

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   lowercase_   :



						def __init__(      self				:    Optional[Any]      , A__				:    str      , A__				:    Any=13      , A__				:    str=[30, 30]      , A__				:    int=2      , A__				:    Dict=3      , A__				:    str=True      , A__				:    Union[str, Any]=True      , A__				:    Any=32      , A__				:    int=5      , A__				:    str=4      , A__				:    List[Any]=37      , A__				:    Union[str, Any]="gelu"      , A__				:    Dict=0.1      , A__				:    Dict=0.1      , A__				:    Tuple=10      , A__				:    Dict=0.02      , A__				:    Any=3      , A__				:    Union[str, Any]=None      , A__				:    Optional[Any]=8      , A__				:    Dict=10      , )   ->					Optional[Any]:
								_snake_case						=							parent
								_snake_case						=							batch_size
								_snake_case						=							image_size
								_snake_case						=							patch_size
								_snake_case						=							num_channels
								_snake_case						=							is_training
								_snake_case						=							use_labels
								_snake_case						=							hidden_size
								_snake_case						=							num_hidden_layers
								_snake_case						=							num_attention_heads
								_snake_case						=							intermediate_size
								_snake_case						=							hidden_act
								_snake_case						=							hidden_dropout_prob
								_snake_case						=							attention_probs_dropout_prob
								_snake_case						=							type_sequence_label_size
								_snake_case						=							initializer_range
								_snake_case						=							num_labels
								_snake_case						=							scope
								_snake_case						=							n_targets
								_snake_case						=							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
								_snake_case						=							(image_size[1] // patch_size) * (image_size[0] // patch_size)
								_snake_case						=							num_patches + 1 + self.num_detection_tokens



						def 						UpperCamelCase_		(      self				:    List[str]		)   ->					str:
								_snake_case						=							floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]		)

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

								_snake_case						=							self.get_config()

								return config, pixel_values, labels



						def 						UpperCamelCase_		(      self				:    Dict		)   ->					List[Any]:
								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=A__      , initializer_range=self.initializer_range      , num_detection_tokens=self.num_detection_tokens      , num_labels=self.num_labels      , )



						def 						UpperCamelCase_		(      self				:    Any      , A__				:    Any      , A__				:    str      , A__				:    Tuple		)   ->					Dict:
								_snake_case						=							YolosModel(config=A__		)
								model.to(A__		)
								model.eval()
								_snake_case						=							model(A__		)
								self.parent.assertEqual(
								    result.last_hidden_state.shape      , (self.batch_size, self.expected_seq_len, self.hidden_size)		)



						def 						UpperCamelCase_		(      self				:    Dict      , A__				:    List[str]      , A__				:    Optional[Any]      , A__				:    str		)   ->					int:
								_snake_case						=							YolosForObjectDetection(A__		)
								model.to(A__		)
								model.eval()

								_snake_case						=							model(pixel_values=A__		)
								_snake_case						=							model(A__		)

								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)		)

								_snake_case						=							model(pixel_values=A__      , labels=A__		)

								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 						UpperCamelCase_		(      self				:    Optional[Any]		)   ->					Tuple:
								_snake_case						=							self.prepare_config_and_inputs()
								_snake_case,					_snake_case,					_snake_case						=							config_and_inputs
								_snake_case						=							{'''pixel_values''': pixel_values}
								return config, inputs_dict



@require_torch
class   lowercase_   (  __lowercase       ,							__lowercase       ,							unittest.TestCase					):
						UpperCamelCase_      :		Optional[int]										=    (YolosModel, YolosForObjectDetection) if is_torch_available() else ()
						UpperCamelCase_      :		int										=    (
						    {"feature-extraction": YolosModel, "object-detection": YolosForObjectDetection} if is_torch_available() else {}
						)

						UpperCamelCase_      :		List[str]										=    False
						UpperCamelCase_      :		List[Any]										=    False
						UpperCamelCase_      :		List[Any]										=    False
						UpperCamelCase_      :		Tuple										=    False



						def 						UpperCamelCase_		(      self				:    Dict      , A__				:    List[Any]      , A__				:    List[str]      , A__				:    Optional[int]=False		)   ->					Optional[int]:
								_snake_case						=							super()._prepare_for_class(A__      , A__      , return_labels=A__		)

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

								return inputs_dict



						def 						UpperCamelCase_		(      self				:    List[Any]		)   ->					List[str]:
								_snake_case						=							YolosModelTester(self		)
								_snake_case						=							ConfigTester(self      , config_class=A__      , has_text_modality=A__      , hidden_size=37		)



						def 						UpperCamelCase_		(      self				:    Optional[int]		)   ->					Dict:
								self.config_tester.run_common_tests()



						def 						UpperCamelCase_		(      self				:    List[Any]		)   ->					str:
								# YOLOS does not use inputs_embeds
								pass



						def 						UpperCamelCase_		(      self				:    Union[str, Any]		)   ->					List[str]:
								_snake_case,					_snake_case						=							self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
										_snake_case						=							model_class(A__		)
										self.assertIsInstance(model.get_input_embeddings()      , (nn.Module)		)
										_snake_case						=							model.get_output_embeddings()
										self.assertTrue(x is None or isinstance(A__      , nn.Linear		)		)



						def 						UpperCamelCase_		(      self				:    List[Any]		)   ->					Optional[Any]:
								_snake_case,					_snake_case						=							self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
										_snake_case						=							model_class(A__		)
										_snake_case						=							inspect.signature(model.forward		)
										# signature.parameters is an OrderedDict => so arg_names order is deterministic
										_snake_case						=							[*signature.parameters.keys()]

										_snake_case						=							['''pixel_values''']
										self.assertListEqual(arg_names[:1]      , A__		)



						def 						UpperCamelCase_		(      self				:    List[str]		)   ->					List[Any]:
								_snake_case						=							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*A__		)



						def 						UpperCamelCase_		(      self				:    Union[str, Any]		)   ->					int:
								_snake_case,					_snake_case						=							self.model_tester.prepare_config_and_inputs_for_common()
								_snake_case						=							True

								# in YOLOS, the seq_len is different
								_snake_case						=							self.model_tester.expected_seq_len
								for model_class in self.all_model_classes:
										_snake_case						=							True
										_snake_case						=							False
										_snake_case						=							True
										_snake_case						=							model_class(A__		)
										model.to(A__		)
										model.eval()
										with torch.no_grad():
												_snake_case						=							model(**self._prepare_for_class(A__      , A__		)		)
										_snake_case						=							outputs.attentions
										self.assertEqual(len(A__		)      , self.model_tester.num_hidden_layers		)

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

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

										# Check attention is always last and order is fine
										_snake_case						=							True
										_snake_case						=							True
										_snake_case						=							model_class(A__		)
										model.to(A__		)
										model.eval()
										with torch.no_grad():
												_snake_case						=							model(**self._prepare_for_class(A__      , A__		)		)

										_snake_case						=							1
										self.assertEqual(out_len + added_hidden_states      , len(A__		)		)

										_snake_case						=							outputs.attentions

										self.assertEqual(len(A__		)      , 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 						UpperCamelCase_		(      self				:    int		)   ->					Dict:
								def check_hidden_states_output(A__				:    Optional[int]      , A__				:    Union[str, Any]      , A__				:    int		):
										_snake_case						=							model_class(A__		)
										model.to(A__		)
										model.eval()

										with torch.no_grad():
												_snake_case						=							model(**self._prepare_for_class(A__      , A__		)		)

										_snake_case						=							outputs.hidden_states

										_snake_case						=							getattr(
										    self.model_tester      , '''expected_num_hidden_layers'''      , self.model_tester.num_hidden_layers + 1		)
										self.assertEqual(len(A__		)      , A__		)

										# YOLOS has a different seq_length
										_snake_case						=							self.model_tester.expected_seq_len

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

								_snake_case,					_snake_case						=							self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
										_snake_case						=							True
										check_hidden_states_output(A__      , A__      , A__		)

										# check that output_hidden_states also work using config
										del inputs_dict["output_hidden_states"]
										_snake_case						=							True

										check_hidden_states_output(A__      , A__      , A__		)



						def 						UpperCamelCase_		(      self				:    Optional[Any]		)   ->					str:
								_snake_case						=							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_object_detection(*A__		)







						@slow
						def 						UpperCamelCase_		(      self				:    List[str]		)   ->					Dict:
								for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										_snake_case						=							YolosModel.from_pretrained(A__		)
										self.assertIsNotNone(A__		)



def 				snake_case_()							->     str:



		"""simple docstring"""






		_snake_case						=							Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''					)
		return image



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



						@cached_property
						def 						UpperCamelCase_		(      self				:    Any		)   ->					str:
								return AutoImageProcessor.from_pretrained('''hustvl/yolos-small'''		) if is_vision_available() else None







						@slow
						def 						UpperCamelCase_		(      self				:    Tuple		)   ->					str:
								_snake_case						=							YolosForObjectDetection.from_pretrained('''hustvl/yolos-small'''		).to(A__		)

								_snake_case						=							self.default_image_processor
								_snake_case						=							prepare_img()
								_snake_case						=							image_processor(images=A__      , return_tensors='''pt'''		).to(A__		)

								# forward pass
								with torch.no_grad():
										_snake_case						=							model(inputs.pixel_values		)

								# verify outputs
								_snake_case						=							torch.Size((1, 100, 92)		)
								self.assertEqual(outputs.logits.shape      , A__		)

								_snake_case						=							torch.tensor(
								    [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]]      , device=A__      , )
								_snake_case						=							torch.tensor(
								    [[0.2559, 0.5455, 0.4706], [0.2989, 0.7279, 0.1875], [0.7732, 0.4017, 0.4462]]      , device=A__		)
								self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]      , A__      , atol=1e-4		)		)
								self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3]      , A__      , atol=1e-4		)		)

								# verify postprocessing
								_snake_case						=							image_processor.post_process_object_detection(
								    A__      , threshold=0.3      , target_sizes=[image.size[::-1]]		)[0]
								_snake_case						=							torch.tensor([0.9994, 0.9790, 0.9964, 0.9972, 0.9861]		).to(A__		)
								_snake_case						=							[75, 75, 17, 63, 17]
								_snake_case						=							torch.tensor([335.0609, 79.3848, 375.4216, 187.2495]		).to(A__		)

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




from __future__ import annotations



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase					)							->     bool:



  """simple docstring"""






  _snake_case						=							get_failure_array(_UpperCamelCase					)

  # 2) Step through text searching for pattern
  _snake_case,					_snake_case						=							0, 0  # index into text, pattern
  while i < len(_UpperCamelCase					):
    if pattern[j] == text[i]:
      if j == (len(_UpperCamelCase					) - 1):
        return True
      j += 1

    # if this is a prefix in our pattern
    # just go back far enough to continue
    elif j > 0:
      _snake_case						=							failure[j - 1]
      continue
    i += 1
  return False



def 				snake_case_(_UpperCamelCase					)							->     list[int]:



  """simple docstring"""






  _snake_case						=							[0]
  _snake_case						=							0
  _snake_case						=							1
  while j < len(_UpperCamelCase					):
    if pattern[i] == pattern[j]:
      i += 1
    elif i > 0:
      _snake_case						=							failure[i - 1]
      continue
    j += 1
    failure.append(_UpperCamelCase					)
  return failure


if __name__ == "__main__":
 # Test 1)
 __A      		=     '''abc1abc12'''
 __A      		=     '''alskfjaldsabc1abc1abc12k23adsfabcabc'''
 __A      		=     '''alskfjaldsk23adsfabcabc'''
 assert kmp(pattern, texta) and not kmp(pattern, texta)

 # Test 2)
 __A      		=     '''ABABX'''
 __A      		=     '''ABABZABABYABABX'''
 assert kmp(pattern, text)

 # Test 3)
 __A      		=     '''AAAB'''
 __A      		=     '''ABAAAAAB'''
 assert kmp(pattern, text)

 # Test 4)
 __A      		=     '''abcdabcy'''
 __A      		=     '''abcxabcdabxabcdabcdabcy'''
 assert kmp(pattern, text)

 # Test 5)
 __A      		=     '''aabaabaaa'''
 assert get_failure_array(pattern) == [0, 1, 0, 1, 2, 3, 4, 5, 2]

