




import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest




class 		__A      (			__snake_case			):
						UpperCamelCase     						=					(DDIMParallelScheduler,)
						UpperCamelCase     						=					(("""eta""", 0.0), ("""num_inference_steps""", 50))

						def     A__						(		self   :List[Any]    , **__snake_case   :Any ):
										'''simple docstring'''

										__magic_name__	:	int								={
										    """num_train_timesteps""": 10_00,
										    """beta_start""": 0.0001,
										    """beta_end""": 0.02,
										    """beta_schedule""": """linear""",
										    """clip_sample""": True,
										}

										config.update(**__UpperCamelCase )
										return config

						def     A__						(		self   :Optional[Any]    , **__snake_case   :Any ):
										'''simple docstring'''

										__magic_name__	:	Any								=self.scheduler_classes[0]
										__magic_name__	:	Union[str, Any]								=self.get_scheduler_config(**__UpperCamelCase )
										__magic_name__	:	Optional[int]								=scheduler_class(**__UpperCamelCase )

										__magic_name__				, __magic_name__	:	Union[str, Any]								=10, 0.0

										__magic_name__	:	Union[str, Any]								=self.dummy_model()
										__magic_name__	:	Optional[Any]								=self.dummy_sample_deter

										scheduler.set_timesteps(__UpperCamelCase )

										for t in scheduler.timesteps:
														__magic_name__	:	Dict								=model(__UpperCamelCase    , __UpperCamelCase )
														__magic_name__	:	Dict								=scheduler.step(__UpperCamelCase    , __UpperCamelCase    , __UpperCamelCase    , __UpperCamelCase ).prev_sample

										return sample

						def     A__						(		self   :Optional[int] ):
										'''simple docstring'''

										for timesteps in [1_00, 5_00, 10_00]:
														self.check_over_configs(num_train_timesteps=__UpperCamelCase )

						def     A__						(		self   :Optional[Any] ):
										'''simple docstring'''

										for steps_offset in [0, 1]:
														self.check_over_configs(steps_offset=__UpperCamelCase )

										__magic_name__	:	Dict								=self.scheduler_classes[0]
										__magic_name__	:	List[str]								=self.get_scheduler_config(steps_offset=1 )
										__magic_name__	:	List[str]								=scheduler_class(**__UpperCamelCase )
										scheduler.set_timesteps(5 )
										assert torch.equal(scheduler.timesteps    , torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1] ) )

						def     A__						(		self   :Union[str, Any] ):
										'''simple docstring'''

										for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]    , [0.002, 0.02, 0.2, 2] ):
														self.check_over_configs(beta_start=__UpperCamelCase    , beta_end=__UpperCamelCase )

						def     A__						(		self   :Union[str, Any] ):
										'''simple docstring'''

										for schedule in ["linear", "squaredcos_cap_v2"]:
														self.check_over_configs(beta_schedule=__UpperCamelCase )

						def     A__						(		self   :Dict ):
										'''simple docstring'''

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

						def     A__						(		self   :Optional[int] ):
										'''simple docstring'''

										for clip_sample in [True, False]:
														self.check_over_configs(clip_sample=__UpperCamelCase )

						def     A__						(		self   :int ):
										'''simple docstring'''

										for timestep_spacing in ["trailing", "leading"]:
														self.check_over_configs(timestep_spacing=__UpperCamelCase )

						def     A__						(		self   :Optional[Any] ):
										'''simple docstring'''

										for rescale_betas_zero_snr in [True, False]:
														self.check_over_configs(rescale_betas_zero_snr=__UpperCamelCase )

						def     A__						(		self   :Tuple ):
										'''simple docstring'''

										self.check_over_configs(thresholding=__UpperCamelCase )
										for threshold in [0.5, 1.0, 2.0]:
														for prediction_type in ["epsilon", "v_prediction"]:
																		self.check_over_configs(
																		    thresholding=__UpperCamelCase    , prediction_type=__UpperCamelCase    , sample_max_value=__UpperCamelCase    , )

						def     A__						(		self   :Any ):
										'''simple docstring'''

										for t in [1, 10, 49]:
														self.check_over_forward(time_step=__UpperCamelCase )

						def     A__						(		self   :Dict ):
										'''simple docstring'''

										for t, num_inference_steps in zip([1, 10, 50]    , [10, 50, 5_00] ):
														self.check_over_forward(time_step=__UpperCamelCase    , num_inference_steps=__UpperCamelCase )

						def     A__						(		self   :Tuple ):
										'''simple docstring'''

										for t, eta in zip([1, 10, 49]    , [0.0, 0.5, 1.0] ):
														self.check_over_forward(time_step=__UpperCamelCase    , eta=__UpperCamelCase )

						def     A__						(		self   :List[str] ):
										'''simple docstring'''

										__magic_name__	:	Tuple								=self.scheduler_classes[0]
										__magic_name__	:	List[Any]								=self.get_scheduler_config()
										__magic_name__	:	Optional[Any]								=scheduler_class(**__UpperCamelCase )

										assert torch.sum(torch.abs(scheduler._get_variance(0    , 0 ) - 0.0 ) ) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(4_20    , 4_00 ) - 0.14771 ) ) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(9_80    , 9_60 ) - 0.32460 ) ) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(0    , 0 ) - 0.0 ) ) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(4_87    , 4_86 ) - 0.00979 ) ) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(9_99    , 9_98 ) - 0.02 ) ) < 1E-5

						def     A__						(		self   :Union[str, Any] ):
										'''simple docstring'''

										__magic_name__	:	Tuple								=self.scheduler_classes[0]
										__magic_name__	:	Optional[Any]								=self.get_scheduler_config()
										__magic_name__	:	Optional[int]								=scheduler_class(**__UpperCamelCase )

										__magic_name__				, __magic_name__	:	Dict								=10, 0.0
										scheduler.set_timesteps(__UpperCamelCase )

										__magic_name__	:	str								=self.dummy_model()
										__magic_name__	:	str								=self.dummy_sample_deter
										__magic_name__	:	List[Any]								=self.dummy_sample_deter + 0.1
										__magic_name__	:	List[Any]								=self.dummy_sample_deter - 0.1

										__magic_name__	:	int								=samplea.shape[0]
										__magic_name__	:	str								=torch.stack([samplea, samplea, samplea]    , dim=0 )
										__magic_name__	:	Optional[int]								=torch.arange(__UpperCamelCase )[0:3, None].repeat(1    , __UpperCamelCase )

										__magic_name__	:	int								=model(samples.flatten(0    , 1 )    , timesteps.flatten(0    , 1 ) )
										__magic_name__	:	Any								=scheduler.batch_step_no_noise(__UpperCamelCase    , timesteps.flatten(0    , 1 )    , samples.flatten(0    , 1 )    , __UpperCamelCase )

										__magic_name__	:	Tuple								=torch.sum(torch.abs(__UpperCamelCase ) )
										__magic_name__	:	Any								=torch.mean(torch.abs(__UpperCamelCase ) )

										assert abs(result_sum.item() - 1147.7904 ) < 1E-2
										assert abs(result_mean.item() - 0.4982 ) < 1E-3

						def     A__						(		self   :Any ):
										'''simple docstring'''

										__magic_name__	:	int								=self.full_loop()

										__magic_name__	:	Optional[int]								=torch.sum(torch.abs(__UpperCamelCase ) )
										__magic_name__	:	Tuple								=torch.mean(torch.abs(__UpperCamelCase ) )

										assert abs(result_sum.item() - 172.0067 ) < 1E-2
										assert abs(result_mean.item() - 0.223967 ) < 1E-3

						def     A__						(		self   :List[Any] ):
										'''simple docstring'''

										__magic_name__	:	List[str]								=self.full_loop(prediction_type="""v_prediction""" )

										__magic_name__	:	Dict								=torch.sum(torch.abs(__UpperCamelCase ) )
										__magic_name__	:	List[Any]								=torch.mean(torch.abs(__UpperCamelCase ) )

										assert abs(result_sum.item() - 52.5302 ) < 1E-2
										assert abs(result_mean.item() - 0.0684 ) < 1E-3

						def     A__						(		self   :Any ):
										'''simple docstring'''

										__magic_name__	:	int								=self.full_loop(set_alpha_to_one=__UpperCamelCase    , beta_start=0.01 )
										__magic_name__	:	str								=torch.sum(torch.abs(__UpperCamelCase ) )
										__magic_name__	:	List[str]								=torch.mean(torch.abs(__UpperCamelCase ) )

										assert abs(result_sum.item() - 149.8295 ) < 1E-2
										assert abs(result_mean.item() - 0.1951 ) < 1E-3


						def     A__						(		self   :Dict ):
										'''simple docstring'''

										__magic_name__	:	Optional[Any]								=self.full_loop(set_alpha_to_one=__UpperCamelCase    , beta_start=0.01 )
										__magic_name__	:	Optional[int]								=torch.sum(torch.abs(__UpperCamelCase ) )
										__magic_name__	:	Tuple								=torch.mean(torch.abs(__UpperCamelCase ) )

										assert abs(result_sum.item() - 149.0784 ) < 1E-2
										assert abs(result_mean.item() - 0.1941 ) < 1E-3










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 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase = None	,     ):
				__magic_name__	:	Optional[int]								={}

				if train_file is not None:
								__magic_name__	:	Optional[int]								=[train_file]
				if eval_file is not None:
								__magic_name__	:	Any								=[eval_file]
				if test_file is not None:
								__magic_name__	:	int								=[test_file]

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

				if len(lowerCamelCase   ) == 1:
								for k in files.keys():
												__magic_name__	:	Dict								=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():
												__magic_name__	:	Optional[Any]								=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]:
												__magic_name__	:	Any								={k: v for k, v in ex.items() if k in input_names}
												__magic_name__	:	Any								=labelaid[ex[label_name]]
												yield (d, label)

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

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

				__magic_name__	:	Union[str, 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.TRAIN in transformed_ds
				    else None
				)

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

				__magic_name__	:	Optional[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:
								__magic_name__	:	Any								=val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]   )   )   )

				__magic_name__	:	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.TEST in transformed_ds
				    else None
				)

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

				return train_ds, val_ds, test_ds, labelaid


UpperCAmelCase_		:				int									=     logging.getLogger(__name__)




@dataclass
class 		__A      :
						UpperCamelCase     						=					field(metadata={"""help""": """Which column contains the label"""}			)
						UpperCamelCase     						=					field(default=UpperCamelCase__					,						metadata={"""help""": """The path of the training file"""}			)
						UpperCamelCase     						=					field(default=UpperCamelCase__					,						metadata={"""help""": """The path of the development file"""}			)
						UpperCamelCase     						=					field(default=UpperCamelCase__					,						metadata={"""help""": """The path of the test file"""}			)
						UpperCamelCase     						=					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."""
						        )
						    }					,						)
						UpperCamelCase     						=					field(
						    default=UpperCamelCase__					,						metadata={"""help""": """Overwrite the cached training and evaluation sets"""}			)




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




def 		lowerCAmelCase_   (	):
				# 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.
				__magic_name__	:	List[Any]								=HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)   )
				__magic_name__				, __magic_name__				, __magic_name__	:	Union[str, 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.

				__magic_name__	:	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	,     )

				__magic_name__				, __magic_name__				, __magic_name__				, __magic_name__	:	Optional[Any]								=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	,     )

				__magic_name__	:	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():
								__magic_name__	:	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:
								__magic_name__	:	Tuple								=np.argmax(p.predictions	,     axis=1   )

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

				# Initialize our Trainer
				__magic_name__	:	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
				__magic_name__	:	List[str]								={}
				if training_args.do_eval:
								logger.info("""*** Evaluate ***"""   )
								__magic_name__	:	List[str]								=trainer.evaluate()
								__magic_name__	:	Optional[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()




