






'''simple docstring'''





import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin





class 		SCREAMING_SNAKE_CASE__ (  __snake_case		,		unittest.TestCase     ):
		_UpperCAmelCase								=DebertaTokenizer
		_UpperCAmelCase								=True
		_UpperCAmelCase								=DebertaTokenizerFast



		def _lowerCAmelCase  (     self:     Tuple)	->Tuple:






								'''simple docstring'''


								super().setUp()

								# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
								a_					  = [
								    '''l''',
								    '''o''',
								    '''w''',
								    '''e''',
								    '''r''',
								    '''s''',
								    '''t''',
								    '''i''',
								    '''d''',
								    '''n''',
								    '''\u0120''',
								    '''\u0120l''',
								    '''\u0120n''',
								    '''\u0120lo''',
								    '''\u0120low''',
								    '''er''',
								    '''\u0120lowest''',
								    '''\u0120newer''',
								    '''\u0120wider''',
								    '''[UNK]''',
								]
								a_					  = dict(zip(a					, range(len(a))))
								a_					  = ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
								a_					  = {'''unk_token''': '''[UNK]'''}

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



		def _lowerCAmelCase  (     self:     List[str]					, **a:     List[str])	->Optional[Any]:






								'''simple docstring'''


								kwargs.update(self.special_tokens_map)
								return self.tokenizer_class.from_pretrained(self.tmpdirname					, **a)



		def _lowerCAmelCase  (     self:     str					, a:     Optional[Any])	->str:






								'''simple docstring'''


								a_					  = '''lower newer'''
								a_					  = '''lower newer'''
								return input_text, output_text



		def _lowerCAmelCase  (     self:     str)	->str:






								'''simple docstring'''


								a_					  = self.get_tokenizer()
								a_					  = '''lower newer'''
								a_					  = ['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
								a_					  = tokenizer.tokenize(a)
								self.assertListEqual(a					, a)

								a_					  = tokens + [tokenizer.unk_token]
								a_					  = [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(a)					, a)



		def _lowerCAmelCase  (     self:     List[str])	->str:






								'''simple docstring'''


								a_					  = self.get_tokenizer()
								a_					  = tokenizer("Hello"					, "World")
								a_					  = [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
								self.assertListEqual(tokd["token_type_ids"]					, a)



		@slow
		def _lowerCAmelCase  (     self:     Optional[int])	->int:






								'''simple docstring'''


								a_					  = self.tokenizer_class.from_pretrained("microsoft/deberta-base")

								a_					  = tokenizer.encode("sequence builders"					, add_special_tokens=a)
								a_					  = tokenizer.encode("multi-sequence build"					, add_special_tokens=a)

								a_					  = tokenizer.encode(
								    "sequence builders"					, add_special_tokens=a					, add_prefix_space=a)
								a_					  = tokenizer.encode(
								    "sequence builders"					, "multi-sequence build"					, add_special_tokens=a					, add_prefix_space=a)

								a_					  = tokenizer.build_inputs_with_special_tokens(a)
								a_					  = tokenizer.build_inputs_with_special_tokens(a					, a)

								assert encoded_sentence == encoded_text_from_decode
								assert encoded_pair == encoded_pair_from_decode




		@slow
		def _lowerCAmelCase  (     self:     List[Any])	->int:






								'''simple docstring'''


								a_					  = [self.tokenizer_class]
								if self.test_rust_tokenizer:
														tokenizer_classes.append(self.rust_tokenizer_class)

								for tokenizer_class in tokenizer_classes:
														a_					  = tokenizer_class.from_pretrained("microsoft/deberta-base")

														a_					  = [
														    '''ALBERT: A Lite BERT for Self-supervised Learning of Language Representations''',
														    '''ALBERT incorporates two parameter reduction techniques''',
														    '''The first one is a factorized embedding parameterization. By decomposing the large vocabulary'''
														    ''' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'''
														    ''' vocabulary embedding.''',
														]

														a_					  = tokenizer(a					, padding=a)
														a_					  = [tokenizer.decode(a					, skip_special_tokens=a) for seq in encoding['''input_ids''']]

														# fmt: off
														a_					  = {
														    '''input_ids''': [
														        [1, 21_18, 1_11_26, 5_65, 35, 83, 2_51_91, 1_63, 1_88_54, 13, 1_21_56, 12, 1_61_01, 2_53_76, 1_38_07, 9, 2_22_05, 2_78_93, 16_35, 2, 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, 21_18, 1_11_26, 5_65, 2_45_36, 80, 4_37_97, 48_78, 73_73, 2, 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_33, 78, 65, 16, 10, 37_24, 15_38, 3_31_83, 1_13_03, 4_37_97, 19_38, 4, 8_70, 2_41_65, 2_91_05, 5, 7_39, 3_26_44, 3_31_83, 1_13_03, 3_61_73, 88, 80, 6_50, 78_21, 4_59_40, 6, 52, 25_59, 5, 18_36, 9, 5, 73_97, 1_31_71, 31, 5, 18_36, 9, 3_26_44, 3_31_83, 1_13_03, 4, 2]
														    ],
														    '''token_type_ids''': [
														        [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, 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]
														    ],
														    '''attention_mask''': [
														        [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],
														        [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],
														        [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]
														    ]
														}
														# fmt: on

														a_					  = [
														    '''ALBERT: A Lite BERT for Self-supervised Learning of Language Representations''',
														    '''ALBERT incorporates two parameter reduction techniques''',
														    '''The first one is a factorized embedding parameterization. By decomposing the large vocabulary'''
														    ''' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'''
														    ''' vocabulary embedding.''',
														]

														self.assertDictEqual(encoding.data					, a)

														for expected, decoded in zip(a					, a):
																				self.assertEqual(a					, a)





'''simple docstring'''




import inspect
import unittest

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

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch
      from torch import nn

      from transformers import YolosForObjectDetection, YolosModel
      from transformers.models.yolos.modeling_yolos import YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import AutoImageProcessor





class 					A							:





     def __init__(				self		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE=13		,     SCREAMING_SNAKE_CASE=[30, 30]		,     SCREAMING_SNAKE_CASE=2		,     SCREAMING_SNAKE_CASE=3		,     SCREAMING_SNAKE_CASE=True		,     SCREAMING_SNAKE_CASE=True		,     SCREAMING_SNAKE_CASE=32		,     SCREAMING_SNAKE_CASE=5		,     SCREAMING_SNAKE_CASE=4		,     SCREAMING_SNAKE_CASE=37		,     SCREAMING_SNAKE_CASE="gelu"		,     SCREAMING_SNAKE_CASE=0.1		,     SCREAMING_SNAKE_CASE=0.1		,     SCREAMING_SNAKE_CASE=10		,     SCREAMING_SNAKE_CASE=0.02		,     SCREAMING_SNAKE_CASE=3		,     SCREAMING_SNAKE_CASE=None		,     SCREAMING_SNAKE_CASE=8		,     SCREAMING_SNAKE_CASE=10		,     )					->							Dict:






         """simple docstring"""






         A			:  List[Any]   =		parent
         A			:  Optional[Any]   =		batch_size
         A			:  int   =		image_size
         A			:  Optional[Any]   =		patch_size
         A			:  Union[str, Any]   =		num_channels
         A			:  Optional[int]   =		is_training
         A			:  List[str]   =		use_labels
         A			:  Any   =		hidden_size
         A			:  Dict   =		num_hidden_layers
         A			:  List[str]   =		num_attention_heads
         A			:  List[Any]   =		intermediate_size
         A			:  List[Any]   =		hidden_act
         A			:  Union[str, Any]   =		hidden_dropout_prob
         A			:  Union[str, Any]   =		attention_probs_dropout_prob
         A			:  Optional[Any]   =		type_sequence_label_size
         A			:  Optional[Any]   =		initializer_range
         A			:  Optional[Any]   =		num_labels
         A			:  Union[str, Any]   =		scope
         A			:  Optional[Any]   =		n_targets
         A			:  str   =		num_detection_tokens
         # we set the expected sequence length (which is used in several tests)
         # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) + num_detection_tokens
         A			:  Dict   =		(image_size[1] // patch_size) * (image_size[0] // patch_size)
         A			:  str   =		num_patches + 1 + self.num_detection_tokens





     def 							__lowerCAmelCase				(				self      )					->							int:






         """simple docstring"""






         A			:  Tuple   =		floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]      )

         A			:  Dict   =		None
         if self.use_labels:
             # labels is a list of Dict (each Dict being the labels for a given example in the batch)
             A			:  int   =		[]
             for i in range(self.batch_size      ):
                 A			:  str   =		{}
                 A			:  Union[str, Any]   =		torch.randint(
                     high=self.num_labels		,     size=(self.n_targets,)		,     device=SCREAMING_SNAKE_CASE      )
                 A			:  Optional[Any]   =		torch.rand(self.n_targets		,     4		,     device=SCREAMING_SNAKE_CASE      )
                 labels.append(SCREAMING_SNAKE_CASE      )

         A			:  Any   =		self.get_config()

         return config, pixel_values, labels





     def 							__lowerCAmelCase				(				self      )					->							str:






         """simple docstring"""






         return YolosConfig(
             image_size=self.image_size		,     patch_size=self.patch_size		,     num_channels=self.num_channels		,     hidden_size=self.hidden_size		,     num_hidden_layers=self.num_hidden_layers		,     num_attention_heads=self.num_attention_heads		,     intermediate_size=self.intermediate_size		,     hidden_act=self.hidden_act		,     hidden_dropout_prob=self.hidden_dropout_prob		,     attention_probs_dropout_prob=self.attention_probs_dropout_prob		,     is_decoder=SCREAMING_SNAKE_CASE		,     initializer_range=self.initializer_range		,     num_detection_tokens=self.num_detection_tokens		,     num_labels=self.num_labels		,     )





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )					->							str:






         """simple docstring"""






         A			:  int   =		YolosModel(config=SCREAMING_SNAKE_CASE      )
         model.to(SCREAMING_SNAKE_CASE      )
         model.eval()
         A			:  Optional[int]   =		model(SCREAMING_SNAKE_CASE      )
         self.parent.assertEqual(
             result.last_hidden_state.shape		,     (self.batch_size, self.expected_seq_len, self.hidden_size)      )





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )					->							str:






         """simple docstring"""






         A			:  int   =		YolosForObjectDetection(SCREAMING_SNAKE_CASE      )
         model.to(SCREAMING_SNAKE_CASE      )
         model.eval()

         A			:  Any   =		model(pixel_values=SCREAMING_SNAKE_CASE      )
         A			:  Tuple   =		model(SCREAMING_SNAKE_CASE      )

         self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.num_detection_tokens, self.num_labels + 1)      )
         self.parent.assertEqual(result.pred_boxes.shape		,     (self.batch_size, self.num_detection_tokens, 4)      )

         A			:  List[str]   =		model(pixel_values=SCREAMING_SNAKE_CASE		,     labels=SCREAMING_SNAKE_CASE      )

         self.parent.assertEqual(result.loss.shape		,     ()      )
         self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.num_detection_tokens, self.num_labels + 1)      )
         self.parent.assertEqual(result.pred_boxes.shape		,     (self.batch_size, self.num_detection_tokens, 4)      )





     def 							__lowerCAmelCase				(				self      )					->							int:






         """simple docstring"""






         A			:  Any   =		self.prepare_config_and_inputs()
         A,    A,    A			:  int   =		config_and_inputs
         A			:  List[Any]   =		{'''pixel_values''': pixel_values}
         return config, inputs_dict





@require_torch
class 					A							(				__snake_case       ,     __snake_case       ,     unittest.TestCase		):
     __magic_name__          =						(YolosModel, YolosForObjectDetection) if is_torch_available() else ()
     __magic_name__          =						(
         {'''feature-extraction''': YolosModel, '''object-detection''': YolosForObjectDetection} if is_torch_available() else {}
     )

     __magic_name__          =						False
     __magic_name__          =						False
     __magic_name__          =						False
     __magic_name__          =						False





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE=False      )					->							Union[str, Any]:






         """simple docstring"""






         A			:  Union[str, Any]   =		super()._prepare_for_class(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     return_labels=SCREAMING_SNAKE_CASE      )

         if return_labels:
             if model_class.__name__ == "YolosForObjectDetection":
                 A			:  str   =		[]
                 for i in range(self.model_tester.batch_size      ):
                     A			:  Optional[Any]   =		{}
                     A			:  int   =		torch.ones(
                         size=(self.model_tester.n_targets,)		,     device=SCREAMING_SNAKE_CASE		,     dtype=torch.long      )
                     A			:  Any   =		torch.ones(
                         self.model_tester.n_targets		,     4		,     device=SCREAMING_SNAKE_CASE		,     dtype=torch.float      )
                     labels.append(SCREAMING_SNAKE_CASE      )
                 A			:  Optional[int]   =		labels

         return inputs_dict





     def 							__lowerCAmelCase				(				self      )					->							Optional[int]:






         """simple docstring"""






         A			:  Tuple   =		YolosModelTester(self      )
         A			:  Any   =		ConfigTester(self		,     config_class=SCREAMING_SNAKE_CASE		,     has_text_modality=SCREAMING_SNAKE_CASE		,     hidden_size=37      )





     def 							__lowerCAmelCase				(				self      )					->							int:






         """simple docstring"""






         self.config_tester.run_common_tests()





     def 							__lowerCAmelCase				(				self      )					->							Any:






         """simple docstring"""






         pass





     def 							__lowerCAmelCase				(				self      )					->							Optional[Any]:






         """simple docstring"""






         A,    A			:  Tuple   =		self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
             A			:  Dict   =		model_class(SCREAMING_SNAKE_CASE      )
             self.assertIsInstance(model.get_input_embeddings()		,     (nn.Module)      )
             A			:  List[Any]   =		model.get_output_embeddings()
             self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE		,     nn.Linear      )      )





     def 							__lowerCAmelCase				(				self      )					->							int:






         """simple docstring"""






         A,    A			:  int   =		self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
             A			:  List[Any]   =		model_class(SCREAMING_SNAKE_CASE      )
             A			:  Optional[Any]   =		inspect.signature(model.forward      )
             # signature.parameters is an OrderedDict => so arg_names order is deterministic
             A			:  List[str]   =		[*signature.parameters.keys()]

             A			:  Dict   =		['''pixel_values''']
             self.assertListEqual(arg_names[:1]		,     SCREAMING_SNAKE_CASE      )





     def 							__lowerCAmelCase				(				self      )					->							Union[str, Any]:






         """simple docstring"""






         A			:  Optional[int]   =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE      )





     def 							__lowerCAmelCase				(				self      )					->							Optional[Any]:






         """simple docstring"""






         A,    A			:  Any   =		self.model_tester.prepare_config_and_inputs_for_common()
         A			:  List[Any]   =		True

         # in YOLOS, the seq_len is different
         A			:  Optional[int]   =		self.model_tester.expected_seq_len
         for model_class in self.all_model_classes:
             A			:  Optional[int]   =		True
             A			:  int   =		False
             A			:  List[str]   =		True
             A			:  str   =		model_class(SCREAMING_SNAKE_CASE      )
             model.to(SCREAMING_SNAKE_CASE      )
             model.eval()
             with torch.no_grad():
                 A			:  Optional[Any]   =		model(**self._prepare_for_class(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )      )
             A			:  int   =		outputs.attentions
             self.assertEqual(len(SCREAMING_SNAKE_CASE      )		,     self.model_tester.num_hidden_layers      )

             # check that output_attentions also work using config
             del inputs_dict["output_attentions"]
             A			:  int   =		True
             A			:  Optional[int]   =		model_class(SCREAMING_SNAKE_CASE      )
             model.to(SCREAMING_SNAKE_CASE      )
             model.eval()
             with torch.no_grad():
                 A			:  List[str]   =		model(**self._prepare_for_class(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )      )
             A			:  List[Any]   =		outputs.attentions
             self.assertEqual(len(SCREAMING_SNAKE_CASE      )		,     self.model_tester.num_hidden_layers      )

             self.assertListEqual(
                 list(attentions[0].shape[-3:]      )		,     [self.model_tester.num_attention_heads, seq_len, seq_len]		,     )
             A			:  Any   =		len(SCREAMING_SNAKE_CASE      )

             # Check attention is always last and order is fine
             A			:  Optional[Any]   =		True
             A			:  Union[str, Any]   =		True
             A			:  Optional[int]   =		model_class(SCREAMING_SNAKE_CASE      )
             model.to(SCREAMING_SNAKE_CASE      )
             model.eval()
             with torch.no_grad():
                 A			:  Tuple   =		model(**self._prepare_for_class(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )      )

             A			:  Optional[int]   =		1
             self.assertEqual(out_len + added_hidden_states		,     len(SCREAMING_SNAKE_CASE      )      )

             A			:  List[str]   =		outputs.attentions

             self.assertEqual(len(SCREAMING_SNAKE_CASE      )		,     self.model_tester.num_hidden_layers      )
             self.assertListEqual(
                 list(self_attentions[0].shape[-3:]      )		,     [self.model_tester.num_attention_heads, seq_len, seq_len]		,     )





     def 							__lowerCAmelCase				(				self      )					->							Tuple:






         """simple docstring"""






         def check_hidden_states_output(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      ):
             A			:  List[Any]   =		model_class(SCREAMING_SNAKE_CASE      )
             model.to(SCREAMING_SNAKE_CASE      )
             model.eval()

             with torch.no_grad():
                 A			:  List[Any]   =		model(**self._prepare_for_class(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )      )

             A			:  Dict   =		outputs.hidden_states

             A			:  List[Any]   =		getattr(
                 self.model_tester		,     '''expected_num_hidden_layers'''		,     self.model_tester.num_hidden_layers + 1      )
             self.assertEqual(len(SCREAMING_SNAKE_CASE      )		,     SCREAMING_SNAKE_CASE      )

             # YOLOS has a different seq_length
             A			:  int   =		self.model_tester.expected_seq_len

             self.assertListEqual(
                 list(hidden_states[0].shape[-2:]      )		,     [seq_length, self.model_tester.hidden_size]		,     )

         A,    A			:  Optional[int]   =		self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
             A			:  List[Any]   =		True
             check_hidden_states_output(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )

             # check that output_hidden_states also work using config
             del inputs_dict["output_hidden_states"]
             A			:  Optional[Any]   =		True

             check_hidden_states_output(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )





     def 							__lowerCAmelCase				(				self      )					->							List[Any]:






         """simple docstring"""






         A			:  Dict   =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_object_detection(*SCREAMING_SNAKE_CASE      )





     @slow
     def 							__lowerCAmelCase				(				self      )					->							Tuple:






         """simple docstring"""






         for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             A			:  int   =		YolosModel.from_pretrained(SCREAMING_SNAKE_CASE      )
             self.assertIsNotNone(SCREAMING_SNAKE_CASE      )







def 						lowerCAmelCase_							(  ):




    '''simple docstring'''



    A			:  List[str]   =		Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
    return image







@require_torch
@require_vision
class 					A							(				unittest.TestCase		):





     @cached_property
     def 							__lowerCAmelCase				(				self      )					->							Dict:






         """simple docstring"""






         return AutoImageProcessor.from_pretrained('''hustvl/yolos-small'''      ) if is_vision_available() else None





     @slow
     def 							__lowerCAmelCase				(				self      )					->							str:






         """simple docstring"""






         A			:  Union[str, Any]   =		YolosForObjectDetection.from_pretrained('''hustvl/yolos-small'''      ).to(SCREAMING_SNAKE_CASE      )

         A			:  str   =		self.default_image_processor
         A			:  Any   =		prepare_img()
         A			:  Any   =		image_processor(images=SCREAMING_SNAKE_CASE		,     return_tensors='''pt'''      ).to(SCREAMING_SNAKE_CASE      )

         # forward pass
         with torch.no_grad():
             A			:  Optional[Any]   =		model(inputs.pixel_values      )

         # verify outputs
         A			:  List[Any]   =		torch.Size((1, 100, 92)      )
         self.assertEqual(outputs.logits.shape		,     SCREAMING_SNAKE_CASE      )

         A			:  Optional[Any]   =		torch.tensor(
             [[-24.0_248, -10.3_024, -14.8_290], [-42.0_392, -16.8_200, -27.4_334], [-27.2_743, -11.8_154, -18.7_148]]		,     device=SCREAMING_SNAKE_CASE		,     )
         A			:  str   =		torch.tensor(
             [[0.2_559, 0.5_455, 0.4_706], [0.2_989, 0.7_279, 0.1_875], [0.7_732, 0.4_017, 0.4_462]]		,     device=SCREAMING_SNAKE_CASE      )
         self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]		,     SCREAMING_SNAKE_CASE		,     atol=1e-4      )      )
         self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3]		,     SCREAMING_SNAKE_CASE		,     atol=1e-4      )      )

         # verify postprocessing
         A			:  int   =		image_processor.post_process_object_detection(
             SCREAMING_SNAKE_CASE		,     threshold=0.3		,     target_sizes=[image.size[::-1]]      )[0]
         A			:  str   =		torch.tensor([0.9_994, 0.9_790, 0.9_964, 0.9_972, 0.9_861]      ).to(SCREAMING_SNAKE_CASE      )
         A			:  Optional[Any]   =		[75, 75, 17, 63, 17]
         A			:  Union[str, Any]   =		torch.tensor([335.0_609, 79.3_848, 375.4_216, 187.2_495]      ).to(SCREAMING_SNAKE_CASE      )

         self.assertEqual(len(results['''scores''']      )		,     5      )
         self.assertTrue(torch.allclose(results['''scores''']		,     SCREAMING_SNAKE_CASE		,     atol=1e-4      )      )
         self.assertSequenceEqual(results['''labels'''].tolist()		,     SCREAMING_SNAKE_CASE      )
         self.assertTrue(torch.allclose(results['''boxes'''][0, :]		,     SCREAMING_SNAKE_CASE      )      )




