



from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType







class 			__lowerCAmelCase    (			A						):
			UpperCamelCase   					=     ['''image_processor''', '''tokenizer''']
			UpperCamelCase   					=     '''BlipImageProcessor'''
			UpperCamelCase   					=     ('''BertTokenizer''', '''BertTokenizerFast''')



			def __init__(						self   :  str ,		A   :  int ,		A   :  List[str])   ->    Dict:



						"""simple docstring"""






						_UpperCAmelCase		       =   False
						super().__init__(_snake_case ,		_snake_case)
						_UpperCAmelCase		       =   self.image_processor



			def __call__(						self   :  Any ,		A   :  Any = None ,		A   :  Optional[int] = None ,		A   :  List[Any] = True ,		A   :  Union[str, Any] = False ,		A   :  Union[str, Any] = None ,		A   :  Optional[Any] = None ,		A   :  int = 0 ,		A   :  Tuple = None ,		A   :  Union[str, Any] = None ,		A   :  Optional[int] = False ,		A   :  Dict = False ,		A   :  List[str] = False ,		A   :  int = False ,		A   :  Dict = False ,		A   :  Optional[Any] = True ,		A   :  List[str] = None ,		**A   :  Optional[Any] ,		)   ->    BatchEncoding:



						"""simple docstring"""






						if images is None and text is None:
									raise ValueError('You have to specify either images or text.')

						# Get only text
						if images is None:
									_UpperCAmelCase		       =   self.tokenizer
									_UpperCAmelCase		       =   self.tokenizer(
									    text=_snake_case ,		add_special_tokens=_snake_case ,		padding=_snake_case ,		truncation=_snake_case ,		max_length=_snake_case ,		stride=_snake_case ,		pad_to_multiple_of=_snake_case ,		return_attention_mask=_snake_case ,		return_overflowing_tokens=_snake_case ,		return_special_tokens_mask=_snake_case ,		return_offsets_mapping=_snake_case ,		return_token_type_ids=_snake_case ,		return_length=_snake_case ,		verbose=_snake_case ,		return_tensors=_snake_case ,		**_snake_case ,		)
									return text_encoding

						# add pixel_values
						_UpperCAmelCase		       =   self.image_processor(_snake_case ,		return_tensors=_snake_case)

						if text is not None:
									_UpperCAmelCase		       =   self.tokenizer(
									    text=_snake_case ,		add_special_tokens=_snake_case ,		padding=_snake_case ,		truncation=_snake_case ,		max_length=_snake_case ,		stride=_snake_case ,		pad_to_multiple_of=_snake_case ,		return_attention_mask=_snake_case ,		return_overflowing_tokens=_snake_case ,		return_special_tokens_mask=_snake_case ,		return_offsets_mapping=_snake_case ,		return_token_type_ids=_snake_case ,		return_length=_snake_case ,		verbose=_snake_case ,		return_tensors=_snake_case ,		**_snake_case ,		)
						else:
									_UpperCAmelCase		       =   None

						if text_encoding is not None:
									encoding_image_processor.update(_snake_case)

						return encoding_image_processor



			def     _lowerCamelCase       (						self   :  Optional[Any] ,		*A   :  Any ,		**A   :  int)   ->    int:



						"""simple docstring"""






						return self.tokenizer.batch_decode(*_snake_case ,		**_snake_case)



			def     _lowerCamelCase       (						self   :  List[Any] ,		*A   :  Union[str, Any] ,		**A   :  str)   ->    Optional[int]:



						"""simple docstring"""






						return self.tokenizer.decode(*_snake_case ,		**_snake_case)



			@property
			def     _lowerCamelCase       (						self   :  Dict)   ->    List[Any]:



						"""simple docstring"""






						_UpperCAmelCase		       =   self.tokenizer.model_input_names
						_UpperCAmelCase		       =   self.image_processor.model_input_names
						return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))


from typing import TYPE_CHECKING

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


A     :	Dict    = {
    'configuration_xlm_roberta': [
        'XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'XLMRobertaConfig',
        'XLMRobertaOnnxConfig',
    ],
}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A     :	Union[str, Any]    = ['XLMRobertaTokenizer']

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

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A     :	List[Any]    = [
					    'XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'XLMRobertaForCausalLM',
					    'XLMRobertaForMaskedLM',
					    'XLMRobertaForMultipleChoice',
					    'XLMRobertaForQuestionAnswering',
					    'XLMRobertaForSequenceClassification',
					    'XLMRobertaForTokenClassification',
					    'XLMRobertaModel',
					    'XLMRobertaPreTrainedModel',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A     :	int    = [
					    'TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFXLMRobertaForCausalLM',
					    'TFXLMRobertaForMaskedLM',
					    'TFXLMRobertaForMultipleChoice',
					    'TFXLMRobertaForQuestionAnswering',
					    'TFXLMRobertaForSequenceClassification',
					    'TFXLMRobertaForTokenClassification',
					    'TFXLMRobertaModel',
					    'TFXLMRobertaPreTrainedModel',
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A     :	Tuple    = [
					    'FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'FlaxXLMRobertaForMaskedLM',
					    'FlaxXLMRobertaForCausalLM',
					    'FlaxXLMRobertaForMultipleChoice',
					    'FlaxXLMRobertaForQuestionAnswering',
					    'FlaxXLMRobertaForSequenceClassification',
					    'FlaxXLMRobertaForTokenClassification',
					    'FlaxXLMRobertaModel',
					    'FlaxXLMRobertaPreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_xlm_roberta import (
					    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    XLMRobertaConfig,
					    XLMRobertaOnnxConfig,
					)

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_xlm_roberta import XLMRobertaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_xlm_roberta_fast import XLMRobertaTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_xlm_roberta import (
										    XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
										    XLMRobertaForCausalLM,
										    XLMRobertaForMaskedLM,
										    XLMRobertaForMultipleChoice,
										    XLMRobertaForQuestionAnswering,
										    XLMRobertaForSequenceClassification,
										    XLMRobertaForTokenClassification,
										    XLMRobertaModel,
										    XLMRobertaPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_xlm_roberta import (
										    TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFXLMRobertaForCausalLM,
										    TFXLMRobertaForMaskedLM,
										    TFXLMRobertaForMultipleChoice,
										    TFXLMRobertaForQuestionAnswering,
										    TFXLMRobertaForSequenceClassification,
										    TFXLMRobertaForTokenClassification,
										    TFXLMRobertaModel,
										    TFXLMRobertaPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_xlm_roberta import (
										    FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
										    FlaxXLMRobertaForCausalLM,
										    FlaxXLMRobertaForMaskedLM,
										    FlaxXLMRobertaForMultipleChoice,
										    FlaxXLMRobertaForQuestionAnswering,
										    FlaxXLMRobertaForSequenceClassification,
										    FlaxXLMRobertaForTokenClassification,
										    FlaxXLMRobertaModel,
										    FlaxXLMRobertaPreTrainedModel,
										)

else:
					import sys

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