




import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin




@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class 		__A      (			snake_case__			):
						UpperCamelCase     						=					42
						UpperCamelCase     						=					None




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase=0.9_9_9	,     lowerCamelCase="cosine"	,     ):
				if alpha_transform_type == "cosine":

								def alpha_bar_fn(lowerCamelCase   ):
												return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2   ) ** 2

				elif alpha_transform_type == "exp":

								def alpha_bar_fn(lowerCamelCase   ):
												return math.exp(t * -1_2.0   )

				else:
								raise ValueError(F"Unsupported alpha_tranform_type: {alpha_transform_type}"   )

				__magic_name__	:	Optional[Any]								=[]
				for i in range(_SCREAMING_SNAKE_CASE   ):
								__magic_name__	:	Optional[int]								=i / num_diffusion_timesteps
								__magic_name__	:	List[str]								=(i + 1) / num_diffusion_timesteps
								betas.append(min(1 - alpha_bar_fn(_SCREAMING_SNAKE_CASE   ) / alpha_bar_fn(_SCREAMING_SNAKE_CASE   )	,     _SCREAMING_SNAKE_CASE   )   )
				return torch.tensor(_SCREAMING_SNAKE_CASE	,     dtype=torch.floataa   )




class 		__A      (			snake_case__					,						snake_case__			):

						@register_to_config
						def __init__(		self   :Union[str, Any]    , __snake_case   :int = 10_00    , __snake_case   :str = "fixed_small_log"    , __snake_case   :bool = True    , __snake_case   :Optional[float] = 1.0    , __snake_case   :str = "epsilon"    , __snake_case   :str = "squaredcos_cap_v2"    , ):
										'''simple docstring'''

										if beta_schedule != "squaredcos_cap_v2":
														raise ValueError("""UnCLIPScheduler only supports `beta_schedule`: 'squaredcos_cap_v2'""" )

										__magic_name__	:	Optional[Any]								=betas_for_alpha_bar(UpperCAmelCase_ )

										__magic_name__	:	Dict								=1.0 - self.betas
										__magic_name__	:	Any								=torch.cumprod(self.alphas    , dim=0 )
										__magic_name__	:	Any								=torch.tensor(1.0 )

										# standard deviation of the initial noise distribution
										__magic_name__	:	Optional[Any]								=1.0

										# setable values
										__magic_name__	:	str								=None
										__magic_name__	:	str								=torch.from_numpy(np.arange(0    , UpperCAmelCase_ )[::-1].copy() )

										__magic_name__	:	Tuple								=variance_type

						def     A__						(		self   :List[Any]    , __snake_case   :torch.FloatTensor    , __snake_case   :Optional[int] = None ):
										'''simple docstring'''

										return sample

						def     A__						(		self   :Optional[int]    , __snake_case   :int    , __snake_case   :Union[str, torch.device] = None ):
										'''simple docstring'''

										__magic_name__	:	Tuple								=num_inference_steps
										__magic_name__	:	Optional[int]								=(self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
										__magic_name__	:	Optional[int]								=(np.arange(0    , UpperCAmelCase_ ) * step_ratio).round()[::-1].copy().astype(np.intaa )
										__magic_name__	:	int								=torch.from_numpy(UpperCAmelCase_ ).to(UpperCAmelCase_ )

						def     A__						(		self   :List[str]    , __snake_case   :Optional[int]    , __snake_case   :List[str]=None    , __snake_case   :Optional[Any]=None    , __snake_case   :List[str]=None ):
										'''simple docstring'''

										if prev_timestep is None:
														__magic_name__	:	Optional[Any]								=t - 1

										__magic_name__	:	Dict								=self.alphas_cumprod[t]
										__magic_name__	:	List[str]								=self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
										__magic_name__	:	Tuple								=1 - alpha_prod_t
										__magic_name__	:	List[str]								=1 - alpha_prod_t_prev

										if prev_timestep == t - 1:
														__magic_name__	:	Any								=self.betas[t]
										else:
														__magic_name__	:	Optional[Any]								=1 - alpha_prod_t / alpha_prod_t_prev

										# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
										# and sample from it to get previous sample
										# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
										__magic_name__	:	Union[str, Any]								=beta_prod_t_prev / beta_prod_t * beta

										if variance_type is None:
														__magic_name__	:	int								=self.config.variance_type

										# hacks - were probably added for training stability
										if variance_type == "fixed_small_log":
														__magic_name__	:	int								=torch.log(torch.clamp(UpperCAmelCase_    , min=1E-20 ) )
														__magic_name__	:	Any								=torch.exp(0.5 * variance )
										elif variance_type == "learned_range":
														# NOTE difference with DDPM scheduler
														__magic_name__	:	Optional[int]								=variance.log()
														__magic_name__	:	List[str]								=beta.log()

														__magic_name__	:	List[str]								=(predicted_variance + 1) / 2
														__magic_name__	:	Union[str, Any]								=frac * max_log + (1 - frac) * min_log

										return variance

						def     A__						(		self   :Union[str, Any]    , __snake_case   :torch.FloatTensor    , __snake_case   :int    , __snake_case   :torch.FloatTensor    , __snake_case   :Optional[int] = None    , __snake_case   :Any=None    , __snake_case   :bool = True    , ):
										'''simple docstring'''

										__magic_name__	:	str								=timestep

										if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
														__magic_name__				, __magic_name__	:	List[Any]								=torch.split(UpperCAmelCase_    , sample.shape[1]    , dim=1 )
										else:
														__magic_name__	:	Tuple								=None

										# 1. compute alphas, betas
										if prev_timestep is None:
														__magic_name__	:	str								=t - 1

										__magic_name__	:	Optional[int]								=self.alphas_cumprod[t]
										__magic_name__	:	Dict								=self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
										__magic_name__	:	List[str]								=1 - alpha_prod_t
										__magic_name__	:	Optional[int]								=1 - alpha_prod_t_prev

										if prev_timestep == t - 1:
														__magic_name__	:	Tuple								=self.betas[t]
														__magic_name__	:	Union[str, Any]								=self.alphas[t]
										else:
														__magic_name__	:	Optional[int]								=1 - alpha_prod_t / alpha_prod_t_prev
														__magic_name__	:	Optional[int]								=1 - beta

										# 2. compute predicted original sample from predicted noise also called
										# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
										if self.config.prediction_type == "epsilon":
														__magic_name__	:	str								=(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
										elif self.config.prediction_type == "sample":
														__magic_name__	:	List[Any]								=model_output
										else:
														raise ValueError(
														    f"prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`"
														    """ for the UnCLIPScheduler.""" )

										# 3. Clip "predicted x_0"
										if self.config.clip_sample:
														__magic_name__	:	List[str]								=torch.clamp(
														    UpperCAmelCase_    , -self.config.clip_sample_range    , self.config.clip_sample_range )

										# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
										# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
										__magic_name__	:	Union[str, Any]								=(alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
										__magic_name__	:	List[Any]								=alpha ** 0.5 * beta_prod_t_prev / beta_prod_t

										# 5. Compute predicted previous sample µ_t
										# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
										__magic_name__	:	Optional[int]								=pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

										# 6. Add noise
										__magic_name__	:	Optional[Any]								=0
										if t > 0:
														__magic_name__	:	str								=randn_tensor(
														    model_output.shape    , dtype=model_output.dtype    , generator=UpperCAmelCase_    , device=model_output.device )

														__magic_name__	:	List[str]								=self._get_variance(
														    UpperCAmelCase_    , predicted_variance=UpperCAmelCase_    , prev_timestep=UpperCAmelCase_    , )

														if self.variance_type == "fixed_small_log":
																		__magic_name__	:	int								=variance
														elif self.variance_type == "learned_range":
																		__magic_name__	:	List[str]								=(0.5 * variance).exp()
														else:
																		raise ValueError(
																		    f"variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`"
																		    """ for the UnCLIPScheduler.""" )

														__magic_name__	:	Tuple								=variance * variance_noise

										__magic_name__	:	Optional[Any]								=pred_prev_sample + variance

										if not return_dict:
														return (pred_prev_sample,)

										return UnCLIPSchedulerOutput(prev_sample=UpperCAmelCase_    , pred_original_sample=UpperCAmelCase_ )


						def     A__						(		self   :int    , __snake_case   :torch.FloatTensor    , __snake_case   :torch.FloatTensor    , __snake_case   :torch.IntTensor    , ):
										'''simple docstring'''

										__magic_name__	:	Union[str, Any]								=self.alphas_cumprod.to(device=original_samples.device    , dtype=original_samples.dtype )
										__magic_name__	:	List[str]								=timesteps.to(original_samples.device )

										__magic_name__	:	Optional[int]								=alphas_cumprod[timesteps] ** 0.5
										__magic_name__	:	List[Any]								=sqrt_alpha_prod.flatten()
										while len(sqrt_alpha_prod.shape ) < len(original_samples.shape ):
														__magic_name__	:	Tuple								=sqrt_alpha_prod.unsqueeze(-1 )

										__magic_name__	:	str								=(1 - alphas_cumprod[timesteps]) ** 0.5
										__magic_name__	:	Union[str, Any]								=sqrt_one_minus_alpha_prod.flatten()
										while len(sqrt_one_minus_alpha_prod.shape ) < len(original_samples.shape ):
														__magic_name__	:	Union[str, Any]								=sqrt_one_minus_alpha_prod.unsqueeze(-1 )

										__magic_name__	:	Optional[int]								=sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
										return noisy_samples










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()




