



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 				_snake_case			(					unittest.TestCase):




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




				def 							A__      (						self					:						Optional[int]						):
							return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}
@require_torch
@require_pytesseract
class 				_snake_case			(					lowercase__    ,	unittest.TestCase):
				UpperCamelCase__				:					List[str]     		=LayoutLMvaImageProcessor if is_pytesseract_available() else None




				def 							A__      (						self					:						Optional[int]						):
							lowercase__					  =				LayoutLMvaImageProcessingTester(self						)




				@property
				def 							A__      (						self					:						Tuple						):
							return self.image_processor_tester.prepare_image_processor_dict()




				def 							A__      (						self					:						int						):
							lowercase__					  =				self.image_processing_class(**self.image_processor_dict						)
							self.assertTrue(hasattr(__lowercase,					"do_resize"						)						)
							self.assertTrue(hasattr(__lowercase,					"size"						)						)
							self.assertTrue(hasattr(__lowercase,					"apply_ocr"						)						)




				def 							A__      (						self					:						Optional[int]						):
							lowercase__					  =				self.image_processing_class.from_dict(self.image_processor_dict						)
							self.assertEqual(image_processor.size,					{"height": 18, "width": 18}						)

							lowercase__					  =				self.image_processing_class.from_dict(self.image_processor_dict,					size=42						)
							self.assertEqual(image_processor.size,					{"height": 42, "width": 42}						)




				def 							A__      (						self					:						Any						):
							pass




				def 							A__      (						self					:						List[Any]						):
							# Initialize image_processing
							lowercase__					  =				self.image_processing_class(**self.image_processor_dict						)
							# create random PIL images
							lowercase__					  =				prepare_image_inputs(self.image_processor_tester,					equal_resolution=__lowercase						)
							for image in image_inputs:
										self.assertIsInstance(__lowercase,					Image.Image						)

							# Test not batched input
							lowercase__					  =				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,					__lowercase						)
							self.assertIsInstance(encoding.boxes,					__lowercase						)

							# Test batched
							lowercase__					  =				image_processing(__lowercase,					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 							A__      (						self					:						Any						):
							# Initialize image_processing
							lowercase__					  =				self.image_processing_class(**self.image_processor_dict						)
							# create random numpy tensors
							lowercase__					  =				prepare_image_inputs(self.image_processor_tester,					equal_resolution=__lowercase,					numpify=__lowercase						)
							for image in image_inputs:
										self.assertIsInstance(__lowercase,					np.ndarray						)

							# Test not batched input
							lowercase__					  =				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__					  =				image_processing(__lowercase,					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 							A__      (						self					:						List[Any]						):
							# Initialize image_processing
							lowercase__					  =				self.image_processing_class(**self.image_processor_dict						)
							# create random PyTorch tensors
							lowercase__					  =				prepare_image_inputs(self.image_processor_tester,					equal_resolution=__lowercase,					torchify=__lowercase						)
							for image in image_inputs:
										self.assertIsInstance(__lowercase,					torch.Tensor						)

							# Test not batched input
							lowercase__					  =				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__					  =				image_processing(__lowercase,					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 							A__      (						self					:						List[str]						):
							# with apply_OCR = True
							lowercase__					  =				LayoutLMvaImageProcessor()

							from datasets import load_dataset

							lowercase__					  =				load_dataset("hf-internal-testing/fixtures_docvqa",					split="test"						)

							lowercase__					  =				Image.open(ds[0]["file"]						).convert("RGB"						)

							lowercase__					  =				image_processing(__lowercase,					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__					  =				[["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__					  =				[[[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,					__lowercase						)
							self.assertListEqual(encoding.boxes,					__lowercase						)

							# with apply_OCR = False
							lowercase__					  =				LayoutLMvaImageProcessor(apply_ocr=__lowercase						)

							lowercase__					  =				image_processing(__lowercase,					return_tensors="pt"						)

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









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

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer


lowercase_								=     logging.get_logger(__name__)


lowercase_								=     {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

lowercase_								=     {
    """vocab_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json""",
    },
}

lowercase_								=     {
    """allenai/led-base-16384""": 1_6384,
}
class 				_snake_case			(					lowercase__):
				UpperCamelCase__				:					int     		=VOCAB_FILES_NAMES
				UpperCamelCase__				:					Any     		=PRETRAINED_VOCAB_FILES_MAP
				UpperCamelCase__				:					Dict     		=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				UpperCamelCase__				:					List[Any]     		=LEDTokenizer
				UpperCamelCase__				:					Tuple     		=["""input_ids""", """attention_mask"""]




				def __init__(						self					:						Optional[Any],					__lowercase					:						Optional[Any]=None,					__lowercase					:						Dict=None,					__lowercase					:						Tuple=None,					__lowercase					:						Union[str, Any]="replace",					__lowercase					:						Tuple="<s>",					__lowercase					:						Optional[Any]="</s>",					__lowercase					:						Tuple="</s>",					__lowercase					:						List[str]="<s>",					__lowercase					:						Tuple="<unk>",					__lowercase					:						Dict="<pad>",					__lowercase					:						Dict="<mask>",					__lowercase					:						Any=False,					__lowercase					:						Any=True,					**__lowercase					:						List[Any],					):
							super().__init__(
							    __lowercase,					__lowercase,					tokenizer_file=__lowercase,					errors=__lowercase,					bos_token=__lowercase,					eos_token=__lowercase,					sep_token=__lowercase,					cls_token=__lowercase,					unk_token=__lowercase,					pad_token=__lowercase,					mask_token=__lowercase,					add_prefix_space=__lowercase,					trim_offsets=__lowercase,					**__lowercase,					)

							lowercase__					  =				json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()						)
							if pre_tok_state.get("add_prefix_space",					__lowercase						) != add_prefix_space:
										lowercase__					  =				getattr(__lowercase,					pre_tok_state.pop("type"						)						)
										lowercase__					  =				add_prefix_space
										lowercase__					  =				pre_tok_class(**__lowercase						)

							lowercase__					  =				add_prefix_space

							# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
							lowercase__					  =				"post_processor"
							lowercase__					  =				getattr(self.backend_tokenizer,					__lowercase,					__lowercase						)
							if tokenizer_component_instance:
										lowercase__					  =				json.loads(tokenizer_component_instance.__getstate__()						)

										# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
										if "sep" in state:
													lowercase__					  =				tuple(state["sep"]						)
										if "cls" in state:
													lowercase__					  =				tuple(state["cls"]						)

										lowercase__					  =				False

										if state.get("add_prefix_space",					__lowercase						) != add_prefix_space:
													lowercase__					  =				add_prefix_space
													lowercase__					  =				True

										if state.get("trim_offsets",					__lowercase						) != trim_offsets:
													lowercase__					  =				trim_offsets
													lowercase__					  =				True

										if changes_to_apply:
													lowercase__					  =				getattr(__lowercase,					state.pop("type"						)						)
													lowercase__					  =				component_class(**__lowercase						)
													setattr(self.backend_tokenizer,					__lowercase,					__lowercase						)




				@property
				# Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
				def 							A__      (						self					:						str						):
							if self._mask_token is None:
										if self.verbose:
													logger.error("Using mask_token, but it is not set yet."						)
										return None
							return str(self._mask_token						)




				@mask_token.setter
				def 							A__      (						self					:						Optional[int],					__lowercase					:						Dict						):
							lowercase__					  =				AddedToken(__lowercase,					lstrip=__lowercase,					rstrip=__lowercase						) if isinstance(__lowercase,					__lowercase						) else value
							lowercase__					  =				value




				def 							A__      (						self					:						Any,					*__lowercase					:						List[Any],					**__lowercase					:						Optional[Any]						):
							lowercase__					  =				kwargs.get("is_split_into_words",					__lowercase						)

							if is_split_into_words and not self.add_prefix_space:
										raise ValueError(
										    F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
										    "to use it with pretokenized inputs."						)

							return super()._batch_encode_plus(*__lowercase,					**__lowercase						)




				def 							A__      (						self					:						int,					*__lowercase					:						Union[str, Any],					**__lowercase					:						List[str]						):
							lowercase__					  =				kwargs.get("is_split_into_words",					__lowercase						)

							if is_split_into_words and not self.add_prefix_space:
										raise ValueError(
										    F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
										    "to use it with pretokenized inputs."						)

							return super()._encode_plus(*__lowercase,					**__lowercase						)




				def 							A__      (						self					:						Optional[Any],					__lowercase					:						str,					__lowercase					:						Optional[str] = None						):
							lowercase__					  =				self._tokenizer.model.save(__lowercase,					name=__lowercase						)
							return tuple(__lowercase						)




				def 							A__      (						self					:						List[str],					__lowercase					:						int,					__lowercase					:						Optional[int]=None						):
							lowercase__					  =				[self.bos_token_id] + token_ids_a + [self.eos_token_id]
							if token_ids_a is None:
										return output

							return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]




				def 							A__      (						self					:						int,					__lowercase					:						List[int],					__lowercase					:						Optional[List[int]] = None						):
							lowercase__					  =				[self.sep_token_id]
							lowercase__					  =				[self.cls_token_id]

							if token_ids_a is None:
										return len(cls + token_ids_a + sep						) * [0]
							return len(cls + token_ids_a + sep + sep + token_ids_a + sep						) * [0]




				def 							A__      (						self					:						Union[str, Any],					__lowercase					:						Union[Dict[str, EncodedInput], BatchEncoding],					__lowercase					:						Optional[int] = None,					__lowercase					:						PaddingStrategy = PaddingStrategy.DO_NOT_PAD,					__lowercase					:						Optional[int] = None,					__lowercase					:						Optional[bool] = None,					):
							lowercase__					  =				super()._pad(
							    encoded_inputs=__lowercase,					max_length=__lowercase,					padding_strategy=__lowercase,					pad_to_multiple_of=__lowercase,					return_attention_mask=__lowercase,					)

							# Load from model defaults
							if return_attention_mask is None:
										lowercase__					  =				"attention_mask" in self.model_input_names

							if return_attention_mask and "global_attention_mask" in encoded_inputs:
										lowercase__					  =				encoded_inputs[self.model_input_names[0]]
										# `global_attention_mask` need to have the same length as other (sequential) inputs.
										lowercase__					  =				len(encoded_inputs["global_attention_mask"]						) != len(__lowercase						)

										if needs_to_be_padded:
													lowercase__					  =				len(__lowercase						) - len(encoded_inputs["global_attention_mask"]						)

													if self.padding_side == "right":
																# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
																lowercase__					  =				(
																    encoded_inputs["global_attention_mask"] + [-1] * difference
																)
													elif self.padding_side == "left":
																lowercase__					  =				[-1] * difference + encoded_inputs[
																    "global_attention_mask"
																]
													else:
																raise ValueError("Invalid padding strategy:" + str(self.padding_side						)						)

							return encoded_inputs




