





"""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 			A__	:


     """simple docstring"""

     def __init__(	self:						Optional[int]     ,		__a:						str     ,		__a:						Union[str, Any]=13     ,		__a:						str=30     ,		__a:						int=2     ,		__a:						Any=3     ,		__a:						Optional[int]=True     ,		__a:						Union[str, Any]=True     ,		__a:						Optional[int]=32     ,		__a:						Any=5     ,		__a:						Optional[Any]=4     ,		__a:						Optional[int]=37     ,		__a:						Optional[int]="gelu"     ,		__a:						Optional[Any]=0.1     ,		__a:						Dict=0.1     ,		__a:						Any=10     ,		__a:						Union[str, Any]=0.02     ,		__a:						str=3     ,		__a:						Dict=0.6     ,		__a:						Optional[Any]=None     ,		)->    Union[str, Any]:
           lowerCamelCase					:					str        =	parent
           lowerCamelCase					:					Optional[int]        =	batch_size
           lowerCamelCase					:					Optional[Any]        =	image_size
           lowerCamelCase					:					List[str]        =	patch_size
           lowerCamelCase					:					Union[str, Any]        =	num_channels
           lowerCamelCase					:					Optional[Any]        =	is_training
           lowerCamelCase					:					Optional[int]        =	use_labels
           lowerCamelCase					:					Union[str, Any]        =	hidden_size
           lowerCamelCase					:					Dict        =	num_hidden_layers
           lowerCamelCase					:					Optional[int]        =	num_attention_heads
           lowerCamelCase					:					Tuple        =	intermediate_size
           lowerCamelCase					:					Optional[Any]        =	hidden_act
           lowerCamelCase					:					List[Any]        =	hidden_dropout_prob
           lowerCamelCase					:					str        =	attention_probs_dropout_prob
           lowerCamelCase					:					List[str]        =	type_sequence_label_size
           lowerCamelCase					:					int        =	initializer_range
           lowerCamelCase					:					Any        =	mask_ratio
           lowerCamelCase					:					Optional[Any]        =	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[Any]        =	(image_size // patch_size) ** 2
           lowerCamelCase					:					Tuple        =	int(math.ceil((1 - mask_ratio) * (num_patches + 1)	)	)

     def 		a__						(	self:						Tuple	)->    str:
           lowerCamelCase					:					Optional[Any]        =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

           lowerCamelCase					:					Union[str, Any]        =	None
           if self.use_labels:
                 lowerCamelCase					:					int        =	ids_tensor([self.batch_size]     ,		self.type_sequence_label_size	)

           lowerCamelCase					:					Optional[Any]        =	self.get_config()

           return config, pixel_values, labels

     def 		a__						(	self:						Optional[Any]	)->    Union[str, 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=__a     ,		initializer_range=self.initializer_range     ,		mask_ratio=self.mask_ratio     ,		)

     def 		a__						(	self:						Any     ,		__a:						Dict     ,		__a:						Any     ,		__a:						int	)->    Union[str, Any]:
           lowerCamelCase					:					Any        =	ViTMAEModel(config=__a	)
           model.to(__a	)
           model.eval()
           lowerCamelCase					:					Optional[int]        =	model(__a	)
           self.parent.assertEqual(result.last_hidden_state.shape     ,		(self.batch_size, self.seq_length, self.hidden_size)	)

     def 		a__						(	self:						Union[str, Any]     ,		__a:						Any     ,		__a:						Union[str, Any]     ,		__a:						Optional[Any]	)->    Tuple:
           lowerCamelCase					:					str        =	ViTMAEForPreTraining(__a	)
           model.to(__a	)
           model.eval()
           lowerCamelCase					:					Any        =	model(__a	)
           lowerCamelCase					:					Dict        =	(self.image_size // self.patch_size) ** 2
           lowerCamelCase					:					str        =	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					:					int        =	1
           lowerCamelCase					:					str        =	ViTMAEForPreTraining(__a	)
           model.to(__a	)
           model.eval()
           lowerCamelCase					:					Union[str, Any]        =	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
           lowerCamelCase					:					List[str]        =	model(__a	)
           lowerCamelCase					:					Optional[Any]        =	self.patch_size**2
           self.parent.assertEqual(result.logits.shape     ,		(self.batch_size, num_patches, expected_num_channels)	)







     def 		a__						(	self:						Union[str, Any]	)->    List[Any]:
           lowerCamelCase					:					Optional[int]        =	self.prepare_config_and_inputs()
           lowerCamelCase					:					int        =	config_and_inputs
           lowerCamelCase					:					Union[str, Any]        =	{"""pixel_values""": pixel_values}
           return config, inputs_dict


@require_torch
class 			A__	(						__lowercase ,     __lowercase ,     unittest.TestCase):


     """simple docstring"""
     snake_case__	:					Union[str, Any]													=(ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
     snake_case__	:					Union[str, Any]													={'''feature-extraction''': ViTMAEModel} if is_torch_available() else {}

     snake_case__	:					int													=False
     snake_case__	:					str													=False
     snake_case__	:					str													=False
     snake_case__	:					Union[str, Any]													=False

     def 		a__						(	self:						Optional[Any]	)->    str:
           lowerCamelCase					:					List[Any]        =	ViTMAEModelTester(self	)
           lowerCamelCase					:					List[Any]        =	ConfigTester(self     ,		config_class=__a     ,		has_text_modality=__a     ,		hidden_size=37	)

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

     @unittest.skip(reason="""ViTMAE does not use inputs_embeds"""	)
     def 		a__						(	self:						Any	)->    Optional[Any]:
           pass

     def 		a__						(	self:						Optional[Any]	)->    Optional[Any]:
           lowerCamelCase					:					Any        =	self.model_tester.prepare_config_and_inputs_for_common()

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

     def 		a__						(	self:						int	)->    str:
           lowerCamelCase					:					Optional[int]        =	self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 lowerCamelCase					:					List[str]        =	model_class(__a	)
                 lowerCamelCase					:					Union[str, Any]        =	inspect.signature(model.forward	)
                 # signature.parameters is an OrderedDict => so arg_names order is deterministic
                 lowerCamelCase					:					List[Any]        =	[*signature.parameters.keys()]

                 lowerCamelCase					:					List[str]        =	["""pixel_values"""]
                 self.assertListEqual(arg_names[:1]     ,		__a	)

     def 		a__						(	self:						int	)->    Dict:
           lowerCamelCase					:					str        =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*__a	)

     def 		a__						(	self:						List[str]	)->    Optional[Any]:
           lowerCamelCase					:					Optional[Any]        =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_pretraining(*__a	)

     def 		a__						(	self:						str     ,		__a:						Union[str, Any]     ,		__a:						List[str]     ,		__a:						List[str]	)->    Any:
           # make masks reproducible
           np.random.seed(2	)

           lowerCamelCase					:					str        =	int((pt_model.config.image_size // pt_model.config.patch_size) ** 2	)
           lowerCamelCase					:					Optional[int]        =	np.random.uniform(size=(self.model_tester.batch_size, num_patches)	)
           lowerCamelCase					:					Optional[int]        =	torch.from_numpy(__a	)

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

           super().check_pt_tf_models(__a     ,		__a     ,		__a	)

     def 		a__						(	self:						Optional[int]	)->    List[str]:
           lowerCamelCase					:					str        =	self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 lowerCamelCase					:					Any        =	model_class(__a	)
                 model.to(__a	)
                 model.eval()
                 # make random mask reproducible
                 torch.manual_seed(2	)
                 with torch.no_grad():
                       lowerCamelCase					:					List[str]        =	model(**self._prepare_for_class(__a     ,		__a	)	)

                 lowerCamelCase					:					Dict        =	outputs[0].cpu().numpy()
                 lowerCamelCase					:					Dict        =	0

                 with tempfile.TemporaryDirectory() as tmpdirname:
                       model.save_pretrained(__a	)
                       lowerCamelCase					:					Dict        =	model_class.from_pretrained(__a	)
                       model.to(__a	)
                       # make random mask reproducible
                       torch.manual_seed(2	)
                       with torch.no_grad():
                             lowerCamelCase					:					Optional[int]        =	model(**self._prepare_for_class(__a     ,		__a	)	)

                       # Make sure we don't have nans
                       lowerCamelCase					:					List[str]        =	after_outputs[0].cpu().numpy()
                       lowerCamelCase					:					Optional[Any]        =	0
                       lowerCamelCase					:					List[str]        =	np.amax(np.abs(out_a - out_a	)	)
                       self.assertLessEqual(__a     ,		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 		a__						(	self:						Union[str, Any]	)->    Union[str, 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 		a__						(	self:						str	)->    Dict:
           pass

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

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

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







     @slow
     def 		a__						(	self:						str	)->    Any:
           for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 lowerCamelCase					:					List[str]        =	ViTMAEModel.from_pretrained(__a	)
                 self.assertIsNotNone(__a	)




def   snake_case		(     )   ->      List[str]:
      lowerCamelCase					:					str        =	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""    )
      return image






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


     """simple docstring"""

     @cached_property
     def 		a__						(	self:						Any	)->    int:
           return ViTImageProcessor.from_pretrained("""facebook/vit-mae-base"""	) if is_vision_available() else None







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

           lowerCamelCase					:					int        =	ViTMAEForPreTraining.from_pretrained("""facebook/vit-mae-base"""	).to(__a	)

           lowerCamelCase					:					Tuple        =	self.default_image_processor
           lowerCamelCase					:					Dict        =	prepare_img()
           lowerCamelCase					:					List[Any]        =	image_processor(images=__a     ,		return_tensors="""pt"""	).to(__a	)

           # 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					:					List[Any]        =	int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2	)
           lowerCamelCase					:					Union[str, Any]        =	np.random.uniform(size=(1, num_patches)	)

           # forward pass
           with torch.no_grad():
                 lowerCamelCase					:					Union[str, Any]        =	model(**__a     ,		noise=torch.from_numpy(__a	).to(device=__a	)	)

           # verify the logits
           lowerCamelCase					:					int        =	torch.Size((1, 196, 768)	)
           self.assertEqual(outputs.logits.shape     ,		__a	)

           lowerCamelCase					:					str        =	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(__a	)     ,		atol=1e-4	)	)








"""simple docstring"""


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


__lowerCamelCase      :Optional[int]	    =		logging.get_logger(__name__)

__lowerCamelCase      :List[str]	    =		{
    'google/realm-cc-news-pretrained-embedder': (
        'https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/config.json'
    ),
    'google/realm-cc-news-pretrained-encoder': (
        'https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/config.json'
    ),
    'google/realm-cc-news-pretrained-scorer': (
        'https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/config.json'
    ),
    'google/realm-cc-news-pretrained-openqa': (
        'https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/config.json'
    ),
    'google/realm-orqa-nq-openqa': 'https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/config.json',
    'google/realm-orqa-nq-reader': 'https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/config.json',
    'google/realm-orqa-wq-openqa': 'https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/config.json',
    'google/realm-orqa-wq-reader': 'https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/config.json',
    # See all REALM models at https://huggingface.co/models?filter=realm
}


class 			A__	(						__lowercase):


     """simple docstring"""
     snake_case__	:					Optional[Any]													='''realm'''

     def __init__(	self:						Union[str, Any]     ,		__a:						List[Any]=30_522     ,		__a:						List[Any]=768     ,		__a:						List[Any]=128     ,		__a:						Union[str, Any]=12     ,		__a:						Union[str, Any]=12     ,		__a:						Optional[Any]=8     ,		__a:						Dict=3_072     ,		__a:						List[Any]="gelu_new"     ,		__a:						List[Any]=0.1     ,		__a:						Tuple=0.1     ,		__a:						Optional[Any]=512     ,		__a:						Optional[int]=2     ,		__a:						str=0.02     ,		__a:						int=1e-1_2     ,		__a:						Optional[Any]=256     ,		__a:						Any=10     ,		__a:						Dict=1e-3     ,		__a:						Optional[Any]=5     ,		__a:						Dict=320     ,		__a:						Tuple=13_353_718     ,		__a:						List[Any]=5_000     ,		__a:						Dict=1     ,		__a:						int=0     ,		__a:						Dict=2     ,		**__a:						List[str]     ,		)->    Any:
           super().__init__(pad_token_id=__a     ,		bos_token_id=__a     ,		eos_token_id=__a     ,		**__a	)

           # Common config
           lowerCamelCase					:					Optional[Any]        =	vocab_size
           lowerCamelCase					:					str        =	max_position_embeddings
           lowerCamelCase					:					Dict        =	hidden_size
           lowerCamelCase					:					Dict        =	retriever_proj_size
           lowerCamelCase					:					Optional[Any]        =	num_hidden_layers
           lowerCamelCase					:					List[str]        =	num_attention_heads
           lowerCamelCase					:					Tuple        =	num_candidates
           lowerCamelCase					:					int        =	intermediate_size
           lowerCamelCase					:					Dict        =	hidden_act
           lowerCamelCase					:					List[str]        =	hidden_dropout_prob
           lowerCamelCase					:					Dict        =	attention_probs_dropout_prob
           lowerCamelCase					:					Optional[int]        =	initializer_range
           lowerCamelCase					:					Dict        =	type_vocab_size
           lowerCamelCase					:					Optional[Any]        =	layer_norm_eps

           # Reader config
           lowerCamelCase					:					List[str]        =	span_hidden_size
           lowerCamelCase					:					Dict        =	max_span_width
           lowerCamelCase					:					Optional[Any]        =	reader_layer_norm_eps
           lowerCamelCase					:					Optional[int]        =	reader_beam_size
           lowerCamelCase					:					List[Any]        =	reader_seq_len

           # Retrieval config
           lowerCamelCase					:					int        =	num_block_records
           lowerCamelCase					:					Dict        =	searcher_beam_size




