



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				(     __snake_case ):






   '''simple docstring'''






   def       A__    (    self:     Optional[int]							)	->      Optional[Any]:
       UpperCAmelCase_     :       Tuple  		=					self.config_class(**self.inputs_dict							)
       self.parent.assertTrue(hasattr(lowerCamelCase_  ,"""hidden_sizes"""							)							)
       self.parent.assertTrue(hasattr(lowerCamelCase_  ,"""num_attention_heads"""							)							)
       self.parent.assertTrue(hasattr(lowerCamelCase_  ,"""num_encoder_blocks"""							)							)







class       _snake_case				:






   '''simple docstring'''






   def __init__(    self:     int  ,lowerCamelCase_:     int  ,lowerCamelCase_:     List[str]=13  ,lowerCamelCase_:     Dict=64  ,lowerCamelCase_:     str=3  ,lowerCamelCase_:     List[str]=4  ,lowerCamelCase_:     Optional[int]=[2, 2, 2, 2]  ,lowerCamelCase_:     Optional[int]=[8, 4, 2, 1]  ,lowerCamelCase_:     Any=[16, 32, 64, 128]  ,lowerCamelCase_:     int=[1, 4, 8, 16]  ,lowerCamelCase_:     Union[str, Any]=[1, 2, 4, 8]  ,lowerCamelCase_:     str=True  ,lowerCamelCase_:     Any=True  ,lowerCamelCase_:     Optional[Any]="gelu"  ,lowerCamelCase_:     str=0.1  ,lowerCamelCase_:     str=0.1  ,lowerCamelCase_:     List[str]=0.0_2  ,lowerCamelCase_:     Optional[Any]=16  ,lowerCamelCase_:     str=3  ,lowerCamelCase_:     int=None  ,)	->      Optional[Any]:
       UpperCAmelCase_     :       Union[str, Any]  		=					parent
       UpperCAmelCase_     :       Tuple  		=					batch_size
       UpperCAmelCase_     :       Optional[int]  		=					image_size
       UpperCAmelCase_     :       Dict  		=					num_channels
       UpperCAmelCase_     :       Union[str, Any]  		=					num_encoder_blocks
       UpperCAmelCase_     :       int  		=					sr_ratios
       UpperCAmelCase_     :       int  		=					depths
       UpperCAmelCase_     :       str  		=					hidden_sizes
       UpperCAmelCase_     :       Union[str, Any]  		=					downsampling_rates
       UpperCAmelCase_     :       List[str]  		=					num_attention_heads
       UpperCAmelCase_     :       List[Any]  		=					is_training
       UpperCAmelCase_     :       int  		=					use_labels
       UpperCAmelCase_     :       Optional[Any]  		=					hidden_act
       UpperCAmelCase_     :       Optional[int]  		=					hidden_dropout_prob
       UpperCAmelCase_     :       List[str]  		=					attention_probs_dropout_prob
       UpperCAmelCase_     :       Any  		=					initializer_range
       UpperCAmelCase_     :       List[str]  		=					decoder_hidden_size
       UpperCAmelCase_     :       Any  		=					num_labels
       UpperCAmelCase_     :       Optional[int]  		=					scope


   def       A__    (    self:     List[str]							)	->      int:
       UpperCAmelCase_     :       Dict  		=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

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

       UpperCAmelCase_     :       str  		=					self.get_config()
       return config, pixel_values, labels


   def       A__    (    self:     Union[str, Any]							)	->      int:
       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       A__    (    self:     Any  ,lowerCamelCase_:     Optional[int]  ,lowerCamelCase_:     Dict  ,lowerCamelCase_:     Union[str, Any]							)	->      Any:
       UpperCAmelCase_     :       Optional[Any]  		=					GLPNModel(config=lowerCamelCase_							)
       model.to(lowerCamelCase_							)
       model.eval()
       UpperCAmelCase_     :       Any  		=					model(lowerCamelCase_							)
       UpperCAmelCase_     :       Any  		=					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       A__    (    self:     List[str]  ,lowerCamelCase_:     List[Any]  ,lowerCamelCase_:     List[str]  ,lowerCamelCase_:     str							)	->      List[str]:
       UpperCAmelCase_     :       Optional[Any]  		=					self.num_labels
       UpperCAmelCase_     :       Any  		=					GLPNForDepthEstimation(lowerCamelCase_							)
       model.to(lowerCamelCase_							)
       model.eval()
       UpperCAmelCase_     :       Optional[int]  		=					model(lowerCamelCase_							)
       self.parent.assertEqual(result.predicted_depth.shape  ,(self.batch_size, self.image_size, self.image_size)							)
       UpperCAmelCase_     :       Optional[int]  		=					model(lowerCamelCase_  ,labels=lowerCamelCase_							)
       self.parent.assertEqual(result.predicted_depth.shape  ,(self.batch_size, self.image_size, self.image_size)							)






   def       A__    (    self:     Tuple							)	->      Tuple:
       UpperCAmelCase_     :       Optional[int]  		=					self.prepare_config_and_inputs()
       UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_     :       Any  		=					config_and_inputs
       UpperCAmelCase_     :       Optional[Any]  		=					{"""pixel_values""": pixel_values}
       return config, inputs_dict







@require_torch
class       _snake_case				(     __snake_case ,							__snake_case ,							unittest.TestCase ):






   '''simple docstring'''






   A__      : int       =						(GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
   A__      : Any       =						(
       {"depth-estimation": GLPNForDepthEstimation, "feature-extraction": GLPNModel} if is_torch_available() else {}
   )

   A__      : List[str]       =						False
   A__      : Optional[int]       =						False
   A__      : str       =						False


   def       A__    (    self:     Union[str, Any]							)	->      Optional[int]:
       UpperCAmelCase_     :       str  		=					GLPNModelTester(self							)
       UpperCAmelCase_     :       Optional[Any]  		=					GLPNConfigTester(self  ,config_class=lowerCamelCase_							)


   def       A__    (    self:     Dict							)	->      Optional[Any]:
       self.config_tester.run_common_tests()


   def       A__    (    self:     Union[str, Any]							)	->      Optional[int]:
       UpperCAmelCase_     :       Tuple  		=					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*lowerCamelCase_							)


   def       A__    (    self:     List[str]							)	->      Optional[int]:
       UpperCAmelCase_     :       str  		=					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_depth_estimation(*lowerCamelCase_							)


   @unittest.skip("""GLPN does not use inputs_embeds"""							)
   def       A__    (    self:     List[str]							)	->      List[Any]:
       pass


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


   def       A__    (    self:     Union[str, Any]							)	->      List[str]:
       UpperCAmelCase_		, UpperCAmelCase_     :       int  		=					self.model_tester.prepare_config_and_inputs_for_common()

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

           UpperCAmelCase_     :       str  		=					["""pixel_values"""]
           self.assertListEqual(arg_names[:1]  ,lowerCamelCase_							)


   def       A__    (    self:     str							)	->      List[Any]:
       UpperCAmelCase_		, UpperCAmelCase_     :       Tuple  		=					self.model_tester.prepare_config_and_inputs_for_common()
       UpperCAmelCase_     :       str  		=					True

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

           UpperCAmelCase_     :       str  		=					sum(self.model_tester.depths							)
           self.assertEqual(len(lowerCamelCase_							)  ,lowerCamelCase_							)

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

           self.assertEqual(len(lowerCamelCase_							)  ,lowerCamelCase_							)

           # verify the first attentions (first block, first layer)
           UpperCAmelCase_     :       Optional[int]  		=					(self.model_tester.image_size // 4) ** 2
           UpperCAmelCase_     :       List[Any]  		=					(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)
           UpperCAmelCase_     :       Tuple  		=					(self.model_tester.image_size // 32) ** 2
           UpperCAmelCase_     :       Optional[int]  		=					(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]  ,)
           UpperCAmelCase_     :       Tuple  		=					len(lowerCamelCase_							)

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

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

           UpperCAmelCase_     :       List[str]  		=					outputs.attentions

           self.assertEqual(len(lowerCamelCase_							)  ,lowerCamelCase_							)
           # verify the first attentions (first block, first layer)
           UpperCAmelCase_     :       Optional[int]  		=					(self.model_tester.image_size // 4) ** 2
           UpperCAmelCase_     :       List[Any]  		=					(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       A__    (    self:     str							)	->      List[Any]:
       def check_hidden_states_output(lowerCamelCase_:     Any  ,lowerCamelCase_:     Tuple  ,lowerCamelCase_:     List[str]							):
           UpperCAmelCase_     :       Optional[int]  		=					model_class(lowerCamelCase_							)
           model.to(lowerCamelCase_							)
           model.eval()

           with torch.no_grad():
               UpperCAmelCase_     :       Optional[int]  		=					model(**self._prepare_for_class(lowerCamelCase_  ,lowerCamelCase_							)							)

           UpperCAmelCase_     :       List[str]  		=					outputs.hidden_states

           UpperCAmelCase_     :       Union[str, Any]  		=					self.model_tester.num_encoder_blocks
           self.assertEqual(len(lowerCamelCase_							)  ,lowerCamelCase_							)

           # 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,
               ]  ,)

       UpperCAmelCase_		, UpperCAmelCase_     :       List[str]  		=					self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           UpperCAmelCase_     :       Optional[Any]  		=					True
           check_hidden_states_output(lowerCamelCase_  ,lowerCamelCase_  ,lowerCamelCase_							)

           # check that output_hidden_states also work using config
           del inputs_dict["output_hidden_states"]
           UpperCAmelCase_     :       Tuple  		=					True

           check_hidden_states_output(lowerCamelCase_  ,lowerCamelCase_  ,lowerCamelCase_							)


   def       A__    (    self:     Dict							)	->      List[str]:
       if not self.model_tester.is_training:
           return

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

       for model_class in self.all_model_classes:
           if model_class in get_values(lowerCamelCase_							):
               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":
               UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_     :       int  		=					inputs_dict["""pixel_values"""].shape
               UpperCAmelCase_     :       Optional[int]  		=					torch.zeros(
                   [self.model_tester.batch_size, height, width]  ,device=lowerCamelCase_							).long()
           UpperCAmelCase_     :       int  		=					model_class(lowerCamelCase_							)
           model.to(lowerCamelCase_							)
           model.train()
           UpperCAmelCase_     :       Union[str, Any]  		=					self._prepare_for_class(lowerCamelCase_  ,lowerCamelCase_  ,return_labels=lowerCamelCase_							)
           UpperCAmelCase_     :       Optional[Any]  		=					model(**lowerCamelCase_							).loss
           loss.backward()






   @slow
   def       A__    (    self:     List[str]							)	->      Optional[int]:
       for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           UpperCAmelCase_     :       List[Any]  		=					GLPNModel.from_pretrained(lowerCamelCase_							)
           self.assertIsNotNone(lowerCamelCase_							)







def       lowerCamelCase_  (    ):



    '''simple docstring'''




    UpperCAmelCase_     :       Union[str, Any]  		=					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       A__    (    self:     Any							)	->      List[str]:
       UpperCAmelCase_     :       str  		=					GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]							)
       UpperCAmelCase_     :       Tuple  		=					GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]							).to(lowerCamelCase_							)

       UpperCAmelCase_     :       Any  		=					prepare_img()
       UpperCAmelCase_     :       List[Any]  		=					image_processor(images=lowerCamelCase_  ,return_tensors="""pt"""							).to(lowerCamelCase_							)

       # forward pass
       with torch.no_grad():
           UpperCAmelCase_     :       Union[str, Any]  		=					model(**lowerCamelCase_							)

       # verify the predicted depth
       UpperCAmelCase_     :       Tuple  		=					torch.Size([1, 480, 640]							)
       self.assertEqual(outputs.predicted_depth.shape  ,lowerCamelCase_							)

       UpperCAmelCase_     :       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(lowerCamelCase_							)

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



"""simple docstring"""




__A	:       dict[str, float]        =						{
    "kilogram": 1,
    "gram": pow(10, 3),
    "milligram": pow(10, 6),
    "metric-ton": pow(10, -3),
    "long-ton": 0.00_09_84_20_73,
    "short-ton": 0.00_11_02_31_22,
    "pound": 2.20_46_24_42_02,
    "stone": 0.15_74_73_17_28,
    "ounce": 35.2_73_99_07_23,
    "carrat": 50_00,
    "atomic-mass-unit": 6.0_2213_6652E26,
}

__A	:       dict[str, float]        =						{
    "kilogram": 1,
    "gram": pow(10, -3),
    "milligram": pow(10, -6),
    "metric-ton": pow(10, 3),
    "long-ton": 10_16.0_46_08,
    "short-ton": 9_07.1_84,
    "pound": 0.45_35_92,
    "stone": 6.3_50_29,
    "ounce": 0.0_28_34_95,
    "carrat": 0.00_02,
    "atomic-mass-unit": 1.6_6054_0199E-27,
}
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__     ,				lowercase__ ):
					"""simple docstring"""
					if to_type not in KILOGRAM_CHART or from_type not in WEIGHT_TYPE_CHART:
										A     =   (
										    F"""Invalid 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"""
										    F"""Supported values are: {", ".join(lowercase__ )}"""
										)
										raise ValueError(lowercase__ )
					return value * KILOGRAM_CHART[to_type] * WEIGHT_TYPE_CHART[from_type]


if __name__ == "__main__":
	import doctest

	doctest.testmod()





