


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,
)


__UpperCamelCase    		=       logging.getLogger(__name__)







@dataclass
class        lowerCamelCase__	:






			"""simple docstring"""






			_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        lowerCamelCase__	:






			"""simple docstring"""






			_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        UpperCamelCase_(				_A   :int				,     _A   :Optional[int]				,     _A   :Optional[int]  )->		List[Any]:
			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        UpperCamelCase_(				)->		Any:
			# 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.

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

			UpperCamelCase__					=       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				,     )

			UpperCamelCase__					=       ("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  )  )

			UpperCamelCase__					=       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__					=       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:
						UpperCamelCase__					=       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  ):
									UpperCamelCase__					=       tokenizer.lang_code_to_id[data_args.tgt_lang]
						else:
									UpperCamelCase__					=       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()  )

			UpperCamelCase__					=       SeqaSeqDataset

			# Get datasets
			UpperCamelCase__					=       (
			    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
			)
			UpperCamelCase__					=       (
			    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
			)
			UpperCamelCase__					=       (
			    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
			UpperCamelCase__					=       (
			    build_compute_metrics_fn(data_args.task				,     _A  ) if training_args.predict_with_generate else None
			)
			UpperCamelCase__					=       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				,     )

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

						UpperCamelCase__					=       trainer.train(
						    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path  ) else None  )
						UpperCamelCase__					=       train_result.metrics
						UpperCamelCase__					=       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 ***"  )

						UpperCamelCase__					=       trainer.evaluate(metric_key_prefix="val"  )
						UpperCamelCase__					=       data_args.n_val
						UpperCamelCase__					=       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 ***"  )

						UpperCamelCase__					=       trainer.predict(test_dataset=_A				,     metric_key_prefix="test"  )
						UpperCamelCase__					=       test_output.metrics
						UpperCamelCase__					=       data_args.n_test

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

									if training_args.predict_with_generate:
												UpperCamelCase__					=       tokenizer.batch_decode(
												    test_output.predictions				,     skip_special_tokens=_A				,     clean_up_tokenization_spaces=_A  )
												UpperCamelCase__					=       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        UpperCamelCase_(				_A   :Optional[int]  )->		Union[str, Any]:
			# For xla_spawn (TPUs)
			main()


if __name__ == "__main__":
						main()






def        UpperCamelCase_(				_A   :int				,     _A   :int  )->		str:
			if number < 0 or shift_amount < 0:
						raise ValueError("both inputs must be positive integers"  )

			UpperCamelCase__					=       str(bin(_A  )  )
			binary_number += "0" * shift_amount
			return binary_number
def        UpperCamelCase_(				_A   :int				,     _A   :int  )->		str:
			if number < 0 or shift_amount < 0:
						raise ValueError("both inputs must be positive integers"  )

			UpperCamelCase__					=       str(bin(_A  )  )[2:]
			if shift_amount >= len(_A  ):
						return "0b0"
			UpperCamelCase__					=       binary_number[: len(_A  ) - shift_amount]
			return "0b" + shifted_binary_number
def        UpperCamelCase_(				_A   :int				,     _A   :int  )->		str:
			if number >= 0:  # Get binary representation of positive number
						UpperCamelCase__					=       "0" + str(bin(_A  )  ).strip("-"  )[2:]
			else:  # Get binary (2's complement) representation of negative number
						UpperCamelCase__					=       len(bin(_A  )[3:]  )  # Find 2's complement of number
						UpperCamelCase__					=       bin(abs(_A  ) - (1 << binary_number_length)  )[3:]
						UpperCamelCase__					=       (
						    "1" + "0" * (binary_number_length - len(_A  )) + binary_number
						)

			if shift_amount >= len(_A  ):
						return "0b" + binary_number[0] * len(_A  )
			return (
			    "0b"
			    + binary_number[0] * shift_amount
			    + binary_number[: len(_A  ) - shift_amount]
			)


if __name__ == "__main__":
						import doctest

						doctest.testmod()


