






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

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

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__    						=			[
			    '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:
			lowerCAmelCase__    						=			[
			    '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:
			lowerCAmelCase__    						=			[
			    '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

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









"""simple docstring"""
lowerCAmelCase__    						=			'\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

lowerCAmelCase__    						=			[{'type': 'code', 'content': INSTALL_CONTENT}]
lowerCAmelCase__    						=			{
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}

