



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


__snake_case	:				str  					=			logging.get_logger(__name__)

class       UpperCamelCase  (					a				):





     """simple docstring"""






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

          # 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:
               A__				=   self.load_generation_config(self.args.generation_config )
               A__				=   gen_config





     @staticmethod
     def 						A__  (					_lowerCamelCase :							Union[str, GenerationConfig] ):

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

          # str or Path
          A__				=   Path(_lowerCamelCase ) if isinstance(_lowerCamelCase	,      _lowerCamelCase ) else gen_config_arg
          A__				=   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():
               A__				=   pretrained_model_name.name
               A__				=   pretrained_model_name.parent
          # dir path
          elif pretrained_model_name.is_dir():
               pass
          # model id or URL
          else:
               A__				=   gen_config_arg

          A__				=   GenerationConfig.from_pretrained(_lowerCamelCase	,      _lowerCamelCase )
          return gen_config





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

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





     def 						A__  (					self :							int	,      _lowerCamelCase :							Dataset	,      _lowerCamelCase :							Optional[List[str]] = None	,      _lowerCamelCase :							str = "test"	,      **_lowerCamelCase :							Optional[Any]	,      ):
          A__				=   gen_kwargs.copy()
          if gen_kwargs.get('''max_length''' ) is None and gen_kwargs.get('''max_new_tokens''' ) is None:
               A__				=   self.args.generation_max_length
          A__				=   (
              gen_kwargs['''num_beams'''] if gen_kwargs.get('''num_beams''' ) is not None else self.args.generation_num_beams
          )
          A__				=   gen_kwargs

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





     def 						A__  (					self :							str	,      _lowerCamelCase :							nn.Module	,      _lowerCamelCase :							Dict[str, Union[torch.Tensor, Any]]	,      _lowerCamelCase :							bool	,      _lowerCamelCase :							Optional[List[str]] = None	,      **_lowerCamelCase :							Optional[int]	,      ):

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

          A__				=   '''labels''' in inputs
          A__				=   self._prepare_inputs(_lowerCamelCase )

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

          if len(_lowerCamelCase ) == 0 and hasattr(self	,      '''_gen_kwargs''' ):
               A__				=   self._gen_kwargs.copy()

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

          # 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:
               A__				=   False

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

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

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

          if has_labels:
               A__				=   inputs['''labels''']
               if labels.shape[-1] < gen_config.max_length:
                    A__				=   self._pad_tensors_to_max_len(_lowerCamelCase	,      gen_config.max_length )
               elif gen_config.max_new_tokens is not None and labels.shape[-1] < gen_config.max_new_tokens + 1:
                    A__				=   self._pad_tensors_to_max_len(_lowerCamelCase	,      gen_config.max_new_tokens + 1 )
          else:
               A__				=   None

          return loss, generated_tokens, labels





     def 						A__  (					self :							Optional[int]	,      _lowerCamelCase :							str	,      _lowerCamelCase :							Optional[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
               A__				=   (
                   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:
                    A__				=   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''' )

          A__				=   pad_token_id * torch.ones(
              (tensor.shape[0], max_length)	,      dtype=tensor.dtype	,      device=tensor.device )
          A__				=   tensor
          return padded_tensor








"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


__snake_case	:				List[Any]  					=			{
    'configuration_unispeech_sat': ['UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'UniSpeechSatConfig'],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __snake_case	:				str  					=			[
     'UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST',
     'UniSpeechSatForAudioFrameClassification',
     'UniSpeechSatForCTC',
     'UniSpeechSatForPreTraining',
     'UniSpeechSatForSequenceClassification',
     'UniSpeechSatForXVector',
     'UniSpeechSatModel',
     'UniSpeechSatPreTrainedModel',
 ]

if TYPE_CHECKING:
 from .configuration_unispeech_sat import UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechSatConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_unispeech_sat import (
      UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST,
      UniSpeechSatForAudioFrameClassification,
      UniSpeechSatForCTC,
      UniSpeechSatForPreTraining,
      UniSpeechSatForSequenceClassification,
      UniSpeechSatForXVector,
      UniSpeechSatModel,
      UniSpeechSatPreTrainedModel,
  )

else:
 import sys

 __snake_case	:				Union[str, Any]  					=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



