






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		,		_UpperCAmelCase		,		_UpperCAmelCase=13		,		_UpperCAmelCase=30		,		_UpperCAmelCase=2		,		_UpperCAmelCase=3		,		_UpperCAmelCase=True		,		_UpperCAmelCase=True		,		_UpperCAmelCase=32		,		_UpperCAmelCase=5		,		_UpperCAmelCase=4		,		_UpperCAmelCase=37		,		_UpperCAmelCase="gelu"		,		_UpperCAmelCase=0.1		,		_UpperCAmelCase=0.1		,		_UpperCAmelCase=10		,		_UpperCAmelCase=0.0_2		,		_UpperCAmelCase=3		,		_UpperCAmelCase=0.6		,		_UpperCAmelCase=None		,		)			-> List[Any]:
     UpperCamelCase_									=     parent
     UpperCamelCase_									=     batch_size
     UpperCamelCase_									=     image_size
     UpperCamelCase_									=     patch_size
     UpperCamelCase_									=     num_channels
     UpperCamelCase_									=     is_training
     UpperCamelCase_									=     use_labels
     UpperCamelCase_									=     hidden_size
     UpperCamelCase_									=     num_hidden_layers
     UpperCamelCase_									=     num_attention_heads
     UpperCamelCase_									=     intermediate_size
     UpperCamelCase_									=     hidden_act
     UpperCamelCase_									=     hidden_dropout_prob
     UpperCamelCase_									=     attention_probs_dropout_prob
     UpperCamelCase_									=     type_sequence_label_size
     UpperCamelCase_									=     initializer_range
     UpperCamelCase_									=     mask_ratio
     UpperCamelCase_									=     scope

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




  def    _UpperCAmelCase     (     self )			-> List[str]:
     UpperCamelCase_									=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

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

     UpperCamelCase_									=     self.get_config()

     return config, pixel_values, labels




  def    _UpperCAmelCase     (     self )			-> 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    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase )			-> List[Any]:
     UpperCamelCase_									=     ViTMAEModel(config=_UpperCAmelCase )
     model.to(_UpperCAmelCase )
     model.eval()
     UpperCamelCase_									=     model(_UpperCAmelCase )
     self.parent.assertEqual(result.last_hidden_state.shape		,		(self.batch_size, self.seq_length, self.hidden_size) )




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

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




  def    _UpperCAmelCase     (     self )			-> List[str]:
     UpperCamelCase_									=     self.prepare_config_and_inputs()
     UpperCamelCase_		,      UpperCamelCase_		,      UpperCamelCase_									=     config_and_inputs
     UpperCamelCase_									=     {'pixel_values': pixel_values}
     return config, inputs_dict




@require_torch
class 						_a       (  UpperCAmelCase__						,       UpperCAmelCase__						,       unittest.TestCase    ):






  """simple docstring"""





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

  A_						=   False
  A_						=   False
  A_						=   False
  A_						=   False




  def    _UpperCAmelCase     (     self )			-> Dict:
     UpperCamelCase_									=     ViTMAEModelTester(self )
     UpperCamelCase_									=     ConfigTester(self		,		config_class=_UpperCAmelCase		,		has_text_modality=_UpperCAmelCase		,		hidden_size=37 )




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




  @unittest.skip(reason='ViTMAE does not use inputs_embeds' )
  def    _UpperCAmelCase     (     self )			-> List[str]:
     pass




  def    _UpperCAmelCase     (     self )			-> str:
     UpperCamelCase_		,      UpperCamelCase_									=     self.model_tester.prepare_config_and_inputs_for_common()

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




  def    _UpperCAmelCase     (     self )			-> Optional[int]:
     UpperCamelCase_		,      UpperCamelCase_									=     self.model_tester.prepare_config_and_inputs_for_common()

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

        UpperCamelCase_									=     ['pixel_values']
        self.assertListEqual(arg_names[:1]		,		_UpperCAmelCase )




  def    _UpperCAmelCase     (     self )			-> Optional[Any]:
     UpperCamelCase_									=     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*_UpperCAmelCase )




  def    _UpperCAmelCase     (     self )			-> Tuple:
     UpperCamelCase_									=     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_pretraining(*_UpperCAmelCase )




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase )			-> str:
     # make masks reproducible
     np.random.seed(2 )

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

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

     super().check_pt_tf_models(_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase )




  def    _UpperCAmelCase     (     self )			-> Any:
     UpperCamelCase_		,      UpperCamelCase_									=     self.model_tester.prepare_config_and_inputs_for_common()

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

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

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

           # Make sure we don't have nans
           UpperCamelCase_									=     after_outputs[0].cpu().numpy()
           UpperCamelCase_									=     0
           UpperCamelCase_									=     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    _UpperCAmelCase     (     self )			-> 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    _UpperCAmelCase     (     self )			-> 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    _UpperCAmelCase     (     self )			-> List[Any]:
     pass




  @unittest.skip(reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load' )
  def    _UpperCAmelCase     (     self )			-> Tuple:
     pass




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




  @slow
  def    _UpperCAmelCase     (     self )			-> Optional[Any]:
     for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        UpperCamelCase_									=     ViTMAEModel.from_pretrained(_UpperCAmelCase )
        self.assertIsNotNone(_UpperCAmelCase )






def     _snake_case    ():
   UpperCamelCase_									=     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
   return image


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






  """simple docstring"""





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




  @slow
  def    _UpperCAmelCase     (     self )			-> List[Any]:
     # make random mask reproducible across the PT and TF model
     np.random.seed(2 )

     UpperCamelCase_									=     ViTMAEForPreTraining.from_pretrained('facebook/vit-mae-base' ).to(_UpperCAmelCase )

     UpperCamelCase_									=     self.default_image_processor
     UpperCamelCase_									=     prepare_img()
     UpperCamelCase_									=     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)
     UpperCamelCase_									=     ViTMAEConfig()
     UpperCamelCase_									=     int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2 )
     UpperCamelCase_									=     np.random.uniform(size=(1, num_patches) )

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

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

     UpperCamelCase_									=     torch.tensor(
         [[-0.0_5_4_8, -1.7_0_2_3, -0.9_3_2_5], [0.3_7_2_1, -0.5_6_7_0, -0.2_2_3_3], [0.8_2_3_5, -1.3_8_7_8, -0.3_5_2_4]] )

     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_sentencepiece_available,
    is_torch_available,
)


snake_case__      :	List[str]       	=      {
    """configuration_speecht5""": [
        """SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP""",
        """SpeechT5Config""",
        """SpeechT5HifiGanConfig""",
    ],
    """feature_extraction_speecht5""": ["""SpeechT5FeatureExtractor"""],
    """processing_speecht5""": ["""SpeechT5Processor"""],
}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case__      :	List[str]       	=      ["""SpeechT5Tokenizer"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case__      :	Any       	=      [
     """SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """SpeechT5ForSpeechToText""",
     """SpeechT5ForSpeechToSpeech""",
     """SpeechT5ForTextToSpeech""",
     """SpeechT5Model""",
     """SpeechT5PreTrainedModel""",
     """SpeechT5HifiGan""",
 ]

if TYPE_CHECKING:
 from .configuration_speechta import (
     SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
     SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
     SpeechTaConfig,
     SpeechTaHifiGanConfig,
 )
 from .feature_extraction_speechta import SpeechTaFeatureExtractor
 from .processing_speechta import SpeechTaProcessor

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_speechta import SpeechTaTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_speechta import (
      SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
      SpeechTaForSpeechToSpeech,
      SpeechTaForSpeechToText,
      SpeechTaForTextToSpeech,
      SpeechTaHifiGan,
      SpeechTaModel,
      SpeechTaPreTrainedModel,
  )

else:
 import sys

 snake_case__      :	Optional[int]       	=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





