




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


lowerCAmelCase__		:					Any							=logging.get_logger(__name__)






def      __lowercase       (			a__							,       a__							,       a__							,       a__       )    ->			Optional[int]:
							def constraint_to_multiple_of(a__							,       a__							,       a__=0							,       a__=None       ):
														__SCREAMING_SNAKE_CASE									= round(val / multiple       ) * multiple

														if max_val is not None and x > max_val:
																					__SCREAMING_SNAKE_CASE									= math.floor(val / multiple       ) * multiple

														if x < min_val:
																					__SCREAMING_SNAKE_CASE									= math.ceil(val / multiple       ) * multiple

														return x

							__SCREAMING_SNAKE_CASE									= (output_size, output_size) if isinstance(_A							,       _A       ) else output_size

							__SCREAMING_SNAKE_CASE									= get_image_size(_A       )
							__SCREAMING_SNAKE_CASE									= output_size

							# determine new height and width
							__SCREAMING_SNAKE_CASE									= output_height / input_height
							__SCREAMING_SNAKE_CASE									= output_width / input_width

							if keep_aspect_ratio:
														# scale as little as possible
														if abs(1 - scale_width       ) < abs(1 - scale_height       ):
																					# fit width
																					__SCREAMING_SNAKE_CASE									= scale_width
														else:
																					# fit height
																					__SCREAMING_SNAKE_CASE									= scale_height

							__SCREAMING_SNAKE_CASE									= constraint_to_multiple_of(scale_height * input_height							,       multiple=_A       )
							__SCREAMING_SNAKE_CASE									= constraint_to_multiple_of(scale_width * input_width							,       multiple=_A       )

							return (new_height, new_width)

class        UpperCAmelCase_  (       lowerCamelCase__	):

						'''simple docstring'''




						UpperCamelCase__      :			List[str]      =  ['''pixel_values''']






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







													'''simple docstring'''

													super().__init__(**__snake_case		)
													__SCREAMING_SNAKE_CASE									= size if size is not None else {'height': 384, 'width': 384}
													__SCREAMING_SNAKE_CASE									= get_size_dict(__snake_case		)
													__SCREAMING_SNAKE_CASE									= do_resize
													__SCREAMING_SNAKE_CASE									= size
													__SCREAMING_SNAKE_CASE									= keep_aspect_ratio
													__SCREAMING_SNAKE_CASE									= ensure_multiple_of
													__SCREAMING_SNAKE_CASE									= resample
													__SCREAMING_SNAKE_CASE									= do_rescale
													__SCREAMING_SNAKE_CASE									= rescale_factor
													__SCREAMING_SNAKE_CASE									= do_normalize
													__SCREAMING_SNAKE_CASE									= image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
													__SCREAMING_SNAKE_CASE									= image_std if image_std is not None else IMAGENET_STANDARD_STD






						def 	_A     (   self				,							_A				,							_A				,							_A = False				,							_A = 1				,							_A = PILImageResampling.BICUBIC				,							_A = None				,							**_A				,							):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= get_size_dict(__snake_case		)
													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()}"""		)
													__SCREAMING_SNAKE_CASE									= get_resize_output_image_size(
													    __snake_case				,							output_size=(size['height'], size['width'])				,							keep_aspect_ratio=__snake_case				,							multiple=__snake_case				,							)
													return resize(__snake_case				,							size=__snake_case				,							resample=__snake_case				,							data_format=__snake_case				,							**__snake_case		)






						def 	_A     (   self				,							_A				,							_A				,							_A = None				,							**_A				,							):







													'''simple docstring'''

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






						def 	_A     (   self				,							_A				,							_A				,							_A				,							_A = None				,							**_A				,							):







													'''simple docstring'''

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






						def 	_A     (   self				,							_A				,							_A = None				,							_A = None				,							_A = None				,							_A = None				,							_A = None				,							_A = None				,							_A = None				,							_A = None				,							_A = None				,							_A = None				,							_A = None				,							_A = ChannelDimension.FIRST				,							**_A				,							):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= do_resize if do_resize is not None else self.do_resize
													__SCREAMING_SNAKE_CASE									= size if size is not None else self.size
													__SCREAMING_SNAKE_CASE									= get_size_dict(__snake_case		)
													__SCREAMING_SNAKE_CASE									= keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
													__SCREAMING_SNAKE_CASE									= ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
													__SCREAMING_SNAKE_CASE									= resample if resample is not None else self.resample
													__SCREAMING_SNAKE_CASE									= do_rescale if do_rescale is not None else self.do_rescale
													__SCREAMING_SNAKE_CASE									= rescale_factor if rescale_factor is not None else self.rescale_factor
													__SCREAMING_SNAKE_CASE									= do_normalize if do_normalize is not None else self.do_normalize
													__SCREAMING_SNAKE_CASE									= image_mean if image_mean is not None else self.image_mean
													__SCREAMING_SNAKE_CASE									= image_std if image_std is not None else self.image_std

													__SCREAMING_SNAKE_CASE									= make_list_of_images(__snake_case		)

													if not valid_images(__snake_case		):
																				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.
													__SCREAMING_SNAKE_CASE									= [to_numpy_array(__snake_case		) for image in images]

													if do_resize:
																				__SCREAMING_SNAKE_CASE									= [self.resize(image=__snake_case				,							size=__snake_case				,							resample=__snake_case		) for image in images]

													if do_rescale:
																				__SCREAMING_SNAKE_CASE									= [self.rescale(image=__snake_case				,							scale=__snake_case		) for image in images]

													if do_normalize:
																				__SCREAMING_SNAKE_CASE									= [self.normalize(image=__snake_case				,							mean=__snake_case				,							std=__snake_case		) for image in images]

													__SCREAMING_SNAKE_CASE									= [to_channel_dimension_format(__snake_case				,							__snake_case		) for image in images]

													__SCREAMING_SNAKE_CASE									= {'pixel_values': images}
													return BatchFeature(data=__snake_case				,							tensor_type=__snake_case		)






						def 	_A     (   self				,							_A				,							_A = None		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= outputs.logits

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

																				if is_torch_tensor(__snake_case		):
																											__SCREAMING_SNAKE_CASE									= target_sizes.numpy()

																				__SCREAMING_SNAKE_CASE									= []

																				for idx in range(len(__snake_case		)		):
																											__SCREAMING_SNAKE_CASE									= torch.nn.functional.interpolate(
																											    logits[idx].unsqueeze(dim=0		)				,							size=target_sizes[idx]				,							mode='bilinear'				,							align_corners=__snake_case		)
																											__SCREAMING_SNAKE_CASE									= resized_logits[0].argmax(dim=0		)
																											semantic_segmentation.append(__snake_case		)
													else:
																				__SCREAMING_SNAKE_CASE									= logits.argmax(dim=1		)
																				__SCREAMING_SNAKE_CASE									= [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]		)]

													return semantic_segmentation











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        UpperCAmelCase_  (       UpperCamelCase_   ,   unittest.TestCase	):

						'''simple docstring'''




						UpperCamelCase__      :			Optional[int]      =  LayoutLMTokenizer
						UpperCamelCase__      :			Any      =  LayoutLMTokenizerFast
						UpperCamelCase__      :			Optional[int]      =  True
						UpperCamelCase__      :			int      =  True






						def 	_A     (   self		):







													'''simple docstring'''

													super().setUp()

													__SCREAMING_SNAKE_CASE									= [
													    '[UNK]',
													    '[CLS]',
													    '[SEP]',
													    'want',
													    '##want',
													    '##ed',
													    'wa',
													    'un',
													    'runn',
													    '##ing',
													    ',',
													    'low',
													    'lowest',
													]
													__SCREAMING_SNAKE_CASE									= 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 	_A     (   self				,							**_A		):







													'''simple docstring'''

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






						def 	_A     (   self				,							_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= 'UNwant\u00E9d,running'
													__SCREAMING_SNAKE_CASE									= 'unwanted, running'
													return input_text, output_text






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= self.tokenizer_class(self.vocab_file		)

													__SCREAMING_SNAKE_CASE									= tokenizer.tokenize('UNwant\u00E9d,running'		)
													self.assertListEqual(_A				,							['un', '##want', '##ed', ',', 'runn', '##ing']		)
													self.assertListEqual(tokenizer.convert_tokens_to_ids(_A		)				,							[7, 4, 5, 10, 8, 9]		)






						def 	_A     (   self		):







													'''simple docstring'''

													pass






