



'''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 _UpperCamelCase		(     unittest.TestCase		):





				'''simple docstring'''





				def __init__(							self  :  int  ,       _lowerCAmelCase  :  Tuple  ,       _lowerCAmelCase  :  Optional[int]=7  ,       _lowerCAmelCase  :  int=3  ,       _lowerCAmelCase  :  List[str]=1_8  ,       _lowerCAmelCase  :  Dict=3_0  ,       _lowerCAmelCase  :  int=4_0_0  ,       _lowerCAmelCase  :  int=True  ,       _lowerCAmelCase  :  str=None  ,       _lowerCAmelCase  :  Optional[Any]=True  ,       ):







											'''simple docstring'''

											__lowercase       	=size if size is not None else {"""height""": 1_8, """width""": 1_8}
											__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 				__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 _UpperCamelCase		(     UpperCAmelCase__      ,       unittest.TestCase		):





				'''simple docstring'''





				lowerCAmelCase__					    =    LayoutLMvaImageProcessor if is_pytesseract_available() else None

				def 				__lowerCamelCase     (							self  :  str):







											'''simple docstring'''

											__lowercase       	=LayoutLMvaImageProcessingTester(self)

				@property
				def 				__lowerCamelCase     (							self  :  Tuple):







											'''simple docstring'''

											return self.image_processor_tester.prepare_image_processor_dict()

				def 				__lowerCamelCase     (							self  :  Union[str, Any]):







											'''simple docstring'''

											__lowercase       	=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 				__lowerCamelCase     (							self  :  Optional[int]):







											'''simple docstring'''

											__lowercase       	=self.image_processing_class.from_dict(self.image_processor_dict)
											self.assertEqual(image_processor.size  ,       {'height': 1_8, 'width': 1_8})

											__lowercase       	=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 				__lowerCamelCase     (							self  :  Union[str, Any]):







											'''simple docstring'''

											pass

				def 				__lowerCamelCase     (							self  :  Tuple):







											'''simple docstring'''

											__lowercase       	=self.image_processing_class(**self.image_processor_dict)
											# create random PIL images
											__lowercase       	=prepare_image_inputs(self.image_processor_tester  ,       equal_resolution=_lowerCAmelCase)
											for image in image_inputs:
																		self.assertIsInstance(_lowerCAmelCase  ,       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  ,       _lowerCAmelCase)
											self.assertIsInstance(encoding.boxes  ,       _lowerCAmelCase)

											# Test batched
											__lowercase       	=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 				__lowerCamelCase     (							self  :  Union[str, Any]):







											'''simple docstring'''

											__lowercase       	=self.image_processing_class(**self.image_processor_dict)
											# create random numpy tensors
											__lowercase       	=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
											__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(_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 				__lowerCamelCase     (							self  :  Optional[Any]):







											'''simple docstring'''

											__lowercase       	=self.image_processing_class(**self.image_processor_dict)
											# create random PyTorch tensors
											__lowercase       	=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
											__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(_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 				__lowerCamelCase     (							self  :  int):







											'''simple docstring'''

											__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(_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
											__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       	=[[[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
											__lowercase       	=LayoutLMvaImageProcessor(apply_ocr=_lowerCAmelCase)

											__lowercase       	=image_processing(_lowerCAmelCase  ,       return_tensors='pt')

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








'''simple docstring'''



import json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin





class _UpperCamelCase		(     A      ,       unittest.TestCase		):





				'''simple docstring'''





				lowerCAmelCase__					    =    BlenderbotSmallTokenizer
				lowerCAmelCase__					    =    False

				def 				__lowerCamelCase     (							self  :  str):







											'''simple docstring'''

											super().setUp()

											__lowercase       	=['__start__', 'adapt', 'act', 'ap@@', 'te', '__end__', '__unk__']
											__lowercase       	=dict(zip(_lowerCAmelCase  ,       range(len(_lowerCAmelCase))))

											__lowercase       	=['#version: 0.2', 'a p', 't e</w>', 'ap t</w>', 'a d', 'ad apt</w>', 'a c', 'ac t</w>', '']
											__lowercase       	={'unk_token': '__unk__', 'bos_token': '__start__', 'eos_token': '__end__'}

											__lowercase       	=os.path.join(self.tmpdirname  ,       VOCAB_FILES_NAMES['vocab_file'])
											__lowercase       	=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 				__lowerCamelCase     (							self  :  int  ,       **_lowerCAmelCase  :  Tuple):







											'''simple docstring'''

											kwargs.update(self.special_tokens_map)
											return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname  ,       **_lowerCAmelCase)

				def 				__lowerCamelCase     (							self  :  Tuple  ,       _lowerCAmelCase  :  Any):







											'''simple docstring'''

											__lowercase       	='adapt act apte'
											__lowercase       	='adapt act apte'
											return input_text, output_text

				def 				__lowerCamelCase     (							self  :  Union[str, Any]):







											'''simple docstring'''

											__lowercase       	=BlenderbotSmallTokenizer(self.vocab_file  ,       self.merges_file  ,       **self.special_tokens_map)
											__lowercase       	='adapt act apte'
											__lowercase       	=['adapt', 'act', 'ap@@', 'te']
											__lowercase       	=tokenizer.tokenize(_lowerCAmelCase)
											self.assertListEqual(_lowerCAmelCase  ,       _lowerCAmelCase)

											__lowercase       	=[tokenizer.bos_token] + tokens + [tokenizer.eos_token]

											__lowercase       	=[0, 1, 2, 3, 4, 5]
											self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCAmelCase)  ,       _lowerCAmelCase)

				def 				__lowerCamelCase     (							self  :  Union[str, Any]):







											'''simple docstring'''

											__lowercase       	=BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot-90M')
											assert tok('sam').input_ids == [1_3_8_4]
											__lowercase       	='I am a small frog.'
											__lowercase       	=tok([src_text]  ,       padding=_lowerCAmelCase  ,       truncation=_lowerCAmelCase)['input_ids']
											__lowercase       	=tok.batch_decode(_lowerCAmelCase  ,       skip_special_tokens=_lowerCAmelCase  ,       clean_up_tokenization_spaces=_lowerCAmelCase)[0]
											assert src_text != decoded  # I wish it did!
											assert decoded == "i am a small frog ."

				def 				__lowerCamelCase     (							self  :  str):







											'''simple docstring'''

											__lowercase       	=BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot-90M')
											__lowercase       	='I am a small frog .'
											__lowercase       	='.'
											__lowercase       	=tok(_lowerCAmelCase)['input_ids']
											__lowercase       	=tok(_lowerCAmelCase)['input_ids']

											assert encoded[-1] == encoded_dot[0]



