
'''simple docstring'''





from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
      import PIL

if is_torch_available():
      import torch


SCREAMING_SNAKE_CASE     =       logging.get_logger(__name__)






class 							UpperCAmelCase_							(    A				):

 '''simple docstring'''
 lowercase_					:    Union[str, Any]				=							["pixel_values"]







 def __init__(					self		:  Tuple			,				snake_case__		:  List[str] = True			,				snake_case__		:  int = None			,				snake_case__		:  Any = PILImageResampling.BILINEAR			,				snake_case__		:  Dict = True			,				snake_case__		:  Optional[int] = 1 / 2_55			,				snake_case__		:  int = True			,				snake_case__		:  Dict = None			,				snake_case__		:  int = True			,				**snake_case__		:  List[Any]			,				):



   '''simple docstring'''




   super().__init__(**lowercase__ )
   UpperCAmelCase__      :					List[Any]				       =							size if size is not None else {"shortest_edge": 2_24}
   UpperCAmelCase__      :					Optional[int]				       =							get_size_dict(lowercase__			,				default_to_square=lowercase__ )
   UpperCAmelCase__      :					Optional[int]				       =							crop_size if crop_size is not None else {"height": 2_56, "width": 2_56}
   UpperCAmelCase__      :					Optional[Any]				       =							get_size_dict(lowercase__			,				param_name="crop_size" )

   UpperCAmelCase__      :					Optional[int]				       =							do_resize
   UpperCAmelCase__      :					List[Any]				       =							size
   UpperCAmelCase__      :					List[Any]				       =							resample
   UpperCAmelCase__      :					Tuple				       =							do_rescale
   UpperCAmelCase__      :					Tuple				       =							rescale_factor
   UpperCAmelCase__      :					List[Any]				       =							do_center_crop
   UpperCAmelCase__      :					Optional[int]				       =							crop_size
   UpperCAmelCase__      :					Dict				       =							do_flip_channel_order







 def 							UpperCamelCase  (					self		:  int			,				snake_case__		:  Any			,				snake_case__		:  str			,				snake_case__		:  Optional[Any] = PIL.Image.BILINEAR			,				snake_case__		:  int = None			,				**snake_case__		:  Any			,				):



   '''simple docstring'''




   UpperCAmelCase__      :					List[Any]				       =							get_size_dict(lowercase__			,				default_to_square=lowercase__ )
   if "shortest_edge" not in size:
     raise ValueError(F"""The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}""" )
   UpperCAmelCase__      :					List[str]				       =							get_resize_output_image_size(lowercase__			,				size=size["shortest_edge"]			,				default_to_square=lowercase__ )
   return resize(lowercase__			,				size=lowercase__			,				resample=lowercase__			,				data_format=lowercase__			,				**lowercase__ )







 def 							UpperCamelCase  (					self		:  int			,				snake_case__		:  List[str]			,				snake_case__		:  str			,				snake_case__		:  Any = None			,				**snake_case__		:  Optional[Any]			,				):



   '''simple docstring'''




   UpperCAmelCase__      :					str				       =							get_size_dict(lowercase__ )
   if "height" not in size or "width" not in size:
     raise ValueError(F"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}""" )
   return center_crop(lowercase__			,				size=(size["height"], size["width"])			,				data_format=lowercase__			,				**lowercase__ )







 def 							UpperCamelCase  (					self		:  Optional[Any]			,				snake_case__		:  Union[str, Any]			,				snake_case__		:  str			,				snake_case__		:  int = None			,				**snake_case__		:  Optional[Any]			,				):



   '''simple docstring'''




   return rescale(lowercase__			,				scale=lowercase__			,				data_format=lowercase__			,				**lowercase__ )







 def 							UpperCamelCase  (					self		:  Any			,				snake_case__		:  str			,				snake_case__		:  Optional[Any] = None ):



   '''simple docstring'''




   return flip_channel_order(lowercase__			,				data_format=lowercase__ )







 def 							UpperCamelCase  (					self		:  Dict			,				snake_case__		:  int			,				snake_case__		:  Optional[Any] = None			,				snake_case__		:  Tuple = None			,				snake_case__		:  List[Any] = None			,				snake_case__		:  Union[str, Any] = None			,				snake_case__		:  Optional[int] = None			,				snake_case__		:  int = None			,				snake_case__		:  Any = None			,				snake_case__		:  Tuple = None			,				snake_case__		:  str = None			,				snake_case__		:  str = ChannelDimension.FIRST			,				**snake_case__		:  Tuple			,				):



   '''simple docstring'''




   UpperCAmelCase__      :					Optional[Any]				       =							do_resize if do_resize is not None else self.do_resize
   UpperCAmelCase__      :					Dict				       =							resample if resample is not None else self.resample
   UpperCAmelCase__      :					List[Any]				       =							do_rescale if do_rescale is not None else self.do_rescale
   UpperCAmelCase__      :					Dict				       =							rescale_factor if rescale_factor is not None else self.rescale_factor
   UpperCAmelCase__      :					Optional[int]				       =							do_center_crop if do_center_crop is not None else self.do_center_crop
   UpperCAmelCase__      :					Optional[int]				       =							(
       do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
   )

   UpperCAmelCase__      :					Dict				       =							size if size is not None else self.size
   UpperCAmelCase__      :					Union[str, Any]				       =							get_size_dict(lowercase__			,				default_to_square=lowercase__ )
   UpperCAmelCase__      :					str				       =							crop_size if crop_size is not None else self.crop_size
   UpperCAmelCase__      :					int				       =							get_size_dict(lowercase__			,				param_name="crop_size" )

   UpperCAmelCase__      :					List[str]				       =							make_list_of_images(lowercase__ )

   if not valid_images(lowercase__ ):
     raise ValueError(
         "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
         "torch.Tensor, tf.Tensor or jax.ndarray." )

   if do_resize and size is None:
     raise ValueError("Size must be specified if do_resize is True." )

   if do_rescale and rescale_factor is None:
     raise ValueError("Rescale factor must be specified if do_rescale is True." )

   if do_center_crop and crop_size is None:
     raise ValueError("Crop size must be specified if do_center_crop is True." )

   # All transformations expect numpy arrays.
   UpperCAmelCase__      :					Dict				       =							[to_numpy_array(lowercase__ ) for image in images]

   if do_resize:
     UpperCAmelCase__      :					Dict				       =							[self.resize(image=lowercase__			,				size=lowercase__			,				resample=lowercase__ ) for image in images]

   if do_center_crop:
     UpperCAmelCase__      :					List[str]				       =							[self.center_crop(image=lowercase__			,				size=lowercase__ ) for image in images]

   if do_rescale:
     UpperCAmelCase__      :					Tuple				       =							[self.rescale(image=lowercase__			,				scale=lowercase__ ) for image in images]

   # the pretrained checkpoints assume images are BGR, not RGB
   if do_flip_channel_order:
     UpperCAmelCase__      :					List[Any]				       =							[self.flip_channel_order(image=lowercase__ ) for image in images]

   UpperCAmelCase__      :					Optional[int]				       =							[to_channel_dimension_format(lowercase__			,				lowercase__ ) for image in images]

   UpperCAmelCase__      :					Tuple				       =							{"pixel_values": images}
   return BatchFeature(data=lowercase__			,				tensor_type=lowercase__ )







 def 							UpperCamelCase  (					self		:  List[str]			,				snake_case__		:  Optional[int]			,				snake_case__		:  Tuple = None ):



   '''simple docstring'''




   UpperCAmelCase__      :					Tuple				       =							outputs.logits

   # Resize logits and compute semantic segmentation maps
   if target_sizes is not None:
     if len(lowercase__ ) != len(lowercase__ ):
       raise ValueError(
           "Make sure that you pass in as many target sizes as the batch dimension of the logits" )

     if is_torch_tensor(lowercase__ ):
       UpperCAmelCase__      :					Tuple				       =							target_sizes.numpy()

     UpperCAmelCase__      :					Optional[Any]				       =							[]

     for idx in range(len(lowercase__ ) ):
       UpperCAmelCase__      :					List[str]				       =							torch.nn.functional.interpolate(
           logits[idx].unsqueeze(dim=0 )			,				size=target_sizes[idx]			,				mode="bilinear"			,				align_corners=lowercase__ )
       UpperCAmelCase__      :					Optional[Any]				       =							resized_logits[0].argmax(dim=0 )
       semantic_segmentation.append(lowercase__ )
   else:
     UpperCAmelCase__      :					List[Any]				       =							logits.argmax(dim=1 )
     UpperCAmelCase__      :					Dict				       =							[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]

   return semantic_segmentation






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__  (					__magic_name__			):



       '''simple docstring'''







       lowercase_						=    ["""image_processor""", """tokenizer"""]
       lowercase_						=    """LayoutLMv2ImageProcessor"""
       lowercase_						=    ("""LayoutXLMTokenizer""", """LayoutXLMTokenizerFast""")

       def __init__(							self ,   lowercase__=None ,   lowercase__=None ,   **lowercase__     ):







             '''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.''' ,   lowercase__ ,   )
                   __A    =kwargs.pop('''feature_extractor'''     )

             __A    =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__(lowercase__ ,   lowercase__     )

       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__ ,   ):







             '''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
             __A    =self.image_processor(images=lowercase__ ,   return_tensors=lowercase__     )

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

             __A    =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=lowercase__ ,   add_special_tokens=lowercase__ ,   padding=lowercase__ ,   truncation=lowercase__ ,   max_length=lowercase__ ,   stride=lowercase__ ,   pad_to_multiple_of=lowercase__ ,   return_token_type_ids=lowercase__ ,   return_attention_mask=lowercase__ ,   return_overflowing_tokens=lowercase__ ,   return_special_tokens_mask=lowercase__ ,   return_offsets_mapping=lowercase__ ,   return_length=lowercase__ ,   verbose=lowercase__ ,   return_tensors=lowercase__ ,   **lowercase__ ,   )

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

             return encoded_inputs

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__     ):







             '''simple docstring'''



             __A    =[]
             for sample_idx in overflow_to_sample_mapping:
                   images_with_overflow.append(images[sample_idx]     )

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

             return images_with_overflow

       def     __UpperCamelCase     (							self ,   *lowercase__ ,   **lowercase__     ):







             '''simple docstring'''



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

       def     __UpperCamelCase     (							self ,   *lowercase__ ,   **lowercase__     ):







             '''simple docstring'''



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

       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



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

       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             warnings.warn(
                 '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' ,   lowercase__ ,   )
             return self.image_processor_class


       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



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


