




'''simple docstring'''







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

import transformers
from transformers import (
    AutoConfig,
    AutoModelForQuestionAnswering,
    AutoTokenizer,
    DataCollatorWithPadding,
    HfArgumentParser,
    SquadDataset,
    Trainer,
    TrainingArguments,
)
from transformers import SquadDataTrainingArguments as DataTrainingArguments
from transformers.trainer_utils import is_main_process


__UpperCamelCase			:      Any			=					logging.getLogger(__name__)







@dataclass
class 				_a   :





     '''simple docstring'''



     lowerCAmelCase_     :    str									=				field(
         metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}  )
     lowerCAmelCase_     :    Optional[str]									=				field(
         default=a_,		metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}  )
     lowerCAmelCase_     :    Optional[str]									=				field(
         default=a_,		metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}  )
     lowerCAmelCase_     :    bool									=				field(default=a_,		metadata={"""help""": """Set this flag to use fast tokenization."""}  )
     # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
     # or just modify its tokenizer_config.json.
     lowerCAmelCase_     :    Optional[str]									=				field(
         default=a_,		metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""},		)



def 					lowerCamelCase__							(			):
     # 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.

     snake_case :			Dict					   = 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.
          snake_case :			Union[str, Any]					   = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]							)							)
     else:
          snake_case :			List[str]					   = 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"""    ,      lowercase_							)

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

     snake_case :			Optional[Any]					   = AutoConfig.from_pretrained(
         model_args.config_name if model_args.config_name else model_args.model_name_or_path    ,      cache_dir=model_args.cache_dir    ,      )
     snake_case :			Dict					   = 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    ,      use_fast=lowercase_    ,      )
     snake_case :			List[Any]					   = AutoModelForQuestionAnswering.from_pretrained(
         model_args.model_name_or_path    ,      from_tf=bool(""".ckpt""" in model_args.model_name_or_path							)    ,      config=lowercase_    ,      cache_dir=model_args.cache_dir    ,      )

     # Get datasets
     snake_case :			List[Any]					   = hasattr(model.config    ,      """lang2id"""							)
     snake_case :			int					   = (
         SquadDataset(
             lowercase_    ,      tokenizer=lowercase_    ,      is_language_sensitive=lowercase_    ,      cache_dir=model_args.cache_dir							)
         if training_args.do_train
         else None
     )
     snake_case :			Dict					   = (
         SquadDataset(
             lowercase_    ,      tokenizer=lowercase_    ,      mode="""dev"""    ,      is_language_sensitive=lowercase_    ,      cache_dir=model_args.cache_dir    ,      )
         if training_args.do_eval
         else None
     )

     # Data collator
     snake_case :			Tuple					   = DataCollatorWithPadding(lowercase_    ,      pad_to_multiple_of=8							) if training_args.fpaa else None

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

     # 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							)



def 					lowerCamelCase__							(			__lowercase							):
     # For xla_spawn (TPUs)
     main()


if __name__ == "__main__":
      main()









'''simple docstring'''







from typing import List

from ...processing_utils import ProcessorMixin
from ...utils import is_torch_available


if is_torch_available():
      import torch







class 				_a   (a__  ):





     '''simple docstring'''



     lowerCAmelCase_     :    Tuple									=				["""image_processor""", """tokenizer"""]
     lowerCAmelCase_     :    Optional[Any]									=				"""OneFormerImageProcessor"""
     lowerCAmelCase_     :    int									=				("""CLIPTokenizer""", """CLIPTokenizerFast""")







     def __init__(							self       ,__a=None       ,__a=None       ,__a = 77       ,__a = 77       ,**__a       )			->							Optional[Any]:
          if image_processor is None:
               raise ValueError("""You need to specify an `image_processor`."""       )
          if tokenizer is None:
               raise ValueError("""You need to specify a `tokenizer`."""       )

          snake_case :			str					   = max_seq_length
          snake_case :			Any					   = task_seq_length

          super().__init__(__a       ,__a       )







     def  snake_case_			(							self       ,__a=None       ,__a=77       )			->							List[str]:
          if text_list is None:
               raise ValueError("""tokens cannot be None."""       )

          snake_case :			int					   = self.tokenizer(__a       ,padding="""max_length"""       ,max_length=__a       ,truncation=__a       )

          snake_case   ,		snake_case :			int					   = tokens["""attention_mask"""], tokens["""input_ids"""]

          snake_case :			Tuple					   = []
          for attn_mask, input_id in zip(__a       ,__a       ):
               snake_case :			Dict					   = torch.tensor(__a       ) * torch.tensor(__a       )
               token_inputs.append(token.unsqueeze(0       )       )

          snake_case :			Optional[int]					   = torch.cat(__a       ,dim=0       )
          return token_inputs







     def __call__(							self       ,__a=None       ,__a=None       ,__a=None       ,**__a       )			->							Optional[int]:

          if task_inputs is None:
               raise ValueError("""You have to specify the task_input. Found None."""       )
          elif images is None:
               raise ValueError("""You have to specify the image. Found None."""       )

          if not all(task in ["""semantic""", """instance""", """panoptic"""] for task in task_inputs       ):
               raise ValueError("""task_inputs must be semantic, instance, or panoptic."""       )

          snake_case :			Union[str, Any]					   = self.image_processor(__a       ,__a       ,__a       ,**__a       )

          if isinstance(__a       ,__a       ):
               snake_case :			Tuple					   = [task_inputs]

          if isinstance(__a       ,__a       ) and all(isinstance(__a       ,__a       ) for task_input in task_inputs       ):
               snake_case :			Optional[Any]					   = []
               for task in task_inputs:
                    snake_case :			Any					   = F'''the task is {task}'''
                    task_token_inputs.append(__a       )
               snake_case :			Optional[int]					   = self._preprocess_text(__a       ,max_length=self.task_seq_length       )
          else:
               raise TypeError("""Task Inputs should be a string or a list of strings."""       )

          if hasattr(__a       ,"""text_inputs"""       ):
               snake_case :			Optional[int]					   = encoded_inputs.text_inputs

               snake_case :			Dict					   = []
               for texts in texts_list:
                    snake_case :			Optional[Any]					   = self._preprocess_text(__a       ,max_length=self.max_seq_length       )
                    text_inputs.append(text_input_list.unsqueeze(0       )       )

               snake_case :			Tuple					   = torch.cat(__a       ,dim=0       )

          return encoded_inputs







     def  snake_case_			(							self       ,__a=None       ,__a=None       ,__a=None       ,**__a       )			->							List[str]:

          if task_inputs is None:
               raise ValueError("""You have to specify the task_input. Found None."""       )
          elif images is None:
               raise ValueError("""You have to specify the image. Found None."""       )

          if not all(task in ["""semantic""", """instance""", """panoptic"""] for task in task_inputs       ):
               raise ValueError("""task_inputs must be semantic, instance, or panoptic."""       )

          snake_case :			Any					   = self.image_processor.encode_inputs(__a       ,__a       ,__a       ,**__a       )

          if isinstance(__a       ,__a       ):
               snake_case :			Optional[int]					   = [task_inputs]

          if isinstance(__a       ,__a       ) and all(isinstance(__a       ,__a       ) for task_input in task_inputs       ):
               snake_case :			Dict					   = []
               for task in task_inputs:
                    snake_case :			str					   = F'''the task is {task}'''
                    task_token_inputs.append(__a       )
               snake_case :			Any					   = self._preprocess_text(__a       ,max_length=self.task_seq_length       )
          else:
               raise TypeError("""Task Inputs should be a string or a list of strings."""       )

          if hasattr(__a       ,"""text_inputs"""       ):
               snake_case :			Dict					   = encoded_inputs.text_inputs

               snake_case :			Tuple					   = []
               for texts in texts_list:
                    snake_case :			Tuple					   = self._preprocess_text(__a       ,max_length=self.max_seq_length       )
                    text_inputs.append(text_input_list.unsqueeze(0       )       )

               snake_case :			Union[str, Any]					   = torch.cat(__a       ,dim=0       )

          return encoded_inputs







     def  snake_case_			(							self       ,*__a       ,**__a       )			->							List[str]:
          return self.image_processor.post_process_semantic_segmentation(*__a       ,**__a       )







     def  snake_case_			(							self       ,*__a       ,**__a       )			->							List[Any]:
          return self.image_processor.post_process_instance_segmentation(*__a       ,**__a       )







     def  snake_case_			(							self       ,*__a       ,**__a       )			->							Optional[Any]:
          return self.image_processor.post_process_panoptic_segmentation(*__a       ,**__a       )



