






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__			:    Optional[int]         =				logging.getLogger(__name__)
@dataclass
class    _UpperCAmelCase      :

				"""simple docstring"""




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



@dataclass
class    _UpperCAmelCase      :

				"""simple docstring"""




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






def 						a    (		lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_       ):






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






def 						a    (		):






				'''simple docstring'''
				# 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(lowerCamelCase_       )

				# Setup logging
				logging.basicConfig(
				    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''	,     datefmt='''%m/%d/%Y %H:%M:%S'''	,     level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN	,     )
				logger.warning(
				    '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'''	,     training_args.local_rank	,     training_args.device	,     training_args.n_gpu	,     bool(training_args.parallel_mode == ParallelMode.DISTRIBUTED       )	,     training_args.fpaa	,     )
				transformers.utils.logging.enable_default_handler()
				transformers.utils.logging.enable_explicit_format()
				# Set the verbosity to info of the Transformers logger (on main process only):
				if is_main_process(training_args.local_rank       ):
								transformers.utils.logging.set_verbosity_info()
				logger.info('''Training/evaluation parameters %s'''	,     lowerCamelCase_       )

				# Set seed
				set_seed(training_args.seed       )

				# Load pretrained model and tokenizer
				#
				# Distributed training:
				# The .from_pretrained methods guarantee that only one local process can concurrently
				# download model & vocab.

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

				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=lowerCamelCase_	,     cache_dir=model_args.cache_dir	,     )

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

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

				# set decoder_start_token_id for MBart
				if model.config.decoder_start_token_id is None and isinstance(lowerCamelCase_	,     (MBartTokenizer, MBartTokenizerFast)       ):
								assert (
								    data_args.tgt_lang is not None and data_args.src_lang is not None
								), "mBart requires --tgt_lang and --src_lang"
								if isinstance(lowerCamelCase_	,     lowerCamelCase_       ):
												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(lowerCamelCase_       )
				if model_args.freeze_encoder:
								freeze_params(model.get_encoder()       )
								assert_all_frozen(model.get_encoder()       )

				lowercase__     				=    SeqaSeqDataset

				# Get datasets
				lowercase__     				=    (
				    dataset_class(
				        lowerCamelCase_	,     type_path='''train'''	,     data_dir=data_args.data_dir	,     n_obs=data_args.n_train	,     max_target_length=data_args.max_target_length	,     max_source_length=data_args.max_source_length	,     prefix=model.config.prefix or ''''''	,     )
				    if training_args.do_train
				    else None
				)
				lowercase__     				=    (
				    dataset_class(
				        lowerCamelCase_	,     type_path='''val'''	,     data_dir=data_args.data_dir	,     n_obs=data_args.n_val	,     max_target_length=data_args.val_max_target_length	,     max_source_length=data_args.max_source_length	,     prefix=model.config.prefix or ''''''	,     )
				    if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
				    else None
				)
				lowercase__     				=    (
				    dataset_class(
				        lowerCamelCase_	,     type_path='''test'''	,     data_dir=data_args.data_dir	,     n_obs=data_args.n_test	,     max_target_length=data_args.test_max_target_length	,     max_source_length=data_args.max_source_length	,     prefix=model.config.prefix or ''''''	,     )
				    if training_args.do_predict
				    else None
				)

				# Initialize our Trainer
				lowercase__     				=    (
				    build_compute_metrics_fn(data_args.task	,     lowerCamelCase_       ) if training_args.predict_with_generate else None
				)
				lowercase__     				=    SeqaSeqTrainer(
				    model=lowerCamelCase_	,     args=lowerCamelCase_	,     data_args=lowerCamelCase_	,     train_dataset=lowerCamelCase_	,     eval_dataset=lowerCamelCase_	,     data_collator=SeqaSeqDataCollator(
				        lowerCamelCase_	,     lowerCamelCase_	,     model.config.decoder_start_token_id	,     training_args.tpu_num_cores       )	,     compute_metrics=lowerCamelCase_	,     tokenizer=lowerCamelCase_	,     )

				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'''	,     lowerCamelCase_	,     training_args.output_dir       )
												all_metrics.update(lowerCamelCase_       )

												# Need to save the state, since Trainer.save_model saves only the tokenizer with the model
												trainer.state.save_to_json(os.path.join(training_args.output_dir	,     '''trainer_state.json'''       )       )

												# For convenience, we also re-save the tokenizer to the same directory,
												# so that you can share your model easily on huggingface.co/models =)
												tokenizer.save_pretrained(training_args.output_dir       )

    # Evaluation
				if training_args.do_eval:
								logger.info('''*** Evaluate ***'''       )

								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'''	,     lowerCamelCase_	,     training_args.output_dir       )
												all_metrics.update(lowerCamelCase_       )

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

								lowercase__     				=    trainer.predict(test_dataset=lowerCamelCase_	,     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'''	,     lowerCamelCase_	,     training_args.output_dir       )
												all_metrics.update(lowerCamelCase_       )

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

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

				return all_metrics






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				# For xla_spawn (TPUs)
				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    _UpperCAmelCase      ( A__			):

				"""simple docstring"""




				lowercase__       							= 42
				lowercase__       							= 42



class    _UpperCAmelCase      ( A__	,A__			):

				"""simple docstring"""




				lowercase__       							= 1




				@register_to_config
				def __init__(  self		:					Union[str, Any],						lowerCamelCase		:					int = 2_000,						lowerCamelCase		:					float = 0.15,						lowerCamelCase		:					float = 0.01,						lowerCamelCase		:					float = 1348.0,						lowerCamelCase		:					float = 1E-5,						lowerCamelCase		:					int = 1,						):

								'''simple docstring'''
								# standard deviation of the initial noise distribution
								lowercase__     				=    sigma_max

								# setable values
								lowercase__     				=    None

								self.set_sigmas(lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						lowerCamelCase						)




				def        lowercase__     (  self		:					List[str],						lowerCamelCase		:					torch.FloatTensor,						lowerCamelCase		:					Optional[int] = None						):

								'''simple docstring'''
								return sample




				def        lowercase__     (  self		:					Dict,						lowerCamelCase		:					int,						lowerCamelCase		:					float = None,						lowerCamelCase		:					Union[str, torch.device] = None						):

								'''simple docstring'''
								lowercase__     				=    sampling_eps if sampling_eps is not None else self.config.sampling_eps

								lowercase__     				=    torch.linspace(1,						lowerCamelCase,						lowerCamelCase,						device=lowerCamelCase						)




				def        lowercase__     (  self		:					str,						lowerCamelCase		:					int,						lowerCamelCase		:					float = None,						lowerCamelCase		:					float = None,						lowerCamelCase		:					float = None						):

								'''simple docstring'''
								lowercase__     				=    sigma_min if sigma_min is not None else self.config.sigma_min
								lowercase__     				=    sigma_max if sigma_max is not None else self.config.sigma_max
								lowercase__     				=    sampling_eps if sampling_eps is not None else self.config.sampling_eps
								if self.timesteps is None:
												self.set_timesteps(lowerCamelCase,						lowerCamelCase						)

								lowercase__     				=    sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
								lowercase__     				=    torch.exp(torch.linspace(math.log(lowerCamelCase						),						math.log(lowerCamelCase						),						lowerCamelCase						)						)
								lowercase__     				=    torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]						)




				def        lowercase__     (  self		:					Optional[int],						lowerCamelCase		:					str,						lowerCamelCase		:					str						):

								'''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		:					Tuple,						lowerCamelCase		:					torch.FloatTensor,						lowerCamelCase		:					int,						lowerCamelCase		:					torch.FloatTensor,						lowerCamelCase		:					Optional[torch.Generator] = None,						lowerCamelCase		:					bool = True,						):

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

								lowercase__     				=    timestep * torch.ones(
								    sample.shape[0],						device=sample.device						)  # torch.repeat_interleave(timestep, sample.shape[0])
								lowercase__     				=    (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
								lowercase__     				=    timesteps.to(self.discrete_sigmas.device						)

								lowercase__     				=    self.discrete_sigmas[timesteps].to(sample.device						)
								lowercase__     				=    self.get_adjacent_sigma(lowerCamelCase,						lowerCamelCase						).to(sample.device						)
								lowercase__     				=    torch.zeros_like(lowerCamelCase						)
								lowercase__     				=    (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
								lowercase__     				=    diffusion.flatten()
								while len(diffusion.shape						) < len(sample.shape						):
												lowercase__     				=    diffusion.unsqueeze(-1						)
								lowercase__     				=    drift - diffusion**2 * model_output

								#  equation 6: sample noise for the diffusion term of
								lowercase__     				=    randn_tensor(
								    sample.shape,						layout=sample.layout,						generator=lowerCamelCase,						device=sample.device,						dtype=sample.dtype						)
								lowercase__     				=    sample - drift  # subtract because `dt` is a small negative timestep
								# TODO is the variable diffusion the correct scaling term for the noise?
								lowercase__     				=    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=lowerCamelCase,						prev_sample_mean=lowerCamelCase						)




				def        lowercase__     (  self		:					int,						lowerCamelCase		:					torch.FloatTensor,						lowerCamelCase		:					torch.FloatTensor,						lowerCamelCase		:					Optional[torch.Generator] = None,						lowerCamelCase		:					bool = True,						):

								'''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
								lowercase__     				=    randn_tensor(sample.shape,						layout=sample.layout,						generator=lowerCamelCase						).to(sample.device						)

								# compute step size from the model_output, the noise, and the snr
								lowercase__     				=    torch.norm(model_output.reshape(model_output.shape[0],						-1						),						dim=-1						).mean()
								lowercase__     				=    torch.norm(noise.reshape(noise.shape[0],						-1						),						dim=-1						).mean()
								lowercase__     				=    (self.config.snr * noise_norm / grad_norm) ** 2 * 2
								lowercase__     				=    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
								lowercase__     				=    step_size.flatten()
								while len(step_size.shape						) < len(sample.shape						):
												lowercase__     				=    step_size.unsqueeze(-1						)
								lowercase__     				=    sample + step_size * model_output
								lowercase__     				=    prev_sample_mean + ((step_size * 2) ** 0.5) * noise

								if not return_dict:
												return (prev_sample,)

								return SchedulerOutput(prev_sample=lowerCamelCase						)




				def        lowercase__     (  self		:					List[str],						lowerCamelCase		:					torch.FloatTensor,						lowerCamelCase		:					torch.FloatTensor,						lowerCamelCase		:					torch.FloatTensor,						):

								'''simple docstring'''
								# Make sure sigmas and timesteps have the same device and dtype as original_samples
								lowercase__     				=    timesteps.to(original_samples.device						)
								lowercase__     				=    self.discrete_sigmas.to(original_samples.device						)[timesteps]
								lowercase__     				=    (
								    noise * sigmas[:, None, None, None]
								    if noise is not None
								    else torch.randn_like(lowerCamelCase						) * sigmas[:, None, None, None]
								)
								lowercase__     				=    noise + original_samples
								return noisy_samples




				def __len__(  self		:					Union[str, Any]						):

								'''simple docstring'''
								return self.config.num_train_timesteps




