



'''simple docstring'''




import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_gpu,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch
						from torch import nn

						from transformers import ViTForImageClassification, ViTForMaskedImageModeling, ViTModel
						from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import ViTImageProcessor







class      _lowerCAmelCase							:


	def __init__(self	,				lowercase	,				lowercase=13	,				lowercase=30	,				lowercase=2	,				lowercase=3	,				lowercase=True	,				lowercase=True	,				lowercase=32	,				lowercase=5	,				lowercase=4	,				lowercase=37	,				lowercase="gelu"	,				lowercase=0.1	,				lowercase=0.1	,				lowercase=10	,				lowercase=0.02	,				lowercase=None	,				lowercase=2	,				):
				A_     :						List[str]     =					parent
				A_     :						str     =					batch_size
				A_     :						Optional[Any]     =					image_size
				A_     :						List[str]     =					patch_size
				A_     :						List[str]     =					num_channels
				A_     :						List[str]     =					is_training
				A_     :						str     =					use_labels
				A_     :						List[str]     =					hidden_size
				A_     :						List[Any]     =					num_hidden_layers
				A_     :						Any     =					num_attention_heads
				A_     :						Any     =					intermediate_size
				A_     :						Optional[Any]     =					hidden_act
				A_     :						Optional[int]     =					hidden_dropout_prob
				A_     :						str     =					attention_probs_dropout_prob
				A_     :						Optional[int]     =					type_sequence_label_size
				A_     :						Any     =					initializer_range
				A_     :						int     =					scope
				A_     :						str     =					encoder_stride

				# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
				A_     :						Dict     =					(image_size // patch_size) ** 2
				A_     :						List[str]     =					num_patches + 1


	def    _a     (self							):
				A_     :						str     =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

				A_     :						Optional[Any]     =					None
				if self.use_labels:
							A_     :						str     =					ids_tensor([self.batch_size]	,				self.type_sequence_label_size							)

				A_     :						Union[str, Any]     =					self.get_config()

				return config, pixel_values, labels


	def    _a     (self							):
				return ViTConfig(
				    image_size=self.image_size	,				patch_size=self.patch_size	,				num_channels=self.num_channels	,				hidden_size=self.hidden_size	,				num_hidden_layers=self.num_hidden_layers	,				num_attention_heads=self.num_attention_heads	,				intermediate_size=self.intermediate_size	,				hidden_act=self.hidden_act	,				hidden_dropout_prob=self.hidden_dropout_prob	,				attention_probs_dropout_prob=self.attention_probs_dropout_prob	,				is_decoder=lowercase	,				initializer_range=self.initializer_range	,				encoder_stride=self.encoder_stride	,				)


	def    _a     (self	,				lowercase	,				lowercase	,				lowercase							):
				A_     :						List[str]     =					ViTModel(config=lowercase							)
				model.to(lowercase							)
				model.eval()
				A_     :						List[str]     =					model(lowercase							)
				self.parent.assertEqual(result.last_hidden_state.shape	,				(self.batch_size, self.seq_length, self.hidden_size)							)


	def    _a     (self	,				lowercase	,				lowercase	,				lowercase							):
				A_     :						List[str]     =					ViTForMaskedImageModeling(config=lowercase							)
				model.to(lowercase							)
				model.eval()
				A_     :						Tuple     =					model(lowercase							)
				self.parent.assertEqual(
				    result.reconstruction.shape	,				(self.batch_size, self.num_channels, self.image_size, self.image_size)							)

				# test greyscale images
				A_     :						Union[str, Any]     =					1
				A_     :						Any     =					ViTForMaskedImageModeling(lowercase							)
				model.to(lowercase							)
				model.eval()

				A_     :						str     =					floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
				A_     :						Optional[int]     =					model(lowercase							)
				self.parent.assertEqual(result.reconstruction.shape	,				(self.batch_size, 1, self.image_size, self.image_size)							)


	def    _a     (self	,				lowercase	,				lowercase	,				lowercase							):
				A_     :						Dict     =					self.type_sequence_label_size
				A_     :						str     =					ViTForImageClassification(lowercase							)
				model.to(lowercase							)
				model.eval()
				A_     :						List[str]     =					model(lowercase	,				labels=lowercase							)
				self.parent.assertEqual(result.logits.shape	,				(self.batch_size, self.type_sequence_label_size)							)

				# test greyscale images
				A_     :						Any     =					1
				A_     :						str     =					ViTForImageClassification(lowercase							)
				model.to(lowercase							)
				model.eval()

				A_     :						Optional[int]     =					floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
				A_     :						Union[str, Any]     =					model(lowercase							)
				self.parent.assertEqual(result.logits.shape	,				(self.batch_size, self.type_sequence_label_size)							)


	def    _a     (self							):
				A_     :						str     =					self.prepare_config_and_inputs()
				(
				    (
				    A_
				),   (
				    A_
				),   (
				    A_
				),   
				)     :						Optional[int]     =					config_and_inputs
				A_     :						Tuple     =					{"""pixel_values""": pixel_values}
				return config, inputs_dict







@require_torch
class      _lowerCAmelCase							(      __UpperCAmelCase				,      __UpperCAmelCase				,      unittest.TestCase			):
	__SCREAMING_SNAKE_CASE       :		Any		      = (
	    (
	        ViTModel,
	        ViTForImageClassification,
	        ViTForMaskedImageModeling,
	    )
	    if is_torch_available()
	    else ()
	)
	__SCREAMING_SNAKE_CASE       :		Union[str, Any]		      = (
	    {'feature-extraction': ViTModel, 'image-classification': ViTForImageClassification}
	    if is_torch_available()
	    else {}
	)
	__SCREAMING_SNAKE_CASE       :		Union[str, Any]		      = True

	__SCREAMING_SNAKE_CASE       :		Dict		      = False
	__SCREAMING_SNAKE_CASE       :		Optional[int]		      = False
	__SCREAMING_SNAKE_CASE       :		Optional[int]		      = False


	def    _a     (self							):
				A_     :						Any     =					ViTModelTester(self							)
				A_     :						str     =					ConfigTester(self	,				config_class=lowercase	,				has_text_modality=lowercase	,				hidden_size=37							)


	def    _a     (self							):
				self.config_tester.run_common_tests()


	@unittest.skip(reason="""ViT does not use inputs_embeds"""							)
	def    _a     (self							):
				pass


	def    _a     (self							):
				A_,   A_     :						Union[str, Any]     =					self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
							A_     :						Union[str, Any]     =					model_class(lowercase							)
							self.assertIsInstance(model.get_input_embeddings()	,				(nn.Module)							)
							A_     :						Tuple     =					model.get_output_embeddings()
							self.assertTrue(x is None or isinstance(lowercase	,				nn.Linear							)							)


	def    _a     (self							):
				A_,   A_     :						List[Any]     =					self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
							A_     :						Optional[Any]     =					model_class(lowercase							)
							A_     :						Any     =					inspect.signature(model.forward							)
							# signature.parameters is an OrderedDict => so arg_names order is deterministic
							A_     :						List[str]     =					[*signature.parameters.keys()]

							A_     :						Dict     =					["""pixel_values"""]
							self.assertListEqual(arg_names[:1]	,				lowercase							)


	def    _a     (self							):
				A_     :						Tuple     =					self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*lowercase							)


	def    _a     (self							):
				A_     :						List[Any]     =					self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_masked_image_modeling(*lowercase							)


	def    _a     (self							):
				A_     :						Dict     =					self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*lowercase							)


	@slow
	def    _a     (self							):
				for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							A_     :						Dict     =					ViTModel.from_pretrained(lowercase							)
							self.assertIsNotNone(lowercase							)






def      a      (			):
			'''simple docstring'''





			A_     :						List[Any]     =					Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
			return image







@require_torch
@require_vision
class      _lowerCAmelCase							(      unittest.TestCase			):


	@cached_property
	def    _a     (self							):
				return ViTImageProcessor.from_pretrained("""google/vit-base-patch16-224"""							) if is_vision_available() else None


	@slow
	def    _a     (self							):
				A_     :						Optional[int]     =					ViTForImageClassification.from_pretrained("""google/vit-base-patch16-224"""							).to(lowercase							)

				A_     :						List[str]     =					self.default_image_processor
				A_     :						Tuple     =					prepare_img()
				A_     :						int     =					image_processor(images=lowercase	,				return_tensors="""pt"""							).to(lowercase							)

				# forward pass
				with torch.no_grad():
							A_     :						str     =					model(**lowercase							)

				# verify the logits
				A_     :						Dict     =					torch.Size((1, 1000)							)
				self.assertEqual(outputs.logits.shape	,				lowercase							)

				A_     :						str     =					torch.tensor([-0.27_44, 0.82_15, -0.08_36]							).to(lowercase							)

				self.assertTrue(torch.allclose(outputs.logits[0, :3]	,				lowercase	,				atol=1E-4							)							)


	@slow
	def    _a     (self							):
				# ViT models have an `interpolate_pos_encoding` argument in their forward method,
				# allowing to interpolate the pre-trained position embeddings in order to use
				# the model on higher resolutions. The DINO model by Facebook AI leverages this
				# to visualize self-attention on higher resolution images.
				A_     :						Optional[int]     =					ViTModel.from_pretrained("""facebook/dino-vits8"""							).to(lowercase							)

				A_     :						List[Any]     =					ViTImageProcessor.from_pretrained("""facebook/dino-vits8"""	,				size=480							)
				A_     :						Dict     =					prepare_img()
				A_     :						str     =					image_processor(images=lowercase	,				return_tensors="""pt"""							)
				A_     :						int     =					inputs.pixel_values.to(lowercase							)

				# forward pass
				with torch.no_grad():
							A_     :						int     =					model(lowercase	,				interpolate_pos_encoding=lowercase							)

				# verify the logits
				A_     :						int     =					torch.Size((1, 3601, 384)							)
				self.assertEqual(outputs.last_hidden_state.shape	,				lowercase							)

				A_     :						List[Any]     =					torch.tensor(
				    [[4.23_40, 4.39_06, -6.66_92], [4.54_63, 1.89_28, -6.72_57], [4.44_29, 0.84_96, -5.85_85]]							).to(lowercase							)

				self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]	,				lowercase	,				atol=1E-4							)							)


	@slow
	@require_accelerate
	@require_torch_gpu
	def    _a     (self							):
				A_     :						List[Any]     =					ViTModel.from_pretrained("""facebook/dino-vits8"""	,				torch_dtype=torch.floataa	,				device_map="""auto"""							)
				A_     :						int     =					self.default_image_processor

				A_     :						Any     =					prepare_img()
				A_     :						List[str]     =					image_processor(images=lowercase	,				return_tensors="""pt"""							)
				A_     :						Any     =					inputs.pixel_values.to(lowercase							)

				# forward pass to make sure inference works in fp16
				with torch.no_grad():
							A_     :						Optional[Any]     =					model(lowercase							)




'''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,
)


lowerCamelCase							:Union[str, Any]       = logging.getLogger(__name__)







@dataclass
class      _lowerCAmelCase							:
	__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      _lowerCAmelCase							:
	__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_024				,      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=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.'
	        )
	    }				,      )
	__SCREAMING_SNAKE_CASE       :		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``.'
	        )
	    }				,      )
	__SCREAMING_SNAKE_CASE       :		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.'
	        )
	    }				,      )
	__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      a      (			lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__   ):
			'''simple docstring'''





			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      a      (			):
			'''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_     :						str     =					parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]   )   )
			else:
						A_,   A_,   A_     :						List[str]     =					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[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_     :						int     =					("""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_     :						Optional[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_     :						Optional[Any]     =					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_     :						int     =					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_     :						Optional[Any]     =					tokenizer.lang_code_to_id[data_args.tgt_lang]
						else:
									A_     :						List[str]     =					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_     :						Union[str, 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_     :						int     =					(
			    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_     :						Tuple     =					(
			    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_     :						Optional[Any]     =					(
			    build_compute_metrics_fn(data_args.task						,				lowerCamelCase__   ) if training_args.predict_with_generate else None
			)
			A_     :						List[str]     =					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_     :						str     =					{}
			# Training
			if training_args.do_train:
						logger.info("""*** Train ***"""   )

						A_     :						List[Any]     =					trainer.train(
						    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path   ) else None   )
						A_     :						Any     =					train_result.metrics
						A_     :						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_     :						Tuple     =					trainer.evaluate(metric_key_prefix="""val"""   )
						A_     :						str     =					data_args.n_val
						A_     :						Optional[Any]     =					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_     :						Tuple     =					data_args.n_test

						if trainer.is_world_process_zero():
									A_     :						List[Any]     =					round(metrics["""test_loss"""]						,				4   )
									handle_metrics("""test"""						,				lowerCamelCase__						,				training_args.output_dir   )
									all_metrics.update(lowerCamelCase__   )

									if training_args.predict_with_generate:
												A_     :						List[Any]     =					tokenizer.batch_decode(
												    test_output.predictions						,				skip_special_tokens=lowerCamelCase__						,				clean_up_tokenization_spaces=lowerCamelCase__   )
												A_     :						Tuple     =					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      a      (			lowerCamelCase__   ):
			'''simple docstring'''





			main()


if __name__ == "__main__":
						main()