


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 A  :






      def __init__(					self						:     List[Any]			,   lowercase_						:     str			,   lowercase_						:     str=13			,   lowercase_						:     List[str]=30			,   lowercase_						:     Dict=2			,   lowercase_						:     List[Any]=3			,   lowercase_						:     str=True			,   lowercase_						:     int=True			,   lowercase_						:     str=32			,   lowercase_						:     List[str]=5			,   lowercase_						:     Any=4			,   lowercase_						:     Optional[Any]=37			,   lowercase_						:     List[str]="gelu"			,   lowercase_						:     Any=0.1			,   lowercase_						:     List[Any]=0.1			,   lowercase_						:     Any=10			,   lowercase_						:     List[str]=0.02			,   lowercase_						:     Union[str, Any]=3			,   lowercase_						:     str=0.6			,   lowercase_						:     Union[str, Any]=None			,   )					->						Optional[Any]:
             """simple docstring"""




             _lowerCamelCase       :		Optional[Any]		=parent
             _lowerCamelCase       :		List[Any]		=batch_size
             _lowerCamelCase       :		int		=image_size
             _lowerCamelCase       :		Union[str, Any]		=patch_size
             _lowerCamelCase       :		str		=num_channels
             _lowerCamelCase       :		int		=is_training
             _lowerCamelCase       :		List[Any]		=use_labels
             _lowerCamelCase       :		Tuple		=hidden_size
             _lowerCamelCase       :		int		=num_hidden_layers
             _lowerCamelCase       :		Tuple		=num_attention_heads
             _lowerCamelCase       :		Union[str, Any]		=intermediate_size
             _lowerCamelCase       :		str		=hidden_act
             _lowerCamelCase       :		Any		=hidden_dropout_prob
             _lowerCamelCase       :		Optional[int]		=attention_probs_dropout_prob
             _lowerCamelCase       :		int		=type_sequence_label_size
             _lowerCamelCase       :		Union[str, Any]		=initializer_range
             _lowerCamelCase       :		Any		=mask_ratio
             _lowerCamelCase       :		Dict		=scope

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






      def        lowerCamelCase       (					self						:     List[Any] )					->						Optional[int]:
             """simple docstring"""




             _lowerCamelCase       :		List[Any]		=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

             _lowerCamelCase       :		List[str]		=None
             if self.use_labels:
                    _lowerCamelCase       :		Tuple		=ids_tensor([self.batch_size]			,   self.type_sequence_label_size )

             _lowerCamelCase       :		List[Any]		=self.get_config()

             return config, pixel_values, labels






      def        lowerCamelCase       (					self						:     Union[str, Any] )					->						Any:
             """simple docstring"""




             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=lowercase_			,   initializer_range=self.initializer_range			,   mask_ratio=self.mask_ratio			,   )






      def        lowerCamelCase       (					self						:     Any			,   lowercase_						:     Union[str, Any]			,   lowercase_						:     List[Any]			,   lowercase_						:     int )					->						int:
             """simple docstring"""




             _lowerCamelCase       :		Optional[Any]		=ViTMAEModel(config=lowercase_ )
             model.to(lowercase_ )
             model.eval()
             _lowerCamelCase       :		Dict		=model(lowercase_ )
             self.parent.assertEqual(result.last_hidden_state.shape			,   (self.batch_size, self.seq_length, self.hidden_size) )






      def        lowerCamelCase       (					self						:     str			,   lowercase_						:     List[Any]			,   lowercase_						:     Dict			,   lowercase_						:     List[str] )					->						Optional[Any]:
             """simple docstring"""




             _lowerCamelCase       :		int		=ViTMAEForPreTraining(lowercase_ )
             model.to(lowercase_ )
             model.eval()
             _lowerCamelCase       :		List[Any]		=model(lowercase_ )
             _lowerCamelCase       :		Union[str, Any]		=(self.image_size // self.patch_size) ** 2
             _lowerCamelCase       :		Optional[int]		=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       :		List[Any]		=1
             _lowerCamelCase       :		str		=ViTMAEForPreTraining(lowercase_ )
             model.to(lowercase_ )
             model.eval()
             _lowerCamelCase       :		Optional[int]		=floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
             _lowerCamelCase       :		Any		=model(lowercase_ )
             _lowerCamelCase       :		str		=self.patch_size**2
             self.parent.assertEqual(result.logits.shape			,   (self.batch_size, num_patches, expected_num_channels) )






      def        lowerCamelCase       (					self						:     Tuple )					->						Any:
             """simple docstring"""




             _lowerCamelCase       :		Optional[int]		=self.prepare_config_and_inputs()
             _lowerCamelCase	,     _lowerCamelCase	,     _lowerCamelCase       :		Union[str, Any]		=config_and_inputs
             _lowerCamelCase       :		List[Any]		={'pixel_values': pixel_values}
             return config, inputs_dict




@require_torch
class A  (   UpperCamelCase_    ,							UpperCamelCase_    ,							unittest.TestCase     ):
      UpperCamelCase__							:  Union[str, Any]      =(ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
      UpperCamelCase__							:  Optional[Any]      ={'feature-extraction': ViTMAEModel} if is_torch_available() else {}

      UpperCamelCase__							:  Dict      =False
      UpperCamelCase__							:  Optional[int]      =False
      UpperCamelCase__							:  Any      =False
      UpperCamelCase__							:  str      =False






      def        lowerCamelCase       (					self						:     int )					->						str:
             """simple docstring"""




             _lowerCamelCase       :		int		=ViTMAEModelTester(self )
             _lowerCamelCase       :		List[Any]		=ConfigTester(self			,   config_class=lowercase_			,   has_text_modality=lowercase_			,   hidden_size=37 )






      def        lowerCamelCase       (					self						:     List[Any] )					->						int:
             """simple docstring"""




             self.config_tester.run_common_tests()






      @unittest.skip(reason='ViTMAE does not use inputs_embeds' )
      def        lowerCamelCase       (					self						:     Tuple )					->						Union[str, Any]:
             """simple docstring"""




             pass






      def        lowerCamelCase       (					self						:     Any )					->						Any:
             """simple docstring"""




             _lowerCamelCase	,     _lowerCamelCase       :		str		=self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                    _lowerCamelCase       :		int		=model_class(lowercase_ )
                    self.assertIsInstance(model.get_input_embeddings()			,   (nn.Module) )
                    _lowerCamelCase       :		List[Any]		=model.get_output_embeddings()
                    self.assertTrue(x is None or isinstance(lowercase_			,   nn.Linear ) )






      def        lowerCamelCase       (					self						:     str )					->						Dict:
             """simple docstring"""




             _lowerCamelCase	,     _lowerCamelCase       :		List[Any]		=self.model_tester.prepare_config_and_inputs_for_common()

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

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






      def        lowerCamelCase       (					self						:     Optional[Any] )					->						str:
             """simple docstring"""




             _lowerCamelCase       :		Any		=self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*lowercase_ )






      def        lowerCamelCase       (					self						:     List[Any] )					->						Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase       :		List[str]		=self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_pretraining(*lowercase_ )






      def        lowerCamelCase       (					self						:     int			,   lowercase_						:     Optional[Any]			,   lowercase_						:     Dict			,   lowercase_						:     Union[str, Any] )					->						List[str]:
             """simple docstring"""




             np.random.seed(2 )

             _lowerCamelCase       :		Any		=int((pt_model.config.image_size // pt_model.config.patch_size) ** 2 )
             _lowerCamelCase       :		Any		=np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
             _lowerCamelCase       :		str		=torch.from_numpy(lowercase_ )

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

             super().check_pt_tf_models(lowercase_			,   lowercase_			,   lowercase_ )






      def        lowerCamelCase       (					self						:     Optional[int] )					->						List[Any]:
             """simple docstring"""




             _lowerCamelCase	,     _lowerCamelCase       :		Optional[int]		=self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                    _lowerCamelCase       :		Optional[int]		=model_class(lowercase_ )
                    model.to(lowercase_ )
                    model.eval()
                    # make random mask reproducible
                    torch.manual_seed(2 )
                    with torch.no_grad():
                           _lowerCamelCase       :		Tuple		=model(**self._prepare_for_class(lowercase_			,   lowercase_ ) )

                    _lowerCamelCase       :		Optional[Any]		=outputs[0].cpu().numpy()
                    _lowerCamelCase       :		Dict		=0

                    with tempfile.TemporaryDirectory() as tmpdirname:
                           model.save_pretrained(lowercase_ )
                           _lowerCamelCase       :		List[str]		=model_class.from_pretrained(lowercase_ )
                           model.to(lowercase_ )
                           # make random mask reproducible
                           torch.manual_seed(2 )
                           with torch.no_grad():
                                  _lowerCamelCase       :		Union[str, Any]		=model(**self._prepare_for_class(lowercase_			,   lowercase_ ) )

                           # Make sure we don't have nans
                           _lowerCamelCase       :		Optional[int]		=after_outputs[0].cpu().numpy()
                           _lowerCamelCase       :		Dict		=0
                           _lowerCamelCase       :		int		=np.amax(np.abs(out_a - out_a ) )
                           self.assertLessEqual(lowercase_			,   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        lowerCamelCase       (					self						:     str )					->						Dict:
             """simple docstring"""




             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        lowerCamelCase       (					self						:     Any )					->						List[str]:
             """simple docstring"""




             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        lowerCamelCase       (					self						:     Optional[int] )					->						Tuple:
             """simple docstring"""




             pass






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




             pass






      @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
      def        lowerCamelCase       (					self						:     List[str] )					->						int:
             """simple docstring"""




             pass






      @slow
      def        lowerCamelCase       (					self						:     int )					->						Any:
             """simple docstring"""




             for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                    _lowerCamelCase       :		Any		=ViTMAEModel.from_pretrained(lowercase_ )
                    self.assertIsNotNone(lowercase_ )




def 	a_      (    ):
       '''simple docstring'''





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




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






      @cached_property
      def        lowerCamelCase       (					self						:     Optional[Any] )					->						List[Any]:
             """simple docstring"""




             return ViTImageProcessor.from_pretrained('facebook/vit-mae-base' ) if is_vision_available() else None






      @slow
      def        lowerCamelCase       (					self						:     List[Any] )					->						List[str]:
             """simple docstring"""




             np.random.seed(2 )

             _lowerCamelCase       :		Tuple		=ViTMAEForPreTraining.from_pretrained('facebook/vit-mae-base' ).to(lowercase_ )

             _lowerCamelCase       :		int		=self.default_image_processor
             _lowerCamelCase       :		Optional[Any]		=prepare_img()
             _lowerCamelCase       :		Optional[Any]		=image_processor(images=lowercase_			,   return_tensors='pt' ).to(lowercase_ )

             # 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       :		Tuple		=ViTMAEConfig()
             _lowerCamelCase       :		Any		=int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2 )
             _lowerCamelCase       :		Optional[Any]		=np.random.uniform(size=(1, num_patches) )

             # forward pass
             with torch.no_grad():
                    _lowerCamelCase       :		int		=model(**lowercase_			,   noise=torch.from_numpy(lowercase_ ).to(device=lowercase_ ) )

             # verify the logits
             _lowerCamelCase       :		List[Any]		=torch.Size((1, 196, 768) )
             self.assertEqual(outputs.logits.shape			,   lowercase_ )

             _lowerCamelCase       :		List[str]		=torch.tensor(
                 [[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]] )

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






# Lint as: python3
import itertools
import os
import re


lowerCamelCase											=       re.compile(R'([A-Z]+)([A-Z][a-z])')
lowerCamelCase											=       re.compile(R'([a-z\d])([A-Z])')

lowerCamelCase											=       re.compile(R'(?<!_)_(?!_)')
lowerCamelCase											=       re.compile(R'(_{2,})')

lowerCamelCase											=       R'^\w+(\.\w+)*$'

lowerCamelCase											=       R'<>:/\|?*'




def 	a_      (    SCREAMING_SNAKE_CASE__					:      Tuple     ):
       '''simple docstring'''





       _lowerCamelCase       :		List[str]		=_uppercase_uppercase_re.sub(r'\1_\2'		,			SCREAMING_SNAKE_CASE__     )
       _lowerCamelCase       :		List[Any]		=_lowercase_uppercase_re.sub(r'\1_\2'		,			SCREAMING_SNAKE_CASE__     )
       return name.lower()




def 	a_      (    SCREAMING_SNAKE_CASE__					:      Union[str, Any]     ):
       '''simple docstring'''





       _lowerCamelCase       :		Tuple		=_single_underscore_re.split(SCREAMING_SNAKE_CASE__     )
       _lowerCamelCase       :		Tuple		=[_multiple_underscores_re.split(SCREAMING_SNAKE_CASE__     ) for n in name]
       return "".join(n.capitalize() for n in itertools.chain.from_iterable(SCREAMING_SNAKE_CASE__     ) if n != ''     )




def 	a_      (    SCREAMING_SNAKE_CASE__					:      List[Any]     ):
       '''simple docstring'''





       if os.path.basename(SCREAMING_SNAKE_CASE__     ) != name:
              raise ValueError(F'''Should be a dataset name, not a path: {name}'''     )
       return camelcase_to_snakecase(SCREAMING_SNAKE_CASE__     )




def 	a_      (    SCREAMING_SNAKE_CASE__					:      Union[str, Any]		,			SCREAMING_SNAKE_CASE__					:      Optional[int]     ):
       '''simple docstring'''





       if os.path.basename(SCREAMING_SNAKE_CASE__     ) != name:
              raise ValueError(F'''Should be a dataset name, not a path: {name}'''     )
       if not re.match(_split_re		,			SCREAMING_SNAKE_CASE__     ):
              raise ValueError(F'''Split name should match \'{_split_re}\'\' but got \'{split}\'.'''     )
       return F'''{filename_prefix_for_name(SCREAMING_SNAKE_CASE__     )}-{split}'''




def 	a_      (    SCREAMING_SNAKE_CASE__					:      Optional[int]		,			SCREAMING_SNAKE_CASE__					:      List[Any]		,			SCREAMING_SNAKE_CASE__					:      Optional[Any]		,			SCREAMING_SNAKE_CASE__					:      int=None     ):
       '''simple docstring'''





       _lowerCamelCase       :		Union[str, Any]		=filename_prefix_for_split(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )
       if filetype_suffix:
              prefix += F'''.{filetype_suffix}'''
       _lowerCamelCase       :		Optional[Any]		=os.path.join(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )
       return F'''{filepath}*'''




def 	a_      (    SCREAMING_SNAKE_CASE__					:      Tuple		,			SCREAMING_SNAKE_CASE__					:      List[str]		,			SCREAMING_SNAKE_CASE__					:      str		,			SCREAMING_SNAKE_CASE__					:      List[str]=None		,			SCREAMING_SNAKE_CASE__					:      Optional[Any]=None     ):
       '''simple docstring'''





       _lowerCamelCase       :		Dict		=filename_prefix_for_split(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )
       _lowerCamelCase       :		Union[str, Any]		=os.path.join(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )

       if shard_lengths:
              _lowerCamelCase       :		Union[str, Any]		=len(SCREAMING_SNAKE_CASE__     )
              _lowerCamelCase       :		Optional[int]		=[F'''{prefix}-{shard_id:05d}-of-{num_shards:05d}''' for shard_id in range(SCREAMING_SNAKE_CASE__     )]
              if filetype_suffix:
                     _lowerCamelCase       :		List[Any]		=[filename + F'''.{filetype_suffix}''' for filename in filenames]
              return filenames
       else:
              _lowerCamelCase       :		Any		=prefix
              if filetype_suffix:
                     filename += F'''.{filetype_suffix}'''
              return [filename]


