



'''simple docstring'''

import inspect
import unittest

from transformers import ViTMSNConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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 torch import nn

							from transformers import ViTMSNForImageClassification, ViTMSNModel
							from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

							from transformers import ViTImageProcessor

class 	__magic_name__						:







							def __init__(							self					:       Optional[int]	,_UpperCAmelCase					:       Optional[int]	,_UpperCAmelCase					:       Union[str, Any]=13	,_UpperCAmelCase					:       Optional[int]=30	,_UpperCAmelCase					:       Union[str, Any]=2	,_UpperCAmelCase					:       List[str]=3	,_UpperCAmelCase					:       Any=True	,_UpperCAmelCase					:       Tuple=True	,_UpperCAmelCase					:       Tuple=32	,_UpperCAmelCase					:       Union[str, Any]=5	,_UpperCAmelCase					:       str=4	,_UpperCAmelCase					:       int=37	,_UpperCAmelCase					:       int="gelu"	,_UpperCAmelCase					:       List[str]=0.1	,_UpperCAmelCase					:       Optional[Any]=0.1	,_UpperCAmelCase					:       List[Any]=10	,_UpperCAmelCase					:       str=0.02	,_UpperCAmelCase					:       List[str]=None	,):
									_a     :      int        =							parent
									_a     :      str        =							batch_size
									_a     :      Tuple        =							image_size
									_a     :      str        =							patch_size
									_a     :      List[str]        =							num_channels
									_a     :      Union[str, Any]        =							is_training
									_a     :      Union[str, Any]        =							use_labels
									_a     :      List[Any]        =							hidden_size
									_a     :      Tuple        =							num_hidden_layers
									_a     :      List[str]        =							num_attention_heads
									_a     :      List[Any]        =							intermediate_size
									_a     :      Union[str, Any]        =							hidden_act
									_a     :      Optional[Any]        =							hidden_dropout_prob
									_a     :      Dict        =							attention_probs_dropout_prob
									_a     :      Optional[Any]        =							type_sequence_label_size
									_a     :      List[str]        =							initializer_range
									_a     :      List[Any]        =							scope

									# in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
									_a     :      int        =							(image_size // patch_size) ** 2
									_a     :      List[str]        =							num_patches + 1







							def 							__lowercase			(							self					:       str		):
									_a     :      Tuple        =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

									_a     :      Any        =							None
									if self.use_labels:
											_a     :      str        =							ids_tensor([self.batch_size]	,self.type_sequence_label_size		)

									_a     :      List[str]        =							self.get_config()

									return config, pixel_values, labels







							def 							__lowercase			(							self					:       List[str]		):
									return ViTMSNConfig(
									    image_size=self.image_size	,patch_size=self.patch_size	,num_channels=self.num_channels	,hidden_size=self.hidden_size	,num_hidden_layers=self.num_hidden_layers	,num_attention_heads=self.num_attention_heads	,intermediate_size=self.intermediate_size	,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	,)







							def 							__lowercase			(							self					:       Tuple	,_UpperCAmelCase					:       Any	,_UpperCAmelCase					:       Optional[Any]	,_UpperCAmelCase					:       Any		):
									_a     :      Optional[int]        =							ViTMSNModel(config=_UpperCAmelCase		)
									model.to(_UpperCAmelCase		)
									model.eval()
									_a     :      Tuple        =							model(_UpperCAmelCase		)
									self.parent.assertEqual(result.last_hidden_state.shape	,(self.batch_size, self.seq_length, self.hidden_size)		)







							def 							__lowercase			(							self					:       Optional[Any]	,_UpperCAmelCase					:       Dict	,_UpperCAmelCase					:       Union[str, Any]	,_UpperCAmelCase					:       Any		):
									_a     :      Optional[int]        =							self.type_sequence_label_size
									_a     :      Any        =							ViTMSNForImageClassification(_UpperCAmelCase		)
									model.to(_UpperCAmelCase		)
									model.eval()
									_a     :      Optional[Any]        =							model(_UpperCAmelCase	,labels=_UpperCAmelCase		)
									print('Pixel and labels shape: {pixel_values.shape}, {labels.shape}'		)
									print('Labels: {labels}'		)
									self.parent.assertEqual(result.logits.shape	,(self.batch_size, self.type_sequence_label_size)		)

									# test greyscale images
									_a     :      Tuple        =							1
									_a     :      Optional[Any]        =							ViTMSNForImageClassification(_UpperCAmelCase		)
									model.to(_UpperCAmelCase		)
									model.eval()

									_a     :      Optional[int]        =							floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
									_a     :      Union[str, Any]        =							model(_UpperCAmelCase		)
									self.parent.assertEqual(result.logits.shape	,(self.batch_size, self.type_sequence_label_size)		)







							def 							__lowercase			(							self					:       Dict		):
									_a     :      int        =							self.prepare_config_and_inputs()
									_a       ,					_a       ,					_a     :      List[str]        =							config_and_inputs
									_a     :      Dict        =							{'pixel_values': pixel_values}
									return config, inputs_dict

@require_torch
class 	__magic_name__						(    _UpperCamelCase			,						_UpperCamelCase			,						unittest.TestCase   ):
							lowerCAmelCase       :    Any   				=					(ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
							lowerCAmelCase       :    Optional[int]   				=					(
							    {'feature-extraction': ViTMSNModel, 'image-classification': ViTMSNForImageClassification}
							    if is_torch_available()
							    else {}
							)

							lowerCAmelCase       :    List[Any]   				=					False
							lowerCAmelCase       :    Optional[int]   				=					False
							lowerCAmelCase       :    Optional[int]   				=					False
							lowerCAmelCase       :    Optional[int]   				=					False







							def 							__lowercase			(							self					:       Tuple		):
									_a     :      List[Any]        =							ViTMSNModelTester(self		)
									_a     :      Dict        =							ConfigTester(self	,config_class=_UpperCAmelCase	,has_text_modality=_UpperCAmelCase	,hidden_size=37		)







							def 							__lowercase			(							self					:       Optional[Any]		):
									self.config_tester.run_common_tests()







							@unittest.skip(reason='ViTMSN does not use inputs_embeds'		)
							def 							__lowercase			(							self					:       str		):
									pass







							def 							__lowercase			(							self					:       List[str]		):
									_a       ,					_a     :      Any        =							self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											_a     :      int        =							model_class(_UpperCAmelCase		)
											self.assertIsInstance(model.get_input_embeddings()	,(nn.Module)		)
											_a     :      int        =							model.get_output_embeddings()
											self.assertTrue(x is None or isinstance(_UpperCAmelCase	,nn.Linear		)		)







							def 							__lowercase			(							self					:       Optional[int]		):
									_a       ,					_a     :      Optional[int]        =							self.model_tester.prepare_config_and_inputs_for_common()

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

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







							def 							__lowercase			(							self					:       int		):
									_a     :      List[str]        =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_UpperCAmelCase		)







							def 							__lowercase			(							self					:       Dict		):
									_a     :      Dict        =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase		)







							@slow
							def 							__lowercase			(							self					:       List[Any]		):
									for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											_a     :      List[str]        =							ViTMSNModel.from_pretrained(_UpperCAmelCase		)
											self.assertIsNotNone(_UpperCAmelCase		)



def      __lowerCamelCase     (      )			->      Tuple:
		_a     :      Tuple        =							Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
		return image

@require_torch
@require_vision
class 	__magic_name__						(    unittest.TestCase   ):







							@cached_property
							def 							__lowercase			(							self					:       Optional[Any]		):
									return ViTImageProcessor.from_pretrained('facebook/vit-msn-small'		) if is_vision_available() else None







							@slow
							def 							__lowercase			(							self					:       Tuple		):
									torch.manual_seed(2		)
									_a     :      List[str]        =							ViTMSNForImageClassification.from_pretrained('facebook/vit-msn-small'		).to(_UpperCAmelCase		)

									_a     :      List[str]        =							self.default_image_processor
									_a     :      Dict        =							prepare_img()
									_a     :      Union[str, Any]        =							image_processor(images=_UpperCAmelCase	,return_tensors='pt'		).to(_UpperCAmelCase		)

									# forward pass
									with torch.no_grad():
											_a     :      List[str]        =							model(**_UpperCAmelCase		)

									# verify the logits
									_a     :      Optional[int]        =							torch.Size((1, 1000)		)
									self.assertEqual(outputs.logits.shape	,_UpperCAmelCase		)

									_a     :      Any        =							torch.tensor([-0.08_03, -0.44_54, -0.23_75]		).to(_UpperCAmelCase		)

									self.assertTrue(torch.allclose(outputs.logits[0, :3]	,_UpperCAmelCase	,atol=1E-4		)		)








"""simple docstring"""





import inspect
import re

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
snake_case__						:   Optional[Any]			 = '''src/transformers'''


# This is to make sure the transformers module imported is the one in the repo.
snake_case__						:   Dict			 = direct_transformers_import(PATH_TO_TRANSFORMERS)

snake_case__						:   Optional[int]			 = transformers.models.auto.configuration_auto.CONFIG_MAPPING

# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
snake_case__						:   Optional[int]			 = re.compile(R'''\[(.+?)\]\((https://huggingface\.co/.+?)\)''')


snake_case__						:   int			 = {
    '''DecisionTransformerConfig''',
    '''EncoderDecoderConfig''',
    '''MusicgenConfig''',
    '''RagConfig''',
    '''SpeechEncoderDecoderConfig''',
    '''TimmBackboneConfig''',
    '''VisionEncoderDecoderConfig''',
    '''VisionTextDualEncoderConfig''',
    '''LlamaConfig''',
}


def    _snake_case  (  _snake_case :	List[str]	):
      lowerCAmelCase    :		Dict				=      None

      # source code of `config_class`
      lowerCAmelCase    :		Union[str, Any]				=      inspect.getsource(_snake_case	)
      lowerCAmelCase    :		List[Any]				=      _re_checkpoint.findall(_snake_case	)

      # Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
      # For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
      for ckpt_name, ckpt_link in checkpoints:
            # allow the link to end with `/`
            if ckpt_link.endswith('''/'''	):
                  lowerCAmelCase    :		List[str]				=      ckpt_link[:-1]

            # verify the checkpoint name corresponds to the checkpoint link
            lowerCAmelCase    :		Optional[int]				=      f'''https://huggingface.co/{ckpt_name}'''
            if ckpt_link == ckpt_link_from_name:
                  lowerCAmelCase    :		List[str]				=      ckpt_name
                  break

      return checkpoint


def    _snake_case  (  ):
      lowerCAmelCase    :		List[Any]				=      []

      for config_class in list(CONFIG_MAPPING.values()	):
            # Skip deprecated models
            if "models.deprecated" in config_class.__module__:
                  continue
            lowerCAmelCase    :		int				=      get_checkpoint_from_config_class(_snake_case	)

            lowerCAmelCase    :		int				=      config_class.__name__
            if checkpoint is None and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
                  configs_without_checkpoint.append(_snake_case	)

      if len(_snake_case	) > 0:
            lowerCAmelCase    :		Dict				=      '''\n'''.join(sorted(_snake_case	)	)
            raise ValueError(f'''The following configurations don\'t contain any valid checkpoint:\n{message}'''	)


if __name__ == "__main__":
 check_config_docstrings_have_checkpoints()


