




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     (						_lowerCAmelCase						):
     """simple docstring"""

     def 	__snake_case				(      self					:				List[Any]     )			->    Dict:
      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      A     :
     """simple docstring"""

     def __init__(      self					:				Union[str, Any]		,					A__					:				Any		,					A__					:				Optional[Any]=1_3		,					A__					:				List[str]=6_4		,					A__					:				Optional[Any]=3		,					A__					:				Dict=4		,					A__					:				int=[2, 2, 2, 2]		,					A__					:				List[Any]=[8, 4, 2, 1]		,					A__					:				List[Any]=[1_6, 3_2, 6_4, 1_2_8]		,					A__					:				int=[1, 4, 8, 1_6]		,					A__					:				int=[1, 2, 4, 8]		,					A__					:				int=True		,					A__					:				Union[str, Any]=True		,					A__					:				Optional[Any]="gelu"		,					A__					:				str=0.1		,					A__					:				Optional[Any]=0.1		,					A__					:				List[Any]=0.02		,					A__					:				Any=1_6		,					A__					:				int=3		,					A__					:				List[str]=None		,					)			->    List[Any]:
      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 	__snake_case				(      self					:				Dict     )			->    List[str]:
      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 	__snake_case				(      self					:				Union[str, Any]     )			->    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					:				str		,					A__					:				Union[str, Any]		,					A__					:				int		,					A__					:				Dict     )			->    int:
      lowerCamelCase__ 					=			GLPNModel(config=A__     )
      model.to(A__     )
      model.eval()
      lowerCamelCase__ 					=			model(A__     )
      lowerCamelCase__ 					=			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 	__snake_case				(      self					:				List[Any]		,					A__					:				Union[str, Any]		,					A__					:				Dict		,					A__					:				str     )			->    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 	__snake_case				(      self					:				str     )			->    str:
      lowerCamelCase__ 					=			self.prepare_config_and_inputs()
      lowerCamelCase__					,						lowerCamelCase__					,						lowerCamelCase__ 					=			config_and_inputs
      lowerCamelCase__ 					=			{'pixel_values': pixel_values}
      return config, inputs_dict






@require_torch
class      A     (						_lowerCAmelCase     ,  _lowerCAmelCase     ,  unittest.TestCase						):
     """simple docstring"""

     _snake_case:		Optional[int]       =(GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
     _snake_case:		Dict       =(
         {"depth-estimation": GLPNForDepthEstimation, "feature-extraction": GLPNModel} if is_torch_available() else {}
     )

     _snake_case:		List[str]       =False
     _snake_case:		List[str]       =False
     _snake_case:		Union[str, Any]       =False




     def 	__snake_case				(      self					:				str     )			->    Optional[Any]:
      lowerCamelCase__ 					=			GLPNModelTester(self     )
      lowerCamelCase__ 					=			GLPNConfigTester(self		,					config_class=A__     )




     def 	__snake_case				(      self					:				str     )			->    Dict:
      self.config_tester.run_common_tests()




     def 	__snake_case				(      self					:				List[Any]     )			->    Optional[Any]:
      lowerCamelCase__ 					=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*A__     )




     def 	__snake_case				(      self					:				List[Any]     )			->    int:
      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 	__snake_case				(      self					:				Union[str, Any]     )			->    Optional[Any]:
      pass




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




     def 	__snake_case				(      self					:				Optional[int]     )			->    str:
      lowerCamelCase__					,						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 	__snake_case				(      self					:				Dict     )			->    List[Any]:
      lowerCamelCase__					,						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 // 3_2) ** 2
       lowerCamelCase__ 					=			(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__ 					=			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 	__snake_case				(      self					:				List[Any]     )			->    Optional[int]:
      def check_hidden_states_output(A__					:				List[Any]		,					A__					:				Optional[int]		,					A__					:				int     ):
       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__					,						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 	__snake_case				(      self					:				Union[str, Any]     )			->    Dict:
      if not self.model_tester.is_training:
       return

      lowerCamelCase__					,						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__					,						lowerCamelCase__					,						lowerCamelCase__					,						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 	__snake_case				(      self					:				List[str]     )			->    Optional[Any]:
      for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       lowerCamelCase__ 					=			GLPNModel.from_pretrained(A__     )
       self.assertIsNotNone(A__     )

def      lowerCAmelCase_     (					)   ->     Optional[Any]:


 '''simple docstring'''




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






@require_torch
@require_vision
@slow
class      A     (						unittest.TestCase						):
     """simple docstring"""

     @slow
     def 	__snake_case				(      self					:				int     )			->    Dict:
      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_8_0, 6_4_0]     )
      self.assertEqual(outputs.predicted_depth.shape		,					A__     )

      lowerCamelCase__ 					=			torch.tensor(
          [[3.4291, 2.7865, 2.5151], [3.2841, 2.7021, 2.3502], [3.1147, 2.4625, 2.2481]]     ).to(A__     )

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





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__SCREAMING_SNAKE_CASE      :				int				      ={"""configuration_nat""": ["""NAT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NatConfig"""]}


try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __SCREAMING_SNAKE_CASE      :				Optional[int]				      =[
          """NAT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """NatForImageClassification""",
          """NatModel""",
          """NatPreTrainedModel""",
          """NatBackbone""",
      ]

if TYPE_CHECKING:
      from .configuration_nat import NAT_PRETRAINED_CONFIG_ARCHIVE_MAP, NatConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_nat import (
                NAT_PRETRAINED_MODEL_ARCHIVE_LIST,
                NatBackbone,
                NatForImageClassification,
                NatModel,
                NatPreTrainedModel,
            )

else:
      import sys

      __SCREAMING_SNAKE_CASE      :				Any				      =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)