


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		   =			logging.getLogger(__name__)




@dataclass
class A    :
      UpperCamelCase__				:    str =field(
          metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}	)
      UpperCamelCase__				:    Optional[str] =field(
          default=UpperCamelCase_       ,				metadata={'help': 'Pretrained config name or path if not the same as model_name'}	)
      UpperCamelCase__				:    Optional[str] =field(
          default=UpperCamelCase_       ,				metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}	)
      UpperCamelCase__				:    Optional[str] =field(
          default=UpperCamelCase_       ,				metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}       ,				)
      UpperCamelCase__				:    bool =field(default=UpperCamelCase_       ,				metadata={'help': 'Whether tp freeze the encoder.'}	)
      UpperCamelCase__				:    bool =field(default=UpperCamelCase_       ,				metadata={'help': 'Whether  to freeze the embeddings.'}	)




@dataclass
class A    :
      UpperCamelCase__				:    str =field(
          metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'}	)
      UpperCamelCase__				:    Optional[str] =field(
          default='summarization'       ,				metadata={'help': 'Task name, summarization (or summarization_{dataset} for pegasus) or translation'}       ,				)
      UpperCamelCase__				:    Optional[int] =field(
          default=1024       ,				metadata={
              'help': (
                  'The maximum total input sequence length after tokenization. Sequences longer '
                  'than this will be truncated, sequences shorter will be padded.'
              )
          }       ,				)
      UpperCamelCase__				:    Optional[int] =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.'
              )
          }       ,				)
      UpperCamelCase__				:    Optional[int] =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``.'
              )
          }       ,				)
      UpperCamelCase__				:    Optional[int] =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.'
              )
          }       ,				)
      UpperCamelCase__				:    Optional[int] =field(default=-1       ,				metadata={'help': '# training examples. -1 means use all.'}	)
      UpperCamelCase__				:    Optional[int] =field(default=-1       ,				metadata={'help': '# validation examples. -1 means use all.'}	)
      UpperCamelCase__				:    Optional[int] =field(default=-1       ,				metadata={'help': '# test examples. -1 means use all.'}	)
      UpperCamelCase__				:    Optional[str] =field(default=UpperCamelCase_       ,				metadata={'help': 'Source language id for translation.'}	)
      UpperCamelCase__				:    Optional[str] =field(default=UpperCamelCase_       ,				metadata={'help': 'Target language id for translation.'}	)
      UpperCamelCase__				:    Optional[int] =field(default=UpperCamelCase_       ,				metadata={'help': '# num_beams to use for evaluation.'}	)
      UpperCamelCase__				:    bool =field(
          default=UpperCamelCase_       ,				metadata={'help': 'If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'}       ,				)




def       a_ (       SCREAMING_SNAKE_CASE__   : Optional[Any]						,     SCREAMING_SNAKE_CASE__   : Optional[int]						,     SCREAMING_SNAKE_CASE__   : List[Any]	):
       '''simple docstring'''





       logger.info(F'''***** {split} metrics *****'''	)
       for key in sorted(metrics.keys()	):
              logger.info(F'''  {key} = {metrics[key]}'''	)
       save_json(SCREAMING_SNAKE_CASE__						,     os.path.join(SCREAMING_SNAKE_CASE__						,     F'''{split}_results.json'''	)	)




def       a_ (       ):
       '''simple docstring'''





       _lowerCamelCase			:						Tuple            =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.
              _lowerCamelCase      ,	_lowerCamelCase      ,	_lowerCamelCase			:						Any            =parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]	)	)
       else:
              _lowerCamelCase      ,	_lowerCamelCase      ,	_lowerCamelCase			:						List[Any]            =parser.parse_args_into_dataclasses()

       check_output_dir(SCREAMING_SNAKE_CASE__	)

       # 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'						,     SCREAMING_SNAKE_CASE__	)

       # 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.

       _lowerCamelCase			:						Dict            =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						,     )

       _lowerCamelCase			:						List[str]            =('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
       for p in extra_model_params:
              if getattr(SCREAMING_SNAKE_CASE__						,     SCREAMING_SNAKE_CASE__						,     SCREAMING_SNAKE_CASE__	):
                     assert hasattr(SCREAMING_SNAKE_CASE__						,     SCREAMING_SNAKE_CASE__	), F'''({config.__class__.__name__}) doesn\'t have a `{p}` attribute'''
                     setattr(SCREAMING_SNAKE_CASE__						,     SCREAMING_SNAKE_CASE__						,     getattr(SCREAMING_SNAKE_CASE__						,     SCREAMING_SNAKE_CASE__	)	)

       _lowerCamelCase			:						Union[str, Any]            =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						,     )
       _lowerCamelCase			:						str            =AutoModelForSeqaSeqLM.from_pretrained(
           model_args.model_name_or_path						,     from_tf='.ckpt' in model_args.model_name_or_path						,     config=SCREAMING_SNAKE_CASE__						,     cache_dir=model_args.cache_dir						,     )

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

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

       # set decoder_start_token_id for MBart
       if model.config.decoder_start_token_id is None and isinstance(SCREAMING_SNAKE_CASE__						,     (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(SCREAMING_SNAKE_CASE__						,     SCREAMING_SNAKE_CASE__	):
                     _lowerCamelCase			:						Tuple            =tokenizer.lang_code_to_id[data_args.tgt_lang]
              else:
                     _lowerCamelCase			:						Dict            =tokenizer.convert_tokens_to_ids(data_args.tgt_lang	)

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

       _lowerCamelCase			:						List[str]            =SeqaSeqDataset

       # Get datasets
       _lowerCamelCase			:						List[Any]            =(
           dataset_class(
               SCREAMING_SNAKE_CASE__						,     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
       )
       _lowerCamelCase			:						int            =(
           dataset_class(
               SCREAMING_SNAKE_CASE__						,     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
       )
       _lowerCamelCase			:						Optional[int]            =(
           dataset_class(
               SCREAMING_SNAKE_CASE__						,     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
       _lowerCamelCase			:						List[Any]            =(
           build_compute_metrics_fn(data_args.task						,     SCREAMING_SNAKE_CASE__	) if training_args.predict_with_generate else None
       )
       _lowerCamelCase			:						Union[str, Any]            =SeqaSeqTrainer(
           model=SCREAMING_SNAKE_CASE__						,     args=SCREAMING_SNAKE_CASE__						,     data_args=SCREAMING_SNAKE_CASE__						,     train_dataset=SCREAMING_SNAKE_CASE__						,     eval_dataset=SCREAMING_SNAKE_CASE__						,     data_collator=SeqaSeqDataCollator(
               SCREAMING_SNAKE_CASE__						,     SCREAMING_SNAKE_CASE__						,     model.config.decoder_start_token_id						,     training_args.tpu_num_cores	)						,     compute_metrics=SCREAMING_SNAKE_CASE__						,     tokenizer=SCREAMING_SNAKE_CASE__						,     )

       _lowerCamelCase			:						List[Any]            ={}
       # Training
       if training_args.do_train:
              logger.info('*** Train ***'	)

              _lowerCamelCase			:						Dict            =trainer.train(
                  model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path	) else None	)
              _lowerCamelCase			:						List[str]            =train_result.metrics
              _lowerCamelCase			:						List[Any]            =data_args.n_train

              trainer.save_model()  # this also saves the tokenizer

              if trainer.is_world_process_zero():
                     handle_metrics('train'						,     SCREAMING_SNAKE_CASE__						,     training_args.output_dir	)
                     all_metrics.update(SCREAMING_SNAKE_CASE__	)

                     # 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 ***'	)

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

              if trainer.is_world_process_zero():
                     handle_metrics('val'						,     SCREAMING_SNAKE_CASE__						,     training_args.output_dir	)
                     all_metrics.update(SCREAMING_SNAKE_CASE__	)

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

              _lowerCamelCase			:						Tuple            =trainer.predict(test_dataset=SCREAMING_SNAKE_CASE__						,     metric_key_prefix='test'	)
              _lowerCamelCase			:						Optional[Any]            =test_output.metrics
              _lowerCamelCase			:						int            =data_args.n_test

              if trainer.is_world_process_zero():
                     _lowerCamelCase			:						List[Any]            =round(metrics['test_loss']						,     4	)
                     handle_metrics('test'						,     SCREAMING_SNAKE_CASE__						,     training_args.output_dir	)
                     all_metrics.update(SCREAMING_SNAKE_CASE__	)

                     if training_args.predict_with_generate:
                            _lowerCamelCase			:						Union[str, Any]            =tokenizer.batch_decode(
                                test_output.predictions						,     skip_special_tokens=SCREAMING_SNAKE_CASE__						,     clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__	)
                            _lowerCamelCase			:						str            =lmap(str.strip						,     SCREAMING_SNAKE_CASE__	)
                            write_txt_file(SCREAMING_SNAKE_CASE__						,     os.path.join(training_args.output_dir						,     'test_generations.txt'	)	)

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

       return all_metrics




def       a_ (       SCREAMING_SNAKE_CASE__   : Tuple	):
       '''simple docstring'''





       main()


if __name__ == "__main__":
 main()






from collections.abc import Iterable
from typing import Any




class A    :






      def __init__(   self		:	Dict     ,  lowercase_		:	int | None = None					)   ->		int:
             """simple docstring"""




             _lowerCamelCase			:						List[Any]            =value
             _lowerCamelCase			:						Node | None            =None  # Added in order to delete a node easier
             _lowerCamelCase			:						Node | None            =None
             _lowerCamelCase			:						Node | None            =None






      def __repr__(   self		:	Dict					)   ->		str:
             """simple docstring"""




             from pprint import pformat

             if self.left is None and self.right is None:
                    return str(self.value					)
             return pformat({F'''{self.value}''': (self.left, self.right)}     ,  indent=1					)




class A    :






      def __init__(   self		:	Union[str, Any]     ,  lowercase_		:	Node | None = None					)   ->		int:
             """simple docstring"""




             _lowerCamelCase			:						Optional[int]            =root






      def __str__(   self		:	Tuple					)   ->		str:
             """simple docstring"""




             return str(self.root					)






      def 			lowerCamelCase			(   self		:	Optional[Any]     ,  lowercase_		:	Node     ,  lowercase_		:	Node | None					)   ->		None:
             """simple docstring"""




             if new_children is not None:  # reset its kids
                    _lowerCamelCase			:						Optional[int]            =node.parent
             if node.parent is not None:  # reset its parent
                    if self.is_right(lowercase_					):  # If it is the right children
                           _lowerCamelCase			:						int            =new_children
                    else:
                           _lowerCamelCase			:						Dict            =new_children
             else:
                    _lowerCamelCase			:						Tuple            =new_children






      def 			lowerCamelCase			(   self		:	Optional[Any]     ,  lowercase_		:	Node					)   ->		bool:
             """simple docstring"""




             if node.parent and node.parent.right:
                    return node == node.parent.right
             return False






      def 			lowerCamelCase			(   self		:	int					)   ->		bool:
             """simple docstring"""




             return self.root is None






      def 			lowerCamelCase			(   self		:	List[Any]     ,  lowercase_		:	Union[str, Any]					)   ->		None:
             """simple docstring"""




             _lowerCamelCase			:						Optional[Any]            =Node(lowercase_					)  # create a new Node
             if self.empty():  # if Tree is empty
                    _lowerCamelCase			:						Union[str, Any]            =new_node  # set its root
             else:  # Tree is not empty
                    _lowerCamelCase			:						Optional[int]            =self.root  # from root
                    if parent_node is None:
                           return
                    while True:  # While we don't get to a leaf
                           if value < parent_node.value:  # We go left
                                  if parent_node.left is None:
                                         _lowerCamelCase			:						Optional[int]            =new_node  # We insert the new node in a leaf
                                         break
                                  else:
                                         _lowerCamelCase			:						Optional[Any]            =parent_node.left
                           else:
                                  if parent_node.right is None:
                                         _lowerCamelCase			:						Optional[Any]            =new_node
                                         break
                                  else:
                                         _lowerCamelCase			:						Optional[int]            =parent_node.right
                    _lowerCamelCase			:						Optional[Any]            =parent_node






      def 			lowerCamelCase			(   self		:	Any     ,  *lowercase_		:	Union[str, Any]					)   ->		None:
             """simple docstring"""




             for value in values:
                    self.__insert(lowercase_					)






      def 			lowerCamelCase			(   self		:	Optional[int]     ,  lowercase_		:	List[Any]					)   ->		Node | None:
             """simple docstring"""




             if self.empty():
                    raise IndexError('Warning: Tree is empty! please use another.'					)
             else:
                    _lowerCamelCase			:						int            =self.root
                    # use lazy evaluation here to avoid NoneType Attribute error
                    while node is not None and node.value is not value:
                           _lowerCamelCase			:						Dict            =node.left if value < node.value else node.right
                    return node






      def 			lowerCamelCase			(   self		:	Tuple     ,  lowercase_		:	Node | None = None					)   ->		Node | None:
             """simple docstring"""




             if node is None:
                    if self.root is None:
                           return None
                    _lowerCamelCase			:						Union[str, Any]            =self.root

             if not self.empty():
                    while node.right is not None:
                           _lowerCamelCase			:						Optional[int]            =node.right
             return node






      def 			lowerCamelCase			(   self		:	Optional[Any]     ,  lowercase_		:	Node | None = None					)   ->		Node | None:
             """simple docstring"""




             if node is None:
                    _lowerCamelCase			:						Union[str, Any]            =self.root
             if self.root is None:
                    return None
             if not self.empty():
                    _lowerCamelCase			:						Optional[int]            =self.root
                    while node.left is not None:
                           _lowerCamelCase			:						List[Any]            =node.left
             return node






      def 			lowerCamelCase			(   self		:	Tuple     ,  lowercase_		:	int					)   ->		None:
             """simple docstring"""




             _lowerCamelCase			:						List[str]            =self.search(lowercase_					)  # Look for the node with that label
             if node is not None:
                    if node.left is None and node.right is None:  # If it has no children
                           self.__reassign_nodes(lowercase_     ,  lowercase_					)
                    elif node.left is None:  # Has only right children
                           self.__reassign_nodes(lowercase_     ,  node.right					)
                    elif node.right is None:  # Has only left children
                           self.__reassign_nodes(lowercase_     ,  node.left					)
                    else:
                           _lowerCamelCase			:						List[str]            =self.get_max(
                               node.left					)  # Gets the max value of the left branch
                           self.remove(tmp_node.value					)  # type: ignore
                           _lowerCamelCase			:						Union[str, Any]            =(
                               tmp_node.value  # type: ignore
                           )  # Assigns the value to the node to delete and keep tree structure






      def 			lowerCamelCase			(   self		:	str     ,  lowercase_		:	Node | None					)   ->		Iterable:
             """simple docstring"""




             if node is not None:
                    yield node  # Preorder Traversal
                    yield from self.preorder_traverse(node.left					)
                    yield from self.preorder_traverse(node.right					)






      def 			lowerCamelCase			(   self		:	str     ,  lowercase_		:	Dict=None					)   ->		Any:
             """simple docstring"""




             if traversal_function is None:
                    return self.preorder_traverse(self.root					)
             else:
                    return traversal_function(self.root					)






      def 			lowerCamelCase			(   self		:	Union[str, Any]     ,  lowercase_		:	list     ,  lowercase_		:	Node | None					)   ->		None:
             """simple docstring"""




             if node:
                    self.inorder(lowercase_     ,  node.left					)
                    arr.append(node.value					)
                    self.inorder(lowercase_     ,  node.right					)






      def 			lowerCamelCase			(   self		:	Union[str, Any]     ,  lowercase_		:	int     ,  lowercase_		:	Node					)   ->		int:
             """simple docstring"""




             _lowerCamelCase			:						list[int]            =[]
             self.inorder(lowercase_     ,  lowercase_					)  # append all values to list using inorder traversal
             return arr[k - 1]




def       a_ (       SCREAMING_SNAKE_CASE__   : Node | None	):
       '''simple docstring'''





       _lowerCamelCase			:						int            =[]
       if curr_node is not None:
              _lowerCamelCase			:						List[Any]            =postorder(curr_node.left	) + postorder(curr_node.right	) + [curr_node]
       return node_list




def       a_ (       ):
       '''simple docstring'''





       _lowerCamelCase			:						Optional[int]            =(8, 3, 6, 1, 10, 14, 13, 4, 7)
       _lowerCamelCase			:						int            =BinarySearchTree()
       for i in testlist:
              t.insert(SCREAMING_SNAKE_CASE__	)

       # Prints all the elements of the list in order traversal
       print(SCREAMING_SNAKE_CASE__	)

       if t.search(6	) is not None:
              print('The value 6 exists'	)
       else:
              print('The value 6 doesn\'t exist'	)

       if t.search(-1	) is not None:
              print('The value -1 exists'	)
       else:
              print('The value -1 doesn\'t exist'	)

       if not t.empty():
              print('Max Value: '						,     t.get_max().value	)  # type: ignore
              print('Min Value: '						,     t.get_min().value	)  # type: ignore

       for i in testlist:
              t.remove(SCREAMING_SNAKE_CASE__	)
              print(SCREAMING_SNAKE_CASE__	)


if __name__ == "__main__":
 import doctest

 doctest.testmod(verbose=True)


