


"""simple docstring"""
from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE					:       Union[str, Any]							   =							{
    """configuration_bart""": ["""BART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BartConfig""", """BartOnnxConfig"""],
    """tokenization_bart""": ["""BartTokenizer"""],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE					:       Dict							   =							["""BartTokenizerFast"""]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE					:       List[Any]							   =							[
			    """BART_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """BartForCausalLM""",
			    """BartForConditionalGeneration""",
			    """BartForQuestionAnswering""",
			    """BartForSequenceClassification""",
			    """BartModel""",
			    """BartPretrainedModel""",
			    """PretrainedBartModel""",
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE					:       Any							   =							[
			    """TFBartForConditionalGeneration""",
			    """TFBartForSequenceClassification""",
			    """TFBartModel""",
			    """TFBartPretrainedModel""",
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE					:       str							   =							[
			    """FlaxBartDecoderPreTrainedModel""",
			    """FlaxBartForCausalLM""",
			    """FlaxBartForConditionalGeneration""",
			    """FlaxBartForQuestionAnswering""",
			    """FlaxBartForSequenceClassification""",
			    """FlaxBartModel""",
			    """FlaxBartPreTrainedModel""",
			]

if TYPE_CHECKING:
			from .configuration_bart import BART_PRETRAINED_CONFIG_ARCHIVE_MAP, BartConfig, BartOnnxConfig
			from .tokenization_bart import BartTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_bart_fast import BartTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_bart import (
						    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
						    BartForCausalLM,
						    BartForConditionalGeneration,
						    BartForQuestionAnswering,
						    BartForSequenceClassification,
						    BartModel,
						    BartPretrainedModel,
						    PretrainedBartModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_bart import (
						    TFBartForConditionalGeneration,
						    TFBartForSequenceClassification,
						    TFBartModel,
						    TFBartPretrainedModel,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_bart import (
						    FlaxBartDecoderPreTrainedModel,
						    FlaxBartForCausalLM,
						    FlaxBartForConditionalGeneration,
						    FlaxBartForQuestionAnswering,
						    FlaxBartForSequenceClassification,
						    FlaxBartModel,
						    FlaxBartPreTrainedModel,
						)

else:
			import sys

			SCREAMING_SNAKE_CASE					:       Tuple							   =							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






'''simple docstring'''

import warnings

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




class 		SCREAMING_SNAKE_CASE_					(_UpperCAmelCase      ):



			"""simple docstring"""






			__magic_name__ :				Tuple   							=       ["""image_processor""", """tokenizer"""]
			__magic_name__ :				List[str]   							=       """AutoImageProcessor"""
			__magic_name__ :				List[str]   							=       """AutoTokenizer"""



			def __init__(							self				,			snake_case_=None				,			snake_case_=None				,			**snake_case_					):
									snake_case :       Any				=    None
									if "feature_extractor" in kwargs:
															warnings.warn(
															    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
															    " instead."				,			snake_case_				,			)
															snake_case :       str				=    kwargs.pop("feature_extractor"					)

									snake_case :       Optional[int]				=    image_processor if image_processor is not None else feature_extractor
									if image_processor is None:
															raise ValueError("You have to specify an image_processor."					)
									if tokenizer is None:
															raise ValueError("You have to specify a tokenizer."					)

									super().__init__(snake_case_				,			snake_case_					)
									snake_case :       Optional[int]				=    self.image_processor



			def __call__(							self				,			snake_case_=None				,			snake_case_=None				,			snake_case_=None				,			**snake_case_					):

									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:
															snake_case :       Optional[int]				=    self.tokenizer(snake_case_				,			return_tensors=snake_case_				,			**snake_case_					)

									if images is not None:
															snake_case :       List[Any]				=    self.image_processor(snake_case_				,			return_tensors=snake_case_				,			**snake_case_					)

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



			def   lowerCamelCase (							self				,			*snake_case_				,			**snake_case_					):
									return self.tokenizer.batch_decode(*snake_case_				,			**snake_case_					)



			def   lowerCamelCase (							self				,			*snake_case_				,			**snake_case_					):
									return self.tokenizer.decode(*snake_case_				,			**snake_case_					)



			@property
			def   lowerCamelCase (							self					):
									snake_case :       List[str]				=    self.tokenizer.model_input_names
									snake_case :       Any				=    self.image_processor.model_input_names
									return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names					)					)



			@property
			def   lowerCamelCase (							self					):
									warnings.warn(
									    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."				,			snake_case_				,			)
									return self.image_processor_class






			@property
			def   lowerCamelCase (							self					):
									warnings.warn(
									    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."				,			snake_case_				,			)
									return self.image_processor





