


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


lowercase_           =	logging.get_logger(__name__)


class        A_					(       __UpperCamelCase    ):




      '''simple docstring'''


      def __init__(				self:       Any						,      a:       Union["PreTrainedModel", nn.Module] = None						,      a:       "TrainingArguments" = None						,      a:       Optional["DataCollator"] = None						,      a:       Optional[Dataset] = None						,      a:       Optional[Union[Dataset, Dict[str, Dataset]]] = None						,      a:       Optional["PreTrainedTokenizerBase"] = None						,      a:       Optional[Callable[[], "PreTrainedModel"]] = None						,      a:       Optional[Callable[["EvalPrediction"], Dict]] = None						,      a:       Optional[List["TrainerCallback"]] = None						,      a:       Tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (None, None)						,      a:       Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None						,      ):
       super().__init__(
           model=a						,      args=a						,      data_collator=a						,      train_dataset=a						,      eval_dataset=a						,      tokenizer=a						,      model_init=a						,      compute_metrics=a						,      callbacks=a						,      optimizers=a						,      preprocess_logits_for_metrics=a						,      )

       # 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:
        __lowerCamelCase		:							int							=						self.load_generation_config(self.args.generation_config				)
        __lowerCamelCase		:							Union[str, Any]							=						gen_config




      @staticmethod
      def      _snake_case	(				a:       Union[str, GenerationConfig]				):

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

       # str or Path
       __lowerCamelCase		:							List[str]							=						Path(a				) if isinstance(a						,      a				) else gen_config_arg
       __lowerCamelCase		:							Dict							=						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():
        __lowerCamelCase		:							Optional[int]							=						pretrained_model_name.name
        __lowerCamelCase		:							Optional[Any]							=						pretrained_model_name.parent
       # dir path
       elif pretrained_model_name.is_dir():
        pass
       # model id or URL
       else:
        __lowerCamelCase		:							Optional[int]							=						gen_config_arg

       __lowerCamelCase		:							List[str]							=						GenerationConfig.from_pretrained(a						,      a				)
       return gen_config




      def      _snake_case	(				self:       List[Any]						,      a:       Optional[Dataset] = None						,      a:       Optional[List[str]] = None						,      a:       str = "eval"						,      **a:       Dict						,      ):
       __lowerCamelCase		:							Union[str, Any]							=						gen_kwargs.copy()
       if gen_kwargs.get('max_length'				) is None and gen_kwargs.get('max_new_tokens'				) is None:
        __lowerCamelCase		:							Optional[int]							=						self.args.generation_max_length
       __lowerCamelCase		:							Union[str, Any]							=						(
           gen_kwargs['num_beams'] if gen_kwargs.get('num_beams'				) is not None else self.args.generation_num_beams
       )
       __lowerCamelCase		:							Optional[int]							=						gen_kwargs

       return super().evaluate(a						,      ignore_keys=a						,      metric_key_prefix=a				)




      def      _snake_case	(				self:       Dict						,      a:       Dataset						,      a:       Optional[List[str]] = None						,      a:       str = "test"						,      **a:       Union[str, Any]						,      ):
       __lowerCamelCase		:							Dict							=						gen_kwargs.copy()
       if gen_kwargs.get('max_length'				) is None and gen_kwargs.get('max_new_tokens'				) is None:
        __lowerCamelCase		:							Tuple							=						self.args.generation_max_length
       __lowerCamelCase		:							Union[str, Any]							=						(
           gen_kwargs['num_beams'] if gen_kwargs.get('num_beams'				) is not None else self.args.generation_num_beams
       )
       __lowerCamelCase		:							Dict							=						gen_kwargs

       return super().predict(a						,      ignore_keys=a						,      metric_key_prefix=a				)




      def      _snake_case	(				self:       List[Any]						,      a:       nn.Module						,      a:       Dict[str, Union[torch.Tensor, Any]]						,      a:       bool						,      a:       Optional[List[str]] = None						,      **a:       Tuple						,      ):

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

       __lowerCamelCase		:							Optional[int]							=						'labels' in inputs
       __lowerCamelCase		:							Dict							=						self._prepare_inputs(a				)

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

       if len(a				) == 0 and hasattr(self						,      '_gen_kwargs'				):
        __lowerCamelCase		:							List[str]							=						self._gen_kwargs.copy()

       if gen_kwargs.get('max_length'				) is None and gen_kwargs.get('max_new_tokens'				) is None:
        __lowerCamelCase		:							Union[str, Any]							=						self.model.config.max_length
       __lowerCamelCase		:							Any							=						(
           gen_kwargs['num_beams'] if gen_kwargs.get('num_beams'				) is not None else self.model.config.num_beams
       )
       __lowerCamelCase		:							Optional[Any]							=						True if is_deepspeed_zeroa_enabled() else False
       __lowerCamelCase		:							str							=						(
           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
       ):
        __lowerCamelCase		:							Optional[Any]							=						{k: v for k, v in inputs.items() if k != 'decoder_input_ids'}
       __lowerCamelCase		:							Union[str, Any]							=						self.model.generate(**a						,      **a				)

       # 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:
        __lowerCamelCase		:							Any							=						False

       # Retrieves GenerationConfig from model.generation_config
       __lowerCamelCase		:							List[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:
        __lowerCamelCase		:							Tuple							=						self._pad_tensors_to_max_len(a						,      gen_config.max_length				)
       elif gen_config.max_new_tokens is not None and generated_tokens.shape[-1] < gen_config.max_new_tokens + 1:
        __lowerCamelCase		:							Tuple							=						self._pad_tensors_to_max_len(a						,      gen_config.max_new_tokens + 1				)

       with torch.no_grad():
        if has_labels:
         with self.compute_loss_context_manager():
          __lowerCamelCase		:							int							=						model(**a				)
         if self.label_smoother is not None:
          __lowerCamelCase		:							Dict							=						self.label_smoother(a						,      inputs['labels']				).mean().detach()
         else:
          __lowerCamelCase		:							int							=						(outputs['loss'] if isinstance(a						,      a				) else outputs[0]).mean().detach()
        else:
         __lowerCamelCase		:							Any							=						None

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

       if has_labels:
        __lowerCamelCase		:							Optional[Any]							=						inputs['labels']
        if labels.shape[-1] < gen_config.max_length:
         __lowerCamelCase		:							List[Any]							=						self._pad_tensors_to_max_len(a						,      gen_config.max_length				)
        elif gen_config.max_new_tokens is not None and labels.shape[-1] < gen_config.max_new_tokens + 1:
         __lowerCamelCase		:							Tuple							=						self._pad_tensors_to_max_len(a						,      gen_config.max_new_tokens + 1				)
       else:
        __lowerCamelCase		:							Tuple							=						None

       return loss, generated_tokens, labels




      def      _snake_case	(				self:       Optional[Any]						,      a:       str						,      a:       List[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
        __lowerCamelCase		:							Tuple							=						(
            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:
         __lowerCamelCase		:							List[Any]							=						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'				)

       __lowerCamelCase		:							Optional[int]							=						pad_token_id * torch.ones(
           (tensor.shape[0], max_length)						,      dtype=tensor.dtype						,      device=tensor.device				)
       __lowerCamelCase		:							Dict							=						tensor
       return padded_tensor





from typing import List

import datasets
from datasets.tasks import ImageClassification

from ..folder_based_builder import folder_based_builder


lowercase_           =	datasets.utils.logging.get_logger(__name__)


class        A_					(       folder_based_builder.FolderBasedBuilderConfig    ):




      '''simple docstring'''


      __snake_case					       =   None
      __snake_case					       =   None



class        A_					(       folder_based_builder.FolderBasedBuilder    ):




      '''simple docstring'''


      __snake_case					       =   datasets.Image()
      __snake_case					       =   """image"""
      __snake_case					       =   ImageFolderConfig
      __snake_case					       =   42  # definition at the bottom of the script
      __snake_case					       =   ImageClassification(image_column="""image"""					,      label_column="""label"""    )



lowercase_           =	[
    '.blp',
    '.bmp',
    '.dib',
    '.bufr',
    '.cur',
    '.pcx',
    '.dcx',
    '.dds',
    '.ps',
    '.eps',
    '.fit',
    '.fits',
    '.fli',
    '.flc',
    '.ftc',
    '.ftu',
    '.gbr',
    '.gif',
    '.grib',
    '.h5',
    '.hdf',
    '.png',
    '.apng',
    '.jp2',
    '.j2k',
    '.jpc',
    '.jpf',
    '.jpx',
    '.j2c',
    '.icns',
    '.ico',
    '.im',
    '.iim',
    '.tif',
    '.tiff',
    '.jfif',
    '.jpe',
    '.jpg',
    '.jpeg',
    '.mpg',
    '.mpeg',
    '.msp',
    '.pcd',
    '.pxr',
    '.pbm',
    '.pgm',
    '.ppm',
    '.pnm',
    '.psd',
    '.bw',
    '.rgb',
    '.rgba',
    '.sgi',
    '.ras',
    '.tga',
    '.icb',
    '.vda',
    '.vst',
    '.webp',
    '.wmf',
    '.emf',
    '.xbm',
    '.xpm',
]
lowercase_           =	IMAGE_EXTENSIONS

