





'''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 lowerCamelCase						(_a					):
		_lowercase  :	Optional[Any]       					=				["""image_processor""", """tokenizer"""]
		_lowercase  :	Tuple       					=				"""LayoutLMv2ImageProcessor"""
		_lowercase  :	Union[str, Any]       					=				("""LayoutXLMTokenizer""", """LayoutXLMTokenizerFast""")







		def __init__(		self     ,     lowercase__=None     ,     lowercase__=None     ,     **lowercase__		)		->						Union[str, Any]:






								"""simple docstring"""





								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		:	str					     =     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__(snake_case_     ,     snake_case_		)







		def __call__(		self     ,     lowercase__     ,     lowercase__ = None     ,     lowercase__ = None     ,     lowercase__ = None     ,     lowercase__ = None     ,     lowercase__ = True     ,     lowercase__ = False     ,     lowercase__ = None     ,     lowercase__ = None     ,     lowercase__ = 0     ,     lowercase__ = None     ,     lowercase__ = None     ,     lowercase__ = None     ,     lowercase__ = False     ,     lowercase__ = False     ,     lowercase__ = False     ,     lowercase__ = False     ,     lowercase__ = True     ,     lowercase__ = None     ,     **lowercase__     ,     )		->						BatchEncoding:






								"""simple docstring"""





								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
								_snake_case		:	Tuple					     =     self.image_processor(images=snake_case_     ,     return_tensors=snake_case_		)

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

								_snake_case		:	Dict					     =     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=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_token_type_ids=snake_case_     ,     return_attention_mask=snake_case_     ,     return_overflowing_tokens=snake_case_     ,     return_special_tokens_mask=snake_case_     ,     return_offsets_mapping=snake_case_     ,     return_length=snake_case_     ,     verbose=snake_case_     ,     return_tensors=snake_case_     ,     **snake_case_     ,     )

								# add pixel values
								_snake_case		:	Tuple					     =     features.pop('''pixel_values'''		)
								if return_overflowing_tokens is True:
														_snake_case		:	Optional[int]					     =     self.get_overflowing_images(snake_case_     ,     encoded_inputs['''overflow_to_sample_mapping''']		)
								_snake_case		:	Any					     =     images

								return encoded_inputs







		def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__		)		->						int:






								"""simple docstring"""





								_snake_case		:	Optional[int]					     =     []
								for sample_idx in overflow_to_sample_mapping:
														images_with_overflow.append(images[sample_idx]		)

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

								return images_with_overflow







		def 				UpperCAmelCase_				(		self     ,     *lowercase__     ,     **lowercase__		)		->						int:






								"""simple docstring"""





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







		def 				UpperCAmelCase_				(		self     ,     *lowercase__     ,     **lowercase__		)		->						Optional[Any]:






								"""simple docstring"""





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







		@property
		def 				UpperCAmelCase_				(		self		)		->						Dict:






								"""simple docstring"""





								return ["input_ids", "bbox", "attention_mask", "image"]







		@property
		def 				UpperCAmelCase_				(		self		)		->						Tuple:






								"""simple docstring"""





								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 				UpperCAmelCase_				(		self		)		->						Dict:






								"""simple docstring"""





								warnings.warn(
								    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''     ,     snake_case_     ,     )
								return self.image_processor









'''simple docstring'''

# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase					:		Dict		=							{'configuration_timm_backbone': ['TimmBackboneConfig']}


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase					:		Union[str, Any]		=							['TimmBackbone']


if TYPE_CHECKING:
   from .configuration_timm_backbone import TimmBackboneConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_timm_backbone import TimmBackbone

else:
   import sys

   UpperCAmelCase					:		Dict		=							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


