
'''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 		__lowercase			(					_lowercase      ):
   lowerCamelCase     :						Tuple		 =     ['image_processor', 'tokenizer']
   lowerCamelCase     :						Tuple		 =     'LayoutLMv2ImageProcessor'
   lowerCamelCase     :						Optional[int]		 =     ('LayoutLMv2Tokenizer', 'LayoutLMv2TokenizerFast')

   def __init__(self     ,				A=None     ,				A=None     ,				**A ):
        lowerCamelCase_   : Dict       =						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     ,				)
             lowerCamelCase_   : Optional[Any]       =						kwargs.pop('''feature_extractor''' )

        lowerCamelCase_   : Any       =						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     ,				A     ,				A = None     ,				A = None     ,				A = None     ,				A = None     ,				A = True     ,				A = False     ,				A = False     ,				A = None     ,				A = 0     ,				A = None     ,				A = None     ,				A = None     ,				A = False     ,				A = False     ,				A = False     ,				A = False     ,				A = True     ,				A = None     ,				**A     ,				):



        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.''' )

        if return_overflowing_tokens is True and return_offsets_mapping is False:
             raise ValueError('''You cannot return overflowing tokens without returning the offsets mapping.''' )

        # first, apply the image processor
        lowerCamelCase_   : int       =						self.image_processor(images=A     ,				return_tensors=A )

        # second, apply the tokenizer
        if text is not None and self.image_processor.apply_ocr and text_pair is None:
             if isinstance(A     ,				A ):
                  lowerCamelCase_   : Optional[int]       =						[text]  # add batch dimension (as the image processor always adds a batch dimension)
             lowerCamelCase_   : Dict       =						features['''words''']

        lowerCamelCase_   : List[str]       =						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=A     ,				add_special_tokens=A     ,				padding=A     ,				truncation=A     ,				max_length=A     ,				stride=A     ,				pad_to_multiple_of=A     ,				return_token_type_ids=A     ,				return_attention_mask=A     ,				return_overflowing_tokens=A     ,				return_special_tokens_mask=A     ,				return_offsets_mapping=A     ,				return_length=A     ,				verbose=A     ,				return_tensors=A     ,				**A     ,				)

        # add pixel values
        lowerCamelCase_   : List[str]       =						features.pop('''pixel_values''' )
        if return_overflowing_tokens is True:
             lowerCamelCase_   : str       =						self.get_overflowing_images(A     ,				encoded_inputs['''overflow_to_sample_mapping'''] )
        lowerCamelCase_   : int       =						images

        return encoded_inputs

   def     UpperCAmelCase__	(self     ,				A     ,				A ):
        lowerCamelCase_   : List[str]       =						[]
        for sample_idx in overflow_to_sample_mapping:
             images_with_overflow.append(images[sample_idx] )

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

        return images_with_overflow

   def     UpperCAmelCase__	(self     ,				*A     ,				**A ):
        return self.tokenizer.batch_decode(*A     ,				**A )

   def     UpperCAmelCase__	(self     ,				*A     ,				**A ):
        return self.tokenizer.decode(*A     ,				**A )

   @property
   def     UpperCAmelCase__	(self ):
        return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]

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




'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


__lowercase							:				List[str]					=       {
    '''configuration_sam''': [
        '''SAM_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SamConfig''',
        '''SamMaskDecoderConfig''',
        '''SamPromptEncoderConfig''',
        '''SamVisionConfig''',
    ],
    '''processing_sam''': ['''SamProcessor'''],
}


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowercase							:				List[Any]					=       [
					    '''SAM_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''SamModel''',
					    '''SamPreTrainedModel''',
					]
try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowercase							:				int					=       [
					    '''TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFSamModel''',
					    '''TFSamPreTrainedModel''',
					]
try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowercase							:				str					=       ['''SamImageProcessor''']


if TYPE_CHECKING:
					from .configuration_sam import (
					    SAM_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    SamConfig,
					    SamMaskDecoderConfig,
					    SamPromptEncoderConfig,
					    SamVisionConfig,
					)
					from .processing_sam import SamProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_sam import SAM_PRETRAINED_MODEL_ARCHIVE_LIST, SamModel, SamPreTrainedModel

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_sam import TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST, TFSamModel, TFSamPreTrainedModel

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .image_processing_sam import SamImageProcessor

else:
					import sys

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


