





'''simple docstring'''


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

from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSeqaSeqLM,
    AutoTokenizer,
    HfArgumentParser,
    MBartTokenizer,
    MBartTokenizerFast,
    set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
    SeqaSeqDataCollator,
    SeqaSeqDataset,
    assert_all_frozen,
    build_compute_metrics_fn,
    check_output_dir,
    freeze_embeds,
    freeze_params,
    lmap,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


lowerCAmelCase :					Dict 							=logging.getLogger(__name__)
@dataclass
class 				a_       :
     __A								=      field(
         metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}							)
     __A								=      field(
         default=lowerCamelCase_     ,						metadata={"help": "Pretrained config name or path if not the same as model_name"}							)
     __A								=      field(
         default=lowerCamelCase_     ,						metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}							)
     __A								=      field(
         default=lowerCamelCase_     ,						metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}     ,						)
     __A								=      field(default=lowerCamelCase_     ,						metadata={"help": "Whether tp freeze the encoder."}							)
     __A								=      field(default=lowerCamelCase_     ,						metadata={"help": "Whether  to freeze the embeddings."}							)







@dataclass
class 				a_       :
     __A								=      field(
         metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}							)
     __A								=      field(
         default="summarization"     ,						metadata={"help": "Task name, summarization (or summarization_{dataset} for pegasus) or translation"}     ,						)
     __A								=      field(
         default=1_024     ,						metadata={
             "help": (
                 "The maximum total input sequence length after tokenization. Sequences longer "
                 "than this will be truncated, sequences shorter will be padded."
             )
         }     ,						)
     __A								=      field(
         default=128     ,						metadata={
             "help": (
                 "The maximum total sequence length for target text after tokenization. Sequences longer "
                 "than this will be truncated, sequences shorter will be padded."
             )
         }     ,						)
     __A								=      field(
         default=142     ,						metadata={
             "help": (
                 "The maximum total sequence length for validation target text after tokenization. Sequences longer "
                 "than this will be truncated, sequences shorter will be padded. "
                 "This argument is also used to override the ``max_length`` param of ``model.generate``, which is used "
                 "during ``evaluate`` and ``predict``."
             )
         }     ,						)
     __A								=      field(
         default=142     ,						metadata={
             "help": (
                 "The maximum total sequence length for test target text after tokenization. Sequences longer "
                 "than this will be truncated, sequences shorter will be padded."
             )
         }     ,						)
     __A								=      field(default=-1     ,						metadata={"help": "# training examples. -1 means use all."}							)
     __A								=      field(default=-1     ,						metadata={"help": "# validation examples. -1 means use all."}							)
     __A								=      field(default=-1     ,						metadata={"help": "# test examples. -1 means use all."}							)
     __A								=      field(default=lowerCamelCase_     ,						metadata={"help": "Source language id for translation."}							)
     __A								=      field(default=lowerCamelCase_     ,						metadata={"help": "Target language id for translation."}							)
     __A								=      field(default=lowerCamelCase_     ,						metadata={"help": "# num_beams to use for evaluation."}							)
     __A								=      field(
         default=lowerCamelCase_     ,						metadata={"help": "If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined."}     ,						)







def 							UpperCAmelCase_  (						__lowerCamelCase : int     ,__lowerCamelCase : Dict     ,__lowerCamelCase : Union[str, Any]			):
   logger.info(F'***** {split} metrics *****'			)
   for key in sorted(metrics.keys()			):
      logger.info(F'  {key} = {metrics[key]}'			)
   save_json(__lowerCAmelCase     ,os.path.join(__lowerCAmelCase     ,F'{split}_results.json'			)			)
def 							UpperCAmelCase_  (						):
   # 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.

   lowercase_      :str         =			HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments)			)

   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.
      lowercase_      ,							lowercase_      ,							lowercase_      :Tuple         =			parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]			)			)
   else:
      lowercase_      ,							lowercase_      ,							lowercase_      :Optional[int]         =			parser.parse_args_into_dataclasses()

   check_output_dir(__lowerCAmelCase			)

   # 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.parallel_mode == ParallelMode.DISTRIBUTED			)     ,training_args.fpaa     ,)
   transformers.utils.logging.enable_default_handler()
   transformers.utils.logging.enable_explicit_format()
   # 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()
   logger.info("Training/evaluation parameters %s"     ,__lowerCAmelCase			)

   # Set seed
   set_seed(training_args.seed			)

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

   lowercase_      :Optional[int]         =			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     ,)

   lowercase_      :Any         =			("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
   for p in extra_model_params:
      if getattr(__lowerCAmelCase     ,__lowerCAmelCase     ,__lowerCAmelCase			):
         assert hasattr(__lowerCAmelCase     ,__lowerCAmelCase			), F'({config.__class__.__name__}) doesn\'t have a `{p}` attribute'
         setattr(__lowerCAmelCase     ,__lowerCAmelCase     ,getattr(__lowerCAmelCase     ,__lowerCAmelCase			)			)

   lowercase_      :Tuple         =			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     ,)
   lowercase_      :int         =			AutoModelForSeqaSeqLM.from_pretrained(
       model_args.model_name_or_path     ,from_tf=".ckpt" in model_args.model_name_or_path     ,config=__lowerCAmelCase     ,cache_dir=model_args.cache_dir     ,)

   # use task specific params
   use_task_specific_params(__lowerCAmelCase     ,data_args.task			)

   # set num_beams for evaluation
   if data_args.eval_beams is None:
      lowercase_      :str         =			model.config.num_beams

   # set decoder_start_token_id for MBart
   if model.config.decoder_start_token_id is None and isinstance(__lowerCAmelCase     ,(MBartTokenizer, MBartTokenizerFast)			):
      assert (
          data_args.tgt_lang is not None and data_args.src_lang is not None
      ), "mBart requires --tgt_lang and --src_lang"
      if isinstance(__lowerCAmelCase     ,__lowerCAmelCase			):
         lowercase_      :List[Any]         =			tokenizer.lang_code_to_id[data_args.tgt_lang]
      else:
         lowercase_      :Optional[Any]         =			tokenizer.convert_tokens_to_ids(data_args.tgt_lang			)

   if model_args.freeze_embeds:
      freeze_embeds(__lowerCAmelCase			)
   if model_args.freeze_encoder:
      freeze_params(model.get_encoder()			)
      assert_all_frozen(model.get_encoder()			)

   lowercase_      :Optional[int]         =			SeqaSeqDataset

   # Get datasets
   lowercase_      :Union[str, Any]         =			(
       dataset_class(
           __lowerCAmelCase     ,type_path="train"     ,data_dir=data_args.data_dir     ,n_obs=data_args.n_train     ,max_target_length=data_args.max_target_length     ,max_source_length=data_args.max_source_length     ,prefix=model.config.prefix or ""     ,)
       if training_args.do_train
       else None
   )
   lowercase_      :List[str]         =			(
       dataset_class(
           __lowerCAmelCase     ,type_path="val"     ,data_dir=data_args.data_dir     ,n_obs=data_args.n_val     ,max_target_length=data_args.val_max_target_length     ,max_source_length=data_args.max_source_length     ,prefix=model.config.prefix or ""     ,)
       if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
       else None
   )
   lowercase_      :List[str]         =			(
       dataset_class(
           __lowerCAmelCase     ,type_path="test"     ,data_dir=data_args.data_dir     ,n_obs=data_args.n_test     ,max_target_length=data_args.test_max_target_length     ,max_source_length=data_args.max_source_length     ,prefix=model.config.prefix or ""     ,)
       if training_args.do_predict
       else None
   )

   # Initialize our Trainer
   lowercase_      :Any         =			(
       build_compute_metrics_fn(data_args.task     ,__lowerCAmelCase			) if training_args.predict_with_generate else None
   )
   lowercase_      :Any         =			SeqaSeqTrainer(
       model=__lowerCAmelCase     ,args=__lowerCAmelCase     ,data_args=__lowerCAmelCase     ,train_dataset=__lowerCAmelCase     ,eval_dataset=__lowerCAmelCase     ,data_collator=SeqaSeqDataCollator(
           __lowerCAmelCase     ,__lowerCAmelCase     ,model.config.decoder_start_token_id     ,training_args.tpu_num_cores			)     ,compute_metrics=__lowerCAmelCase     ,tokenizer=__lowerCAmelCase     ,)

   lowercase_      :List[str]         =			{}
   # Training
   if training_args.do_train:
      logger.info("*** Train ***"			)

      lowercase_      :str         =			trainer.train(
          model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path			) else None			)
      lowercase_      :int         =			train_result.metrics
      lowercase_      :Dict         =			data_args.n_train

      trainer.save_model()  # this also saves the tokenizer

      if trainer.is_world_process_zero():
         handle_metrics("train"     ,__lowerCAmelCase     ,training_args.output_dir			)
         all_metrics.update(__lowerCAmelCase			)

         # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
         trainer.state.save_to_json(os.path.join(training_args.output_dir     ,"trainer_state.json"			)			)

         # For convenience, we also re-save the tokenizer to the same directory,
         # so that you can share your model easily on huggingface.co/models =)
         tokenizer.save_pretrained(training_args.output_dir			)

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

      lowercase_      :int         =			trainer.evaluate(metric_key_prefix="val"			)
      lowercase_      :List[str]         =			data_args.n_val
      lowercase_      :Union[str, Any]         =			round(metrics["val_loss"]     ,4			)

      if trainer.is_world_process_zero():
         handle_metrics("val"     ,__lowerCAmelCase     ,training_args.output_dir			)
         all_metrics.update(__lowerCAmelCase			)

   if training_args.do_predict:
      logger.info("*** Predict ***"			)

      lowercase_      :List[Any]         =			trainer.predict(test_dataset=__lowerCAmelCase     ,metric_key_prefix="test"			)
      lowercase_      :Dict         =			test_output.metrics
      lowercase_      :str         =			data_args.n_test

      if trainer.is_world_process_zero():
         lowercase_      :Any         =			round(metrics["test_loss"]     ,4			)
         handle_metrics("test"     ,__lowerCAmelCase     ,training_args.output_dir			)
         all_metrics.update(__lowerCAmelCase			)

         if training_args.predict_with_generate:
            lowercase_      :List[str]         =			tokenizer.batch_decode(
                test_output.predictions     ,skip_special_tokens=__lowerCAmelCase     ,clean_up_tokenization_spaces=__lowerCAmelCase			)
            lowercase_      :Optional[Any]         =			lmap(str.strip     ,__lowerCAmelCase			)
            write_txt_file(__lowerCAmelCase     ,os.path.join(training_args.output_dir     ,"test_generations.txt"			)			)

   if trainer.is_world_process_zero():
      save_json(__lowerCAmelCase     ,os.path.join(training_args.output_dir     ,"all_results.json"			)			)

   return all_metrics
def 							UpperCAmelCase_  (						__lowerCamelCase : Tuple			):
   # For xla_spawn (TPUs)
   main()


if __name__ == "__main__":
 main()









'''simple docstring'''


import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig


lowerCAmelCase :					Any 							={
    '''facebook/maskformer-swin-base-ade''': (
        '''https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json'''
    )
    # See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}

lowerCAmelCase :					int 							=logging.get_logger(__name__)
class 				a_       (    _lowerCAmelCase							):
     __A								=      "maskformer"
     __A								=      {"hidden_size": "mask_feature_size"}
     __A								=      ["resnet", "swin"]
     __A								=      ["detr"]





     def __init__(			self		: List[Any]     ,				lowercase		: int = 256     ,				lowercase		: int = 256     ,				lowercase		: float = 0.1     ,				lowercase		: bool = False     ,				lowercase		: Optional[Dict] = None     ,				lowercase		: Optional[Dict] = None     ,				lowercase		: float = 0.02     ,				lowercase		: float = 1.0     ,				lowercase		: float = 1.0     ,				lowercase		: float = 1.0     ,				lowercase		: float = 20.0     ,				lowercase		: Optional[bool] = None     ,				**lowercase		: Any     ,				):





        """simple docstring"""




        if backbone_config is None:
           # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
           lowercase_      :Any         =			SwinConfig(
               image_size=384     ,				in_channels=3     ,				patch_size=4     ,				embed_dim=128     ,				depths=[2, 2, 18, 2]     ,				num_heads=[4, 8, 16, 32]     ,				window_size=12     ,				drop_path_rate=0.3     ,				out_features=["stage1", "stage2", "stage3", "stage4"]     ,				)

        if isinstance(lowercase     ,				lowercase						):
           lowercase_      :Optional[int]         =			backbone_config.pop("model_type"						)
           lowercase_      :Optional[int]         =			CONFIG_MAPPING[backbone_model_type]
           lowercase_      :int         =			config_class.from_dict(lowercase						)

        # 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 MaskFormer. '
               F'Supported model types: {",".join(self.backbones_supported						)}'						)

        if decoder_config is None:
           # fall back to https://huggingface.co/facebook/detr-resnet-50
           lowercase_      :Optional[Any]         =			DetrConfig()
        else:
           # verify that the decoder is supported
           lowercase_      :Tuple         =			(
               decoder_config.pop("model_type"						) if isinstance(lowercase     ,				lowercase						) else decoder_config.model_type
           )
           if decoder_type not in self.decoders_supported:
              raise ValueError(
                  F'Transformer Decoder {decoder_type} not supported, please use one of'
                  F' {",".join(self.decoders_supported						)}'						)
           if isinstance(lowercase     ,				lowercase						):
              lowercase_      :str         =			CONFIG_MAPPING[decoder_type]
              lowercase_      :List[str]         =			config_class.from_dict(lowercase						)

        lowercase_      :str         =			backbone_config
        lowercase_      :Union[str, Any]         =			decoder_config
        # main feature dimension for the model
        lowercase_      :Any         =			fpn_feature_size
        lowercase_      :Optional[int]         =			mask_feature_size
        # initializer
        lowercase_      :List[Any]         =			init_std
        lowercase_      :Union[str, Any]         =			init_xavier_std
        # Hungarian matcher && loss
        lowercase_      :List[str]         =			cross_entropy_weight
        lowercase_      :int         =			dice_weight
        lowercase_      :List[str]         =			mask_weight
        lowercase_      :Optional[Any]         =			use_auxiliary_loss
        lowercase_      :str         =			no_object_weight
        lowercase_      :int         =			output_auxiliary_logits

        lowercase_      :Optional[Any]         =			self.decoder_config.encoder_attention_heads
        lowercase_      :int         =			self.decoder_config.num_hidden_layers
        super().__init__(**lowercase						)





     @classmethod
     def 							lowercase__				(			cls		: Tuple     ,				lowercase		: PretrainedConfig     ,				lowercase		: PretrainedConfig     ,				**lowercase		: Union[str, Any]						):





        """simple docstring"""




        return cls(
            backbone_config=lowercase     ,				decoder_config=lowercase     ,				**lowercase     ,				)







     def 							lowercase__				(			self		: Optional[Any]						):





        """simple docstring"""




        lowercase_      :str         =			copy.deepcopy(self.__dict__						)
        lowercase_      :int         =			self.backbone_config.to_dict()
        lowercase_      :List[Any]         =			self.decoder_config.to_dict()
        lowercase_      :Optional[Any]         =			self.__class__.model_type
        return output


