



'''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,
)


snake_case_			=     logging.getLogger(__name__)


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



@dataclass
class 	a__     :
	__magic_name__		:    str	    =     field(
	    metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}	)
	__magic_name__		:    Optional[str]	    =     field(
	    default="summarization",			metadata={"help": "Task name, summarization (or summarization_{dataset} for pegasus) or translation"},			)
	__magic_name__		:    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."
	        )
	    },			)
	__magic_name__		:    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."
	        )
	    },			)
	__magic_name__		:    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``."
	        )
	    },			)
	__magic_name__		:    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."
	        )
	    },			)
	__magic_name__		:    Optional[int]	    =     field(default=-1,			metadata={"help": "# training examples. -1 means use all."}	)
	__magic_name__		:    Optional[int]	    =     field(default=-1,			metadata={"help": "# validation examples. -1 means use all."}	)
	__magic_name__		:    Optional[int]	    =     field(default=-1,			metadata={"help": "# test examples. -1 means use all."}	)
	__magic_name__		:    Optional[str]	    =     field(default=_lowercase,			metadata={"help": "Source language id for translation."}	)
	__magic_name__		:    Optional[str]	    =     field(default=_lowercase,			metadata={"help": "Target language id for translation."}	)
	__magic_name__		:    Optional[int]	    =     field(default=_lowercase,			metadata={"help": "# num_beams to use for evaluation."}	)
	__magic_name__		:    bool	    =     field(
	    default=_lowercase,			metadata={"help": "If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined."},			)






def       __lowercase  (_SCREAMING_SNAKE_CASE      :Any    ,	_SCREAMING_SNAKE_CASE      :Union[str, Any]    ,	_SCREAMING_SNAKE_CASE      :Tuple       ):
				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       __lowercase  ():
				# 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.

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

				SCREAMING_SNAKE_CASE  :						List[str]      					= 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    ,	)

				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= ('''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       )       )

				SCREAMING_SNAKE_CASE  :						List[str]      					= 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    ,	)
				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= 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:
								SCREAMING_SNAKE_CASE  :						List[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       ):
												SCREAMING_SNAKE_CASE  :						List[Any]      					= tokenizer.lang_code_to_id[data_args.tgt_lang]
								else:
												SCREAMING_SNAKE_CASE  :						int      					= 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()       )

				SCREAMING_SNAKE_CASE  :						Any      					= SeqaSeqDataset

				# Get datasets
				SCREAMING_SNAKE_CASE  :						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
				)
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= (
				    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
				)
				SCREAMING_SNAKE_CASE  :						List[Any]      					= (
				    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
				SCREAMING_SNAKE_CASE  :						Tuple      					= (
				    build_compute_metrics_fn(data_args.task    ,	_SCREAMING_SNAKE_CASE       ) if training_args.predict_with_generate else None
				)
				SCREAMING_SNAKE_CASE  :						Tuple      					= 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    ,	)

				SCREAMING_SNAKE_CASE  :						Any      					= {}
				# Training
				if training_args.do_train:
								logger.info('''*** Train ***'''       )

								SCREAMING_SNAKE_CASE  :						Any      					= trainer.train(
								    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path       ) else None       )
								SCREAMING_SNAKE_CASE  :						List[Any]      					= train_result.metrics
								SCREAMING_SNAKE_CASE  :						Optional[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 ***'''       )

								SCREAMING_SNAKE_CASE  :						Optional[Any]      					= trainer.evaluate(metric_key_prefix='''val'''       )
								SCREAMING_SNAKE_CASE  :						List[Any]      					= data_args.n_val
								SCREAMING_SNAKE_CASE  :						str      					= 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 ***'''       )

								SCREAMING_SNAKE_CASE  :						List[str]      					= trainer.predict(test_dataset=_SCREAMING_SNAKE_CASE    ,	metric_key_prefix='''test'''       )
								SCREAMING_SNAKE_CASE  :						List[str]      					= test_output.metrics
								SCREAMING_SNAKE_CASE  :						Dict      					= data_args.n_test

								if trainer.is_world_process_zero():
												SCREAMING_SNAKE_CASE  :						Union[str, 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:
																SCREAMING_SNAKE_CASE  :						int      					= tokenizer.batch_decode(
																    test_output.predictions    ,	skip_special_tokens=_SCREAMING_SNAKE_CASE    ,	clean_up_tokenization_spaces=_SCREAMING_SNAKE_CASE       )
																SCREAMING_SNAKE_CASE  :						Any      					= 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       __lowercase  (_SCREAMING_SNAKE_CASE      :List[Any]       ):
				# For xla_spawn (TPUs)
				main()


if __name__ == "__main__":
					main()









'''simple docstring'''

from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case_			=     logging.get_logger(__name__)

snake_case_			=     {
    """microsoft/markuplm-base""": """https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json""",
    """microsoft/markuplm-large""": """https://huggingface.co/microsoft/markuplm-large/resolve/main/config.json""",
}


class 	a__     (				_lowercase	):
	__magic_name__		:    List[Any]	    =     "markuplm"


	def __init__(self : List[Any],					__UpperCAmelCase : int=30522,					__UpperCAmelCase : Tuple=768,					__UpperCAmelCase : str=12,					__UpperCAmelCase : List[Any]=12,					__UpperCAmelCase : Tuple=3072,					__UpperCAmelCase : Union[str, Any]="gelu",					__UpperCAmelCase : Union[str, Any]=0.1,					__UpperCAmelCase : str=0.1,					__UpperCAmelCase : Union[str, Any]=512,					__UpperCAmelCase : Dict=2,					__UpperCAmelCase : int=0.02,					__UpperCAmelCase : Optional[Any]=1e-12,					__UpperCAmelCase : Union[str, Any]=0,					__UpperCAmelCase : Optional[Any]=0,					__UpperCAmelCase : Union[str, Any]=2,					__UpperCAmelCase : int=256,					__UpperCAmelCase : Tuple=1024,					__UpperCAmelCase : Optional[Any]=216,					__UpperCAmelCase : str=1001,					__UpperCAmelCase : Optional[Any]=32,					__UpperCAmelCase : Dict=50,					__UpperCAmelCase : Union[str, Any]="absolute",					__UpperCAmelCase : Tuple=True,					__UpperCAmelCase : List[str]=None,					**__UpperCAmelCase : Union[str, Any],					)     ->			Union[str, Any]:

					"""simple docstring"""

					super().__init__(
					    pad_token_id=__UpperCAmelCase,					bos_token_id=__UpperCAmelCase,					eos_token_id=__UpperCAmelCase,					**__UpperCAmelCase,					)
					SCREAMING_SNAKE_CASE  :						List[Any]      					= vocab_size
					SCREAMING_SNAKE_CASE  :						Dict      					= hidden_size
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= num_hidden_layers
					SCREAMING_SNAKE_CASE  :						Dict      					= num_attention_heads
					SCREAMING_SNAKE_CASE  :						Any      					= hidden_act
					SCREAMING_SNAKE_CASE  :						Tuple      					= intermediate_size
					SCREAMING_SNAKE_CASE  :						Tuple      					= hidden_dropout_prob
					SCREAMING_SNAKE_CASE  :						List[str]      					= attention_probs_dropout_prob
					SCREAMING_SNAKE_CASE  :						List[str]      					= max_position_embeddings
					SCREAMING_SNAKE_CASE  :						Dict      					= type_vocab_size
					SCREAMING_SNAKE_CASE  :						Tuple      					= initializer_range
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= layer_norm_eps
					SCREAMING_SNAKE_CASE  :						int      					= position_embedding_type
					SCREAMING_SNAKE_CASE  :						List[Any]      					= use_cache
					SCREAMING_SNAKE_CASE  :						int      					= classifier_dropout
					# additional properties
					SCREAMING_SNAKE_CASE  :						List[Any]      					= max_depth
					SCREAMING_SNAKE_CASE  :						Any      					= max_xpath_tag_unit_embeddings
					SCREAMING_SNAKE_CASE  :						int      					= max_xpath_subs_unit_embeddings
					SCREAMING_SNAKE_CASE  :						Dict      					= tag_pad_id
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= subs_pad_id
					SCREAMING_SNAKE_CASE  :						int      					= xpath_unit_hidden_size




