'''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__	(							_SCREAMING_SNAKE_CASE      ):






   """simple docstring"""
   def 		_lowerCamelCase			(self					)     ->  Any:
        lowercase_       :							str  					=       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							,					_a							,					_a=13							,					_a=64							,					_a=3							,					_a=4							,					_a=[2, 2, 2, 2]							,					_a=[8, 4, 2, 1]							,					_a=[16, 32, 64, 128]							,					_a=[1, 4, 8, 16]							,					_a=[1, 2, 4, 8]							,					_a=True							,					_a=True							,					_a="gelu"							,					_a=0.1							,					_a=0.1							,					_a=0.02							,					_a=16							,					_a=3							,					_a=None							,					)     ->  Dict:
        lowercase_       :							Optional[Any]  					=       parent
        lowercase_       :							Optional[Any]  					=       batch_size
        lowercase_       :							List[str]  					=       image_size
        lowercase_       :							int  					=       num_channels
        lowercase_       :							List[Any]  					=       num_encoder_blocks
        lowercase_       :							int  					=       sr_ratios
        lowercase_       :							Optional[Any]  					=       depths
        lowercase_       :							Tuple  					=       hidden_sizes
        lowercase_       :							Tuple  					=       downsampling_rates
        lowercase_       :							Union[str, Any]  					=       num_attention_heads
        lowercase_       :							str  					=       is_training
        lowercase_       :							Tuple  					=       use_labels
        lowercase_       :							Union[str, Any]  					=       hidden_act
        lowercase_       :							Optional[Any]  					=       hidden_dropout_prob
        lowercase_       :							List[Any]  					=       attention_probs_dropout_prob
        lowercase_       :							Union[str, Any]  					=       initializer_range
        lowercase_       :							List[Any]  					=       decoder_hidden_size
        lowercase_       :							Optional[int]  					=       num_labels
        lowercase_       :							Optional[Any]  					=       scope
   def 		_lowerCamelCase			(self					)     ->  List[Any]:
        lowercase_       :							Optional[Any]  					=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

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

        lowercase_       :							List[str]  					=       self.get_config()
        return config, pixel_values, labels
   def 		_lowerCamelCase			(self					)     ->  Union[str, 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 		_lowerCamelCase			(self							,					_a							,					_a							,					_a					)     ->  Dict:
        lowercase_       :							Optional[Any]  					=       GLPNModel(config=_a					)
        model.to(_a					)
        model.eval()
        lowercase_       :							int  					=       model(_a					)
        lowercase_       :							List[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							,					_a							,					_a							,					_a					)     ->  Optional[Any]:
        lowercase_       :							Optional[int]  					=       self.num_labels
        lowercase_       :							Tuple  					=       GLPNForDepthEstimation(_a					)
        model.to(_a					)
        model.eval()
        lowercase_       :							str  					=       model(_a					)
        self.parent.assertEqual(result.predicted_depth.shape							,					(self.batch_size, self.image_size, self.image_size)					)
        lowercase_       :							Dict  					=       model(_a							,					labels=_a					)
        self.parent.assertEqual(result.predicted_depth.shape							,					(self.batch_size, self.image_size, self.image_size)					)





   def 		_lowerCamelCase			(self					)     ->  Any:
        lowercase_       :							Union[str, Any]  					=       self.prepare_config_and_inputs()
        lowercase_					,lowercase_					,lowercase_       :							List[str]  					=       config_and_inputs
        lowercase_       :							Dict  					=       {'pixel_values': pixel_values}
        return config, inputs_dict



@require_torch
class    UpperCAmelCase__	(							_SCREAMING_SNAKE_CASE       ,  _SCREAMING_SNAKE_CASE       ,  unittest.TestCase      ):






   """simple docstring"""
   A				:							Tuple			     =     (GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
   A				:							int			     =     (
       {"depth-estimation": GLPNForDepthEstimation, "feature-extraction": GLPNModel} if is_torch_available() else {}
   )

   A				:							Dict			     =     False
   A				:							List[str]			     =     False
   A				:							Optional[Any]			     =     False
   def 		_lowerCamelCase			(self					)     ->  Optional[int]:
        lowercase_       :							Any  					=       GLPNModelTester(self					)
        lowercase_       :							Optional[int]  					=       GLPNConfigTester(self							,					config_class=_a					)
   def 		_lowerCamelCase			(self					)     ->  Dict:
        self.config_tester.run_common_tests()
   def 		_lowerCamelCase			(self					)     ->  Union[str, Any]:
        lowercase_       :							int  					=       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*_a					)
   def 		_lowerCamelCase			(self					)     ->  Union[str, Any]:
        lowercase_       :							Optional[int]  					=       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 		_lowerCamelCase			(self					)     ->  List[str]:
        pass
   @unittest.skip('GLPN does not have get_input_embeddings method and get_output_embeddings methods'					)
   def 		_lowerCamelCase			(self					)     ->  Dict:
        pass
   def 		_lowerCamelCase			(self					)     ->  List[str]:
        lowercase_					,lowercase_       :							Union[str, Any]  					=       self.model_tester.prepare_config_and_inputs_for_common()

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

             lowercase_       :							Union[str, Any]  					=       ['pixel_values']
             self.assertListEqual(arg_names[:1]							,					_a					)
   def 		_lowerCamelCase			(self					)     ->  Union[str, Any]:
        lowercase_					,lowercase_       :							Union[str, Any]  					=       self.model_tester.prepare_config_and_inputs_for_common()
        lowercase_       :							Dict  					=       True

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

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

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

             self.assertEqual(len(_a					)							,					_a					)

             # verify the first attentions (first block, first layer)
             lowercase_       :							int  					=       (self.model_tester.image_size // 4) ** 2
             lowercase_       :							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)
             lowercase_       :							Union[str, Any]  					=       (self.model_tester.image_size // 32) ** 2
             lowercase_       :							Optional[int]  					=       (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]							,					)
             lowercase_       :							Optional[int]  					=       len(_a					)

             # Check attention is always last and order is fine
             lowercase_       :							Any  					=       True
             lowercase_       :							int  					=       True
             lowercase_       :							Any  					=       model_class(_a					)
             model.to(_a					)
             model.eval()
             with torch.no_grad():
                  lowercase_       :							List[Any]  					=       model(**self._prepare_for_class(_a							,					_a					)					)

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

             lowercase_       :							List[Any]  					=       outputs.attentions

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

             with torch.no_grad():
                  lowercase_       :							Optional[Any]  					=       model(**self._prepare_for_class(_a							,					_a					)					)

             lowercase_       :							int  					=       outputs.hidden_states

             lowercase_       :							List[Any]  					=       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,
                 ]							,					)

        lowercase_					,lowercase_       :							List[str]  					=       self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
             lowercase_       :							List[Any]  					=       True
             check_hidden_states_output(_a							,					_a							,					_a					)

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

             check_hidden_states_output(_a							,					_a							,					_a					)
   def 		_lowerCamelCase			(self					)     ->  List[str]:
        if not self.model_tester.is_training:
             return

        lowercase_					,lowercase_       :							Dict  					=       self.model_tester.prepare_config_and_inputs_for_common()
        lowercase_       :							Optional[int]  					=       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":
                  lowercase_					,lowercase_					,lowercase_					,lowercase_       :							Any  					=       inputs_dict['pixel_values'].shape
                  lowercase_       :							Dict  					=       torch.zeros(
                      [self.model_tester.batch_size, height, width]							,					device=_a					).long()
             lowercase_       :							str  					=       model_class(_a					)
             model.to(_a					)
             model.train()
             lowercase_       :							Tuple  					=       self._prepare_for_class(_a							,					_a							,					return_labels=_a					)
             lowercase_       :							Optional[Any]  					=       model(**_a					).loss
             loss.backward()





   @slow
   def 		_lowerCamelCase			(self					)     ->  Optional[int]:
        for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             lowercase_       :							Any  					=       GLPNModel.from_pretrained(_a					)
             self.assertIsNotNone(_a					)



def _UpperCamelCase (							):
     lowercase_       :							Dict  					=       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
     return image






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






   """simple docstring"""
   @slow
   def 		_lowerCamelCase			(self					)     ->  int:
        lowercase_       :							Any  					=       GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]					)
        lowercase_       :							str  					=       GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]					).to(_a					)

        lowercase_       :							Optional[Any]  					=       prepare_img()
        lowercase_       :							str  					=       image_processor(images=_a							,					return_tensors='pt'					).to(_a					)

        # forward pass
        with torch.no_grad():
             lowercase_       :							List[str]  					=       model(**_a					)

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

        lowercase_       :							Dict  					=       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					)					)










def        __SCREAMING_SNAKE_CASE (	lowercase_   )							->						str:

    """simple docstring"""


    return " ".join(sorted(set(sentence.split()   )   )   )


if __name__ == "__main__":
 import doctest

 doctest.testmod()






