






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 			SCREAMING_SNAKE_CASE      :
     '''simple docstring'''


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

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


     def 					_A							(					self							:						Any   ):
       SCREAMING_SNAKE_CASE							:   Dict						    =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

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

       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     self.get_config()

       return config, pixel_values, labels


     def 					_A							(					self							:						List[str]   ):
       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=UpperCAmelCase_       ,			initializer_range=self.initializer_range       ,			mask_ratio=self.mask_ratio       ,			)


     def 					_A							(					self							:						Union[str, Any]       ,			UpperCAmelCase_							:						Optional[int]       ,			UpperCAmelCase_							:						Tuple       ,			UpperCAmelCase_							:						Tuple   ):
       SCREAMING_SNAKE_CASE							:   str						    =     ViTMAEModel(config=UpperCAmelCase_   )
       model.to(UpperCAmelCase_   )
       model.eval()
       SCREAMING_SNAKE_CASE							:   int						    =     model(UpperCAmelCase_   )
       self.parent.assertEqual(result.last_hidden_state.shape       ,			(self.batch_size, self.seq_length, self.hidden_size)   )


     def 					_A							(					self							:						Union[str, Any]       ,			UpperCAmelCase_							:						int       ,			UpperCAmelCase_							:						Dict       ,			UpperCAmelCase_							:						List[str]   ):
       SCREAMING_SNAKE_CASE							:   Any						    =     ViTMAEForPreTraining(UpperCAmelCase_   )
       model.to(UpperCAmelCase_   )
       model.eval()
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     model(UpperCAmelCase_   )
       SCREAMING_SNAKE_CASE							:   Dict						    =     (self.image_size // self.patch_size) ** 2
       SCREAMING_SNAKE_CASE							:   Tuple						    =     self.patch_size**2 * self.num_channels
       self.parent.assertEqual(result.logits.shape       ,			(self.batch_size, num_patches, expected_num_channels)   )

       # test greyscale images
       SCREAMING_SNAKE_CASE							:   Any						    =     1
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     ViTMAEForPreTraining(UpperCAmelCase_   )
       model.to(UpperCAmelCase_   )
       model.eval()
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     floats_tensor([self.batch_size, 1, self.image_size, self.image_size]   )
       SCREAMING_SNAKE_CASE							:   str						    =     model(UpperCAmelCase_   )
       SCREAMING_SNAKE_CASE							:   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]   ):
       SCREAMING_SNAKE_CASE							:   Any						    =     self.prepare_config_and_inputs()
       SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     config_and_inputs
       SCREAMING_SNAKE_CASE							:   Any						    =     {"pixel_values": pixel_values}
       return config, inputs_dict





@require_torch
class 			SCREAMING_SNAKE_CASE      (      lowerCAmelCase   ,		lowerCAmelCase   ,		unittest.TestCase       ):
     '''simple docstring'''


     UpperCamelCase_				:		List[Any]								=						(ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
     UpperCamelCase_				:		Union[str, Any]								=						{'''feature-extraction''': ViTMAEModel} if is_torch_available() else {}

     UpperCamelCase_				:		Dict								=						False
     UpperCamelCase_				:		Tuple								=						False
     UpperCamelCase_				:		Tuple								=						False
     UpperCamelCase_				:		int								=						False


     def 					_A							(					self							:						Optional[Any]   ):
       SCREAMING_SNAKE_CASE							:   int						    =     ViTMAEModelTester(self   )
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     ConfigTester(self       ,			config_class=UpperCAmelCase_       ,			has_text_modality=UpperCAmelCase_       ,			hidden_size=37   )


     def 					_A							(					self							:						Any   ):
       self.config_tester.run_common_tests()


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


     def 					_A							(					self							:						str   ):
       SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE							:   Any						    =     self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
         SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     model_class(UpperCAmelCase_   )
         self.assertIsInstance(model.get_input_embeddings()       ,			(nn.Module)   )
         SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     model.get_output_embeddings()
         self.assertTrue(x is None or isinstance(UpperCAmelCase_       ,			nn.Linear   )   )


     def 					_A							(					self							:						List[Any]   ):
       SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE							:   str						    =     self.model_tester.prepare_config_and_inputs_for_common()

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

         SCREAMING_SNAKE_CASE							:   Dict						    =     ["pixel_values"]
         self.assertListEqual(arg_names[:1]       ,			UpperCAmelCase_   )


     def 					_A							(					self							:						List[Any]   ):
       SCREAMING_SNAKE_CASE							:   List[str]						    =     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*UpperCAmelCase_   )


     def 					_A							(					self							:						Any   ):
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_pretraining(*UpperCAmelCase_   )


     def 					_A							(					self							:						List[str]       ,			UpperCAmelCase_							:						Union[str, Any]       ,			UpperCAmelCase_							:						List[Any]       ,			UpperCAmelCase_							:						Union[str, Any]   ):
       # make masks reproducible
       np.random.seed(2   )

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

       # Add `noise` argument.
       # PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
       SCREAMING_SNAKE_CASE							:   List[str]						    =     pt_noise

       super().check_pt_tf_models(UpperCAmelCase_       ,			UpperCAmelCase_       ,			UpperCAmelCase_   )


     def 					_A							(					self							:						Tuple   ):
       SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE							:   Dict						    =     self.model_tester.prepare_config_and_inputs_for_common()

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

         SCREAMING_SNAKE_CASE							:   List[Any]						    =     outputs[0].cpu().numpy()
         SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     0

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

           # Make sure we don't have nans
           SCREAMING_SNAKE_CASE							:   List[Any]						    =     after_outputs[0].cpu().numpy()
           SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     0
           SCREAMING_SNAKE_CASE							:   Dict						    =     np.amax(np.abs(out_a - out_a   )   )
           self.assertLessEqual(UpperCAmelCase_       ,			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							:						Tuple   ):
       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							:						Tuple   ):
       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]   ):
       pass


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


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






     @slow
     def 					_A							(					self							:						Optional[Any]   ):
       for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         SCREAMING_SNAKE_CASE							:   Optional[int]						    =     ViTMAEModel.from_pretrained(UpperCAmelCase_   )
         self.assertIsNotNone(UpperCAmelCase_   )





def 	lowerCamelCase__      (  ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE							:   List[str]						    =     Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"					)
  return image


@require_torch
@require_vision
class 			SCREAMING_SNAKE_CASE      (      unittest.TestCase       ):
     '''simple docstring'''


     @cached_property
     def 					_A							(					self							:						Dict   ):
       return ViTImageProcessor.from_pretrained("facebook/vit-mae-base"   ) if is_vision_available() else None






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

       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     ViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base"   ).to(UpperCAmelCase_   )

       SCREAMING_SNAKE_CASE							:   List[Any]						    =     self.default_image_processor
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     prepare_img()
       SCREAMING_SNAKE_CASE							:   int						    =     image_processor(images=UpperCAmelCase_       ,			return_tensors="pt"   ).to(UpperCAmelCase_   )

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

       # forward pass
       with torch.no_grad():
         SCREAMING_SNAKE_CASE							:   str						    =     model(**UpperCAmelCase_       ,			noise=torch.from_numpy(UpperCAmelCase_   ).to(device=UpperCAmelCase_   )   )

       # verify the logits
       SCREAMING_SNAKE_CASE							:   Tuple						    =     torch.Size((1, 196, 768)   )
       self.assertEqual(outputs.logits.shape       ,			UpperCAmelCase_   )

       SCREAMING_SNAKE_CASE							:   int						    =     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(UpperCAmelCase_   )       ,			atol=1E-4   )   )














from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCAmelCase__							  =						{
    "configuration_biogpt": ["BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BioGptConfig"],
    "tokenization_biogpt": ["BioGptTokenizer"],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase__							  =						[
         "BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "BioGptForCausalLM",
         "BioGptForTokenClassification",
         "BioGptForSequenceClassification",
         "BioGptModel",
         "BioGptPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
     from .tokenization_biogpt import BioGptTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_biogpt import (
              BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
              BioGptForCausalLM,
              BioGptForSequenceClassification,
              BioGptForTokenClassification,
              BioGptModel,
              BioGptPreTrainedModel,
          )


else:
     import sys

     UpperCAmelCase__							  =						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


