



'''simple docstring'''



from typing import TYPE_CHECKING

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


lowerCAmelCase	:		Union[str, Any]				=   {
    '''configuration_distilbert''': [
        '''DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''DistilBertConfig''',
        '''DistilBertOnnxConfig''',
    ],
    '''tokenization_distilbert''': ['''DistilBertTokenizer'''],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase	:		Tuple				=   ['''DistilBertTokenizerFast''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase	:		Tuple				=   [
	    '''DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''DistilBertForMaskedLM''',
	    '''DistilBertForMultipleChoice''',
	    '''DistilBertForQuestionAnswering''',
	    '''DistilBertForSequenceClassification''',
	    '''DistilBertForTokenClassification''',
	    '''DistilBertModel''',
	    '''DistilBertPreTrainedModel''',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase	:		List[str]				=   [
	    '''TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TFDistilBertForMaskedLM''',
	    '''TFDistilBertForMultipleChoice''',
	    '''TFDistilBertForQuestionAnswering''',
	    '''TFDistilBertForSequenceClassification''',
	    '''TFDistilBertForTokenClassification''',
	    '''TFDistilBertMainLayer''',
	    '''TFDistilBertModel''',
	    '''TFDistilBertPreTrainedModel''',
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase	:		Optional[int]				=   [
	    '''FlaxDistilBertForMaskedLM''',
	    '''FlaxDistilBertForMultipleChoice''',
	    '''FlaxDistilBertForQuestionAnswering''',
	    '''FlaxDistilBertForSequenceClassification''',
	    '''FlaxDistilBertForTokenClassification''',
	    '''FlaxDistilBertModel''',
	    '''FlaxDistilBertPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_distilbert import (
	    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    DistilBertConfig,
	    DistilBertOnnxConfig,
	)
	from .tokenization_distilbert import DistilBertTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_distilbert_fast import DistilBertTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_distilbert import (
		    DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    DistilBertForMaskedLM,
		    DistilBertForMultipleChoice,
		    DistilBertForQuestionAnswering,
		    DistilBertForSequenceClassification,
		    DistilBertForTokenClassification,
		    DistilBertModel,
		    DistilBertPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_distilbert import (
		    TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFDistilBertForMaskedLM,
		    TFDistilBertForMultipleChoice,
		    TFDistilBertForQuestionAnswering,
		    TFDistilBertForSequenceClassification,
		    TFDistilBertForTokenClassification,
		    TFDistilBertMainLayer,
		    TFDistilBertModel,
		    TFDistilBertPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_distilbert import (
		    FlaxDistilBertForMaskedLM,
		    FlaxDistilBertForMultipleChoice,
		    FlaxDistilBertForQuestionAnswering,
		    FlaxDistilBertForSequenceClassification,
		    FlaxDistilBertForTokenClassification,
		    FlaxDistilBertModel,
		    FlaxDistilBertPreTrainedModel,
		)

else:
	import sys

	lowerCAmelCase	:		List[str]				=   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




'''simple docstring'''



import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union

import torch

from ..utils import BaseOutput


lowerCAmelCase	:		Optional[Any]				=   '''scheduler_config.json'''






class 					UpperCAmelCase__	(		UpperCamelCase__ ):
					a      :     str        =							1
					a      :     Optional[int]        =							2
					a      :     int        =							3
					a      :     Union[str, Any]        =							4
					a      :     int        =							5
					a      :     Optional[int]        =							6
					a      :     str        =							7
					a      :     List[Any]        =							8
					a      :     List[str]        =							9
					a      :     List[str]        =							1_0
					a      :     int        =							1_1
					a      :     Any        =							1_2
					a      :     Any        =							1_3
					a      :     Tuple        =							1_4






@dataclass
class 					UpperCAmelCase__	(		UpperCamelCase__ ):
					a      :     torch.FloatTensor






class 					UpperCAmelCase__	:
					a      :     Tuple        =							SCHEDULER_CONFIG_NAME
					a      :     Union[str, Any]        =							[]
					a      :     str        =							True



					@classmethod
					def        UpperCAmelCase_ (    cls			,				UpperCamelCase = None			,				UpperCamelCase = None			,				UpperCamelCase=False			,				**UpperCamelCase			,				)     ->			int:
						__lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase												=  cls.load_config(
						    pretrained_model_name_or_path=UpperCamelCase			,				subfolder=UpperCamelCase			,				return_unused_kwargs=UpperCamelCase			,				return_commit_hash=UpperCamelCase			,				**UpperCamelCase			,				)
						return cls.from_config(UpperCamelCase			,				return_unused_kwargs=UpperCamelCase			,				**UpperCamelCase     )



					def        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase = False			,				**UpperCamelCase     )     ->			Dict:
						self.save_config(save_directory=UpperCamelCase			,				push_to_hub=UpperCamelCase			,				**UpperCamelCase     )



					@property
					def        UpperCAmelCase_ (    self     )     ->			str:
						return self._get_compatibles()



					@classmethod
					def        UpperCAmelCase_ (    cls     )     ->			Tuple:
						__lowerCAmelCase												=  list(set([cls.__name__] + cls._compatibles     )     )
						__lowerCAmelCase												=  importlib.import_module(__name__.split("."     )[0]     )
						__lowerCAmelCase												=  [
						    getattr(UpperCamelCase			,				UpperCamelCase     ) for c in compatible_classes_str if hasattr(UpperCamelCase			,				UpperCamelCase     )
						]
						return compatible_classes