
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, List, Optional

import numpy as np
import torch
from utils_hans import HansDataset, InputFeatures, hans_processors, hans_tasks_num_labels

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import is_main_process


_SCREAMING_SNAKE_CASE   						=    logging.getLogger(__name__)







@dataclass
class 					UpperCAmelCase_				:
    UpperCAmelCase_			:       Any						 =							field(
        metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}    )
    UpperCAmelCase_			:       int						 =							field(
        default=UpperCamelCase_     ,     metadata={'help': 'Pretrained config name or path if not the same as model_name'}    )
    UpperCAmelCase_			:       List[str]						 =							field(
        default=UpperCamelCase_     ,     metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}    )
    UpperCAmelCase_			:       List[Any]						 =							field(
        default=UpperCamelCase_     ,     metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}     ,     )







@dataclass
class 					UpperCAmelCase_				:
    UpperCAmelCase_			:       Optional[Any]						 =							field(
        metadata={'help': 'The name of the task to train selected in the list: ' + ', '.join(hans_processors.keys()    )}    )
    UpperCAmelCase_			:       Optional[Any]						 =							field(
        metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'}    )
    UpperCAmelCase_			:       List[Any]						 =							field(
        default=128     ,     metadata={
            'help': (
                'The maximum total input sequence length after tokenization. Sequences longer '
                'than this will be truncated, sequences shorter will be padded.'
            )
        }     ,     )
    UpperCAmelCase_			:       List[str]						 =							field(
        default=UpperCamelCase_     ,     metadata={'help': 'Overwrite the cached training and evaluation sets'}    )

def 				__lowercase	(						lowerCamelCase__					)				->Dict[str, torch.Tensor]:

  '''simple docstring'''




  a__     		=     default_data_collator(snake_case_					)
  a__     		=     batch.pop("pairID" ,				snake_case_					)
  return batch
def 				__lowercase	(						)				->Optional[int]:

  '''simple docstring'''




  a__     		=     HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)					)
  a__     		=     parser.parse_args_into_dataclasses()

  if (
      os.path.exists(training_args.output_dir					)
      and os.listdir(training_args.output_dir					)
      and training_args.do_train
      and not training_args.overwrite_output_dir
  ):
    raise ValueError(
        f"Output directory ({training_args.output_dir}) already exists and is not empty. Use"
        " --overwrite_output_dir to overcome."					)

  # Setup logging
  logging.basicConfig(
      format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" ,				datefmt="%m/%d/%Y %H:%M:%S" ,				level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN ,				)
  logger.warning(
      "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s" ,				training_args.local_rank ,				training_args.device ,				training_args.n_gpu ,				bool(training_args.local_rank != -1					) ,				training_args.fpaa ,				)
  # Set the verbosity to info of the Transformers logger (on main process only):
  if is_main_process(training_args.local_rank					):
    transformers.utils.logging.set_verbosity_info()
    transformers.utils.logging.enable_default_handler()
    transformers.utils.logging.enable_explicit_format()
  logger.info("Training/evaluation parameters %s" ,				snake_case_					)

  # Set seed
  set_seed(training_args.seed					)

  try:
    a__     		=     hans_tasks_num_labels[data_args.task_name]
  except KeyError:
    raise ValueError("Task not found: %s" % (data_args.task_name)					)

  # Load pretrained model and tokenizer
  #
  # Distributed training:
  # The .from_pretrained methods guarantee that only one local process can concurrently
  # download model & vocab.

  a__     		=     AutoConfig.from_pretrained(
      model_args.config_name if model_args.config_name else model_args.model_name_or_path ,				num_labels=snake_case_ ,				finetuning_task=data_args.task_name ,				cache_dir=model_args.cache_dir ,				)
  a__     		=     AutoTokenizer.from_pretrained(
      model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path ,				cache_dir=model_args.cache_dir ,				)
  a__     		=     AutoModelForSequenceClassification.from_pretrained(
      model_args.model_name_or_path ,				from_tf=bool(".ckpt" in model_args.model_name_or_path					) ,				config=snake_case_ ,				cache_dir=model_args.cache_dir ,				)

  # Get datasets
  a__     		=     (
      HansDataset(
          data_dir=data_args.data_dir ,				tokenizer=snake_case_ ,				task=data_args.task_name ,				max_seq_length=data_args.max_seq_length ,				overwrite_cache=data_args.overwrite_cache ,				)
      if training_args.do_train
      else None
  )
  a__     		=     (
      HansDataset(
          data_dir=data_args.data_dir ,				tokenizer=snake_case_ ,				task=data_args.task_name ,				max_seq_length=data_args.max_seq_length ,				overwrite_cache=data_args.overwrite_cache ,				evaluate=snake_case_ ,				)
      if training_args.do_eval
      else None
  )

  # Initialize our Trainer
  a__     		=     Trainer(
      model=snake_case_ ,				args=snake_case_ ,				train_dataset=snake_case_ ,				eval_dataset=snake_case_ ,				data_collator=snake_case_ ,				)

  # Training
  if training_args.do_train:
    trainer.train(
        model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path					) else None					)
    trainer.save_model()
    # For convenience, we also re-save the tokenizer to the same directory,
    # so that you can share your model easily on huggingface.co/models =)
    if trainer.is_world_master():
      tokenizer.save_pretrained(training_args.output_dir					)

    # Evaluation
  if training_args.do_eval:
    logger.info("*** Evaluate ***"					)

    a__     		=     trainer.predict(snake_case_					)
    a__     		=     output.predictions
    a__     		=     np.argmax(snake_case_ ,				axis=1					)

    a__     		=     [ex.pairID for ex in eval_dataset]
    a__     		=     os.path.join(training_args.output_dir ,				"hans_predictions.txt"					)
    a__     		=     eval_dataset.get_labels()
    if trainer.is_world_master():
      with open(snake_case_ ,				"w"					) as writer:
        writer.write("pairID,gold_label\n"					)
        for pid, pred in zip(snake_case_ ,				snake_case_					):
          writer.write("ex" + str(snake_case_					) + "," + label_list[int(snake_case_					)] + "\n"					)

    trainer._log(output.metrics					)
def 				__lowercase	(						lowerCamelCase__					)				->Union[str, Any]:

  '''simple docstring'''




  main()


if __name__ == "__main__":
     main()


import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE   						=    logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE   						=    {
    """facebook/sam-vit-huge""": """https://huggingface.co/facebook/sam-vit-huge/resolve/main/config.json""",
    """facebook/sam-vit-large""": """https://huggingface.co/facebook/sam-vit-large/resolve/main/config.json""",
    """facebook/sam-vit-base""": """https://huggingface.co/facebook/sam-vit-base/resolve/main/config.json""",
}







class 					UpperCAmelCase_				(							_a    ):




    def __init__(					self  :   List[str]      ,      lowerCAmelCase  :   str=256      ,      lowerCAmelCase  :   Any=1024      ,      lowerCAmelCase  :   List[str]=16      ,      lowerCAmelCase  :   Union[str, Any]=16      ,      lowerCAmelCase  :   int=4      ,      lowerCAmelCase  :   List[Any]="gelu"      ,      lowerCAmelCase  :   str=1e-6      ,      **lowerCAmelCase  :   Union[str, Any]      ,      )	-> Union[str, Any]:
      super().__init__(**lowerCAmelCase   )
      a__     		=     hidden_size
      a__     		=     image_size
      a__     		=     patch_size
      a__     		=     image_size // patch_size
      a__     		=     mask_input_channels
      a__     		=     num_point_embeddings
      a__     		=     hidden_act
      a__     		=     layer_norm_eps







class 					UpperCAmelCase_				(							_a    ):




    def __init__(					self  :   Dict      ,      lowerCAmelCase  :   str=256      ,      lowerCAmelCase  :   Union[str, Any]="relu"      ,      lowerCAmelCase  :   Optional[int]=2048      ,      lowerCAmelCase  :   Any=2      ,      lowerCAmelCase  :   Any=8      ,      lowerCAmelCase  :   Optional[Any]=2      ,      lowerCAmelCase  :   Optional[Any]=3      ,      lowerCAmelCase  :   Union[str, Any]=3      ,      lowerCAmelCase  :   List[str]=256      ,      lowerCAmelCase  :   Optional[Any]=1e-6      ,      **lowerCAmelCase  :   Any      ,      )	-> int:
      super().__init__(**lowerCAmelCase   )
      a__     		=     hidden_size
      a__     		=     hidden_act
      a__     		=     mlp_dim
      a__     		=     num_hidden_layers
      a__     		=     num_attention_heads
      a__     		=     attention_downsample_rate
      a__     		=     num_multimask_outputs
      a__     		=     iou_head_depth
      a__     		=     iou_head_hidden_dim
      a__     		=     layer_norm_eps







class 					UpperCAmelCase_				(							_a    ):




    def __init__(					self  :   Optional[Any]      ,      lowerCAmelCase  :   Optional[Any]=768      ,      lowerCAmelCase  :   int=256      ,      lowerCAmelCase  :   Tuple=12      ,      lowerCAmelCase  :   List[str]=12      ,      lowerCAmelCase  :   Tuple=3      ,      lowerCAmelCase  :   Any=1024      ,      lowerCAmelCase  :   Dict=16      ,      lowerCAmelCase  :   Dict="gelu"      ,      lowerCAmelCase  :   List[Any]=1e-0_6      ,      lowerCAmelCase  :   Any=0.0      ,      lowerCAmelCase  :   Optional[Any]=1e-1_0      ,      lowerCAmelCase  :   Tuple=True      ,      lowerCAmelCase  :   Tuple=4.0      ,      lowerCAmelCase  :   Optional[Any]=True      ,      lowerCAmelCase  :   List[str]=True      ,      lowerCAmelCase  :   Dict=14      ,      lowerCAmelCase  :   Optional[int]=[2, 5, 8, 11]      ,      lowerCAmelCase  :   List[str]=128      ,      lowerCAmelCase  :   List[str]=None      ,      **lowerCAmelCase  :   str      ,      )	-> Optional[int]:
      super().__init__(**lowerCAmelCase   )

      a__     		=     hidden_size
      a__     		=     output_channels
      a__     		=     num_hidden_layers
      a__     		=     num_attention_heads
      a__     		=     num_channels
      a__     		=     image_size
      a__     		=     patch_size
      a__     		=     hidden_act
      a__     		=     layer_norm_eps
      a__     		=     attention_dropout
      a__     		=     initializer_range
      a__     		=     qkv_bias
      a__     		=     mlp_ratio
      a__     		=     use_abs_pos
      a__     		=     use_rel_pos
      a__     		=     window_size
      a__     		=     global_attn_indexes
      a__     		=     num_pos_feats
      a__     		=     int(hidden_size * mlp_ratio   ) if mlp_dim is None else mlp_dim







class 					UpperCAmelCase_				(							_a    ):
    UpperCAmelCase_			:       Optional[Any]						 =							'sam'
    UpperCAmelCase_			:       List[Any]						 =							True




    def __init__(					self  :   Dict      ,      lowerCAmelCase  :   Any=None      ,      lowerCAmelCase  :   Optional[Any]=None      ,      lowerCAmelCase  :   Optional[Any]=None      ,      lowerCAmelCase  :   str=0.02      ,      **lowerCAmelCase  :   int      ,      )	-> Dict:
      super().__init__(**lowerCAmelCase   )
      a__     		=     vision_config if vision_config is not None else {}
      a__     		=     prompt_encoder_config if prompt_encoder_config is not None else {}
      a__     		=     mask_decoder_config if mask_decoder_config is not None else {}

      if isinstance(lowerCAmelCase      ,      lowerCAmelCase   ):
        a__     		=     vision_config.to_dict()
      if isinstance(lowerCAmelCase      ,      lowerCAmelCase   ):
        a__     		=     prompt_encoder_config.to_dict()
      if isinstance(lowerCAmelCase      ,      lowerCAmelCase   ):
        a__     		=     mask_decoder_config.to_dict()

      a__     		=     SamVisionConfig(**lowerCAmelCase   )
      a__     		=     SamPromptEncoderConfig(**lowerCAmelCase   )
      a__     		=     SamMaskDecoderConfig(**lowerCAmelCase   )
      a__     		=     initializer_range




    def 						__lowerCamelCase (					self  :   Optional[Any]   )	-> Union[str, Any]:
      a__     		=     copy.deepcopy(self.__dict__   )
      a__     		=     self.vision_config.to_dict()
      a__     		=     self.prompt_encoder_config.to_dict()
      a__     		=     self.mask_decoder_config.to_dict()
      a__     		=     self.__class__.model_type
      return output

