






from copy import deepcopy
from pathlib import Path
from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from torch import nn
from torch.utils.data import Dataset

from .deepspeed import is_deepspeed_zeroa_enabled
from .generation.configuration_utils import GenerationConfig
from .trainer import Trainer
from .utils import logging


if TYPE_CHECKING:
       from .data.data_collator import DataCollator
       from .modeling_utils import PreTrainedModel
       from .tokenization_utils_base import PreTrainedTokenizerBase
       from .trainer_callback import TrainerCallback
       from .trainer_utils import EvalPrediction, PredictionOutput
       from .training_args import TrainingArguments


a		:	int      					=			logging.get_logger(__name__)
class    a   (			lowerCAmelCase__       ):
      def __init__(				self :      Optional[Any]		,				__lowercase :      Any = None		,				__lowercase :      int = None		,				__lowercase :      str = None		,				__lowercase :      str = None		,				__lowercase :      Any = None		,				__lowercase :      List[str] = None		,				__lowercase :      Tuple = None		,				__lowercase :      List[Any] = None		,				__lowercase :      Union[str, Any] = None		,				__lowercase :      List[Any] = (None, None)		,				__lowercase :      Optional[int] = None		,				)							->				List[str]:
        super().__init__(
            model=UpperCamelCase__		,				args=UpperCamelCase__		,				data_collator=UpperCamelCase__		,				train_dataset=UpperCamelCase__		,				eval_dataset=UpperCamelCase__		,				tokenizer=UpperCamelCase__		,				model_init=UpperCamelCase__		,				compute_metrics=UpperCamelCase__		,				callbacks=UpperCamelCase__		,				optimizers=UpperCamelCase__		,				preprocess_logits_for_metrics=UpperCamelCase__		,				)

        # Override self.model.generation_config if a GenerationConfig is specified in args.
        # Priority: args.generation_config > model.generation_config > default GenerationConfig.
        if self.args.generation_config is not None:
          __UpperCAmelCase							:     List[str]									=  self.load_generation_config(self.args.generation_config )
          __UpperCAmelCase							:     Any									=  gen_config
      @staticmethod
      def      UpperCAmelCase				(				__lowercase :      Tuple )							->				GenerationConfig:

        # GenerationConfig provided, nothing to do
        if isinstance(UpperCamelCase__		,				UpperCamelCase__ ):
          return deepcopy(UpperCamelCase__ )

        # str or Path
        __UpperCAmelCase							:     int									=  Path(UpperCamelCase__ ) if isinstance(UpperCamelCase__		,				UpperCamelCase__ ) else gen_config_arg
        __UpperCAmelCase							:     Optional[int]									=  None

        # Figuring if it is path pointing to a file, pointing to a directory or else a model id or URL
        # This step is required in order to determine config_file_name
        if pretrained_model_name.is_file():
          __UpperCAmelCase							:     int									=  pretrained_model_name.name
          __UpperCAmelCase							:     Tuple									=  pretrained_model_name.parent
        # dir path
        elif pretrained_model_name.is_dir():
          pass
        # model id or URL
        else:
          __UpperCAmelCase							:     List[Any]									=  gen_config_arg

        __UpperCAmelCase							:     str									=  GenerationConfig.from_pretrained(UpperCamelCase__		,				UpperCamelCase__ )
        return gen_config
      def      UpperCAmelCase				(				self :      List[Any]		,				__lowercase :      Union[str, Any] = None		,				__lowercase :      List[str] = None		,				__lowercase :      Tuple = "eval"		,				**__lowercase :      str		,				)							->				Dict[str, float]:
        __UpperCAmelCase							:     str									=  gen_kwargs.copy()
        if gen_kwargs.get("""max_length""" ) is None and gen_kwargs.get("""max_new_tokens""" ) is None:
          __UpperCAmelCase							:     int									=  self.args.generation_max_length
        __UpperCAmelCase							:     Tuple									=  (
            gen_kwargs["num_beams"] if gen_kwargs.get("""num_beams""" ) is not None else self.args.generation_num_beams
        )
        __UpperCAmelCase							:     Tuple									=  gen_kwargs

        return super().evaluate(UpperCamelCase__		,				ignore_keys=UpperCamelCase__		,				metric_key_prefix=UpperCamelCase__ )
      def      UpperCAmelCase				(				self :      List[Any]		,				__lowercase :      List[str]		,				__lowercase :      Tuple = None		,				__lowercase :      Tuple = "test"		,				**__lowercase :      Union[str, Any]		,				)							->				"PredictionOutput":
        __UpperCAmelCase							:     Optional[Any]									=  gen_kwargs.copy()
        if gen_kwargs.get("""max_length""" ) is None and gen_kwargs.get("""max_new_tokens""" ) is None:
          __UpperCAmelCase							:     Optional[int]									=  self.args.generation_max_length
        __UpperCAmelCase							:     Any									=  (
            gen_kwargs["num_beams"] if gen_kwargs.get("""num_beams""" ) is not None else self.args.generation_num_beams
        )
        __UpperCAmelCase							:     List[str]									=  gen_kwargs

        return super().predict(UpperCamelCase__		,				ignore_keys=UpperCamelCase__		,				metric_key_prefix=UpperCamelCase__ )
      def      UpperCAmelCase				(				self :      List[str]		,				__lowercase :      int		,				__lowercase :      Dict		,				__lowercase :      Any		,				__lowercase :      Dict = None		,				**__lowercase :      Tuple		,				)							->				Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:

        if not self.args.predict_with_generate or prediction_loss_only:
          return super().prediction_step(
              UpperCamelCase__		,				UpperCamelCase__		,				prediction_loss_only=UpperCamelCase__		,				ignore_keys=UpperCamelCase__ )

        __UpperCAmelCase							:     Optional[int]									=  "labels" in inputs
        __UpperCAmelCase							:     Optional[int]									=  self._prepare_inputs(UpperCamelCase__ )

        # XXX: adapt synced_gpus for fairscale as well
        # Priority (handled in generate):
        # gen_kwargs > model.generation_config > default GenerationConfig()

        if len(UpperCamelCase__ ) == 0 and hasattr(self		,				"""_gen_kwargs""" ):
          __UpperCAmelCase							:     Union[str, Any]									=  self._gen_kwargs.copy()

        if gen_kwargs.get("""max_length""" ) is None and gen_kwargs.get("""max_new_tokens""" ) is None:
          __UpperCAmelCase							:     Any									=  self.model.config.max_length
        __UpperCAmelCase							:     Tuple									=  (
            gen_kwargs["num_beams"] if gen_kwargs.get("""num_beams""" ) is not None else self.model.config.num_beams
        )
        __UpperCAmelCase							:     Union[str, Any]									=  True if is_deepspeed_zeroa_enabled() else False
        __UpperCAmelCase							:     Dict									=  (
            gen_kwargs["synced_gpus"] if gen_kwargs.get("""synced_gpus""" ) is not None else default_synced_gpus
        )

        # If the `decoder_input_ids` was created from `labels`, evict the former, so that the model can freely generate
        # (otherwise, it would continue generating from the padded `decoder_input_ids`)
        if (
            "labels" in inputs
            and "decoder_input_ids" in inputs
            and inputs["labels"].shape == inputs["decoder_input_ids"].shape
        ):
          __UpperCAmelCase							:     Dict									=  {k: v for k, v in inputs.items() if k != "decoder_input_ids"}
        __UpperCAmelCase							:     List[Any]									=  self.model.generate(**UpperCamelCase__		,				**UpperCamelCase__ )

        # Temporary hack to ensure the generation config is not initialized for each iteration of the evaluation loop
        # TODO: remove this hack when the legacy code that initializes generation_config from a model config is
        # removed in https://github.com/huggingface/transformers/blob/98d88b23f54e5a23e741833f1e973fdf600cc2c5/src/transformers/generation/utils.py#L1183
        if self.model.generation_config._from_model_config:
          __UpperCAmelCase							:     Dict									=  False

        # Retrieves GenerationConfig from model.generation_config
        __UpperCAmelCase							:     str									=  self.model.generation_config
        # in case the batch is shorter than max length, the output should be padded
        if generated_tokens.shape[-1] < gen_config.max_length:
          __UpperCAmelCase							:     List[str]									=  self._pad_tensors_to_max_len(UpperCamelCase__		,				gen_config.max_length )
        elif gen_config.max_new_tokens is not None and generated_tokens.shape[-1] < gen_config.max_new_tokens + 1:
          __UpperCAmelCase							:     Tuple									=  self._pad_tensors_to_max_len(UpperCamelCase__		,				gen_config.max_new_tokens + 1 )

        with torch.no_grad():
          if has_labels:
            with self.compute_loss_context_manager():
              __UpperCAmelCase							:     Optional[Any]									=  model(**UpperCamelCase__ )
            if self.label_smoother is not None:
              __UpperCAmelCase							:     Dict									=  self.label_smoother(UpperCamelCase__		,				inputs["""labels"""] ).mean().detach()
            else:
              __UpperCAmelCase							:     List[str]									=  (outputs["loss"] if isinstance(UpperCamelCase__		,				UpperCamelCase__ ) else outputs[0]).mean().detach()
          else:
            __UpperCAmelCase							:     Union[str, Any]									=  None

        if self.args.prediction_loss_only:
          return loss, None, None

        if has_labels:
          __UpperCAmelCase							:     Optional[int]									=  inputs["labels"]
          if labels.shape[-1] < gen_config.max_length:
            __UpperCAmelCase							:     List[Any]									=  self._pad_tensors_to_max_len(UpperCamelCase__		,				gen_config.max_length )
          elif gen_config.max_new_tokens is not None and labels.shape[-1] < gen_config.max_new_tokens + 1:
            __UpperCAmelCase							:     int									=  self._pad_tensors_to_max_len(UpperCamelCase__		,				gen_config.max_new_tokens + 1 )
        else:
          __UpperCAmelCase							:     str									=  None

        return loss, generated_tokens, labels




      def      UpperCAmelCase				(				self :      List[str]		,				__lowercase :      Optional[Any]		,				__lowercase :      Dict )							->				Union[str, Any]:
        if self.tokenizer is not None and hasattr(self.tokenizer		,				"""pad_token_id""" ):
          # If PAD token is not defined at least EOS token has to be defined
          __UpperCAmelCase							:     Dict									=  (
              self.tokenizer.pad_token_id if self.tokenizer.pad_token_id is not None else self.tokenizer.eos_token_id
          )
        else:
          if self.model.config.pad_token_id is not None:
            __UpperCAmelCase							:     Optional[int]									=  self.model.config.pad_token_id
          else:
            raise ValueError("""Pad_token_id must be set in the configuration of the model, in order to pad tensors""" )

        __UpperCAmelCase							:     Any									=  pad_token_id * torch.ones(
            (tensor.shape[0], max_length)		,				dtype=tensor.dtype		,				device=tensor.device )
        __UpperCAmelCase							:     Tuple									=  tensor
        return padded_tensor










from sklearn.neural_network import MLPClassifier

a		:	int      					=			[[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
a		:	List[str]      					=			[0, 1, 0, 0]


a		:	str      					=			MLPClassifier(
    solver="lbfgs", alpha=1e-5, hidden_layer_sizes=(5, 2), random_state=1
)

clf.fit(X, y)


a		:	Union[str, Any]      					=			[[0.0, 0.0], [0.0, 1.0], [1.0, 1.0]]
a		:	Optional[int]      					=			clf.predict(test)






def     lowerCamelCase__				(   __lowerCamelCase      :					Dict		):
  return list(__lowerCamelCase		)


if __name__ == "__main__":
       import doctest

       doctest.testmod()


