

"""simple docstring"""




from typing import Any, Dict, List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
      from PIL import Image

      from ..image_utils import load_image

if is_torch_available():
      from ..models.auto.modeling_auto import (
          MODEL_FOR_IMAGE_SEGMENTATION_MAPPING,
          MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING,
          MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
          MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING,
      )


__UpperCAmelCase	=			logging.get_logger(__name__)


__UpperCAmelCase	=			Dict[str, Any]
__UpperCAmelCase	=			List[Prediction]







@add_end_docstrings(__lowerCamelCase   )
class        __lowercase     (      __lowerCamelCase   ):

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






   '''simple docstring'''
   super().__init__(*A   ,**A      )

   if self.framework == "tf":
    raise ValueError(f"The {self.__class__} is only available in PyTorch."      )

   requires_backends(self   ,"""vision"""      )
   self.check_model_type(
       dict(
           MODEL_FOR_IMAGE_SEGMENTATION_MAPPING.items()
           + MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING.items()
           + MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING.items()
           + MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING.items()      )      )

  def 				__lowercase    (					self						:     List[str]   ,**A						:     Optional[Any]      ):






   '''simple docstring'''
   UpperCAmelCase__   :					List[str]   				=   {}
   UpperCAmelCase__   :					Optional[Any]   				=   {}
   if "subtask" in kwargs:
    UpperCAmelCase__   :					Optional[int]   				=   kwargs["""subtask"""]
    UpperCAmelCase__   :					List[str]   				=   kwargs["""subtask"""]
   if "threshold" in kwargs:
    UpperCAmelCase__   :					str   				=   kwargs["""threshold"""]
   if "mask_threshold" in kwargs:
    UpperCAmelCase__   :					Union[str, Any]   				=   kwargs["""mask_threshold"""]
   if "overlap_mask_area_threshold" in kwargs:
    UpperCAmelCase__   :					List[Any]   				=   kwargs["""overlap_mask_area_threshold"""]

   return preprocess_kwargs, {}, postprocess_kwargs

  def __call__(					self						:     Any   ,A						:     Tuple   ,**A						:     int      ):






   '''simple docstring'''
   return super().__call__(A   ,**A      )

  def 				__lowercase    (					self						:     int   ,A						:     Tuple   ,A						:     int=None      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Dict   				=   load_image(A      )
   UpperCAmelCase__   :					Optional[Any]   				=   [(image.height, image.width)]
   if self.model.config.__class__.__name__ == "OneFormerConfig":
    if subtask is None:
     UpperCAmelCase__   :					List[Any]   				=   {}
    else:
     UpperCAmelCase__   :					Optional[int]   				=   {"""task_inputs""": [subtask]}
    UpperCAmelCase__   :					List[str]   				=   self.image_processor(images=[image]   ,return_tensors="""pt"""   ,**A      )
    UpperCAmelCase__   :					Union[str, Any]   				=   self.tokenizer(
        inputs["""task_inputs"""]   ,padding="""max_length"""   ,max_length=self.model.config.task_seq_len   ,return_tensors=self.framework   ,)["""input_ids"""]
   else:
    UpperCAmelCase__   :					Tuple   				=   self.image_processor(images=[image]   ,return_tensors="""pt"""      )
   UpperCAmelCase__   :					List[Any]   				=   target_size
   return inputs

  def 				__lowercase    (					self						:     List[Any]   ,A						:     Union[str, Any]      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Optional[Any]   				=   model_inputs.pop("""target_size"""      )
   UpperCAmelCase__   :					Any   				=   self.model(**A      )
   UpperCAmelCase__   :					List[str]   				=   target_size
   return model_outputs




  def 				__lowercase    (					self						:     Optional[int]   ,A						:     List[str]   ,A						:     Dict=None   ,A						:     Optional[int]=0.9   ,A						:     str=0.5   ,A						:     int=0.5      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Optional[int]   				=   None
   if subtask in {"panoptic", None} and hasattr(self.image_processor   ,"""post_process_panoptic_segmentation"""      ):
    UpperCAmelCase__   :					List[str]   				=   self.image_processor.post_process_panoptic_segmentation
   elif subtask in {"instance", None} and hasattr(self.image_processor   ,"""post_process_instance_segmentation"""      ):
    UpperCAmelCase__   :					List[Any]   				=   self.image_processor.post_process_instance_segmentation

   if fn is not None:
    UpperCAmelCase__   :					int   				=   fn(
        A   ,threshold=A   ,mask_threshold=A   ,overlap_mask_area_threshold=A   ,target_sizes=model_outputs["""target_size"""]   ,)[0]

    UpperCAmelCase__   :					List[Any]   				=   []
    UpperCAmelCase__   :					str   				=   outputs["""segmentation"""]

    for segment in outputs["segments_info"]:
     UpperCAmelCase__   :					Dict   				=   (segmentation == segment["""id"""]) * 255
     UpperCAmelCase__   :					Union[str, Any]   				=   Image.fromarray(mask.numpy().astype(np.uinta      )   ,mode="""L"""      )
     UpperCAmelCase__   :					List[Any]   				=   self.model.config.idalabel[segment["""label_id"""]]
     UpperCAmelCase__   :					Optional[Any]   				=   segment["""score"""]
     annotation.append({"""score""": score, """label""": label, """mask""": mask}      )

   elif subtask in {"semantic", None} and hasattr(self.image_processor   ,"""post_process_semantic_segmentation"""      ):
    UpperCAmelCase__   :					List[Any]   				=   self.image_processor.post_process_semantic_segmentation(
        A   ,target_sizes=model_outputs["""target_size"""]      )[0]

    UpperCAmelCase__   :					List[str]   				=   []
    UpperCAmelCase__   :					List[Any]   				=   outputs.numpy()
    UpperCAmelCase__   :					str   				=   np.unique(A      )

    for label in labels:
     UpperCAmelCase__   :					Union[str, Any]   				=   (segmentation == label) * 255
     UpperCAmelCase__   :					Dict   				=   Image.fromarray(mask.astype(np.uinta      )   ,mode="""L"""      )
     UpperCAmelCase__   :					List[Any]   				=   self.model.config.idalabel[label]
     annotation.append({"""score""": None, """label""": label, """mask""": mask}      )
   else:
    raise ValueError(f"Subtask {subtask} is not supported for model {type(self.model      )}"      )
   return annotation








"""simple docstring"""




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


__UpperCAmelCase	=			logging.getLogger(__name__)







@dataclass
class        __lowercase     :
  snake_case_			    =    field(
      metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}   )
  snake_case_			    =    field(
      default=__lowerCamelCase		,     metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}   )
  snake_case_			    =    field(
      default=__lowerCamelCase		,     metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}   )
  snake_case_			    =    field(
      default=__lowerCamelCase		,     metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}		,     )







@dataclass
class        __lowercase     :
  snake_case_			    =    field(
      metadata={"""help""": """The name of the task to train selected in the list: """ + """, """.join(hans_processors.keys()   )}   )
  snake_case_			    =    field(
      metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""}   )
  snake_case_			    =    field(
      default=1_2_8		,     metadata={
          """help""": (
              """The maximum total input sequence length after tokenization. Sequences longer """
              """than this will be truncated, sequences shorter will be padded."""
          )
      }		,     )
  snake_case_			    =    field(
      default=__lowerCamelCase		,     metadata={"""help""": """Overwrite the cached training and evaluation sets"""}   )







def 					lowerCAmelCase  (   __UpperCamelCase ):
 '''simple docstring'''





 UpperCAmelCase__   :					Optional[Any]   				=   default_data_collator(__UpperCamelCase )
 UpperCAmelCase__   :					List[str]   				=   batch.pop("""pairID"""							,      __UpperCamelCase )
 return batch
def 					lowerCAmelCase  (   ):
 '''simple docstring'''





 UpperCAmelCase__   :					Dict   				=   HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
 UpperCAmelCase__				, UpperCAmelCase__				, UpperCAmelCase__   :					Union[str, Any]   				=   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"""							,      __UpperCamelCase )

 # Set seed
 set_seed(training_args.seed )

 try:
  UpperCAmelCase__   :					Optional[int]   				=   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.

 UpperCAmelCase__   :					Tuple   				=   AutoConfig.from_pretrained(
     model_args.config_name if model_args.config_name else model_args.model_name_or_path							,      num_labels=__UpperCamelCase							,      finetuning_task=data_args.task_name							,      cache_dir=model_args.cache_dir							,      )
 UpperCAmelCase__   :					int   				=   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							,      )
 UpperCAmelCase__   :					List[Any]   				=   AutoModelForSequenceClassification.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							,      )

 # Get datasets
 UpperCAmelCase__   :					Optional[int]   				=   (
     HansDataset(
         data_dir=data_args.data_dir							,      tokenizer=__UpperCamelCase							,      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
 )
 UpperCAmelCase__   :					str   				=   (
     HansDataset(
         data_dir=data_args.data_dir							,      tokenizer=__UpperCamelCase							,      task=data_args.task_name							,      max_seq_length=data_args.max_seq_length							,      overwrite_cache=data_args.overwrite_cache							,      evaluate=__UpperCamelCase							,      )
     if training_args.do_eval
     else None
 )

 # Initialize our Trainer
 UpperCAmelCase__   :					List[Any]   				=   Trainer(
     model=__UpperCamelCase							,      args=__UpperCamelCase							,      train_dataset=__UpperCamelCase							,      eval_dataset=__UpperCamelCase							,      data_collator=__UpperCamelCase							,      )

 # 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 ***""" )

  UpperCAmelCase__   :					Optional[Any]   				=   trainer.predict(__UpperCamelCase )
  UpperCAmelCase__   :					List[str]   				=   output.predictions
  UpperCAmelCase__   :					Dict   				=   np.argmax(__UpperCamelCase							,      axis=1 )

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

  trainer._log(output.metrics )
def 					lowerCAmelCase  (   __UpperCamelCase ):
 '''simple docstring'''





 main()


if __name__ == "__main__":
      main()





