'''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 			__snake_case   (     lowerCAmelCase__   ):
 def 	UpperCAmelCase__       (  self						)	->							Any:
       '''simple docstring'''





       UpperCAmelCase    : List[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 			__snake_case   :
 def __init__(  self		,   snake_case__		,   snake_case__=13		,   snake_case__=64		,   snake_case__=3		,   snake_case__=4		,   snake_case__=[2, 2, 2, 2]		,   snake_case__=[8, 4, 2, 1]		,   snake_case__=[16, 32, 64, 128]		,   snake_case__=[1, 4, 8, 16]		,   snake_case__=[1, 2, 4, 8]		,   snake_case__=True		,   snake_case__=True		,   snake_case__="gelu"		,   snake_case__=0.1		,   snake_case__=0.1		,   snake_case__=0.02		,   snake_case__=16		,   snake_case__=3		,   snake_case__=None		,   )	->							List[str]:
       '''simple docstring'''





       UpperCAmelCase    : Optional[Any]      =parent
       UpperCAmelCase    : int      =batch_size
       UpperCAmelCase    : str      =image_size
       UpperCAmelCase    : Tuple      =num_channels
       UpperCAmelCase    : Any      =num_encoder_blocks
       UpperCAmelCase    : List[Any]      =sr_ratios
       UpperCAmelCase    : Dict      =depths
       UpperCAmelCase    : Dict      =hidden_sizes
       UpperCAmelCase    : Dict      =downsampling_rates
       UpperCAmelCase    : Union[str, Any]      =num_attention_heads
       UpperCAmelCase    : Any      =is_training
       UpperCAmelCase    : Optional[Any]      =use_labels
       UpperCAmelCase    : int      =hidden_act
       UpperCAmelCase    : Optional[int]      =hidden_dropout_prob
       UpperCAmelCase    : Tuple      =attention_probs_dropout_prob
       UpperCAmelCase    : Tuple      =initializer_range
       UpperCAmelCase    : Optional[Any]      =decoder_hidden_size
       UpperCAmelCase    : Optional[int]      =num_labels
       UpperCAmelCase    : str      =scope
 def 	UpperCAmelCase__       (  self						)	->							Union[str, Any]:
       '''simple docstring'''





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

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

       UpperCAmelCase    : Optional[int]      =self.get_config()
       return config, pixel_values, labels
 def 	UpperCAmelCase__       (  self						)	->							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 	UpperCAmelCase__       (  self		,   snake_case__		,   snake_case__		,   snake_case__						)	->							Any:
       '''simple docstring'''





       UpperCAmelCase    : Optional[int]      =GLPNModel(config=_lowerCamelCase						)
       model.to(_lowerCamelCase						)
       model.eval()
       UpperCAmelCase    : Dict      =model(_lowerCamelCase						)
       UpperCAmelCase    : 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 	UpperCAmelCase__       (  self		,   snake_case__		,   snake_case__		,   snake_case__						)	->							int:
       '''simple docstring'''





       UpperCAmelCase    : Optional[Any]      =self.num_labels
       UpperCAmelCase    : int      =GLPNForDepthEstimation(_lowerCamelCase						)
       model.to(_lowerCamelCase						)
       model.eval()
       UpperCAmelCase    : List[Any]      =model(_lowerCamelCase						)
       self.parent.assertEqual(result.predicted_depth.shape		,   (self.batch_size, self.image_size, self.image_size)						)
       UpperCAmelCase    : Optional[Any]      =model(_lowerCamelCase		,   labels=_lowerCamelCase						)
       self.parent.assertEqual(result.predicted_depth.shape		,   (self.batch_size, self.image_size, self.image_size)						)






 def 	UpperCAmelCase__       (  self						)	->							int:
       '''simple docstring'''





       UpperCAmelCase    : Union[str, Any]      =self.prepare_config_and_inputs()
       UpperCAmelCase    : Dict      =config_and_inputs
       UpperCAmelCase    : str      ={"""pixel_values""": pixel_values}
       return config, inputs_dict







@require_torch
class 			__snake_case   (     lowerCAmelCase__					,   lowerCAmelCase__					,   unittest.TestCase   ):
 __lowerCamelCase    :   str     =      (GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
 __lowerCamelCase    :   Union[str, Any]     =      (
     {"""depth-estimation""": GLPNForDepthEstimation, """feature-extraction""": GLPNModel} if is_torch_available() else {}
 )

 __lowerCamelCase    :   Tuple     =      False
 __lowerCamelCase    :   Dict     =      False
 __lowerCamelCase    :   List[str]     =      False
 def 	UpperCAmelCase__       (  self						)	->							Dict:
       '''simple docstring'''





       UpperCAmelCase    : str      =GLPNModelTester(self						)
       UpperCAmelCase    : int      =GLPNConfigTester(self		,   config_class=_lowerCamelCase						)
 def 	UpperCAmelCase__       (  self						)	->							int:
       '''simple docstring'''





       self.config_tester.run_common_tests()
 def 	UpperCAmelCase__       (  self						)	->							Dict:
       '''simple docstring'''





       UpperCAmelCase    : int      =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_lowerCamelCase						)
 def 	UpperCAmelCase__       (  self						)	->							Tuple:
       '''simple docstring'''





       UpperCAmelCase    : 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 	UpperCAmelCase__       (  self						)	->							List[Any]:
       '''simple docstring'''





       pass
 @unittest.skip('''GLPN does not have get_input_embeddings method and get_output_embeddings methods'''						)
 def 	UpperCAmelCase__       (  self						)	->							Dict:
       '''simple docstring'''





       pass
 def 	UpperCAmelCase__       (  self						)	->							List[Any]:
       '''simple docstring'''





       UpperCAmelCase    : Union[str, Any]      =self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             UpperCAmelCase    : List[str]      =model_class(_lowerCamelCase						)
             UpperCAmelCase    : Dict      =inspect.signature(model.forward						)
             # signature.parameters is an OrderedDict => so arg_names order is deterministic
             UpperCAmelCase    : int      =[*signature.parameters.keys()]

             UpperCAmelCase    : str      =["""pixel_values"""]
             self.assertListEqual(arg_names[:1]		,   _lowerCamelCase						)
 def 	UpperCAmelCase__       (  self						)	->							List[str]:
       '''simple docstring'''





       UpperCAmelCase    : Any      =self.model_tester.prepare_config_and_inputs_for_common()
       UpperCAmelCase    : int      =True

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

             UpperCAmelCase    : Tuple      =sum(self.model_tester.depths						)
             self.assertEqual(len(_lowerCamelCase						)		,   _lowerCamelCase						)

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

             self.assertEqual(len(_lowerCamelCase						)		,   _lowerCamelCase						)

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

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

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

             UpperCAmelCase    : Tuple      =outputs.attentions

             self.assertEqual(len(_lowerCamelCase						)		,   _lowerCamelCase						)
             # verify the first attentions (first block, first layer)
             UpperCAmelCase    : Any      =(self.model_tester.image_size // 4) ** 2
             UpperCAmelCase    : Optional[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 	UpperCAmelCase__       (  self						)	->							Dict:
       '''simple docstring'''





       def check_hidden_states_output(snake_case__		,   snake_case__		,   snake_case__						):
             UpperCAmelCase    : Optional[Any]      =model_class(_lowerCamelCase						)
             model.to(_lowerCamelCase						)
             model.eval()

             with torch.no_grad():
                   UpperCAmelCase    : Dict      =model(**self._prepare_for_class(_lowerCamelCase		,   _lowerCamelCase						)						)

             UpperCAmelCase    : Optional[Any]      =outputs.hidden_states

             UpperCAmelCase    : Optional[Any]      =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,
                 ]		,   )

       UpperCAmelCase    : int      =self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             UpperCAmelCase    : Optional[int]      =True
             check_hidden_states_output(_lowerCamelCase		,   _lowerCamelCase		,   _lowerCamelCase						)

             # check that output_hidden_states also work using config
             del inputs_dict["output_hidden_states"]
             UpperCAmelCase    : List[Any]      =True

             check_hidden_states_output(_lowerCamelCase		,   _lowerCamelCase		,   _lowerCamelCase						)
 def 	UpperCAmelCase__       (  self						)	->							Optional[int]:
       '''simple docstring'''





       if not self.model_tester.is_training:
             return

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






 @slow
 def 	UpperCAmelCase__       (  self						)	->							Dict:
       '''simple docstring'''





       for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             UpperCAmelCase    : Dict      =GLPNModel.from_pretrained(_lowerCamelCase						)
             self.assertIsNotNone(_lowerCamelCase						)





def   lowerCAmelCase_ (				)->  Optional[int]:






      '''simple docstring'''





      UpperCAmelCase    : Optional[Any]      =Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
      return image







@require_torch
@require_vision
@slow
class 			__snake_case   (     unittest.TestCase   ):
 @slow
 def 	UpperCAmelCase__       (  self						)	->							Dict:
       '''simple docstring'''





       UpperCAmelCase    : Optional[Any]      =GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)
       UpperCAmelCase    : List[str]      =GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]						).to(_lowerCamelCase						)

       UpperCAmelCase    : Optional[Any]      =prepare_img()
       UpperCAmelCase    : Tuple      =image_processor(images=_lowerCamelCase		,   return_tensors='''pt'''						).to(_lowerCamelCase						)

       # forward pass
       with torch.no_grad():
             UpperCAmelCase    : int      =model(**_lowerCamelCase						)

       # verify the predicted depth
       UpperCAmelCase    : Tuple      =torch.Size([1, 480, 640]						)
       self.assertEqual(outputs.predicted_depth.shape		,   _lowerCamelCase						)

       UpperCAmelCase    : List[str]      =torch.tensor(
           [[3.4291, 2.7865, 2.5151], [3.2841, 2.7021, 2.3502], [3.1147, 2.4625, 2.2481]]						).to(_lowerCamelCase						)

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

import json
import os
import tempfile
import unittest

from transformers.modelcard import ModelCard







class 			__snake_case   (     unittest.TestCase   ):
 def 	UpperCAmelCase__       (  self						)	->							Optional[Any]:
       '''simple docstring'''





       UpperCAmelCase    : List[str]      ={
           '''model_details''': {
               '''Organization''': '''testing''',
               '''Model date''': '''today''',
               '''Model version''': '''v2.1, Developed by Test Corp in 2019.''',
               '''Architecture''': '''Convolutional Neural Network.''',
           },
           '''metrics''': '''BLEU and ROUGE-1''',
           '''evaluation_data''': {
               '''Datasets''': {'''BLEU''': '''My-great-dataset-v1''', '''ROUGE-1''': '''My-short-dataset-v2.1'''},
               '''Preprocessing''': '''See details on https://arxiv.org/pdf/1810.03993.pdf''',
           },
           '''training_data''': {
               '''Dataset''': '''English Wikipedia dump dated 2018-12-01''',
               '''Preprocessing''': (
                   '''Using SentencePiece vocabulary of size 52k tokens. See details on'''
                   ''' https://arxiv.org/pdf/1810.03993.pdf'''
               ),
           },
           '''quantitative_analyses''': {'''BLEU''': 55.1, '''ROUGE-1''': 76},
       }
 def 	UpperCAmelCase__       (  self						)	->							Dict:
       '''simple docstring'''





       UpperCAmelCase    : Optional[Any]      =ModelCard.from_dict(self.inputs_dict						)
       self.assertTrue(hasattr(snake_case__		,   '''model_details'''						)						)
       self.assertTrue(hasattr(snake_case__		,   '''intended_use'''						)						)
       self.assertTrue(hasattr(snake_case__		,   '''factors'''						)						)
       self.assertTrue(hasattr(snake_case__		,   '''metrics'''						)						)
       self.assertTrue(hasattr(snake_case__		,   '''evaluation_data'''						)						)
       self.assertTrue(hasattr(snake_case__		,   '''training_data'''						)						)
       self.assertTrue(hasattr(snake_case__		,   '''quantitative_analyses'''						)						)
       self.assertTrue(hasattr(snake_case__		,   '''ethical_considerations'''						)						)
       self.assertTrue(hasattr(snake_case__		,   '''caveats_and_recommendations'''						)						)
 def 	UpperCAmelCase__       (  self						)	->							Tuple:
       '''simple docstring'''





       UpperCAmelCase    : Optional[Any]      =ModelCard.from_dict(self.inputs_dict						)
       UpperCAmelCase    : int      =json.loads(modelcard.to_json_string()						)
       for key, value in self.inputs_dict.items():
             self.assertEqual(obj[key]		,   snake_case__						)
 def 	UpperCAmelCase__       (  self						)	->							Tuple:
       '''simple docstring'''





       UpperCAmelCase    : List[str]      =ModelCard.from_dict(self.inputs_dict						)

       with tempfile.TemporaryDirectory() as tmpdirname:
             UpperCAmelCase    : Optional[Any]      =os.path.join(snake_case__		,   '''modelcard.json'''						)
             model_card_first.to_json_file(snake_case__						)
             UpperCAmelCase    : Tuple      =ModelCard.from_json_file(snake_case__						)

       self.assertEqual(model_card_second.to_dict()		,   model_card_first.to_dict()						)






 def 	UpperCAmelCase__       (  self						)	->							str:
       '''simple docstring'''





       UpperCAmelCase    : Optional[int]      =ModelCard.from_dict(self.inputs_dict						)

       with tempfile.TemporaryDirectory() as tmpdirname:
             model_card_first.save_pretrained(snake_case__						)
             UpperCAmelCase    : Dict      =ModelCard.from_pretrained(snake_case__						)

       self.assertEqual(model_card_second.to_dict()		,   model_card_first.to_dict()						)
