





'''simple docstring'''




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   A_	(  lowerCAmelCase_			):
 _lowerCamelCase  : Optional[Any]           =							["""image_processor""", """tokenizer"""]
 _lowerCamelCase  : Dict           =							"""BlipImageProcessor"""
 _lowerCamelCase  : int           =							("""BertTokenizer""", """BertTokenizerFast""")




 def __init__(					self       :    str	,			snake_case_       :    Dict	,			snake_case_       :    Any    ):
      _UpperCAmelCase     							=   False
      super().__init__(snake_case_	,			snake_case_    )
      _UpperCAmelCase     							=   self.image_processor




 def __call__(					self       :    Tuple	,			snake_case_       :    ImageInput = None	,			snake_case_       :    Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None	,			snake_case_       :    bool = True	,			snake_case_       :    Union[bool, str, PaddingStrategy] = False	,			snake_case_       :    Union[bool, str, TruncationStrategy] = None	,			snake_case_       :    Optional[int] = None	,			snake_case_       :    int = 0	,			snake_case_       :    Optional[int] = None	,			snake_case_       :    Optional[bool] = None	,			snake_case_       :    bool = False	,			snake_case_       :    bool = False	,			snake_case_       :    bool = False	,			snake_case_       :    bool = False	,			snake_case_       :    bool = False	,			snake_case_       :    bool = True	,			snake_case_       :    Optional[Union[str, TensorType]] = None	,			**snake_case_       :    Optional[Any]	,			):
      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      lowercase	(					self       :    Optional[int]	,			*snake_case_       :    Union[str, Any]	,			**snake_case_       :    List[str]    ):
      return self.tokenizer.batch_decode(*snake_case_	,			**snake_case_    )




 def      lowercase	(					self       :    Union[str, Any]	,			*snake_case_       :    int	,			**snake_case_       :    Optional[int]    ):
      return self.tokenizer.decode(*snake_case_	,			**snake_case_    )




 @property
 def      lowercase	(					self       :    Any    ):
      _UpperCAmelCase     							=   self.tokenizer.model_input_names
      _UpperCAmelCase     							=   self.image_processor.model_input_names
      return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names    )    )








'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowerCamelCase							:   str  						=	{
    'configuration_rag': ['RagConfig'],
    'retrieval_rag': ['RagRetriever'],
    'tokenization_rag': ['RagTokenizer'],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase							:   Dict  						=	[
     'RagModel',
     'RagPreTrainedModel',
     'RagSequenceForGeneration',
     'RagTokenForGeneration',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase							:   Optional[int]  						=	[
     'TFRagModel',
     'TFRagPreTrainedModel',
     'TFRagSequenceForGeneration',
     'TFRagTokenForGeneration',
 ]


if TYPE_CHECKING:
 from .configuration_rag import RagConfig
 from .retrieval_rag import RagRetriever
 from .tokenization_rag import RagTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_rag import (
      TFRagModel,
      TFRagPreTrainedModel,
      TFRagSequenceForGeneration,
      TFRagTokenForGeneration,
  )

else:
 import sys

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




