





'''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        __lowerCAmelCase  (					__magic_name__				):






       """simple docstring"""





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

            '''simple docstring'''




            _UpperCamelCase       					= 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        __lowerCAmelCase  :






       """simple docstring"""





       def __init__(							self     :	Optional[int] ,				lowerCAmelCase__     :	Any ,				lowerCAmelCase__     :	List[str]=13 ,				lowerCAmelCase__     :	Any=64 ,				lowerCAmelCase__     :	Union[str, Any]=3 ,				lowerCAmelCase__     :	Union[str, Any]=4 ,				lowerCAmelCase__     :	Dict=[2, 2, 2, 2] ,				lowerCAmelCase__     :	Optional[Any]=[8, 4, 2, 1] ,				lowerCAmelCase__     :	str=[16, 32, 64, 128] ,				lowerCAmelCase__     :	Optional[int]=[1, 4, 8, 16] ,				lowerCAmelCase__     :	Union[str, Any]=[1, 2, 4, 8] ,				lowerCAmelCase__     :	int=True ,				lowerCAmelCase__     :	List[str]=True ,				lowerCAmelCase__     :	List[str]="gelu" ,				lowerCAmelCase__     :	int=0.1 ,				lowerCAmelCase__     :	Union[str, Any]=0.1 ,				lowerCAmelCase__     :	str=0.02 ,				lowerCAmelCase__     :	Union[str, Any]=16 ,				lowerCAmelCase__     :	Optional[int]=3 ,				lowerCAmelCase__     :	Tuple=None ,				)					->  str:

            '''simple docstring'''




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





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

            '''simple docstring'''




            _UpperCamelCase       					= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

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

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





       def snake_case__		(							self     :	int  )					->  Union[str, 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 snake_case__		(							self     :	int ,				lowerCAmelCase__     :	Any ,				lowerCAmelCase__     :	str ,				lowerCAmelCase__     :	Dict  )					->  Union[str, Any]:

            '''simple docstring'''




            _UpperCamelCase       					= GLPNModel(config=lowerCAmelCase__  )
            model.to(lowerCAmelCase__  )
            model.eval()
            _UpperCamelCase       					= model(lowerCAmelCase__  )
            _UpperCamelCase       					= _UpperCamelCase       					= 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     :	int ,				lowerCAmelCase__     :	int ,				lowerCAmelCase__     :	Any ,				lowerCAmelCase__     :	Any  )					->  List[str]:

            '''simple docstring'''




            _UpperCamelCase       					= self.num_labels
            _UpperCamelCase       					= GLPNForDepthEstimation(lowerCAmelCase__  )
            model.to(lowerCAmelCase__  )
            model.eval()
            _UpperCamelCase       					= model(lowerCAmelCase__  )
            self.parent.assertEqual(result.predicted_depth.shape ,				(self.batch_size, self.image_size, self.image_size)  )
            _UpperCamelCase       					= model(lowerCAmelCase__ ,				labels=lowerCAmelCase__  )
            self.parent.assertEqual(result.predicted_depth.shape ,				(self.batch_size, self.image_size, self.image_size)  )







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

            '''simple docstring'''




            _UpperCamelCase       					= self.prepare_config_and_inputs()
            _UpperCamelCase					,       _UpperCamelCase					,       _UpperCamelCase       					= config_and_inputs
            _UpperCamelCase       					= {'''pixel_values''': pixel_values}
            return config, inputs_dict

@require_torch
class        __lowerCAmelCase  (					__magic_name__		,		__magic_name__		,		unittest.TestCase				):






       """simple docstring"""
       _snake_case			:  str		       =					(GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
       _snake_case			:  Any		       =					(
           {'depth-estimation': GLPNForDepthEstimation, 'feature-extraction': GLPNModel} if is_torch_available() else {}
       )

       _snake_case			:  Tuple		       =					False
       _snake_case			:  List[str]		       =					False
       _snake_case			:  List[Any]		       =					False





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

            '''simple docstring'''




            _UpperCamelCase       					= GLPNModelTester(self  )
            _UpperCamelCase       					= GLPNConfigTester(self ,				config_class=lowerCAmelCase__  )





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

            '''simple docstring'''




            self.config_tester.run_common_tests()





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

            '''simple docstring'''




            _UpperCamelCase       					= self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*lowerCAmelCase__  )





       def snake_case__		(							self     :	Optional[int]  )					->  int:

            '''simple docstring'''




            _UpperCamelCase       					= 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 snake_case__		(							self     :	Optional[int]  )					->  Dict:

            '''simple docstring'''




            pass





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

            '''simple docstring'''




            pass





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

            '''simple docstring'''




            _UpperCamelCase					,       _UpperCamelCase       					= self.model_tester.prepare_config_and_inputs_for_common()

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

                 _UpperCamelCase       					= ['''pixel_values''']
                 self.assertListEqual(arg_names[:1] ,				lowerCAmelCase__  )





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

            '''simple docstring'''




            _UpperCamelCase					,       _UpperCamelCase       					= self.model_tester.prepare_config_and_inputs_for_common()
            _UpperCamelCase       					= True

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

                 _UpperCamelCase       					= sum(self.model_tester.depths  )
                 self.assertEqual(len(lowerCAmelCase__  ) ,				lowerCAmelCase__  )

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

                 self.assertEqual(len(lowerCAmelCase__  ) ,				lowerCAmelCase__  )

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

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

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

                 _UpperCamelCase       					= outputs.attentions

                 self.assertEqual(len(lowerCAmelCase__  ) ,				lowerCAmelCase__  )
                 # verify the first attentions (first block, first layer)
                 _UpperCamelCase       					= (self.model_tester.image_size // 4) ** 2
                 _UpperCamelCase       					= (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     :	str  )					->  int:

            '''simple docstring'''




            def check_hidden_states_output(lowerCAmelCase__     :	Optional[int] ,				lowerCAmelCase__     :	List[Any] ,				lowerCAmelCase__     :	Optional[Any]  ):
                 _UpperCamelCase       					= model_class(lowerCAmelCase__  )
                 model.to(lowerCAmelCase__  )
                 model.eval()

                 with torch.no_grad():
                      _UpperCamelCase       					= model(**self._prepare_for_class(lowerCAmelCase__ ,				lowerCAmelCase__  )  )

                 _UpperCamelCase       					= outputs.hidden_states

                 _UpperCamelCase       					= 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					,       _UpperCamelCase       					= self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                 _UpperCamelCase       					= True
                 check_hidden_states_output(lowerCAmelCase__ ,				lowerCAmelCase__ ,				lowerCAmelCase__  )

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

                 check_hidden_states_output(lowerCAmelCase__ ,				lowerCAmelCase__ ,				lowerCAmelCase__  )





       def snake_case__		(							self     :	List[Any]  )					->  Optional[int]:

            '''simple docstring'''




            if not self.model_tester.is_training:
                 return

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







       @slow
       def snake_case__		(							self     :	List[str]  )					->  Union[str, Any]:

            '''simple docstring'''




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







def       a__       (       )	->	Any:





     """simple docstring"""

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




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






       """simple docstring"""





       @slow
       def snake_case__		(							self     :	Tuple  )					->  str:

            '''simple docstring'''




            _UpperCamelCase       					= GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]  )
            _UpperCamelCase       					= GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]  ).to(lowerCAmelCase__  )

            _UpperCamelCase       					= prepare_img()
            _UpperCamelCase       					= image_processor(images=lowerCAmelCase__ ,				return_tensors='''pt'''  ).to(lowerCAmelCase__  )

            # forward pass
            with torch.no_grad():
                 _UpperCamelCase       					= model(**lowerCAmelCase__  )

            # verify the predicted depth
            _UpperCamelCase       					= torch.Size([1, 480, 640]  )
            self.assertEqual(outputs.predicted_depth.shape ,				lowerCAmelCase__  )

            _UpperCamelCase       					= 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  )  )










'''simple docstring'''

import re
import tempfile
import unittest
from pathlib import Path

from datasets.utils.metadata import DatasetMetadata







def       a__       (       lowercase      :  str   )	->	str:





     """simple docstring"""

     _UpperCamelCase       					= min(re.search('''^ +''',      lowercase   ).end() if t.startswith(''' '''   ) else 0 for t in string.splitlines()   )
     return "\n".join([line[indent_level:] for line in string.splitlines() if indent_level < len(lowercase   )]   )


lowercase__		:    List[str] =       '\\n---\nlanguage:\n- zh\n- en\ntask_ids:\n- sentiment-classification\n---\n# Begin of markdown\n\nSome cool dataset card\n'

lowercase__		:    str =       '\\n---\n---\n# Begin of markdown\n\nSome cool dataset card\n'


lowercase__		:    List[Any] =       '\\n# Begin of markdown\n\nSome cool dataset card\n'




class        __lowerCAmelCase  (					unittest.TestCase				):






       """simple docstring"""





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

            '''simple docstring'''




            with tempfile.TemporaryDirectory() as tmp_dir:
                 _UpperCamelCase       					= Path(lowerCAmelCase__  ) / '''README.md'''
                 with open(lowerCAmelCase__ ,				'''w+'''  ) as readme_file:
                      readme_file.write(lowerCAmelCase__  )
                 _UpperCamelCase       					= DatasetMetadata.from_readme(lowerCAmelCase__  )
                 self.assertDictEqual(lowerCAmelCase__ ,				{'''language''': ['''zh''', '''en'''], '''task_ids''': ['''sentiment-classification''']}  )

                 with open(lowerCAmelCase__ ,				'''w+'''  ) as readme_file:
                      readme_file.write(lowerCAmelCase__  )
                 _UpperCamelCase       					= DatasetMetadata.from_readme(lowerCAmelCase__  )
                 self.assertDictEqual(lowerCAmelCase__ ,				{}  )

                 with open(lowerCAmelCase__ ,				'''w+'''  ) as readme_file:
                      readme_file.write(lowerCAmelCase__  )
                 _UpperCamelCase       					= DatasetMetadata.from_readme(lowerCAmelCase__  )
                 self.assertEqual(lowerCAmelCase__ ,				{}  )







       def snake_case__		(							self     :	Dict  )					->  Any:

            '''simple docstring'''




            _UpperCamelCase       					= _dedent(
                '''\
            annotations_creators:
            - found
            language_creators:
            - found
            language:
            - en
            license:
            - unknown
            multilinguality:
            - monolingual
            pretty_name: Test Dataset
            size_categories:
            - 10K<n<100K
            source_datasets:
            - extended|other-yahoo-webscope-l6
            task_categories:
            - question-answering
            task_ids:
            - open-domain-qa
            '''  )
            assert DatasetMetadata.from_yaml_string(lowerCAmelCase__  )

            _UpperCamelCase       					= _dedent(
                '''\
            annotations_creators:
            - found
            language:
            - en
            license:
            - unknown
            multilinguality:
            - monolingual
            pretty_name: Test Dataset
            size_categories:
            - 10K<n<100K
            source_datasets:
            - extended|other-yahoo-webscope-l6
            task_categories:
            - question-answering
            task_ids:
            - open-domain-qa
            task_ids:
            - open-domain-qa
            '''  )
            with self.assertRaises(lowerCAmelCase__  ):
                 DatasetMetadata.from_yaml_string(lowerCAmelCase__  )

            _UpperCamelCase       					= _dedent(
                '''\
            annotations_creators:
            - found
            language_creators:
            - found
            language:
            - en
            license:
            - unknown
            multilinguality:
            - monolingual
            pretty_name: Test Dataset
            size_categories:
            - 10K<n<100K
            source_datasets:
            - extended|other-yahoo-webscope-l6
            task_categories:
            - text-classification
            task_ids:
            - multi-class-classification
            paperswithcode_id:
            - squad
            configs:
            - en
            train-eval-index:
            - config: en
              task: text-classification
              task_id: multi_class_classification
              splits:
                train_split: train
                eval_split: test
              col_mapping:
                text: text
                label: target
              metrics:
                - type: accuracy
                  name: Accuracy
            extra_gated_prompt: |
              By clicking on “Access repository” below, you also agree to ImageNet Terms of Access:
              [RESEARCHER_FULLNAME] (the "Researcher") has requested permission to use the ImageNet database (the "Database") at Princeton University and Stanford University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions:
              1. Researcher shall use the Database only for non-commercial research and educational purposes.
            extra_gated_fields:
              Company: text
              Country: text
              I agree to use this model for non-commerical use ONLY: checkbox
            '''  )
            assert DatasetMetadata.from_yaml_string(lowerCAmelCase__  )



