

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 						__magic_name__    (       unittest.TestCase    ):


	def __init__(						self		: Optional[Any]			,						lowerCAmelCase__		: Union[str, Any]			,						lowerCAmelCase__		: Union[str, Any]=7			,						lowerCAmelCase__		: List[str]=3			,						lowerCAmelCase__		: List[str]=1_8			,						lowerCAmelCase__		: Union[str, Any]=3_0			,						lowerCAmelCase__		: List[str]=4_0_0			,						lowerCAmelCase__		: int=True			,						lowerCAmelCase__		: List[Any]=None			,						lowerCAmelCase__		: str=True			,						)    ->      List[Any]:
			UpperCAmelCase					      =  size if size is not None else {"height": 1_8, "width": 1_8}
			UpperCAmelCase					      =  parent
			UpperCAmelCase					      =  batch_size
			UpperCAmelCase					      =  num_channels
			UpperCAmelCase					      =  image_size
			UpperCAmelCase					      =  min_resolution
			UpperCAmelCase					      =  max_resolution
			UpperCAmelCase					      =  do_resize
			UpperCAmelCase					      =  size
			UpperCAmelCase					      =  apply_ocr


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






@require_torch
@require_pytesseract
class 						__magic_name__    (       _snake_case	,						unittest.TestCase    ):
	UpperCAmelCase							    =						LayoutLMvaImageProcessor if is_pytesseract_available() else None


	def _UpperCamelCase	(						self		: int    )    ->      Union[str, Any]:
			UpperCAmelCase					      =  LayoutLMvaImageProcessingTester(self    )


	@property
	def _UpperCamelCase	(						self		: int    )    ->      Union[str, Any]:
			return self.image_processor_tester.prepare_image_processor_dict()


	def _UpperCamelCase	(						self		: Any    )    ->      str:
			UpperCAmelCase					      =  self.image_processing_class(**self.image_processor_dict    )
			self.assertTrue(hasattr(lowerCAmelCase__			,						"do_resize"    )    )
			self.assertTrue(hasattr(lowerCAmelCase__			,						"size"    )    )
			self.assertTrue(hasattr(lowerCAmelCase__			,						"apply_ocr"    )    )


	def _UpperCamelCase	(						self		: str    )    ->      Optional[Any]:
			UpperCAmelCase					      =  self.image_processing_class.from_dict(self.image_processor_dict    )
			self.assertEqual(image_processor.size			,						{"height": 1_8, "width": 1_8}    )

			UpperCAmelCase					      =  self.image_processing_class.from_dict(self.image_processor_dict			,						size=4_2    )
			self.assertEqual(image_processor.size			,						{"height": 4_2, "width": 4_2}    )


	def _UpperCamelCase	(						self		: Any    )    ->      int:
			pass


	def _UpperCamelCase	(						self		: Tuple    )    ->      Union[str, Any]:
			# Initialize image_processing
			UpperCAmelCase					      =  self.image_processing_class(**self.image_processor_dict    )
			# create random PIL images
			UpperCAmelCase					      =  prepare_image_inputs(self.image_processor_tester			,						equal_resolution=lowerCAmelCase__    )
			for image in image_inputs:
					self.assertIsInstance(lowerCAmelCase__			,						Image.Image    )

			# Test not batched input
			UpperCAmelCase					      =  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			,						lowerCAmelCase__    )
			self.assertIsInstance(encoding.boxes			,						lowerCAmelCase__    )

			# Test batched
			UpperCAmelCase					      =  image_processing(lowerCAmelCase__			,						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[str]    )    ->      Tuple:
			# Initialize image_processing
			UpperCAmelCase					      =  self.image_processing_class(**self.image_processor_dict    )
			# create random numpy tensors
			UpperCAmelCase					      =  prepare_image_inputs(self.image_processor_tester			,						equal_resolution=lowerCAmelCase__			,						numpify=lowerCAmelCase__    )
			for image in image_inputs:
					self.assertIsInstance(lowerCAmelCase__			,						np.ndarray    )

			# Test not batched input
			UpperCAmelCase					      =  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
			UpperCAmelCase					      =  image_processing(lowerCAmelCase__			,						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		: int    )    ->      int:
			# Initialize image_processing
			UpperCAmelCase					      =  self.image_processing_class(**self.image_processor_dict    )
			# create random PyTorch tensors
			UpperCAmelCase					      =  prepare_image_inputs(self.image_processor_tester			,						equal_resolution=lowerCAmelCase__			,						torchify=lowerCAmelCase__    )
			for image in image_inputs:
					self.assertIsInstance(lowerCAmelCase__			,						torch.Tensor    )

			# Test not batched input
			UpperCAmelCase					      =  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
			UpperCAmelCase					      =  image_processing(lowerCAmelCase__			,						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		: Dict    )    ->      Tuple:
			# with apply_OCR = True
			UpperCAmelCase					      =  LayoutLMvaImageProcessor()

			from datasets import load_dataset

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

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

			UpperCAmelCase					      =  image_processing(lowerCAmelCase__			,						return_tensors="pt"    )

			self.assertEqual(encoding.pixel_values.shape			,						(1, 3, 2_2_4, 2_2_4)    )
			self.assertEqual(len(encoding.words    )			,						len(encoding.boxes    )    )

			# fmt: off
			# the words and boxes were obtained with Tesseract 4.1.1
			UpperCAmelCase					      =  [["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
			UpperCAmelCase					      =  [[[1_4_1, 5_7, 2_1_4, 6_9], [2_2_8, 5_8, 2_5_2, 6_9], [1_4_1, 7_5, 2_1_6, 8_8], [2_3_0, 7_9, 2_8_0, 8_8], [1_4_2, 2_6_0, 2_1_8, 2_7_3], [2_3_0, 2_6_1, 2_5_5, 2_7_3], [1_4_3, 2_7_9, 2_1_8, 2_9_0], [2_3_1, 2_8_2, 2_9_0, 2_9_1], [1_4_3, 3_4_2, 2_1_8, 3_5_4], [2_3_1, 3_4_5, 2_8_9, 3_5_5], [2_0_2, 3_6_2, 2_2_7, 3_7_3], [1_4_3, 3_7_9, 2_2_0, 3_9_2], [2_3_1, 3_8_2, 2_9_1, 3_9_4], [1_4_4, 7_1_4, 2_2_0, 7_2_6], [2_3_1, 7_1_5, 2_5_6, 7_2_6], [1_4_4, 7_3_2, 2_2_0, 7_4_5], [2_3_2, 7_3_6, 2_9_1, 7_4_7], [1_4_4, 7_6_9, 2_1_8, 7_8_2], [2_3_1, 7_7_0, 2_5_6, 7_8_2], [1_4_1, 7_8_8, 2_0_2, 8_0_1], [2_1_5, 7_9_1, 2_7_4, 8_0_4], [1_4_3, 8_2_6, 2_0_4, 8_3_8], [2_1_5, 8_2_6, 2_4_0, 8_3_8], [1_4_2, 8_4_4, 2_0_2, 8_5_7], [2_1_5, 8_4_7, 2_7_4, 8_5_9], [3_3_4, 5_7, 4_2_7, 6_9], [4_4_0, 5_7, 5_2_2, 6_9], [3_6_9, 7_5, 4_6_1, 8_8], [4_6_9, 7_5, 5_1_6, 8_8], [5_2_8, 7_6, 5_6_2, 8_8], [5_7_0, 7_6, 6_6_7, 8_8], [6_7_5, 7_5, 7_1_1, 8_7], [7_2_1, 7_9, 7_7_8, 8_8], [7_8_9, 7_5, 8_4_0, 8_8], [3_6_9, 9_7, 4_7_0, 1_0_7], [4_8_4, 9_4, 5_0_7, 1_0_6], [5_1_8, 9_4, 5_6_2, 1_0_7], [5_7_6, 9_4, 6_5_5, 1_1_0], [6_6_8, 9_4, 7_9_2, 1_0_9], [8_0_4, 9_5, 8_2_9, 1_0_7], [3_6_9, 1_1_3, 4_6_5, 1_2_5], [4_7_7, 1_1_6, 5_4_7, 1_2_5], [5_6_2, 1_1_3, 6_5_8, 1_2_5], [6_7_1, 1_1_6, 7_4_8, 1_2_5], [7_6_1, 1_1_3, 8_1_1, 1_2_5], [3_6_9, 1_3_1, 4_6_5, 1_4_3], [4_7_7, 1_3_3, 5_4_8, 1_4_3], [5_6_3, 1_3_0, 6_9_8, 1_4_5], [7_1_0, 1_3_0, 8_0_2, 1_4_6], [3_3_6, 1_7_1, 4_1_2, 1_8_3], [4_2_3, 1_7_1, 5_7_2, 1_8_3], [5_8_2, 1_7_0, 7_1_6, 1_8_4], [7_2_8, 1_7_1, 8_1_7, 1_8_7], [8_2_9, 1_7_1, 8_4_4, 1_8_6], [3_3_8, 1_9_7, 4_8_2, 2_1_2], [5_0_7, 1_9_6, 5_5_7, 2_0_9], [5_6_9, 1_9_6, 5_9_5, 2_0_8], [6_1_0, 1_9_6, 7_0_2, 2_0_9], [5_0_5, 2_1_4, 5_8_3, 2_2_6], [5_9_5, 2_1_4, 6_5_6, 2_2_7], [6_7_0, 2_1_5, 8_0_7, 2_2_7], [3_3_5, 2_5_9, 5_4_3, 2_7_4], [5_5_6, 2_5_9, 7_0_8, 2_7_2], [3_7_2, 2_7_9, 4_2_2, 2_9_1], [4_3_5, 2_7_9, 4_6_0, 2_9_1], [4_7_4, 2_7_9, 5_7_4, 2_9_2], [5_8_7, 2_7_8, 6_6_4, 2_9_1], [6_7_6, 2_7_8, 7_3_8, 2_9_1], [7_5_1, 2_7_9, 8_3_4, 2_9_1], [3_7_2, 2_9_8, 4_3_4, 3_1_0], [3_3_5, 3_4_1, 4_8_3, 3_5_4], [4_9_7, 3_4_1, 6_5_5, 3_5_4], [6_6_7, 3_4_1, 7_2_8, 3_5_4], [7_4_0, 3_4_1, 8_2_5, 3_5_4], [3_3_5, 3_6_0, 4_3_0, 3_7_2], [4_4_2, 3_6_0, 5_3_4, 3_7_2], [5_4_5, 3_5_9, 6_8_7, 3_7_2], [6_9_7, 3_6_0, 7_5_4, 3_7_2], [7_6_5, 3_6_0, 8_2_3, 3_7_3], [3_3_4, 3_7_8, 4_2_8, 3_9_1], [4_4_0, 3_7_8, 5_7_7, 3_9_4], [5_9_0, 3_7_8, 7_0_5, 3_9_1], [7_2_0, 3_7_8, 8_0_1, 3_9_1], [3_3_4, 3_9_7, 4_0_0, 4_0_9], [3_7_0, 4_1_6, 5_2_9, 4_2_9], [5_4_4, 4_1_6, 5_7_6, 4_3_2], [5_8_7, 4_1_6, 6_6_5, 4_2_8], [6_7_7, 4_1_6, 8_1_4, 4_2_9], [3_7_2, 4_3_5, 4_5_2, 4_5_0], [4_6_5, 4_3_4, 4_9_5, 4_4_7], [5_1_1, 4_3_4, 6_0_0, 4_4_7], [6_1_1, 4_3_6, 6_3_7, 4_4_7], [6_4_9, 4_3_6, 6_9_4, 4_5_1], [7_0_5, 4_3_8, 8_2_4, 4_4_7], [3_6_9, 4_5_3, 4_5_2, 4_6_6], [4_6_4, 4_5_4, 5_0_9, 4_6_6], [5_2_2, 4_5_3, 6_1_1, 4_6_9], [6_2_5, 4_5_3, 7_9_2, 4_6_9], [3_7_0, 4_7_2, 5_5_6, 4_8_8], [5_7_0, 4_7_2, 6_8_4, 4_8_7], [6_9_7, 4_7_2, 7_1_8, 4_8_5], [7_3_2, 4_7_2, 8_3_5, 4_8_8], [3_6_9, 4_9_0, 4_1_1, 5_0_3], [4_2_5, 4_9_0, 4_8_4, 5_0_3], [4_9_6, 4_9_0, 6_3_5, 5_0_6], [6_4_5, 4_9_0, 7_0_7, 5_0_3], [7_1_8, 4_9_1, 7_6_1, 5_0_3], [7_7_1, 4_9_0, 8_4_0, 5_0_3], [3_3_6, 5_1_0, 3_7_4, 5_2_1], [3_8_8, 5_1_0, 4_4_7, 5_2_2], [4_6_0, 5_1_0, 4_8_9, 5_2_1], [5_0_3, 5_1_0, 5_8_0, 5_2_2], [5_9_2, 5_0_9, 7_3_6, 5_2_5], [7_4_5, 5_0_9, 7_7_0, 5_2_2], [7_8_1, 5_0_9, 8_4_0, 5_2_2], [3_3_8, 5_2_8, 4_3_4, 5_4_1], [4_4_8, 5_2_8, 5_9_6, 5_4_1], [6_0_9, 5_2_7, 6_8_7, 5_4_0], [7_0_0, 5_2_8, 7_9_2, 5_4_1], [3_3_6, 5_4_6, 3_9_7, 5_5_9], [4_0_7, 5_4_6, 4_3_1, 5_5_9], [4_4_3, 5_4_6, 5_2_5, 5_6_0], [5_3_7, 5_4_6, 6_8_0, 5_6_2], [6_8_8, 5_4_6, 7_1_4, 5_5_9], [7_2_2, 5_4_6, 8_3_7, 5_6_2], [3_3_6, 5_6_5, 4_4_9, 5_8_1], [4_6_1, 5_6_5, 4_8_5, 5_7_7], [4_9_7, 5_6_5, 6_6_5, 5_8_1], [6_8_1, 5_6_5, 7_1_8, 5_7_7], [7_3_2, 5_6_5, 8_3_7, 5_8_0], [3_3_7, 5_8_4, 4_3_8, 5_9_7], [4_5_2, 5_8_3, 5_2_1, 5_9_6], [5_3_5, 5_8_4, 6_7_7, 5_9_9], [6_9_0, 5_8_3, 7_8_7, 5_9_6], [8_0_1, 5_8_3, 8_2_5, 5_9_6], [3_3_8, 6_0_2, 4_7_8, 6_1_5], [4_9_2, 6_0_2, 5_3_0, 6_1_4], [5_4_3, 6_0_2, 6_3_8, 6_1_5], [6_5_0, 6_0_2, 6_7_6, 6_1_4], [6_8_8, 6_0_2, 7_8_8, 6_1_5], [8_0_2, 6_0_2, 8_4_3, 6_1_4], [3_3_7, 6_2_1, 5_0_2, 6_3_3], [5_1_6, 6_2_1, 6_1_5, 6_3_7], [6_2_9, 6_2_1, 7_7_4, 6_3_6], [7_8_9, 6_2_1, 8_2_7, 6_3_3], [3_3_7, 6_3_9, 4_1_8, 6_5_2], [4_3_2, 6_4_0, 5_7_1, 6_5_3], [5_8_7, 6_3_9, 7_3_1, 6_5_5], [7_4_3, 6_3_9, 7_6_9, 6_5_2], [7_8_0, 6_3_9, 8_4_1, 6_5_2], [3_3_8, 6_5_8, 4_4_0, 6_7_3], [4_5_5, 6_5_8, 4_9_1, 6_7_0], [5_0_8, 6_5_8, 6_0_2, 6_7_1], [6_1_6, 6_5_8, 6_3_8, 6_7_0], [6_5_4, 6_5_8, 8_3_5, 6_7_4], [3_3_7, 6_7_7, 4_2_9, 6_8_9], [3_3_7, 7_1_4, 4_8_2, 7_2_6], [4_9_5, 7_1_4, 5_4_8, 7_2_6], [5_6_1, 7_1_4, 6_8_3, 7_2_6], [3_3_8, 7_7_0, 4_6_1, 7_8_2], [4_7_4, 7_6_9, 5_5_4, 7_8_5], [4_8_9, 7_8_8, 5_6_2, 8_0_3], [5_7_6, 7_8_8, 6_4_3, 8_0_1], [6_5_6, 7_8_7, 7_5_1, 8_0_4], [7_6_4, 7_8_8, 8_4_4, 8_0_1], [3_3_4, 8_2_5, 4_2_1, 8_3_8], [4_3_0, 8_2_4, 5_7_4, 8_3_8], [5_8_4, 8_2_4, 7_2_3, 8_4_1], [3_3_5, 8_4_4, 4_5_0, 8_5_7], [4_6_4, 8_4_3, 5_8_3, 8_6_0], [6_2_8, 8_6_2, 7_5_5, 8_7_5], [7_6_9, 8_6_1, 8_4_8, 8_7_8]]]  # noqa: E231
			# fmt: on

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

			# with apply_OCR = False
			UpperCAmelCase					      =  LayoutLMvaImageProcessor(apply_ocr=lowerCAmelCase__    )

			UpperCAmelCase					      =  image_processing(lowerCAmelCase__			,						return_tensors="pt"    )

			self.assertEqual(encoding.pixel_values.shape			,						(1, 3, 2_2_4, 2_2_4)    )






import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 						__magic_name__    (       _snake_case	,						unittest.TestCase    ):
 UpperCAmelCase							    =						LEDTokenizer
 UpperCAmelCase							    =						LEDTokenizerFast
 UpperCAmelCase							    =						True


 def _UpperCamelCase	(						self		: Tuple    )    ->      Union[str, Any]:
   super().setUp()
   UpperCAmelCase					      =  [
       "l",
       "o",
       "w",
       "e",
       "r",
       "s",
       "t",
       "i",
       "d",
       "n",
       "\u0120",
       "\u0120l",
       "\u0120n",
       "\u0120lo",
       "\u0120low",
       "er",
       "\u0120lowest",
       "\u0120newer",
       "\u0120wider",
       "<unk>",
   ]
   UpperCAmelCase					      =  dict(zip(lowerCAmelCase__			,						range(len(lowerCAmelCase__    )    )    )    )
   UpperCAmelCase					      =  ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
   UpperCAmelCase					      =  {"unk_token": "<unk>"}

   UpperCAmelCase					      =  os.path.join(self.tmpdirname			,						VOCAB_FILES_NAMES["vocab_file"]    )
   UpperCAmelCase					      =  os.path.join(self.tmpdirname			,						VOCAB_FILES_NAMES["merges_file"]    )
   with open(self.vocab_file			,						"w"			,						encoding="utf-8"    ) as fp:
     fp.write(json.dumps(lowerCAmelCase__    ) + "\n"    )
   with open(self.merges_file			,						"w"			,						encoding="utf-8"    ) as fp:
     fp.write("\n".join(lowerCAmelCase__    )    )


 def _UpperCamelCase	(						self		: Union[str, Any]			,						**lowerCAmelCase__		: Optional[int]    )    ->      Optional[int]:
   kwargs.update(self.special_tokens_map    )
   return self.tokenizer_class.from_pretrained(self.tmpdirname			,						**lowerCAmelCase__    )


 def _UpperCamelCase	(						self		: str			,						**lowerCAmelCase__		: str    )    ->      Optional[int]:
   kwargs.update(self.special_tokens_map    )
   return self.rust_tokenizer_class.from_pretrained(self.tmpdirname			,						**lowerCAmelCase__    )


 def _UpperCamelCase	(						self		: List[str]			,						lowerCAmelCase__		: List[Any]    )    ->      List[Any]:
   return "lower newer", "lower newer"


 @cached_property
 def _UpperCamelCase	(						self		: Dict    )    ->      str:
   return LEDTokenizer.from_pretrained("allenai/led-base-16384"    )


 @cached_property
 def _UpperCamelCase	(						self		: int    )    ->      Tuple:
   return LEDTokenizerFast.from_pretrained("allenai/led-base-16384"    )


 @require_torch
 def _UpperCamelCase	(						self		: Tuple    )    ->      List[str]:
   UpperCAmelCase					      =  ["A long paragraph for summarization.", "Another paragraph for summarization."]
   UpperCAmelCase					      =  [0, 2_5_0, 2_5_1, 1_7_8_1_8, 1_3, 3_9_1_8_6, 1_9_3_8, 4, 2]

   for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
     UpperCAmelCase					      =  tokenizer(lowerCAmelCase__			,						max_length=len(lowerCAmelCase__    )			,						padding=lowerCAmelCase__			,						return_tensors="pt"    )
     self.assertIsInstance(lowerCAmelCase__			,						lowerCAmelCase__    )

     self.assertEqual((2, 9)			,						batch.input_ids.shape    )
     self.assertEqual((2, 9)			,						batch.attention_mask.shape    )
     UpperCAmelCase					      =  batch.input_ids.tolist()[0]
     self.assertListEqual(lowerCAmelCase__			,						lowerCAmelCase__    )


 @require_torch
 def _UpperCamelCase	(						self		: Union[str, Any]    )    ->      List[Any]:
   UpperCAmelCase					      =  ["A long paragraph for summarization.", "Another paragraph for summarization."]
   for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
     UpperCAmelCase					      =  tokenizer(lowerCAmelCase__			,						padding=lowerCAmelCase__			,						return_tensors="pt"    )
     self.assertIn("input_ids"			,						lowerCAmelCase__    )
     self.assertIn("attention_mask"			,						lowerCAmelCase__    )
     self.assertNotIn("labels"			,						lowerCAmelCase__    )
     self.assertNotIn("decoder_attention_mask"			,						lowerCAmelCase__    )


 @require_torch
 def _UpperCamelCase	(						self		: int    )    ->      int:
   UpperCAmelCase					      =  [
       "Summary of the text.",
       "Another summary.",
   ]
   for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
     UpperCAmelCase					      =  tokenizer(text_target=lowerCAmelCase__			,						max_length=3_2			,						padding="max_length"			,						return_tensors="pt"    )
     self.assertEqual(3_2			,						targets["input_ids"].shape[1]    )


 @require_torch
 def _UpperCamelCase	(						self		: Any    )    ->      int:
   for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
     UpperCAmelCase					      =  tokenizer(
         ["I am a small frog" * 1_0_2_4, "I am a small frog"]			,						padding=lowerCAmelCase__			,						truncation=lowerCAmelCase__			,						return_tensors="pt"    )
     self.assertIsInstance(lowerCAmelCase__			,						lowerCAmelCase__    )
     self.assertEqual(batch.input_ids.shape			,						(2, 5_1_2_2)    )


 @require_torch
 def _UpperCamelCase	(						self		: Dict    )    ->      Tuple:
   UpperCAmelCase					      =  ["A long paragraph for summarization."]
   UpperCAmelCase					      =  [
       "Summary of the text.",
   ]
   for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
     UpperCAmelCase					      =  tokenizer(lowerCAmelCase__			,						return_tensors="pt"    )
     UpperCAmelCase					      =  tokenizer(text_target=lowerCAmelCase__			,						return_tensors="pt"    )
     UpperCAmelCase					      =  inputs["input_ids"]
     UpperCAmelCase					      =  targets["input_ids"]
     self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()    )
     self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()    )
     self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()    )
     self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()    )


 @require_torch
 def _UpperCamelCase	(						self		: Optional[int]    )    ->      Optional[Any]:
   for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
     UpperCAmelCase					      =  ["Summary of the text.", "Another summary."]
     UpperCAmelCase					      =  [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

     UpperCAmelCase					      =  tokenizer(lowerCAmelCase__			,						padding=lowerCAmelCase__    )
     UpperCAmelCase					      =  [[0] * len(lowerCAmelCase__    ) for x in encoded_output["input_ids"]]
     UpperCAmelCase					      =  tokenizer.pad(lowerCAmelCase__    )
     self.assertSequenceEqual(outputs["global_attention_mask"]			,						lowerCAmelCase__    )


 def _UpperCamelCase	(						self		: List[str]    )    ->      int:
   pass


 def _UpperCamelCase	(						self		: Optional[Any]    )    ->      Union[str, Any]:
   for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
     with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"    ):
       UpperCAmelCase					      =  self.rust_tokenizer_class.from_pretrained(lowerCAmelCase__			,						**lowerCAmelCase__    )
       UpperCAmelCase					      =  self.tokenizer_class.from_pretrained(lowerCAmelCase__			,						**lowerCAmelCase__    )
       UpperCAmelCase					      =  "A, <mask> AllenNLP sentence."
       UpperCAmelCase					      =  tokenizer_r.encode_plus(lowerCAmelCase__			,						add_special_tokens=lowerCAmelCase__			,						return_token_type_ids=lowerCAmelCase__    )
       UpperCAmelCase					      =  tokenizer_p.encode_plus(lowerCAmelCase__			,						add_special_tokens=lowerCAmelCase__			,						return_token_type_ids=lowerCAmelCase__    )
       self.assertEqual(sum(tokens_r["token_type_ids"]    )			,						sum(tokens_p["token_type_ids"]    )    )
       self.assertEqual(
           sum(tokens_r["attention_mask"]    ) / len(tokens_r["attention_mask"]    )			,						sum(tokens_p["attention_mask"]    ) / len(tokens_p["attention_mask"]    )			,						)

       UpperCAmelCase					      =  tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]    )
       UpperCAmelCase					      =  tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]    )
       self.assertSequenceEqual(tokens_p["input_ids"]			,						[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]    )
       self.assertSequenceEqual(tokens_r["input_ids"]			,						[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]    )

       self.assertSequenceEqual(
           lowerCAmelCase__			,						["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]    )
       self.assertSequenceEqual(
           lowerCAmelCase__			,						["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]    )



