

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a		={
    """configuration_rembert""": ["""REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RemBertConfig""", """RemBertOnnxConfig"""]
}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=["""RemBertTokenizer"""]

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=["""RemBertTokenizerFast"""]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=[
					    """REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """RemBertForCausalLM""",
					    """RemBertForMaskedLM""",
					    """RemBertForMultipleChoice""",
					    """RemBertForQuestionAnswering""",
					    """RemBertForSequenceClassification""",
					    """RemBertForTokenClassification""",
					    """RemBertLayer""",
					    """RemBertModel""",
					    """RemBertPreTrainedModel""",
					    """load_tf_weights_in_rembert""",
					]


try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=[
					    """TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """TFRemBertForCausalLM""",
					    """TFRemBertForMaskedLM""",
					    """TFRemBertForMultipleChoice""",
					    """TFRemBertForQuestionAnswering""",
					    """TFRemBertForSequenceClassification""",
					    """TFRemBertForTokenClassification""",
					    """TFRemBertLayer""",
					    """TFRemBertModel""",
					    """TFRemBertPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_rembert import RemBertTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_rembert_fast import RemBertTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_rembert import (
										    REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    RemBertForCausalLM,
										    RemBertForMaskedLM,
										    RemBertForMultipleChoice,
										    RemBertForQuestionAnswering,
										    RemBertForSequenceClassification,
										    RemBertForTokenClassification,
										    RemBertLayer,
										    RemBertModel,
										    RemBertPreTrainedModel,
										    load_tf_weights_in_rembert,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_rembert import (
										    TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFRemBertForCausalLM,
										    TFRemBertForMaskedLM,
										    TFRemBertForMultipleChoice,
										    TFRemBertForQuestionAnswering,
										    TFRemBertForSequenceClassification,
										    TFRemBertForTokenClassification,
										    TFRemBertLayer,
										    TFRemBertModel,
										    TFRemBertPreTrainedModel,
										)


else:
					import sys

					a		=_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)








'''simple docstring'''




import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


__snake_case    						=logging.getLogger(__name__)

@dataclass
@add_start_docstrings(TrainingArguments.__doc__		)
class 		UpperCAmelCase_      (  __lowercase		):
     lowerCamelCase				:	bool		=     field(default=__lowercase       ,	metadata={'''help''': '''Whether to use SortishSampler or not.'''}		)
     lowerCamelCase				:	bool		=     field(
         default=__lowercase       ,	metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}		)
     lowerCamelCase				:	Optional[int]		=     field(
         default=__lowercase       ,	metadata={
             '''help''': (
                 '''The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default '''
                 '''to the `max_length` value of the model configuration.'''
             )
         }       ,	)
     lowerCamelCase				:	Optional[int]		=     field(
         default=__lowercase       ,	metadata={
             '''help''': (
                 '''The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default '''
                 '''to the `num_beams` value of the model configuration.'''
             )
         }       ,	)
     lowerCamelCase				:	Optional[Union[str, Path, GenerationConfig]]		=     field(
         default=__lowercase       ,	metadata={
             '''help''': '''Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'''
         }       ,	)





     def 							__UpperCAmelCase      ( self       :      Dict    )				->  List[str]:
        lowerCAmelCase     =			super().to_dict()
        for k, v in d.items():
           if isinstance(UpperCAmelCase__    ,      UpperCAmelCase__    ):
              lowerCAmelCase     =			v.to_dict()
        return d





