




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_ ):





     """simple docstring"""



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






         """simple docstring"""
         a__      =       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:





     """simple docstring"""



     def __init__(      self :       Tuple						,				A :       Optional[int]						,				A :       str=13						,				A :       List[str]=64						,				A :       List[Any]=3						,				A :       str=4						,				A :       str=[2, 2, 2, 2]						,				A :       str=[8, 4, 2, 1]						,				A :       Any=[16, 32, 64, 1_28]						,				A :       Dict=[1, 4, 8, 16]						,				A :       Dict=[1, 2, 4, 8]						,				A :       List[Any]=True						,				A :       Optional[Any]=True						,				A :       Optional[Any]="gelu"						,				A :       List[str]=0.1						,				A :       Dict=0.1						,				A :       List[str]=0.0_2						,				A :       Optional[int]=16						,				A :       int=3						,				A :       int=None						,				)			->			Dict:






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

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






         """simple docstring"""
         a__      =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

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

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

     def 				__SCREAMING_SNAKE_CASE		(      self :       Any    )			->			List[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						,				A :       Optional[Any]						,				A :       Optional[int]						,				A :       List[str]    )			->			Any:






         """simple docstring"""
         a__      =       GLPNModel(config=__a    )
         model.to(__a    )
         model.eval()
         a__      =       model(__a    )
         a__      =       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 :       List[str]						,				A :       Optional[Any]						,				A :       Union[str, Any]						,				A :       Dict    )			->			Tuple:






         """simple docstring"""
         a__      =       self.num_labels
         a__      =       GLPNForDepthEstimation(__a    )
         model.to(__a    )
         model.eval()
         a__      =       model(__a    )
         self.parent.assertEqual(result.predicted_depth.shape						,				(self.batch_size, self.image_size, self.image_size)    )
         a__      =       model(__a						,				labels=__a    )
         self.parent.assertEqual(result.predicted_depth.shape						,				(self.batch_size, self.image_size, self.image_size)    )






     def 				__SCREAMING_SNAKE_CASE		(      self :       Union[str, Any]    )			->			int:






         """simple docstring"""
         a__      =       self.prepare_config_and_inputs()
         a__      =       config_and_inputs
         a__      =       {'pixel_values': pixel_values}
         return config, inputs_dict







@require_torch
class 		__UpperCAmelCase(		lowerCAmelCase_       ,	lowerCAmelCase_       ,	unittest.TestCase ):





     """simple docstring"""



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

     __UpperCAmelCase	: Tuple						=    False
     __UpperCAmelCase	: List[Any]						=    False
     __UpperCAmelCase	: List[Any]						=    False

     def 				__SCREAMING_SNAKE_CASE		(      self :       Tuple    )			->			Any:






         """simple docstring"""
         a__      =       GLPNModelTester(self    )
         a__      =       GLPNConfigTester(self						,				config_class=__a    )

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






         """simple docstring"""
         self.config_tester.run_common_tests()

     def 				__SCREAMING_SNAKE_CASE		(      self :       Optional[int]    )			->			Union[str, Any]:






         """simple docstring"""
         a__      =       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*__a    )

     def 				__SCREAMING_SNAKE_CASE		(      self :       int    )			->			Any:






         """simple docstring"""
         a__      =       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 				__SCREAMING_SNAKE_CASE		(      self :       Union[str, Any]    )			->			Union[str, Any]:






         """simple docstring"""
         pass

     @unittest.skip("""GLPN does not have get_input_embeddings method and get_output_embeddings methods"""    )
     def 				__SCREAMING_SNAKE_CASE		(      self :       Dict    )			->			Dict:






         """simple docstring"""
         pass

     def 				__SCREAMING_SNAKE_CASE		(      self :       Dict    )			->			Optional[int]:






         """simple docstring"""
         a__      =       self.model_tester.prepare_config_and_inputs_for_common()

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

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

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






         """simple docstring"""
         a__      =       self.model_tester.prepare_config_and_inputs_for_common()
         a__      =       True

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

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

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

             self.assertEqual(len(__a    )						,				__a    )

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

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

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

             a__      =       outputs.attentions

             self.assertEqual(len(__a    )						,				__a    )
             # verify the first attentions (first block, first layer)
             a__      =       (self.model_tester.image_size // 4) ** 2
             a__      =       (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 :       List[Any]    )			->			int:






         """simple docstring"""





         def check_hidden_states_output(A :       Optional[int]						,				A :       Union[str, Any]						,				A :       str    ):
             a__      =       model_class(__a    )
             model.to(__a    )
             model.eval()

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

             a__      =       outputs.hidden_states

             a__      =       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,
                 ]						,				)

         a__      =       self.model_tester.prepare_config_and_inputs_for_common()

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

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

             check_hidden_states_output(__a						,				__a						,				__a    )

     def 				__SCREAMING_SNAKE_CASE		(      self :       Any    )			->			Dict:






         """simple docstring"""





         if not self.model_tester.is_training:
             return

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






     @slow
     def 				__SCREAMING_SNAKE_CASE		(      self :       List[str]    )			->			str:






         """simple docstring"""





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






def        __lowerCAmelCase ():
    a__      =       Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""		)
    return image







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





     """simple docstring"""



     @slow
     def 				__SCREAMING_SNAKE_CASE		(      self :       Tuple    )			->			List[str]:






         """simple docstring"""
         a__      =       GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]    )
         a__      =       GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]    ).to(__a    )

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

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

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

         a__      =       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(__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, is_vision_available


SCREAMING_SNAKE_CASE_						:					List[str]			   =       {'''configuration_vit_hybrid''': ['''VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTHybridConfig''']}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE_						:					Tuple			   =       [
     '''VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''ViTHybridForImageClassification''',
     '''ViTHybridModel''',
     '''ViTHybridPreTrainedModel''',
 ]

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE_						:					int			   =       ['''ViTHybridImageProcessor''']


if TYPE_CHECKING:
 from .configuration_vit_hybrid import VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTHybridConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_vit_hybrid import (
      VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST,
      ViTHybridForImageClassification,
      ViTHybridModel,
      ViTHybridPreTrainedModel,
  )

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .image_processing_vit_hybrid import ViTHybridImageProcessor


else:
 import sys

 SCREAMING_SNAKE_CASE_						:					Any			   =       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






