAKE_CASE:Union[str, Any]						      =					model(snake_case      ,       labels=snake_case )
    SCREAMING_SNAKE_CASE:str						      =					outputs.loss.repeat(args.batch_size )
    losses.append(accelerator.gather(snake_case ) )

    if args.max_eval_steps > 0 and step >= args.max_eval_steps:
      break
  SCREAMING_SNAKE_CASE:Optional[Any]						      =					torch.mean(torch.cat(snake_case ) )
  try:
    SCREAMING_SNAKE_CASE:List[str]						      =					torch.exp(snake_case )
  except OverflowError:
    SCREAMING_SNAKE_CASE:str						      =					float("inf" )
  return loss.item(), perplexity.item()


# Setup Accelerator
A_       						=							Accelerator()

# Parse configuration
A_       						=							HfArgumentParser(EvaluationArguments)
A_       						=							parser.parse_args()
set_seed(args.seed)

# Logging
A_       						=							logging.getLogger(__name__)
logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)

# Load model and tokenizer
A_       						=							AutoModelForCausalLM.from_pretrained(args.model_ckpt)
A_       						=							AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
A_       						=							create_dataloader(args)

# Prepare everything with our `accelerator`.
A_						,	A_       						=							accelerator.prepare(model, eval_dataloader)

# Evaluate and save the last checkpoint
logger.info("Evaluating and saving model after training")
A_						,	A_       						=							evaluate(args)


logger.info(f'''loss/eval: {eval_loss}, perplexity: {perplexity}''')









'''simple docstring'''

import copy
from typing import Dict, List, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


A_       						=							{
    "facebook/mask2former-swin-small-coco-instance": (
        "https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json"
    )
    # See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}

A_       						=							logging.get_logger(__name__)





class 							_snake_case (       _a						):
   _A       :     List[Any]       =					'''mask2former'''
   _A       :     str       =					['''swin''']
   _A       :     Tuple       =					{'''hidden_size''': '''hidden_dim'''}
   def __init__( self			:      Tuple     ,SCREAMING_SNAKE_CASE__			:      Optional[Dict] = None     ,SCREAMING_SNAKE_CASE__			:      int = 256     ,SCREAMING_SNAKE_CASE__			:      int = 256     ,SCREAMING_SNAKE_CASE__			:      int = 256     ,SCREAMING_SNAKE_CASE__			:      int = 1_024     ,SCREAMING_SNAKE_CASE__			:      str = "relu"     ,SCREAMING_SNAKE_CASE__			:      int = 6     ,SCREAMING_SNAKE_CASE__			:      int = 10     ,SCREAMING_SNAKE_CASE__			:      int = 8     ,SCREAMING_SNAKE_CASE__			:      float = 0.0     ,SCREAMING_SNAKE_CASE__			:      int = 2_048     ,SCREAMING_SNAKE_CASE__			:      bool = False     ,SCREAMING_SNAKE_CASE__			:      bool = False     ,SCREAMING_SNAKE_CASE__			:      int = 4     ,SCREAMING_SNAKE_CASE__			:      int = 255     ,SCREAMING_SNAKE_CASE__			:      int = 100     ,SCREAMING_SNAKE_CASE__			:      float = 0.1     ,SCREAMING_SNAKE_CASE__			:      float = 2.0     ,SCREAMING_SNAKE_CASE__			:      float = 5.0     ,SCREAMING_SNAKE_CASE__			:      float = 5.0     ,SCREAMING_SNAKE_CASE__			:      int = 12_544     ,SCREAMING_SNAKE_CASE__			:      float = 3.0     ,SCREAMING_SNAKE_CASE__			:      float = 0.75     ,SCREAMING_SNAKE_CASE__			:      float = 0.02     ,SCREAMING_SNAKE_CASE__			:      float = 1.0     ,SCREAMING_SNAKE_CASE__			:      bool = True     ,SCREAMING_SNAKE_CASE__			:      List[int] = [4, 8, 16, 32]     ,SCREAMING_SNAKE_CASE__			:      bool = None     ,**SCREAMING_SNAKE_CASE__			:      Dict     ,):
     if backbone_config is None:
       logger.info("`backbone_config` is `None`. Initializing the config with the default `Swin` backbone."							)
       SCREAMING_SNAKE_CASE:Union[str, Any]						      =					CONFIG_MAPPING["swin"](
           image_size=224     ,in_channels=3     ,patch_size=4     ,embed_dim=96     ,depths=[2, 2, 18, 2]     ,num_heads=[3, 6, 12, 24]     ,window_size=7     ,drop_path_rate=0.3     ,use_absolute_embeddings=SCREAMING_SNAKE_CASE__     ,out_features=["stage1", "stage2", "stage3", "stage4"]     ,)

     if isinstance(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							):
       SCREAMING_SNAKE_CASE:Any						      =					backbone_config.pop("model_type"							)
       SCREAMING_SNAKE_CASE:List[str]						      =					CONFIG_MAPPING[backbone_model_type]
       SCREAMING_SNAKE_CASE:List[str]						      =					config_class.from_dict(SCREAMING_SNAKE_CASE__							)

     # verify that the backbone is supported
     if backbone_config.model_type not in self.backbones_supported:
       logger.warning_once(
           F'''Backbone {backbone_config.model_type} is not a supported model and may not be compatible with Mask2Former. '''
           F'''Supported model types: {",".join(self.backbones_supported							)}'''							)

     SCREAMING_SNAKE_CASE:List[str]						      =					backbone_config
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					feature_size
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					mask_feature_size
     SCREAMING_SNAKE_CASE:str						      =					hidden_dim
     SCREAMING_SNAKE_CASE:Optional[int]						      =					encoder_feedforward_dim
     SCREAMING_SNAKE_CASE:Tuple						      =					activation_function
     SCREAMING_SNAKE_CASE:Optional[int]						      =					encoder_layers
     SCREAMING_SNAKE_CASE:List[Any]						      =					decoder_layers
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					num_attention_heads
     SCREAMING_SNAKE_CASE:Dict						      =					dropout
     SCREAMING_SNAKE_CASE:List[str]						      =					dim_feedforward
     SCREAMING_SNAKE_CASE:int						      =					pre_norm
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					enforce_input_projection
     SCREAMING_SNAKE_CASE:Any						      =					common_stride
     SCREAMING_SNAKE_CASE:int						      =					ignore_value
     SCREAMING_SNAKE_CASE:List[Any]						      =					num_queries
     SCREAMING_SNAKE_CASE:Dict						      =					no_object_weight
     SCREAMING_SNAKE_CASE:str						      =					class_weight
     SCREAMING_SNAKE_CASE:Tuple						      =					mask_weight
     SCREAMING_SNAKE_CASE:Optional[int]						      =					dice_weight
     SCREAMING_SNAKE_CASE:int						      =					train_num_points
     SCREAMING_SNAKE_CASE:str						      =					oversample_ratio
     SCREAMING_SNAKE_CASE:str						      =					importance_sample_ratio
     SCREAMING_SNAKE_CASE:str						      =					init_std
     SCREAMING_SNAKE_CASE:Any						      =					init_xavier_std
     SCREAMING_SNAKE_CASE:List[Any]						      =					use_auxiliary_loss
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					feature_strides
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					output_auxiliary_logits
     SCREAMING_SNAKE_CASE:Dict						      =					decoder_layers

     super().__init__(**SCREAMING_SNAKE_CASE__							)
   @classmethod
   def     __UpperCamelCase						( cls			:      Tuple     ,SCREAMING_SNAKE_CASE__			:      PretrainedConfig     ,**SCREAMING_SNAKE_CASE__			:      Union[str, Any]							):
     return cls(
         backbone_config=SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__     ,)







   def     __UpperCamelCase						( self			:      List[Any]							):
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					copy.deepcopy(self.__dict__							)
     SCREAMING_SNAKE_CASE:str						      =					self.backbone_config.to_dict()
     SCREAMING_SNAKE_CASE:List[str]						      =					self.__class__.model_type
     return output






