
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,
)


_A   =			logging.getLogger(__name__)







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







@dataclass
class 					lowerCamelCase		:
     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=10_24							,			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=1_28							,			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=1_42							,			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=1_42							,			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=A_							,			metadata={"help": "Source language id for translation."}  )
     UpperCAmelCase__    :							Optional[str]									=		field(default=A_							,			metadata={"help": "Target language id for translation."}  )
     UpperCAmelCase__    :							Optional[int]									=		field(default=A_							,			metadata={"help": "# num_beams to use for evaluation."}  )
     UpperCAmelCase__    :							bool									=		field(
         default=A_							,			metadata={"help": "If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined."}							,			)



def 						lowercase_      (       A__			,		A__			,		A__		)							->							Dict:
    """simple docstring"""

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



def 						lowercase_      (       )							->							Tuple:
    """simple docstring"""

    snake_case								= 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.
        snake_case					,							snake_case					,							snake_case								= parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]		)		)
    else:
        snake_case					,							snake_case					,							snake_case								= parser.parse_args_into_dataclasses()

    check_output_dir(A__		)

    # 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"			,		A__		)

    # 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.

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

    snake_case								= 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			,		)
    snake_case								= AutoModelForSeqaSeqLM.from_pretrained(
        model_args.model_name_or_path			,		from_tf=".ckpt" in model_args.model_name_or_path			,		config=A__			,		cache_dir=model_args.cache_dir			,		)

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

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

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

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

    snake_case								= SeqaSeqDataset

    # Get datasets
    snake_case								= (
        dataset_class(
            A__			,		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
    )
    snake_case								= (
        dataset_class(
            A__			,		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
    )
    snake_case								= (
        dataset_class(
            A__			,		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
    snake_case								= (
        build_compute_metrics_fn(data_args.task			,		A__		) if training_args.predict_with_generate else None
    )
    snake_case								= SeqaSeqTrainer(
        model=A__			,		args=A__			,		data_args=A__			,		train_dataset=A__			,		eval_dataset=A__			,		data_collator=SeqaSeqDataCollator(
            A__			,		A__			,		model.config.decoder_start_token_id			,		training_args.tpu_num_cores		)			,		compute_metrics=A__			,		tokenizer=A__			,		)

    snake_case								= {}
    # Training
    if training_args.do_train:
        logger.info("*** Train ***"		)

        snake_case								= trainer.train(
            model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path		) else None		)
        snake_case								= train_result.metrics
        snake_case								= data_args.n_train

        trainer.save_model()  # this also saves the tokenizer

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

            # 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 ***"		)

        snake_case								= trainer.evaluate(metric_key_prefix="val"		)
        snake_case								= data_args.n_val
        snake_case								= round(metrics["val_loss"]			,		4		)

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

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

        snake_case								= trainer.predict(test_dataset=A__			,		metric_key_prefix="test"		)
        snake_case								= test_output.metrics
        snake_case								= data_args.n_test

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

            if training_args.predict_with_generate:
                snake_case								= tokenizer.batch_decode(
                    test_output.predictions			,		skip_special_tokens=A__			,		clean_up_tokenization_spaces=A__		)
                snake_case								= lmap(str.strip			,		A__		)
                write_txt_file(A__			,		os.path.join(training_args.output_dir			,		"test_generations.txt"		)		)

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

    return all_metrics



def 						lowercase_      (       A__		)							->							Optional[Any]:
    """simple docstring"""

    main()


if __name__ == "__main__":
   main()



from collections import defaultdict







class 					lowerCamelCase		:





     def __init__(self				:   Tuple    ,   _A				:   Optional[int]    ,   _A				:   List[str]       )						->						Union[str, Any]:
         snake_case								= total  # total no of tasks (N)

         # DP table will have a dimension of (2^M)*N
         # initially all values are set to -1
         snake_case								= [
             [-1 for i in range(total + 1       )] for j in range(2 ** len(_A       )       )
         ]

         snake_case								= defaultdict(_A       )  # stores the list of persons for each task

         # final_mask is used to check if all persons are included by setting all bits
         # to 1
         snake_case								= (1 << len(_A       )) - 1





     def 		UpperCAmelCase(self				:   str    ,   _A				:   Optional[Any]    ,   _A				:   List[Any]       )						->						str:
         # if mask == self.finalmask all persons are distributed tasks, return 1
         if mask == self.final_mask:
             return 1

         # if not everyone gets the task and no more tasks are available, return 0
         if task_no > self.total_tasks:
             return 0

         # if case already considered
         if self.dp[mask][task_no] != -1:
             return self.dp[mask][task_no]

         # Number of ways when we don't this task in the arrangement
         snake_case								= self.count_ways_until(_A    ,   task_no + 1       )

         # now assign the tasks one by one to all possible persons and recursively
         # assign for the remaining tasks.
         if task_no in self.task:
             for p in self.task[task_no]:
                 # if p is already given a task
                 if mask & (1 << p):
                     continue

                 # assign this task to p and change the mask value. And recursively
                 # assign tasks with the new mask value.
                 total_ways_util += self.count_ways_until(mask | (1 << p)    ,   task_no + 1       )

        # save the value.
         snake_case								= total_ways_util

         return self.dp[mask][task_no]





     def 		UpperCAmelCase(self				:   Any    ,   _A				:   Dict       )						->						Optional[Any]:
         # Store the list of persons for each task
         for i in range(len(_A       )       ):
             for j in task_performed[i]:
                 self.task[j].append(_A       )

        # call the function to fill the DP table, final answer is stored in dp[0][1]
         return self.count_ways_until(0    ,   1       )


if __name__ == "__main__":
   _A   =			5  # total no of tasks (the value of N)

   # the list of tasks that can be done by M persons.
   _A   =			[[1, 3, 4], [1, 2, 5], [3, 4]]
   print(
       AssignmentUsingBitmask(task_performed, total_tasks).count_no_of_ways(
           task_performed
       )
   )

