




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 			__lowerCamelCase	:




    """simple docstring"""

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

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


    def        a				(	self					:					str       )				->	Any:
         lowerCAmelCase__          =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

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

         lowerCAmelCase__          =			self.get_config()

         return config, pixel_values, labels


    def        a				(	self					:					Optional[int]       )				->	Tuple:
         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        a				(	self					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					List[Any]       ,     SCREAMING_SNAKE_CASE__					:					Dict       ,     SCREAMING_SNAKE_CASE__					:					Any       )				->	List[Any]:
         lowerCAmelCase__          =			ViTMSNModel(config=SCREAMING_SNAKE_CASE__       )
         model.to(SCREAMING_SNAKE_CASE__       )
         model.eval()
         lowerCAmelCase__          =			model(SCREAMING_SNAKE_CASE__       )
         self.parent.assertEqual(result.last_hidden_state.shape       ,     (self.batch_size, self.seq_length, self.hidden_size)       )


    def        a				(	self					:					Optional[Any]       ,     SCREAMING_SNAKE_CASE__					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]       )				->	Optional[int]:
         lowerCAmelCase__          =			self.type_sequence_label_size
         lowerCAmelCase__          =			ViTMSNForImageClassification(SCREAMING_SNAKE_CASE__       )
         model.to(SCREAMING_SNAKE_CASE__       )
         model.eval()
         lowerCAmelCase__          =			model(SCREAMING_SNAKE_CASE__       ,     labels=SCREAMING_SNAKE_CASE__       )
         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
         lowerCAmelCase__          =			1
         lowerCAmelCase__          =			ViTMSNForImageClassification(SCREAMING_SNAKE_CASE__       )
         model.to(SCREAMING_SNAKE_CASE__       )
         model.eval()

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


    def        a				(	self					:					Optional[int]       )				->	List[str]:
         lowerCAmelCase__          =			self.prepare_config_and_inputs()
         lowerCAmelCase__          =			config_and_inputs
         lowerCAmelCase__          =			{'pixel_values': pixel_values}
         return config, inputs_dict






@require_torch
class 			__lowerCamelCase	(   snake_case_     ,    snake_case_     ,    unittest.TestCase   ):




    """simple docstring"""

    snake_case__ =				(ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
    snake_case__ =				(
        {"feature-extraction": ViTMSNModel, "image-classification": ViTMSNForImageClassification}
        if is_torch_available()
        else {}
    )

    snake_case__ =				False
    snake_case__ =				False
    snake_case__ =				False
    snake_case__ =				False


    def        a				(	self					:					Union[str, Any]       )				->	Optional[int]:
         lowerCAmelCase__          =			ViTMSNModelTester(self       )
         lowerCAmelCase__          =			ConfigTester(self       ,     config_class=SCREAMING_SNAKE_CASE__       ,     has_text_modality=SCREAMING_SNAKE_CASE__       ,     hidden_size=37       )


    def        a				(	self					:					List[Any]       )				->	int:
         self.config_tester.run_common_tests()


    @unittest.skip(reason="ViTMSN does not use inputs_embeds"       )
    def        a				(	self					:					Dict       )				->	Dict:
         pass


    def        a				(	self					:					str       )				->	Tuple:
         lowerCAmelCase__          =			self.model_tester.prepare_config_and_inputs_for_common()

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


    def        a				(	self					:					Dict       )				->	int:
         lowerCAmelCase__          =			self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              lowerCAmelCase__          =			model_class(SCREAMING_SNAKE_CASE__       )
              lowerCAmelCase__          =			inspect.signature(model.forward       )
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              lowerCAmelCase__          =			[*signature.parameters.keys()]

              lowerCAmelCase__          =			['pixel_values']
              self.assertListEqual(arg_names[:1]       ,     SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					List[str]       )				->	Dict:
         lowerCAmelCase__          =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					Tuple       )				->	int:
         lowerCAmelCase__          =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE__       )


    @slow
    def        a				(	self					:					Union[str, Any]       )				->	Any:





         for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              lowerCAmelCase__          =			ViTMSNModel.from_pretrained(SCREAMING_SNAKE_CASE__       )
              self.assertIsNotNone(SCREAMING_SNAKE_CASE__       )






def 							_A       (						):
     """simple docstring"""

     lowerCAmelCase__          =			Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"      )
     return image






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




    """simple docstring"""

    @cached_property
    def        a				(	self					:					Tuple       )				->	Dict:
         return ViTImageProcessor.from_pretrained("facebook/vit-msn-small"       ) if is_vision_available() else None


    @slow
    def        a				(	self					:					int       )				->	str:
         torch.manual_seed(2       )
         lowerCAmelCase__          =			ViTMSNForImageClassification.from_pretrained("facebook/vit-msn-small"       ).to(SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			self.default_image_processor
         lowerCAmelCase__          =			prepare_img()
         lowerCAmelCase__          =			image_processor(images=SCREAMING_SNAKE_CASE__       ,     return_tensors="pt"       ).to(SCREAMING_SNAKE_CASE__       )

         # forward pass
         with torch.no_grad():
              lowerCAmelCase__          =			model(**SCREAMING_SNAKE_CASE__       )

         # verify the logits
         lowerCAmelCase__          =			torch.Size((1, 1_000)       )
         self.assertEqual(outputs.logits.shape       ,     SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			torch.tensor([-0.0_803, -0.4_454, -0.2_375]       ).to(SCREAMING_SNAKE_CASE__       )

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






import fire

from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer



def 							_A       (						lowerCAmelCase_					: str       ,    lowerCAmelCase_					: str       ,    **lowerCAmelCase_					: str      ):
     """simple docstring"""

     lowerCAmelCase__          =			AutoConfig.from_pretrained(lowerCAmelCase_       ,    **lowerCAmelCase_      )
     lowerCAmelCase__          =			AutoModelForSeqaSeqLM.from_config(lowerCAmelCase_      )
     model.save_pretrained(lowerCAmelCase_      )
     AutoTokenizer.from_pretrained(lowerCAmelCase_      ).save_pretrained(lowerCAmelCase_      )
     return model


if __name__ == "__main__":
     fire.Fire(save_randomly_initialized_version)
