



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        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_ = None							,				)					->		Any:
    _lowercase :							List[Any]   	=	{}

    if train_file is not None:
        _lowercase :							str   	=	[train_file]
    if eval_file is not None:
        _lowercase :							List[Any]   	=	[eval_file]
    if test_file is not None:
        _lowercase :							Union[str, Any]   	=	[test_file]

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

    if len(lowerCamelCase_				) == 1:
        for k in files.keys():
            _lowercase :							Tuple   	=	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 :							str   	=	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 :							Tuple   	=	{k: v for k, v in ex.items() if k in input_names}
            _lowercase :							str   	=	labelaid[ex[label_name]]
            yield (d, label)

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

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

    _lowercase :							int   	=	(
        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 :							Union[str, Any]   	=	train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]				)				)				)

    _lowercase :							Any   	=	(
        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 :							Union[str, Any]   	=	val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]				)				)				)

    _lowercase :							List[str]   	=	(
        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 :							Optional[int]   	=	test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]				)				)				)

    return train_ds, val_ds, test_ds, labelaid


SCREAMING_SNAKE_CASE :   List[str]							      =				logging.getLogger(__name__)






@dataclass
class _lowerCamelCase:
       lowercase_				:			int									=    field(metadata={"""help""": """Which column contains the label"""}     )
       lowercase_				:			str									=    field(default=_a,				metadata={"""help""": """The path of the training file"""}     )
       lowercase_				:			Optional[str]									=    field(default=_a,				metadata={"""help""": """The path of the development file"""}     )
       lowercase_				:			Optional[str]									=    field(default=_a,				metadata={"""help""": """The path of the test file"""}     )
       lowercase_				:			int									=    field(
           default=1_28,				metadata={
               """help""": (
                   """The maximum total input sequence length after tokenization. Sequences longer """
                   """than this will be truncated, sequences shorter will be padded."""
               )
           },				)
       lowercase_				:			bool									=    field(
           default=_a,				metadata={"""help""": """Overwrite the cached training and evaluation sets"""}     )






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



def        UpperCamelCase_(   )					->		Dict:
    # 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 :							List[Any]   	=	HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)				)
    _lowercase :							List[Any]   	=	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 :							Dict   	=	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 :							Dict   	=	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 :							Optional[Any]   	=	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 :							Union[str, Any]   	=	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 :							Dict   	=	np.argmax(p.predictions							,				axis=1				)

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

    # Initialize our Trainer
    _lowercase :							int   	=	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 :							Optional[int]   	=	{}
    if training_args.do_eval:
        logger.info('*** Evaluate ***'				)
        _lowercase :							Union[str, Any]   	=	trainer.evaluate()
        _lowercase :							Union[str, Any]   	=	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()






import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest






class _lowerCamelCase(		_a     ):
       lowercase_				:			List[Any]									=    (DPMSolverSinglestepScheduler,)
       lowercase_				:			List[str]									=    (("""num_inference_steps""", 25),)
       def     UpperCamelCase  (      self,   **lowerCamelCase) ->     Union[str, Any]:




           """simple docstring"""

           _lowercase :							Optional[Any]   	=	{
               'num_train_timesteps': 10_00,
               'beta_start': 0.0_0_0_1,
               'beta_end': 0.0_2,
               'beta_schedule': 'linear',
               'solver_order': 2,
               'prediction_type': 'epsilon',
               'thresholding': False,
               'sample_max_value': 1.0,
               'algorithm_type': 'dpmsolver++',
               'solver_type': 'midpoint',
               'lambda_min_clipped': -float('inf'),
               'variance_type': None,
           }

           config.update(**lowerCamelCase)
           return config
       def     UpperCamelCase  (      self,   lowerCamelCase=0,   **lowerCamelCase) ->     Any:




           """simple docstring"""

           _lowercase :							Dict   	=	dict(self.forward_default_kwargs)
           _lowercase :							Union[str, Any]   	=	kwargs.pop('num_inference_steps',   lowerCamelCase)
           _lowercase :							Optional[int]   	=	self.dummy_sample
           _lowercase :							Optional[int]   	=	0.1 * sample
           _lowercase :							Optional[int]   	=	[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

           for scheduler_class in self.scheduler_classes:
               _lowercase :							Any   	=	self.get_scheduler_config(**lowerCamelCase)
               _lowercase :							List[Any]   	=	scheduler_class(**lowerCamelCase)
               scheduler.set_timesteps(lowerCamelCase)
               # copy over dummy past residuals
               _lowercase :							List[str]   	=	dummy_past_residuals[: scheduler.config.solver_order]

               with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(lowerCamelCase)
                   _lowercase :							Optional[Any]   	=	scheduler_class.from_pretrained(lowerCamelCase)
                   new_scheduler.set_timesteps(lowerCamelCase)
                   # copy over dummy past residuals
                   _lowercase :							List[Any]   	=	dummy_past_residuals[: new_scheduler.config.solver_order]

               _lowercase	,       _lowercase :							List[Any]   	=	sample, sample
               for t in range(lowerCamelCase,   time_step + scheduler.config.solver_order + 1):
                   _lowercase :							Dict   	=	scheduler.step(lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   **lowerCamelCase).prev_sample
                   _lowercase :							int   	=	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     UpperCamelCase  (      self) ->     Optional[int]:




           """simple docstring"""

           pass
       def     UpperCamelCase  (      self,   lowerCamelCase=0,   **lowerCamelCase) ->     str:




           """simple docstring"""

           _lowercase :							Optional[int]   	=	dict(self.forward_default_kwargs)
           _lowercase :							List[str]   	=	kwargs.pop('num_inference_steps',   lowerCamelCase)
           _lowercase :							List[str]   	=	self.dummy_sample
           _lowercase :							str   	=	0.1 * sample
           _lowercase :							Optional[Any]   	=	[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

           for scheduler_class in self.scheduler_classes:
               _lowercase :							Any   	=	self.get_scheduler_config()
               _lowercase :							List[str]   	=	scheduler_class(**lowerCamelCase)
               scheduler.set_timesteps(lowerCamelCase)

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

               with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(lowerCamelCase)
                   _lowercase :							List[Any]   	=	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 :							List[str]   	=	dummy_past_residuals[: new_scheduler.config.solver_order]

               _lowercase :							Optional[int]   	=	scheduler.step(lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   **lowerCamelCase).prev_sample
               _lowercase :							List[Any]   	=	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     UpperCamelCase  (      self,   lowerCamelCase=None,   **lowerCamelCase) ->     Optional[Any]:




           """simple docstring"""

           if scheduler is None:
               _lowercase :							str   	=	self.scheduler_classes[0]
               _lowercase :							int   	=	self.get_scheduler_config(**lowerCamelCase)
               _lowercase :							Optional[Any]   	=	scheduler_class(**lowerCamelCase)

           _lowercase :							List[Any]   	=	self.scheduler_classes[0]
           _lowercase :							Optional[int]   	=	self.get_scheduler_config(**lowerCamelCase)
           _lowercase :							Optional[Any]   	=	scheduler_class(**lowerCamelCase)

           _lowercase :							List[Any]   	=	10
           _lowercase :							List[str]   	=	self.dummy_model()
           _lowercase :							Dict   	=	self.dummy_sample_deter
           scheduler.set_timesteps(lowerCamelCase)

           for i, t in enumerate(scheduler.timesteps):
               _lowercase :							Optional[int]   	=	model(lowerCamelCase,   lowerCamelCase)
               _lowercase :							Optional[int]   	=	scheduler.step(lowerCamelCase,   lowerCamelCase,   lowerCamelCase).prev_sample

           return sample
       def     UpperCamelCase  (      self) ->     Tuple:




           """simple docstring"""

           _lowercase :							Dict   	=	DPMSolverSinglestepScheduler(**self.get_scheduler_config())
           _lowercase :							Optional[int]   	=	50
           _lowercase :							Union[str, Any]   	=	self.dummy_model()
           _lowercase :							Tuple   	=	self.dummy_sample_deter
           scheduler.set_timesteps(lowerCamelCase)

           # make sure that the first t is uneven
           for i, t in enumerate(scheduler.timesteps[3:]):
               _lowercase :							Optional[Any]   	=	model(lowerCamelCase,   lowerCamelCase)
               _lowercase :							int   	=	scheduler.step(lowerCamelCase,   lowerCamelCase,   lowerCamelCase).prev_sample

           _lowercase :							Optional[int]   	=	torch.mean(torch.abs(lowerCamelCase))

           assert abs(result_mean.item() - 0.2_5_7_4) < 1E-3
       def     UpperCamelCase  (      self) ->     Tuple:




           """simple docstring"""

           for timesteps in [25, 50, 1_00, 9_99, 10_00]:
               self.check_over_configs(num_train_timesteps=lowerCamelCase)
       def     UpperCamelCase  (      self) ->     Optional[Any]:




           """simple docstring"""

           _lowercase :							List[str]   	=	DPMSolverSinglestepScheduler(**self.get_scheduler_config())
           _lowercase :							List[str]   	=	self.full_loop(scheduler=lowerCamelCase)
           _lowercase :							str   	=	torch.mean(torch.abs(lowerCamelCase))

           assert abs(result_mean.item() - 0.2_7_9_1) < 1E-3

           _lowercase :							str   	=	DEISMultistepScheduler.from_config(scheduler.config)
           _lowercase :							List[str]   	=	DPMSolverMultistepScheduler.from_config(scheduler.config)
           _lowercase :							Tuple   	=	UniPCMultistepScheduler.from_config(scheduler.config)
           _lowercase :							Any   	=	DPMSolverSinglestepScheduler.from_config(scheduler.config)

           _lowercase :							Any   	=	self.full_loop(scheduler=lowerCamelCase)
           _lowercase :							Optional[int]   	=	torch.mean(torch.abs(lowerCamelCase))

           assert abs(result_mean.item() - 0.2_7_9_1) < 1E-3
       def     UpperCamelCase  (      self) ->     List[str]:




           """simple docstring"""

           self.check_over_configs(thresholding=lowerCamelCase)
           for order in [1, 2, 3]:
               for solver_type in ["midpoint", "heun"]:
                   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,   algorithm_type='dpmsolver++',   solver_order=lowerCamelCase,   solver_type=lowerCamelCase,   )
       def     UpperCamelCase  (      self) ->     Union[str, Any]:




           """simple docstring"""

           for prediction_type in ["epsilon", "v_prediction"]:
               self.check_over_configs(prediction_type=lowerCamelCase)
       def     UpperCamelCase  (      self) ->     List[Any]:




           """simple docstring"""

           for algorithm_type in ["dpmsolver", "dpmsolver++"]:
               for solver_type in ["midpoint", "heun"]:
                   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,   algorithm_type=lowerCamelCase,   )
                           _lowercase :							Optional[Any]   	=	self.full_loop(
                               solver_order=lowerCamelCase,   solver_type=lowerCamelCase,   prediction_type=lowerCamelCase,   algorithm_type=lowerCamelCase,   )
                           assert not torch.isnan(lowerCamelCase).any(), "Samples have nan numbers"
       def     UpperCamelCase  (      self) ->     str:




           """simple docstring"""

           self.check_over_configs(lower_order_final=lowerCamelCase)
           self.check_over_configs(lower_order_final=lowerCamelCase)
       def     UpperCamelCase  (      self) ->     List[str]:




           """simple docstring"""

           self.check_over_configs(lambda_min_clipped=-float('inf'))
           self.check_over_configs(lambda_min_clipped=-5.1)
       def     UpperCamelCase  (      self) ->     Union[str, Any]:




           """simple docstring"""

           self.check_over_configs(variance_type=lowerCamelCase)
           self.check_over_configs(variance_type='learned_range')
       def     UpperCamelCase  (      self) ->     Dict:




           """simple docstring"""

           for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
               self.check_over_forward(num_inference_steps=lowerCamelCase,   time_step=0)
       def     UpperCamelCase  (      self) ->     List[str]:




           """simple docstring"""

           _lowercase :							int   	=	self.full_loop()
           _lowercase :							Union[str, Any]   	=	torch.mean(torch.abs(lowerCamelCase))

           assert abs(result_mean.item() - 0.2_7_9_1) < 1E-3
       def     UpperCamelCase  (      self) ->     List[Any]:




           """simple docstring"""

           _lowercase :							Tuple   	=	self.full_loop(use_karras_sigmas=lowerCamelCase)
           _lowercase :							List[str]   	=	torch.mean(torch.abs(lowerCamelCase))

           assert abs(result_mean.item() - 0.2_2_4_8) < 1E-3
       def     UpperCamelCase  (      self) ->     List[str]:




           """simple docstring"""

           _lowercase :							Tuple   	=	self.full_loop(prediction_type='v_prediction')
           _lowercase :							str   	=	torch.mean(torch.abs(lowerCamelCase))

           assert abs(result_mean.item() - 0.1_4_5_3) < 1E-3
       def     UpperCamelCase  (      self) ->     Union[str, Any]:




           """simple docstring"""

           _lowercase :							Tuple   	=	self.full_loop(prediction_type='v_prediction',   use_karras_sigmas=lowerCamelCase)
           _lowercase :							str   	=	torch.mean(torch.abs(lowerCamelCase))

           assert abs(result_mean.item() - 0.0_6_4_9) < 1E-3




       def     UpperCamelCase  (      self) ->     Dict:




           """simple docstring"""

           _lowercase :							List[Any]   	=	self.scheduler_classes[0]
           _lowercase :							Optional[int]   	=	self.get_scheduler_config(thresholding=lowerCamelCase,   dynamic_thresholding_ratio=0)
           _lowercase :							Any   	=	scheduler_class(**lowerCamelCase)

           _lowercase :							str   	=	10
           _lowercase :							List[str]   	=	self.dummy_model()
           _lowercase :							Tuple   	=	self.dummy_sample_deter.half()
           scheduler.set_timesteps(lowerCamelCase)

           for i, t in enumerate(scheduler.timesteps):
               _lowercase :							Tuple   	=	model(lowerCamelCase,   lowerCamelCase)
               _lowercase :							Dict   	=	scheduler.step(lowerCamelCase,   lowerCamelCase,   lowerCamelCase).prev_sample

           assert sample.dtype == torch.floataa


