



import os
import tempfile
import unittest

from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device

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


if is_torch_available():
	import torch

	from transformers import (
	    DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
	    DistilBertForMaskedLM,
	    DistilBertForMultipleChoice,
	    DistilBertForQuestionAnswering,
	    DistilBertForSequenceClassification,
	    DistilBertForTokenClassification,
	    DistilBertModel,
	)







class 							_a							(					_UpperCAmelCase ):

			def __init__(self,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_=13,					SCREAMING_SNAKE_CASE_=7,					SCREAMING_SNAKE_CASE_=True,					SCREAMING_SNAKE_CASE_=True,					SCREAMING_SNAKE_CASE_=False,					SCREAMING_SNAKE_CASE_=True,					SCREAMING_SNAKE_CASE_=99,					SCREAMING_SNAKE_CASE_=32,					SCREAMING_SNAKE_CASE_=5,					SCREAMING_SNAKE_CASE_=4,					SCREAMING_SNAKE_CASE_=37,					SCREAMING_SNAKE_CASE_="gelu",					SCREAMING_SNAKE_CASE_=0.1,					SCREAMING_SNAKE_CASE_=0.1,					SCREAMING_SNAKE_CASE_=512,					SCREAMING_SNAKE_CASE_=16,					SCREAMING_SNAKE_CASE_=2,					SCREAMING_SNAKE_CASE_=0.0_2,					SCREAMING_SNAKE_CASE_=3,					SCREAMING_SNAKE_CASE_=4,					SCREAMING_SNAKE_CASE_=None,					)    ->					Optional[Any]:
						UpperCAmelCase_:						str					=			parent
						UpperCAmelCase_:						str					=			batch_size
						UpperCAmelCase_:						str					=			seq_length
						UpperCAmelCase_:						Optional[Any]					=			is_training
						UpperCAmelCase_:						Optional[Any]					=			use_input_mask
						UpperCAmelCase_:						Union[str, Any]					=			use_token_type_ids
						UpperCAmelCase_:						Any					=			use_labels
						UpperCAmelCase_:						int					=			vocab_size
						UpperCAmelCase_:						Optional[Any]					=			hidden_size
						UpperCAmelCase_:						List[Any]					=			num_hidden_layers
						UpperCAmelCase_:						List[str]					=			num_attention_heads
						UpperCAmelCase_:						List[str]					=			intermediate_size
						UpperCAmelCase_:						Union[str, Any]					=			hidden_act
						UpperCAmelCase_:						Optional[Any]					=			hidden_dropout_prob
						UpperCAmelCase_:						List[str]					=			attention_probs_dropout_prob
						UpperCAmelCase_:						Union[str, Any]					=			max_position_embeddings
						UpperCAmelCase_:						int					=			type_vocab_size
						UpperCAmelCase_:						Union[str, Any]					=			type_sequence_label_size
						UpperCAmelCase_:						List[Any]					=			initializer_range
						UpperCAmelCase_:						Optional[Any]					=			num_labels
						UpperCAmelCase_:						List[Any]					=			num_choices
						UpperCAmelCase_:						Any					=			scope

			def 			__snake_case    (self )    ->					List[Any]:
						UpperCAmelCase_:						Optional[Any]					=			ids_tensor([self.batch_size, self.seq_length],					self.vocab_size )

						UpperCAmelCase_:						Tuple					=			None
						if self.use_input_mask:
									UpperCAmelCase_:						List[Any]					=			random_attention_mask([self.batch_size, self.seq_length] )

						UpperCAmelCase_:						Dict					=			None
						UpperCAmelCase_:						List[str]					=			None
						UpperCAmelCase_:						Optional[int]					=			None
						if self.use_labels:
									UpperCAmelCase_:						Union[str, Any]					=			ids_tensor([self.batch_size],					self.type_sequence_label_size )
									UpperCAmelCase_:						Union[str, Any]					=			ids_tensor([self.batch_size, self.seq_length],					self.num_labels )
									UpperCAmelCase_:						int					=			ids_tensor([self.batch_size],					self.num_choices )

						UpperCAmelCase_:						Dict					=			self.get_config()

						return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels

			def 			__snake_case    (self )    ->					int:
						return DistilBertConfig(
						    vocab_size=self.vocab_size,					dim=self.hidden_size,					n_layers=self.num_hidden_layers,					n_heads=self.num_attention_heads,					hidden_dim=self.intermediate_size,					hidden_act=self.hidden_act,					dropout=self.hidden_dropout_prob,					attention_dropout=self.attention_probs_dropout_prob,					max_position_embeddings=self.max_position_embeddings,					initializer_range=self.initializer_range,					)

			def 			__snake_case    (self,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_ )    ->					List[Any]:
						UpperCAmelCase_:						Any					=			DistilBertModel(config=a__ )
						model.to(a__ )
						model.eval()
						UpperCAmelCase_:						Optional[Any]					=			model(a__,					a__ )
						UpperCAmelCase_:						List[str]					=			model(a__ )
						self.parent.assertEqual(result.last_hidden_state.shape,					(self.batch_size, self.seq_length, self.hidden_size) )

			def 			__snake_case    (self,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_ )    ->					Dict:
						UpperCAmelCase_:						Optional[int]					=			DistilBertForMaskedLM(config=a__ )
						model.to(a__ )
						model.eval()
						UpperCAmelCase_:						Optional[Any]					=			model(a__,					attention_mask=a__,					labels=a__ )
						self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.seq_length, self.vocab_size) )

			def 			__snake_case    (self,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_ )    ->					Optional[int]:
						UpperCAmelCase_:						Union[str, Any]					=			DistilBertForQuestionAnswering(config=a__ )
						model.to(a__ )
						model.eval()
						UpperCAmelCase_:						Optional[Any]					=			model(
						    a__,					attention_mask=a__,					start_positions=a__,					end_positions=a__ )
						self.parent.assertEqual(result.start_logits.shape,					(self.batch_size, self.seq_length) )
						self.parent.assertEqual(result.end_logits.shape,					(self.batch_size, self.seq_length) )

			def 			__snake_case    (self,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_ )    ->					int:
						UpperCAmelCase_:						Tuple					=			self.num_labels
						UpperCAmelCase_:						Tuple					=			DistilBertForSequenceClassification(a__ )
						model.to(a__ )
						model.eval()
						UpperCAmelCase_:						Optional[int]					=			model(a__,					attention_mask=a__,					labels=a__ )
						self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.num_labels) )

			def 			__snake_case    (self,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_ )    ->					List[str]:
						UpperCAmelCase_:						List[Any]					=			self.num_labels
						UpperCAmelCase_:						List[str]					=			DistilBertForTokenClassification(config=a__ )
						model.to(a__ )
						model.eval()

						UpperCAmelCase_:						int					=			model(a__,					attention_mask=a__,					labels=a__ )
						self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.seq_length, self.num_labels) )

			def 			__snake_case    (self,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_ )    ->					Optional[Any]:
						UpperCAmelCase_:						Tuple					=			self.num_choices
						UpperCAmelCase_:						str					=			DistilBertForMultipleChoice(config=a__ )
						model.to(a__ )
						model.eval()
						UpperCAmelCase_:						List[Any]					=			input_ids.unsqueeze(1 ).expand(-1,					self.num_choices,					-1 ).contiguous()
						UpperCAmelCase_:						Dict					=			input_mask.unsqueeze(1 ).expand(-1,					self.num_choices,					-1 ).contiguous()
						UpperCAmelCase_:						Union[str, Any]					=			model(
						    a__,					attention_mask=a__,					labels=a__,					)
						self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.num_choices) )

			def 			__snake_case    (self )    ->					Optional[int]:
						UpperCAmelCase_:						Optional[Any]					=			self.prepare_config_and_inputs()
						((UpperCAmelCase_)	,				(UpperCAmelCase_)	,				(UpperCAmelCase_)	,				(UpperCAmelCase_)	,				(UpperCAmelCase_)	,				(UpperCAmelCase_)):						Optional[Any]					=			config_and_inputs
						UpperCAmelCase_:						Any					=			{"""input_ids""": input_ids, """attention_mask""": input_mask}
						return config, inputs_dict







@require_torch
class 							_a							(					_UpperCAmelCase      ,						_UpperCAmelCase      ,						unittest.TestCase ):
			A							=     (
			    (
			        DistilBertModel,
			        DistilBertForMaskedLM,
			        DistilBertForMultipleChoice,
			        DistilBertForQuestionAnswering,
			        DistilBertForSequenceClassification,
			        DistilBertForTokenClassification,
			    )
			    if is_torch_available()
			    else None
			)
			A							=     (
			    {
			        'feature-extraction': DistilBertModel,
			        'fill-mask': DistilBertForMaskedLM,
			        'question-answering': DistilBertForQuestionAnswering,
			        'text-classification': DistilBertForSequenceClassification,
			        'token-classification': DistilBertForTokenClassification,
			        'zero-shot': DistilBertForSequenceClassification,
			    }
			    if is_torch_available()
			    else {}
			)
			A							=     True
			A							=     True
			A							=     True
			A							=     True

			def 			__snake_case    (self )    ->					int:
						UpperCAmelCase_:						int					=			DistilBertModelTester(self )
						UpperCAmelCase_:						int					=			ConfigTester(self,					config_class=a__,					dim=37 )

			def 			__snake_case    (self )    ->					Optional[Any]:
						self.config_tester.run_common_tests()

			def 			__snake_case    (self )    ->					Optional[int]:
						UpperCAmelCase_:						Union[str, Any]					=			self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_distilbert_model(*a__ )

			def 			__snake_case    (self )    ->					str:
						UpperCAmelCase_:						int					=			self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_distilbert_for_masked_lm(*a__ )

			def 			__snake_case    (self )    ->					List[str]:
						UpperCAmelCase_:						Dict					=			self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_distilbert_for_question_answering(*a__ )

			def 			__snake_case    (self )    ->					Optional[Any]:
						UpperCAmelCase_:						Dict					=			self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_distilbert_for_sequence_classification(*a__ )

			def 			__snake_case    (self )    ->					Optional[int]:
						UpperCAmelCase_:						Optional[Any]					=			self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_distilbert_for_token_classification(*a__ )

			def 			__snake_case    (self )    ->					List[Any]:
						UpperCAmelCase_:						Union[str, Any]					=			self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_distilbert_for_multiple_choice(*a__ )

			@slow
			def 			__snake_case    (self )    ->					Any:



						for model_name in DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									UpperCAmelCase_:						Dict					=			DistilBertModel.from_pretrained(a__ )
									self.assertIsNotNone(a__ )

			@slow
			@require_torch_gpu
			def 			__snake_case    (self )    ->					Optional[int]:
						UpperCAmelCase_	,				UpperCAmelCase_:						List[str]					=			self.model_tester.prepare_config_and_inputs_for_common()
						for model_class in self.all_model_classes:
									# BertForMultipleChoice behaves incorrectly in JIT environments.
									if model_class == DistilBertForMultipleChoice:
												return

									UpperCAmelCase_:						str					=			True
									UpperCAmelCase_:						Optional[int]					=			model_class(config=a__ )

									UpperCAmelCase_:						List[Any]					=			self._prepare_for_class(a__,					a__ )
									UpperCAmelCase_:						Union[str, Any]					=			torch.jit.trace(
									    a__,					(inputs_dict["""input_ids"""].to("""cpu""" ), inputs_dict["""attention_mask"""].to("""cpu""" )) )

									with tempfile.TemporaryDirectory() as tmp:
												torch.jit.save(a__,					os.path.join(a__,					"""traced_model.pt""" ) )
												UpperCAmelCase_:						str					=			torch.jit.load(os.path.join(a__,					"""traced_model.pt""" ),					map_location=a__ )
												loaded(inputs_dict["""input_ids"""].to(a__ ),					inputs_dict["""attention_mask"""].to(a__ ) )







@require_torch
class 							_a							(					unittest.TestCase ):

			@slow
			def 			__snake_case    (self )    ->					str:
						UpperCAmelCase_:						Optional[int]					=			DistilBertModel.from_pretrained("""distilbert-base-uncased""" )
						UpperCAmelCase_:						int					=			torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]] )
						UpperCAmelCase_:						Any					=			torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
						with torch.no_grad():
									UpperCAmelCase_:						Tuple					=			model(a__,					attention_mask=a__ )[0]
						UpperCAmelCase_:						Union[str, Any]					=			torch.Size((1, 11, 768) )
						self.assertEqual(output.shape,					a__ )
						UpperCAmelCase_:						List[Any]					=			torch.tensor(
						    [[[-0.1_6_3_9, 0.3_2_9_9, 0.1_6_4_8], [-0.1_7_4_6, 0.3_2_8_9, 0.1_7_1_0], [-0.1_8_8_4, 0.3_3_5_7, 0.1_8_1_0]]] )

						self.assertTrue(torch.allclose(output[:, 1:4, 1:4],					a__,					atol=1E-4 ) )













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






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






def lowerCamelCase__    (							snake_case_	:  List[Any]       ,      snake_case_	:  List[str]       ,      snake_case_	:  Dict       )      ->				str:
   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 lowerCamelCase__    (							)      ->				Optional[Any]:
   # 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.

   __snake_case							       =   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.
      __snake_case       ,	__snake_case       ,	__snake_case							       =   parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]       )       )
   else:
      __snake_case       ,	__snake_case       ,	__snake_case							       =   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.

   __snake_case							       =   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       ,      )

   __snake_case							       =   ('''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_       )       )

   __snake_case							       =   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       ,      )
   __snake_case							       =   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:
      __snake_case							       =   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_       ):
         __snake_case							       =   tokenizer.lang_code_to_id[data_args.tgt_lang]
      else:
         __snake_case							       =   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()       )

   __snake_case							       =   SeqaSeqDataset

   # Get datasets
   __snake_case							       =   (
       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
   )
   __snake_case							       =   (
       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
   )
   __snake_case							       =   (
       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
   __snake_case							       =   (
       build_compute_metrics_fn(data_args.task       ,      snake_case_       ) if training_args.predict_with_generate else None
   )
   __snake_case							       =   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_       ,      )

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

      __snake_case							       =   trainer.train(
          model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path       ) else None       )
      __snake_case							       =   train_result.metrics
      __snake_case							       =   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 ***'''       )

      __snake_case							       =   trainer.evaluate(metric_key_prefix='''val'''       )
      __snake_case							       =   data_args.n_val
      __snake_case							       =   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 ***'''       )

      __snake_case							       =   trainer.predict(test_dataset=snake_case_       ,      metric_key_prefix='''test'''       )
      __snake_case							       =   test_output.metrics
      __snake_case							       =   data_args.n_test

      if trainer.is_world_process_zero():
         __snake_case							       =   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:
            __snake_case							       =   tokenizer.batch_decode(
                test_output.predictions       ,      skip_special_tokens=snake_case_       ,      clean_up_tokenization_spaces=snake_case_       )
            __snake_case							       =   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 lowerCamelCase__    (							snake_case_	:  Optional[Any]       )      ->				Tuple:
   # For xla_spawn (TPUs)
   main()


if __name__ == "__main__":
    main()

