

import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest



class   lowercase_   (  __lowercase					):
      UpperCamelCase_      :		Dict										=    (DDIMParallelScheduler,)
      UpperCamelCase_      :		List[Any]										=    (("eta", 0.0), ("num_inference_steps", 5_0))



      def 						UpperCamelCase_		(      self				:    Dict      , **A__				:    int		)   ->					Tuple:
        _snake_case						=							{
            '''num_train_timesteps''': 1000,
            '''beta_start''': 0.0001,
            '''beta_end''': 0.02,
            '''beta_schedule''': '''linear''',
            '''clip_sample''': True,
        }

        config.update(**A__		)
        return config



      def 						UpperCamelCase_		(      self				:    Dict      , **A__				:    str		)   ->					List[str]:
        _snake_case						=							self.scheduler_classes[0]
        _snake_case						=							self.get_scheduler_config(**A__		)
        _snake_case						=							scheduler_class(**A__		)

        _snake_case,					_snake_case						=							10, 0.0

        _snake_case						=							self.dummy_model()
        _snake_case						=							self.dummy_sample_deter

        scheduler.set_timesteps(A__		)

        for t in scheduler.timesteps:
          _snake_case						=							model(A__      , A__		)
          _snake_case						=							scheduler.step(A__      , A__      , A__      , A__		).prev_sample

        return sample



      def 						UpperCamelCase_		(      self				:    Dict		)   ->					str:
        for timesteps in [100, 500, 1000]:
          self.check_over_configs(num_train_timesteps=A__		)



      def 						UpperCamelCase_		(      self				:    Any		)   ->					Dict:
        for steps_offset in [0, 1]:
          self.check_over_configs(steps_offset=A__		)

        _snake_case						=							self.scheduler_classes[0]
        _snake_case						=							self.get_scheduler_config(steps_offset=1		)
        _snake_case						=							scheduler_class(**A__		)
        scheduler.set_timesteps(5		)
        assert torch.equal(scheduler.timesteps      , torch.LongTensor([801, 601, 401, 201, 1]		)		)



      def 						UpperCamelCase_		(      self				:    Union[str, Any]		)   ->					Optional[Any]:
        for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]      , [0.002, 0.02, 0.2, 2]		):
          self.check_over_configs(beta_start=A__      , beta_end=A__		)



      def 						UpperCamelCase_		(      self				:    str		)   ->					Any:
        for schedule in ["linear", "squaredcos_cap_v2"]:
          self.check_over_configs(beta_schedule=A__		)



      def 						UpperCamelCase_		(      self				:    Optional[int]		)   ->					List[str]:
        for prediction_type in ["epsilon", "v_prediction"]:
          self.check_over_configs(prediction_type=A__		)



      def 						UpperCamelCase_		(      self				:    Any		)   ->					List[str]:
        for clip_sample in [True, False]:
          self.check_over_configs(clip_sample=A__		)



      def 						UpperCamelCase_		(      self				:    str		)   ->					str:
        for timestep_spacing in ["trailing", "leading"]:
          self.check_over_configs(timestep_spacing=A__		)



      def 						UpperCamelCase_		(      self				:    int		)   ->					Tuple:
        for rescale_betas_zero_snr in [True, False]:
          self.check_over_configs(rescale_betas_zero_snr=A__		)



      def 						UpperCamelCase_		(      self				:    List[str]		)   ->					Optional[Any]:
        self.check_over_configs(thresholding=A__		)
        for threshold in [0.5, 1.0, 2.0]:
          for prediction_type in ["epsilon", "v_prediction"]:
            self.check_over_configs(
                thresholding=A__      , prediction_type=A__      , sample_max_value=A__      , )



      def 						UpperCamelCase_		(      self				:    Union[str, Any]		)   ->					str:
        for t in [1, 10, 49]:
          self.check_over_forward(time_step=A__		)



      def 						UpperCamelCase_		(      self				:    Dict		)   ->					Union[str, Any]:
        for t, num_inference_steps in zip([1, 10, 50]      , [10, 50, 500]		):
          self.check_over_forward(time_step=A__      , num_inference_steps=A__		)



      def 						UpperCamelCase_		(      self				:    str		)   ->					Any:
        for t, eta in zip([1, 10, 49]      , [0.0, 0.5, 1.0]		):
          self.check_over_forward(time_step=A__      , eta=A__		)



      def 						UpperCamelCase_		(      self				:    Tuple		)   ->					Union[str, Any]:
        _snake_case						=							self.scheduler_classes[0]
        _snake_case						=							self.get_scheduler_config()
        _snake_case						=							scheduler_class(**A__		)

        assert torch.sum(torch.abs(scheduler._get_variance(0      , 0		) - 0.0		)		) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(420      , 400		) - 0.1_4771		)		) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(980      , 960		) - 0.3_2460		)		) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(0      , 0		) - 0.0		)		) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(487      , 486		) - 0.0_0979		)		) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(999      , 998		) - 0.02		)		) < 1e-5



      def 						UpperCamelCase_		(      self				:    List[Any]		)   ->					Optional[int]:
        _snake_case						=							self.scheduler_classes[0]
        _snake_case						=							self.get_scheduler_config()
        _snake_case						=							scheduler_class(**A__		)

        _snake_case,					_snake_case						=							10, 0.0
        scheduler.set_timesteps(A__		)

        _snake_case						=							self.dummy_model()
        _snake_case						=							self.dummy_sample_deter
        _snake_case						=							self.dummy_sample_deter + 0.1
        _snake_case						=							self.dummy_sample_deter - 0.1

        _snake_case						=							samplea.shape[0]
        _snake_case						=							torch.stack([samplea, samplea, samplea]      , dim=0		)
        _snake_case						=							torch.arange(A__		)[0:3, None].repeat(1      , A__		)

        _snake_case						=							model(samples.flatten(0      , 1		)      , timesteps.flatten(0      , 1		)		)
        _snake_case						=							scheduler.batch_step_no_noise(A__      , timesteps.flatten(0      , 1		)      , samples.flatten(0      , 1		)      , A__		)

        _snake_case						=							torch.sum(torch.abs(A__		)		)
        _snake_case						=							torch.mean(torch.abs(A__		)		)

        assert abs(result_sum.item() - 1147.7904		) < 1e-2
        assert abs(result_mean.item() - 0.4982		) < 1e-3



      def 						UpperCamelCase_		(      self				:    int		)   ->					List[Any]:
        _snake_case						=							self.full_loop()

        _snake_case						=							torch.sum(torch.abs(A__		)		)
        _snake_case						=							torch.mean(torch.abs(A__		)		)

        assert abs(result_sum.item() - 172.0067		) < 1e-2
        assert abs(result_mean.item() - 0.22_3967		) < 1e-3



      def 						UpperCamelCase_		(      self				:    Union[str, Any]		)   ->					Optional[Any]:
        _snake_case						=							self.full_loop(prediction_type='''v_prediction'''		)

        _snake_case						=							torch.sum(torch.abs(A__		)		)
        _snake_case						=							torch.mean(torch.abs(A__		)		)

        assert abs(result_sum.item() - 52.5302		) < 1e-2
        assert abs(result_mean.item() - 0.0684		) < 1e-3



      def 						UpperCamelCase_		(      self				:    Any		)   ->					Union[str, Any]:
        # We specify different beta, so that the first alpha is 0.99
        _snake_case						=							self.full_loop(set_alpha_to_one=A__      , beta_start=0.01		)
        _snake_case						=							torch.sum(torch.abs(A__		)		)
        _snake_case						=							torch.mean(torch.abs(A__		)		)

        assert abs(result_sum.item() - 149.8295		) < 1e-2
        assert abs(result_mean.item() - 0.1951		) < 1e-3







      def 						UpperCamelCase_		(      self				:    Dict		)   ->					Dict:
        # We specify different beta, so that the first alpha is 0.99
        _snake_case						=							self.full_loop(set_alpha_to_one=A__      , beta_start=0.01		)
        _snake_case						=							torch.sum(torch.abs(A__		)		)
        _snake_case						=							torch.mean(torch.abs(A__		)		)

        assert abs(result_sum.item() - 149.0784		) < 1e-2
        assert abs(result_mean.item() - 0.1941		) < 1e-3




import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSeqaSeqLM,
    AutoTokenizer,
    HfArgumentParser,
    MBartTokenizer,
    MBartTokenizerFast,
    set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
    SeqaSeqDataCollator,
    SeqaSeqDataset,
    assert_all_frozen,
    build_compute_metrics_fn,
    check_output_dir,
    freeze_embeds,
    freeze_params,
    lmap,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


__A      		=     logging.getLogger(__name__)



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



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



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase					)							->     List[Any]:



  """simple docstring"""






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



def 				snake_case_()							->     List[Any]:



  """simple docstring"""






  _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(_UpperCamelCase					)

  # 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'''			,	_UpperCamelCase					)

  # 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(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase					):
      assert hasattr(_UpperCamelCase			,	_UpperCamelCase					), F"""({config.__class__.__name__}) doesn't have a `{p}` attribute"""
      setattr(_UpperCamelCase			,	_UpperCamelCase			,	getattr(_UpperCamelCase			,	_UpperCamelCase					)					)

  _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=_UpperCamelCase			,	cache_dir=model_args.cache_dir			,	)

  # use task specific params
  use_task_specific_params(_UpperCamelCase			,	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(_UpperCamelCase			,	(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(_UpperCamelCase			,	_UpperCamelCase					):
      _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(_UpperCamelCase					)
  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(
          _UpperCamelCase			,	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(
          _UpperCamelCase			,	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(
          _UpperCamelCase			,	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			,	_UpperCamelCase					) if training_args.predict_with_generate else None
  )
  _snake_case						=							SeqaSeqTrainer(
      model=_UpperCamelCase			,	args=_UpperCamelCase			,	data_args=_UpperCamelCase			,	train_dataset=_UpperCamelCase			,	eval_dataset=_UpperCamelCase			,	data_collator=SeqaSeqDataCollator(
          _UpperCamelCase			,	_UpperCamelCase			,	model.config.decoder_start_token_id			,	training_args.tpu_num_cores					)			,	compute_metrics=_UpperCamelCase			,	tokenizer=_UpperCamelCase			,	)

  _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'''			,	_UpperCamelCase			,	training_args.output_dir					)
      all_metrics.update(_UpperCamelCase					)

      # 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'''			,	_UpperCamelCase			,	training_args.output_dir					)
      all_metrics.update(_UpperCamelCase					)

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

    _snake_case						=							trainer.predict(test_dataset=_UpperCamelCase			,	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'''			,	_UpperCamelCase			,	training_args.output_dir					)
      all_metrics.update(_UpperCamelCase					)

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

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

  return all_metrics



def 				snake_case_(_UpperCamelCase					)							->     List[str]:



  """simple docstring"""






  main()


if __name__ == "__main__":
 main()

