





import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


__UpperCamelCase:	Union[str, Any] = get_tests_dir("""fixtures/test_sentencepiece.model""")


@require_sentencepiece
@require_tokenizers
class       __lowerCAmelCase					(       _UpperCamelCase   ,    unittest.TestCase      ):


	'''simple docstring'''





	_A													=				XLMRobertaTokenizer
	_A													=				XLMRobertaTokenizerFast
	_A													=				True
	_A													=				True

	def    snake_case__(						self:						Any			):
			super().setUp()

			# We have a SentencePiece fixture for testing
			lowercase__					:      Tuple      =		XLMRobertaTokenizer(lowerCamelCase_,					keep_accents=lowerCamelCase_			)
			tokenizer.save_pretrained(self.tmpdirname			)

	def    snake_case__(						self:						Any			):
			lowercase__					:      Optional[Any]      =		'<pad>'
			lowercase__					:      Tuple      =		1

			self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCamelCase_			),					lowerCamelCase_			)
			self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCamelCase_			),					lowerCamelCase_			)

	def    snake_case__(						self:						str			):
			lowercase__					:      List[Any]      =		list(self.get_tokenizer().get_vocab().keys()			)

			self.assertEqual(vocab_keys[0],					'<s>'			)
			self.assertEqual(vocab_keys[1],					'<pad>'			)
			self.assertEqual(vocab_keys[-1],					'<mask>'			)
			self.assertEqual(len(lowerCamelCase_			),					1002			)

	def    snake_case__(						self:						Dict			):
			self.assertEqual(self.get_tokenizer().vocab_size,					1002			)

	def    snake_case__(						self:						Optional[Any]			):
			lowercase__					:      List[str]      =		XLMRobertaTokenizer(lowerCamelCase_,					keep_accents=lowerCamelCase_			)

			lowercase__					:      List[str]      =		tokenizer.tokenize('This is a test'			)
			self.assertListEqual(lowerCamelCase_,					['▁This', '▁is', '▁a', '▁t', 'est']			)

			self.assertListEqual(
			    tokenizer.convert_tokens_to_ids(lowerCamelCase_			),					[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]],					)

			lowercase__					:      Any      =		tokenizer.tokenize('I was born in 92000, and this is falsé.'			)
			self.assertListEqual(
			    lowerCamelCase_,					[
			        SPIECE_UNDERLINE + 'I',
			        SPIECE_UNDERLINE + 'was',
			        SPIECE_UNDERLINE + 'b',
			        'or',
			        'n',
			        SPIECE_UNDERLINE + 'in',
			        SPIECE_UNDERLINE + '',
			        '9',
			        '2',
			        '0',
			        '0',
			        '0',
			        ',',
			        SPIECE_UNDERLINE + 'and',
			        SPIECE_UNDERLINE + 'this',
			        SPIECE_UNDERLINE + 'is',
			        SPIECE_UNDERLINE + 'f',
			        'al',
			        's',
			        'é',
			        '.',
			    ],					)
			lowercase__					:      List[Any]      =		tokenizer.convert_tokens_to_ids(lowerCamelCase_			)
			self.assertListEqual(
			    lowerCamelCase_,					[
			        value + tokenizer.fairseq_offset
			        for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
			        #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
			    ],					)

			lowercase__					:      List[str]      =		tokenizer.convert_ids_to_tokens(lowerCamelCase_			)
			self.assertListEqual(
			    lowerCamelCase_,					[
			        SPIECE_UNDERLINE + 'I',
			        SPIECE_UNDERLINE + 'was',
			        SPIECE_UNDERLINE + 'b',
			        'or',
			        'n',
			        SPIECE_UNDERLINE + 'in',
			        SPIECE_UNDERLINE + '',
			        '<unk>',
			        '2',
			        '0',
			        '0',
			        '0',
			        ',',
			        SPIECE_UNDERLINE + 'and',
			        SPIECE_UNDERLINE + 'this',
			        SPIECE_UNDERLINE + 'is',
			        SPIECE_UNDERLINE + 'f',
			        'al',
			        's',
			        '<unk>',
			        '.',
			    ],					)

	def    snake_case__(						self:						Optional[Any]			):
			if not self.test_slow_tokenizer:
					# as we don't have a slow version, we can't compare the outputs between slow and fast versions
					return

			lowercase__					:      str      =		(self.rust_tokenizer_class, 'hf-internal-testing/tiny-xlm-roberta', {})
			for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
					with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""			):
							lowercase__					:      List[str]      =		self.rust_tokenizer_class.from_pretrained(lowerCamelCase_,					**lowerCamelCase_			)
							lowercase__					:      Optional[Any]      =		self.tokenizer_class.from_pretrained(lowerCamelCase_,					**lowerCamelCase_			)

							lowercase__					:      Any      =		tempfile.mkdtemp()

							lowercase__					:      str      =		tokenizer_r.save_pretrained(lowerCamelCase_			)
							lowercase__					:      int      =		tokenizer_p.save_pretrained(lowerCamelCase_			)

							# Checks it save with the same files + the tokenizer.json file for the fast one
							self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files			)			)
							lowercase__					:      List[Any]      =		tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f			)
							self.assertSequenceEqual(lowerCamelCase_,					lowerCamelCase_			)

							# Checks everything loads correctly in the same way
							lowercase__					:      Any      =		tokenizer_r.from_pretrained(lowerCamelCase_			)
							lowercase__					:      Optional[Any]      =		tokenizer_p.from_pretrained(lowerCamelCase_			)

							# Check special tokens are set accordingly on Rust and Python
							for key in tokenizer_pp.special_tokens_map:
									self.assertTrue(hasattr(lowerCamelCase_,					lowerCamelCase_			)			)
									# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
									# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

							shutil.rmtree(lowerCamelCase_			)

							# Save tokenizer rust, legacy_format=True
							lowercase__					:      List[str]      =		tempfile.mkdtemp()

							lowercase__					:      int      =		tokenizer_r.save_pretrained(lowerCamelCase_,					legacy_format=lowerCamelCase_			)
							lowercase__					:      Optional[Any]      =		tokenizer_p.save_pretrained(lowerCamelCase_			)

							# Checks it save with the same files
							self.assertSequenceEqual(lowerCamelCase_,					lowerCamelCase_			)

							# Checks everything loads correctly in the same way
							lowercase__					:      List[str]      =		tokenizer_r.from_pretrained(lowerCamelCase_			)
							lowercase__					:      Optional[Any]      =		tokenizer_p.from_pretrained(lowerCamelCase_			)

							# Check special tokens are set accordingly on Rust and Python
							for key in tokenizer_pp.special_tokens_map:
									self.assertTrue(hasattr(lowerCamelCase_,					lowerCamelCase_			)			)

							shutil.rmtree(lowerCamelCase_			)

							# Save tokenizer rust, legacy_format=False
							lowercase__					:      List[Any]      =		tempfile.mkdtemp()

							lowercase__					:      Optional[Any]      =		tokenizer_r.save_pretrained(lowerCamelCase_,					legacy_format=lowerCamelCase_			)
							lowercase__					:      Optional[Any]      =		tokenizer_p.save_pretrained(lowerCamelCase_			)

							# Checks it saved the tokenizer.json file
							self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files			)			)

							# Checks everything loads correctly in the same way
							lowercase__					:      int      =		tokenizer_r.from_pretrained(lowerCamelCase_			)
							lowercase__					:      Optional[int]      =		tokenizer_p.from_pretrained(lowerCamelCase_			)

							# Check special tokens are set accordingly on Rust and Python
							for key in tokenizer_pp.special_tokens_map:
									self.assertTrue(hasattr(lowerCamelCase_,					lowerCamelCase_			)			)

							shutil.rmtree(lowerCamelCase_			)

	@cached_property
	def    snake_case__(						self:						int			):
			return XLMRobertaTokenizer.from_pretrained('xlm-roberta-base'			)

	def    snake_case__(						self:						Dict			):
			with tempfile.NamedTemporaryFile() as f:
					shutil.copyfile(lowerCamelCase_,					f.name			)
					lowercase__					:      Any      =		XLMRobertaTokenizer(f.name,					keep_accents=lowerCamelCase_			)
					lowercase__					:      str      =		pickle.dumps(lowerCamelCase_			)
			pickle.loads(lowerCamelCase_			)

	def    snake_case__(						self:						Any			):
			if not self.test_rust_tokenizer:
					return

			lowercase__					:      Optional[Any]      =		self.get_tokenizer()
			lowercase__					:      str      =		self.get_rust_tokenizer()

			lowercase__					:      List[Any]      =		'I was born in 92000, and this is falsé.'

			lowercase__					:      Dict      =		tokenizer.tokenize(lowerCamelCase_			)
			lowercase__					:      Optional[Any]      =		rust_tokenizer.tokenize(lowerCamelCase_			)
			self.assertListEqual(lowerCamelCase_,					lowerCamelCase_			)

			lowercase__					:      int      =		tokenizer.encode(lowerCamelCase_,					add_special_tokens=lowerCamelCase_			)
			lowercase__					:      Any      =		rust_tokenizer.encode(lowerCamelCase_,					add_special_tokens=lowerCamelCase_			)
			self.assertListEqual(lowerCamelCase_,					lowerCamelCase_			)

			lowercase__					:      Optional[int]      =		self.get_rust_tokenizer()
			lowercase__					:      str      =		tokenizer.encode(lowerCamelCase_			)
			lowercase__					:      List[str]      =		rust_tokenizer.encode(lowerCamelCase_			)
			self.assertListEqual(lowerCamelCase_,					lowerCamelCase_			)

	@slow
	def    snake_case__(						self:						Optional[Any]			):
			lowercase__					:      Dict      =		'Hello World!'
			lowercase__					:      str      =		[0, 35378, 6661, 38, 2]
			# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
			# xlmr.eval()
			# xlmr.encode(symbols)

			self.assertListEqual(lowerCamelCase_,					self.big_tokenizer.encode(lowerCamelCase_			)			)

	@slow
	def    snake_case__(						self:						Any			):
			lowercase__					:      int      =		(
			    'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'
			    ' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'
			)
			lowercase__					:      Any      =		[
			    0,
			    3293,
			    83,
			    10,
			    4552,
			    4989,
			    7986,
			    678,
			    10,
			    5915,
			    111,
			    179459,
			    124850,
			    4,
			    6044,
			    237,
			    12,
			    6,
			    5,
			    6,
			    4,
			    6780,
			    705,
			    15,
			    1388,
			    44,
			    378,
			    10114,
			    711,
			    152,
			    20,
			    6,
			    5,
			    22376,
			    642,
			    1221,
			    15190,
			    34153,
			    450,
			    5608,
			    959,
			    1119,
			    57702,
			    136,
			    186,
			    47,
			    1098,
			    29367,
			    47,
			    # 4426, # What fairseq tokenizes from "<unk>": "_<"
			    # 3678, # What fairseq tokenizes from "<unk>": "unk"
			    # 2740, # What fairseq tokenizes from "<unk>": ">"
			    3,  # What we tokenize from "<unk>": "<unk>"
			    6,  # Residue from the tokenization: an extra sentencepiece underline
			    4,
			    6044,
			    237,
			    6284,
			    50901,
			    528,
			    31,
			    90,
			    34,
			    927,
			    2,
			]
			# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
			# xlmr.eval()
			# xlmr.encode(symbols)

			self.assertListEqual(lowerCamelCase_,					self.big_tokenizer.encode(lowerCamelCase_			)			)





	@slow
	def    snake_case__(						self:						int			):
			# fmt: off
			lowercase__					:      Optional[Any]      =		{'input_ids': [[0, 11062, 82772, 7, 15, 82772, 538, 51529, 237, 17198, 1290, 206, 9, 215175, 1314, 136, 17198, 1290, 206, 9, 56359, 42, 122009, 9, 16466, 16, 87344, 4537, 9, 4717, 78381, 6, 159958, 7, 15, 24480, 618, 4, 527, 22693, 5428, 4, 2777, 24480, 9874, 4, 43523, 594, 4, 803, 18392, 33189, 18, 4, 43523, 24447, 12399, 100, 24955, 83658, 9626, 144057, 15, 839, 22335, 16, 136, 24955, 83658, 83479, 15, 39102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 122009, 115774, 23, 805, 1328, 46876, 7, 136, 53894, 1940, 42227, 41159, 17721, 823, 425, 4, 27512, 98722, 206, 136, 5531, 4970, 919, 17336, 5, 2], [0, 20080, 618, 83, 82775, 47, 479, 9, 1517, 73, 53894, 333, 80581, 110117, 18811, 5256, 1295, 51, 152526, 297, 7986, 390, 124416, 538, 35431, 214, 98, 15044, 25737, 136, 7108, 43701, 23, 756, 135355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 581, 63773, 119455, 6, 147797, 88203, 7, 645, 70, 21, 3285, 10269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
			# fmt: on

			self.tokenizer_integration_test_util(
			    expected_encoding=lowerCamelCase_,					model_name='xlm-roberta-base',					revision='d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3',					)









import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
			import torch

if is_vision_available():
			from PIL import Image

			from transformers import YolosImageProcessor


class       __lowerCAmelCase					(       unittest.TestCase      ):


	'''simple docstring'''





	def __init__(						self:						Optional[int],					lowerCamelCase_:						Dict,					lowerCamelCase_:						Tuple=7,					lowerCamelCase_:						Dict=3,					lowerCamelCase_:						Optional[Any]=30,					lowerCamelCase_:						str=400,					lowerCamelCase_:						List[Any]=True,					lowerCamelCase_:						List[Any]=None,					lowerCamelCase_:						Dict=True,					lowerCamelCase_:						Optional[Any]=[0.5, 0.5, 0.5],					lowerCamelCase_:						Dict=[0.5, 0.5, 0.5],					lowerCamelCase_:						Dict=True,					lowerCamelCase_:						List[str]=1 / 255,					lowerCamelCase_:						Dict=True,					):
			# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
			lowercase__					:      str      =		size if size is not None else {'shortest_edge': 18, 'longest_edge': 1333}
			lowercase__					:      Optional[int]      =		parent
			lowercase__					:      List[Any]      =		batch_size
			lowercase__					:      Optional[int]      =		num_channels
			lowercase__					:      Optional[Any]      =		min_resolution
			lowercase__					:      Union[str, Any]      =		max_resolution
			lowercase__					:      Tuple      =		do_resize
			lowercase__					:      str      =		size
			lowercase__					:      Dict      =		do_normalize
			lowercase__					:      Optional[int]      =		image_mean
			lowercase__					:      Dict      =		image_std
			lowercase__					:      int      =		do_rescale
			lowercase__					:      Any      =		rescale_factor
			lowercase__					:      Union[str, Any]      =		do_pad

	def    snake_case__(						self:						str			):
			return {
			    "do_resize": self.do_resize,
			    "size": self.size,
			    "do_normalize": self.do_normalize,
			    "image_mean": self.image_mean,
			    "image_std": self.image_std,
			    "do_rescale": self.do_rescale,
			    "rescale_factor": self.rescale_factor,
			    "do_pad": self.do_pad,
			}





	def    snake_case__(						self:						Tuple,					lowerCamelCase_:						Tuple,					lowerCamelCase_:						int=False			):
			if not batched:
					lowercase__					:      Any      =		image_inputs[0]
					if isinstance(lowerCamelCase_,					Image.Image			):
							lowercase__   ,				lowercase__					:      List[str]      =		image.size
					else:
							lowercase__   ,				lowercase__					:      str      =		image.shape[1], image.shape[2]
					if w < h:
							lowercase__					:      Optional[Any]      =		int(self.size['shortest_edge'] * h / w			)
							lowercase__					:      Optional[int]      =		self.size['shortest_edge']
					elif w > h:
							lowercase__					:      List[str]      =		self.size['shortest_edge']
							lowercase__					:      Any      =		int(self.size['shortest_edge'] * w / h			)
					else:
							lowercase__					:      List[str]      =		self.size['shortest_edge']
							lowercase__					:      List[Any]      =		self.size['shortest_edge']

			else:
					lowercase__					:      int      =		[]
					for image in image_inputs:
							lowercase__   ,				lowercase__					:      Dict      =		self.get_expected_values([image]			)
							expected_values.append((expected_height, expected_width)			)
					lowercase__					:      int      =		max(lowerCamelCase_,					key=lambda lowerCamelCase_			: item[0]			)[0]
					lowercase__					:      Tuple      =		max(lowerCamelCase_,					key=lambda lowerCamelCase_			: item[1]			)[1]

			return expected_height, expected_width


@require_torch
@require_vision
class       __lowerCAmelCase					(       _UpperCamelCase   ,    unittest.TestCase      ):


	'''simple docstring'''





	_A													=				YolosImageProcessor if is_vision_available() else None

	def    snake_case__(						self:						int			):
			lowercase__					:      Optional[int]      =		YolosImageProcessingTester(self			)

	@property
	def    snake_case__(						self:						Union[str, Any]			):
			return self.image_processor_tester.prepare_image_processor_dict()

	def    snake_case__(						self:						Dict			):
			lowercase__					:      Optional[Any]      =		self.image_processing_class(**self.image_processor_dict			)
			self.assertTrue(hasattr(lowerCamelCase_,					'image_mean'			)			)
			self.assertTrue(hasattr(lowerCamelCase_,					'image_std'			)			)
			self.assertTrue(hasattr(lowerCamelCase_,					'do_normalize'			)			)
			self.assertTrue(hasattr(lowerCamelCase_,					'do_resize'			)			)
			self.assertTrue(hasattr(lowerCamelCase_,					'size'			)			)

	def    snake_case__(						self:						List[Any]			):
			lowercase__					:      Tuple      =		self.image_processing_class.from_dict(self.image_processor_dict			)
			self.assertEqual(image_processor.size,					{'shortest_edge': 18, 'longest_edge': 1333}			)
			self.assertEqual(image_processor.do_pad,					lowerCamelCase_			)

			lowercase__					:      Optional[int]      =		self.image_processing_class.from_dict(
			    self.image_processor_dict,					size=42,					max_size=84,					pad_and_return_pixel_mask=lowerCamelCase_			)
			self.assertEqual(image_processor.size,					{'shortest_edge': 42, 'longest_edge': 84}			)
			self.assertEqual(image_processor.do_pad,					lowerCamelCase_			)

	def    snake_case__(						self:						str			):
			pass

	def    snake_case__(						self:						Optional[Any]			):
			# Initialize image_processing
			lowercase__					:      Optional[int]      =		self.image_processing_class(**self.image_processor_dict			)
			# create random PIL images
			lowercase__					:      Union[str, Any]      =		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__					:      Tuple      =		image_processing(image_inputs[0],					return_tensors='pt'			).pixel_values

			lowercase__   ,				lowercase__					:      List[Any]      =		self.image_processor_tester.get_expected_values(lowerCamelCase_			)

			self.assertEqual(
			    encoded_images.shape,					(1, self.image_processor_tester.num_channels, expected_height, expected_width),					)

			# Test batched
			lowercase__   ,				lowercase__					:      Optional[Any]      =		self.image_processor_tester.get_expected_values(lowerCamelCase_,					batched=lowerCamelCase_			)

			lowercase__					:      Dict      =		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,
			        expected_height,
			        expected_width,
			    ),					)

	def    snake_case__(						self:						str			):
			# Initialize image_processing
			lowercase__					:      str      =		self.image_processing_class(**self.image_processor_dict			)
			# create random numpy tensors
			lowercase__					:      int      =		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__					:      List[Any]      =		image_processing(image_inputs[0],					return_tensors='pt'			).pixel_values

			lowercase__   ,				lowercase__					:      Optional[int]      =		self.image_processor_tester.get_expected_values(lowerCamelCase_			)

			self.assertEqual(
			    encoded_images.shape,					(1, self.image_processor_tester.num_channels, expected_height, expected_width),					)

			# Test batched
			lowercase__					:      Any      =		image_processing(lowerCamelCase_,					return_tensors='pt'			).pixel_values

			lowercase__   ,				lowercase__					:      Optional[int]      =		self.image_processor_tester.get_expected_values(lowerCamelCase_,					batched=lowerCamelCase_			)

			self.assertEqual(
			    encoded_images.shape,					(
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.num_channels,
			        expected_height,
			        expected_width,
			    ),					)

	def    snake_case__(						self:						Union[str, Any]			):
			# Initialize image_processing
			lowercase__					:      Optional[int]      =		self.image_processing_class(**self.image_processor_dict			)
			# create random PyTorch tensors
			lowercase__					:      Dict      =		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__					:      List[str]      =		image_processing(image_inputs[0],					return_tensors='pt'			).pixel_values

			lowercase__   ,				lowercase__					:      Union[str, Any]      =		self.image_processor_tester.get_expected_values(lowerCamelCase_			)

			self.assertEqual(
			    encoded_images.shape,					(1, self.image_processor_tester.num_channels, expected_height, expected_width),					)

			# Test batched
			lowercase__					:      List[Any]      =		image_processing(lowerCamelCase_,					return_tensors='pt'			).pixel_values

			lowercase__   ,				lowercase__					:      Union[str, Any]      =		self.image_processor_tester.get_expected_values(lowerCamelCase_,					batched=lowerCamelCase_			)

			self.assertEqual(
			    encoded_images.shape,					(
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.num_channels,
			        expected_height,
			        expected_width,
			    ),					)

	def    snake_case__(						self:						int			):
			# Initialize image_processings
			lowercase__					:      Tuple      =		self.image_processing_class(**self.image_processor_dict			)
			lowercase__					:      List[str]      =		self.image_processing_class(do_resize=lowerCamelCase_,					do_normalize=lowerCamelCase_,					do_rescale=lowerCamelCase_			)
			# create random PyTorch tensors
			lowercase__					:      Tuple      =		prepare_image_inputs(self.image_processor_tester,					equal_resolution=lowerCamelCase_,					torchify=lowerCamelCase_			)
			for image in image_inputs:
					self.assertIsInstance(lowerCamelCase_,					torch.Tensor			)

			# Test whether the method "pad" and calling the image processor return the same tensors
			lowercase__					:      Optional[int]      =		image_processing_a.pad(lowerCamelCase_,					return_tensors='pt'			)
			lowercase__					:      int      =		image_processing_a(lowerCamelCase_,					return_tensors='pt'			)

			self.assertTrue(
			    torch.allclose(encoded_images_with_method['pixel_values'],					encoded_images['pixel_values'],					atol=1E-4			)			)

	@slow
	def    snake_case__(						self:						List[str]			):
			# prepare image and target
			lowercase__					:      Any      =		Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
			with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt',					'r'			) as f:
					lowercase__					:      List[str]      =		json.loads(f.read()			)

			lowercase__					:      List[str]      =		{'image_id': 39769, 'annotations': target}

			# encode them
			lowercase__					:      Any      =		YolosImageProcessor.from_pretrained('hustvl/yolos-small'			)
			lowercase__					:      Optional[Any]      =		image_processing(images=lowerCamelCase_,					annotations=lowerCamelCase_,					return_tensors='pt'			)

			# verify pixel values
			lowercase__					:      Optional[Any]      =		torch.Size([1, 3, 800, 1066]			)
			self.assertEqual(encoding['pixel_values'].shape,					lowerCamelCase_			)

			lowercase__					:      int      =		torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]			)
			self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3],					lowerCamelCase_,					atol=1E-4			)			)

			# verify area
			lowercase__					:      Optional[int]      =		torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['area'],					lowerCamelCase_			)			)
			# verify boxes
			lowercase__					:      int      =		torch.Size([6, 4]			)
			self.assertEqual(encoding['labels'][0]['boxes'].shape,					lowerCamelCase_			)
			lowercase__					:      Tuple      =		torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0],					lowerCamelCase_,					atol=1E-3			)			)
			# verify image_id
			lowercase__					:      Tuple      =		torch.tensor([39769]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'],					lowerCamelCase_			)			)
			# verify is_crowd
			lowercase__					:      Union[str, Any]      =		torch.tensor([0, 0, 0, 0, 0, 0]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'],					lowerCamelCase_			)			)
			# verify class_labels
			lowercase__					:      Tuple      =		torch.tensor([75, 75, 63, 65, 17, 17]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'],					lowerCamelCase_			)			)
			# verify orig_size
			lowercase__					:      Optional[int]      =		torch.tensor([480, 640]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'],					lowerCamelCase_			)			)
			# verify size
			lowercase__					:      List[Any]      =		torch.tensor([800, 1066]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['size'],					lowerCamelCase_			)			)





	@slow
	def    snake_case__(						self:						Dict			):
			# prepare image, target and masks_path
			lowercase__					:      Optional[int]      =		Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
			with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt',					'r'			) as f:
					lowercase__					:      Optional[Any]      =		json.loads(f.read()			)

			lowercase__					:      List[Any]      =		{'file_name': '000000039769.png', 'image_id': 39769, 'segments_info': target}

			lowercase__					:      Optional[int]      =		pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'			)

			# encode them
			lowercase__					:      List[Any]      =		YolosImageProcessor(format='coco_panoptic'			)
			lowercase__					:      Dict      =		image_processing(images=lowerCamelCase_,					annotations=lowerCamelCase_,					masks_path=lowerCamelCase_,					return_tensors='pt'			)

			# verify pixel values
			lowercase__					:      str      =		torch.Size([1, 3, 800, 1066]			)
			self.assertEqual(encoding['pixel_values'].shape,					lowerCamelCase_			)

			lowercase__					:      List[str]      =		torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]			)
			self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3],					lowerCamelCase_,					atol=1E-4			)			)

			# verify area
			lowercase__					:      Optional[int]      =		torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['area'],					lowerCamelCase_			)			)
			# verify boxes
			lowercase__					:      Dict      =		torch.Size([6, 4]			)
			self.assertEqual(encoding['labels'][0]['boxes'].shape,					lowerCamelCase_			)
			lowercase__					:      List[str]      =		torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0],					lowerCamelCase_,					atol=1E-3			)			)
			# verify image_id
			lowercase__					:      Any      =		torch.tensor([39769]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'],					lowerCamelCase_			)			)
			# verify is_crowd
			lowercase__					:      List[str]      =		torch.tensor([0, 0, 0, 0, 0, 0]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'],					lowerCamelCase_			)			)
			# verify class_labels
			lowercase__					:      List[str]      =		torch.tensor([17, 17, 63, 75, 75, 93]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'],					lowerCamelCase_			)			)
			# verify masks
			lowercase__					:      Optional[Any]      =		822873
			self.assertEqual(encoding['labels'][0]['masks'].sum().item(),					lowerCamelCase_			)
			# verify orig_size
			lowercase__					:      Optional[int]      =		torch.tensor([480, 640]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'],					lowerCamelCase_			)			)
			# verify size
			lowercase__					:      Dict      =		torch.tensor([800, 1066]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['size'],					lowerCamelCase_			)			)


