






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__       		=  logging.getLogger(__name__)




@dataclass
class       snake_case__ :

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




@dataclass
class       snake_case__ :

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






def 	UpperCamelCase	(							snake_case__  : Any						,snake_case__  : Tuple						,snake_case__  : Optional[Any]	):




     '''simple docstring'''






     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 	UpperCamelCase	(							):




     '''simple docstring'''






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

     __snake_case      :List[Any] 							=    ("""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      :Optional[int] 							=    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      :Optional[Any] 							=    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      :Optional[Any] 							=    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      :Tuple 							=    tokenizer.lang_code_to_id[data_args.tgt_lang]
          else:
               __snake_case      :str 							=    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      :Optional[int] 							=    SeqaSeqDataset

     # Get datasets
     __snake_case      :List[str] 							=    (
         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      :Tuple 							=    (
         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      :Optional[int] 							=    (
         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      :Union[str, Any] 							=    (
         build_compute_metrics_fn(data_args.task						,snake_case__	) if training_args.predict_with_generate else None
     )
     __snake_case      :Union[str, Any] 							=    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      :List[str] 							=    {}
     # Training
     if training_args.do_train:
          logger.info("""*** Train ***"""	)

          __snake_case      :List[str] 							=    trainer.train(
              model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path	) else None	)
          __snake_case      :List[str] 							=    train_result.metrics
          __snake_case      :Union[str, Any] 							=    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      :Optional[int] 							=    trainer.evaluate(metric_key_prefix="""val"""	)
          __snake_case      :Optional[Any] 							=    data_args.n_val
          __snake_case      :Optional[int] 							=    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      :int 							=    trainer.predict(test_dataset=snake_case__						,metric_key_prefix="""test"""	)
          __snake_case      :Dict 							=    test_output.metrics
          __snake_case      :Optional[Any] 							=    data_args.n_test

          if trainer.is_world_process_zero():
               __snake_case      :Any 							=    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      :Union[str, Any] 							=    tokenizer.batch_decode(
                        test_output.predictions						,skip_special_tokens=snake_case__						,clean_up_tokenization_spaces=snake_case__	)
                    __snake_case      :List[str] 							=    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 	UpperCamelCase	(							snake_case__  : str	):




     '''simple docstring'''






     main()


if __name__ == "__main__":
     main()










# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput




@dataclass
class       snake_case__ (				lowercase_):

       '''simple docstring'''
       lowerCamelCase				:					torch.FloatTensor
       lowerCamelCase				:					torch.FloatTensor




class       snake_case__ (				lowercase_						,     lowercase_):

       '''simple docstring'''
       lowerCamelCase				:					int								=			1



       @register_to_config
       def __init__(				self      ,    a__ = 20_00      ,    a__ = 0.15      ,    a__ = 0.01      ,    a__ = 13_48.0      ,    a__ = 1e-5      ,    a__ = 1      ,    )			->   Any:






            '''simple docstring'''





            __snake_case      :int 							=    sigma_max

            # setable values
            __snake_case      :Any 							=    None

            self.set_sigmas(a__      ,    a__      ,    a__      ,    a__     )



       def        __lowercase    (				self      ,    a__      ,    a__ = None     )			->   torch.FloatTensor:






            '''simple docstring'''





            return sample



       def        __lowercase    (				self      ,    a__      ,    a__ = None      ,    a__ = None     )			->   Union[str, Any]:






            '''simple docstring'''





            __snake_case      :List[Any] 							=    sampling_eps if sampling_eps is not None else self.config.sampling_eps

            __snake_case      :int 							=    torch.linspace(1      ,    a__      ,    a__      ,    device=a__     )



       def        __lowercase    (				self      ,    a__      ,    a__ = None      ,    a__ = None      ,    a__ = None     )			->   Union[str, Any]:






            '''simple docstring'''





            __snake_case      :Union[str, Any] 							=    sigma_min if sigma_min is not None else self.config.sigma_min
            __snake_case      :Any 							=    sigma_max if sigma_max is not None else self.config.sigma_max
            __snake_case      :Any 							=    sampling_eps if sampling_eps is not None else self.config.sampling_eps
            if self.timesteps is None:
                 self.set_timesteps(a__      ,    a__     )

            __snake_case      :int 							=    sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
            __snake_case      :List[str] 							=    torch.exp(torch.linspace(math.log(a__     )      ,    math.log(a__     )      ,    a__     )     )
            __snake_case      :Any 							=    torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]     )



       def        __lowercase    (				self      ,    a__      ,    a__     )			->   Tuple:






            '''simple docstring'''





            return torch.where(
                timesteps == 0      ,    torch.zeros_like(t.to(timesteps.device     )     )      ,    self.discrete_sigmas[timesteps - 1].to(timesteps.device     )      ,    )



       def        __lowercase    (				self      ,    a__      ,    a__      ,    a__      ,    a__ = None      ,    a__ = True      ,    )			->   Union[SdeVeOutput, Tuple]:






            '''simple docstring'''





            if self.timesteps is None:
                 raise ValueError(
                     """`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""     )

            __snake_case      :Optional[int] 							=    timestep * torch.ones(
                sample.shape[0]      ,    device=sample.device     )  # torch.repeat_interleave(timestep, sample.shape[0])
            __snake_case      :Tuple 							=    (timestep * (len(self.timesteps     ) - 1)).long()

            # mps requires indices to be in the same device, so we use cpu as is the default with cuda
            __snake_case      :str 							=    timesteps.to(self.discrete_sigmas.device     )

            __snake_case      :str 							=    self.discrete_sigmas[timesteps].to(sample.device     )
            __snake_case      :Union[str, Any] 							=    self.get_adjacent_sigma(a__      ,    a__     ).to(sample.device     )
            __snake_case      :Dict 							=    torch.zeros_like(a__     )
            __snake_case      :str 							=    (sigma**2 - adjacent_sigma**2) ** 0.5

            # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
            # also equation 47 shows the analog from SDE models to ancestral sampling methods
            __snake_case      :Optional[Any] 							=    diffusion.flatten()
            while len(diffusion.shape     ) < len(sample.shape     ):
                 __snake_case      :int 							=    diffusion.unsqueeze(-1     )
            __snake_case      :Optional[Any] 							=    drift - diffusion**2 * model_output

            #  equation 6: sample noise for the diffusion term of
            __snake_case      :str 							=    randn_tensor(
                sample.shape      ,    layout=sample.layout      ,    generator=a__      ,    device=sample.device      ,    dtype=sample.dtype     )
            __snake_case      :str 							=    sample - drift  # subtract because `dt` is a small negative timestep
            # TODO is the variable diffusion the correct scaling term for the noise?
            __snake_case      :str 							=    prev_sample_mean + diffusion * noise  # add impact of diffusion field g

            if not return_dict:
                 return (prev_sample, prev_sample_mean)

            return SdeVeOutput(prev_sample=a__      ,    prev_sample_mean=a__     )



       def        __lowercase    (				self      ,    a__      ,    a__      ,    a__ = None      ,    a__ = True      ,    )			->   Union[SchedulerOutput, Tuple]:






            '''simple docstring'''





            if self.timesteps is None:
                 raise ValueError(
                     """`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""     )

            # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
            # sample noise for correction
            __snake_case      :Any 							=    randn_tensor(sample.shape      ,    layout=sample.layout      ,    generator=a__     ).to(sample.device     )

            # compute step size from the model_output, the noise, and the snr
            __snake_case      :List[Any] 							=    torch.norm(model_output.reshape(model_output.shape[0]      ,    -1     )      ,    dim=-1     ).mean()
            __snake_case      :Tuple 							=    torch.norm(noise.reshape(noise.shape[0]      ,    -1     )      ,    dim=-1     ).mean()
            __snake_case      :Dict 							=    (self.config.snr * noise_norm / grad_norm) ** 2 * 2
            __snake_case      :int 							=    step_size * torch.ones(sample.shape[0]     ).to(sample.device     )
            # self.repeat_scalar(step_size, sample.shape[0])

            # compute corrected sample: model_output term and noise term
            __snake_case      :Tuple 							=    step_size.flatten()
            while len(step_size.shape     ) < len(sample.shape     ):
                 __snake_case      :Optional[int] 							=    step_size.unsqueeze(-1     )
            __snake_case      :List[str] 							=    sample + step_size * model_output
            __snake_case      :Tuple 							=    prev_sample_mean + ((step_size * 2) ** 0.5) * noise

            if not return_dict:
                 return (prev_sample,)

            return SchedulerOutput(prev_sample=a__     )



       def        __lowercase    (				self      ,    a__      ,    a__      ,    a__      ,    )			->   torch.FloatTensor:






            '''simple docstring'''





            __snake_case      :Tuple 							=    timesteps.to(original_samples.device     )
            __snake_case      :Optional[Any] 							=    self.discrete_sigmas.to(original_samples.device     )[timesteps]
            __snake_case      :int 							=    (
                noise * sigmas[:, None, None, None]
                if noise is not None
                else torch.randn_like(a__     ) * sigmas[:, None, None, None]
            )
            __snake_case      :str 							=    noise + original_samples
            return noisy_samples



       def __len__(				self     )			->   Optional[int]:






            '''simple docstring'''





            return self.config.num_train_timesteps


