

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   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__ = None  ,		):
						snake_case_   =	{}

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

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

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

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

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

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

						snake_case_   =	(
						    tf.data.Dataset.from_generator(
						        SCREAMING_SNAKE_CASE__  ,		({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:
												snake_case_   =	train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]       )       )       )

						snake_case_   =	(
						    tf.data.Dataset.from_generator(
						        SCREAMING_SNAKE_CASE__  ,		({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:
												snake_case_   =	val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]       )       )       )

						snake_case_   =	(
						    tf.data.Dataset.from_generator(
						        SCREAMING_SNAKE_CASE__  ,		({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:
												snake_case_   =	test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]       )       )       )

						return train_ds, val_ds, test_ds, labelaid


lowerCAmelCase_       	=	logging.getLogger(__name__)





@dataclass
class 	snake_case_   :







			'''simple docstring'''

			SCREAMING_SNAKE_CASE     :   int									=				field(metadata={"help": "Which column contains the label"}					)
			SCREAMING_SNAKE_CASE     :   str									=				field(default=__A	,       metadata={"help": "The path of the training file"}					)
			SCREAMING_SNAKE_CASE     :   Optional[str]									=				field(default=__A	,       metadata={"help": "The path of the development file"}					)
			SCREAMING_SNAKE_CASE     :   Optional[str]									=				field(default=__A	,       metadata={"help": "The path of the test file"}					)
			SCREAMING_SNAKE_CASE     :   int									=				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."
			        )
			    }	,       )
			SCREAMING_SNAKE_CASE     :   bool									=				field(
			    default=__A	,       metadata={"help": "Overwrite the cached training and evaluation sets"}					)






@dataclass
class 	snake_case_   :







			'''simple docstring'''

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






def   __SCREAMING_SNAKE_CASE					():
						# 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.
						snake_case_   =	HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)       )
						snake_case_,    snake_case_,    snake_case_   =	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.

						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_,    snake_case_,    snake_case_,    snake_case_   =	get_tfds(
						    train_file=data_args.train_file  ,		eval_file=data_args.dev_file  ,		test_file=data_args.test_file  ,		tokenizer=SCREAMING_SNAKE_CASE__  ,		label_column_id=data_args.label_column_id  ,		max_seq_length=data_args.max_seq_length  ,		)

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

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

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

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

						# Initialize our Trainer
						snake_case_   =	TFTrainer(
						    model=SCREAMING_SNAKE_CASE__  ,		args=SCREAMING_SNAKE_CASE__  ,		train_dataset=SCREAMING_SNAKE_CASE__  ,		eval_dataset=SCREAMING_SNAKE_CASE__  ,		compute_metrics=SCREAMING_SNAKE_CASE__  ,		)

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

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

												with open(SCREAMING_SNAKE_CASE__  ,		'''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(SCREAMING_SNAKE_CASE__       )

						return results


if __name__ == "__main__":
					main()


import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest


class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    lowerCamelCase			=							(DPMSolverSinglestepScheduler,)
    lowerCamelCase			=							(('num_inference_steps', 25),)


    def 						snake_case__     (      self :       Tuple,**lowercase_ :       Dict		)->    Optional[int]:





        '''simple docstring'''







        A__          =   {
            'num_train_timesteps': 1_0_0_0,
            'beta_start': 0.0_001,
            'beta_end': 0.02,
            '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(**lowercase_		)
        return config


    def 						snake_case__     (      self :       str,lowercase_ :       Optional[Any]=0,**lowercase_ :       Any		)->    List[Any]:





        '''simple docstring'''







        A__          =   dict(self.forward_default_kwargs		)
        A__          =   kwargs.pop('num_inference_steps',lowercase_		)
        A__          =   self.dummy_sample
        A__          =   0.1 * sample
        A__          =   [residual + 0.2, residual + 0.15, residual + 0.10]

        for scheduler_class in self.scheduler_classes:
            A__          =   self.get_scheduler_config(**lowercase_		)
            A__          =   scheduler_class(**lowercase_		)
            scheduler.set_timesteps(lowercase_		)
            # copy over dummy past residuals
            A__          =   dummy_past_residuals[: scheduler.config.solver_order]

            with tempfile.TemporaryDirectory() as tmpdirname:
                scheduler.save_config(lowercase_		)
                A__          =   scheduler_class.from_pretrained(lowercase_		)
                new_scheduler.set_timesteps(lowercase_		)
                # copy over dummy past residuals
                A__          =   dummy_past_residuals[: new_scheduler.config.solver_order]

            A__      ,						A__          =   sample, sample
            for t in range(lowercase_,time_step + scheduler.config.solver_order + 1		):
                A__          =   scheduler.step(lowercase_,lowercase_,lowercase_,**lowercase_		).prev_sample
                A__          =   new_scheduler.step(lowercase_,lowercase_,lowercase_,**lowercase_		).prev_sample

                assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"


    def 						snake_case__     (      self :       List[str]		)->    List[Any]:





        '''simple docstring'''







        pass


    def 						snake_case__     (      self :       Tuple,lowercase_ :       Union[str, Any]=0,**lowercase_ :       Union[str, Any]		)->    Union[str, Any]:





        '''simple docstring'''







        A__          =   dict(self.forward_default_kwargs		)
        A__          =   kwargs.pop('num_inference_steps',lowercase_		)
        A__          =   self.dummy_sample
        A__          =   0.1 * sample
        A__          =   [residual + 0.2, residual + 0.15, residual + 0.10]

        for scheduler_class in self.scheduler_classes:
            A__          =   self.get_scheduler_config()
            A__          =   scheduler_class(**lowercase_		)
            scheduler.set_timesteps(lowercase_		)

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

            with tempfile.TemporaryDirectory() as tmpdirname:
                scheduler.save_config(lowercase_		)
                A__          =   scheduler_class.from_pretrained(lowercase_		)
                # copy over dummy past residuals
                new_scheduler.set_timesteps(lowercase_		)

                # copy over dummy past residual (must be after setting timesteps)
                A__          =   dummy_past_residuals[: new_scheduler.config.solver_order]

            A__          =   scheduler.step(lowercase_,lowercase_,lowercase_,**lowercase_		).prev_sample
            A__          =   new_scheduler.step(lowercase_,lowercase_,lowercase_,**lowercase_		).prev_sample

            assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"


    def 						snake_case__     (      self :       Optional[Any],lowercase_ :       Optional[int]=None,**lowercase_ :       int		)->    int:





        '''simple docstring'''







        if scheduler is None:
            A__          =   self.scheduler_classes[0]
            A__          =   self.get_scheduler_config(**lowercase_		)
            A__          =   scheduler_class(**lowercase_		)

        A__          =   self.scheduler_classes[0]
        A__          =   self.get_scheduler_config(**lowercase_		)
        A__          =   scheduler_class(**lowercase_		)

        A__          =   1_0
        A__          =   self.dummy_model()
        A__          =   self.dummy_sample_deter
        scheduler.set_timesteps(lowercase_		)

        for i, t in enumerate(scheduler.timesteps		):
            A__          =   model(lowercase_,lowercase_		)
            A__          =   scheduler.step(lowercase_,lowercase_,lowercase_		).prev_sample

        return sample


    def 						snake_case__     (      self :       Any		)->    str:





        '''simple docstring'''







        A__          =   DPMSolverSinglestepScheduler(**self.get_scheduler_config()		)
        A__          =   5_0
        A__          =   self.dummy_model()
        A__          =   self.dummy_sample_deter
        scheduler.set_timesteps(lowercase_		)

        # make sure that the first t is uneven
        for i, t in enumerate(scheduler.timesteps[3:]		):
            A__          =   model(lowercase_,lowercase_		)
            A__          =   scheduler.step(lowercase_,lowercase_,lowercase_		).prev_sample

        A__          =   torch.mean(torch.abs(lowercase_		)		)

        assert abs(result_mean.item() - 0.2_574		) < 1E-3


    def 						snake_case__     (      self :       Optional[Any]		)->    List[Any]:





        '''simple docstring'''







        for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
            self.check_over_configs(num_train_timesteps=lowercase_		)


    def 						snake_case__     (      self :       int		)->    Optional[Any]:





        '''simple docstring'''







        A__          =   DPMSolverSinglestepScheduler(**self.get_scheduler_config()		)
        A__          =   self.full_loop(scheduler=lowercase_		)
        A__          =   torch.mean(torch.abs(lowercase_		)		)

        assert abs(result_mean.item() - 0.2_791		) < 1E-3

        A__          =   DEISMultistepScheduler.from_config(scheduler.config		)
        A__          =   DPMSolverMultistepScheduler.from_config(scheduler.config		)
        A__          =   UniPCMultistepScheduler.from_config(scheduler.config		)
        A__          =   DPMSolverSinglestepScheduler.from_config(scheduler.config		)

        A__          =   self.full_loop(scheduler=lowercase_		)
        A__          =   torch.mean(torch.abs(lowercase_		)		)

        assert abs(result_mean.item() - 0.2_791		) < 1E-3


    def 						snake_case__     (      self :       Tuple		)->    Any:





        '''simple docstring'''







        self.check_over_configs(thresholding=lowercase_		)
        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=lowercase_,prediction_type=lowercase_,sample_max_value=lowercase_,algorithm_type='dpmsolver++',solver_order=lowercase_,solver_type=lowercase_,)


    def 						snake_case__     (      self :       List[Any]		)->    int:





        '''simple docstring'''







        for prediction_type in ["epsilon", "v_prediction"]:
            self.check_over_configs(prediction_type=lowercase_		)


    def 						snake_case__     (      self :       Dict		)->    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=lowercase_,solver_type=lowercase_,prediction_type=lowercase_,algorithm_type=lowercase_,)
                        A__          =   self.full_loop(
                            solver_order=lowercase_,solver_type=lowercase_,prediction_type=lowercase_,algorithm_type=lowercase_,)
                        assert not torch.isnan(lowercase_		).any(), "Samples have nan numbers"


    def 						snake_case__     (      self :       Optional[int]		)->    Tuple:





        '''simple docstring'''







        self.check_over_configs(lower_order_final=lowercase_		)
        self.check_over_configs(lower_order_final=lowercase_		)


    def 						snake_case__     (      self :       Tuple		)->    Optional[int]:





        '''simple docstring'''







        self.check_over_configs(lambda_min_clipped=-float('inf'		)		)
        self.check_over_configs(lambda_min_clipped=-5.1		)


    def 						snake_case__     (      self :       Optional[Any]		)->    Tuple:





        '''simple docstring'''







        self.check_over_configs(variance_type=lowercase_		)
        self.check_over_configs(variance_type='learned_range'		)


    def 						snake_case__     (      self :       str		)->    Any:





        '''simple docstring'''







        for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
            self.check_over_forward(num_inference_steps=lowercase_,time_step=0		)


    def 						snake_case__     (      self :       Tuple		)->    Tuple:





        '''simple docstring'''







        A__          =   self.full_loop()
        A__          =   torch.mean(torch.abs(lowercase_		)		)

        assert abs(result_mean.item() - 0.2_791		) < 1E-3


    def 						snake_case__     (      self :       Any		)->    Union[str, Any]:





        '''simple docstring'''







        A__          =   self.full_loop(use_karras_sigmas=lowercase_		)
        A__          =   torch.mean(torch.abs(lowercase_		)		)

        assert abs(result_mean.item() - 0.2_248		) < 1E-3


    def 						snake_case__     (      self :       Union[str, Any]		)->    Tuple:





        '''simple docstring'''







        A__          =   self.full_loop(prediction_type='v_prediction'		)
        A__          =   torch.mean(torch.abs(lowercase_		)		)

        assert abs(result_mean.item() - 0.1_453		) < 1E-3


    def 						snake_case__     (      self :       Tuple		)->    int:





        '''simple docstring'''







        A__          =   self.full_loop(prediction_type='v_prediction',use_karras_sigmas=lowercase_		)
        A__          =   torch.mean(torch.abs(lowercase_		)		)

        assert abs(result_mean.item() - 0.0_649		) < 1E-3


    def 						snake_case__     (      self :       List[Any]		)->    int:





        '''simple docstring'''







        A__          =   self.scheduler_classes[0]
        A__          =   self.get_scheduler_config(thresholding=lowercase_,dynamic_thresholding_ratio=0		)
        A__          =   scheduler_class(**lowercase_		)

        A__          =   1_0
        A__          =   self.dummy_model()
        A__          =   self.dummy_sample_deter.half()
        scheduler.set_timesteps(lowercase_		)

        for i, t in enumerate(scheduler.timesteps		):
            A__          =   model(lowercase_,lowercase_		)
            A__          =   scheduler.step(lowercase_,lowercase_,lowercase_		).prev_sample

        assert sample.dtype == torch.floataa

