





'''simple docstring'''






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 				UpperCAmelCase  (    lowerCAmelCase_     ):




     def        lowercase__					(    self    :				str						)       ->   List[Any]:
           _lowerCAmelCase							  =							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 				UpperCAmelCase  :




     def __init__(    self    :				Any    ,    __snake_case    :				List[str]    ,    __snake_case    :				List[Any]=13    ,    __snake_case    :				Any=64    ,    __snake_case    :				int=3    ,    __snake_case    :				Any=4    ,    __snake_case    :				List[Any]=[2, 2, 2, 2]    ,    __snake_case    :				List[Any]=[8, 4, 2, 1]    ,    __snake_case    :				List[Any]=[16, 32, 64, 1_28]    ,    __snake_case    :				Tuple=[1, 4, 8, 16]    ,    __snake_case    :				Union[str, Any]=[1, 2, 4, 8]    ,    __snake_case    :				int=True    ,    __snake_case    :				List[str]=True    ,    __snake_case    :				List[Any]="gelu"    ,    __snake_case    :				str=0.1    ,    __snake_case    :				List[Any]=0.1    ,    __snake_case    :				List[Any]=0.02    ,    __snake_case    :				int=16    ,    __snake_case    :				List[str]=3    ,    __snake_case    :				Optional[int]=None    ,    )       ->   Optional[int]:
           _lowerCAmelCase							  =							parent
           _lowerCAmelCase							  =							batch_size
           _lowerCAmelCase							  =							image_size
           _lowerCAmelCase							  =							num_channels
           _lowerCAmelCase							  =							num_encoder_blocks
           _lowerCAmelCase							  =							sr_ratios
           _lowerCAmelCase							  =							depths
           _lowerCAmelCase							  =							hidden_sizes
           _lowerCAmelCase							  =							downsampling_rates
           _lowerCAmelCase							  =							num_attention_heads
           _lowerCAmelCase							  =							is_training
           _lowerCAmelCase							  =							use_labels
           _lowerCAmelCase							  =							hidden_act
           _lowerCAmelCase							  =							hidden_dropout_prob
           _lowerCAmelCase							  =							attention_probs_dropout_prob
           _lowerCAmelCase							  =							initializer_range
           _lowerCAmelCase							  =							decoder_hidden_size
           _lowerCAmelCase							  =							num_labels
           _lowerCAmelCase							  =							scope




     def        lowercase__					(    self    :				Optional[Any]						)       ->   Optional[Any]:
           _lowerCAmelCase							  =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

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

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




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




     def        lowercase__					(    self    :				Optional[Any]						)       ->   Dict:
           _lowerCAmelCase							  =							self.prepare_config_and_inputs()
           _lowerCAmelCase							  =							config_and_inputs
           _lowerCAmelCase							  =							{'pixel_values': pixel_values}
           return config, inputs_dict




@require_torch
class 				UpperCAmelCase  (    lowerCAmelCase_							,	lowerCAmelCase_							,	unittest.TestCase     ):
     _lowercase:    int		  =     (GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
     _lowercase:    str		  =     (
         {'''depth-estimation''': GLPNForDepthEstimation, '''feature-extraction''': GLPNModel} if is_torch_available() else {}
     )

     _lowercase:    Any		  =     False
     _lowercase:    str		  =     False
     _lowercase:    Any		  =     False




     def        lowercase__					(    self    :				Dict						)       ->   List[Any]:
           _lowerCAmelCase							  =							GLPNModelTester(self						)
           _lowerCAmelCase							  =							GLPNConfigTester(self    ,    config_class=__a						)




     def        lowercase__					(    self    :				str						)       ->   Optional[Any]:
           self.config_tester.run_common_tests()




     def        lowercase__					(    self    :				str						)       ->   List[str]:
           _lowerCAmelCase							  =							self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*__a						)




     def        lowercase__					(    self    :				int						)       ->   Dict:
           _lowerCAmelCase							  =							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        lowercase__					(    self    :				Dict						)       ->   str:
           pass




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




     def        lowercase__					(    self    :				Union[str, Any]						)       ->   Tuple:
           _lowerCAmelCase							  =							self.model_tester.prepare_config_and_inputs_for_common()

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

                 _lowerCAmelCase							  =							['pixel_values']
                 self.assertListEqual(arg_names[:1]    ,    __a						)




     def        lowercase__					(    self    :				Optional[Any]						)       ->   List[Any]:
           _lowerCAmelCase							  =							self.model_tester.prepare_config_and_inputs_for_common()
           _lowerCAmelCase							  =							True

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

                 _lowerCAmelCase							  =							sum(self.model_tester.depths						)
                 self.assertEqual(len(__a						)    ,    __a						)

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

                 self.assertEqual(len(__a						)    ,    __a						)

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

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

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

                 _lowerCAmelCase							  =							outputs.attentions

                 self.assertEqual(len(__a						)    ,    __a						)
                 # verify the first attentions (first block, first layer)
                 _lowerCAmelCase							  =							(self.model_tester.image_size // 4) ** 2
                 _lowerCAmelCase							  =							(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        lowercase__					(    self    :				int						)       ->   Tuple:





           def check_hidden_states_output(__snake_case    :				Any    ,    __snake_case    :				List[str]    ,    __snake_case    :				Tuple						):
                 _lowerCAmelCase							  =							model_class(__a						)
                 model.to(__a						)
                 model.eval()

                 with torch.no_grad():
                       _lowerCAmelCase							  =							model(**self._prepare_for_class(__a    ,    __a						)						)

                 _lowerCAmelCase							  =							outputs.hidden_states

                 _lowerCAmelCase							  =							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,
                     ]    ,    )

           _lowerCAmelCase							  =							self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 _lowerCAmelCase							  =							True
                 check_hidden_states_output(__a    ,    __a    ,    __a						)

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

                 check_hidden_states_output(__a    ,    __a    ,    __a						)




     def        lowercase__					(    self    :				Dict						)       ->   Tuple:





           if not self.model_tester.is_training:
                 return

           _lowerCAmelCase							  =							self.model_tester.prepare_config_and_inputs_for_common()
           _lowerCAmelCase							  =							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":
                       _lowerCAmelCase							  =							inputs_dict['pixel_values'].shape
                       _lowerCAmelCase							  =							torch.zeros(
                           [self.model_tester.batch_size, height, width]    ,    device=__a						).long()
                 _lowerCAmelCase							  =							model_class(__a						)
                 model.to(__a						)
                 model.train()
                 _lowerCAmelCase							  =							self._prepare_for_class(__a    ,    __a    ,    return_labels=__a						)
                 _lowerCAmelCase							  =							model(**__a						).loss
                 loss.backward()




     @slow
     def        lowercase__					(    self    :				Union[str, Any]						)       ->   int:





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




def      UpperCamelCase__   (	):





      """simple docstring"""


      _lowerCAmelCase							  =							Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)
      return image





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




     @slow
     def        lowercase__					(    self    :				Tuple						)       ->   List[Any]:
           _lowerCAmelCase							  =							GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)
           _lowerCAmelCase							  =							GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]						).to(__a						)

           _lowerCAmelCase							  =							prepare_img()
           _lowerCAmelCase							  =							image_processor(images=__a    ,    return_tensors="""pt"""						).to(__a						)

           # forward pass
           with torch.no_grad():
                 _lowerCAmelCase							  =							model(**__a						)

           # verify the predicted depth
           _lowerCAmelCase							  =							torch.Size([1, 4_80, 6_40]						)
           self.assertEqual(outputs.predicted_depth.shape    ,    __a						)

           _lowerCAmelCase							  =							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(__a						)

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













'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


A__       :     Tuple          ={
    '''configuration_oneformer''': ['''ONEFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''OneFormerConfig'''],
    '''processing_oneformer''': ['''OneFormerProcessor'''],
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__       :     List[str]          =['''OneFormerImageProcessor''']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__       :     Dict          =[
          '''ONEFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''OneFormerForUniversalSegmentation''',
          '''OneFormerModel''',
          '''OneFormerPreTrainedModel''',
      ]

if TYPE_CHECKING:
      from .configuration_oneformer import ONEFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, OneFormerConfig
      from .processing_oneformer import OneFormerProcessor

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .image_processing_oneformer import OneFormerImageProcessor
      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_oneformer import (
                ONEFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                OneFormerForUniversalSegmentation,
                OneFormerModel,
                OneFormerPreTrainedModel,
            )


else:
      import sys

      A__       :     Optional[Any]          =_LazyModule(__name__, globals()['''__file__'''], _import_structure)






