



"""simple docstring"""

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase__					:						Dict												=			logging.get_logger(__name__)

lowercase__					:						Union[str, Any]												=			{
    "roberta-base": "https://huggingface.co/roberta-base/resolve/main/config.json",
    "roberta-large": "https://huggingface.co/roberta-large/resolve/main/config.json",
    "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/config.json",
    "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/config.json",
    "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json",
    "roberta-large-openai-detector": "https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json",
}




class  _UpperCAmelCase (    lowercase__):
 _lowerCAmelCase     :    List[str]	      =	"""roberta"""

 def __init__(  self	: int						,  lowercase_	: Dict=50265						,  lowercase_	: Optional[int]=768						,  lowercase_	: Optional[Any]=12						,  lowercase_	: Optional[int]=12						,  lowercase_	: int=3072						,  lowercase_	: Dict="gelu"						,  lowercase_	: Union[str, Any]=0.1						,  lowercase_	: int=0.1						,  lowercase_	: Tuple=512						,  lowercase_	: List[str]=2						,  lowercase_	: Any=0.02						,  lowercase_	: Optional[int]=1E-12						,  lowercase_	: List[Any]=1						,  lowercase_	: int=0						,  lowercase_	: Any=2						,  lowercase_	: Tuple="absolute"						,  lowercase_	: Tuple=True						,  lowercase_	: str=None						,  **lowercase_	: Optional[Any]						,  ):
     super().__init__(pad_token_id=__lowerCamelCase						,  bos_token_id=__lowerCamelCase						,  eos_token_id=__lowerCamelCase						,  **__lowerCamelCase       )

     snake_case_				:  List[Any]       =			vocab_size
     snake_case_				:  str       =			hidden_size
     snake_case_				:  int       =			num_hidden_layers
     snake_case_				:  Optional[Any]       =			num_attention_heads
     snake_case_				:  Optional[Any]       =			hidden_act
     snake_case_				:  Any       =			intermediate_size
     snake_case_				:  Tuple       =			hidden_dropout_prob
     snake_case_				:  Any       =			attention_probs_dropout_prob
     snake_case_				:  Tuple       =			max_position_embeddings
     snake_case_				:  int       =			type_vocab_size
     snake_case_				:  Any       =			initializer_range
     snake_case_				:  Dict       =			layer_norm_eps
     snake_case_				:  int       =			position_embedding_type
     snake_case_				:  Any       =			use_cache
     snake_case_				:  int       =			classifier_dropout





class  _UpperCAmelCase (    lowercase__):

 @property
 def 		_snake_case      (  self	: Union[str, Any]       ):



     if self.task == "multiple-choice":
         snake_case_				:  int       =			{0: "batch", 1: "choice", 2: "sequence"}
     else:
         snake_case_				:  Optional[Any]       =			{0: "batch", 1: "sequence"}
     return OrderedDict(
         [
             ('''input_ids''', dynamic_axis),
             ('''attention_mask''', dynamic_axis),
         ]       )










"""simple docstring"""

import math
import time
from typing import Dict, List, Optional

from torch.utils.data import Dataset

from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics


if is_torch_tpu_available(check_device=False):
     import torch_xla.core.xla_model as xm
     import torch_xla.debug.metrics as met




class  _UpperCAmelCase (    lowerCAmelCase__):

 def __init__(  self	: List[Any]						,  *lowercase_	: List[Any]						,  lowercase_	: Optional[Any]=None						,  lowercase_	: Tuple=None						,  **lowercase_	: Union[str, Any]       ):
     super().__init__(*lowercase_						,  **lowercase_       )
     snake_case_				:  str       =			eval_examples
     snake_case_				:  Optional[Any]       =			post_process_function

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

     snake_case_				:  Optional[int]       =			self.eval_dataset if eval_dataset is None else eval_dataset
     snake_case_				:  Optional[Any]       =			self.get_eval_dataloader(lowercase_       )
     snake_case_				:  Dict       =			self.eval_examples if eval_examples is None else eval_examples

     # Temporarily disable metric computation, we will do it in the loop here.
     snake_case_				:  Optional[int]       =			self.compute_metrics
     snake_case_				:  str       =			None
     snake_case_				:  int       =			time.time()
     snake_case_				:  Any       =			self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
     try:
         snake_case_				:  str       =			eval_loop(
             lowercase_						,  description='''Evaluation'''						,  prediction_loss_only=True if compute_metrics is None else None						,  ignore_keys=lowercase_						,  metric_key_prefix=lowercase_						,  )
     finally:
         snake_case_				:  Optional[int]       =			compute_metrics
     snake_case_				:  Dict       =			self.args.eval_batch_size * self.args.world_size
     if f"{metric_key_prefix}_jit_compilation_time" in output.metrics:
         start_time += output.metrics[f"{metric_key_prefix}_jit_compilation_time"]
     output.metrics.update(
         speed_metrics(
             lowercase_						,  lowercase_						,  num_samples=output.num_samples						,  num_steps=math.ceil(output.num_samples / total_batch_size       )						,  )       )

     if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
         # Only the main node write the results by default
         snake_case_				:  Tuple       =			self.post_process_function(lowercase_						,  lowercase_						,  lowercase_       )
         snake_case_				:  Optional[int]       =			self.compute_metrics(lowercase_       )

         # Prefix all keys with metric_key_prefix + '_'
         for key in list(metrics.keys()       ):
             if not key.startswith(f"{metric_key_prefix}_"       ):
                 snake_case_				:  int       =			metrics.pop(lowercase_       )

         metrics.update(output.metrics       )
     else:
         snake_case_				:  Tuple       =			output.metrics

     if self.args.should_log:
         # Only the main node log the results by default
         self.log(lowercase_       )

     if self.args.tpu_metrics_debug or self.args.debug:
         # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
         xm.master_print(met.metrics_report()       )

     snake_case_				:  str       =			self.callback_handler.on_evaluate(self.args						,  self.state						,  self.control						,  lowercase_       )
     return metrics






 def 		_snake_case      (  self	: Any						,  lowercase_	: int						,  lowercase_	: List[str]						,  lowercase_	: str=None						,  lowercase_	: str = "test"						,  **lowercase_	: Dict       ):
     snake_case_				:  Dict       =			gen_kwargs.copy()

     snake_case_				:  Dict       =			self.get_test_dataloader(lowercase_       )

     # Temporarily disable metric computation, we will do it in the loop here.
     snake_case_				:  Tuple       =			self.compute_metrics
     snake_case_				:  int       =			None
     snake_case_				:  List[Any]       =			time.time()
     snake_case_				:  int       =			self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
     try:
         snake_case_				:  List[str]       =			eval_loop(
             lowercase_						,  description='''Prediction'''						,  prediction_loss_only=True if compute_metrics is None else None						,  ignore_keys=lowercase_						,  metric_key_prefix=lowercase_						,  )
     finally:
         snake_case_				:  str       =			compute_metrics

     snake_case_				:  int       =			self.args.eval_batch_size * self.args.world_size
     if f"{metric_key_prefix}_jit_compilation_time" in output.metrics:
         start_time += output.metrics[f"{metric_key_prefix}_jit_compilation_time"]
     output.metrics.update(
         speed_metrics(
             lowercase_						,  lowercase_						,  num_samples=output.num_samples						,  num_steps=math.ceil(output.num_samples / total_batch_size       )						,  )       )
     if self.post_process_function is None or self.compute_metrics is None:
         return output

     snake_case_				:  Tuple       =			self.post_process_function(lowercase_						,  lowercase_						,  lowercase_						,  '''predict'''       )
     snake_case_				:  Dict       =			self.compute_metrics(lowercase_       )

     # Prefix all keys with metric_key_prefix + '_'
     for key in list(metrics.keys()       ):
         if not key.startswith(f"{metric_key_prefix}_"       ):
             snake_case_				:  Union[str, Any]       =			metrics.pop(lowercase_       )
     metrics.update(output.metrics       )
     return PredictionOutput(predictions=predictions.predictions						,  label_ids=predictions.label_ids						,  metrics=lowercase_       )





