


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      _lowercase					(				lowercase__):


       """simple docstring"""




       def 		lowerCAmelCase	(  self				:     List[Any]			):


             '''simple docstring'''



             lowerCamelCase__						:							Optional[Any]       =    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      _lowercase					:


       """simple docstring"""




       def __init__(  self				:     Any   , __lowerCamelCase				:     str   , __lowerCamelCase				:     Dict=13   , __lowerCamelCase				:     int=64   , __lowerCamelCase				:     str=3   , __lowerCamelCase				:     Union[str, Any]=4   , __lowerCamelCase				:     str=[2, 2, 2, 2]   , __lowerCamelCase				:     Tuple=[8, 4, 2, 1]   , __lowerCamelCase				:     Dict=[16, 32, 64, 128]   , __lowerCamelCase				:     Dict=[1, 4, 8, 16]   , __lowerCamelCase				:     int=[1, 2, 4, 8]   , __lowerCamelCase				:     Union[str, Any]=True   , __lowerCamelCase				:     Union[str, Any]=True   , __lowerCamelCase				:     List[Any]="gelu"   , __lowerCamelCase				:     Any=0.1   , __lowerCamelCase				:     Dict=0.1   , __lowerCamelCase				:     int=0.0_2   , __lowerCamelCase				:     Any=16   , __lowerCamelCase				:     Optional[Any]=3   , __lowerCamelCase				:     str=None   , ):


             '''simple docstring'''



             lowerCamelCase__						:							Optional[Any]       =    parent
             lowerCamelCase__						:							Any       =    batch_size
             lowerCamelCase__						:							Any       =    image_size
             lowerCamelCase__						:							Union[str, Any]       =    num_channels
             lowerCamelCase__						:							Any       =    num_encoder_blocks
             lowerCamelCase__						:							List[Any]       =    sr_ratios
             lowerCamelCase__						:							List[Any]       =    depths
             lowerCamelCase__						:							Tuple       =    hidden_sizes
             lowerCamelCase__						:							Any       =    downsampling_rates
             lowerCamelCase__						:							List[str]       =    num_attention_heads
             lowerCamelCase__						:							Tuple       =    is_training
             lowerCamelCase__						:							List[Any]       =    use_labels
             lowerCamelCase__						:							str       =    hidden_act
             lowerCamelCase__						:							Union[str, Any]       =    hidden_dropout_prob
             lowerCamelCase__						:							int       =    attention_probs_dropout_prob
             lowerCamelCase__						:							Dict       =    initializer_range
             lowerCamelCase__						:							List[str]       =    decoder_hidden_size
             lowerCamelCase__						:							int       =    num_labels
             lowerCamelCase__						:							str       =    scope





       def 		lowerCAmelCase	(  self				:     int			):


             '''simple docstring'''



             lowerCamelCase__						:							str       =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

             lowerCamelCase__						:							Optional[int]       =    None
             if self.use_labels:
                   lowerCamelCase__						:							str       =    ids_tensor([self.batch_size, self.image_size, self.image_size]   , self.num_labels			)

             lowerCamelCase__						:							Tuple       =    self.get_config()
             return config, pixel_values, labels





       def 		lowerCAmelCase	(  self				:     Optional[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 		lowerCAmelCase	(  self				:     Optional[int]   , __lowerCamelCase				:     str   , __lowerCamelCase				:     Optional[int]   , __lowerCamelCase				:     Any			):


             '''simple docstring'''



             lowerCamelCase__						:							List[str]       =    GLPNModel(config=__lowerCamelCase			)
             model.to(__lowerCamelCase			)
             model.eval()
             lowerCamelCase__						:							str       =    model(__lowerCamelCase			)
             lowerCamelCase__						:							Union[str, 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 		lowerCAmelCase	(  self				:     Any   , __lowerCamelCase				:     Optional[Any]   , __lowerCamelCase				:     Optional[Any]   , __lowerCamelCase				:     Union[str, Any]			):


             '''simple docstring'''



             lowerCamelCase__						:							Any       =    self.num_labels
             lowerCamelCase__						:							List[str]       =    GLPNForDepthEstimation(__lowerCamelCase			)
             model.to(__lowerCamelCase			)
             model.eval()
             lowerCamelCase__						:							List[Any]       =    model(__lowerCamelCase			)
             self.parent.assertEqual(result.predicted_depth.shape   , (self.batch_size, self.image_size, self.image_size)			)
             lowerCamelCase__						:							str       =    model(__lowerCamelCase   , labels=__lowerCamelCase			)
             self.parent.assertEqual(result.predicted_depth.shape   , (self.batch_size, self.image_size, self.image_size)			)





       def 		lowerCAmelCase	(  self				:     int			):


             '''simple docstring'''



             lowerCamelCase__						:							int       =    self.prepare_config_and_inputs()
             lowerCamelCase__       ,  lowerCamelCase__       ,  lowerCamelCase__						:							Dict       =    config_and_inputs
             lowerCamelCase__						:							Any       =    {"pixel_values": pixel_values}
             return config, inputs_dict







@require_torch
class      _lowercase					(				lowercase__				,    lowercase__				,    unittest.TestCase):


       """simple docstring"""




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

       A__   		=     False
       A__   		=     False
       A__   		=     False





       def 		lowerCAmelCase	(  self				:     Tuple			):


             '''simple docstring'''



             lowerCamelCase__						:							Any       =    GLPNModelTester(self			)
             lowerCamelCase__						:							int       =    GLPNConfigTester(self   , config_class=__lowerCamelCase			)





       def 		lowerCAmelCase	(  self				:     str			):


             '''simple docstring'''



             self.config_tester.run_common_tests()





       def 		lowerCAmelCase	(  self				:     Union[str, Any]			):


             '''simple docstring'''



             lowerCamelCase__						:							Optional[Any]       =    self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*__lowerCamelCase			)





       def 		lowerCAmelCase	(  self				:     Optional[Any]			):


             '''simple docstring'''



             lowerCamelCase__						:							List[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 		lowerCAmelCase	(  self				:     List[str]			):


             '''simple docstring'''



             pass





       @unittest.skip("GLPN does not have get_input_embeddings method and get_output_embeddings methods"			)
       def 		lowerCAmelCase	(  self				:     str			):


             '''simple docstring'''



             pass





       def 		lowerCAmelCase	(  self				:     List[Any]			):


             '''simple docstring'''



             lowerCamelCase__       ,  lowerCamelCase__						:							str       =    self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                   lowerCamelCase__						:							Union[str, Any]       =    model_class(__lowerCamelCase			)
                   lowerCamelCase__						:							Any       =    inspect.signature(model.forward			)
                   # signature.parameters is an OrderedDict => so arg_names order is deterministic
                   lowerCamelCase__						:							int       =    [*signature.parameters.keys()]

                   lowerCamelCase__						:							Union[str, Any]       =    ["pixel_values"]
                   self.assertListEqual(arg_names[:1]   , __lowerCamelCase			)





       def 		lowerCAmelCase	(  self				:     Union[str, Any]			):


             '''simple docstring'''



             lowerCamelCase__       ,  lowerCamelCase__						:							Union[str, Any]       =    self.model_tester.prepare_config_and_inputs_for_common()
             lowerCamelCase__						:							Dict       =    True

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

                   lowerCamelCase__						:							List[Any]       =    sum(self.model_tester.depths			)
                   self.assertEqual(len(__lowerCamelCase			)   , __lowerCamelCase			)

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

                   self.assertEqual(len(__lowerCamelCase			)   , __lowerCamelCase			)

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

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

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

                   lowerCamelCase__						:							Any       =    outputs.attentions

                   self.assertEqual(len(__lowerCamelCase			)   , __lowerCamelCase			)
                   # verify the first attentions (first block, first layer)
                   lowerCamelCase__						:							List[Any]       =    (self.model_tester.image_size // 4) ** 2
                   lowerCamelCase__						:							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 		lowerCAmelCase	(  self				:     Dict			):


             '''simple docstring'''



             def check_hidden_states_output(__lowerCamelCase				:     Dict   , __lowerCamelCase				:     Optional[int]   , __lowerCamelCase				:     Optional[Any]			):
                   lowerCamelCase__						:							List[Any]       =    model_class(__lowerCamelCase			)
                   model.to(__lowerCamelCase			)
                   model.eval()

                   with torch.no_grad():
                         lowerCamelCase__						:							Union[str, Any]       =    model(**self._prepare_for_class(__lowerCamelCase   , __lowerCamelCase			)			)

                   lowerCamelCase__						:							Optional[int]       =    outputs.hidden_states

                   lowerCamelCase__						:							Dict       =    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,
                       ]   , )

             lowerCamelCase__       ,  lowerCamelCase__						:							Dict       =    self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                   lowerCamelCase__						:							int       =    True
                   check_hidden_states_output(__lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase			)

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

                   check_hidden_states_output(__lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase			)





       def 		lowerCAmelCase	(  self				:     int			):


             '''simple docstring'''



             if not self.model_tester.is_training:
                   return

             lowerCamelCase__       ,  lowerCamelCase__						:							int       =    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(__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":
                         lowerCamelCase__       ,  lowerCamelCase__       ,  lowerCamelCase__       ,  lowerCamelCase__						:							List[str]       =    inputs_dict["pixel_values"].shape
                         lowerCamelCase__						:							List[str]       =    torch.zeros(
                             [self.model_tester.batch_size, height, width]   , device=__lowerCamelCase			).long()
                   lowerCamelCase__						:							List[Any]       =    model_class(__lowerCamelCase			)
                   model.to(__lowerCamelCase			)
                   model.train()
                   lowerCamelCase__						:							Optional[Any]       =    self._prepare_for_class(__lowerCamelCase   , __lowerCamelCase   , return_labels=__lowerCamelCase			)
                   lowerCamelCase__						:							Union[str, Any]       =    model(**__lowerCamelCase			).loss
                   loss.backward()





       @slow
       def 		lowerCAmelCase	(  self				:     Union[str, Any]			):


             '''simple docstring'''



             for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   lowerCamelCase__						:							Any       =    GLPNModel.from_pretrained(__lowerCamelCase			)
                   self.assertIsNotNone(__lowerCamelCase			)



def  lowercase_						(						):

      """simple docstring"""




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







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


       """simple docstring"""




       @slow
       def 		lowerCAmelCase	(  self				:     Dict			):


             '''simple docstring'''



             lowerCamelCase__						:							str       =    GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]			)
             lowerCamelCase__						:							Any       =    GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]			).to(__lowerCamelCase			)

             lowerCamelCase__						:							Tuple       =    prepare_img()
             lowerCamelCase__						:							Any       =    image_processor(images=__lowerCamelCase   , return_tensors="pt"			).to(__lowerCamelCase			)

             # forward pass
             with torch.no_grad():
                   lowerCamelCase__						:							Tuple       =    model(**__lowerCamelCase			)

             # verify the predicted depth
             lowerCamelCase__						:							Optional[int]       =    torch.Size([1, 480, 640]			)
             self.assertEqual(outputs.predicted_depth.shape   , __lowerCamelCase			)

             lowerCamelCase__						:							Union[str, Any]       =    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			)			)






def  lowercase_						(						_A     :       int		):

      """simple docstring"""




      if not isinstance(_A       ,   _A		):
            raise ValueError("double_factorial() only accepts integral values"		)
      if num < 0:
            raise ValueError("double_factorial() not defined for negative values"		)
      lowerCamelCase__						:							Union[str, Any]       =    1
      for i in range(_A       ,   0       ,   -2		):
            value *= i
      return value


if __name__ == "__main__":
    import doctest

    doctest.testmod()


