
"""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 	_lowercase       (    unittest.TestCase		):




	def __init__(			self		,			UpperCAmelCase_		,			UpperCAmelCase_=7		,			UpperCAmelCase_=3		,			UpperCAmelCase_=18		,			UpperCAmelCase_=30		,			UpperCAmelCase_=400		,			UpperCAmelCase_=True		,			UpperCAmelCase_=None		,			UpperCAmelCase_=True		,			)		->							Dict:
			lowerCamelCase					:      int          =   size if size is not None else {"height": 18, "width": 18}
			lowerCamelCase					:      List[Any]          =   parent
			lowerCamelCase					:      Tuple          =   batch_size
			lowerCamelCase					:      Optional[int]          =   num_channels
			lowerCamelCase					:      Tuple          =   image_size
			lowerCamelCase					:      Tuple          =   min_resolution
			lowerCamelCase					:      Optional[int]          =   max_resolution
			lowerCamelCase					:      str          =   do_resize
			lowerCamelCase					:      int          =   size
			lowerCamelCase					:      Tuple          =   apply_ocr




	def       _UpperCamelCase   (			self )		->							List[Any]:
			return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}






@require_torch
@require_pytesseract
class 	_lowercase       (    _a    ,	unittest.TestCase		):
	lowercase_				=     LayoutLMvaImageProcessor if is_pytesseract_available() else None




	def       _UpperCamelCase   (			self )		->							Any:
			lowerCamelCase					:      int          =   LayoutLMvaImageProcessingTester(self )




	@property
	def       _UpperCamelCase   (			self )		->							Any:
			return self.image_processor_tester.prepare_image_processor_dict()




	def       _UpperCamelCase   (			self )		->							List[str]:
			lowerCamelCase					:      Tuple          =   self.image_processing_class(**self.image_processor_dict )
			self.assertTrue(hasattr(_a		,			'do_resize' ) )
			self.assertTrue(hasattr(_a		,			'size' ) )
			self.assertTrue(hasattr(_a		,			'apply_ocr' ) )




	def       _UpperCamelCase   (			self )		->							Optional[Any]:
			lowerCamelCase					:      str          =   self.image_processing_class.from_dict(self.image_processor_dict )
			self.assertEqual(image_processor.size		,			{'height': 18, 'width': 18} )

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




	def       _UpperCamelCase   (			self )		->							str:
			pass




	def       _UpperCamelCase   (			self )		->							Any:
			# Initialize image_processing
			lowerCamelCase					:      Any          =   self.image_processing_class(**self.image_processor_dict )
			# create random PIL images
			lowerCamelCase					:      int          =   prepare_image_inputs(self.image_processor_tester		,			equal_resolution=_a )
			for image in image_inputs:
					self.assertIsInstance(_a		,			Image.Image )

			# Test not batched input
			lowerCamelCase					:      Optional[Any]          =   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		,			_a )
			self.assertIsInstance(encoding.boxes		,			_a )

			# Test batched
			lowerCamelCase					:      Optional[Any]          =   image_processing(_a		,			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       _UpperCamelCase   (			self )		->							Optional[Any]:
			# Initialize image_processing
			lowerCamelCase					:      Any          =   self.image_processing_class(**self.image_processor_dict )
			# create random numpy tensors
			lowerCamelCase					:      List[str]          =   prepare_image_inputs(self.image_processor_tester		,			equal_resolution=_a		,			numpify=_a )
			for image in image_inputs:
					self.assertIsInstance(_a		,			np.ndarray )

			# Test not batched input
			lowerCamelCase					:      Optional[int]          =   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
			lowerCamelCase					:      List[str]          =   image_processing(_a		,			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       _UpperCamelCase   (			self )		->							Union[str, Any]:
			# Initialize image_processing
			lowerCamelCase					:      Dict          =   self.image_processing_class(**self.image_processor_dict )
			# create random PyTorch tensors
			lowerCamelCase					:      Optional[Any]          =   prepare_image_inputs(self.image_processor_tester		,			equal_resolution=_a		,			torchify=_a )
			for image in image_inputs:
					self.assertIsInstance(_a		,			torch.Tensor )

			# Test not batched input
			lowerCamelCase					:      List[str]          =   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
			lowerCamelCase					:      Dict          =   image_processing(_a		,			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       _UpperCamelCase   (			self )		->							List[Any]:
			# with apply_OCR = True
			lowerCamelCase					:      Union[str, Any]          =   LayoutLMvaImageProcessor()

			from datasets import load_dataset

			lowerCamelCase					:      Union[str, Any]          =   load_dataset('hf-internal-testing/fixtures_docvqa'		,			split='test' )

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

			lowerCamelCase					:      List[Any]          =   image_processing(_a		,			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
			lowerCamelCase					:      List[Any]          =   [["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
			lowerCamelCase					:      List[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		,			_a )
			self.assertListEqual(encoding.boxes		,			_a )

			# with apply_OCR = False
			lowerCamelCase					:      Any          =   LayoutLMvaImageProcessor(apply_ocr=_a )

			lowerCamelCase					:      Dict          =   image_processing(_a		,			return_tensors='pt' )

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








"""simple docstring"""

import logging
import random

import ray

from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex


_A						=			logging.getLogger(__name__)






class 	_lowercase       :




	def __init__(			self )		->							Tuple:
			lowerCamelCase					:      Dict          =   False




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_ )		->							Any:
			if not self.initialized:
					lowerCamelCase					:      Optional[int]          =   RagRetriever(
					    UpperCAmelCase_		,			question_encoder_tokenizer=UpperCAmelCase_		,			generator_tokenizer=UpperCAmelCase_		,			index=UpperCAmelCase_		,			init_retrieval=UpperCAmelCase_		,			)
					lowerCamelCase					:      Union[str, Any]          =   True




	def       _UpperCamelCase   (			self )		->							Union[str, Any]:
			self.retriever.index.init_index()




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_ )		->							str:
			lowerCamelCase     ,   lowerCamelCase					:      int          =   self.retriever._main_retrieve(UpperCAmelCase_		,			UpperCAmelCase_ )
			return doc_ids, retrieved_doc_embeds






class 	_lowercase       (    __UpperCAmelCase		):




	def __init__(			self		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_=None )		->							Dict:
			if index is not None and index.is_initialized() and len(UpperCAmelCase_ ) > 0:
					raise ValueError(
					    'When using Ray for distributed fine-tuning, '
					    'you\'ll need to provide the paths instead, '
					    'as the dataset and the index are loaded '
					    'separately. More info in examples/rag/use_own_knowledge_dataset.py ' )
			super().__init__(
			    UpperCAmelCase_		,			question_encoder_tokenizer=UpperCAmelCase_		,			generator_tokenizer=UpperCAmelCase_		,			index=UpperCAmelCase_		,			init_retrieval=UpperCAmelCase_		,			)
			lowerCamelCase					:      List[str]          =   retrieval_workers
			if len(self.retrieval_workers ) > 0:
					ray.get(
					    [
					        worker.create_rag_retriever.remote(UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_ )
					        for worker in self.retrieval_workers
					    ] )




	def       _UpperCamelCase   (			self )		->							Dict:
			logger.info('initializing retrieval' )

			if len(self.retrieval_workers ) > 0:
					ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers] )
			else:
					# Non-distributed training. Load index into this same process.
					self.index.init_index()




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_ )		->							List[Any]:
			if len(self.retrieval_workers ) > 0:
					# Select a random retrieval actor.
					lowerCamelCase					:      Tuple          =   self.retrieval_workers[random.randint(0		,			len(self.retrieval_workers ) - 1 )]
					lowerCamelCase     ,   lowerCamelCase					:      Tuple          =   ray.get(random_worker.retrieve.remote(UpperCAmelCase_		,			UpperCAmelCase_ ) )
			else:
					lowerCamelCase     ,   lowerCamelCase					:      int          =   self._main_retrieve(UpperCAmelCase_		,			UpperCAmelCase_ )
			return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(UpperCAmelCase_ )




	@classmethod
	def       _UpperCamelCase   (			cls		,			UpperCAmelCase_		,			UpperCAmelCase_=None		,			**UpperCAmelCase_ )		->							Dict:
			return super(UpperCAmelCase_		,			cls ).get_tokenizers(UpperCAmelCase_		,			UpperCAmelCase_		,			**UpperCAmelCase_ )




	@classmethod
	def       _UpperCamelCase   (			cls		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_=None		,			**UpperCAmelCase_ )		->							Dict:
			lowerCamelCase					:      str          =   kwargs.pop('config'		,			UpperCAmelCase_ ) or RagConfig.from_pretrained(UpperCAmelCase_		,			**UpperCAmelCase_ )
			lowerCamelCase					:      Any          =   RagTokenizer.from_pretrained(UpperCAmelCase_		,			config=UpperCAmelCase_ )
			lowerCamelCase					:      Any          =   rag_tokenizer.question_encoder
			lowerCamelCase					:      Union[str, Any]          =   rag_tokenizer.generator
			if indexed_dataset is not None:
					lowerCamelCase					:      int          =   'custom'
					lowerCamelCase					:      int          =   CustomHFIndex(config.retrieval_vector_size		,			UpperCAmelCase_ )
			else:
					lowerCamelCase					:      Optional[Any]          =   cls._build_index(UpperCAmelCase_ )
			return cls(
			    UpperCAmelCase_		,			question_encoder_tokenizer=UpperCAmelCase_		,			generator_tokenizer=UpperCAmelCase_		,			retrieval_workers=UpperCAmelCase_		,			index=UpperCAmelCase_		,			)






