

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 							A_			(		snake_case__ ):


     def   UpperCAmelCase  (							self      :		Optional[Any] )							->     List[Any]:
      __lowerCAmelCase:       str            =				self.config_class(**self.inputs_dict )
      self.parent.assertTrue(hasattr(UpperCAmelCase      ,  'hidden_sizes' ) )
      self.parent.assertTrue(hasattr(UpperCAmelCase      ,  'num_attention_heads' ) )
      self.parent.assertTrue(hasattr(UpperCAmelCase      ,  'num_encoder_blocks' ) )







class 							A_			:


     def __init__(							self      :		Dict      ,  UpperCAmelCase      :		Optional[Any]      ,  UpperCAmelCase      :		Optional[Any]=1_3      ,  UpperCAmelCase      :		Any=6_4      ,  UpperCAmelCase      :		Optional[int]=3      ,  UpperCAmelCase      :		int=4      ,  UpperCAmelCase      :		List[str]=[2, 2, 2, 2]      ,  UpperCAmelCase      :		Tuple=[8, 4, 2, 1]      ,  UpperCAmelCase      :		List[str]=[1_6, 3_2, 6_4, 1_2_8]      ,  UpperCAmelCase      :		Dict=[1, 4, 8, 1_6]      ,  UpperCAmelCase      :		Tuple=[1, 2, 4, 8]      ,  UpperCAmelCase      :		str=True      ,  UpperCAmelCase      :		List[Any]=True      ,  UpperCAmelCase      :		str="gelu"      ,  UpperCAmelCase      :		Optional[Any]=0.1      ,  UpperCAmelCase      :		Union[str, Any]=0.1      ,  UpperCAmelCase      :		Dict=0.02      ,  UpperCAmelCase      :		Optional[int]=1_6      ,  UpperCAmelCase      :		int=3      ,  UpperCAmelCase      :		List[Any]=None      ,  )							->     Optional[Any]:
      __lowerCAmelCase:       Optional[int]            =				parent
      __lowerCAmelCase:       Optional[Any]            =				batch_size
      __lowerCAmelCase:       Tuple            =				image_size
      __lowerCAmelCase:       Any            =				num_channels
      __lowerCAmelCase:       Union[str, Any]            =				num_encoder_blocks
      __lowerCAmelCase:       Tuple            =				sr_ratios
      __lowerCAmelCase:       Dict            =				depths
      __lowerCAmelCase:       Any            =				hidden_sizes
      __lowerCAmelCase:       List[str]            =				downsampling_rates
      __lowerCAmelCase:       Optional[Any]            =				num_attention_heads
      __lowerCAmelCase:       List[str]            =				is_training
      __lowerCAmelCase:       Optional[int]            =				use_labels
      __lowerCAmelCase:       Optional[int]            =				hidden_act
      __lowerCAmelCase:       Optional[Any]            =				hidden_dropout_prob
      __lowerCAmelCase:       int            =				attention_probs_dropout_prob
      __lowerCAmelCase:       List[Any]            =				initializer_range
      __lowerCAmelCase:       Union[str, Any]            =				decoder_hidden_size
      __lowerCAmelCase:       Optional[int]            =				num_labels
      __lowerCAmelCase:       List[str]            =				scope


     def   UpperCAmelCase  (							self      :		List[Any] )							->     Tuple:
      __lowerCAmelCase:       str            =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

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

      __lowerCAmelCase:       Any            =				self.get_config()
      return config, pixel_values, labels


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


     def   UpperCAmelCase  (							self      :		Tuple )							->     int:
      __lowerCAmelCase:       str            =				self.prepare_config_and_inputs()
      __lowerCAmelCase     ,							__lowerCAmelCase     ,							__lowerCAmelCase:       Dict            =				config_and_inputs
      __lowerCAmelCase:       List[Any]            =				{'pixel_values': pixel_values}
      return config, inputs_dict







@require_torch
class 							A_			(		snake_case__							,     snake_case__							,     unittest.TestCase ):
     _lowercase      :					List[Any]       =     (GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
     _lowercase      :					str       =     (
         {'depth-estimation': GLPNForDepthEstimation, 'feature-extraction': GLPNModel} if is_torch_available() else {}
     )

     _lowercase      :					List[str]       =     False
     _lowercase      :					Dict       =     False
     _lowercase      :					Union[str, Any]       =     False


     def   UpperCAmelCase  (							self      :		Optional[int] )							->     Tuple:
      __lowerCAmelCase:       Union[str, Any]            =				GLPNModelTester(self )
      __lowerCAmelCase:       List[Any]            =				GLPNConfigTester(self      ,  config_class=UpperCAmelCase )


     def   UpperCAmelCase  (							self      :		Union[str, Any] )							->     Optional[int]:
      self.config_tester.run_common_tests()


     def   UpperCAmelCase  (							self      :		str )							->     Dict:
      __lowerCAmelCase:       Tuple            =				self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*UpperCAmelCase )


     def   UpperCAmelCase  (							self      :		Optional[int] )							->     Union[str, Any]:
      __lowerCAmelCase:       List[str]            =				self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_depth_estimation(*UpperCAmelCase )


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


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


     def   UpperCAmelCase  (							self      :		Dict )							->     Any:
      __lowerCAmelCase     ,							__lowerCAmelCase:       Dict            =				self.model_tester.prepare_config_and_inputs_for_common()

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

       __lowerCAmelCase:       Dict            =				['pixel_values']
       self.assertListEqual(arg_names[:1]      ,  UpperCAmelCase )


     def   UpperCAmelCase  (							self      :		int )							->     Tuple:
      __lowerCAmelCase     ,							__lowerCAmelCase:       Tuple            =				self.model_tester.prepare_config_and_inputs_for_common()
      __lowerCAmelCase:       Optional[int]            =				True

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

       __lowerCAmelCase:       Dict            =				sum(self.model_tester.depths )
       self.assertEqual(len(UpperCAmelCase )      ,  UpperCAmelCase )

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

       self.assertEqual(len(UpperCAmelCase )      ,  UpperCAmelCase )

       # verify the first attentions (first block, first layer)
       __lowerCAmelCase:       List[str]            =				(self.model_tester.image_size // 4) ** 2
       __lowerCAmelCase:       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)
       __lowerCAmelCase:       Optional[int]            =				(self.model_tester.image_size // 3_2) ** 2
       __lowerCAmelCase:       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]      ,  )
       __lowerCAmelCase:       Any            =				len(UpperCAmelCase )

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

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

       __lowerCAmelCase:       Optional[int]            =				outputs.attentions

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

       with torch.no_grad():
        __lowerCAmelCase:       Tuple            =				model(**self._prepare_for_class(UpperCAmelCase      ,  UpperCAmelCase ) )

       __lowerCAmelCase:       Union[str, Any]            =				outputs.hidden_states

       __lowerCAmelCase:       Tuple            =				self.model_tester.num_encoder_blocks
       self.assertEqual(len(UpperCAmelCase )      ,  UpperCAmelCase )

       # 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,
           ]      ,  )

      __lowerCAmelCase     ,							__lowerCAmelCase:       Union[str, Any]            =				self.model_tester.prepare_config_and_inputs_for_common()

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

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

       check_hidden_states_output(UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase )


     def   UpperCAmelCase  (							self      :		Union[str, Any] )							->     Dict:
      if not self.model_tester.is_training:
       return

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

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


     @slow
     def   UpperCAmelCase  (							self      :		int )							->     Optional[int]:
      for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       __lowerCAmelCase:       Dict            =				GLPNModel.from_pretrained(UpperCAmelCase )
       self.assertIsNotNone(UpperCAmelCase )







def _a			(	)   ->   List[Any]:

 """simple docstring"""






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






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


     @slow
     def   UpperCAmelCase  (							self      :		List[Any] )							->     Optional[Any]:
      __lowerCAmelCase:       str            =				GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
      __lowerCAmelCase:       Optional[int]            =				GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(UpperCAmelCase )

      __lowerCAmelCase:       List[Any]            =				prepare_img()
      __lowerCAmelCase:       Optional[int]            =				image_processor(images=UpperCAmelCase      ,  return_tensors='pt' ).to(UpperCAmelCase )

      # forward pass
      with torch.no_grad():
       __lowerCAmelCase:       int            =				model(**UpperCAmelCase )

      # verify the predicted depth
      __lowerCAmelCase:       Any            =				torch.Size([1, 4_8_0, 6_4_0] )
      self.assertEqual(outputs.predicted_depth.shape      ,  UpperCAmelCase )

      __lowerCAmelCase:       str            =				torch.tensor(
          [[3.4291, 2.7865, 2.5151], [3.2841, 2.7021, 2.3502], [3.1147, 2.4625, 2.2481]] ).to(UpperCAmelCase )

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








import torch

from transformers import PreTrainedModel

from .custom_configuration import CustomConfig, NoSuperInitConfig






class 							A_			(		snake_case__ ):
     _lowercase      :					Any       =     CustomConfig


     def __init__(							self      :		List[Any]      ,  UpperCAmelCase      :		Optional[Any] )							->     int:
      super().__init__(UpperCAmelCase )
      __lowerCAmelCase:       Tuple            =				torch.nn.Linear(config.hidden_size      ,  config.hidden_size )


     def   UpperCAmelCase  (							self      :		Optional[int]      ,  UpperCAmelCase      :		Tuple )							->     Union[str, Any]:
      return self.linear(UpperCAmelCase )


     def   UpperCAmelCase  (							self      :		str      ,  UpperCAmelCase      :		str )							->     Optional[int]:
      pass







class 							A_			(		snake_case__ ):
     _lowercase      :					str       =     NoSuperInitConfig


     def __init__(							self      :		List[Any]      ,  UpperCAmelCase      :		Union[str, Any] )							->     int:
      super().__init__(UpperCAmelCase )
      __lowerCAmelCase:       int            =				torch.nn.Linear(config.attribute      ,  config.attribute )


     def   UpperCAmelCase  (							self      :		Tuple      ,  UpperCAmelCase      :		Tuple )							->     Tuple:
      return self.linear(UpperCAmelCase )


     def   UpperCAmelCase  (							self      :		Optional[Any]      ,  UpperCAmelCase      :		Any )							->     str:
      pass





