





from typing import TYPE_CHECKING

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


__lowercase       :List[str]       =  {
    "configuration_roberta": ["ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "RobertaConfig", "RobertaOnnxConfig"],
    "tokenization_roberta": ["RobertaTokenizer"],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowercase       :str       =  ["RobertaTokenizerFast"]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowercase       :Union[str, Any]       =  [
				    "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:
				__lowercase       :Any       =  [
				    "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:
				__lowercase       :Any       =  [
				    "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

				__lowercase       :str       =  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding



class    _a     (				lowercase__							):






							"""simple docstring"""

							snake_case_							 =						["image_processor", "tokenizer"]
							snake_case_							 =						"CLIPImageProcessor"
							snake_case_							 =						("CLIPTokenizer", "CLIPTokenizerFast")

							def __init__(      self				:     Any							,	a				:     List[Any]=None							,	a				:     Any=None							,	**a				:     int    )	->int:
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					None
													if "feature_extractor" in kwargs:
																			warnings.warn(
																			    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
																			    " instead."							,	a							,	)
																			SCREAMING_SNAKE_CASE__     :				List[Any]   =					kwargs.pop("feature_extractor"    )

													SCREAMING_SNAKE_CASE__     :				int   =					image_processor if image_processor is not None else feature_extractor
													if image_processor is None:
																			raise ValueError("You need to specify an `image_processor`."    )
													if tokenizer is None:
																			raise ValueError("You need to specify a `tokenizer`."    )

													super().__init__(a							,	a    )

							def __call__(      self				:     Tuple							,	a				:     Tuple=None							,	a				:     Union[str, Any]=None							,	a				:     List[str]=None							,	**a				:     Optional[Any]    )	->Optional[Any]:

													if text is None and images is None:
																			raise ValueError("You have to specify either text or images. Both cannot be none."    )

													if text is not None:
																			SCREAMING_SNAKE_CASE__     :				str   =					self.tokenizer(a							,	return_tensors=a							,	**a    )

													if images is not None:
																			SCREAMING_SNAKE_CASE__     :				int   =					self.image_processor(a							,	return_tensors=a							,	**a    )

													if text is not None and images is not None:
																			SCREAMING_SNAKE_CASE__     :				Tuple   =					image_features.pixel_values
																			return encoding
													elif text is not None:
																			return encoding
													else:
																			return BatchEncoding(data=dict(**a    )							,	tensor_type=a    )

							def      A_  (      self				:     Optional[int]							,	*a				:     Any							,	**a				:     List[str]    )	->Any:
													return self.tokenizer.batch_decode(*a							,	**a    )

							def      A_  (      self				:     Any							,	*a				:     Optional[int]							,	**a				:     Dict    )	->Any:
													return self.tokenizer.decode(*a							,	**a    )

							@property
							def      A_  (      self				:     List[str]    )	->Union[str, Any]:
													SCREAMING_SNAKE_CASE__     :				Dict   =					self.tokenizer.model_input_names
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					self.image_processor.model_input_names
													return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names    )    )

							@property
							def      A_  (      self				:     Optional[int]    )	->List[Any]:
													warnings.warn(
													    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."							,	a							,	)
													return self.image_processor_class






							@property
							def      A_  (      self				:     Dict    )	->str:
													warnings.warn(
													    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."							,	a							,	)
													return self.image_processor