'''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


snake_case__     :			str      			=  logging.get_logger(__name__)






class      __SCREAMING_SNAKE_CASE						(       SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''






  def __init__( self      ,		snake_case_ = None      ,		snake_case_ = None      ,		snake_case_ = None      ,		snake_case_ = None      ,		snake_case_ = None      ,		snake_case_ = None      ,		snake_case_ = None      ,		snake_case_ = None      ,		snake_case_ = None      ,		snake_case_ = (None, None)      ,		snake_case_ = None      ,		):




       '''simple docstring'''

       super().__init__(
           model=_SCREAMING_SNAKE_CASE      ,		args=_SCREAMING_SNAKE_CASE      ,		data_collator=_SCREAMING_SNAKE_CASE      ,		train_dataset=_SCREAMING_SNAKE_CASE      ,		eval_dataset=_SCREAMING_SNAKE_CASE      ,		tokenizer=_SCREAMING_SNAKE_CASE      ,		model_init=_SCREAMING_SNAKE_CASE      ,		compute_metrics=_SCREAMING_SNAKE_CASE      ,		callbacks=_SCREAMING_SNAKE_CASE      ,		optimizers=_SCREAMING_SNAKE_CASE      ,		preprocess_logits_for_metrics=_SCREAMING_SNAKE_CASE      ,		)

       # 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_							:   Dict   =      self.load_generation_config(self.args.generation_config						)
            UpperCAmelCase_							:   List[str]   =      gen_config


  @staticmethod
  def _UpperCamelCase     ( snake_case_						):




       '''simple docstring'''

       if isinstance(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE						):
            return deepcopy(_SCREAMING_SNAKE_CASE						)

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

       UpperCAmelCase_							:   List[Any]   =      GenerationConfig.from_pretrained(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE						)
       return gen_config


  def _UpperCamelCase     ( self      ,		snake_case_ = None      ,		snake_case_ = None      ,		snake_case_ = "eval"      ,		**snake_case_      ,		):




       '''simple docstring'''

       UpperCAmelCase_							:   Dict   =      gen_kwargs.copy()
       if gen_kwargs.get('max_length'						) is None and gen_kwargs.get('max_new_tokens'						) is None:
            UpperCAmelCase_							:   Tuple   =      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_							:   Tuple   =      gen_kwargs

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


  def _UpperCamelCase     ( self      ,		snake_case_      ,		snake_case_ = None      ,		snake_case_ = "test"      ,		**snake_case_      ,		):




       '''simple docstring'''

       UpperCAmelCase_							:   Tuple   =      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_							:   int   =      (
           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().predict(_SCREAMING_SNAKE_CASE      ,		ignore_keys=_SCREAMING_SNAKE_CASE      ,		metric_key_prefix=_SCREAMING_SNAKE_CASE						)


  def _UpperCamelCase     ( self      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_ = None      ,		**snake_case_      ,		):




       '''simple docstring'''

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

       UpperCAmelCase_							:   int   =      "labels" in inputs
       UpperCAmelCase_							:   List[Any]   =      self._prepare_inputs(_SCREAMING_SNAKE_CASE						)

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

       if len(_SCREAMING_SNAKE_CASE						) == 0 and hasattr(self      ,		'_gen_kwargs'						):
            UpperCAmelCase_							:   Tuple   =      self._gen_kwargs.copy()

       if gen_kwargs.get('max_length'						) is None and gen_kwargs.get('max_new_tokens'						) is None:
            UpperCAmelCase_							:   Optional[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_							:   Optional[int]   =      True if is_deepspeed_zeroa_enabled() else False
       UpperCAmelCase_							:   int   =      (
           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_							:   int   =      {k: v for k, v in inputs.items() if k != "decoder_input_ids"}
       UpperCAmelCase_							:   Union[str, Any]   =      self.model.generate(**_SCREAMING_SNAKE_CASE      ,		**_SCREAMING_SNAKE_CASE						)

       # 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_							:   int   =      False

       # Retrieves GenerationConfig from model.generation_config
       UpperCAmelCase_							:   Optional[int]   =      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_							:   Optional[Any]   =      self._pad_tensors_to_max_len(_SCREAMING_SNAKE_CASE      ,		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(_SCREAMING_SNAKE_CASE      ,		gen_config.max_new_tokens + 1						)

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

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

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

       return loss, generated_tokens, labels






  def _UpperCamelCase     ( self      ,		snake_case_      ,		snake_case_						):




       '''simple docstring'''

       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_							:   str   =      (
                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_							:   str   =      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_							:   List[Any]   =      pad_token_id * torch.ones(
           (tensor.shape[0], max_length)      ,		dtype=tensor.dtype      ,		device=tensor.device						)
       UpperCAmelCase_							:   List[str]   =      tensor
       return padded_tensor














from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase     :							Any	     =			logging.get_logger(__name__)

lowercase     :							int	     =			{
    # pcqm4mv1 now deprecated
    '''graphormer-base''': '''https://huggingface.co/clefourrier/graphormer-base-pcqm4mv2/resolve/main/config.json''',
    # See all Graphormer models at https://huggingface.co/models?filter=graphormer
}




class 						UpperCAmelCase_  (		SCREAMING_SNAKE_CASE__    ):


						'''simple docstring'''




						A   :      Optional[int]						 =    'graphormer'
						A   :      List[str]						 =    ['past_key_values']






						def __init__(		self						, _SCREAMING_SNAKE_CASE = 1						, _SCREAMING_SNAKE_CASE = 512 * 9						, _SCREAMING_SNAKE_CASE = 512 * 3						, _SCREAMING_SNAKE_CASE = 512						, _SCREAMING_SNAKE_CASE = 512						, _SCREAMING_SNAKE_CASE = 512						, _SCREAMING_SNAKE_CASE = 128						, _SCREAMING_SNAKE_CASE = 5						, _SCREAMING_SNAKE_CASE = 1024						, _SCREAMING_SNAKE_CASE = "multi_hop"						, _SCREAMING_SNAKE_CASE = 512						, _SCREAMING_SNAKE_CASE = False						, _SCREAMING_SNAKE_CASE = 12						, _SCREAMING_SNAKE_CASE = 768						, _SCREAMING_SNAKE_CASE = 768						, _SCREAMING_SNAKE_CASE = 32						, _SCREAMING_SNAKE_CASE = 0.1						, _SCREAMING_SNAKE_CASE = 0.1						, _SCREAMING_SNAKE_CASE = 0.0						, _SCREAMING_SNAKE_CASE = False						, _SCREAMING_SNAKE_CASE = False						, _SCREAMING_SNAKE_CASE = False						, _SCREAMING_SNAKE_CASE = "gelu"						, _SCREAMING_SNAKE_CASE = None						, _SCREAMING_SNAKE_CASE = False						, _SCREAMING_SNAKE_CASE = 0						, _SCREAMING_SNAKE_CASE = False						, _SCREAMING_SNAKE_CASE = 0.0						, _SCREAMING_SNAKE_CASE = 8						, _SCREAMING_SNAKE_CASE = None						, _SCREAMING_SNAKE_CASE = None						, _SCREAMING_SNAKE_CASE = True						, _SCREAMING_SNAKE_CASE = True						, _SCREAMING_SNAKE_CASE=0						, _SCREAMING_SNAKE_CASE=1						, _SCREAMING_SNAKE_CASE=2						, **_SCREAMING_SNAKE_CASE						, )       -> List[str]:
								snake_case_ :							str         =				num_classes
								snake_case_ :							Union[str, Any]         =				num_atoms
								snake_case_ :							Dict         =				num_in_degree
								snake_case_ :							Union[str, Any]         =				num_out_degree
								snake_case_ :							List[Any]         =				num_edges
								snake_case_ :							Union[str, Any]         =				num_spatial
								snake_case_ :							int         =				num_edge_dis
								snake_case_ :							List[Any]         =				edge_type
								snake_case_ :							Any         =				multi_hop_max_dist
								snake_case_ :							Tuple         =				spatial_pos_max
								snake_case_ :							Union[str, Any]         =				max_nodes
								snake_case_ :							str         =				num_hidden_layers
								snake_case_ :							str         =				embedding_dim
								snake_case_ :							List[str]         =				embedding_dim
								snake_case_ :							Dict         =				ffn_embedding_dim
								snake_case_ :							Any         =				num_attention_heads
								snake_case_ :							List[str]         =				dropout
								snake_case_ :							List[str]         =				attention_dropout
								snake_case_ :							Optional[Any]         =				layerdrop
								snake_case_ :							Optional[int]         =				encoder_normalize_before
								snake_case_ :							Union[str, Any]         =				pre_layernorm
								snake_case_ :							int         =				apply_graphormer_init
								snake_case_ :							List[Any]         =				activation_fn
								snake_case_ :							str         =				embed_scale
								snake_case_ :							int         =				freeze_embeddings
								snake_case_ :							Optional[Any]         =				num_trans_layers_to_freeze
								snake_case_ :							int         =				share_input_output_embed
								snake_case_ :							int         =				traceable
								snake_case_ :							Tuple         =				q_noise
								snake_case_ :							Tuple         =				qn_block_size

								# These parameters are here for future extensions
								# atm, the model only supports self attention
								snake_case_ :							List[Any]         =				kdim
								snake_case_ :							Tuple         =				vdim
								snake_case_ :							Any         =				self_attention
								snake_case_ :							Any         =				bias

								super().__init__(
								    pad_token_id=_SCREAMING_SNAKE_CASE						, bos_token_id=_SCREAMING_SNAKE_CASE						, eos_token_id=_SCREAMING_SNAKE_CASE						, **_SCREAMING_SNAKE_CASE						, )
