




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


SCREAMING_SNAKE_CASE__	:				int  =       logging.get_logger(__name__)




class 		UpperCamelCase__     (lowerCAmelCase__					):

    '''simple docstring'''



    def __init__(   self					,  UpperCamelCase__ = None					,  UpperCamelCase__ = None					,  UpperCamelCase__ = None					,  UpperCamelCase__ = None					,  UpperCamelCase__ = None					,  UpperCamelCase__ = None					,  UpperCamelCase__ = None					,  UpperCamelCase__ = None					,  UpperCamelCase__ = None					,  UpperCamelCase__ = (None, None)					,  UpperCamelCase__ = 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:
                  lowerCamelCase       :				List[str]      			= self.load_generation_config(self.args.generation_config					)
                  lowerCamelCase       :				Any      			= gen_config







    @staticmethod
    def    _lowercase					(   UpperCamelCase__					)					->				GenerationConfig:

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

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

           lowerCamelCase       :				str      			= GenerationConfig.from_pretrained(UpperCamelCase__					,  UpperCamelCase__					)
           return gen_config







    def    _lowercase					(   self					,  UpperCamelCase__ = None					,  UpperCamelCase__ = None					,  UpperCamelCase__ = "eval"					,  **UpperCamelCase__					,  )					->				Dict[str, float]:
           lowerCamelCase       :				str      			= gen_kwargs.copy()
           if gen_kwargs.get("max_length"					) is None and gen_kwargs.get("max_new_tokens"					) is None:
                  lowerCamelCase       :				int      			= self.args.generation_max_length
           lowerCamelCase       :				Tuple      			= (
               gen_kwargs["num_beams"] if gen_kwargs.get("num_beams"					) is not None else self.args.generation_num_beams
           )
           lowerCamelCase       :				Tuple      			= gen_kwargs

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







    def    _lowercase					(   self					,  UpperCamelCase__					,  UpperCamelCase__ = None					,  UpperCamelCase__ = "test"					,  **UpperCamelCase__					,  )					->				"PredictionOutput":
           lowerCamelCase       :				Optional[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       :				Any      			= (
               gen_kwargs["num_beams"] if gen_kwargs.get("num_beams"					) is not None else self.args.generation_num_beams
           )
           lowerCamelCase       :				List[str]      			= gen_kwargs

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







    def    _lowercase					(   self					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__ = None					,  **UpperCamelCase__					,  )					->				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__					)

           lowerCamelCase       :				Optional[int]      			= "labels" in inputs
           lowerCamelCase       :				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"					):
                  lowerCamelCase       :				Union[str, Any]      			= self._gen_kwargs.copy()

           if gen_kwargs.get("max_length"					) is None and gen_kwargs.get("max_new_tokens"					) is None:
                  lowerCamelCase       :				Any      			= self.model.config.max_length
           lowerCamelCase       :				Tuple      			= (
               gen_kwargs["num_beams"] if gen_kwargs.get("num_beams"					) is not None else self.model.config.num_beams
           )
           lowerCamelCase       :				Union[str, Any]      			= True if is_deepspeed_zeroa_enabled() else False
           lowerCamelCase       :				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
           ):
                  lowerCamelCase       :				Dict      			= {k: v for k, v in inputs.items() if k != "decoder_input_ids"}
           lowerCamelCase       :				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:
                  lowerCamelCase       :				Dict      			= False

           # Retrieves GenerationConfig from model.generation_config
           lowerCamelCase       :				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       :				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:
                  lowerCamelCase       :				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():
                                lowerCamelCase       :				Optional[Any]      			= model(**UpperCamelCase__					)
                         if self.label_smoother is not None:
                                lowerCamelCase       :				Dict      			= self.label_smoother(UpperCamelCase__					,  inputs["labels"]					).mean().detach()
                         else:
                                lowerCamelCase       :				List[str]      			= (outputs["loss"] if isinstance(UpperCamelCase__					,  UpperCamelCase__					) else outputs[0]).mean().detach()
                  else:
                         lowerCamelCase       :				Union[str, Any]      			= None

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

           if has_labels:
                  lowerCamelCase       :				Optional[int]      			= inputs["labels"]
                  if labels.shape[-1] < gen_config.max_length:
                         lowerCamelCase       :				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:
                         lowerCamelCase       :				int      			= self._pad_tensors_to_max_len(UpperCamelCase__					,  gen_config.max_new_tokens + 1					)
           else:
                  lowerCamelCase       :				str      			= None

           return loss, generated_tokens, labels







    def    _lowercase					(   self					,  UpperCamelCase__					,  UpperCamelCase__					)					->				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
                  lowerCamelCase       :				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:
                         lowerCamelCase       :				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"					)

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












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


SCREAMING_SNAKE_CASE__	:				Dict  =       {
    'configuration_oneformer': ['ONEFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OneFormerConfig'],
    'processing_oneformer': ['OneFormerProcessor'],
}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__	:				Optional[Any]  =       ['OneFormerImageProcessor']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__	:				Optional[int]  =       [
           'ONEFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
           'OneFormerForUniversalSegmentation',
           'OneFormerModel',
           'OneFormerPreTrainedModel',
       ]

if TYPE_CHECKING:
       from .configuration_oneformer import ONEFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, OneFormerConfig
       from .processing_oneformer import OneFormerProcessor

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .image_processing_oneformer import OneFormerImageProcessor
       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_oneformer import (
                  ONEFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  OneFormerForUniversalSegmentation,
                  OneFormerModel,
                  OneFormerPreTrainedModel,
              )


else:
       import sys

       SCREAMING_SNAKE_CASE__	:				str  =       _LazyModule(__name__, globals()['__file__'], _import_structure)






