


"""simple docstring"""



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

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
  import torch

if is_vision_available():
  import PIL


__snake_case         =					logging.get_logger(__name__)







def 						__lowerCAmelCase		(		lowercase						:						np.ndarray		,							lowercase						:						Union[int, Iterable[int]]		,							lowercase						:						bool		,							lowercase						:						int				)			->		Tuple[int, int]:


    """simple docstring"""





    def constraint_to_multiple_of(lowercase						:						Tuple		,							lowercase						:						int		,							lowercase						:						int=0		,							lowercase						:						Tuple=None				):
        snake_case					:   Dict         =					round(val / multiple				) * multiple

        if max_val is not None and x > max_val:
            snake_case					:   Optional[int]         =					math.floor(val / multiple				) * multiple

        if x < min_val:
            snake_case					:   List[Any]         =					math.ceil(val / multiple				) * multiple

        return x

    snake_case					:   Tuple         =					(output_size, output_size) if isinstance(lowercase		,							lowercase				) else output_size

    snake_case      ,snake_case					:   Tuple         =					get_image_size(lowercase				)
    snake_case      ,snake_case					:   Union[str, Any]         =					output_size

    # determine new height and width
    snake_case					:   Dict         =					output_height / input_height
    snake_case					:   str         =					output_width / input_width

    if keep_aspect_ratio:
        # scale as little as possible
        if abs(1 - scale_width				) < abs(1 - scale_height				):
            # fit width
            snake_case					:   List[Any]         =					scale_width
        else:
            # fit height
            snake_case					:   Optional[Any]         =					scale_height

    snake_case					:   Any         =					constraint_to_multiple_of(scale_height * input_height		,							multiple=lowercase				)
    snake_case					:   Union[str, Any]         =					constraint_to_multiple_of(scale_width * input_width		,							multiple=lowercase				)

    return (new_height, new_width)







class _lowerCAmelCase       (   snake_case_			):
   __UpperCAmelCase				: Union[str, Any]													=						['''pixel_values''']







   def __init__(			self		,			UpperCamelCase__ = True		,			UpperCamelCase__ = None		,			UpperCamelCase__ = PILImageResampling.BILINEAR		,			UpperCamelCase__ = False		,			UpperCamelCase__ = 1		,			UpperCamelCase__ = True		,			UpperCamelCase__ = 1 / 255		,			UpperCamelCase__ = True		,			UpperCamelCase__ = None		,			UpperCamelCase__ = None		,			**UpperCamelCase__		,			)			->	None:

       '''simple docstring'''




       super().__init__(**UpperCamelCase__		)
       snake_case					:   int         =					size if size is not None else {"height": 384, "width": 384}
       snake_case					:   Any         =					get_size_dict(UpperCamelCase__		)
       snake_case					:   Any         =					do_resize
       snake_case					:   Tuple         =					size
       snake_case					:   Dict         =					keep_aspect_ratio
       snake_case					:   Optional[Any]         =					ensure_multiple_of
       snake_case					:   List[Any]         =					resample
       snake_case					:   Optional[Any]         =					do_rescale
       snake_case					:   Optional[Any]         =					rescale_factor
       snake_case					:   List[Any]         =					do_normalize
       snake_case					:   Optional[Any]         =					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       snake_case					:   Union[str, Any]         =					image_std if image_std is not None else IMAGENET_STANDARD_STD







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__ = False		,			UpperCamelCase__ = 1		,			UpperCamelCase__ = PILImageResampling.BICUBIC		,			UpperCamelCase__ = None		,			**UpperCamelCase__		,			)			->	np.ndarray:

       '''simple docstring'''




       snake_case					:   List[str]         =					get_size_dict(UpperCamelCase__		)
       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()}'		)
       snake_case					:   List[str]         =					get_resize_output_image_size(
           UpperCamelCase__		,			output_size=(size["height"], size["width"])		,			keep_aspect_ratio=UpperCamelCase__		,			multiple=UpperCamelCase__		,			)
       return resize(UpperCamelCase__		,			size=UpperCamelCase__		,			resample=UpperCamelCase__		,			data_format=UpperCamelCase__		,			**UpperCamelCase__		)







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__ = None		,			**UpperCamelCase__		,			)			->	Dict:

       '''simple docstring'''




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







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__ = None		,			**UpperCamelCase__		,			)			->	np.ndarray:

       '''simple docstring'''




       return normalize(UpperCamelCase__		,			mean=UpperCamelCase__		,			std=UpperCamelCase__		,			data_format=UpperCamelCase__		,			**UpperCamelCase__		)







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		,			UpperCamelCase__ = None		,			UpperCamelCase__ = None		,			UpperCamelCase__ = None		,			UpperCamelCase__ = None		,			UpperCamelCase__ = None		,			UpperCamelCase__ = None		,			UpperCamelCase__ = None		,			UpperCamelCase__ = None		,			UpperCamelCase__ = None		,			UpperCamelCase__ = None		,			UpperCamelCase__ = None		,			UpperCamelCase__ = ChannelDimension.FIRST		,			**UpperCamelCase__		,			)			->	PIL.Image.Image:

       '''simple docstring'''




       snake_case					:   int         =					do_resize if do_resize is not None else self.do_resize
       snake_case					:   int         =					size if size is not None else self.size
       snake_case					:   List[str]         =					get_size_dict(UpperCamelCase__		)
       snake_case					:   Tuple         =					keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
       snake_case					:   Union[str, Any]         =					ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
       snake_case					:   str         =					resample if resample is not None else self.resample
       snake_case					:   List[str]         =					do_rescale if do_rescale is not None else self.do_rescale
       snake_case					:   Any         =					rescale_factor if rescale_factor is not None else self.rescale_factor
       snake_case					:   List[Any]         =					do_normalize if do_normalize is not None else self.do_normalize
       snake_case					:   str         =					image_mean if image_mean is not None else self.image_mean
       snake_case					:   List[str]         =					image_std if image_std is not None else self.image_std

       snake_case					:   int         =					make_list_of_images(UpperCamelCase__		)

       if not valid_images(UpperCamelCase__		):
           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 or resample is None:
           raise ValueError("Size and resample 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_normalize and (image_mean is None or image_std is None):
           raise ValueError("Image mean and std must be specified if do_normalize is True."		)

       # All transformations expect numpy arrays.
       snake_case					:   Optional[int]         =					[to_numpy_array(UpperCamelCase__		) for image in images]

       if do_resize:
           snake_case					:   Optional[int]         =					[self.resize(image=UpperCamelCase__		,			size=UpperCamelCase__		,			resample=UpperCamelCase__		) for image in images]

       if do_rescale:
           snake_case					:   int         =					[self.rescale(image=UpperCamelCase__		,			scale=UpperCamelCase__		) for image in images]

       if do_normalize:
           snake_case					:   Union[str, Any]         =					[self.normalize(image=UpperCamelCase__		,			mean=UpperCamelCase__		,			std=UpperCamelCase__		) for image in images]

       snake_case					:   Tuple         =					[to_channel_dimension_format(UpperCamelCase__		,			UpperCamelCase__		) for image in images]

       snake_case					:   Optional[int]         =					{"pixel_values": images}
       return BatchFeature(data=UpperCamelCase__		,			tensor_type=UpperCamelCase__		)







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		,			UpperCamelCase__ = None		)			->	Any:

       '''simple docstring'''




       snake_case					:   Any         =					outputs.logits

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

           if is_torch_tensor(UpperCamelCase__		):
               snake_case					:   List[Any]         =					target_sizes.numpy()

           snake_case					:   Dict         =					[]

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

       return semantic_segmentation









"""simple docstring"""



import os
import unittest

from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class _lowerCAmelCase       (   snake_case_					,							unittest.TestCase			):
   __UpperCAmelCase				: List[Any]													=						LayoutLMTokenizer
   __UpperCAmelCase				: List[Any]													=						LayoutLMTokenizerFast
   __UpperCAmelCase				: List[Any]													=						True
   __UpperCAmelCase				: Dict													=						True







   def 				lowerCamelCase	(			self		)			->	str:

       '''simple docstring'''




       super().setUp()

       snake_case					:   Dict         =					[
           "[UNK]",
           "[CLS]",
           "[SEP]",
           "want",
           "##want",
           "##ed",
           "wa",
           "un",
           "runn",
           "##ing",
           ",",
           "low",
           "lowest",
       ]
       snake_case					:   List[str]         =					os.path.join(self.tmpdirname		,			VOCAB_FILES_NAMES["vocab_file"]		)
       with open(self.vocab_file		,			"w"		,			encoding="utf-8"		) as vocab_writer:
           vocab_writer.write("".join([x + "\n" for x in vocab_tokens]		)		)







   def 				lowerCamelCase	(			self		,			**UpperCamelCase__		)			->	Dict:

       '''simple docstring'''




       return LayoutLMTokenizer.from_pretrained(self.tmpdirname		,			**UpperCamelCase__		)







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		)			->	Tuple:

       '''simple docstring'''




       snake_case					:   str         =					"UNwant\u00E9d,running"
       snake_case					:   Optional[int]         =					"unwanted, running"
       return input_text, output_text







   def 				lowerCamelCase	(			self		)			->	Any:

       '''simple docstring'''




       snake_case					:   List[str]         =					self.tokenizer_class(self.vocab_file		)

       snake_case					:   Optional[Any]         =					tokenizer.tokenize("UNwant\u00E9d,running"		)
       self.assertListEqual(UpperCamelCase__		,			["un", "##want", "##ed", ",", "runn", "##ing"]		)
       self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase__		)		,			[7, 4, 5, 10, 8, 9]		)







   def 				lowerCamelCase	(			self		)			->	Dict:

       '''simple docstring'''




       pass





