



"""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   a__		(						SCREAMING_SNAKE_CASE__     ):


   def        lowercase				(							self      :							Dict			)    ->       Optional[Any]:
         lowercase  :      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   a__		:


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


   def        lowercase				(							self      :							Dict			)    ->       List[str]:
         lowercase  :      Optional[Any]					=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

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

         lowercase  :      Dict					=   self.get_config()
         return config, pixel_values, labels


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






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




@require_torch
class   a__		(						SCREAMING_SNAKE_CASE__,  SCREAMING_SNAKE_CASE__,  unittest.TestCase     ):
   _lowerCamelCase												=				(GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
   _lowerCamelCase												=				(
       {'depth-estimation': GLPNForDepthEstimation, 'feature-extraction': GLPNModel} if is_torch_available() else {}
   )

   _lowerCamelCase												=				False
   _lowerCamelCase												=				False
   _lowerCamelCase												=				False


   def        lowercase				(							self      :							Optional[int]			)    ->       int:
         lowercase  :      Tuple					=   GLPNModelTester(self			)
         lowercase  :      Any					=   GLPNConfigTester(self,     config_class=lowerCAmelCase			)


   def        lowercase				(							self      :							List[str]			)    ->       Union[str, Any]:
         self.config_tester.run_common_tests()


   def        lowercase				(							self      :							Optional[int]			)    ->       Any:
         lowercase  :      Dict					=   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*lowerCAmelCase			)


   def        lowercase				(							self      :							Optional[Any]			)    ->       Union[str, Any]:
         lowercase  :      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        lowercase				(							self      :							Optional[int]			)    ->       Tuple:
         pass


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


   def        lowercase				(							self      :							List[str]			)    ->       Union[str, Any]:
         lowercase	,    lowercase  :      List[Any]					=   self.model_tester.prepare_config_and_inputs_for_common()

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

               lowercase  :      List[Any]					=   ['pixel_values']
               self.assertListEqual(arg_names[:1],     lowerCAmelCase			)


   def        lowercase				(							self      :							Any			)    ->       Optional[Any]:
         lowercase	,    lowercase  :      List[str]					=   self.model_tester.prepare_config_and_inputs_for_common()
         lowercase  :      int					=   True

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

               lowercase  :      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"]
               lowercase  :      Optional[Any]					=   True
               lowercase  :      Optional[Any]					=   model_class(lowerCAmelCase			)
               model.to(lowerCAmelCase			)
               model.eval()
               with torch.no_grad():
                     lowercase  :      Optional[Any]					=   model(**self._prepare_for_class(lowerCAmelCase,     lowerCAmelCase			)			)
               lowercase  :      int					=   outputs.attentions

               self.assertEqual(len(lowerCAmelCase			),     lowerCAmelCase			)

               # verify the first attentions (first block, first layer)
               lowercase  :      Tuple					=   (self.model_tester.image_size // 4) ** 2
               lowercase  :      Optional[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  :      Optional[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  :      List[Any]					=   len(lowerCAmelCase			)

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

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

               lowercase  :      List[Any]					=   outputs.attentions

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

               with torch.no_grad():
                     lowercase  :      Dict					=   model(**self._prepare_for_class(lowerCAmelCase,     lowerCAmelCase			)			)

               lowercase  :      Any					=   outputs.hidden_states

               lowercase  :      Union[str, 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,
                   ],     )

         lowercase	,    lowercase  :      List[Any]					=   self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
               lowercase  :      Optional[Any]					=   True
               check_hidden_states_output(lowerCAmelCase,     lowerCAmelCase,     lowerCAmelCase			)

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

               check_hidden_states_output(lowerCAmelCase,     lowerCAmelCase,     lowerCAmelCase			)


   def        lowercase				(							self      :							str			)    ->       Dict:
         if not self.model_tester.is_training:
               return

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






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




def   lowercase__     (			)     ->			Dict:






      '''simple docstring'''


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





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


   @slow
   def        lowercase				(							self      :							Optional[int]			)    ->       Optional[Any]:
         lowercase  :      Union[str, Any]					=   GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]			)
         lowercase  :      List[Any]					=   GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]			).to(lowerCAmelCase			)

         lowercase  :      List[Any]					=   prepare_img()
         lowercase  :      Union[str, Any]					=   image_processor(images=lowerCAmelCase,     return_tensors='pt'			).to(lowerCAmelCase			)

         # forward pass
         with torch.no_grad():
               lowercase  :      Dict					=   model(**lowerCAmelCase			)

         # verify the predicted depth
         lowercase  :      Any					=   torch.Size([1, 480, 640]			)
         self.assertEqual(outputs.predicted_depth.shape,     lowerCAmelCase			)

         lowercase  :      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			)			)





"""simple docstring"""




import re
import tempfile
import unittest
from pathlib import Path

from datasets.utils.metadata import DatasetMetadata


def   lowercase__     (			_UpperCAmelCase				)     ->			str:






      '''simple docstring'''


      lowercase  :      List[str]					=   min(re.search('^ +'	,      _UpperCAmelCase				).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(_UpperCAmelCase				)]				)


_UpperCamelCase:				Any    							=  '\\n---\nlanguage:\n- zh\n- en\ntask_ids:\n- sentiment-classification\n---\n# Begin of markdown\n\nSome cool dataset card\n'

_UpperCamelCase:				Dict    							=  '\\n---\n---\n# Begin of markdown\n\nSome cool dataset card\n'


_UpperCamelCase:				Dict    							=  '\\n# Begin of markdown\n\nSome cool dataset card\n'





class   a__		(						unittest.TestCase     ):


   def        lowercase				(							self      :							int			)    ->       Dict:
         with tempfile.TemporaryDirectory() as tmp_dir:
               lowercase  :      Tuple					=   Path(lowerCAmelCase			) / 'README.md'
               with open(lowerCAmelCase,     'w+'			) as readme_file:
                     readme_file.write(lowerCAmelCase			)
               lowercase  :      List[str]					=   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			)
               lowercase  :      str					=   DatasetMetadata.from_readme(lowerCAmelCase			)
               self.assertDictEqual(lowerCAmelCase,     {}			)

               with open(lowerCAmelCase,     'w+'			) as readme_file:
                     readme_file.write(lowerCAmelCase			)
               lowercase  :      Optional[Any]					=   DatasetMetadata.from_readme(lowerCAmelCase			)
               self.assertEqual(lowerCAmelCase,     {}			)






   def        lowercase				(							self      :							Optional[Any]			)    ->       List[Any]:
         lowercase  :      Union[str, Any]					=   _dedent(
             '\\n            annotations_creators:\n            - found\n            language_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - question-answering\n            task_ids:\n            - open-domain-qa\n            '			)
         assert DatasetMetadata.from_yaml_string(lowerCAmelCase			)

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

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