



"""simple docstring"""







import copy
import inspect

from transformers.testing_utils import require_torch, torch_device
from transformers.utils.backbone_utils import BackboneType





@require_torch
class   UpperCamelCase	:




	"""simple docstring"""
	SCREAMING_SNAKE_CASE_							:       Tuple						       =  ()
	SCREAMING_SNAKE_CASE_							:       Optional[Any]						       =  True






	def    lowerCamelCase__					(     self   ):
			_lowercase				: Any     =   self.config_class

			# test default config
			_lowercase				: List[str]     =   config_class()
			self.assertIsNotNone(UpperCAmelCase_   )
			_lowercase				: Optional[Any]     =   ["""stem"""] + [f"""stage{idx}""" for idx in range(1				,len(config.depths   ) + 1   )]
			self.assertEqual(config.stage_names				,UpperCAmelCase_   )
			self.assertTrue(set(config.out_features   ).issubset(set(config.stage_names   )   )   )

			# Test out_features and out_indices are correctly set
			# out_features and out_indices both None
			_lowercase				: Optional[Any]     =   config_class(out_features=UpperCAmelCase_				,out_indices=UpperCAmelCase_   )
			self.assertEqual(config.out_features				,[config.stage_names[-1]]   )
			self.assertEqual(config.out_indices				,[len(config.stage_names   ) - 1]   )

			# out_features and out_indices both set
			_lowercase				: Any     =   config_class(out_features=["""stem""", """stage1"""]				,out_indices=[0, 1]   )
			self.assertEqual(config.out_features				,["""stem""", """stage1"""]   )
			self.assertEqual(config.out_indices				,[0, 1]   )

			# Only out_features set
			_lowercase				: str     =   config_class(out_features=["""stage1""", """stage3"""]   )
			self.assertEqual(config.out_features				,["""stage1""", """stage3"""]   )
			self.assertEqual(config.out_indices				,[1, 3]   )

			# Only out_indices set
			_lowercase				: Any     =   config_class(out_indices=[0, 2]   )
			self.assertEqual(config.out_features				,[config.stage_names[0], config.stage_names[2]]   )
			self.assertEqual(config.out_indices				,[0, 2]   )

			# Error raised when out_indices do not correspond to out_features
			with self.assertRaises(UpperCAmelCase_   ):
					_lowercase				: str     =   config_class(out_features=["""stage1""", """stage2"""]				,out_indices=[0, 2]   )






	def    lowerCamelCase__					(     self   ):
			_lowercase     ,  _lowercase				: Tuple     =   self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
					_lowercase				: List[str]     =   model_class(UpperCAmelCase_   )
					_lowercase				: int     =   inspect.signature(model.forward   )
					# signature.parameters is an OrderedDict => so arg_names order is deterministic
					_lowercase				: Optional[Any]     =   [*signature.parameters.keys()]
					_lowercase				: Dict     =   ["""pixel_values"""]
					self.assertListEqual(arg_names[:1]				,UpperCAmelCase_   )






	def    lowerCamelCase__					(     self   ):
			_lowercase     ,  _lowercase				: Tuple     =   self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
					_lowercase				: Dict     =   model_class(UpperCAmelCase_   )
					self.assertEqual(len(model.channels   )				,len(config.out_features   )   )
					_lowercase				: Union[str, Any]     =   model.num_features
					_lowercase				: Optional[Any]     =   [config.stage_names.index(UpperCAmelCase_   ) for feat in config.out_features]
					_lowercase				: List[str]     =   [num_features[idx] for idx in out_indices]
					self.assertListEqual(model.channels				,UpperCAmelCase_   )

					_lowercase				: List[Any]     =   copy.deepcopy(UpperCAmelCase_   )
					_lowercase				: List[Any]     =   None
					_lowercase				: Optional[int]     =   model_class(UpperCAmelCase_   )
					self.assertEqual(len(model.channels   )				,1   )
					self.assertListEqual(model.channels				,[num_features[-1]]   )

					_lowercase				: int     =   copy.deepcopy(UpperCAmelCase_   )
					_lowercase				: Optional[int]     =   None
					_lowercase				: List[str]     =   model_class(UpperCAmelCase_   )
					self.assertEqual(len(model.channels   )				,1   )
					self.assertListEqual(model.channels				,[num_features[-1]]   )






	def    lowerCamelCase__					(     self   ):
			_lowercase     ,  _lowercase				: Optional[Any]     =   self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
					_lowercase				: Optional[int]     =   model_class(UpperCAmelCase_   )
					model.to(UpperCAmelCase_   )
					model.eval()
					_lowercase				: Optional[int]     =   model(**UpperCAmelCase_   )

					self.assertEqual(len(result.feature_maps   )				,len(config.out_features   )   )
					self.assertEqual(len(model.channels   )				,len(config.out_features   )   )
					self.assertEqual(len(result.feature_maps   )				,len(config.out_indices   )   )
					self.assertEqual(len(model.channels   )				,len(config.out_indices   )   )

					# Check output of last stage is taken if out_features=None, out_indices=None
					_lowercase				: str     =   copy.deepcopy(UpperCAmelCase_   )
					_lowercase				: Dict     =   None
					_lowercase				: Union[str, Any]     =   model_class(UpperCAmelCase_   )
					model.to(UpperCAmelCase_   )
					model.eval()
					_lowercase				: int     =   model(**UpperCAmelCase_   )

					self.assertEqual(len(result.feature_maps   )				,1   )
					self.assertEqual(len(model.channels   )				,1   )

					_lowercase				: List[str]     =   copy.deepcopy(UpperCAmelCase_   )
					_lowercase				: Any     =   None
					_lowercase				: int     =   model_class(UpperCAmelCase_   )
					model.to(UpperCAmelCase_   )
					model.eval()
					_lowercase				: Optional[int]     =   model(**UpperCAmelCase_   )

					self.assertEqual(len(result.feature_maps   )				,1   )
					self.assertEqual(len(model.channels   )				,1   )

					# Check backbone can be initialized with fresh weights
					_lowercase				: int     =   copy.deepcopy(UpperCAmelCase_   )
					_lowercase				: int     =   False
					_lowercase				: int     =   model_class(UpperCAmelCase_   )
					model.to(UpperCAmelCase_   )
					model.eval()
					_lowercase				: Optional[int]     =   model(**UpperCAmelCase_   )






	def    lowerCamelCase__					(     self   ):
			_lowercase     ,  _lowercase				: Optional[int]     =   self.model_tester.prepare_config_and_inputs_for_common()

			for backbone_class in self.all_model_classes:
					_lowercase				: List[Any]     =   backbone_class(UpperCAmelCase_   )

					self.assertTrue(hasattr(UpperCAmelCase_				,"""backbone_type"""   )   )
					self.assertTrue(hasattr(UpperCAmelCase_				,"""stage_names"""   )   )
					self.assertTrue(hasattr(UpperCAmelCase_				,"""num_features"""   )   )
					self.assertTrue(hasattr(UpperCAmelCase_				,"""out_indices"""   )   )
					self.assertTrue(hasattr(UpperCAmelCase_				,"""out_features"""   )   )
					self.assertTrue(hasattr(UpperCAmelCase_				,"""out_feature_channels"""   )   )
					self.assertTrue(hasattr(UpperCAmelCase_				,"""channels"""   )   )

					self.assertIsInstance(backbone.backbone_type				,UpperCAmelCase_   )
					# Verify num_features has been initialized in the backbone init
					self.assertIsNotNone(backbone.num_features   )
					self.assertTrue(len(backbone.channels   ) == len(backbone.out_indices   )   )
					self.assertTrue(len(backbone.stage_names   ) == len(backbone.num_features   )   )
					self.assertTrue(len(backbone.channels   ) <= len(backbone.num_features   )   )
					self.assertTrue(len(backbone.out_feature_channels   ) == len(backbone.stage_names   )   )






	def    lowerCamelCase__					(     self   ):
			_lowercase     ,  _lowercase				: int     =   self.model_tester.prepare_config_and_inputs_for_common()
			_lowercase				: int     =   inputs_dict["""pixel_values"""].shape[0]

			for backbone_class in self.all_model_classes:
					_lowercase				: List[str]     =   backbone_class(UpperCAmelCase_   )
					backbone.to(UpperCAmelCase_   )
					backbone.eval()

					_lowercase				: str     =   backbone(**UpperCAmelCase_   )

					# Test default outputs and verify feature maps
					self.assertIsInstance(outputs.feature_maps				,UpperCAmelCase_   )
					self.assertTrue(len(outputs.feature_maps   ) == len(backbone.channels   )   )
					for feature_map, n_channels in zip(outputs.feature_maps				,backbone.channels   ):
							self.assertTrue(feature_map.shape[:2]				,(batch_size, n_channels)   )
					self.assertIsNone(outputs.hidden_states   )
					self.assertIsNone(outputs.attentions   )

					# Test output_hidden_states=True
					_lowercase				: List[Any]     =   backbone(**UpperCAmelCase_				,output_hidden_states=UpperCAmelCase_   )
					self.assertIsNotNone(outputs.hidden_states   )
					self.assertTrue(len(outputs.hidden_states   )				,len(backbone.stage_names   )   )
					for hidden_state, n_channels in zip(outputs.hidden_states				,backbone.channels   ):
							self.assertTrue(hidden_state.shape[:2]				,(batch_size, n_channels)   )

					# Test output_attentions=True
					if self.has_attentions:
							_lowercase				: Any     =   backbone(**UpperCAmelCase_				,output_attentions=UpperCAmelCase_   )
							self.assertIsNotNone(outputs.attentions   )











import json
import os
import tempfile
import unittest

from transformers.modelcard import ModelCard
class 	a_(		unittest.TestCase	):



     """simple docstring"""



     def     __UpperCamelCase						(		self					:	Optional[int])							->	List[str]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		{
          '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': 7_6},
      }





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

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		ModelCard.from_dict(self.inputs_dict)
      self.assertTrue(hasattr(lowerCAmelCase__  ,  'model_details'))
      self.assertTrue(hasattr(lowerCAmelCase__  ,  'intended_use'))
      self.assertTrue(hasattr(lowerCAmelCase__  ,  'factors'))
      self.assertTrue(hasattr(lowerCAmelCase__  ,  'metrics'))
      self.assertTrue(hasattr(lowerCAmelCase__  ,  'evaluation_data'))
      self.assertTrue(hasattr(lowerCAmelCase__  ,  'training_data'))
      self.assertTrue(hasattr(lowerCAmelCase__  ,  'quantitative_analyses'))
      self.assertTrue(hasattr(lowerCAmelCase__  ,  'ethical_considerations'))
      self.assertTrue(hasattr(lowerCAmelCase__  ,  'caveats_and_recommendations'))





     def     __UpperCamelCase						(		self					:	Any)							->	Any:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		ModelCard.from_dict(self.inputs_dict)
      SCREAMING_SNAKE_CASE				    =		json.loads(modelcard.to_json_string())
      for key, value in self.inputs_dict.items():
       self.assertEqual(obj[key]  ,  lowerCAmelCase__)





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

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		ModelCard.from_dict(self.inputs_dict)

      with tempfile.TemporaryDirectory() as tmpdirname:
       SCREAMING_SNAKE_CASE				    =		os.path.join(lowerCAmelCase__  ,  'modelcard.json')
       model_card_first.to_json_file(lowerCAmelCase__)
       SCREAMING_SNAKE_CASE				    =		ModelCard.from_json_file(lowerCAmelCase__)

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





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

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		ModelCard.from_dict(self.inputs_dict)

      with tempfile.TemporaryDirectory() as tmpdirname:
       model_card_first.save_pretrained(lowerCAmelCase__)
       SCREAMING_SNAKE_CASE				    =		ModelCard.from_pretrained(lowerCAmelCase__)

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




