






'''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 _lowercase	:




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




							def 		lowerCamelCase_				(							self:				Optional[int]     ):
														lowerCamelCase__					:	Optional[Any]         =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

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

														lowerCamelCase__					:	Tuple         =       self.get_config()

														return config, pixel_values, labels




							def 		lowerCamelCase_				(							self:				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=UpperCamelCase__			,							initializer_range=self.initializer_range			,							mask_ratio=self.mask_ratio			,							)




							def 		lowerCamelCase_				(							self:				Optional[Any]			,							UpperCamelCase__:				Dict			,							UpperCamelCase__:				List[Any]			,							UpperCamelCase__:				List[Any]     ):
														lowerCamelCase__					:	Union[str, Any]         =       ViTMAEModel(config=UpperCamelCase__     )
														model.to(UpperCamelCase__     )
														model.eval()
														lowerCamelCase__					:	Union[str, Any]         =       model(UpperCamelCase__     )
														self.parent.assertEqual(result.last_hidden_state.shape			,							(self.batch_size, self.seq_length, self.hidden_size)     )




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




							def 		lowerCamelCase_				(							self:				Optional[Any]     ):
														lowerCamelCase__					:	List[str]         =       self.prepare_config_and_inputs()
														lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__					:	Dict         =       config_and_inputs
														lowerCamelCase__					:	Optional[int]         =       {"""pixel_values""": pixel_values}
														return config, inputs_dict



@require_torch
class _lowercase	(				_lowercase				,      _lowercase				,      unittest.TestCase		):
							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 		lowerCamelCase_				(							self:				Tuple     ):
														lowerCamelCase__					:	str         =       ViTMAEModelTester(self     )
														lowerCamelCase__					:	List[str]         =       ConfigTester(self			,							config_class=UpperCamelCase__			,							has_text_modality=UpperCamelCase__			,							hidden_size=37     )




							def 		lowerCamelCase_				(							self:				Dict     ):
														self.config_tester.run_common_tests()




							@unittest.skip(reason="""ViTMAE does not use inputs_embeds"""     )
							def 		lowerCamelCase_				(							self:				Dict     ):
														pass




							def 		lowerCamelCase_				(							self:				Any     ):
														lowerCamelCase__  ,		lowerCamelCase__					:	List[Any]         =       self.model_tester.prepare_config_and_inputs_for_common()

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




							def 		lowerCamelCase_				(							self:				List[Any]     ):
														lowerCamelCase__  ,		lowerCamelCase__					:	List[str]         =       self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					lowerCamelCase__					:	Tuple         =       model_class(UpperCamelCase__     )
																					lowerCamelCase__					:	List[Any]         =       inspect.signature(model.forward     )
																					# signature.parameters is an OrderedDict => so arg_names order is deterministic
																					lowerCamelCase__					:	Optional[Any]         =       [*signature.parameters.keys()]

																					lowerCamelCase__					:	Union[str, Any]         =       ["""pixel_values"""]
																					self.assertListEqual(arg_names[:1]			,							UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				int     ):
														lowerCamelCase__					:	Any         =       self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				Tuple     ):
														lowerCamelCase__					:	Any         =       self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_pretraining(*UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				List[Any]			,							UpperCamelCase__:				List[str]			,							UpperCamelCase__:				List[str]			,							UpperCamelCase__:				int     ):
														# make masks reproducible
														np.random.seed(2     )

														lowerCamelCase__					:	Optional[int]         =       int((pt_model.config.image_size // pt_model.config.patch_size) ** 2     )
														lowerCamelCase__					:	str         =       np.random.uniform(size=(self.model_tester.batch_size, num_patches)     )
														lowerCamelCase__					:	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
														lowerCamelCase__					:	List[str]         =       pt_noise

														super().check_pt_tf_models(UpperCamelCase__			,							UpperCamelCase__			,							UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				List[Any]     ):
														lowerCamelCase__  ,		lowerCamelCase__					:	List[str]         =       self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					lowerCamelCase__					:	Optional[Any]         =       model_class(UpperCamelCase__     )
																					model.to(UpperCamelCase__     )
																					model.eval()
																					# make random mask reproducible
																					torch.manual_seed(2     )
																					with torch.no_grad():
																												lowerCamelCase__					:	Dict         =       model(**self._prepare_for_class(UpperCamelCase__			,							UpperCamelCase__     )     )

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

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

																												# Make sure we don't have nans
																												lowerCamelCase__					:	Optional[int]         =       after_outputs[0].cpu().numpy()
																												lowerCamelCase__					:	List[str]         =       0
																												lowerCamelCase__					:	Any         =       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
    to get deterministic results."""     )
							def 		lowerCamelCase_				(							self:				Optional[int]     ):
														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:				Tuple     ):
														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:				Union[str, Any]     ):
														pass




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




							@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""     )
							def 		lowerCamelCase_				(							self:				Union[str, Any]     ):
														pass




							@slow
							def 		lowerCamelCase_				(							self:				Tuple     ):
														for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					lowerCamelCase__					:	Union[str, Any]         =       ViTMAEModel.from_pretrained(UpperCamelCase__     )
																					self.assertIsNotNone(UpperCamelCase__     )





def  SCREAMING_SNAKE_CASE_       ()      ->       Dict:
							lowerCamelCase__					:	int         =       Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""      )
							return image



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




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




							@slow
							def 		lowerCamelCase_				(							self:				Union[str, Any]     ):
														# make random mask reproducible across the PT and TF model
														np.random.seed(2     )

														lowerCamelCase__					:	Any         =       ViTMAEForPreTraining.from_pretrained("""facebook/vit-mae-base"""     ).to(UpperCamelCase__     )

														lowerCamelCase__					:	Optional[int]         =       self.default_image_processor
														lowerCamelCase__					:	Union[str, Any]         =       prepare_img()
														lowerCamelCase__					:	Optional[Any]         =       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)
														lowerCamelCase__					:	Tuple         =       ViTMAEConfig()
														lowerCamelCase__					:	Tuple         =       int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2     )
														lowerCamelCase__					:	Dict         =       np.random.uniform(size=(1, num_patches)     )

														# forward pass
														with torch.no_grad():
																					lowerCamelCase__					:	List[str]         =       model(**UpperCamelCase__			,							noise=torch.from_numpy(UpperCamelCase__     ).to(device=UpperCamelCase__     )     )

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

														lowerCamelCase__					:	str         =       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     )     )





import functools
import operator

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


UpperCAmelCase__   	=							logging.get_logger(__name__)

UpperCAmelCase__   	=							{
    '''microsoft/wavlm-base''': '''https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json''',
    # See all WavLM models at https://huggingface.co/models?filter=wavlm
}







class       lowerCamelCase__   (       lowerCAmelCase):
     SCREAMING_SNAKE_CASE__      			=			'''wavlm'''





     def __init__(self		,     UpperCAmelCase=3_2		,     UpperCAmelCase=7_6_8		,     UpperCAmelCase=1_2		,     UpperCAmelCase=1_2		,     UpperCAmelCase=3_0_7_2		,     UpperCAmelCase="gelu"		,     UpperCAmelCase=0.1		,     UpperCAmelCase=0.1		,     UpperCAmelCase=0.1		,     UpperCAmelCase=0.0		,     UpperCAmelCase=0.1		,     UpperCAmelCase=0.1		,     UpperCAmelCase=0.02		,     UpperCAmelCase=1e-5		,     UpperCAmelCase="group"		,     UpperCAmelCase="gelu"		,     UpperCAmelCase=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2)		,     UpperCAmelCase=(5, 2, 2, 2, 2, 2, 2)		,     UpperCAmelCase=(1_0, 3, 3, 3, 3, 2, 2)		,     UpperCAmelCase=False		,     UpperCAmelCase=1_2_8		,     UpperCAmelCase=1_6		,     UpperCAmelCase=3_2_0		,     UpperCAmelCase=8_0_0		,     UpperCAmelCase=False		,     UpperCAmelCase=True		,     UpperCAmelCase=0.05		,     UpperCAmelCase=1_0		,     UpperCAmelCase=2		,     UpperCAmelCase=0.0		,     UpperCAmelCase=1_0		,     UpperCAmelCase=3_2_0		,     UpperCAmelCase=2		,     UpperCAmelCase=0.1		,     UpperCAmelCase=1_0_0		,     UpperCAmelCase=2_5_6		,     UpperCAmelCase=2_5_6		,     UpperCAmelCase=0.1		,     UpperCAmelCase="mean"		,     UpperCAmelCase=False		,     UpperCAmelCase=False		,     UpperCAmelCase=2_5_6		,     UpperCAmelCase=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 1_5_0_0)		,     UpperCAmelCase=(5, 3, 3, 1, 1)		,     UpperCAmelCase=(1, 2, 3, 1, 1)		,     UpperCAmelCase=5_1_2		,     UpperCAmelCase=8_0		,     UpperCAmelCase=0		,     UpperCAmelCase=1		,     UpperCAmelCase=2		,     UpperCAmelCase=False		,     UpperCAmelCase=3		,     UpperCAmelCase=2		,     UpperCAmelCase=3		,     UpperCAmelCase=None		,     **UpperCAmelCase		,     )     ->   Optional[Any]:
      super().__init__(**UpperCAmelCase		,     pad_token_id=UpperCAmelCase		,     bos_token_id=UpperCAmelCase		,     eos_token_id=UpperCAmelCase       )
      _lowercase							      =hidden_size
      _lowercase							      =feat_extract_norm
      _lowercase							      =feat_extract_activation
      _lowercase							      =list(UpperCAmelCase       )
      _lowercase							      =list(UpperCAmelCase       )
      _lowercase							      =list(UpperCAmelCase       )
      _lowercase							      =conv_bias
      _lowercase							      =num_buckets
      _lowercase							      =max_bucket_distance
      _lowercase							      =num_conv_pos_embeddings
      _lowercase							      =num_conv_pos_embedding_groups
      _lowercase							      =len(self.conv_dim       )
      _lowercase							      =num_hidden_layers
      _lowercase							      =intermediate_size
      _lowercase							      =hidden_act
      _lowercase							      =num_attention_heads
      _lowercase							      =hidden_dropout
      _lowercase							      =attention_dropout
      _lowercase							      =activation_dropout
      _lowercase							      =feat_proj_dropout
      _lowercase							      =final_dropout
      _lowercase							      =layerdrop
      _lowercase							      =layer_norm_eps
      _lowercase							      =initializer_range
      _lowercase							      =num_ctc_classes
      _lowercase							      =vocab_size
      _lowercase							      =do_stable_layer_norm
      _lowercase							      =use_weighted_layer_sum
      _lowercase							      =classifier_proj_size

      if (
          (len(self.conv_stride       ) != self.num_feat_extract_layers)
          or (len(self.conv_kernel       ) != self.num_feat_extract_layers)
          or (len(self.conv_dim       ) != self.num_feat_extract_layers)
      ):
       raise ValueError(
           '''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
           ''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
           f" {len(self.conv_dim       )}`, `len(config.conv_stride) = {len(self.conv_stride       )}`,"
           f" `len(config.conv_kernel) = {len(self.conv_kernel       )}`."       )

      # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
      _lowercase							      =apply_spec_augment
      _lowercase							      =mask_time_prob
      _lowercase							      =mask_time_length
      _lowercase							      =mask_time_min_masks
      _lowercase							      =mask_feature_prob
      _lowercase							      =mask_feature_length

      # parameters for pretraining with codevector quantized representations
      _lowercase							      =num_codevectors_per_group
      _lowercase							      =num_codevector_groups
      _lowercase							      =contrastive_logits_temperature
      _lowercase							      =num_negatives
      _lowercase							      =codevector_dim
      _lowercase							      =proj_codevector_dim
      _lowercase							      =diversity_loss_weight

      # ctc loss
      _lowercase							      =ctc_loss_reduction
      _lowercase							      =ctc_zero_infinity

      # adapter
      _lowercase							      =add_adapter
      _lowercase							      =adapter_kernel_size
      _lowercase							      =adapter_stride
      _lowercase							      =num_adapter_layers
      _lowercase							      =output_hidden_size or hidden_size

      # SequenceClassification-specific parameter. Feel free to ignore for other classes.
      _lowercase							      =classifier_proj_size

      # XVector-specific parameters. Feel free to ignore for other classes.
      _lowercase							      =list(UpperCAmelCase       )
      _lowercase							      =list(UpperCAmelCase       )
      _lowercase							      =list(UpperCAmelCase       )
      _lowercase							      =xvector_output_dim





     @property
     def      __A					(self       )     ->   int:
      return functools.reduce(operator.mul		,     self.conv_stride		,     1       )

