






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

import datasets
import numpy as np
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()






def 						a    (		lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_ = None	,     ):






				'''simple docstring'''
				lowercase__     				=    {}

				if train_file is not None:
								lowercase__     				=    [train_file]
				if eval_file is not None:
								lowercase__     				=    [eval_file]
				if test_file is not None:
								lowercase__     				=    [test_file]

				lowercase__     				=    datasets.load_dataset('''csv'''	,     data_files=lowerCamelCase_       )
				lowercase__     				=    list(ds[list(files.keys()       )[0]].features.keys()       )
				lowercase__     				=    features_name.pop(lowerCamelCase_       )
				lowercase__     				=    list(set(ds[list(files.keys()       )[0]][label_name]       )       )
				lowercase__     				=    {label: i for i, label in enumerate(lowerCamelCase_       )}
				lowercase__     				=    tokenizer.model_input_names
				lowercase__     				=    {}

				if len(lowerCamelCase_       ) == 1:
								for k in files.keys():
												lowercase__     				=    ds[k].map(
												    lambda lowerCamelCase_       : tokenizer.batch_encode_plus(
												        example[features_name[0]]	,     truncation=lowerCamelCase_	,     max_length=lowerCamelCase_	,     padding='''max_length'''       )	,     batched=lowerCamelCase_	,     )
				elif len(lowerCamelCase_       ) == 2:
								for k in files.keys():
												lowercase__     				=    ds[k].map(
												    lambda lowerCamelCase_       : tokenizer.batch_encode_plus(
												        (example[features_name[0]], example[features_name[1]])	,     truncation=lowerCamelCase_	,     max_length=lowerCamelCase_	,     padding='''max_length'''	,     )	,     batched=lowerCamelCase_	,     )

				def gen_train():
								for ex in transformed_ds[datasets.Split.TRAIN]:
												lowercase__     				=    {k: v for k, v in ex.items() if k in input_names}
												lowercase__     				=    labelaid[ex[label_name]]
												yield (d, label)

				def gen_val():
								for ex in transformed_ds[datasets.Split.VALIDATION]:
												lowercase__     				=    {k: v for k, v in ex.items() if k in input_names}
												lowercase__     				=    labelaid[ex[label_name]]
												yield (d, label)

				def gen_test():
								for ex in transformed_ds[datasets.Split.TEST]:
												lowercase__     				=    {k: v for k, v in ex.items() if k in input_names}
												lowercase__     				=    labelaid[ex[label_name]]
												yield (d, label)

				lowercase__     				=    (
				    tf.data.Dataset.from_generator(
				        lowerCamelCase_	,     ({k: tf.intaa for k in input_names}, tf.intaa)	,     ({k: tf.TensorShape([None]       ) for k in input_names}, tf.TensorShape([]       ))	,     )
				    if datasets.Split.TRAIN in transformed_ds
				    else None
				)

				if train_ds is not None:
								lowercase__     				=    train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]       )       )       )

				lowercase__     				=    (
				    tf.data.Dataset.from_generator(
				        lowerCamelCase_	,     ({k: tf.intaa for k in input_names}, tf.intaa)	,     ({k: tf.TensorShape([None]       ) for k in input_names}, tf.TensorShape([]       ))	,     )
				    if datasets.Split.VALIDATION in transformed_ds
				    else None
				)

				if val_ds is not None:
								lowercase__     				=    val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]       )       )       )

				lowercase__     				=    (
				    tf.data.Dataset.from_generator(
				        lowerCamelCase_	,     ({k: tf.intaa for k in input_names}, tf.intaa)	,     ({k: tf.TensorShape([None]       ) for k in input_names}, tf.TensorShape([]       ))	,     )
				    if datasets.Split.TEST in transformed_ds
				    else None
				)

				if test_ds is not None:
								lowercase__     				=    test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]       )       )       )

				return train_ds, val_ds, test_ds, labelaid


A__			:    List[Any]         =				logging.getLogger(__name__)
@dataclass
class    _UpperCAmelCase      :

				"""simple docstring"""




				lowercase__       							= field(metadata={"""help""": """Which column contains the label"""}			)
				lowercase__       							= field(default=A__	,metadata={"""help""": """The path of the training file"""}			)
				lowercase__       							= field(default=A__	,metadata={"""help""": """The path of the development file"""}			)
				lowercase__       							= field(default=A__	,metadata={"""help""": """The path of the test file"""}			)
				lowercase__       							= field(
				    default=128	,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=A__	,metadata={"""help""": """Overwrite the cached training and evaluation sets"""}			)



@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""": """Set this flag to use fast tokenization."""}			)
				# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
				# or just modify its tokenizer_config.json.
				lowercase__       							= field(
				    default=A__	,metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}	,)






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, TFTrainingArguments)       )
				lowercase__				,		lowercase__				,		lowercase__     				=    parser.parse_args_into_dataclasses()

				if (
				    os.path.exists(training_args.output_dir       )
				    and os.listdir(training_args.output_dir       )
				    and training_args.do_train
				    and not training_args.overwrite_output_dir
				):
								raise ValueError(
								    F"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
								    ''' --overwrite_output_dir to overcome.'''       )

				# Setup logging
				logging.basicConfig(
				    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''	,     datefmt='''%m/%d/%Y %H:%M:%S'''	,     level=logging.INFO	,     )
				logger.info(
				    F"""n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1       )}, """
				    F"""16-bits training: {training_args.fpaa}"""       )
				logger.info(F"""Training/evaluation parameters {training_args}"""       )

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

				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__				,		lowercase__				,		lowercase__				,		lowercase__     				=    get_tfds(
				    train_file=data_args.train_file	,     eval_file=data_args.dev_file	,     test_file=data_args.test_file	,     tokenizer=lowerCamelCase_	,     label_column_id=data_args.label_column_id	,     max_seq_length=data_args.max_seq_length	,     )

				lowercase__     				=    AutoConfig.from_pretrained(
				    model_args.config_name if model_args.config_name else model_args.model_name_or_path	,     num_labels=len(lowerCamelCase_       )	,     labelaid=lowerCamelCase_	,     idalabel={id: label for label, id in labelaid.items()}	,     finetuning_task='''text-classification'''	,     cache_dir=model_args.cache_dir	,     )

				with training_args.strategy.scope():
								lowercase__     				=    TFAutoModelForSequenceClassification.from_pretrained(
								    model_args.model_name_or_path	,     from_pt=bool('''.bin''' in model_args.model_name_or_path       )	,     config=lowerCamelCase_	,     cache_dir=model_args.cache_dir	,     )

				def compute_metrics(lowerCamelCase_       ) -> Dict:
								lowercase__     				=    np.argmax(p.predictions	,     axis=1       )

								return {"acc": (preds == p.label_ids).mean()}

				# Initialize our Trainer
				lowercase__     				=    TFTrainer(
				    model=lowerCamelCase_	,     args=lowerCamelCase_	,     train_dataset=lowerCamelCase_	,     eval_dataset=lowerCamelCase_	,     compute_metrics=lowerCamelCase_	,     )

				# Training
				if training_args.do_train:
								trainer.train()
								trainer.save_model()
								tokenizer.save_pretrained(training_args.output_dir       )

				# Evaluation
				lowercase__     				=    {}
				if training_args.do_eval:
								logger.info('''*** Evaluate ***'''       )
								lowercase__     				=    trainer.evaluate()
								lowercase__     				=    os.path.join(training_args.output_dir	,     '''eval_results.txt'''       )

								with open(lowerCamelCase_	,     '''w'''       ) as writer:
												logger.info('''***** Eval results *****'''       )

												for key, value in result.items():
																logger.info(F"""  {key} = {value}"""       )
																writer.write(F"""{key} = {value}\n"""       )

												results.update(lowerCamelCase_       )

				return results


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




