



import inspect
import unittest

from transformers import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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


if is_torch_available():
      import torch

      from transformers import MODEL_MAPPING, GLPNConfig, GLPNForDepthEstimation, GLPNModel
      from transformers.models.glpn.modeling_glpn import GLPN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import GLPNImageProcessor

class    _SCREAMING_SNAKE_CASE						(_a			):





     def 						__snake_case     (							self			:				List[Any]					)->Optional[int]:
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						self.config_class(**self.inputs_dict					)
      self.parent.assertTrue(hasattr(_A  ,	"hidden_sizes"					)					)
      self.parent.assertTrue(hasattr(_A  ,	"num_attention_heads"					)					)
      self.parent.assertTrue(hasattr(_A  ,	"num_encoder_blocks"					)					)






class    _SCREAMING_SNAKE_CASE						:





     def __init__(							self			:				str  ,	UpperCamelCase			:				List[Any]  ,	UpperCamelCase			:				Optional[int]=1_3  ,	UpperCamelCase			:				str=6_4  ,	UpperCamelCase			:				Optional[Any]=3  ,	UpperCamelCase			:				List[Any]=4  ,	UpperCamelCase			:				List[Any]=[2, 2, 2, 2]  ,	UpperCamelCase			:				str=[8, 4, 2, 1]  ,	UpperCamelCase			:				Tuple=[1_6, 3_2, 6_4, 1_2_8]  ,	UpperCamelCase			:				Optional[int]=[1, 4, 8, 1_6]  ,	UpperCamelCase			:				Dict=[1, 2, 4, 8]  ,	UpperCamelCase			:				Dict=True  ,	UpperCamelCase			:				Dict=True  ,	UpperCamelCase			:				Optional[int]="gelu"  ,	UpperCamelCase			:				int=0.1  ,	UpperCamelCase			:				Union[str, Any]=0.1  ,	UpperCamelCase			:				List[str]=0.0_2  ,	UpperCamelCase			:				List[str]=1_6  ,	UpperCamelCase			:				Optional[int]=3  ,	UpperCamelCase			:				Optional[Any]=None  ,	)->List[Any]:
      __SCREAMING_SNAKE_CASE							:     List[Any]							=						parent
      __SCREAMING_SNAKE_CASE							:     Any							=						batch_size
      __SCREAMING_SNAKE_CASE							:     str							=						image_size
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						num_channels
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						num_encoder_blocks
      __SCREAMING_SNAKE_CASE							:     str							=						sr_ratios
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						depths
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						hidden_sizes
      __SCREAMING_SNAKE_CASE							:     Dict							=						downsampling_rates
      __SCREAMING_SNAKE_CASE							:     List[Any]							=						num_attention_heads
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						is_training
      __SCREAMING_SNAKE_CASE							:     int							=						use_labels
      __SCREAMING_SNAKE_CASE							:     Dict							=						hidden_act
      __SCREAMING_SNAKE_CASE							:     Any							=						hidden_dropout_prob
      __SCREAMING_SNAKE_CASE							:     List[Any]							=						attention_probs_dropout_prob
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						initializer_range
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						decoder_hidden_size
      __SCREAMING_SNAKE_CASE							:     List[str]							=						num_labels
      __SCREAMING_SNAKE_CASE							:     int							=						scope





     def 						__snake_case     (							self			:				List[Any]					)->List[Any]:
      __SCREAMING_SNAKE_CASE							:     List[Any]							=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

      __SCREAMING_SNAKE_CASE							:     int							=						None
      if self.use_labels:
       __SCREAMING_SNAKE_CASE							:     Any							=						ids_tensor([self.batch_size, self.image_size, self.image_size]  ,	self.num_labels					)

      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						self.get_config()
      return config, pixel_values, labels





     def 						__snake_case     (							self			:				Union[str, Any]					)->List[Any]:
      return GLPNConfig(
          image_size=self.image_size  ,	num_channels=self.num_channels  ,	num_encoder_blocks=self.num_encoder_blocks  ,	depths=self.depths  ,	hidden_sizes=self.hidden_sizes  ,	num_attention_heads=self.num_attention_heads  ,	hidden_act=self.hidden_act  ,	hidden_dropout_prob=self.hidden_dropout_prob  ,	attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,	initializer_range=self.initializer_range  ,	decoder_hidden_size=self.decoder_hidden_size  ,	)





     def 						__snake_case     (							self			:				Any  ,	UpperCamelCase			:				List[str]  ,	UpperCamelCase			:				Dict  ,	UpperCamelCase			:				List[Any]					)->List[str]:
      __SCREAMING_SNAKE_CASE							:     int							=						GLPNModel(config=_A					)
      model.to(_A					)
      model.eval()
      __SCREAMING_SNAKE_CASE							:     int							=						model(_A					)
      __SCREAMING_SNAKE_CASE							:     Tuple							=						self.image_size // (self.downsampling_rates[-1] * 2)
      self.parent.assertEqual(
          result.last_hidden_state.shape  ,	(self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)					)





     def 						__snake_case     (							self			:				List[str]  ,	UpperCamelCase			:				Union[str, Any]  ,	UpperCamelCase			:				List[str]  ,	UpperCamelCase			:				Optional[Any]					)->Dict:
      __SCREAMING_SNAKE_CASE							:     List[Any]							=						self.num_labels
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						GLPNForDepthEstimation(_A					)
      model.to(_A					)
      model.eval()
      __SCREAMING_SNAKE_CASE							:     int							=						model(_A					)
      self.parent.assertEqual(result.predicted_depth.shape  ,	(self.batch_size, self.image_size, self.image_size)					)
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						model(_A  ,	labels=_A					)
      self.parent.assertEqual(result.predicted_depth.shape  ,	(self.batch_size, self.image_size, self.image_size)					)






     def 						__snake_case     (							self			:				int					)->Optional[int]:
      __SCREAMING_SNAKE_CASE							:     Any							=						self.prepare_config_and_inputs()
      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							:     List[str]							=						config_and_inputs
      __SCREAMING_SNAKE_CASE							:     Dict							=						{"pixel_values": pixel_values}
      return config, inputs_dict






@require_torch
class    _SCREAMING_SNAKE_CASE						(_a      ,     _a      ,     unittest.TestCase			):
     lowerCAmelCase       =      (GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
     lowerCAmelCase       =      (
         {"""depth-estimation""": GLPNForDepthEstimation, """feature-extraction""": GLPNModel} if is_torch_available() else {}
     )

     lowerCAmelCase       =      False
     lowerCAmelCase       =      False
     lowerCAmelCase       =      False





     def 						__snake_case     (							self			:				Union[str, Any]					)->Optional[Any]:
      __SCREAMING_SNAKE_CASE							:     int							=						GLPNModelTester(self					)
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						GLPNConfigTester(self  ,	config_class=_A					)





     def 						__snake_case     (							self			:				List[Any]					)->List[Any]:
      self.config_tester.run_common_tests()





     def 						__snake_case     (							self			:				Tuple					)->Tuple:
      __SCREAMING_SNAKE_CASE							:     Any							=						self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*_A					)





     def 						__snake_case     (							self			:				Dict					)->Union[str, Any]:
      __SCREAMING_SNAKE_CASE							:     Tuple							=						self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_depth_estimation(*_A					)





     @unittest.skip("GLPN does not use inputs_embeds"					)
     def 						__snake_case     (							self			:				Tuple					)->Optional[int]:
      pass





     @unittest.skip("GLPN does not have get_input_embeddings method and get_output_embeddings methods"					)
     def 						__snake_case     (							self			:				Tuple					)->Dict:
      pass





     def 						__snake_case     (							self			:				Union[str, Any]					)->Any:
      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							:     Optional[Any]							=						self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
       __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						model_class(_A					)
       __SCREAMING_SNAKE_CASE							:     int							=						inspect.signature(model.forward					)
       # signature.parameters is an OrderedDict => so arg_names order is deterministic
       __SCREAMING_SNAKE_CASE							:     Any							=						[*signature.parameters.keys()]

       __SCREAMING_SNAKE_CASE							:     Optional[int]							=						["pixel_values"]
       self.assertListEqual(arg_names[:1]  ,	_A					)





     def 						__snake_case     (							self			:				Optional[int]					)->int:
      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							:     Optional[Any]							=						self.model_tester.prepare_config_and_inputs_for_common()
      __SCREAMING_SNAKE_CASE							:     List[str]							=						True

      for model_class in self.all_model_classes:
       __SCREAMING_SNAKE_CASE							:     int							=						True
       __SCREAMING_SNAKE_CASE							:     Tuple							=						False
       __SCREAMING_SNAKE_CASE							:     List[str]							=						True
       __SCREAMING_SNAKE_CASE							:     Any							=						model_class(_A					)
       model.to(_A					)
       model.eval()
       with torch.no_grad():
        __SCREAMING_SNAKE_CASE							:     Dict							=						model(**self._prepare_for_class(_A  ,	_A					)					)
       __SCREAMING_SNAKE_CASE							:     List[Any]							=						outputs.attentions

       __SCREAMING_SNAKE_CASE							:     Any							=						sum(self.model_tester.depths					)
       self.assertEqual(len(_A					)  ,	_A					)

       # check that output_attentions also work using config
       del inputs_dict["output_attentions"]
       __SCREAMING_SNAKE_CASE							:     List[str]							=						True
       __SCREAMING_SNAKE_CASE							:     int							=						model_class(_A					)
       model.to(_A					)
       model.eval()
       with torch.no_grad():
        __SCREAMING_SNAKE_CASE							:     int							=						model(**self._prepare_for_class(_A  ,	_A					)					)
       __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						outputs.attentions

       self.assertEqual(len(_A					)  ,	_A					)

       # verify the first attentions (first block, first layer)
       __SCREAMING_SNAKE_CASE							:     Dict							=						(self.model_tester.image_size // 4) ** 2
       __SCREAMING_SNAKE_CASE							:     Optional[int]							=						(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
       self.assertListEqual(
           list(attentions[0].shape[-3:]					)  ,	[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]  ,	)

       # verify the last attentions (last block, last layer)
       __SCREAMING_SNAKE_CASE							:     Dict							=						(self.model_tester.image_size // 3_2) ** 2
       __SCREAMING_SNAKE_CASE							:     List[Any]							=						(self.model_tester.image_size // (3_2 * self.model_tester.sr_ratios[-1])) ** 2
       self.assertListEqual(
           list(attentions[-1].shape[-3:]					)  ,	[self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len]  ,	)
       __SCREAMING_SNAKE_CASE							:     List[str]							=						len(_A					)

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

       self.assertEqual(out_len + 1  ,	len(_A					)					)

       __SCREAMING_SNAKE_CASE							:     List[Any]							=						outputs.attentions

       self.assertEqual(len(_A					)  ,	_A					)
       # verify the first attentions (first block, first layer)
       __SCREAMING_SNAKE_CASE							:     Tuple							=						(self.model_tester.image_size // 4) ** 2
       __SCREAMING_SNAKE_CASE							:     Dict							=						(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
       self.assertListEqual(
           list(self_attentions[0].shape[-3:]					)  ,	[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]  ,	)





     def 						__snake_case     (							self			:				int					)->List[str]:
      def check_hidden_states_output(UpperCamelCase			:				int  ,	UpperCamelCase			:				str  ,	UpperCamelCase			:				Tuple					):
       __SCREAMING_SNAKE_CASE							:     str							=						model_class(_A					)
       model.to(_A					)
       model.eval()

       with torch.no_grad():
        __SCREAMING_SNAKE_CASE							:     str							=						model(**self._prepare_for_class(_A  ,	_A					)					)

       __SCREAMING_SNAKE_CASE							:     Tuple							=						outputs.hidden_states

       __SCREAMING_SNAKE_CASE							:     Dict							=						self.model_tester.num_encoder_blocks
       self.assertEqual(len(_A					)  ,	_A					)

       # verify the first hidden states (first block)
       self.assertListEqual(
           list(hidden_states[0].shape[-3:]					)  ,	[
               self.model_tester.hidden_sizes[0],
               self.model_tester.image_size // 4,
               self.model_tester.image_size // 4,
           ]  ,	)

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

      for model_class in self.all_model_classes:
       __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						True
       check_hidden_states_output(_A  ,	_A  ,	_A					)

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

       check_hidden_states_output(_A  ,	_A  ,	_A					)





     def 						__snake_case     (							self			:				List[str]					)->Optional[Any]:
      if not self.model_tester.is_training:
       return

      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							:     Optional[int]							=						self.model_tester.prepare_config_and_inputs_for_common()
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						True

      for model_class in self.all_model_classes:
       if model_class in get_values(_A					):
        continue
       # TODO: remove the following 3 lines once we have a MODEL_FOR_DEPTH_ESTIMATION_MAPPING
       # this can then be incorporated into _prepare_for_class in test_modeling_common.py
       if model_class.__name__ == "GLPNForDepthEstimation":
        __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							:     List[Any]							=						inputs_dict["pixel_values"].shape
        __SCREAMING_SNAKE_CASE							:     Any							=						torch.zeros(
            [self.model_tester.batch_size, height, width]  ,	device=_A					).long()
       __SCREAMING_SNAKE_CASE							:     Optional[int]							=						model_class(_A					)
       model.to(_A					)
       model.train()
       __SCREAMING_SNAKE_CASE							:     List[Any]							=						self._prepare_for_class(_A  ,	_A  ,	return_labels=_A					)
       __SCREAMING_SNAKE_CASE							:     Optional[int]							=						model(**_A					).loss
       loss.backward()






     @slow
     def 						__snake_case     (							self			:				List[str]					)->List[str]:
      for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       __SCREAMING_SNAKE_CASE							:     Any							=						GLPNModel.from_pretrained(_A					)
       self.assertIsNotNone(_A					)






def 						_lowerCAmelCase    (					):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"  )
 return image






@require_torch
@require_vision
@slow
class    _SCREAMING_SNAKE_CASE						(unittest.TestCase			):





     @slow
     def 						__snake_case     (							self			:				Optional[Any]					)->Tuple:
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]					)
      __SCREAMING_SNAKE_CASE							:     List[str]							=						GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]					).to(_A					)

      __SCREAMING_SNAKE_CASE							:     Tuple							=						prepare_img()
      __SCREAMING_SNAKE_CASE							:     Tuple							=						image_processor(images=_A  ,	return_tensors="pt"					).to(_A					)

      # forward pass
      with torch.no_grad():
       __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						model(**_A					)

      # verify the predicted depth
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						torch.Size([1, 4_8_0, 6_4_0]					)
      self.assertEqual(outputs.predicted_depth.shape  ,	_A					)

      __SCREAMING_SNAKE_CASE							:     List[str]							=						torch.tensor(
          [[3.4_2_9_1, 2.7_8_6_5, 2.5_1_5_1], [3.2_8_4_1, 2.7_0_2_1, 2.3_5_0_2], [3.1_1_4_7, 2.4_6_2_5, 2.2_4_8_1]]					).to(_A					)

      self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3]  ,	_A  ,	atol=1E-4					)					)









import random


def 							__lowercase     (	UpperCAmelCase__   ):

					"""simple docstring"""






					def is_sorted(UpperCAmelCase__   ):
										for i in range(len(UpperCAmelCase__   ) - 1   ):
															if collection[i] > collection[i + 1]:
																				return False
										return True

					while not is_sorted(UpperCAmelCase__   ):
										random.shuffle(UpperCAmelCase__   )
					return collection


if __name__ == "__main__":
		lowerCamelCase   					=  input('''Enter numbers separated by a comma:\n''').strip()
		lowerCamelCase   					=  [int(item) for item in user_input.split(''',''')]
		print(bogo_sort(unsorted))






