






"""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,
)


A_    =							logging.getLogger(__name__)






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






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




def      _lowerCAmelCase		(							UpperCAmelCase__       :					Tuple,						UpperCAmelCase__       :					List[Any],						UpperCAmelCase__       :					Tuple      )		->Optional[int]:
						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      _lowerCAmelCase		(							)		->str:
						A__						:  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.
												A__						:  Any		  =  parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]      )      )
						else:
												A__						:  Union[str, Any]		  =  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.

						A__						:  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,						)

						A__						:  List[str]		  =  ("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      )      )

						A__						:  Dict		  =  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,						)
						A__						:  List[str]		  =  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:
												A__						:  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      ):
																		A__						:  Tuple		  =  tokenizer.lang_code_to_id[data_args.tgt_lang]
												else:
																		A__						:  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()      )

						A__						:  Union[str, Any]		  =  SeqaSeqDataset

						# Get datasets
						A__						:  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
						)
						A__						:  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
						)
						A__						:  Optional[int]		  =  (
						    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
						A__						:  Any		  =  (
						    build_compute_metrics_fn(data_args.task,						_lowerCamelCase      ) if training_args.predict_with_generate else None
						)
						A__						:  Tuple		  =  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,						)

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

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

												A__						:  Optional[int]		  =  trainer.evaluate(metric_key_prefix="""val"""      )
												A__						:  Dict		  =  data_args.n_val
												A__						:  List[str]		  =  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 ***"""      )

												A__						:  Any		  =  trainer.predict(test_dataset=_lowerCamelCase,						metric_key_prefix="""test"""      )
												A__						:  int		  =  test_output.metrics
												A__						:  List[Any]		  =  data_args.n_test

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

																		if training_args.predict_with_generate:
																								A__						:  int		  =  tokenizer.batch_decode(
																								    test_output.predictions,						skip_special_tokens=_lowerCamelCase,						clean_up_tokenization_spaces=_lowerCamelCase      )
																								A__						:  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      _lowerCAmelCase		(							UpperCAmelCase__       :					List[Any]      )		->Optional[int]:
						main()


if __name__ == "__main__":
						main()














"""simple docstring"""



from __future__ import annotations






class 							__SCREAMING_SNAKE_CASE						:


							def __init__(			self      :						Any			,    snake_case      :						str			,    snake_case      :						str							):
													'''simple docstring'''





													A__  ,  A__						:  List[str]		  =  text, pattern
													A__  ,  A__						:  List[str]		  =  len(snake_case							), len(snake_case							)


							def 						_UpperCamelCase       (			self      :						str			,    snake_case      :						str							):
													'''simple docstring'''





													for i in range(self.patLen - 1			,    -1			,    -1							):
																			if char == self.pattern[i]:
																									return i
													return -1


							def 						_UpperCamelCase       (			self      :						Any			,    snake_case      :						int							):
													'''simple docstring'''





													for i in range(self.patLen - 1			,    -1			,    -1							):
																			if self.pattern[i] != self.text[current_pos + i]:
																									return current_pos + i
													return -1


							def 						_UpperCamelCase       (			self      :						Union[str, Any]							):
													'''simple docstring'''





													A__						:  str		  =  []
													for i in range(self.textLen - self.patLen + 1							):
																			A__						:  List[Any]		  =  self.mismatch_in_text(snake_case							)
																			if mismatch_index == -1:
																									positions.append(snake_case							)
																			else:
																									A__						:  Dict		  =  self.match_in_pattern(self.text[mismatch_index]							)
																									A__						:  Union[str, Any]		  =  (
																									    mismatch_index - match_index
																									)  # shifting index lgtm [py/multiple-definition]
													return positions




A_    =							'''ABAABA'''
A_    =							'''AB'''
A_    =							BoyerMooreSearch(text, pattern)
A_    =							bms.bad_character_heuristic()

if len(positions) == 0:
						print('''No match found''')
else:
						print('''Pattern found in following positions: ''')
						print(positions)






