

from typing import TYPE_CHECKING

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


A											=     {
    'configuration_roberta': ['ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RobertaConfig', 'RobertaOnnxConfig'],
    'tokenization_roberta': ['RobertaTokenizer'],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A											=     ['RobertaTokenizerFast']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A											=     [
		    'ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'RobertaForCausalLM',
		    'RobertaForMaskedLM',
		    'RobertaForMultipleChoice',
		    'RobertaForQuestionAnswering',
		    'RobertaForSequenceClassification',
		    'RobertaForTokenClassification',
		    'RobertaModel',
		    'RobertaPreTrainedModel',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A											=     [
		    'TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFRobertaForCausalLM',
		    'TFRobertaForMaskedLM',
		    'TFRobertaForMultipleChoice',
		    'TFRobertaForQuestionAnswering',
		    'TFRobertaForSequenceClassification',
		    'TFRobertaForTokenClassification',
		    'TFRobertaMainLayer',
		    'TFRobertaModel',
		    'TFRobertaPreTrainedModel',
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A											=     [
		    'FlaxRobertaForCausalLM',
		    'FlaxRobertaForMaskedLM',
		    'FlaxRobertaForMultipleChoice',
		    'FlaxRobertaForQuestionAnswering',
		    'FlaxRobertaForSequenceClassification',
		    'FlaxRobertaForTokenClassification',
		    'FlaxRobertaModel',
		    'FlaxRobertaPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
		from .tokenization_roberta import RobertaTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_roberta_fast import RobertaTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_roberta import (
				    ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
				    RobertaForCausalLM,
				    RobertaForMaskedLM,
				    RobertaForMultipleChoice,
				    RobertaForQuestionAnswering,
				    RobertaForSequenceClassification,
				    RobertaForTokenClassification,
				    RobertaModel,
				    RobertaPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_roberta import (
				    TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFRobertaForCausalLM,
				    TFRobertaForMaskedLM,
				    TFRobertaForMultipleChoice,
				    TFRobertaForQuestionAnswering,
				    TFRobertaForSequenceClassification,
				    TFRobertaForTokenClassification,
				    TFRobertaMainLayer,
				    TFRobertaModel,
				    TFRobertaPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_roberta import (
				    FlaxRobertaForCausalLM,
				    FlaxRobertaForMaskedLM,
				    FlaxRobertaForMultipleChoice,
				    FlaxRobertaForQuestionAnswering,
				    FlaxRobertaForSequenceClassification,
				    FlaxRobertaForTokenClassification,
				    FlaxRobertaModel,
				    FlaxRobertaPreTrainedModel,
				)

else:
		import sys

		A											=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


A											=     logging.get_logger(__name__)

A											=     {
    'google/bit-50': 'https://huggingface.co/google/bit-50/resolve/main/config.json',
}


class SCREAMING_SNAKE_CASE  (		__snake_case							,						__snake_case							):






			"""simple docstring"""




			__A						  =  """bit"""
			__A						  =  ["""preactivation""", """bottleneck"""]
			__A						  =  ["""SAME""", """VALID"""]


			def __init__(    self ,				__UpperCamelCase=3 ,				__UpperCamelCase=64 ,				__UpperCamelCase=[2_56, 5_12, 10_24, 20_48] ,				__UpperCamelCase=[3, 4, 6, 3] ,				__UpperCamelCase="preactivation" ,				__UpperCamelCase="relu" ,				__UpperCamelCase=None ,				__UpperCamelCase=32 ,				__UpperCamelCase=0.0 ,				__UpperCamelCase=False ,				__UpperCamelCase=32 ,				__UpperCamelCase=1 ,				__UpperCamelCase=None ,				__UpperCamelCase=None ,				**__UpperCamelCase ,				):

				"""simple docstring"""


				super().__init__(**__UpperCamelCase							)
				if layer_type not in self.layer_types:
					raise ValueError(f"""layer_type={layer_type} is not one of {','.join(self.layer_types							)}"""							)
				if global_padding is not None:
					if global_padding.upper() in self.supported_padding:
						snake_case_      		= global_padding.upper()
					else:
						raise ValueError(f"""Padding strategy {global_padding} not supported"""							)
				snake_case_      		= num_channels
				snake_case_      		= embedding_size
				snake_case_      		= hidden_sizes
				snake_case_      		= depths
				snake_case_      		= layer_type
				snake_case_      		= hidden_act
				snake_case_      		= global_padding
				snake_case_      		= num_groups
				snake_case_      		= drop_path_rate
				snake_case_      		= embedding_dynamic_padding
				snake_case_      		= output_stride
				snake_case_      		= width_factor

				snake_case_      		= ['stem'] + [f"""stage{idx}""" for idx in range(1 ,				len(__UpperCamelCase							) + 1							)]
				snake_case_		,       snake_case_      		= get_aligned_output_features_output_indices(
				    out_features=__UpperCamelCase ,				out_indices=__UpperCamelCase ,				stage_names=self.stage_names							)
