


'''simple docstring'''






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_       :     str 							=     logging.get_logger(__name__)




class 			a  (  _SCREAMING_SNAKE_CASE							):
 def __init__(			self   ,					__magic_name__ = None   ,					__magic_name__ = None   ,					__magic_name__ = None   ,					__magic_name__ = None   ,					__magic_name__ = None   ,					__magic_name__ = None   ,					__magic_name__ = None   ,					__magic_name__ = None   ,					__magic_name__ = None   ,					__magic_name__ = (None, None)   ,					__magic_name__ = None   ,					)       ->				str:
      super().__init__(
          model=__magic_name__   ,					args=__magic_name__   ,					data_collator=__magic_name__   ,					train_dataset=__magic_name__   ,					eval_dataset=__magic_name__   ,					tokenizer=__magic_name__   ,					model_init=__magic_name__   ,					compute_metrics=__magic_name__   ,					callbacks=__magic_name__   ,					optimizers=__magic_name__   ,					preprocess_logits_for_metrics=__magic_name__   ,					)

      # 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 				__UpperCAmelCase		(			__magic_name__    )       ->				GenerationConfig:

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

      # str or Path
      _a         =				Path(__magic_name__    ) if isinstance(__magic_name__   ,					__magic_name__    ) 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(__magic_name__   ,					__magic_name__    )
      return gen_config
 def 				__UpperCAmelCase		(			self   ,					__magic_name__ = None   ,					__magic_name__ = None   ,					__magic_name__ = "eval"   ,					**__magic_name__   ,					)       ->				Dict[str, float]:
      _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(__magic_name__   ,					ignore_keys=__magic_name__   ,					metric_key_prefix=__magic_name__    )
 def 				__UpperCAmelCase		(			self   ,					__magic_name__   ,					__magic_name__ = None   ,					__magic_name__ = "test"   ,					**__magic_name__   ,					)       ->				"PredictionOutput":
      _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(__magic_name__   ,					ignore_keys=__magic_name__   ,					metric_key_prefix=__magic_name__    )
 def 				__UpperCAmelCase		(			self   ,					__magic_name__   ,					__magic_name__   ,					__magic_name__   ,					__magic_name__ = None   ,					**__magic_name__   ,					)       ->				Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:

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

      _a         =				'labels' in inputs
      _a         =				self._prepare_inputs(__magic_name__    )

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

      if len(__magic_name__    ) == 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(**__magic_name__   ,					**__magic_name__    )

      # 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(__magic_name__   ,					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(__magic_name__   ,					gen_config.max_new_tokens + 1    )

      with torch.no_grad():
           if has_labels:
                with self.compute_loss_context_manager():
                     _a         =				model(**__magic_name__    )
                if self.label_smoother is not None:
                     _a         =				self.label_smoother(__magic_name__   ,					inputs['labels']    ).mean().detach()
                else:
                     _a         =				(outputs['loss'] if isinstance(__magic_name__   ,					__magic_name__    ) 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(__magic_name__   ,					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(__magic_name__   ,					gen_config.max_new_tokens + 1    )
      else:
           _a         =				None

      return loss, generated_tokens, labels




 def 				__UpperCAmelCase		(			self   ,					__magic_name__   ,					__magic_name__    )       ->				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'''






import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


a_       :     List[str] 							=     logging.get_logger(__name__)

a_       :     Union[str, Any] 							=     {
    "shi-labs/oneformer_ade20k_swin_tiny": (
        "https://huggingface.co/shi-labs/oneformer_ade20k_swin_tiny/blob/main/config.json"
    ),
    # See all OneFormer models at https://huggingface.co/models?filter=oneformer
}




class 			a  (  _SCREAMING_SNAKE_CASE							):
 _lowerCAmelCase			       =						"""oneformer"""
 _lowerCAmelCase			       =						{"""hidden_size""": """hidden_dim"""}
 def __init__(			self   ,					__magic_name__ = None   ,					__magic_name__ = 2_55   ,					__magic_name__ = 1_50   ,					__magic_name__ = 0.1   ,					__magic_name__ = 2.0   ,					__magic_name__ = 5.0   ,					__magic_name__ = 5.0   ,					__magic_name__ = 0.5   ,					__magic_name__ = 0.0_7   ,					__magic_name__ = 1_25_44   ,					__magic_name__ = 3.0   ,					__magic_name__ = 0.7_5   ,					__magic_name__ = 0.0_2   ,					__magic_name__ = 1.0   ,					__magic_name__ = 1e-05   ,					__magic_name__ = False   ,					__magic_name__ = True   ,					__magic_name__ = True   ,					__magic_name__ = [4, 8, 16, 32]   ,					__magic_name__ = 77   ,					__magic_name__ = 2_56   ,					__magic_name__ = 77   ,					__magic_name__ = 6   ,					__magic_name__ = 4_94_08   ,					__magic_name__ = 2   ,					__magic_name__ = 16   ,					__magic_name__ = 2_56   ,					__magic_name__ = 2_56   ,					__magic_name__ = 2_56   ,					__magic_name__ = 10_24   ,					__magic_name__ = "GN"   ,					__magic_name__ = 6   ,					__magic_name__ = 10   ,					__magic_name__ = True   ,					__magic_name__ = 8   ,					__magic_name__ = 0.1   ,					__magic_name__ = 20_48   ,					__magic_name__ = False   ,					__magic_name__ = False   ,					__magic_name__ = 2   ,					__magic_name__ = 4   ,					**__magic_name__   ,					)       ->				Dict:
      if backbone_config is None:
           logger.info('`backbone_config` is unset. Initializing the config with the default `Swin` backbone.'    )
           _a         =				CONFIG_MAPPING['swin'](
               image_size=2_24   ,					in_channels=3   ,					patch_size=4   ,					embed_dim=96   ,					depths=[2, 2, 6, 2]   ,					num_heads=[3, 6, 12, 24]   ,					window_size=7   ,					drop_path_rate=0.3   ,					use_absolute_embeddings=__magic_name__   ,					out_features=['stage1', 'stage2', 'stage3', 'stage4']   ,					)
      elif isinstance(__magic_name__   ,					__magic_name__    ):
           _a         =				backbone_config.get('model_type'    )
           _a         =				CONFIG_MAPPING[backbone_model_type]
           _a         =				config_class.from_dict(__magic_name__    )

      _a         =				backbone_config

      _a         =				ignore_value
      _a         =				num_queries
      _a         =				no_object_weight
      _a         =				class_weight
      _a         =				mask_weight
      _a         =				dice_weight
      _a         =				contrastive_weight
      _a         =				contrastive_temperature
      _a         =				train_num_points
      _a         =				oversample_ratio
      _a         =				importance_sample_ratio
      _a         =				init_std
      _a         =				init_xavier_std
      _a         =				layer_norm_eps
      _a         =				is_training
      _a         =				use_auxiliary_loss
      _a         =				output_auxiliary_logits
      _a         =				strides
      _a         =				task_seq_len
      _a         =				text_encoder_width
      _a         =				text_encoder_context_length
      _a         =				text_encoder_num_layers
      _a         =				text_encoder_vocab_size
      _a         =				text_encoder_proj_layers
      _a         =				text_encoder_n_ctx
      _a         =				conv_dim
      _a         =				mask_dim
      _a         =				hidden_dim
      _a         =				encoder_feedforward_dim
      _a         =				norm
      _a         =				encoder_layers
      _a         =				decoder_layers
      _a         =				use_task_norm
      _a         =				num_attention_heads
      _a         =				dropout
      _a         =				dim_feedforward
      _a         =				pre_norm
      _a         =				enforce_input_proj
      _a         =				query_dec_layers
      _a         =				common_stride
      _a         =				decoder_layers

      super().__init__(**__magic_name__    )




 def 				__UpperCAmelCase		(			self    )       ->				Dict[str, any]:
      _a         =				copy.deepcopy(self.__dict__    )
      _a         =				self.backbone_config.to_dict()
      _a         =				self.__class__.model_type
      return output

