




import inspect
import math
import tempfile
import unittest

import numpy as np

from transformers import ViTMAEConfig
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 ViTMAEForPreTraining, ViTMAEModel
     from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import ViTImageProcessor





class 			__lowerCamelCase	:




    """simple docstring"""

    def __init__(	self					:					int       ,     SCREAMING_SNAKE_CASE__					:					List[str]       ,     SCREAMING_SNAKE_CASE__					:					Dict=13       ,     SCREAMING_SNAKE_CASE__					:					List[Any]=30       ,     SCREAMING_SNAKE_CASE__					:					List[Any]=2       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]=3       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]=True       ,     SCREAMING_SNAKE_CASE__					:					Tuple=True       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]=32       ,     SCREAMING_SNAKE_CASE__					:					Dict=5       ,     SCREAMING_SNAKE_CASE__					:					Dict=4       ,     SCREAMING_SNAKE_CASE__					:					Dict=37       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]="gelu"       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]=0.1       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]=0.1       ,     SCREAMING_SNAKE_CASE__					:					Dict=10       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]=0.02       ,     SCREAMING_SNAKE_CASE__					:					Any=3       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]=0.6       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]=None       ,     )				->	Optional[int]:
         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__          =			mask_ratio
         lowerCAmelCase__          =			scope

         # in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
         # (we add 1 for the [CLS] token)
         lowerCAmelCase__          =			(image_size // patch_size) ** 2
         lowerCAmelCase__          =			int(math.ceil((1 - mask_ratio) * (num_patches + 1)       )       )


    def        a				(	self					:					Union[str, Any]       )				->	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					:					int       )				->	List[Any]:
         return ViTMAEConfig(
             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       ,     is_decoder=SCREAMING_SNAKE_CASE__       ,     initializer_range=self.initializer_range       ,     mask_ratio=self.mask_ratio       ,     )


    def        a				(	self					:					Dict       ,     SCREAMING_SNAKE_CASE__					:					List[str]       ,     SCREAMING_SNAKE_CASE__					:					str       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]       )				->	List[str]:
         lowerCAmelCase__          =			ViTMAEModel(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					:					Any       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]       ,     SCREAMING_SNAKE_CASE__					:					Dict       )				->	Tuple:
         lowerCAmelCase__          =			ViTMAEForPreTraining(SCREAMING_SNAKE_CASE__       )
         model.to(SCREAMING_SNAKE_CASE__       )
         model.eval()
         lowerCAmelCase__          =			model(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			(self.image_size // self.patch_size) ** 2
         lowerCAmelCase__          =			self.patch_size**2 * self.num_channels
         self.parent.assertEqual(result.logits.shape       ,     (self.batch_size, num_patches, expected_num_channels)       )

         # test greyscale images
         lowerCAmelCase__          =			1
         lowerCAmelCase__          =			ViTMAEForPreTraining(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__       )
         lowerCAmelCase__          =			self.patch_size**2
         self.parent.assertEqual(result.logits.shape       ,     (self.batch_size, num_patches, expected_num_channels)       )


    def        a				(	self					:					Union[str, Any]       )				->	Tuple:
         lowerCAmelCase__          =			self.prepare_config_and_inputs()
         lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__          =			config_and_inputs
         lowerCAmelCase__          =			{"pixel_values": pixel_values}
         return config, inputs_dict






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




    """simple docstring"""

    snake_case__ =				(ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
    snake_case__ =				{"feature-extraction": ViTMAEModel} if is_torch_available() else {}

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


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


    def        a				(	self					:					Union[str, Any]       )				->	int:
         self.config_tester.run_common_tests()


    @unittest.skip(reason="ViTMAE does not use inputs_embeds"       )
    def        a				(	self					:					Dict       )				->	Union[str, Any]:
         pass


    def        a				(	self					:					Optional[Any]       )				->	List[Any]:
         lowerCAmelCase__     ,		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					:					int       )				->	Optional[int]:
         lowerCAmelCase__     ,		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					:					Dict       )				->	Tuple:
         lowerCAmelCase__          =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					Optional[int]       )				->	List[str]:
         lowerCAmelCase__          =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_pretraining(*SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					str       ,     SCREAMING_SNAKE_CASE__					:					Dict       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]       )				->	Optional[int]:
         # make masks reproducible
         np.random.seed(2       )

         lowerCAmelCase__          =			int((pt_model.config.image_size // pt_model.config.patch_size) ** 2       )
         lowerCAmelCase__          =			np.random.uniform(size=(self.model_tester.batch_size, num_patches)       )
         lowerCAmelCase__          =			torch.from_numpy(SCREAMING_SNAKE_CASE__       )

         # Add `noise` argument.
         # PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
         lowerCAmelCase__          =			pt_noise

         super().check_pt_tf_models(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					List[Any]       )				->	Optional[Any]:
         lowerCAmelCase__     ,		lowerCAmelCase__          =			self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              lowerCAmelCase__          =			model_class(SCREAMING_SNAKE_CASE__       )
              model.to(SCREAMING_SNAKE_CASE__       )
              model.eval()
              # make random mask reproducible
              torch.manual_seed(2       )
              with torch.no_grad():
                   lowerCAmelCase__          =			model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )       )

              lowerCAmelCase__          =			outputs[0].cpu().numpy()
              lowerCAmelCase__          =			0

              with tempfile.TemporaryDirectory() as tmpdirname:
                   model.save_pretrained(SCREAMING_SNAKE_CASE__       )
                   lowerCAmelCase__          =			model_class.from_pretrained(SCREAMING_SNAKE_CASE__       )
                   model.to(SCREAMING_SNAKE_CASE__       )
                   # make random mask reproducible
                   torch.manual_seed(2       )
                   with torch.no_grad():
                        lowerCAmelCase__          =			model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )       )

                   # Make sure we don't have nans
                   lowerCAmelCase__          =			after_outputs[0].cpu().numpy()
                   lowerCAmelCase__          =			0
                   lowerCAmelCase__          =			np.amax(np.abs(out_a - out_a       )       )
                   self.assertLessEqual(SCREAMING_SNAKE_CASE__       ,     1e-5       )


    @unittest.skip(
        reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results."       )
    def        a				(	self					:					Optional[Any]       )				->	Optional[int]:
         pass


    @unittest.skip(
        reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results."       )
    def        a				(	self					:					List[str]       )				->	List[str]:
         pass


    @unittest.skip(
        reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results."       )
    def        a				(	self					:					Optional[Any]       )				->	str:
         pass


    @unittest.skip(reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load"       )
    def        a				(	self					:					Tuple       )				->	List[Any]:
         pass


    @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."       )
    def        a				(	self					:					Dict       )				->	int:
         pass


    @slow
    def        a				(	self					:					int       )				->	List[Any]:
         for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              lowerCAmelCase__          =			ViTMAEModel.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       )				->	Tuple:
         return ViTImageProcessor.from_pretrained("facebook/vit-mae-base"       ) if is_vision_available() else None


    @slow
    def        a				(	self					:					Optional[Any]       )				->	Union[str, Any]:
         # make random mask reproducible across the PT and TF model
         np.random.seed(2       )

         lowerCAmelCase__          =			ViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base"       ).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__       )

         # prepare a noise vector that will be also used for testing the TF model
         # (this way we can ensure that the PT and TF models operate on the same inputs)
         lowerCAmelCase__          =			ViTMAEConfig()
         lowerCAmelCase__          =			int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2       )
         lowerCAmelCase__          =			np.random.uniform(size=(1, num_patches)       )

         # forward pass
         with torch.no_grad():
              lowerCAmelCase__          =			model(**SCREAMING_SNAKE_CASE__       ,     noise=torch.from_numpy(SCREAMING_SNAKE_CASE__       ).to(device=SCREAMING_SNAKE_CASE__       )       )

         # verify the logits
         lowerCAmelCase__          =			torch.Size((1, 196, 768)       )
         self.assertEqual(outputs.logits.shape       ,     SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			torch.tensor(
             [[-0.0_548, -1.7_023, -0.9_325], [0.3_721, -0.5_670, -0.2_233], [0.8_235, -1.3_878, -0.3_524]]       )

         self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]       ,     expected_slice.to(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)
