

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 							UpperCAmelCase_						:
							"""simple docstring"""







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



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



def   __UpperCAmelCase					(      __a :     Dict						,__a :     Optional[int]						,__a :     List[str]      )     ->	List[Any]:

					"""simple docstring"""

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



def   __UpperCAmelCase					(      )     ->	Union[str, Any]:

					"""simple docstring"""

					_a      :       Union[str, Any]   =						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 ,   _a ,   _a      :       Dict   =						parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]      )      )
					else:
										_a ,   _a ,   _a      :       Union[str, Any]   =						parser.parse_args_into_dataclasses()

					check_output_dir(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'''						,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.

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

					_a      :       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						,)
					_a      :       List[str]   =						AutoModelForSeqaSeqLM.from_pretrained(
					    model_args.model_name_or_path						,from_tf='''.ckpt''' in model_args.model_name_or_path						,config=snake_case__						,cache_dir=model_args.cache_dir						,)

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

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

					# set decoder_start_token_id for MBart
					if model.config.decoder_start_token_id is None and isinstance(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(snake_case__						,snake_case__      ):
															_a      :       Optional[int]   =						tokenizer.lang_code_to_id[data_args.tgt_lang]
										else:
															_a      :       Any   =						tokenizer.convert_tokens_to_ids(data_args.tgt_lang      )

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

					_a      :       List[str]   =						SeqaSeqDataset

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

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

										_a      :       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      :       Dict   =						data_args.n_train

										trainer.save_model()  # this also saves the tokenizer

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

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

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

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

										_a      :       Optional[Any]   =						trainer.predict(test_dataset=snake_case__						,metric_key_prefix='''test'''      )
										_a      :       Optional[int]   =						test_output.metrics
										_a      :       str   =						data_args.n_test

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

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

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

					return all_metrics



def   __UpperCAmelCase					(      __a :     List[str]      )     ->	List[str]:

					"""simple docstring"""

					main()


if __name__ == "__main__":
			main()





from __future__ import annotations




def 							a		(				snake_case__:   Optional[int]		,  snake_case__:   Optional[int]		,  snake_case__:   Any		,  snake_case__:   Optional[int]   ):  # noqa: E741


				'''simple docstring'''
				while r - l > 1:
								lowercase_	    =   (l + r) // 2
								if v[m] >= key:
												lowercase_	    =   m
								else:
												lowercase_	    =   m  # noqa: E741
				return r




def 							a		(				snake_case__:   list[int]   ):


				'''simple docstring'''
				if len(snake_case__   ) == 0:
								return 0

				lowercase_	    =   [0] * len(snake_case__   )
				lowercase_	    =   1

				lowercase_	    =   v[0]

				for i in range(1		,  len(snake_case__   )   ):
								if v[i] < tail[0]:
												lowercase_	    =   v[i]
								elif v[i] > tail[length - 1]:
												lowercase_	    =   v[i]
												length += 1
								else:
												lowercase_	    =   v[i]

				return length


if __name__ == "__main__":
						import doctest

						doctest.testmod()




