




import os
import unittest

from transformers import MobileBertTokenizer, MobileBertTokenizerFast
from transformers.models.bert.tokenization_bert import (
    VOCAB_FILES_NAMES,
    BasicTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english




@require_tokenizers
class 		__A      (			UpperCamelCase_					,						unittest.TestCase			):

						'''simple docstring'''





						UpperCamelCase     						=					MobileBertTokenizer
						UpperCamelCase     						=					MobileBertTokenizerFast
						UpperCamelCase     						=					True
						UpperCamelCase     						=					True
						UpperCamelCase     						=					filter_non_english
						UpperCamelCase     						=					"""google/mobilebert-uncased"""

						def     A__						(		self   :List[Any] ):
										'''simple docstring'''

										super().setUp()

										__magic_name__	:	str								=[
										    '[UNK]',
										    '[CLS]',
										    '[SEP]',
										    '[PAD]',
										    '[MASK]',
										    'want',
										    '##want',
										    '##ed',
										    'wa',
										    'un',
										    'runn',
										    '##ing',
										    ',',
										    'low',
										    'lowest',
										]
										__magic_name__	:	Tuple								=os.path.join(self.tmpdirname    , VOCAB_FILES_NAMES["""vocab_file"""] )
										with open(self.vocab_file    , """w"""    , encoding="""utf-8""" ) as vocab_writer:
														vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens] ) )

										__magic_name__	:	str								=[
										    (tokenizer_def[0], self.pre_trained_model_path, tokenizer_def[2])  # else the 'google/' prefix is stripped
										    for tokenizer_def in self.tokenizers_list
										]

						def     A__						(		self   :Optional[Any]    , __snake_case   :Dict ):
										'''simple docstring'''

										__magic_name__	:	Any								='UNwant\u00E9d,running'
										__magic_name__	:	Union[str, Any]								='unwanted, running'
										return input_text, output_text

						def     A__						(		self   :List[str] ):
										'''simple docstring'''

										__magic_name__	:	Dict								=self.tokenizer_class(self.vocab_file )

										__magic_name__	:	int								=tokenizer.tokenize("""UNwant\u00E9d,running""" )
										self.assertListEqual(__a    , ["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""] )
										self.assertListEqual(tokenizer.convert_tokens_to_ids(__a )    , [9, 6, 7, 12, 10, 11] )

						def     A__						(		self   :Optional[Any] ):
										'''simple docstring'''

										if not self.test_rust_tokenizer:
														return

										__magic_name__	:	Dict								=self.get_tokenizer()
										__magic_name__	:	Optional[Any]								=self.get_rust_tokenizer()

										__magic_name__	:	int								='UNwant\u00E9d,running'

										__magic_name__	:	Tuple								=tokenizer.tokenize(__a )
										__magic_name__	:	int								=rust_tokenizer.tokenize(__a )
										self.assertListEqual(__a    , __a )

										__magic_name__	:	Union[str, Any]								=tokenizer.encode(__a    , add_special_tokens=__a )
										__magic_name__	:	int								=rust_tokenizer.encode(__a    , add_special_tokens=__a )
										self.assertListEqual(__a    , __a )

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

										# With lower casing
										__magic_name__	:	Tuple								=self.get_tokenizer(do_lower_case=__a )
										__magic_name__	:	int								=self.get_rust_tokenizer(do_lower_case=__a )

										__magic_name__	:	Optional[int]								='UNwant\u00E9d,running'

										__magic_name__	:	str								=tokenizer.tokenize(__a )
										__magic_name__	:	Any								=rust_tokenizer.tokenize(__a )
										self.assertListEqual(__a    , __a )

										__magic_name__	:	List[str]								=tokenizer.encode(__a    , add_special_tokens=__a )
										__magic_name__	:	Dict								=rust_tokenizer.encode(__a    , add_special_tokens=__a )
										self.assertListEqual(__a    , __a )

										__magic_name__	:	Optional[Any]								=self.get_rust_tokenizer()
										__magic_name__	:	Union[str, Any]								=tokenizer.encode(__a )
										__magic_name__	:	Optional[Any]								=rust_tokenizer.encode(__a )
										self.assertListEqual(__a    , __a )

						def     A__						(		self   :Dict ):
										'''simple docstring'''

										__magic_name__	:	str								=BasicTokenizer()

										self.assertListEqual(tokenizer.tokenize("""ah\u535A\u63A8zz""" )    , ["""ah""", """\u535A""", """\u63A8""", """zz"""] )

						def     A__						(		self   :Union[str, Any] ):
										'''simple docstring'''

										__magic_name__	:	Dict								=BasicTokenizer(do_lower_case=__a )

										self.assertListEqual(
										    tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU?  """ )    , ["""hello""", """!""", """how""", """are""", """you""", """?"""] )
										self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" )    , ["""hello"""] )

						def     A__						(		self   :str ):
										'''simple docstring'''

										__magic_name__	:	Dict								=BasicTokenizer(do_lower_case=__a    , strip_accents=__a )

										self.assertListEqual(
										    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """ )    , ["""hällo""", """!""", """how""", """are""", """you""", """?"""] )
										self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" )    , ["""h\u00E9llo"""] )

						def     A__						(		self   :Dict ):
										'''simple docstring'''

										__magic_name__	:	int								=BasicTokenizer(do_lower_case=__a    , strip_accents=__a )

										self.assertListEqual(
										    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """ )    , ["""hallo""", """!""", """how""", """are""", """you""", """?"""] )
										self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" )    , ["""hello"""] )

						def     A__						(		self   :List[Any] ):
										'''simple docstring'''

										__magic_name__	:	Union[str, Any]								=BasicTokenizer(do_lower_case=__a )

										self.assertListEqual(
										    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """ )    , ["""hallo""", """!""", """how""", """are""", """you""", """?"""] )
										self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" )    , ["""hello"""] )

						def     A__						(		self   :List[str] ):
										'''simple docstring'''

										__magic_name__	:	int								=BasicTokenizer(do_lower_case=__a )

										self.assertListEqual(
										    tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU?  """ )    , ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?"""] )

						def     A__						(		self   :str ):
										'''simple docstring'''

										__magic_name__	:	Optional[int]								=BasicTokenizer(do_lower_case=__a    , strip_accents=__a )

										self.assertListEqual(
										    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """ )    , ["""HäLLo""", """!""", """how""", """Are""", """yoU""", """?"""] )

						def     A__						(		self   :Tuple ):
										'''simple docstring'''

										__magic_name__	:	int								=BasicTokenizer(do_lower_case=__a    , strip_accents=__a )

										self.assertListEqual(
										    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """ )    , ["""HaLLo""", """!""", """how""", """Are""", """yoU""", """?"""] )

						def     A__						(		self   :Optional[int] ):
										'''simple docstring'''

										__magic_name__	:	Optional[Any]								=BasicTokenizer(do_lower_case=__a    , never_split=["""[UNK]"""] )

										self.assertListEqual(
										    tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU? [UNK]""" )    , ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?""", """[UNK]"""] )

						def     A__						(		self   :str ):
										'''simple docstring'''

										__magic_name__	:	Optional[int]								=['[UNK]', '[CLS]', '[SEP]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing']

										__magic_name__	:	Optional[Any]								={}
										for i, token in enumerate(__a ):
														__magic_name__	:	str								=i
										__magic_name__	:	Optional[Any]								=WordpieceTokenizer(vocab=__a    , unk_token="""[UNK]""" )

										self.assertListEqual(tokenizer.tokenize("""""" )    , [] )

										self.assertListEqual(tokenizer.tokenize("""unwanted running""" )    , ["""un""", """##want""", """##ed""", """runn""", """##ing"""] )

										self.assertListEqual(tokenizer.tokenize("""unwantedX running""" )    , ["""[UNK]""", """runn""", """##ing"""] )

						def     A__						(		self   :List[Any] ):
										'''simple docstring'''

										self.assertTrue(_is_whitespace(""" """ ) )
										self.assertTrue(_is_whitespace("""\t""" ) )
										self.assertTrue(_is_whitespace("""\r""" ) )
										self.assertTrue(_is_whitespace("""\n""" ) )
										self.assertTrue(_is_whitespace("""\u00A0""" ) )

										self.assertFalse(_is_whitespace("""A""" ) )
										self.assertFalse(_is_whitespace("""-""" ) )

						def     A__						(		self   :Optional[int] ):
										'''simple docstring'''

										self.assertTrue(_is_control("""\u0005""" ) )

										self.assertFalse(_is_control("""A""" ) )
										self.assertFalse(_is_control(""" """ ) )
										self.assertFalse(_is_control("""\t""" ) )
										self.assertFalse(_is_control("""\r""" ) )

						def     A__						(		self   :str ):
										'''simple docstring'''

										self.assertTrue(_is_punctuation("""-""" ) )
										self.assertTrue(_is_punctuation("""$""" ) )
										self.assertTrue(_is_punctuation("""`""" ) )
										self.assertTrue(_is_punctuation(""".""" ) )

										self.assertFalse(_is_punctuation("""A""" ) )
										self.assertFalse(_is_punctuation(""" """ ) )

						def     A__						(		self   :List[Any] ):
										'''simple docstring'''

										__magic_name__	:	Any								=self.get_tokenizer()
										__magic_name__	:	Union[str, Any]								=self.get_rust_tokenizer()

										# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
										self.assertListEqual([tokenizer.tokenize(__a ) for t in ["""Test""", """\xad""", """test"""]]    , [["""[UNK]"""], [], ["""[UNK]"""]] )

										self.assertListEqual(
										    [rust_tokenizer.tokenize(__a ) for t in ["""Test""", """\xad""", """test"""]]    , [["""[UNK]"""], [], ["""[UNK]"""]] )

						@slow
						def     A__						(		self   :Optional[int] ):
										'''simple docstring'''

										__magic_name__	:	int								=self.tokenizer_class.from_pretrained("""google/mobilebert-uncased""" )

										__magic_name__	:	Optional[Any]								=tokenizer.encode("""sequence builders"""    , add_special_tokens=__a )
										__magic_name__	:	Optional[int]								=tokenizer.encode("""multi-sequence build"""    , add_special_tokens=__a )

										__magic_name__	:	List[Any]								=tokenizer.build_inputs_with_special_tokens(__a )
										__magic_name__	:	str								=tokenizer.build_inputs_with_special_tokens(__a    , __a )

										assert encoded_sentence == [1_01] + text + [1_02]
										assert encoded_pair == [1_01] + text + [1_02] + text_a + [1_02]

						def     A__						(		self   :List[str] ):
										'''simple docstring'''

										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
														with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})" ):
																		__magic_name__	:	Optional[Any]								=self.rust_tokenizer_class.from_pretrained(__a    , **__a )

																		__magic_name__	:	Dict								=f"A, naïve {tokenizer_r.mask_token} AllenNLP sentence."
																		__magic_name__	:	Union[str, Any]								=tokenizer_r.encode_plus(
																		    __a    , return_attention_mask=__a    , return_token_type_ids=__a    , return_offsets_mapping=__a    , add_special_tokens=__a    , )

																		__magic_name__	:	List[Any]								=tokenizer_r.do_lower_case if hasattr(__a    , """do_lower_case""" ) else False
																		__magic_name__	:	Tuple								=(
																		    [
																		        ((0, 0), tokenizer_r.cls_token),
																		        ((0, 1), 'A'),
																		        ((1, 2), ','),
																		        ((3, 5), 'na'),
																		        ((5, 6), '##ï'),
																		        ((6, 8), '##ve'),
																		        ((9, 15), tokenizer_r.mask_token),
																		        ((16, 21), 'Allen'),
																		        ((21, 23), '##NL'),
																		        ((23, 24), '##P'),
																		        ((25, 33), 'sentence'),
																		        ((33, 34), '.'),
																		        ((0, 0), tokenizer_r.sep_token),
																		    ]
																		    if not do_lower_case
																		    else [
																		        ((0, 0), tokenizer_r.cls_token),
																		        ((0, 1), 'a'),
																		        ((1, 2), ','),
																		        ((3, 8), 'naive'),
																		        ((9, 15), tokenizer_r.mask_token),
																		        ((16, 21), 'allen'),
																		        ((21, 23), '##nl'),
																		        ((23, 24), '##p'),
																		        ((25, 33), 'sentence'),
																		        ((33, 34), '.'),
																		        ((0, 0), tokenizer_r.sep_token),
																		    ]
																		)

																		self.assertEqual(
																		    [e[1] for e in expected_results]    , tokenizer_r.convert_ids_to_tokens(tokens["""input_ids"""] ) )
																		self.assertEqual([e[0] for e in expected_results]    , tokens["""offset_mapping"""] )


						def     A__						(		self   :Any ):
										'''simple docstring'''

										__magic_name__	:	Tuple								=['的', '人', '有']
										__magic_name__	:	List[str]								=''.join(__a )
										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
														with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})" ):
																		__magic_name__	:	Any								=True
																		__magic_name__	:	int								=self.tokenizer_class.from_pretrained(__a    , **__a )
																		__magic_name__	:	int								=self.rust_tokenizer_class.from_pretrained(__a    , **__a )

																		__magic_name__	:	List[Any]								=tokenizer_p.encode(__a    , add_special_tokens=__a )
																		__magic_name__	:	Optional[Any]								=tokenizer_r.encode(__a    , add_special_tokens=__a )

																		__magic_name__	:	int								=tokenizer_r.convert_ids_to_tokens(__a )
																		__magic_name__	:	List[Any]								=tokenizer_p.convert_ids_to_tokens(__a )

																		# it is expected that each Chinese character is not preceded by "##"
																		self.assertListEqual(__a    , __a )
																		self.assertListEqual(__a    , __a )

																		__magic_name__	:	Tuple								=False
																		__magic_name__	:	List[Any]								=self.rust_tokenizer_class.from_pretrained(__a    , **__a )
																		__magic_name__	:	Optional[int]								=self.tokenizer_class.from_pretrained(__a    , **__a )

																		__magic_name__	:	str								=tokenizer_r.encode(__a    , add_special_tokens=__a )
																		__magic_name__	:	int								=tokenizer_p.encode(__a    , add_special_tokens=__a )

																		__magic_name__	:	str								=tokenizer_r.convert_ids_to_tokens(__a )
																		__magic_name__	:	Any								=tokenizer_p.convert_ids_to_tokens(__a )

																		# it is expected that only the first Chinese character is not preceded by "##".
																		__magic_name__	:	Optional[Any]								=[
																		    f"##{token}" if idx != 0 else token for idx, token in enumerate(__a )
																		]
																		self.assertListEqual(__a    , __a )
																		self.assertListEqual(__a    , __a )










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 DetaImageProcessor




class 		__A      (			unittest.TestCase			):

						def __init__(		self   :List[str]    , __snake_case   :str    , __snake_case   :List[Any]=7    , __snake_case   :List[str]=3    , __snake_case   :int=30    , __snake_case   :int=4_00    , __snake_case   :List[str]=True    , __snake_case   :List[str]=None    , __snake_case   :Tuple=True    , __snake_case   :Any=[0.5, 0.5, 0.5]    , __snake_case   :Any=[0.5, 0.5, 0.5]    , __snake_case   :Union[str, Any]=True    , __snake_case   :str=1 / 2_55    , __snake_case   :int=True    , ):
										'''simple docstring'''

										__magic_name__	:	Optional[int]								=size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 13_33}
										__magic_name__	:	str								=parent
										__magic_name__	:	Tuple								=batch_size
										__magic_name__	:	Optional[Any]								=num_channels
										__magic_name__	:	Optional[int]								=min_resolution
										__magic_name__	:	Optional[Any]								=max_resolution
										__magic_name__	:	Optional[Any]								=do_resize
										__magic_name__	:	Any								=size
										__magic_name__	:	str								=do_normalize
										__magic_name__	:	str								=image_mean
										__magic_name__	:	List[Any]								=image_std
										__magic_name__	:	Optional[int]								=do_rescale
										__magic_name__	:	List[str]								=rescale_factor
										__magic_name__	:	Optional[int]								=do_pad

						def     A__						(		self   :Optional[int] ):
										'''simple docstring'''

										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     A__						(		self   :List[str]    , __snake_case   :Any    , __snake_case   :List[Any]=False ):
										'''simple docstring'''

										if not batched:
														__magic_name__	:	Any								=image_inputs[0]
														if isinstance(__snake_case    , Image.Image ):
																		__magic_name__				, __magic_name__	:	Dict								=image.size
														else:
																		__magic_name__				, __magic_name__	:	Dict								=image.shape[1], image.shape[2]
														if w < h:
																		__magic_name__	:	Any								=int(self.size["""shortest_edge"""] * h / w )
																		__magic_name__	:	Dict								=self.size["""shortest_edge"""]
														elif w > h:
																		__magic_name__	:	Union[str, Any]								=self.size["""shortest_edge"""]
																		__magic_name__	:	Optional[int]								=int(self.size["""shortest_edge"""] * w / h )
														else:
																		__magic_name__	:	Any								=self.size["""shortest_edge"""]
																		__magic_name__	:	List[str]								=self.size["""shortest_edge"""]

										else:
														__magic_name__	:	List[str]								=[]
														for image in image_inputs:
																		__magic_name__				, __magic_name__	:	Tuple								=self.get_expected_values([image] )
																		expected_values.append((expected_height, expected_width) )
														__magic_name__	:	Optional[int]								=max(__snake_case    , key=lambda __snake_case : item[0] )[0]
														__magic_name__	:	Tuple								=max(__snake_case    , key=lambda __snake_case : item[1] )[1]

										return expected_height, expected_width




@require_torch
@require_vision
class 		__A      (			UpperCamelCase__					,						unittest.TestCase			):
						UpperCamelCase     						=					DetaImageProcessor if is_vision_available() else None

						def     A__						(		self   :Optional[int] ):
										'''simple docstring'''

										__magic_name__	:	int								=DetaImageProcessingTester(self )

						@property
						def     A__						(		self   :List[str] ):
										'''simple docstring'''

										return self.image_processor_tester.prepare_image_processor_dict()

						def     A__						(		self   :Optional[Any] ):
										'''simple docstring'''

										__magic_name__	:	List[str]								=self.image_processing_class(**self.image_processor_dict )
										self.assertTrue(hasattr(__snake_case    , """image_mean""" ) )
										self.assertTrue(hasattr(__snake_case    , """image_std""" ) )
										self.assertTrue(hasattr(__snake_case    , """do_normalize""" ) )
										self.assertTrue(hasattr(__snake_case    , """do_resize""" ) )
										self.assertTrue(hasattr(__snake_case    , """do_rescale""" ) )
										self.assertTrue(hasattr(__snake_case    , """do_pad""" ) )
										self.assertTrue(hasattr(__snake_case    , """size""" ) )

						def     A__						(		self   :List[Any] ):
										'''simple docstring'''

										__magic_name__	:	Optional[Any]								=self.image_processing_class.from_dict(self.image_processor_dict )
										self.assertEqual(image_processor.size    , {"""shortest_edge""": 18, """longest_edge""": 13_33} )
										self.assertEqual(image_processor.do_pad    , __snake_case )

						def     A__						(		self   :Any ):
										'''simple docstring'''

										pass

						def     A__						(		self   :str ):
										'''simple docstring'''

										__magic_name__	:	Tuple								=self.image_processing_class(**self.image_processor_dict )
										# create random PIL images
										__magic_name__	:	Tuple								=prepare_image_inputs(self.image_processor_tester    , equal_resolution=__snake_case )
										for image in image_inputs:
														self.assertIsInstance(__snake_case    , Image.Image )

										# Test not batched input
										__magic_name__	:	Tuple								=image_processing(image_inputs[0]    , return_tensors="""pt""" ).pixel_values

										__magic_name__				, __magic_name__	:	str								=self.image_processor_tester.get_expected_values(__snake_case )

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

										# Test batched
										__magic_name__				, __magic_name__	:	int								=self.image_processor_tester.get_expected_values(__snake_case    , batched=__snake_case )

										__magic_name__	:	Dict								=image_processing(__snake_case    , 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     A__						(		self   :List[str] ):
										'''simple docstring'''

										__magic_name__	:	int								=self.image_processing_class(**self.image_processor_dict )
										# create random numpy tensors
										__magic_name__	:	Optional[Any]								=prepare_image_inputs(self.image_processor_tester    , equal_resolution=__snake_case    , numpify=__snake_case )
										for image in image_inputs:
														self.assertIsInstance(__snake_case    , np.ndarray )

										# Test not batched input
										__magic_name__	:	str								=image_processing(image_inputs[0]    , return_tensors="""pt""" ).pixel_values

										__magic_name__				, __magic_name__	:	int								=self.image_processor_tester.get_expected_values(__snake_case )

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

										# Test batched
										__magic_name__	:	List[str]								=image_processing(__snake_case    , return_tensors="""pt""" ).pixel_values

										__magic_name__				, __magic_name__	:	Any								=self.image_processor_tester.get_expected_values(__snake_case    , batched=__snake_case )

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

						def     A__						(		self   :Optional[Any] ):
										'''simple docstring'''

										__magic_name__	:	List[Any]								=self.image_processing_class(**self.image_processor_dict )
										# create random PyTorch tensors
										__magic_name__	:	Dict								=prepare_image_inputs(self.image_processor_tester    , equal_resolution=__snake_case    , torchify=__snake_case )
										for image in image_inputs:
														self.assertIsInstance(__snake_case    , torch.Tensor )

										# Test not batched input
										__magic_name__	:	Tuple								=image_processing(image_inputs[0]    , return_tensors="""pt""" ).pixel_values

										__magic_name__				, __magic_name__	:	int								=self.image_processor_tester.get_expected_values(__snake_case )

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

										# Test batched
										__magic_name__	:	Dict								=image_processing(__snake_case    , return_tensors="""pt""" ).pixel_values

										__magic_name__				, __magic_name__	:	Tuple								=self.image_processor_tester.get_expected_values(__snake_case    , batched=__snake_case )

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

						@slow
						def     A__						(		self   :str ):
										'''simple docstring'''

										__magic_name__	:	Optional[int]								=Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
										with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""    , """r""" ) as f:
														__magic_name__	:	Optional[int]								=json.loads(f.read() )

										__magic_name__	:	str								={"""image_id""": 3_97_69, """annotations""": target}

										# encode them
										__magic_name__	:	Optional[Any]								=DetaImageProcessor()
										__magic_name__	:	Any								=image_processing(images=__snake_case    , annotations=__snake_case    , return_tensors="""pt""" )

										# verify pixel values
										__magic_name__	:	Tuple								=torch.Size([1, 3, 8_00, 10_66] )
										self.assertEqual(encoding["""pixel_values"""].shape    , __snake_case )

										__magic_name__	:	Optional[int]								=torch.tensor([0.2796, 0.3138, 0.3481] )
										self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]    , __snake_case    , atol=1E-4 ) )

										# verify area
										__magic_name__	:	Dict								=torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]    , __snake_case ) )
										# verify boxes
										__magic_name__	:	Optional[int]								=torch.Size([6, 4] )
										self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape    , __snake_case )
										__magic_name__	:	int								=torch.tensor([0.5503, 0.2765, 0.0604, 0.2215] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]    , __snake_case    , atol=1E-3 ) )
										# verify image_id
										__magic_name__	:	Any								=torch.tensor([3_97_69] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]    , __snake_case ) )
										# verify is_crowd
										__magic_name__	:	Optional[Any]								=torch.tensor([0, 0, 0, 0, 0, 0] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]    , __snake_case ) )
										# verify class_labels
										__magic_name__	:	List[str]								=torch.tensor([75, 75, 63, 65, 17, 17] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]    , __snake_case ) )
										# verify orig_size
										__magic_name__	:	Optional[Any]								=torch.tensor([4_80, 6_40] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]    , __snake_case ) )
										# verify size
										__magic_name__	:	Tuple								=torch.tensor([8_00, 10_66] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]    , __snake_case ) )


						@slow
						def     A__						(		self   :int ):
										'''simple docstring'''

										__magic_name__	:	List[str]								=Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
										with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"""    , """r""" ) as f:
														__magic_name__	:	str								=json.loads(f.read() )

										__magic_name__	:	Optional[Any]								={"""file_name""": """000000039769.png""", """image_id""": 3_97_69, """segments_info""": target}

										__magic_name__	:	List[str]								=pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic""" )

										# encode them
										__magic_name__	:	int								=DetaImageProcessor(format="""coco_panoptic""" )
										__magic_name__	:	Tuple								=image_processing(images=__snake_case    , annotations=__snake_case    , masks_path=__snake_case    , return_tensors="""pt""" )

										# verify pixel values
										__magic_name__	:	Union[str, Any]								=torch.Size([1, 3, 8_00, 10_66] )
										self.assertEqual(encoding["""pixel_values"""].shape    , __snake_case )

										__magic_name__	:	str								=torch.tensor([0.2796, 0.3138, 0.3481] )
										self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]    , __snake_case    , atol=1E-4 ) )

										# verify area
										__magic_name__	:	str								=torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]    , __snake_case ) )
										# verify boxes
										__magic_name__	:	Dict								=torch.Size([6, 4] )
										self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape    , __snake_case )
										__magic_name__	:	Optional[Any]								=torch.tensor([0.2625, 0.5437, 0.4688, 0.8625] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]    , __snake_case    , atol=1E-3 ) )
										# verify image_id
										__magic_name__	:	Dict								=torch.tensor([3_97_69] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]    , __snake_case ) )
										# verify is_crowd
										__magic_name__	:	int								=torch.tensor([0, 0, 0, 0, 0, 0] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]    , __snake_case ) )
										# verify class_labels
										__magic_name__	:	List[Any]								=torch.tensor([17, 17, 63, 75, 75, 93] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]    , __snake_case ) )
										# verify masks
										__magic_name__	:	List[Any]								=82_28_73
										self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()    , __snake_case )
										# verify orig_size
										__magic_name__	:	Union[str, Any]								=torch.tensor([4_80, 6_40] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]    , __snake_case ) )
										# verify size
										__magic_name__	:	List[Any]								=torch.tensor([8_00, 10_66] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]    , __snake_case ) )




