



'''simple docstring'''


import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest




class      __UpperCamelCase       (_UpperCAmelCase			):
	__A			  =					(UniPCMultistepScheduler,)
	__A			  =					(('''num_inference_steps''', 25),)







	def   _a   (    self ,				**_lowerCAmelCase						)    ->    Any:
							'''simple docstring'''






							lowercase        =							{
							    """num_train_timesteps""": 1000,
							    """beta_start""": 0.0001,
							    """beta_end""": 0.02,
							    """beta_schedule""": """linear""",
							    """solver_order""": 2,
							    """solver_type""": """bh2""",
							}

							config.update(**_lowerCAmelCase						)
							return config







	def   _a   (    self ,				_lowerCAmelCase=0 ,				**_lowerCAmelCase						)    ->    Tuple:
							'''simple docstring'''






							lowercase        =							dict(self.forward_default_kwargs						)
							lowercase        =							kwargs.pop("""num_inference_steps""" ,				_lowerCAmelCase						)
							lowercase        =							self.dummy_sample
							lowercase        =							0.1 * sample
							lowercase        =							[residual + 0.2, residual + 0.15, residual + 0.10]

							for scheduler_class in self.scheduler_classes:
													lowercase        =							self.get_scheduler_config(**_lowerCAmelCase						)
													lowercase        =							scheduler_class(**_lowerCAmelCase						)
													scheduler.set_timesteps(_lowerCAmelCase						)
													# copy over dummy past residuals
													lowercase        =							dummy_past_residuals[: scheduler.config.solver_order]

													with tempfile.TemporaryDirectory() as tmpdirname:
																			scheduler.save_config(_lowerCAmelCase						)
																			lowercase        =							scheduler_class.from_pretrained(_lowerCAmelCase						)
																			new_scheduler.set_timesteps(_lowerCAmelCase						)
																			# copy over dummy past residuals
																			lowercase        =							dummy_past_residuals[: new_scheduler.config.solver_order]

													lowercase     ,   lowercase        =							sample, sample
													for t in range(_lowerCAmelCase ,				time_step + scheduler.config.solver_order + 1						):
																			lowercase        =							scheduler.step(_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase ,				**_lowerCAmelCase						).prev_sample
																			lowercase        =							new_scheduler.step(_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase ,				**_lowerCAmelCase						).prev_sample

																			assert torch.sum(torch.abs(output - new_output						)						) < 1E-5, "Scheduler outputs are not identical"







	def   _a   (    self ,				_lowerCAmelCase=0 ,				**_lowerCAmelCase						)    ->    str:
							'''simple docstring'''






							lowercase        =							dict(self.forward_default_kwargs						)
							lowercase        =							kwargs.pop("""num_inference_steps""" ,				_lowerCAmelCase						)
							lowercase        =							self.dummy_sample
							lowercase        =							0.1 * sample
							lowercase        =							[residual + 0.2, residual + 0.15, residual + 0.10]

							for scheduler_class in self.scheduler_classes:
													lowercase        =							self.get_scheduler_config()
													lowercase        =							scheduler_class(**_lowerCAmelCase						)
													scheduler.set_timesteps(_lowerCAmelCase						)

													# copy over dummy past residuals (must be after setting timesteps)
													lowercase        =							dummy_past_residuals[: scheduler.config.solver_order]

													with tempfile.TemporaryDirectory() as tmpdirname:
																			scheduler.save_config(_lowerCAmelCase						)
																			lowercase        =							scheduler_class.from_pretrained(_lowerCAmelCase						)
																			# copy over dummy past residuals
																			new_scheduler.set_timesteps(_lowerCAmelCase						)

																			# copy over dummy past residual (must be after setting timesteps)
																			lowercase        =							dummy_past_residuals[: new_scheduler.config.solver_order]

													lowercase        =							scheduler.step(_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase ,				**_lowerCAmelCase						).prev_sample
													lowercase        =							new_scheduler.step(_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase ,				**_lowerCAmelCase						).prev_sample

													assert torch.sum(torch.abs(output - new_output						)						) < 1E-5, "Scheduler outputs are not identical"







	def   _a   (    self ,				_lowerCAmelCase=None ,				**_lowerCAmelCase						)    ->    List[Any]:
							'''simple docstring'''






							if scheduler is None:
													lowercase        =							self.scheduler_classes[0]
													lowercase        =							self.get_scheduler_config(**_lowerCAmelCase						)
													lowercase        =							scheduler_class(**_lowerCAmelCase						)

							lowercase        =							self.scheduler_classes[0]
							lowercase        =							self.get_scheduler_config(**_lowerCAmelCase						)
							lowercase        =							scheduler_class(**_lowerCAmelCase						)

							lowercase        =							10
							lowercase        =							self.dummy_model()
							lowercase        =							self.dummy_sample_deter
							scheduler.set_timesteps(_lowerCAmelCase						)

							for i, t in enumerate(scheduler.timesteps						):
													lowercase        =							model(_lowerCAmelCase ,				_lowerCAmelCase						)
													lowercase        =							scheduler.step(_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase						).prev_sample

							return sample







	def   _a   (    self						)    ->    Dict:
							'''simple docstring'''






							lowercase        =							dict(self.forward_default_kwargs						)

							lowercase        =							kwargs.pop("""num_inference_steps""" ,				_lowerCAmelCase						)

							for scheduler_class in self.scheduler_classes:
													lowercase        =							self.get_scheduler_config()
													lowercase        =							scheduler_class(**_lowerCAmelCase						)

													lowercase        =							self.dummy_sample
													lowercase        =							0.1 * sample

													if num_inference_steps is not None and hasattr(_lowerCAmelCase ,				"""set_timesteps"""						):
																			scheduler.set_timesteps(_lowerCAmelCase						)
													elif num_inference_steps is not None and not hasattr(_lowerCAmelCase ,				"""set_timesteps"""						):
																			lowercase        =							num_inference_steps

													# copy over dummy past residuals (must be done after set_timesteps)
													lowercase        =							[residual + 0.2, residual + 0.15, residual + 0.10]
													lowercase        =							dummy_past_residuals[: scheduler.config.solver_order]

													lowercase        =							scheduler.timesteps[5]
													lowercase        =							scheduler.timesteps[6]

													lowercase        =							scheduler.step(_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase ,				**_lowerCAmelCase						).prev_sample
													lowercase        =							scheduler.step(_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase ,				**_lowerCAmelCase						).prev_sample

													self.assertEqual(output_a.shape ,				sample.shape						)
													self.assertEqual(output_a.shape ,				output_a.shape						)







	def   _a   (    self						)    ->    Optional[Any]:
							'''simple docstring'''






							lowercase        =							UniPCMultistepScheduler(**self.get_scheduler_config()						)
							lowercase        =							self.full_loop(scheduler=_lowerCAmelCase						)
							lowercase        =							torch.mean(torch.abs(_lowerCAmelCase						)						)

							assert abs(result_mean.item() - 0.2464						) < 1E-3

							lowercase        =							DPMSolverSinglestepScheduler.from_config(scheduler.config						)
							lowercase        =							DEISMultistepScheduler.from_config(scheduler.config						)
							lowercase        =							DPMSolverMultistepScheduler.from_config(scheduler.config						)
							lowercase        =							UniPCMultistepScheduler.from_config(scheduler.config						)

							lowercase        =							self.full_loop(scheduler=_lowerCAmelCase						)
							lowercase        =							torch.mean(torch.abs(_lowerCAmelCase						)						)

							assert abs(result_mean.item() - 0.2464						) < 1E-3







	def   _a   (    self						)    ->    Optional[int]:
							'''simple docstring'''






							for timesteps in [25, 50, 100, 999, 1000]:
													self.check_over_configs(num_train_timesteps=_lowerCAmelCase						)







	def   _a   (    self						)    ->    List[str]:
							'''simple docstring'''






							self.check_over_configs(thresholding=_lowerCAmelCase						)
							for order in [1, 2, 3]:
													for solver_type in ["bh1", "bh2"]:
																			for threshold in [0.5, 1.0, 2.0]:
																									for prediction_type in ["epsilon", "sample"]:
																															self.check_over_configs(
																															    thresholding=_lowerCAmelCase ,				prediction_type=_lowerCAmelCase ,				sample_max_value=_lowerCAmelCase ,				solver_order=_lowerCAmelCase ,				solver_type=_lowerCAmelCase ,				)







	def   _a   (    self						)    ->    Dict:
							'''simple docstring'''






							for prediction_type in ["epsilon", "v_prediction"]:
													self.check_over_configs(prediction_type=_lowerCAmelCase						)







	def   _a   (    self						)    ->    Union[str, Any]:
							'''simple docstring'''






							for solver_type in ["bh1", "bh2"]:
													for order in [1, 2, 3]:
																			for prediction_type in ["epsilon", "sample"]:
																									self.check_over_configs(
																									    solver_order=_lowerCAmelCase ,				solver_type=_lowerCAmelCase ,				prediction_type=_lowerCAmelCase ,				)
																									lowercase        =							self.full_loop(
																									    solver_order=_lowerCAmelCase ,				solver_type=_lowerCAmelCase ,				prediction_type=_lowerCAmelCase ,				)
																									assert not torch.isnan(_lowerCAmelCase						).any(), "Samples have nan numbers"







	def   _a   (    self						)    ->    List[str]:
							'''simple docstring'''






							self.check_over_configs(lower_order_final=_lowerCAmelCase						)
							self.check_over_configs(lower_order_final=_lowerCAmelCase						)







	def   _a   (    self						)    ->    str:
							'''simple docstring'''






							for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
													self.check_over_forward(num_inference_steps=_lowerCAmelCase ,				time_step=0						)







	def   _a   (    self						)    ->    int:
							'''simple docstring'''






							lowercase        =							self.full_loop()
							lowercase        =							torch.mean(torch.abs(_lowerCAmelCase						)						)

							assert abs(result_mean.item() - 0.2464						) < 1E-3







	def   _a   (    self						)    ->    int:
							'''simple docstring'''






							lowercase        =							self.full_loop(prediction_type="""v_prediction"""						)
							lowercase        =							torch.mean(torch.abs(_lowerCAmelCase						)						)

							assert abs(result_mean.item() - 0.1014						) < 1E-3







	def   _a   (    self						)    ->    Tuple:
							'''simple docstring'''






							lowercase        =							self.scheduler_classes[0]
							lowercase        =							self.get_scheduler_config(thresholding=_lowerCAmelCase ,				dynamic_thresholding_ratio=0						)
							lowercase        =							scheduler_class(**_lowerCAmelCase						)

							lowercase        =							10
							lowercase        =							self.dummy_model()
							lowercase        =							self.dummy_sample_deter.half()
							scheduler.set_timesteps(_lowerCAmelCase						)

							for i, t in enumerate(scheduler.timesteps						):
													lowercase        =							model(_lowerCAmelCase ,				_lowerCAmelCase						)
													lowercase        =							scheduler.step(_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase						).prev_sample

							assert sample.dtype == torch.floataa







	def   _a   (    self ,				**_lowerCAmelCase						)    ->    Optional[Any]:
							'''simple docstring'''






							for scheduler_class in self.scheduler_classes:
													lowercase        =							self.get_scheduler_config(**_lowerCAmelCase						)
													lowercase        =							scheduler_class(**_lowerCAmelCase						)

													scheduler.set_timesteps(scheduler.config.num_train_timesteps						)
													assert len(scheduler.timesteps.unique()						) == scheduler.num_inference_steps







'''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,
)


lowercase_      :				Tuple   					=    logging.getLogger(__name__)




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




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







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







def      SCREAMING_SNAKE_CASE				(				):
						# 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        =							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        =							parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]						)						)
						else:
												lowercase     ,   lowercase     ,   lowercase        =							parser.parse_args_into_dataclasses()

						check_output_dir(lowercase_						)

						# 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""" ,  lowercase_						)

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

						lowercase        =							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        =							AutoModelForSeqaSeqLM.from_pretrained(
						    model_args.model_name_or_path ,  from_tf=""".ckpt""" in model_args.model_name_or_path ,  config=lowercase_ ,  cache_dir=model_args.cache_dir ,  )

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

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

						# set decoder_start_token_id for MBart
						if model.config.decoder_start_token_id is None and isinstance(lowercase_ ,  (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(lowercase_ ,  lowercase_						):
																		lowercase        =							tokenizer.lang_code_to_id[data_args.tgt_lang]
												else:
																		lowercase        =							tokenizer.convert_tokens_to_ids(data_args.tgt_lang						)

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

						lowercase        =							SeqaSeqDataset

						# Get datasets
						lowercase        =							(
						    dataset_class(
						        lowercase_ ,  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        =							(
						    dataset_class(
						        lowercase_ ,  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        =							(
						    dataset_class(
						        lowercase_ ,  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        =							(
						    build_compute_metrics_fn(data_args.task ,  lowercase_						) if training_args.predict_with_generate else None
						)
						lowercase        =							SeqaSeqTrainer(
						    model=lowercase_ ,  args=lowercase_ ,  data_args=lowercase_ ,  train_dataset=lowercase_ ,  eval_dataset=lowercase_ ,  data_collator=SeqaSeqDataCollator(
						        lowercase_ ,  lowercase_ ,  model.config.decoder_start_token_id ,  training_args.tpu_num_cores						) ,  compute_metrics=lowercase_ ,  tokenizer=lowercase_ ,  )

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

												lowercase        =							trainer.train(
												    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path						) else None						)
												lowercase        =							train_result.metrics
												lowercase        =							data_args.n_train

												trainer.save_model()  # this also saves the tokenizer

												if trainer.is_world_process_zero():
																		handle_metrics("""train""" ,  lowercase_ ,  training_args.output_dir						)
																		all_metrics.update(lowercase_						)

																		# 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        =							trainer.evaluate(metric_key_prefix="""val"""						)
												lowercase        =							data_args.n_val
												lowercase        =							round(metrics["""val_loss"""] ,  4						)

												if trainer.is_world_process_zero():
																		handle_metrics("""val""" ,  lowercase_ ,  training_args.output_dir						)
																		all_metrics.update(lowercase_						)

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

												lowercase        =							trainer.predict(test_dataset=lowercase_ ,  metric_key_prefix="""test"""						)
												lowercase        =							test_output.metrics
												lowercase        =							data_args.n_test

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

																		if training_args.predict_with_generate:
																								lowercase        =							tokenizer.batch_decode(
																								    test_output.predictions ,  skip_special_tokens=lowercase_ ,  clean_up_tokenization_spaces=lowercase_						)
																								lowercase        =							lmap(str.strip ,  lowercase_						)
																								write_txt_file(lowercase_ ,  os.path.join(training_args.output_dir ,  """test_generations.txt"""						)						)

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

						return all_metrics







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      Dict						):
						# For xla_spawn (TPUs)
						main()


if __name__ == "__main__":
						main()


