





import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput




def 		lowerCAmelCase_    (  __UpperCAmelCase:			Optional[int]		,   __UpperCAmelCase:			List[Any]=0.999		,   __UpperCAmelCase:			Tuple="cosine"		,   )      ->	Optional[Any]:
    if alpha_transform_type == "cosine":

        def alpha_bar_fn(__UpperCAmelCase:			List[Any]      ):
            return math.cos((t + 0.008) / 1.008 * math.pi / 2      ) ** 2

    elif alpha_transform_type == "exp":

        def alpha_bar_fn(__UpperCAmelCase:			List[Any]      ):
            return math.exp(t * -12.0      )

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

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






class     lowercase__       (	__lowerCamelCase			,				__lowerCamelCase				):

   '''simple docstring'''


   a						:							Optional[Any]   		=		[e.name for e in KarrasDiffusionSchedulers]
   a						:							Union[str, Any]   		=		2




   @register_to_config
   def __init__(    self,		__magic_name__ = 1000,		__magic_name__ = 0.0_0085,		__magic_name__ = 0.012,		__magic_name__ = "linear",		__magic_name__ = None,		__magic_name__ = "epsilon",		__magic_name__ = "linspace",		__magic_name__ = 0,		) ->      Tuple:






       """simple docstring"""
       if trained_betas is not None:
           UpperCamelCase__							:				int          =      torch.tensor(__magic_name__,		dtype=torch.floataa							)
       elif beta_schedule == "linear":
           UpperCamelCase__							:				Dict          =      torch.linspace(__magic_name__,		__magic_name__,		__magic_name__,		dtype=torch.floataa							)
       elif beta_schedule == "scaled_linear":
           # this schedule is very specific to the latent diffusion model.
           UpperCamelCase__							:				List[str]          =      (
               torch.linspace(beta_start**0.5,		beta_end**0.5,		__magic_name__,		dtype=torch.floataa							) ** 2
           )
       elif beta_schedule == "squaredcos_cap_v2":
           # Glide cosine schedule
           UpperCamelCase__							:				str          =      betas_for_alpha_bar(__magic_name__							)
       else:
           raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}"							)

       UpperCamelCase__							:				Optional[int]          =      1.0 - self.betas
       UpperCamelCase__							:				List[Any]          =      torch.cumprod(self.alphas,		dim=0							)

       #  set all values
       self.set_timesteps(__magic_name__,		__magic_name__,		__magic_name__							)




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__=None							) ->      str:






       """simple docstring"""
       if schedule_timesteps is None:
           UpperCamelCase__							:				Dict          =      self.timesteps

       UpperCamelCase__							:				Tuple          =      (schedule_timesteps == timestep).nonzero()

       # The sigma index that is taken for the **very** first `step`
       # is always the second index (or the last index if there is only 1)
       # This way we can ensure we don't accidentally skip a sigma in
       # case we start in the middle of the denoising schedule (e.g. for image-to-image)
       if len(self._index_counter							) == 0:
           UpperCamelCase__							:				List[str]          =      1 if len(__magic_name__							) > 1 else 0
       else:
           UpperCamelCase__							:				List[Any]          =      timestep.cpu().item() if torch.is_tensor(__magic_name__							) else timestep
           UpperCamelCase__							:				int          =      self._index_counter[timestep_int]

       return indices[pos].item()




   @property
   def       UpperCamelCase__  (    self							) ->      List[str]:






       """simple docstring"""
       # standard deviation of the initial noise distribution
       if self.config.timestep_spacing in ["linspace", "trailing"]:
           return self.sigmas.max()

       return (self.sigmas.max() ** 2 + 1) ** 0.5




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__,		) ->      torch.FloatTensor:






       """simple docstring"""
       UpperCamelCase__							:				Tuple          =      self.index_for_timestep(__magic_name__							)

       if self.state_in_first_order:
           UpperCamelCase__							:				str          =      self.sigmas[step_index]
       else:
           UpperCamelCase__							:				Optional[int]          =      self.sigmas_interpol[step_index]

       UpperCamelCase__							:				Optional[int]          =      sample / ((sigma**2 + 1) ** 0.5)
       return sample




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__ = None,		__magic_name__ = None,		) ->      str:






       """simple docstring"""
       UpperCamelCase__							:				Dict          =      num_inference_steps

       UpperCamelCase__							:				Tuple          =      num_train_timesteps or self.config.num_train_timesteps

       # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
       if self.config.timestep_spacing == "linspace":
           UpperCamelCase__							:				Union[str, Any]          =      np.linspace(0,		num_train_timesteps - 1,		__magic_name__,		dtype=__magic_name__							)[::-1].copy()
       elif self.config.timestep_spacing == "leading":
           UpperCamelCase__							:				Union[str, Any]          =      num_train_timesteps // self.num_inference_steps
           # creates integer timesteps by multiplying by ratio
           # casting to int to avoid issues when num_inference_step is power of 3
           UpperCamelCase__							:				List[str]          =      (np.arange(0,		__magic_name__							) * step_ratio).round()[::-1].copy().astype(__magic_name__							)
           timesteps += self.config.steps_offset
       elif self.config.timestep_spacing == "trailing":
           UpperCamelCase__							:				Optional[Any]          =      num_train_timesteps / self.num_inference_steps
           # creates integer timesteps by multiplying by ratio
           # casting to int to avoid issues when num_inference_step is power of 3
           UpperCamelCase__							:				List[str]          =      (np.arange(__magic_name__,		0,		-step_ratio							)).round().copy().astype(__magic_name__							)
           timesteps -= 1
       else:
           raise ValueError(
               f"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."							)

       UpperCamelCase__							:				int          =      np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5							)
       UpperCamelCase__							:				Optional[Any]          =      torch.from_numpy(np.log(__magic_name__							)							).to(__magic_name__							)

       UpperCamelCase__							:				Any          =      np.interp(__magic_name__,		np.arange(0,		len(__magic_name__							)							),		__magic_name__							)
       UpperCamelCase__							:				Union[str, Any]          =      np.concatenate([sigmas, [0.0]]							).astype(np.floataa							)
       UpperCamelCase__							:				Any          =      torch.from_numpy(__magic_name__							).to(device=__magic_name__							)

       # interpolate sigmas
       UpperCamelCase__							:				int          =      sigmas.log().lerp(sigmas.roll(1							).log(),		0.5							).exp()

       UpperCamelCase__							:				List[str]          =      torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2							), sigmas[-1:]]							)
       UpperCamelCase__							:				str          =      torch.cat(
           [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2							), sigmas_interpol[-1:]]							)

       if str(__magic_name__							).startswith('''mps'''							):
           # mps does not support float64
           UpperCamelCase__							:				Optional[Any]          =      torch.from_numpy(__magic_name__							).to(__magic_name__,		dtype=torch.floataa							)
       else:
           UpperCamelCase__							:				List[Any]          =      torch.from_numpy(__magic_name__							).to(__magic_name__							)

       # interpolate timesteps
       UpperCamelCase__							:				str          =      self.sigma_to_t(__magic_name__							).to(__magic_name__,		dtype=timesteps.dtype							)
       UpperCamelCase__							:				Dict          =      torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None]),		dim=-1							).flatten()

       UpperCamelCase__							:				Optional[int]          =      torch.cat([timesteps[:1], interleaved_timesteps]							)

       UpperCamelCase__							:				List[str]          =      None

       # for exp beta schedules, such as the one for `pipeline_shap_e.py`
       # we need an index counter
       UpperCamelCase__							:				Dict          =      defaultdict(__magic_name__							)




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      Optional[Any]:






       """simple docstring"""
       # get log sigma
       UpperCamelCase__							:				Any          =      sigma.log()

       # get distribution
       UpperCamelCase__							:				List[str]          =      log_sigma - self.log_sigmas[:, None]

       # get sigmas range
       UpperCamelCase__							:				int          =      dists.ge(0							).cumsum(dim=0							).argmax(dim=0							).clamp(max=self.log_sigmas.shape[0] - 2							)
       UpperCamelCase__							:				Optional[Any]          =      low_idx + 1

       UpperCamelCase__							:				str          =      self.log_sigmas[low_idx]
       UpperCamelCase__							:				int          =      self.log_sigmas[high_idx]

       # interpolate sigmas
       UpperCamelCase__							:				List[Any]          =      (low - log_sigma) / (low - high)
       UpperCamelCase__							:				str          =      w.clamp(0,		1							)

       # transform interpolation to time range
       UpperCamelCase__							:				Tuple          =      (1 - w) * low_idx + w * high_idx
       UpperCamelCase__							:				int          =      t.view(sigma.shape							)
       return t




   @property
   def       UpperCamelCase__  (    self							) ->      Union[str, Any]:






       """simple docstring"""
       return self.sample is None




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__ = True,		) ->      Union[SchedulerOutput, Tuple]:






       """simple docstring"""
       UpperCamelCase__							:				List[str]          =      self.index_for_timestep(__magic_name__							)

       # advance index counter by 1
       UpperCamelCase__							:				int          =      timestep.cpu().item() if torch.is_tensor(__magic_name__							) else timestep
       self._index_counter[timestep_int] += 1

       if self.state_in_first_order:
           UpperCamelCase__							:				Optional[Any]          =      self.sigmas[step_index]
           UpperCamelCase__							:				Union[str, Any]          =      self.sigmas_interpol[step_index + 1]
           UpperCamelCase__							:				List[Any]          =      self.sigmas[step_index + 1]
       else:
           # 2nd order / KDPM2's method
           UpperCamelCase__							:				Tuple          =      self.sigmas[step_index - 1]
           UpperCamelCase__							:				Tuple          =      self.sigmas_interpol[step_index]
           UpperCamelCase__							:				Dict          =      self.sigmas[step_index]

       # currently only gamma=0 is supported. This usually works best anyways.
       # We can support gamma in the future but then need to scale the timestep before
       # passing it to the model which requires a change in API
       UpperCamelCase__							:				Optional[int]          =      0
       UpperCamelCase__							:				List[Any]          =      sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

       # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
       if self.config.prediction_type == "epsilon":
           UpperCamelCase__							:				Union[str, Any]          =      sigma_hat if self.state_in_first_order else sigma_interpol
           UpperCamelCase__							:				List[str]          =      sample - sigma_input * model_output
       elif self.config.prediction_type == "v_prediction":
           UpperCamelCase__							:				List[Any]          =      sigma_hat if self.state_in_first_order else sigma_interpol
           UpperCamelCase__							:				Optional[Any]          =      model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
               sample / (sigma_input**2 + 1)
           )
       elif self.config.prediction_type == "sample":
           raise NotImplementedError('''prediction_type not implemented yet: sample'''							)
       else:
           raise ValueError(
               f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"							)

       if self.state_in_first_order:
           # 2. Convert to an ODE derivative for 1st order
           UpperCamelCase__							:				List[Any]          =      (sample - pred_original_sample) / sigma_hat
           # 3. delta timestep
           UpperCamelCase__							:				List[str]          =      sigma_interpol - sigma_hat

           # store for 2nd order step
           UpperCamelCase__							:				Dict          =      sample
       else:
           # DPM-Solver-2
           # 2. Convert to an ODE derivative for 2nd order
           UpperCamelCase__							:				List[str]          =      (sample - pred_original_sample) / sigma_interpol

           # 3. delta timestep
           UpperCamelCase__							:				Union[str, Any]          =      sigma_next - sigma_hat

           UpperCamelCase__							:				Union[str, Any]          =      self.sample
           UpperCamelCase__							:				Dict          =      None

       UpperCamelCase__							:				Optional[int]          =      sample + derivative * dt

       if not return_dict:
           return (prev_sample,)

       return SchedulerOutput(prev_sample=__magic_name__							)




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__,		__magic_name__,		) ->      torch.FloatTensor:






       """simple docstring"""
       # Make sure sigmas and timesteps have the same device and dtype as original_samples
       UpperCamelCase__							:				List[str]          =      self.sigmas.to(device=original_samples.device,		dtype=original_samples.dtype							)
       if original_samples.device.type == "mps" and torch.is_floating_point(__magic_name__							):
           # mps does not support float64
           UpperCamelCase__							:				Union[str, Any]          =      self.timesteps.to(original_samples.device,		dtype=torch.floataa							)
           UpperCamelCase__							:				Tuple          =      timesteps.to(original_samples.device,		dtype=torch.floataa							)
       else:
           UpperCamelCase__							:				str          =      self.timesteps.to(original_samples.device							)
           UpperCamelCase__							:				int          =      timesteps.to(original_samples.device							)

       UpperCamelCase__							:				Any          =      [self.index_for_timestep(__magic_name__,		__magic_name__							) for t in timesteps]

       UpperCamelCase__							:				List[str]          =      sigmas[step_indices].flatten()
       while len(sigma.shape							) < len(original_samples.shape							):
           UpperCamelCase__							:				int          =      sigma.unsqueeze(-1							)

       UpperCamelCase__							:				List[str]          =      original_samples + noise * sigma
       return noisy_samples




   def __len__(    self							) ->      Any:






       """simple docstring"""
       return self.config.num_train_timesteps








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_    (  __UpperCAmelCase:			str		,   __UpperCAmelCase:			str		,   __UpperCAmelCase:			str		,   __UpperCAmelCase:			PreTrainedTokenizer		,   __UpperCAmelCase:			int		,   __UpperCAmelCase:			Optional[int] = None		,   )      ->	List[Any]:
    UpperCamelCase__							:				Dict          =      {}

    if train_file is not None:
        UpperCamelCase__							:				str          =      [train_file]
    if eval_file is not None:
        UpperCamelCase__							:				Union[str, Any]          =      [eval_file]
    if test_file is not None:
        UpperCamelCase__							:				Tuple          =      [test_file]

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

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

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

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

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

    UpperCamelCase__							:				Tuple          =      (
        tf.data.Dataset.from_generator(
            __UpperCAmelCase		,   ({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:
        UpperCamelCase__							:				Optional[Any]          =      train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]      )      )      )

    UpperCamelCase__							:				int          =      (
        tf.data.Dataset.from_generator(
            __UpperCAmelCase		,   ({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:
        UpperCamelCase__							:				Dict          =      val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]      )      )      )

    UpperCamelCase__							:				Optional[Any]          =      (
        tf.data.Dataset.from_generator(
            __UpperCAmelCase		,   ({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:
        UpperCamelCase__							:				Union[str, Any]          =      test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]      )      )      )

    return train_ds, val_ds, test_ds, labelaid


UpperCAmelCase_     							= logging.getLogger(__name__)






@dataclass
class     lowercase__       :

   '''simple docstring'''


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






@dataclass
class     lowercase__       :

   '''simple docstring'''


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




def 		lowerCAmelCase_    (  )      ->	int:
    # 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.
    UpperCamelCase__							:				Tuple          =      HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)      )
    UpperCamelCase__   ,UpperCamelCase__   ,UpperCamelCase__							:				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.

    UpperCamelCase__							:				List[str]          =      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		,   )

    UpperCamelCase__   ,UpperCamelCase__   ,UpperCamelCase__   ,UpperCamelCase__							:				Optional[int]          =      get_tfds(
        train_file=data_args.train_file		,   eval_file=data_args.dev_file		,   test_file=data_args.test_file		,   tokenizer=__UpperCAmelCase		,   label_column_id=data_args.label_column_id		,   max_seq_length=data_args.max_seq_length		,   )

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

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

    def compute_metrics(__UpperCAmelCase:			EvalPrediction      ) -> Dict:
        UpperCamelCase__							:				Optional[Any]          =      np.argmax(p.predictions		,   axis=1      )

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

    # Initialize our Trainer
    UpperCamelCase__							:				Union[str, Any]          =      TFTrainer(
        model=__UpperCAmelCase		,   args=__UpperCAmelCase		,   train_dataset=__UpperCAmelCase		,   eval_dataset=__UpperCAmelCase		,   compute_metrics=__UpperCAmelCase		,   )

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

    # Evaluation
    UpperCamelCase__							:				List[str]          =      {}
    if training_args.do_eval:
        logger.info('''*** Evaluate ***'''      )
        UpperCamelCase__							:				Tuple          =      trainer.evaluate()
        UpperCamelCase__							:				Optional[int]          =      os.path.join(training_args.output_dir		,   '''eval_results.txt'''      )

        with open(__UpperCAmelCase		,   '''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(__UpperCAmelCase      )

    return results


if __name__ == "__main__":
  main()

