

'''simple docstring'''





import unittest

import numpy as np

from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
							import torch

if is_pytesseract_available():
							from PIL import Image

							from transformers import LayoutLMvaImageProcessor





class 		__SCREAMING_SNAKE_CASE							(			unittest.TestCase      ):







				def __init__(       self     :		Optional[int]		,					UpperCAmelCase__     :		int		,					UpperCAmelCase__     :		List[str]=7		,					UpperCAmelCase__     :		Optional[Any]=3		,					UpperCAmelCase__     :		Tuple=18		,					UpperCAmelCase__     :		str=30		,					UpperCAmelCase__     :		Union[str, Any]=400		,					UpperCAmelCase__     :		Union[str, Any]=True		,					UpperCAmelCase__     :		List[str]=None		,					UpperCAmelCase__     :		Any=True		,					):
										'''simple docstring'''
										lowercase :							Union[str, Any]    		=size if size is not None else {'''height''': 18, '''width''': 18}
										lowercase :							Union[str, Any]    		=parent
										lowercase :							Optional[int]    		=batch_size
										lowercase :							Optional[int]    		=num_channels
										lowercase :							List[str]    		=image_size
										lowercase :							Dict    		=min_resolution
										lowercase :							str    		=max_resolution
										lowercase :							int    		=do_resize
										lowercase :							int    		=size
										lowercase :							Union[str, Any]    		=apply_ocr







				def  lowerCamelCase_	(       self     :		List[Any]   ):
										'''simple docstring'''
										return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}





@require_torch
@require_pytesseract
class 		__SCREAMING_SNAKE_CASE							(			lowercase__						,							unittest.TestCase      ):
				lowerCamelCase_		       =    LayoutLMvaImageProcessor if is_pytesseract_available() else None







				def  lowerCamelCase_	(       self     :		Dict   ):
										'''simple docstring'''
										lowercase :							Tuple    		=LayoutLMvaImageProcessingTester(self   )







				@property
				def  lowerCamelCase_	(       self     :		int   ):
										'''simple docstring'''
										return self.image_processor_tester.prepare_image_processor_dict()







				def  lowerCamelCase_	(       self     :		List[str]   ):
										'''simple docstring'''
										lowercase :							Dict    		=self.image_processing_class(**self.image_processor_dict   )
										self.assertTrue(hasattr(UpperCAmelCase__		,					'''do_resize'''   )   )
										self.assertTrue(hasattr(UpperCAmelCase__		,					'''size'''   )   )
										self.assertTrue(hasattr(UpperCAmelCase__		,					'''apply_ocr'''   )   )







				def  lowerCamelCase_	(       self     :		Optional[Any]   ):
										'''simple docstring'''
										lowercase :							Optional[int]    		=self.image_processing_class.from_dict(self.image_processor_dict   )
										self.assertEqual(image_processor.size		,					{'''height''': 18, '''width''': 18}   )

										lowercase :							Tuple    		=self.image_processing_class.from_dict(self.image_processor_dict		,					size=42   )
										self.assertEqual(image_processor.size		,					{'''height''': 42, '''width''': 42}   )







				def  lowerCamelCase_	(       self     :		int   ):
										'''simple docstring'''
										pass







				def  lowerCamelCase_	(       self     :		Tuple   ):
										'''simple docstring'''
										# Initialize image_processing
										lowercase :							List[Any]    		=self.image_processing_class(**self.image_processor_dict   )
										# create random PIL images
										lowercase :							Any    		=prepare_image_inputs(self.image_processor_tester		,					equal_resolution=UpperCAmelCase__   )
										for image in image_inputs:
																self.assertIsInstance(UpperCAmelCase__		,					Image.Image   )

										# Test not batched input
										lowercase :							Optional[int]    		=image_processing(image_inputs[0]		,					return_tensors='''pt'''   )
										self.assertEqual(
										    encoding.pixel_values.shape		,					(
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size['''height'''],
										        self.image_processor_tester.size['''width'''],
										    )		,					)

										self.assertIsInstance(encoding.words		,					UpperCAmelCase__   )
										self.assertIsInstance(encoding.boxes		,					UpperCAmelCase__   )

										# Test batched
										lowercase :							Union[str, Any]    		=image_processing(UpperCAmelCase__		,					return_tensors='''pt'''   ).pixel_values
										self.assertEqual(
										    encoded_images.shape		,					(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size['''height'''],
										        self.image_processor_tester.size['''width'''],
										    )		,					)







				def  lowerCamelCase_	(       self     :		str   ):
										'''simple docstring'''
										# Initialize image_processing
										lowercase :							Dict    		=self.image_processing_class(**self.image_processor_dict   )
										# create random numpy tensors
										lowercase :							Optional[Any]    		=prepare_image_inputs(self.image_processor_tester		,					equal_resolution=UpperCAmelCase__		,					numpify=UpperCAmelCase__   )
										for image in image_inputs:
																self.assertIsInstance(UpperCAmelCase__		,					np.ndarray   )

										# Test not batched input
										lowercase :							Tuple    		=image_processing(image_inputs[0]		,					return_tensors='''pt'''   ).pixel_values
										self.assertEqual(
										    encoded_images.shape		,					(
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size['''height'''],
										        self.image_processor_tester.size['''width'''],
										    )		,					)

										# Test batched
										lowercase :							Optional[int]    		=image_processing(UpperCAmelCase__		,					return_tensors='''pt'''   ).pixel_values
										self.assertEqual(
										    encoded_images.shape		,					(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size['''height'''],
										        self.image_processor_tester.size['''width'''],
										    )		,					)







				def  lowerCamelCase_	(       self     :		Tuple   ):
										'''simple docstring'''
										# Initialize image_processing
										lowercase :							int    		=self.image_processing_class(**self.image_processor_dict   )
										# create random PyTorch tensors
										lowercase :							List[Any]    		=prepare_image_inputs(self.image_processor_tester		,					equal_resolution=UpperCAmelCase__		,					torchify=UpperCAmelCase__   )
										for image in image_inputs:
																self.assertIsInstance(UpperCAmelCase__		,					torch.Tensor   )

										# Test not batched input
										lowercase :							Optional[Any]    		=image_processing(image_inputs[0]		,					return_tensors='''pt'''   ).pixel_values
										self.assertEqual(
										    encoded_images.shape		,					(
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size['''height'''],
										        self.image_processor_tester.size['''width'''],
										    )		,					)

										# Test batched
										lowercase :							Optional[Any]    		=image_processing(UpperCAmelCase__		,					return_tensors='''pt'''   ).pixel_values
										self.assertEqual(
										    encoded_images.shape		,					(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size['''height'''],
										        self.image_processor_tester.size['''width'''],
										    )		,					)







				def  lowerCamelCase_	(       self     :		Any   ):
										'''simple docstring'''
										# with apply_OCR = True
										lowercase :							Tuple    		=LayoutLMvaImageProcessor()

										from datasets import load_dataset

										lowercase :							List[Any]    		=load_dataset('''hf-internal-testing/fixtures_docvqa'''		,					split='''test'''   )

										lowercase :							Dict    		=Image.open(ds[0]['''file''']   ).convert('''RGB'''   )

										lowercase :							Tuple    		=image_processing(UpperCAmelCase__		,					return_tensors='''pt'''   )

										self.assertEqual(encoding.pixel_values.shape		,					(1, 3, 224, 224)   )
										self.assertEqual(len(encoding.words   )		,					len(encoding.boxes   )   )

										# fmt: off
										# the words and boxes were obtained with Tesseract 4.1.1
										lowercase :							Tuple    		=[['''11:14''', '''to''', '''11:39''', '''a.m''', '''11:39''', '''to''', '''11:44''', '''a.m.''', '''11:44''', '''a.m.''', '''to''', '''12:25''', '''p.m.''', '''12:25''', '''to''', '''12:58''', '''p.m.''', '''12:58''', '''to''', '''4:00''', '''p.m.''', '''2:00''', '''to''', '''5:00''', '''p.m.''', '''Coffee''', '''Break''', '''Coffee''', '''will''', '''be''', '''served''', '''for''', '''men''', '''and''', '''women''', '''in''', '''the''', '''lobby''', '''adjacent''', '''to''', '''exhibit''', '''area.''', '''Please''', '''move''', '''into''', '''exhibit''', '''area.''', '''(Exhibits''', '''Open)''', '''TRRF''', '''GENERAL''', '''SESSION''', '''(PART''', '''|)''', '''Presiding:''', '''Lee''', '''A.''', '''Waller''', '''TRRF''', '''Vice''', '''President''', '''“Introductory''', '''Remarks”''', '''Lee''', '''A.''', '''Waller,''', '''TRRF''', '''Vice''', '''Presi-''', '''dent''', '''Individual''', '''Interviews''', '''with''', '''TRRF''', '''Public''', '''Board''', '''Members''', '''and''', '''Sci-''', '''entific''', '''Advisory''', '''Council''', '''Mem-''', '''bers''', '''Conducted''', '''by''', '''TRRF''', '''Treasurer''', '''Philip''', '''G.''', '''Kuehn''', '''to''', '''get''', '''answers''', '''which''', '''the''', '''public''', '''refrigerated''', '''warehousing''', '''industry''', '''is''', '''looking''', '''for.''', '''Plus''', '''questions''', '''from''', '''the''', '''floor.''', '''Dr.''', '''Emil''', '''M.''', '''Mrak,''', '''University''', '''of''', '''Cal-''', '''ifornia,''', '''Chairman,''', '''TRRF''', '''Board;''', '''Sam''', '''R.''', '''Cecil,''', '''University''', '''of''', '''Georgia''', '''College''', '''of''', '''Agriculture;''', '''Dr.''', '''Stanley''', '''Charm,''', '''Tufts''', '''University''', '''School''', '''of''', '''Medicine;''', '''Dr.''', '''Robert''', '''H.''', '''Cotton,''', '''ITT''', '''Continental''', '''Baking''', '''Company;''', '''Dr.''', '''Owen''', '''Fennema,''', '''University''', '''of''', '''Wis-''', '''consin;''', '''Dr.''', '''Robert''', '''E.''', '''Hardenburg,''', '''USDA.''', '''Questions''', '''and''', '''Answers''', '''Exhibits''', '''Open''', '''Capt.''', '''Jack''', '''Stoney''', '''Room''', '''TRRF''', '''Scientific''', '''Advisory''', '''Council''', '''Meeting''', '''Ballroom''', '''Foyer''']]  # noqa: E231
										lowercase :							Optional[Any]    		=[[[141, 57, 214, 69], [228, 58, 252, 69], [141, 75, 216, 88], [230, 79, 280, 88], [142, 260, 218, 273], [230, 261, 255, 273], [143, 279, 218, 290], [231, 282, 290, 291], [143, 342, 218, 354], [231, 345, 289, 355], [202, 362, 227, 373], [143, 379, 220, 392], [231, 382, 291, 394], [144, 714, 220, 726], [231, 715, 256, 726], [144, 732, 220, 745], [232, 736, 291, 747], [144, 769, 218, 782], [231, 770, 256, 782], [141, 788, 202, 801], [215, 791, 274, 804], [143, 826, 204, 838], [215, 826, 240, 838], [142, 844, 202, 857], [215, 847, 274, 859], [334, 57, 427, 69], [440, 57, 522, 69], [369, 75, 461, 88], [469, 75, 516, 88], [528, 76, 562, 88], [570, 76, 667, 88], [675, 75, 711, 87], [721, 79, 778, 88], [789, 75, 840, 88], [369, 97, 470, 107], [484, 94, 507, 106], [518, 94, 562, 107], [576, 94, 655, 110], [668, 94, 792, 109], [804, 95, 829, 107], [369, 113, 465, 125], [477, 116, 547, 125], [562, 113, 658, 125], [671, 116, 748, 125], [761, 113, 811, 125], [369, 131, 465, 143], [477, 133, 548, 143], [563, 130, 698, 145], [710, 130, 802, 146], [336, 171, 412, 183], [423, 171, 572, 183], [582, 170, 716, 184], [728, 171, 817, 187], [829, 171, 844, 186], [338, 197, 482, 212], [507, 196, 557, 209], [569, 196, 595, 208], [610, 196, 702, 209], [505, 214, 583, 226], [595, 214, 656, 227], [670, 215, 807, 227], [335, 259, 543, 274], [556, 259, 708, 272], [372, 279, 422, 291], [435, 279, 460, 291], [474, 279, 574, 292], [587, 278, 664, 291], [676, 278, 738, 291], [751, 279, 834, 291], [372, 298, 434, 310], [335, 341, 483, 354], [497, 341, 655, 354], [667, 341, 728, 354], [740, 341, 825, 354], [335, 360, 430, 372], [442, 360, 534, 372], [545, 359, 687, 372], [697, 360, 754, 372], [765, 360, 823, 373], [334, 378, 428, 391], [440, 378, 577, 394], [590, 378, 705, 391], [720, 378, 801, 391], [334, 397, 400, 409], [370, 416, 529, 429], [544, 416, 576, 432], [587, 416, 665, 428], [677, 416, 814, 429], [372, 435, 452, 450], [465, 434, 495, 447], [511, 434, 600, 447], [611, 436, 637, 447], [649, 436, 694, 451], [705, 438, 824, 447], [369, 453, 452, 466], [464, 454, 509, 466], [522, 453, 611, 469], [625, 453, 792, 469], [370, 472, 556, 488], [570, 472, 684, 487], [697, 472, 718, 485], [732, 472, 835, 488], [369, 490, 411, 503], [425, 490, 484, 503], [496, 490, 635, 506], [645, 490, 707, 503], [718, 491, 761, 503], [771, 490, 840, 503], [336, 510, 374, 521], [388, 510, 447, 522], [460, 510, 489, 521], [503, 510, 580, 522], [592, 509, 736, 525], [745, 509, 770, 522], [781, 509, 840, 522], [338, 528, 434, 541], [448, 528, 596, 541], [609, 527, 687, 540], [700, 528, 792, 541], [336, 546, 397, 559], [407, 546, 431, 559], [443, 546, 525, 560], [537, 546, 680, 562], [688, 546, 714, 559], [722, 546, 837, 562], [336, 565, 449, 581], [461, 565, 485, 577], [497, 565, 665, 581], [681, 565, 718, 577], [732, 565, 837, 580], [337, 584, 438, 597], [452, 583, 521, 596], [535, 584, 677, 599], [690, 583, 787, 596], [801, 583, 825, 596], [338, 602, 478, 615], [492, 602, 530, 614], [543, 602, 638, 615], [650, 602, 676, 614], [688, 602, 788, 615], [802, 602, 843, 614], [337, 621, 502, 633], [516, 621, 615, 637], [629, 621, 774, 636], [789, 621, 827, 633], [337, 639, 418, 652], [432, 640, 571, 653], [587, 639, 731, 655], [743, 639, 769, 652], [780, 639, 841, 652], [338, 658, 440, 673], [455, 658, 491, 670], [508, 658, 602, 671], [616, 658, 638, 670], [654, 658, 835, 674], [337, 677, 429, 689], [337, 714, 482, 726], [495, 714, 548, 726], [561, 714, 683, 726], [338, 770, 461, 782], [474, 769, 554, 785], [489, 788, 562, 803], [576, 788, 643, 801], [656, 787, 751, 804], [764, 788, 844, 801], [334, 825, 421, 838], [430, 824, 574, 838], [584, 824, 723, 841], [335, 844, 450, 857], [464, 843, 583, 860], [628, 862, 755, 875], [769, 861, 848, 878]]]  # noqa: E231
										# fmt: on

										self.assertListEqual(encoding.words		,					UpperCAmelCase__   )
										self.assertListEqual(encoding.boxes		,					UpperCAmelCase__   )

										# with apply_OCR = False
										lowercase :							Union[str, Any]    		=LayoutLMvaImageProcessor(apply_ocr=UpperCAmelCase__   )

										lowercase :							str    		=image_processing(UpperCAmelCase__		,					return_tensors='''pt'''   )

										self.assertEqual(encoding.pixel_values.shape		,					(1, 3, 224, 224)   )








'''simple docstring'''





import math

import flax.linen as nn
import jax.numpy as jnp






def 				_lowerCAmelCase			(   __magic_name__						:     jnp.ndarray   , __magic_name__						:     int   , __magic_name__						:     float = 1   , __magic_name__						:     float = 1   , __magic_name__						:     float = 1.0E4   , __magic_name__						:     bool = False   , __magic_name__						:     float = 1.0   , )				->  jnp.ndarray:
						assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
						assert embedding_dim % 2 == 0, f'''Embedding dimension {embedding_dim} should be even'''
						lowercase :							int    		=float(embedding_dim // 2						)
						lowercase :							Optional[int]    		=math.log(max_timescale / min_timescale						) / (num_timescales - freq_shift)
						lowercase :							Any    		=min_timescale * jnp.exp(jnp.arange(__magic_name__   , dtype=jnp.floataa						) * -log_timescale_increment						)
						lowercase :							List[Any]    		=jnp.expand_dims(__magic_name__   , 1						) * jnp.expand_dims(__magic_name__   , 0						)

						# scale embeddings
						lowercase :							Tuple    		=scale * emb

						if flip_sin_to_cos:
												lowercase :							Dict    		=jnp.concatenate([jnp.cos(__magic_name__						), jnp.sin(__magic_name__						)]   , axis=1						)
						else:
												lowercase :							Any    		=jnp.concatenate([jnp.sin(__magic_name__						), jnp.cos(__magic_name__						)]   , axis=1						)
						lowercase :							List[str]    		=jnp.reshape(__magic_name__   , [jnp.shape(__magic_name__						)[0], embedding_dim]						)
						return signal





class 		__SCREAMING_SNAKE_CASE							(			nn.Module      ):
				lowerCamelCase_		       =    32
				lowerCamelCase_		       =    jnp.floataa







				@nn.compact
				def __call__(       self     :		Tuple		,					UpperCAmelCase__     :		int   ):
										'''simple docstring'''
										lowercase :							List[Any]    		=nn.Dense(self.time_embed_dim		,					dtype=self.dtype		,					name='''linear_1'''   )(UpperCAmelCase__   )
										lowercase :							Any    		=nn.silu(UpperCAmelCase__   )
										lowercase :							int    		=nn.Dense(self.time_embed_dim		,					dtype=self.dtype		,					name='''linear_2'''   )(UpperCAmelCase__   )
										return temb





class 		__SCREAMING_SNAKE_CASE							(			nn.Module      ):
				lowerCamelCase_		       =    32
				lowerCamelCase_		       =    False
				lowerCamelCase_		       =    1







				@nn.compact
				def __call__(       self     :		int		,					UpperCAmelCase__     :		str   ):
										'''simple docstring'''
										return get_sinusoidal_embeddings(
										    UpperCAmelCase__		,					embedding_dim=self.dim		,					flip_sin_to_cos=self.flip_sin_to_cos		,					freq_shift=self.freq_shift   )





