






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        snake_case_				(							lowercase_   ):







   """simple docstring"""



   def       SCREAMING_SNAKE_CASE__					(     self:						Any						)	->		Optional[int]:


       """simple docstring"""



       _lowercase     			=						self.config_class(**self.inputs_dict						)
       self.parent.assertTrue(hasattr(snake_case__ ,							'hidden_sizes'						)						)
       self.parent.assertTrue(hasattr(snake_case__ ,							'num_attention_heads'						)						)
       self.parent.assertTrue(hasattr(snake_case__ ,							'num_encoder_blocks'						)						)






class        snake_case_				:







   """simple docstring"""



   def __init__(     self:						Tuple ,							snake_case__:						Dict ,							snake_case__:						Any=13 ,							snake_case__:						Dict=64 ,							snake_case__:						Tuple=3 ,							snake_case__:						List[Any]=4 ,							snake_case__:						str=[2, 2, 2, 2] ,							snake_case__:						Any=[8, 4, 2, 1] ,							snake_case__:						List[Any]=[16, 32, 64, 128] ,							snake_case__:						Tuple=[1, 4, 8, 16] ,							snake_case__:						int=[1, 2, 4, 8] ,							snake_case__:						Optional[int]=True ,							snake_case__:						Optional[Any]=True ,							snake_case__:						str="gelu" ,							snake_case__:						List[str]=0.1 ,							snake_case__:						str=0.1 ,							snake_case__:						Dict=0.02 ,							snake_case__:						Tuple=16 ,							snake_case__:						List[Any]=3 ,							snake_case__:						List[str]=None ,							)	->		Tuple:


       """simple docstring"""



       _lowercase     			=						parent
       _lowercase     			=						batch_size
       _lowercase     			=						image_size
       _lowercase     			=						num_channels
       _lowercase     			=						num_encoder_blocks
       _lowercase     			=						sr_ratios
       _lowercase     			=						depths
       _lowercase     			=						hidden_sizes
       _lowercase     			=						downsampling_rates
       _lowercase     			=						num_attention_heads
       _lowercase     			=						is_training
       _lowercase     			=						use_labels
       _lowercase     			=						hidden_act
       _lowercase     			=						hidden_dropout_prob
       _lowercase     			=						attention_probs_dropout_prob
       _lowercase     			=						initializer_range
       _lowercase     			=						decoder_hidden_size
       _lowercase     			=						num_labels
       _lowercase     			=						scope

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


       """simple docstring"""



       _lowercase     			=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

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

       _lowercase     			=						self.get_config()
       return config, pixel_values, labels

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


       """simple docstring"""



       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       SCREAMING_SNAKE_CASE__					(     self:						str ,							snake_case__:						Any ,							snake_case__:						str ,							snake_case__:						Any						)	->		Optional[Any]:


       """simple docstring"""



       _lowercase     			=						GLPNModel(config=snake_case__						)
       model.to(snake_case__						)
       model.eval()
       _lowercase     			=						model(snake_case__						)
       _lowercase     			=						_lowercase     			=						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       SCREAMING_SNAKE_CASE__					(     self:						Dict ,							snake_case__:						List[Any] ,							snake_case__:						Optional[Any] ,							snake_case__:						Union[str, Any]						)	->		List[str]:


       """simple docstring"""



       _lowercase     			=						self.num_labels
       _lowercase     			=						GLPNForDepthEstimation(snake_case__						)
       model.to(snake_case__						)
       model.eval()
       _lowercase     			=						model(snake_case__						)
       self.parent.assertEqual(result.predicted_depth.shape ,							(self.batch_size, self.image_size, self.image_size)						)
       _lowercase     			=						model(snake_case__ ,							labels=snake_case__						)
       self.parent.assertEqual(result.predicted_depth.shape ,							(self.batch_size, self.image_size, self.image_size)						)

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


       """simple docstring"""



       _lowercase     			=						self.prepare_config_and_inputs()
       _lowercase			,     _lowercase			,     _lowercase     			=						config_and_inputs
       _lowercase     			=						{'pixel_values': pixel_values}
       return config, inputs_dict






@require_torch
class        snake_case_				(							lowercase_   ,      lowercase_   ,      unittest.TestCase   ):







   """simple docstring"""



   __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       SCREAMING_SNAKE_CASE__					(     self:						Optional[int]						)	->		Optional[int]:


       """simple docstring"""



       _lowercase     			=						GLPNModelTester(self						)
       _lowercase     			=						GLPNConfigTester(self ,							config_class=snake_case__						)

   def       SCREAMING_SNAKE_CASE__					(     self:						Optional[int]						)	->		Any:


       """simple docstring"""



       self.config_tester.run_common_tests()

   def       SCREAMING_SNAKE_CASE__					(     self:						str						)	->		Dict:


       """simple docstring"""



       _lowercase     			=						self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*snake_case__						)

   def       SCREAMING_SNAKE_CASE__					(     self:						Optional[Any]						)	->		int:


       """simple docstring"""



       _lowercase     			=						self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_depth_estimation(*snake_case__						)

   @unittest.skip('GLPN does not use inputs_embeds'						)
   def       SCREAMING_SNAKE_CASE__					(     self:						Any						)	->		Optional[int]:


       """simple docstring"""



       pass

   @unittest.skip('GLPN does not have get_input_embeddings method and get_output_embeddings methods'						)
   def       SCREAMING_SNAKE_CASE__					(     self:						str						)	->		Optional[Any]:


       """simple docstring"""



       pass

   def       SCREAMING_SNAKE_CASE__					(     self:						Any						)	->		str:


       """simple docstring"""



       _lowercase			,     _lowercase     			=						self.model_tester.prepare_config_and_inputs_for_common()

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

           _lowercase     			=						['pixel_values']
           self.assertListEqual(arg_names[:1] ,							snake_case__						)

   def       SCREAMING_SNAKE_CASE__					(     self:						Any						)	->		Tuple:


       """simple docstring"""



       _lowercase			,     _lowercase     			=						self.model_tester.prepare_config_and_inputs_for_common()
       _lowercase     			=						True

       for model_class in self.all_model_classes:
           _lowercase     			=						True
           _lowercase     			=						False
           _lowercase     			=						True
           _lowercase     			=						model_class(snake_case__						)
           model.to(snake_case__						)
           model.eval()
           with torch.no_grad():
               _lowercase     			=						model(**self._prepare_for_class(snake_case__ ,							snake_case__						)						)
           _lowercase     			=						outputs.attentions

           _lowercase     			=						sum(self.model_tester.depths						)
           self.assertEqual(len(snake_case__						) ,							snake_case__						)

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

           self.assertEqual(len(snake_case__						) ,							snake_case__						)

           # verify the first attentions (first block, first layer)
           _lowercase     			=						(self.model_tester.image_size // 4) ** 2
           _lowercase     			=						(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)
           _lowercase     			=						(self.model_tester.image_size // 32) ** 2
           _lowercase     			=						(self.model_tester.image_size // (32 * 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] ,							)
           _lowercase     			=						len(snake_case__						)

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

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

           _lowercase     			=						outputs.attentions

           self.assertEqual(len(snake_case__						) ,							snake_case__						)
           # verify the first attentions (first block, first layer)
           _lowercase     			=						(self.model_tester.image_size // 4) ** 2
           _lowercase     			=						(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       SCREAMING_SNAKE_CASE__					(     self:						Optional[Any]						)	->		Dict:


       """simple docstring"""



       def check_hidden_states_output(snake_case__:						Dict ,							snake_case__:						Optional[Any] ,							snake_case__:						Union[str, Any]						):
           _lowercase     			=						model_class(snake_case__						)
           model.to(snake_case__						)
           model.eval()

           with torch.no_grad():
               _lowercase     			=						model(**self._prepare_for_class(snake_case__ ,							snake_case__						)						)

           _lowercase     			=						outputs.hidden_states

           _lowercase     			=						self.model_tester.num_encoder_blocks
           self.assertEqual(len(snake_case__						) ,							snake_case__						)

           # 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,
               ] ,							)

       _lowercase			,     _lowercase     			=						self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           _lowercase     			=						True
           check_hidden_states_output(snake_case__ ,							snake_case__ ,							snake_case__						)

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

           check_hidden_states_output(snake_case__ ,							snake_case__ ,							snake_case__						)

   def       SCREAMING_SNAKE_CASE__					(     self:						str						)	->		Optional[Any]:


       """simple docstring"""



       if not self.model_tester.is_training:
           return

       _lowercase			,     _lowercase     			=						self.model_tester.prepare_config_and_inputs_for_common()
       _lowercase     			=						True

       for model_class in self.all_model_classes:
           if model_class in get_values(snake_case__						):
               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":
               _lowercase			,     _lowercase			,     _lowercase			,     _lowercase     			=						inputs_dict['pixel_values'].shape
               _lowercase     			=						torch.zeros(
                   [self.model_tester.batch_size, height, width] ,							device=snake_case__						).long()
           _lowercase     			=						model_class(snake_case__						)
           model.to(snake_case__						)
           model.train()
           _lowercase     			=						self._prepare_for_class(snake_case__ ,							snake_case__ ,							return_labels=snake_case__						)
           _lowercase     			=						model(**snake_case__						).loss
           loss.backward()

   @slow
   def       SCREAMING_SNAKE_CASE__					(     self:						Dict						)	->		Tuple:


       """simple docstring"""



       for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           _lowercase     			=						GLPNModel.from_pretrained(snake_case__						)
           self.assertIsNotNone(snake_case__						)






def 							UpperCAmelCase   ( ):


    '''simple docstring'''



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

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







   """simple docstring"""



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


       """simple docstring"""



       _lowercase     			=						GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)
       _lowercase     			=						GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]						).to(snake_case__						)

       _lowercase     			=						prepare_img()
       _lowercase     			=						image_processor(images=snake_case__ ,							return_tensors='pt'						).to(snake_case__						)

       # forward pass
       with torch.no_grad():
           _lowercase     			=						model(**snake_case__						)

       # verify the predicted depth
       _lowercase     			=						torch.Size([1, 480, 640]						)
       self.assertEqual(outputs.predicted_depth.shape ,							snake_case__						)

       _lowercase     			=						torch.tensor(
           [[3.42_91, 2.78_65, 2.51_51], [3.28_41, 2.70_21, 2.35_02], [3.11_47, 2.46_25, 2.24_81]]						).to(snake_case__						)

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








import math
def 							UpperCAmelCase   ( _lowercase   ):


    '''simple docstring'''



    _lowercase     			=						math.log(2,   10   ) * number
    _lowercase     			=						round((log_number - int(_lowercase   )),   15   )

    return difference
def 							UpperCAmelCase   ( _lowercase = 678910   ):


    '''simple docstring'''



    _lowercase     			=						90
    _lowercase     			=						0

    _lowercase     			=						math.log(1.23,   10   )
    _lowercase     			=						math.log(1.24,   10   )
    _lowercase     			=						0

    while position < number:
        _lowercase     			=						log_difference(_lowercase   )

        if difference >= upper_limit:
            power_iterator += 93

        elif difference < lower_limit:
            power_iterator += 196

        else:
            _lowercase     			=						power_iterator
            power_iterator += 196
            position += 1

    return previous_power


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   print(f"{solution() = }")
