"""simple docstring"""




import collections
import inspect
import unittest

from transformers import FocalNetConfig
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_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch
     from torch import nn

     from transformers import (
         FocalNetBackbone,
         FocalNetForImageClassification,
         FocalNetForMaskedImageModeling,
         FocalNetModel,
     )
     from transformers.models.focalnet.modeling_focalnet import FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
     from PIL import Image

     from transformers import AutoImageProcessor
class 		__A							:






     def __init__(	self       ,  a__       ,  a__=13       ,  a__=32       ,  a__=2       ,  a__=3       ,  a__=16       ,  a__=[32, 64, 128]       ,  a__=[1, 2, 1]       ,  a__=[2, 2, 4]       ,  a__=2       ,  a__=2.0       ,  a__=True       ,  a__=0.0       ,  a__=0.0       ,  a__=0.1       ,  a__="gelu"       ,  a__=False       ,  a__=True       ,  a__=0.0_2       ,  a__=1e-5       ,  a__=True       ,  a__=None       ,  a__=True       ,  a__=10       ,  a__=8       ,  a__=["stage1", "stage2"]       ,  a__=[1, 2]       ,  ):
       _lowerCAmelCase		: str						    =  parent
       _lowerCAmelCase		: Tuple						    =  batch_size
       _lowerCAmelCase		: Optional[Any]						    =  image_size
       _lowerCAmelCase		: int						    =  patch_size
       _lowerCAmelCase		: Dict						    =  num_channels
       _lowerCAmelCase		: Tuple						    =  embed_dim
       _lowerCAmelCase		: Tuple						    =  hidden_sizes
       _lowerCAmelCase		: Tuple						    =  depths
       _lowerCAmelCase		: List[Any]						    =  num_heads
       _lowerCAmelCase		: Optional[int]						    =  window_size
       _lowerCAmelCase		: Optional[int]						    =  mlp_ratio
       _lowerCAmelCase		: Optional[Any]						    =  qkv_bias
       _lowerCAmelCase		: Tuple						    =  hidden_dropout_prob
       _lowerCAmelCase		: Any						    =  attention_probs_dropout_prob
       _lowerCAmelCase		: int						    =  drop_path_rate
       _lowerCAmelCase		: List[Any]						    =  hidden_act
       _lowerCAmelCase		: List[Any]						    =  use_absolute_embeddings
       _lowerCAmelCase		: Union[str, Any]						    =  patch_norm
       _lowerCAmelCase		: Union[str, Any]						    =  layer_norm_eps
       _lowerCAmelCase		: Any						    =  initializer_range
       _lowerCAmelCase		: Union[str, Any]						    =  is_training
       _lowerCAmelCase		: Optional[int]						    =  scope
       _lowerCAmelCase		: Any						    =  use_labels
       _lowerCAmelCase		: Tuple						    =  type_sequence_label_size
       _lowerCAmelCase		: Tuple						    =  encoder_stride
       _lowerCAmelCase		: str						    =  out_features
       _lowerCAmelCase		: Tuple						    =  out_indices






     def 					__A						(	self						):
       _lowerCAmelCase		: Dict						    =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

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

       _lowerCAmelCase		: Dict						    =  self.get_config()

       return config, pixel_values, labels






     def 					__A						(	self						):
       return FocalNetConfig(
           image_size=self.image_size       ,  patch_size=self.patch_size       ,  num_channels=self.num_channels       ,  embed_dim=self.embed_dim       ,  hidden_sizes=self.hidden_sizes       ,  depths=self.depths       ,  num_heads=self.num_heads       ,  window_size=self.window_size       ,  mlp_ratio=self.mlp_ratio       ,  qkv_bias=self.qkv_bias       ,  hidden_dropout_prob=self.hidden_dropout_prob       ,  attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,  drop_path_rate=self.drop_path_rate       ,  hidden_act=self.hidden_act       ,  use_absolute_embeddings=self.use_absolute_embeddings       ,  path_norm=self.patch_norm       ,  layer_norm_eps=self.layer_norm_eps       ,  initializer_range=self.initializer_range       ,  encoder_stride=self.encoder_stride       ,  out_features=self.out_features       ,  out_indices=self.out_indices       ,  )






     def 					__A						(	self       ,  a__       ,  a__       ,  a__						):
       _lowerCAmelCase		: List[Any]						    =  FocalNetModel(config=a__						)
       model.to(a__						)
       model.eval()
       _lowerCAmelCase		: List[str]						    =  model(a__						)

       _lowerCAmelCase		: str						    =  ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths						) - 1))
       _lowerCAmelCase		: List[Any]						    =  int(config.embed_dim * 2 ** (len(config.depths						) - 1)						)

       self.parent.assertEqual(result.last_hidden_state.shape       ,  (self.batch_size, expected_seq_len, expected_dim)						)






     def 					__A						(	self       ,  a__       ,  a__       ,  a__						):
       _lowerCAmelCase		: Dict						    =  FocalNetBackbone(config=a__						)
       model.to(a__						)
       model.eval()
       _lowerCAmelCase		: str						    =  model(a__						)

       # verify feature maps
       self.parent.assertEqual(len(result.feature_maps						)       ,  len(config.out_features						)						)
       self.parent.assertListEqual(list(result.feature_maps[0].shape						)       ,  [self.batch_size, self.image_size, 8, 8]						)

       # verify channels
       self.parent.assertEqual(len(model.channels						)       ,  len(config.out_features						)						)
       self.parent.assertListEqual(model.channels       ,  config.hidden_sizes[:-1]						)

       # verify backbone works with out_features=None
       _lowerCAmelCase		: Union[str, Any]						    =  None
       _lowerCAmelCase		: Optional[int]						    =  FocalNetBackbone(config=a__						)
       model.to(a__						)
       model.eval()
       _lowerCAmelCase		: Union[str, Any]						    =  model(a__						)

       # verify feature maps
       self.parent.assertEqual(len(result.feature_maps						)       ,  1						)
       self.parent.assertListEqual(list(result.feature_maps[0].shape						)       ,  [self.batch_size, self.image_size * 2, 4, 4]						)

       # verify channels
       self.parent.assertEqual(len(model.channels						)       ,  1						)
       self.parent.assertListEqual(model.channels       ,  [config.hidden_sizes[-1]]						)






     def 					__A						(	self       ,  a__       ,  a__       ,  a__						):
       _lowerCAmelCase		: List[str]						    =  FocalNetForMaskedImageModeling(config=a__						)
       model.to(a__						)
       model.eval()
       _lowerCAmelCase		: str						    =  model(a__						)
       self.parent.assertEqual(
           result.reconstruction.shape       ,  (self.batch_size, self.num_channels, self.image_size, self.image_size)						)

       # test greyscale images
       _lowerCAmelCase		: Dict						    =  1
       _lowerCAmelCase		: Any						    =  FocalNetForMaskedImageModeling(a__						)
       model.to(a__						)
       model.eval()

       _lowerCAmelCase		: Optional[int]						    =  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
       _lowerCAmelCase		: Optional[Any]						    =  model(a__						)
       self.parent.assertEqual(result.reconstruction.shape       ,  (self.batch_size, 1, self.image_size, self.image_size)						)






     def 					__A						(	self       ,  a__       ,  a__       ,  a__						):
       _lowerCAmelCase		: str						    =  self.type_sequence_label_size
       _lowerCAmelCase		: Tuple						    =  FocalNetForImageClassification(a__						)
       model.to(a__						)
       model.eval()
       _lowerCAmelCase		: Any						    =  model(a__       ,  labels=a__						)
       self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.type_sequence_label_size)						)

       # test greyscale images
       _lowerCAmelCase		: str						    =  1
       _lowerCAmelCase		: int						    =  FocalNetForImageClassification(a__						)
       model.to(a__						)
       model.eval()

       _lowerCAmelCase		: str						    =  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
       _lowerCAmelCase		: Optional[Any]						    =  model(a__						)
       self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.type_sequence_label_size)						)






     def 					__A						(	self						):
       _lowerCAmelCase		: List[Any]						    =  self.prepare_config_and_inputs()

       _lowerCAmelCase		: Any						    =  config_and_inputs
       _lowerCAmelCase		: Optional[int]						    =  {"""pixel_values""": pixel_values}
       return config, inputs_dict







@require_torch
class 		__A							(				SCREAMING_SNAKE_CASE_					,			SCREAMING_SNAKE_CASE_					,			unittest.TestCase	):
     _UpperCamelCase							:    Any	      =	(
         (
             FocalNetModel,
             FocalNetForImageClassification,
             FocalNetForMaskedImageModeling,
             FocalNetBackbone,
         )
         if is_torch_available()
         else ()
     )
     _UpperCamelCase							:    Optional[Any]	      =	(
         {"feature-extraction": FocalNetModel, "image-classification": FocalNetForImageClassification}
         if is_torch_available()
         else {}
     )
     _UpperCamelCase							:    Optional[Any]	      =	False

     _UpperCamelCase							:    List[Any]	      =	False
     _UpperCamelCase							:    Optional[Any]	      =	False
     _UpperCamelCase							:    Optional[Any]	      =	False
     _UpperCamelCase							:    Any	      =	False






     def 					__A						(	self						):
       _lowerCAmelCase		: Optional[Any]						    =  FocalNetModelTester(self						)
       _lowerCAmelCase		: int						    =  ConfigTester(self       ,  config_class=a__       ,  embed_dim=37       ,  has_text_modality=a__						)






     def 					__A						(	self						):
       self.create_and_test_config_common_properties()
       self.config_tester.create_and_test_config_to_json_string()
       self.config_tester.create_and_test_config_to_json_file()
       self.config_tester.create_and_test_config_from_and_save_pretrained()
       self.config_tester.create_and_test_config_with_num_labels()
       self.config_tester.check_config_can_be_init_without_params()
       self.config_tester.check_config_arguments_init()






     def 					__A						(	self						):
       return






     def 					__A						(	self						):
       _lowerCAmelCase		: Optional[Any]						    =  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*a__						)






     def 					__A						(	self						):
       _lowerCAmelCase		: Any						    =  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_backbone(*a__						)






     def 					__A						(	self						):
       _lowerCAmelCase		: int						    =  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_image_modeling(*a__						)






     def 					__A						(	self						):
       _lowerCAmelCase		: Dict						    =  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*a__						)






     @unittest.skip(reason="""FocalNet does not use inputs_embeds"""						)
     def 					__A						(	self						):
       pass






     @unittest.skip(reason="""FocalNet does not use feedforward chunking"""						)
     def 					__A						(	self						):
       pass






     def 					__A						(	self						):
       _lowerCAmelCase		: Optional[int]						    =  self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes[:-1]:
         _lowerCAmelCase		: List[Any]						    =  model_class(a__						)
         self.assertIsInstance(model.get_input_embeddings()       ,  (nn.Module)						)
         _lowerCAmelCase		: Optional[Any]						    =  model.get_output_embeddings()
         self.assertTrue(x is None or isinstance(a__       ,  nn.Linear						)						)






     def 					__A						(	self						):
       _lowerCAmelCase		: Union[str, Any]						    =  self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes[:-1]:
         _lowerCAmelCase		: str						    =  model_class(a__						)
         _lowerCAmelCase		: List[str]						    =  inspect.signature(model.forward						)
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         _lowerCAmelCase		: Any						    =  [*signature.parameters.keys()]

         _lowerCAmelCase		: Dict						    =  ["""pixel_values"""]
         self.assertListEqual(arg_names[:1]       ,  a__						)






     def 					__A						(	self       ,  a__       ,  a__       ,  a__       ,  a__						):
       _lowerCAmelCase		: Tuple						    =  model_class(a__						)
       model.to(a__						)
       model.eval()

       with torch.no_grad():
         _lowerCAmelCase		: List[str]						    =  model(**self._prepare_for_class(a__       ,  a__						)						)

       _lowerCAmelCase		: Union[str, Any]						    =  outputs.hidden_states

       _lowerCAmelCase		: List[str]						    =  getattr(
           self.model_tester       ,  """expected_num_hidden_layers"""       ,  len(self.model_tester.depths						) + 1						)
       self.assertEqual(len(a__						)       ,  a__						)

       # FocalNet has a different seq_length
       _lowerCAmelCase		: Optional[Any]						    =  (
           config.patch_size
           if isinstance(config.patch_size       ,  collections.abc.Iterable						)
           else (config.patch_size, config.patch_size)
       )

       _lowerCAmelCase		: Optional[Any]						    =  (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

       self.assertListEqual(
           list(hidden_states[0].shape[-2:]						)       ,  [num_patches, self.model_tester.embed_dim]       ,  )

       _lowerCAmelCase		: Any						    =  outputs.reshaped_hidden_states
       self.assertEqual(len(a__						)       ,  a__						)

       _lowerCAmelCase		: Union[str, Any]						    =  reshaped_hidden_states[0].shape
       _lowerCAmelCase		: List[str]						    =  (
           reshaped_hidden_states[0].view(a__       ,  a__       ,  height * width						).permute(0       ,  2       ,  1						)
       )
       self.assertListEqual(
           list(reshaped_hidden_states.shape[-2:]						)       ,  [num_patches, self.model_tester.embed_dim]       ,  )






     def 					__A						(	self						):
       _lowerCAmelCase		: List[Any]						    =  self.model_tester.prepare_config_and_inputs_for_common()

       _lowerCAmelCase		: List[Any]						    =  (
           self.model_tester.image_size
           if isinstance(self.model_tester.image_size       ,  collections.abc.Iterable						)
           else (self.model_tester.image_size, self.model_tester.image_size)
       )

       for model_class in self.all_model_classes[:-1]:
         _lowerCAmelCase		: int						    =  True
         self.check_hidden_states_output(a__       ,  a__       ,  a__       ,  a__						)

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         _lowerCAmelCase		: Any						    =  True

         self.check_hidden_states_output(a__       ,  a__       ,  a__       ,  a__						)






     def 					__A						(	self						):
       _lowerCAmelCase		: Optional[Any]						    =  self.model_tester.prepare_config_and_inputs_for_common()
       _lowerCAmelCase		: Optional[Any]						    =  3

       _lowerCAmelCase		: Any						    =  (
           self.model_tester.image_size
           if isinstance(self.model_tester.image_size       ,  collections.abc.Iterable						)
           else (self.model_tester.image_size, self.model_tester.image_size)
       )
       _lowerCAmelCase		: int						    =  (
           config.patch_size
           if isinstance(config.patch_size       ,  collections.abc.Iterable						)
           else (config.patch_size, config.patch_size)
       )

       _lowerCAmelCase		: Optional[int]						    =  image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
       _lowerCAmelCase		: Dict						    =  image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

       for model_class in self.all_model_classes[:-1]:
         _lowerCAmelCase		: str						    =  True
         self.check_hidden_states_output(a__       ,  a__       ,  a__       ,  (padded_height, padded_width)						)

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         _lowerCAmelCase		: int						    =  True
         self.check_hidden_states_output(a__       ,  a__       ,  a__       ,  (padded_height, padded_width)						)






     @slow
     def 					__A						(	self						):
       for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _lowerCAmelCase		: Any						    =  FocalNetModel.from_pretrained(a__						)
         self.assertIsNotNone(a__						)






     def 					__A						(	self						):
       _lowerCAmelCase		: Dict						    =  self.model_tester.prepare_config_and_inputs_for_common()

       _lowerCAmelCase		: Any						    =  _config_zero_init(a__						)
       for model_class in self.all_model_classes:
         _lowerCAmelCase		: Optional[int]						    =  model_class(config=a__						)
         for name, param in model.named_parameters():
           if "embeddings" not in name and param.requires_grad:
             self.assertIn(
                 ((param.data.mean() * 1e9).round() / 1e9).item()       ,  [0.0, 1.0]       ,  msg=F"Parameter {name} of model {model_class} seems not properly initialized"       ,  )







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






     @cached_property
     def 					__A						(	self						):
       # TODO update organization
       return AutoImageProcessor.from_pretrained("""microsoft/focalnet-tiny"""						) if is_vision_available() else None






     @slow
     def 					__A						(	self						):
       _lowerCAmelCase		: List[str]						    =  FocalNetForImageClassification.from_pretrained("""microsoft/focalnet-tiny"""						).to(a__						)
       _lowerCAmelCase		: Dict						    =  self.default_image_processor

       _lowerCAmelCase		: List[str]						    =  Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)
       _lowerCAmelCase		: str						    =  image_processor(images=a__       ,  return_tensors="""pt"""						).to(a__						)

       # forward pass
       with torch.no_grad():
         _lowerCAmelCase		: Dict						    =  model(**a__						)

       # verify the logits
       _lowerCAmelCase		: Tuple						    =  torch.Size((1, 1000)						)
       self.assertEqual(outputs.logits.shape       ,  a__						)
       _lowerCAmelCase		: Any						    =  torch.tensor([0.2_1_6_6, -0.4_3_6_8, 0.2_1_9_1]						).to(a__						)
       self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,  a__       ,  atol=1e-4						)						)
       self.assertTrue(outputs.logits.argmax(dim=-1						).item()       ,  281						)







@require_torch
class 		__A							(				SCREAMING_SNAKE_CASE_					,			unittest.TestCase	):
     _UpperCamelCase							:    Any	      =	(FocalNetBackbone,) if is_torch_available() else ()
     _UpperCamelCase							:    int	      =	FocalNetConfig

     _UpperCamelCase							:    List[str]	      =	False






     def 					__A						(	self						):
       _lowerCAmelCase		: Any						    =  FocalNetModelTester(self						)


"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_a   :	Tuple 					=     {'configuration_wavlm': ['WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'WavLMConfig']}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _a   :	str 					=     [
         'WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST',
         'WavLMForAudioFrameClassification',
         'WavLMForCTC',
         'WavLMForSequenceClassification',
         'WavLMForXVector',
         'WavLMModel',
         'WavLMPreTrainedModel',
     ]

if TYPE_CHECKING:
     from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_wavlm import (
              WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
              WavLMForAudioFrameClassification,
              WavLMForCTC,
              WavLMForSequenceClassification,
              WavLMForXVector,
              WavLMModel,
              WavLMPreTrainedModel,
          )

else:
     import sys

     _a   :	Any 					=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




