



'''simple docstring'''





import warnings
from typing import List, Optional, Union

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


class 					snake_case			(UpperCamelCase ):
       lowerCAmelCase__	:Union[str, Any]     =       ["image_processor", "tokenizer"]
       lowerCAmelCase__	:Dict     =       "LayoutLMv3ImageProcessor"
       lowerCAmelCase__	:List[Any]     =       ("LayoutLMv3Tokenizer", "LayoutLMv3TokenizerFast")



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

           lowercase__									=    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__(UpperCAmelCase_  ,UpperCAmelCase_   )



       def __call__(					self  ,UpperCAmelCase_  ,UpperCAmelCase_ = None  ,UpperCAmelCase_ = None  ,UpperCAmelCase_ = None  ,UpperCAmelCase_ = None  ,UpperCAmelCase_ = True  ,UpperCAmelCase_ = False  ,UpperCAmelCase_ = None  ,UpperCAmelCase_ = None  ,UpperCAmelCase_ = 0  ,UpperCAmelCase_ = None  ,UpperCAmelCase_ = None  ,UpperCAmelCase_ = None  ,UpperCAmelCase_ = False  ,UpperCAmelCase_ = False  ,UpperCAmelCase_ = False  ,UpperCAmelCase_ = False  ,UpperCAmelCase_ = True  ,UpperCAmelCase_ = None  ,**UpperCAmelCase_  ,)	->				BatchEncoding:
           # verify input
           if self.image_processor.apply_ocr and (boxes is not None):
               raise ValueError(
                   "You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."   )

           if self.image_processor.apply_ocr and (word_labels is not None):
               raise ValueError(
                   "You cannot provide word labels if you initialized the image processor with apply_ocr set to True."   )

           # first, apply the image processor
           lowercase__									=    self.image_processor(images=UpperCAmelCase_  ,return_tensors=UpperCAmelCase_   )

           # second, apply the tokenizer
           if text is not None and self.image_processor.apply_ocr and text_pair is None:
               if isinstance(UpperCAmelCase_  ,UpperCAmelCase_   ):
                   lowercase__									=    [text]  # add batch dimension (as the image processor always adds a batch dimension)
               lowercase__									=    features["words"]

           lowercase__									=    self.tokenizer(
               text=text if text is not None else features["words"]  ,text_pair=text_pair if text_pair is not None else None  ,boxes=boxes if boxes is not None else features["boxes"]  ,word_labels=UpperCAmelCase_  ,add_special_tokens=UpperCAmelCase_  ,padding=UpperCAmelCase_  ,truncation=UpperCAmelCase_  ,max_length=UpperCAmelCase_  ,stride=UpperCAmelCase_  ,pad_to_multiple_of=UpperCAmelCase_  ,return_token_type_ids=UpperCAmelCase_  ,return_attention_mask=UpperCAmelCase_  ,return_overflowing_tokens=UpperCAmelCase_  ,return_special_tokens_mask=UpperCAmelCase_  ,return_offsets_mapping=UpperCAmelCase_  ,return_length=UpperCAmelCase_  ,verbose=UpperCAmelCase_  ,return_tensors=UpperCAmelCase_  ,**UpperCAmelCase_  ,)

           # add pixel values
           lowercase__									=    features.pop("pixel_values"   )
           if return_overflowing_tokens is True:
               lowercase__									=    self.get_overflowing_images(UpperCAmelCase_  ,encoded_inputs["overflow_to_sample_mapping"]   )
           lowercase__									=    images

           return encoded_inputs



       def 				_a   (					self  ,UpperCAmelCase_  ,UpperCAmelCase_   )	->				Any:
           # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
           lowercase__									=    []
           for sample_idx in overflow_to_sample_mapping:
               images_with_overflow.append(images[sample_idx]   )

           if len(UpperCAmelCase_   ) != len(UpperCAmelCase_   ):
               raise ValueError(
                   "Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
                   F''' {len(UpperCAmelCase_   )} and {len(UpperCAmelCase_   )}'''   )

           return images_with_overflow



       def 				_a   (					self  ,*UpperCAmelCase_  ,**UpperCAmelCase_   )	->				List[Any]:
           return self.tokenizer.batch_decode(*UpperCAmelCase_  ,**UpperCAmelCase_   )



       def 				_a   (					self  ,*UpperCAmelCase_  ,**UpperCAmelCase_   )	->				Dict:
           return self.tokenizer.decode(*UpperCAmelCase_  ,**UpperCAmelCase_   )



       @property
       def 				_a   (					self   )	->				Dict:
           return ["input_ids", "bbox", "attention_mask", "pixel_values"]



       @property
       def 				_a   (					self   )	->				Tuple:
           warnings.warn(
               "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."  ,UpperCAmelCase_  ,)
           return self.image_processor_class







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









'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE__          =  {
    "configuration_clap": [
        "CLAP_PRETRAINED_MODEL_ARCHIVE_LIST",
        "ClapAudioConfig",
        "ClapConfig",
        "ClapTextConfig",
    ],
    "processing_clap": ["ClapProcessor"],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE__          =  [
     "CLAP_PRETRAINED_MODEL_ARCHIVE_LIST",
     "ClapModel",
     "ClapPreTrainedModel",
     "ClapTextModel",
     "ClapTextModelWithProjection",
     "ClapAudioModel",
     "ClapAudioModelWithProjection",
 ]
 SCREAMING_SNAKE_CASE__          =  ["ClapFeatureExtractor"]

if TYPE_CHECKING:
 from .configuration_clap import (
     CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
     ClapAudioConfig,
     ClapConfig,
     ClapTextConfig,
 )
 from .processing_clap import ClapProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_clap import ClapFeatureExtractor
  from .modeling_clap import (
      CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
      ClapAudioModel,
      ClapAudioModelWithProjection,
      ClapModel,
      ClapPreTrainedModel,
      ClapTextModel,
      ClapTextModelWithProjection,
  )


else:
 import sys

 SCREAMING_SNAKE_CASE__          =  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




