






'''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:      Tuple  				=				logging.getLogger(__name__)






@dataclass
class 							__magic_name__     :







	"""simple docstring"""



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






@dataclass
class 							__magic_name__     :







	"""simple docstring"""



	SCREAMING_SNAKE_CASE_					:      str        =  field(
	    metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'}				)
	SCREAMING_SNAKE_CASE_					:      Optional[str]        =  field(
	    default='summarization',						metadata={'help': 'Task name, summarization (or summarization_{dataset} for pegasus) or translation'},						)
	SCREAMING_SNAKE_CASE_					:      Optional[int]        =  field(
	    default=1_0_2_4,						metadata={
	        'help': (
	            'The maximum total input sequence length after tokenization. Sequences longer '
	            'than this will be truncated, sequences shorter will be padded.'
	        )
	    },						)
	SCREAMING_SNAKE_CASE_					:      Optional[int]        =  field(
	    default=1_2_8,						metadata={
	        'help': (
	            'The maximum total sequence length for target text after tokenization. Sequences longer '
	            'than this will be truncated, sequences shorter will be padded.'
	        )
	    },						)
	SCREAMING_SNAKE_CASE_					:      Optional[int]        =  field(
	    default=1_4_2,						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``.'
	        )
	    },						)
	SCREAMING_SNAKE_CASE_					:      Optional[int]        =  field(
	    default=1_4_2,						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.'
	        )
	    },						)
	SCREAMING_SNAKE_CASE_					:      Optional[int]        =  field(default=-1,						metadata={'help': '# training examples. -1 means use all.'}				)
	SCREAMING_SNAKE_CASE_					:      Optional[int]        =  field(default=-1,						metadata={'help': '# validation examples. -1 means use all.'}				)
	SCREAMING_SNAKE_CASE_					:      Optional[int]        =  field(default=-1,						metadata={'help': '# test examples. -1 means use all.'}				)
	SCREAMING_SNAKE_CASE_					:      Optional[str]        =  field(default=UpperCAmelCase_,						metadata={'help': 'Source language id for translation.'}				)
	SCREAMING_SNAKE_CASE_					:      Optional[str]        =  field(default=UpperCAmelCase_,						metadata={'help': 'Target language id for translation.'}				)
	SCREAMING_SNAKE_CASE_					:      Optional[int]        =  field(default=UpperCAmelCase_,						metadata={'help': '# num_beams to use for evaluation.'}				)
	SCREAMING_SNAKE_CASE_					:      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		:	Any						,			a		:	Dict						,			a		:	Optional[Any]							)			->			Any:





						"""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 	_UpperCAmelCase    (  )			->			str:





						"""simple docstring"""
						# 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.

						lowercase_      :   List[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.
												lowercase_  ,      lowercase_  ,      lowercase_      :   Optional[Any]        =			parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]							)							)
						else:
												lowercase_  ,      lowercase_  ,      lowercase_      :   Union[str, Any]        =			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.

						lowercase_      :   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						,			)

						lowercase_      :   Union[str, Any]        =			('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							)							)

						lowercase_      :   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						,			)
						lowercase_      :   Optional[Any]        =			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:
												lowercase_      :   List[Any]        =			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							):
																		lowercase_      :   Union[str, Any]        =			tokenizer.lang_code_to_id[data_args.tgt_lang]
												else:
																		lowercase_      :   Dict        =			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()							)

						lowercase_      :   str        =			SeqaSeqDataset

						# Get datasets
						lowercase_      :   Tuple        =			(
						    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
						)
						lowercase_      :   Optional[int]        =			(
						    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
						)
						lowercase_      :   Dict        =			(
						    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
						lowercase_      :   List[str]        =			(
						    build_compute_metrics_fn(data_args.task						,			a							) if training_args.predict_with_generate else None
						)
						lowercase_      :   Union[str, Any]        =			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						,			)

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

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

												lowercase_      :   str        =			trainer.evaluate(metric_key_prefix='val'							)
												lowercase_      :   Dict        =			data_args.n_val
												lowercase_      :   Optional[int]        =			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 ***'							)

												lowercase_      :   List[Any]        =			trainer.predict(test_dataset=a						,			metric_key_prefix='test'							)
												lowercase_      :   Optional[Any]        =			test_output.metrics
												lowercase_      :   Union[str, Any]        =			data_args.n_test

												if trainer.is_world_process_zero():
																		lowercase_      :   Tuple        =			round(metrics['test_loss']						,			4							)
																		handle_metrics('test'						,			a						,			training_args.output_dir							)
																		all_metrics.update(a							)

																		if training_args.predict_with_generate:
																								lowercase_      :   Tuple        =			tokenizer.batch_decode(
																								    test_output.predictions						,			skip_special_tokens=a						,			clean_up_tokenization_spaces=a							)
																								lowercase_      :   Any        =			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]							)			->			int:





						"""simple docstring"""
						# For xla_spawn (TPUs)
						main()


if __name__ == "__main__":
						main()








'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A:      Dict  				=				logging.get_logger(__name__)

A:      Optional[Any]  				=				{
    "google/vit-base-patch16-224": "https://huggingface.co/vit-base-patch16-224/resolve/main/config.json",
    # See all ViT models at https://huggingface.co/models?filter=vit
}






class 							__magic_name__     (	UpperCAmelCase_				):







	"""simple docstring"""



	SCREAMING_SNAKE_CASE_					:      Optional[Any]        =  'vit'
	def __init__(							self  ,      _lowercase=768  ,      _lowercase=12  ,      _lowercase=12  ,      _lowercase=3072  ,      _lowercase="gelu"  ,      _lowercase=0.0  ,      _lowercase=0.0  ,      _lowercase=0.02  ,      _lowercase=1E-1_2  ,      _lowercase=224  ,      _lowercase=16  ,      _lowercase=3  ,      _lowercase=True  ,      _lowercase=16  ,      **_lowercase  ,      )						->  List[str]:
							super().__init__(**_lowercase       )

							lowercase_      :   Optional[int]        =			hidden_size
							lowercase_      :   str        =			num_hidden_layers
							lowercase_      :   str        =			num_attention_heads
							lowercase_      :   int        =			intermediate_size
							lowercase_      :   List[Any]        =			hidden_act
							lowercase_      :   Any        =			hidden_dropout_prob
							lowercase_      :   List[str]        =			attention_probs_dropout_prob
							lowercase_      :   str        =			initializer_range
							lowercase_      :   List[str]        =			layer_norm_eps
							lowercase_      :   Any        =			image_size
							lowercase_      :   Tuple        =			patch_size
							lowercase_      :   Optional[Any]        =			num_channels
							lowercase_      :   str        =			qkv_bias
							lowercase_      :   List[str]        =			encoder_stride






class 							__magic_name__     (	UpperCAmelCase_				):







	"""simple docstring"""



	SCREAMING_SNAKE_CASE_					:      Any        =  version.parse('1.11'				)
	@property
	def        lowerCamelCase__		(							self       )						->  Mapping[str, Mapping[int, str]]:
							return OrderedDict(
							    [
							        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
							    ]       )







	@property
	def        lowerCamelCase__		(							self       )						->  float:
							return 1E-4
