





import copy
import inspect

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







@require_torch
class 						_lowerCAmelCase:

  """simple docstring"""







  lowerCamelCase__      					=   ()
  lowerCamelCase__      					=   True

  def 		SCREAMING_SNAKE_CASE__	( self     )-> Optional[int]:
     __A				 =       self.config_class

     # test default config
     __A				 =       config_class()
     self.assertIsNotNone(UpperCAmelCase     )
     __A				 =       ['''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
     __A				 =       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
     __A				 =       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
     __A				 =       config_class(out_features=['''stage1''', '''stage3''']     )
     self.assertEqual(config.out_features  ,    ['''stage1''', '''stage3''']     )
     self.assertEqual(config.out_indices  ,    [1, 3]     )

     # Only out_indices set
     __A				 =       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     ):
        __A				 =       config_class(out_features=['''stage1''', '''stage2''']  ,    out_indices=[0, 2]     )

  def 		SCREAMING_SNAKE_CASE__	( self     )-> Any:
     __A ,						__A				 =       self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        __A				 =       model_class(UpperCAmelCase     )
        __A				 =       inspect.signature(model.forward     )
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        __A				 =       [*signature.parameters.keys()]
        __A				 =       ['''pixel_values''']
        self.assertListEqual(arg_names[:1]  ,    UpperCAmelCase     )

  def 		SCREAMING_SNAKE_CASE__	( self     )-> Any:
     __A ,						__A				 =       self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        __A				 =       model_class(UpperCAmelCase     )
        self.assertEqual(len(model.channels     )  ,    len(config.out_features     )     )
        __A				 =       model.num_features
        __A				 =       [config.stage_names.index(UpperCAmelCase     ) for feat in config.out_features]
        __A				 =       [num_features[idx] for idx in out_indices]
        self.assertListEqual(model.channels  ,    UpperCAmelCase     )

        __A				 =       copy.deepcopy(UpperCAmelCase     )
        __A				 =       None
        __A				 =       model_class(UpperCAmelCase     )
        self.assertEqual(len(model.channels     )  ,    1     )
        self.assertListEqual(model.channels  ,    [num_features[-1]]     )

        __A				 =       copy.deepcopy(UpperCAmelCase     )
        __A				 =       None
        __A				 =       model_class(UpperCAmelCase     )
        self.assertEqual(len(model.channels     )  ,    1     )
        self.assertListEqual(model.channels  ,    [num_features[-1]]     )

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

     for model_class in self.all_model_classes:
        __A				 =       model_class(UpperCAmelCase     )
        model.to(UpperCAmelCase     )
        model.eval()
        __A				 =       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
        __A				 =       copy.deepcopy(UpperCAmelCase     )
        __A				 =       None
        __A				 =       model_class(UpperCAmelCase     )
        model.to(UpperCAmelCase     )
        model.eval()
        __A				 =       model(**UpperCAmelCase     )

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

        __A				 =       copy.deepcopy(UpperCAmelCase     )
        __A				 =       None
        __A				 =       model_class(UpperCAmelCase     )
        model.to(UpperCAmelCase     )
        model.eval()
        __A				 =       model(**UpperCAmelCase     )

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

        # Check backbone can be initialized with fresh weights
        __A				 =       copy.deepcopy(UpperCAmelCase     )
        __A				 =       False
        __A				 =       model_class(UpperCAmelCase     )
        model.to(UpperCAmelCase     )
        model.eval()
        __A				 =       model(**UpperCAmelCase     )

  def 		SCREAMING_SNAKE_CASE__	( self     )-> Tuple:
     __A ,						__A				 =       self.model_tester.prepare_config_and_inputs_for_common()

     for backbone_class in self.all_model_classes:
        __A				 =       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 		SCREAMING_SNAKE_CASE__	( self     )-> List[Any]:
     __A ,						__A				 =       self.model_tester.prepare_config_and_inputs_for_common()
     __A				 =       inputs_dict['''pixel_values'''].shape[0]

     for backbone_class in self.all_model_classes:
        __A				 =       backbone_class(UpperCAmelCase     )
        backbone.to(UpperCAmelCase     )
        backbone.eval()

        __A				 =       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
        __A				 =       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:
           __A				 =       backbone(**UpperCAmelCase  ,    output_attentions=UpperCAmelCase     )
           self.assertIsNotNone(outputs.attentions     )








import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


_UpperCamelCase   :							str     =	"""
Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.

In March 2021, Hugging Face raised $40 million in a Series B funding round.[3]

On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]
"""







class 						_lowerCAmelCase(						unittest.TestCase		,   _a):

  """simple docstring"""







  def 		SCREAMING_SNAKE_CASE__	( self     )-> int:
     __A				 =       load_tool('''summarization'''     )
     self.tool.setup()
     __A				 =       load_tool('''summarization'''  ,    remote=UpperCAmelCase     )

  def 		SCREAMING_SNAKE_CASE__	( self     )-> List[str]:
     __A				 =       self.tool(UpperCAmelCase     )
     self.assertEqual(
         UpperCAmelCase  ,    '''Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'''  ,    )

  def 		SCREAMING_SNAKE_CASE__	( self     )-> Dict:
     __A				 =       self.remote_tool(UpperCAmelCase     )
     self.assertEqual(
         UpperCAmelCase  ,    '''Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'''  ,    )

  def 		SCREAMING_SNAKE_CASE__	( self     )-> Union[str, Any]:
     __A				 =       self.tool(text=UpperCAmelCase     )
     self.assertEqual(
         UpperCAmelCase  ,    '''Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'''  ,    )





  def 		SCREAMING_SNAKE_CASE__	( self     )-> Optional[Any]:
     __A				 =       self.remote_tool(text=UpperCAmelCase     )
     self.assertEqual(
         UpperCAmelCase  ,    '''Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'''  ,    )

