





'''simple docstring'''




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							:			List[Any]   ,       __snake_case							:			List[Any]   ,       __snake_case							:			Union[str, Any]=13   ,       __snake_case							:			str=30   ,       __snake_case							:			List[str]=2   ,       __snake_case							:			Optional[int]=3   ,       __snake_case							:			Tuple=True   ,       __snake_case							:			List[str]=True   ,       __snake_case							:			Tuple=32   ,       __snake_case							:			int=5   ,       __snake_case							:			int=4   ,       __snake_case							:			Tuple=37   ,       __snake_case							:			str="gelu"   ,       __snake_case							:			str=0.1   ,       __snake_case							:			Tuple=0.1   ,       __snake_case							:			List[str]=10   ,       __snake_case							:			Union[str, Any]=0.02   ,       __snake_case							:			Any=3   ,       __snake_case							:			str=0.6   ,       __snake_case							:			Union[str, Any]=None   ,       )->	str:
        snake_case 						=   parent
        snake_case 						=   batch_size
        snake_case 						=   image_size
        snake_case 						=   patch_size
        snake_case 						=   num_channels
        snake_case 						=   is_training
        snake_case 						=   use_labels
        snake_case 						=   hidden_size
        snake_case 						=   num_hidden_layers
        snake_case 						=   num_attention_heads
        snake_case 						=   intermediate_size
        snake_case 						=   hidden_act
        snake_case 						=   hidden_dropout_prob
        snake_case 						=   attention_probs_dropout_prob
        snake_case 						=   type_sequence_label_size
        snake_case 						=   initializer_range
        snake_case 						=   mask_ratio
        snake_case 						=   scope

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



     def      lowerCAmelCase			(   self							:			str						)->	Union[str, Any]:
        snake_case 						=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

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

        snake_case 						=   self.get_config()

        return config, pixel_values, labels



     def      lowerCAmelCase			(   self							:			List[Any]						)->	Optional[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=__snake_case   ,       initializer_range=self.initializer_range   ,       mask_ratio=self.mask_ratio   ,       )



     def      lowerCAmelCase			(   self							:			List[Any]   ,       __snake_case							:			Optional[Any]   ,       __snake_case							:			Dict   ,       __snake_case							:			Dict						)->	Union[str, Any]:
        snake_case 						=   ViTMAEModel(config=__snake_case						)
        model.to(__snake_case						)
        model.eval()
        snake_case 						=   model(__snake_case						)
        self.parent.assertEqual(result.last_hidden_state.shape   ,       (self.batch_size, self.seq_length, self.hidden_size)						)



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

        # test greyscale images
        snake_case 						=   1
        snake_case 						=   ViTMAEForPreTraining(__snake_case						)
        model.to(__snake_case						)
        model.eval()
        snake_case 						=   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
        snake_case 						=   model(__snake_case						)
        snake_case 						=   self.patch_size**2
        self.parent.assertEqual(result.logits.shape   ,       (self.batch_size, num_patches, expected_num_channels)						)



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




@require_torch
class 			_lowerCAmelCase		(			A__							,     A__							,     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      lowerCAmelCase			(   self							:			List[str]						)->	str:
        snake_case 						=   ViTMAEModelTester(self						)
        snake_case 						=   ConfigTester(self   ,       config_class=__snake_case   ,       has_text_modality=__snake_case   ,       hidden_size=37						)



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



     @unittest.skip(reason="""ViTMAE does not use inputs_embeds"""						)
     def      lowerCAmelCase			(   self							:			Tuple						)->	int:
        pass



     def      lowerCAmelCase			(   self							:			Union[str, Any]						)->	List[str]:
        snake_case						,    snake_case 						=   self.model_tester.prepare_config_and_inputs_for_common()

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



     def      lowerCAmelCase			(   self							:			Optional[Any]						)->	Optional[Any]:
        snake_case						,    snake_case 						=   self.model_tester.prepare_config_and_inputs_for_common()

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

           snake_case 						=   ["""pixel_values"""]
           self.assertListEqual(arg_names[:1]   ,       __snake_case						)



     def      lowerCAmelCase			(   self							:			int						)->	Any:
        snake_case 						=   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__snake_case						)



     def      lowerCAmelCase			(   self							:			Optional[Any]						)->	Optional[Any]:
        snake_case 						=   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_pretraining(*__snake_case						)



     def      lowerCAmelCase			(   self							:			Dict   ,       __snake_case							:			str   ,       __snake_case							:			Optional[Any]   ,       __snake_case							:			Optional[Any]						)->	Any:
        # make masks reproducible
        np.random.seed(2						)

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

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

        super().check_pt_tf_models(__snake_case   ,       __snake_case   ,       __snake_case						)



     def      lowerCAmelCase			(   self							:			int						)->	Tuple:
        snake_case						,    snake_case 						=   self.model_tester.prepare_config_and_inputs_for_common()

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

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

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

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



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



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



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



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



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



     @slow
     def      lowerCAmelCase			(   self							:			Any						)->	int:
        for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           snake_case 						=   ViTMAEModel.from_pretrained(__snake_case						)
           self.assertIsNotNone(__snake_case						)





def 	__lowerCamelCase			(      )		->  Optional[int]:
   snake_case 						=   Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""		)
   return image


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






     """simple docstring"""






     @cached_property
     def      lowerCAmelCase			(   self							:			Dict						)->	Union[str, Any]:
        return ViTImageProcessor.from_pretrained("""facebook/vit-mae-base"""						) if is_vision_available() else None



     @slow
     def      lowerCAmelCase			(   self							:			Tuple						)->	int:
        # make random mask reproducible across the PT and TF model
        np.random.seed(2						)

        snake_case 						=   ViTMAEForPreTraining.from_pretrained("""facebook/vit-mae-base"""						).to(__snake_case						)

        snake_case 						=   self.default_image_processor
        snake_case 						=   prepare_img()
        snake_case 						=   image_processor(images=__snake_case   ,       return_tensors="""pt"""						).to(__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)
        snake_case 						=   ViTMAEConfig()
        snake_case 						=   int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2						)
        snake_case 						=   np.random.uniform(size=(1, num_patches)						)

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

        # verify the logits
        snake_case 						=   torch.Size((1, 1_96, 7_68)						)
        self.assertEqual(outputs.logits.shape   ,       __snake_case						)

        snake_case 						=   torch.tensor(
            [[-0.05_48, -1.70_23, -0.93_25], [0.37_21, -0.56_70, -0.22_33], [0.82_35, -1.38_78, -0.35_24]]						)

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








'''simple docstring'''




import os
from collections.abc import Iterator





def 	__lowerCamelCase			(      __lowerCAmelCase       :							str = "."		)		->  Iterator[str]:
   for dir_path, dir_names, filenames in os.walk(__lowerCAmelCase		):
      snake_case 						=   [d for d in dir_names if d != """scripts""" and d[0] not in """._"""]
      for filename in filenames:
         if filename == "__init__.py":
            continue
         if os.path.splitext(__lowerCAmelCase		)[1] in (".py", ".ipynb"):
            yield os.path.join(__lowerCAmelCase      ,    __lowerCAmelCase		).lstrip("""./"""		)





def 	__lowerCamelCase			(      __lowerCAmelCase       :							List[str]		)		->  Any:
   return F'''{i * "  "}*''' if i else "\n##"





def 	__lowerCamelCase			(      __lowerCAmelCase       :							str      ,    __lowerCAmelCase       :							str		)		->  str:
   snake_case 						=   old_path.split(os.sep		)
   for i, new_part in enumerate(new_path.split(os.sep		)		):
      if (i + 1 > len(__lowerCAmelCase		) or old_parts[i] != new_part) and new_part:
         print(F'''{md_prefix(__lowerCAmelCase		)} {new_part.replace("_"      ,    " "		).title()}'''		)
   return new_path





def 	__lowerCamelCase			(      __lowerCAmelCase       :							str = "."		)		->  None:
   snake_case 						=   """"""
   for filepath in sorted(good_file_paths(__lowerCAmelCase		)		):
      snake_case						,    snake_case 						=   os.path.split(__lowerCAmelCase		)
      if filepath != old_path:
         snake_case 						=   print_path(__lowerCAmelCase      ,    __lowerCAmelCase		)
      snake_case 						=   (filepath.count(os.sep		) + 1) if filepath else 0
      snake_case 						=   F'''{filepath}/{filename}'''.replace(""" """      ,    """%20"""		)
      snake_case 						=   os.path.splitext(filename.replace("""_"""      ,    """ """		).title()		)[0]
      print(F'''{md_prefix(__lowerCAmelCase		)} [{filename}]({url})'''		)


if __name__ == "__main__":
 print_directory_md(".")

