


'''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 	a       :
 def __init__(	self ,			__magic_name__ ,			__magic_name__=13 ,			__magic_name__=30 ,			__magic_name__=2 ,			__magic_name__=3 ,			__magic_name__=True ,			__magic_name__=True ,			__magic_name__=32 ,			__magic_name__=5 ,			__magic_name__=4 ,			__magic_name__=37 ,			__magic_name__="gelu" ,			__magic_name__=0.1 ,			__magic_name__=0.1 ,			__magic_name__=10 ,			__magic_name__=0.0_2 ,			__magic_name__=None ,			)   ->							Dict:
      _a      =							parent
      _a      =							batch_size
      _a      =							image_size
      _a      =							patch_size
      _a      =							num_channels
      _a      =							is_training
      _a      =							use_labels
      _a      =							hidden_size
      _a      =							num_hidden_layers
      _a      =							num_attention_heads
      _a      =							intermediate_size
      _a      =							hidden_act
      _a      =							hidden_dropout_prob
      _a      =							attention_probs_dropout_prob
      _a      =							type_sequence_label_size
      _a      =							initializer_range
      _a      =							scope

      # in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
      _a      =							(image_size // patch_size) ** 2
      _a      =							num_patches + 1
 def 							__UpperCAmelCase						(	self  )   ->							List[str]:
      _a      =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

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

      _a      =							self.get_config()

      return config, pixel_values, labels
 def 							__UpperCAmelCase						(	self  )   ->							Optional[Any]:
      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 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__  )   ->							List[Any]:
      _a      =							ViTMSNModel(config=__magic_name__  )
      model.to(__magic_name__  )
      model.eval()
      _a      =							model(__magic_name__  )
      self.parent.assertEqual(result.last_hidden_state.shape ,			(self.batch_size, self.seq_length, self.hidden_size)  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__  )   ->							Union[str, Any]:
      _a      =							self.type_sequence_label_size
      _a      =							ViTMSNForImageClassification(__magic_name__  )
      model.to(__magic_name__  )
      model.eval()
      _a      =							model(__magic_name__ ,			labels=__magic_name__  )
      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      =							1
      _a      =							ViTMSNForImageClassification(__magic_name__  )
      model.to(__magic_name__  )
      model.eval()

      _a      =							floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
      _a      =							model(__magic_name__  )
      self.parent.assertEqual(result.logits.shape ,			(self.batch_size, self.type_sequence_label_size)  )




 def 							__UpperCAmelCase						(	self  )   ->							Union[str, Any]:
      _a      =							self.prepare_config_and_inputs()
      _a ,				_a ,				_a      =							config_and_inputs
      _a      =							{'pixel_values': pixel_values}
      return config, inputs_dict





@require_torch
class 	a       (       _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			unittest.TestCase    ):
 _lowerCAmelCase	   =		(ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
 _lowerCAmelCase	   =		(
     {"""feature-extraction""": ViTMSNModel, """image-classification""": ViTMSNForImageClassification}
     if is_torch_available()
     else {}
 )

 _lowerCAmelCase	   =		False
 _lowerCAmelCase	   =		False
 _lowerCAmelCase	   =		False
 _lowerCAmelCase	   =		False
 def 							__UpperCAmelCase						(	self  )   ->							int:
      _a      =							ViTMSNModelTester(self  )
      _a      =							ConfigTester(self ,			config_class=__magic_name__ ,			has_text_modality=__magic_name__ ,			hidden_size=37  )
 def 							__UpperCAmelCase						(	self  )   ->							Optional[int]:
      self.config_tester.run_common_tests()
 @unittest.skip(reason='ViTMSN does not use inputs_embeds'  )
 def 							__UpperCAmelCase						(	self  )   ->							Optional[int]:
      pass
 def 							__UpperCAmelCase						(	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(__magic_name__  )
           self.assertIsInstance(model.get_input_embeddings() ,			(nn.Module)  )
           _a      =							model.get_output_embeddings()
           self.assertTrue(x is None or isinstance(__magic_name__ ,			nn.Linear  )  )
 def 							__UpperCAmelCase						(	self  )   ->							List[str]:
      _a ,				_a      =							self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
           _a      =							model_class(__magic_name__  )
           _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] ,			__magic_name__  )
 def 							__UpperCAmelCase						(	self  )   ->							Any:
      _a      =							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__magic_name__  )
 def 							__UpperCAmelCase						(	self  )   ->							Any:
      _a      =							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*__magic_name__  )




 @slow
 def 							__UpperCAmelCase						(	self  )   ->							int:
      for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           _a      =							ViTMSNModel.from_pretrained(__magic_name__  )
           self.assertIsNotNone(__magic_name__  )






def 		_A			()				->					Dict:




     '''simple docstring'''



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






@require_torch
@require_vision
class 	a       (       unittest.TestCase    ):
 @cached_property
 def 							__UpperCAmelCase						(	self  )   ->							Any:
      return ViTImageProcessor.from_pretrained('facebook/vit-msn-small'  ) if is_vision_available() else None




 @slow
 def 							__UpperCAmelCase						(	self  )   ->							int:
      torch.manual_seed(2  )
      _a      =							ViTMSNForImageClassification.from_pretrained('facebook/vit-msn-small'  ).to(__magic_name__  )

      _a      =							self.default_image_processor
      _a      =							prepare_img()
      _a      =							image_processor(images=__magic_name__ ,			return_tensors='pt'  ).to(__magic_name__  )

      # forward pass
      with torch.no_grad():
           _a      =							model(**__magic_name__  )

      # verify the logits
      _a      =							torch.Size((1, 10_00)  )
      self.assertEqual(outputs.logits.shape ,			__magic_name__  )

      _a      =							torch.tensor([-0.0_8_0_3, -0.4_4_5_4, -0.2_3_7_5]  ).to(__magic_name__  )

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





'''simple docstring'''






import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a_   :      List[str]				    =      logging.get_logger(__name__)

a_   :      str				    =      {
    "microsoft/git-base": "https://huggingface.co/microsoft/git-base/resolve/main/config.json",
}




class 	a       (       _SCREAMING_SNAKE_CASE    ):
 _lowerCAmelCase	   =		"""git_vision_model"""
 def __init__(	self ,			__magic_name__=7_68 ,			__magic_name__=30_72 ,			__magic_name__=12 ,			__magic_name__=12 ,			__magic_name__=3 ,			__magic_name__=2_24 ,			__magic_name__=16 ,			__magic_name__="quick_gelu" ,			__magic_name__=1e-5 ,			__magic_name__=0.0 ,			__magic_name__=0.0_2 ,			**__magic_name__ ,			)   ->							Union[str, Any]:
      super().__init__(**__magic_name__  )

      _a      =							hidden_size
      _a      =							intermediate_size
      _a      =							num_hidden_layers
      _a      =							num_attention_heads
      _a      =							num_channels
      _a      =							patch_size
      _a      =							image_size
      _a      =							initializer_range
      _a      =							attention_dropout
      _a      =							layer_norm_eps
      _a      =							hidden_act




 @classmethod
 def 							__UpperCAmelCase						(	cls ,			__magic_name__ ,			**__magic_name__  )   ->							"PretrainedConfig":
      cls._set_token_in_kwargs(__magic_name__  )

      _a ,				_a      =							cls.get_config_dict(__magic_name__ ,			**__magic_name__  )

      # get the vision config dict if we are loading from GITConfig
      if config_dict.get('model_type'  ) == "git":
           _a      =							config_dict['vision_config']

      if "model_type" in config_dict and hasattr(cls ,			'model_type'  ) and config_dict["model_type"] != cls.model_type:
           logger.warning(
               f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
               f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'  )

      return cls.from_dict(__magic_name__ ,			**__magic_name__  )





class 	a       (       _SCREAMING_SNAKE_CASE    ):
 _lowerCAmelCase	   =		"""git"""
 def __init__(	self ,			__magic_name__=None ,			__magic_name__=3_05_22 ,			__magic_name__=7_68 ,			__magic_name__=6 ,			__magic_name__=12 ,			__magic_name__=30_72 ,			__magic_name__="gelu" ,			__magic_name__=0.1 ,			__magic_name__=0.1 ,			__magic_name__=10_24 ,			__magic_name__=0.0_2 ,			__magic_name__=1e-12 ,			__magic_name__=0 ,			__magic_name__="absolute" ,			__magic_name__=True ,			__magic_name__=False ,			__magic_name__=1_01 ,			__magic_name__=1_02 ,			__magic_name__=None ,			**__magic_name__ ,			)   ->							Optional[int]:
      super().__init__(bos_token_id=__magic_name__ ,			eos_token_id=__magic_name__ ,			pad_token_id=__magic_name__ ,			**__magic_name__  )

      if vision_config is None:
           _a      =							{}
           logger.info('vision_config is None. initializing the GitVisionConfig with default values.'  )

      _a      =							GitVisionConfig(**__magic_name__  )
      _a      =							vocab_size
      _a      =							hidden_size
      _a      =							num_hidden_layers
      _a      =							num_attention_heads
      _a      =							hidden_act
      _a      =							intermediate_size
      _a      =							hidden_dropout_prob
      _a      =							attention_probs_dropout_prob
      _a      =							max_position_embeddings
      _a      =							initializer_range
      _a      =							layer_norm_eps
      _a      =							position_embedding_type
      _a      =							use_cache
      _a      =							tie_word_embeddings
      _a      =							num_image_with_embedding

      _a      =							bos_token_id
      _a      =							eos_token_id




 def 							__UpperCAmelCase						(	self  )   ->							List[str]:
      _a      =							copy.deepcopy(self.__dict__  )
      _a      =							self.vision_config.to_dict()
      _a      =							self.__class__.model_type
      return output

