



'''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				:						List[Any]		,    __snake_case				:						Dict		,    __snake_case				:						Union[str, Any]=13		,    __snake_case				:						Optional[int]=32		,    __snake_case				:						Dict=2		,    __snake_case				:						List[str]=3		,    __snake_case				:						Tuple=16		,    __snake_case				:						Tuple=[32, 64, 1_28]		,    __snake_case				:						Optional[int]=[1, 2, 1]		,    __snake_case				:						List[Any]=[2, 2, 4]		,    __snake_case				:						Dict=2		,    __snake_case				:						Optional[int]=2.0		,    __snake_case				:						str=True		,    __snake_case				:						Dict=0.0		,    __snake_case				:						Union[str, Any]=0.0		,    __snake_case				:						Optional[Any]=0.1		,    __snake_case				:						List[Any]="gelu"		,    __snake_case				:						Any=False		,    __snake_case				:						Tuple=True		,    __snake_case				:						Tuple=0.02		,    __snake_case				:						str=1e-5		,    __snake_case				:						str=True		,    __snake_case				:						Optional[int]=None		,    __snake_case				:						Union[str, Any]=True		,    __snake_case				:						Dict=10		,    __snake_case				:						Any=8		,    __snake_case				:						Optional[Any]=["stage1", "stage2"]		,    __snake_case				:						Optional[Any]=[1, 2]		,    ):
      a		:      int       =  parent
      a		:      Union[str, Any]       =  batch_size
      a		:      Any       =  image_size
      a		:      Tuple       =  patch_size
      a		:      Union[str, Any]       =  num_channels
      a		:      int       =  embed_dim
      a		:      Tuple       =  hidden_sizes
      a		:      List[Any]       =  depths
      a		:      List[Any]       =  num_heads
      a		:      Tuple       =  window_size
      a		:      int       =  mlp_ratio
      a		:      Dict       =  qkv_bias
      a		:      Any       =  hidden_dropout_prob
      a		:      int       =  attention_probs_dropout_prob
      a		:      List[str]       =  drop_path_rate
      a		:      Dict       =  hidden_act
      a		:      Dict       =  use_absolute_embeddings
      a		:      List[Any]       =  patch_norm
      a		:      List[str]       =  layer_norm_eps
      a		:      Any       =  initializer_range
      a		:      Dict       =  is_training
      a		:      Optional[int]       =  scope
      a		:      Tuple       =  use_labels
      a		:      int       =  type_sequence_label_size
      a		:      Tuple       =  encoder_stride
      a		:      str       =  out_features
      a		:      List[str]       =  out_indices







    def     lowercase_ (   self				:						Union[str, Any]			):
      a		:      int       =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

      a		:      Optional[Any]       =  None
      if self.use_labels:
        a		:      str       =  ids_tensor([self.batch_size]		,    self.type_sequence_label_size			)

      a		:      int       =  self.get_config()

      return config, pixel_values, labels







    def     lowercase_ (   self				:						Any			):
      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     lowercase_ (   self				:						List[str]		,    __snake_case				:						List[str]		,    __snake_case				:						Tuple		,    __snake_case				:						List[str]			):
      a		:      List[Any]       =  FocalNetModel(config=__snake_case			)
      model.to(__snake_case			)
      model.eval()
      a		:      List[str]       =  model(__snake_case			)

      a		:      List[Any]       =  ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths			) - 1))
      a		:      Optional[int]       =  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     lowercase_ (   self				:						str		,    __snake_case				:						int		,    __snake_case				:						Tuple		,    __snake_case				:						Tuple			):
      a		:      List[str]       =  FocalNetBackbone(config=__snake_case			)
      model.to(__snake_case			)
      model.eval()
      a		:      Dict       =  model(__snake_case			)

      # 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
      a		:      str       =  None
      a		:      Dict       =  FocalNetBackbone(config=__snake_case			)
      model.to(__snake_case			)
      model.eval()
      a		:      int       =  model(__snake_case			)

      # 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     lowercase_ (   self				:						int		,    __snake_case				:						Optional[int]		,    __snake_case				:						Union[str, Any]		,    __snake_case				:						str			):
      a		:      int       =  FocalNetForMaskedImageModeling(config=__snake_case			)
      model.to(__snake_case			)
      model.eval()
      a		:      List[str]       =  model(__snake_case			)
      self.parent.assertEqual(
          result.reconstruction.shape		,    (self.batch_size, self.num_channels, self.image_size, self.image_size)			)

      # test greyscale images
      a		:      str       =  1
      a		:      Optional[Any]       =  FocalNetForMaskedImageModeling(__snake_case			)
      model.to(__snake_case			)
      model.eval()

      a		:      List[str]       =  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
      a		:      str       =  model(__snake_case			)
      self.parent.assertEqual(result.reconstruction.shape		,    (self.batch_size, 1, self.image_size, self.image_size)			)







    def     lowercase_ (   self				:						str		,    __snake_case				:						Any		,    __snake_case				:						List[Any]		,    __snake_case				:						Optional[int]			):
      a		:      Dict       =  self.type_sequence_label_size
      a		:      Tuple       =  FocalNetForImageClassification(__snake_case			)
      model.to(__snake_case			)
      model.eval()
      a		:      str       =  model(__snake_case		,    labels=__snake_case			)
      self.parent.assertEqual(result.logits.shape		,    (self.batch_size, self.type_sequence_label_size)			)

      # test greyscale images
      a		:      List[Any]       =  1
      a		:      str       =  FocalNetForImageClassification(__snake_case			)
      model.to(__snake_case			)
      model.eval()

      a		:      Tuple       =  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
      a		:      Optional[Any]       =  model(__snake_case			)
      self.parent.assertEqual(result.logits.shape		,    (self.batch_size, self.type_sequence_label_size)			)







    def     lowercase_ (   self				:						Union[str, Any]			):
      a		:      List[Any]       =  self.prepare_config_and_inputs()

      a     ,			a     ,			a		:      Optional[Any]       =  config_and_inputs
      a		:      List[str]       =  {'pixel_values': pixel_values}
      return config, inputs_dict

@require_torch
class 							a__(      lowerCamelCase__	,				lowerCamelCase__	,				unittest.TestCase			):
    lowercase__				=					(
        (
            FocalNetModel,
            FocalNetForImageClassification,
            FocalNetForMaskedImageModeling,
            FocalNetBackbone,
        )
        if is_torch_available()
        else ()
    )
    lowercase__				=					(
        {"""feature-extraction""": FocalNetModel, """image-classification""": FocalNetForImageClassification}
        if is_torch_available()
        else {}
    )
    lowercase__				=					False

    lowercase__				=					False
    lowercase__				=					False
    lowercase__				=					False
    lowercase__				=					False







    def     lowercase_ (   self				:						int			):
      a		:      Union[str, Any]       =  FocalNetModelTester(self			)
      a		:      Any       =  ConfigTester(self		,    config_class=__snake_case		,    embed_dim=37		,    has_text_modality=__snake_case			)







    def     lowercase_ (   self				:						Union[str, Any]			):
      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     lowercase_ (   self				:						str			):
      return







    def     lowercase_ (   self				:						Tuple			):
      a		:      List[Any]       =  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__snake_case			)







    def     lowercase_ (   self				:						str			):
      a		:      Union[str, Any]       =  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_backbone(*__snake_case			)







    def     lowercase_ (   self				:						List[Any]			):
      a		:      Dict       =  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_image_modeling(*__snake_case			)







    def     lowercase_ (   self				:						Tuple			):
      a		:      Dict       =  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*__snake_case			)







    @unittest.skip(reason='FocalNet does not use inputs_embeds'			)
    def     lowercase_ (   self				:						Tuple			):
      pass







    @unittest.skip(reason='FocalNet does not use feedforward chunking'			)
    def     lowercase_ (   self				:						Dict			):
      pass







    def     lowercase_ (   self				:						Optional[int]			):
      a     ,			a		:      Optional[Any]       =  self.model_tester.prepare_config_and_inputs_for_common()

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







    def     lowercase_ (   self				:						List[str]			):
      a     ,			a		:      Dict       =  self.model_tester.prepare_config_and_inputs_for_common()

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

        a		:      Optional[Any]       =  ['pixel_values']
        self.assertListEqual(arg_names[:1]		,    __snake_case			)







    def     lowercase_ (   self				:						List[Any]		,    __snake_case				:						Any		,    __snake_case				:						List[Any]		,    __snake_case				:						List[str]		,    __snake_case				:						Dict			):
      a		:      Optional[int]       =  model_class(__snake_case			)
      model.to(__snake_case			)
      model.eval()

      with torch.no_grad():
        a		:      List[Any]       =  model(**self._prepare_for_class(__snake_case		,    __snake_case			)			)

      a		:      List[Any]       =  outputs.hidden_states

      a		:      List[Any]       =  getattr(
          self.model_tester		,    'expected_num_hidden_layers'		,    len(self.model_tester.depths			) + 1			)
      self.assertEqual(len(__snake_case			)		,    __snake_case			)

      # FocalNet has a different seq_length
      a		:      Tuple       =  (
          config.patch_size
          if isinstance(config.patch_size		,    collections.abc.Iterable			)
          else (config.patch_size, config.patch_size)
      )

      a		:      int       =  (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]		,    )

      a		:      Optional[Any]       =  outputs.reshaped_hidden_states
      self.assertEqual(len(__snake_case			)		,    __snake_case			)

      a     ,			a     ,			a     ,			a		:      int       =  reshaped_hidden_states[0].shape
      a		:      Any       =  (
          reshaped_hidden_states[0].view(__snake_case		,    __snake_case		,    height * width			).permute(0		,    2		,    1			)
      )
      self.assertListEqual(
          list(reshaped_hidden_states.shape[-2:]			)		,    [num_patches, self.model_tester.embed_dim]		,    )







    def     lowercase_ (   self				:						Any			):
      a     ,			a		:      List[Any]       =  self.model_tester.prepare_config_and_inputs_for_common()

      a		:      Optional[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]:
        a		:      int       =  True
        self.check_hidden_states_output(__snake_case		,    __snake_case		,    __snake_case		,    __snake_case			)

        # check that output_hidden_states also work using config
        del inputs_dict["output_hidden_states"]
        a		:      Optional[int]       =  True

        self.check_hidden_states_output(__snake_case		,    __snake_case		,    __snake_case		,    __snake_case			)







    def     lowercase_ (   self				:						int			):
      a     ,			a		:      List[str]       =  self.model_tester.prepare_config_and_inputs_for_common()
      a		:      int       =  3

      a		:      Dict       =  (
          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)
      )
      a		:      Optional[int]       =  (
          config.patch_size
          if isinstance(config.patch_size		,    collections.abc.Iterable			)
          else (config.patch_size, config.patch_size)
      )

      a		:      List[Any]       =  image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
      a		:      int       =  image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

      for model_class in self.all_model_classes[:-1]:
        a		:      Any       =  True
        self.check_hidden_states_output(__snake_case		,    __snake_case		,    __snake_case		,    (padded_height, padded_width)			)

        # check that output_hidden_states also work using config
        del inputs_dict["output_hidden_states"]
        a		:      List[Any]       =  True
        self.check_hidden_states_output(__snake_case		,    __snake_case		,    __snake_case		,    (padded_height, padded_width)			)







    @slow
    def     lowercase_ (   self				:						Dict			):
      for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        a		:      Optional[Any]       =  FocalNetModel.from_pretrained(__snake_case			)
        self.assertIsNotNone(__snake_case			)







    def     lowercase_ (   self				:						int			):
      a     ,			a		:      List[Any]       =  self.model_tester.prepare_config_and_inputs_for_common()

      a		:      Any       =  _config_zero_init(__snake_case			)
      for model_class in self.all_model_classes:
        a		:      Tuple       =  model_class(config=__snake_case			)
        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     lowercase_ (   self				:						Optional[int]			):
      # TODO update organization
      return AutoImageProcessor.from_pretrained('microsoft/focalnet-tiny'			) if is_vision_available() else None







    @slow
    def     lowercase_ (   self				:						List[Any]			):
      a		:      Tuple       =  FocalNetForImageClassification.from_pretrained('microsoft/focalnet-tiny'			).to(__snake_case			)
      a		:      List[str]       =  self.default_image_processor

      a		:      Optional[Any]       =  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
      a		:      int       =  image_processor(images=__snake_case		,    return_tensors='pt'			).to(__snake_case			)

      # forward pass
      with torch.no_grad():
        a		:      Tuple       =  model(**__snake_case			)

      # verify the logits
      a		:      int       =  torch.Size((1, 10_00)			)
      self.assertEqual(outputs.logits.shape		,    __snake_case			)
      a		:      str       =  torch.tensor([0.2166, -0.4368, 0.2191]			).to(__snake_case			)
      self.assertTrue(torch.allclose(outputs.logits[0, :3]		,    __snake_case		,    atol=1e-4			)			)
      self.assertTrue(outputs.logits.argmax(dim=-1			).item()		,    2_81			)

@require_torch
class 							a__(      lowerCamelCase__	,				unittest.TestCase			):
    lowercase__				=					(FocalNetBackbone,) if is_torch_available() else ()
    lowercase__				=					FocalNetConfig

    lowercase__				=					False







    def     lowercase_ (   self				:						Any			):
      a		:      Union[str, Any]       =  FocalNetModelTester(self			)




'''simple docstring'''






import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def    lowerCamelCase__ (		_A       ):
  return (data["data"], data["target"])
def    lowerCamelCase__ (		_A				,    _A				,    _A       ):
  a		:      Any       =  XGBRegressor(verbosity=0				,    random_state=42       )
  xgb.fit(_A				,    _A       )
  # Predict target for test data
  a		:      Any       =  xgb.predict(_A       )
  a		:      Any       =  predictions.reshape(len(_A       )				,    1       )
  return predictions
def    lowerCamelCase__ (		):
  a		:      Optional[int]       =  fetch_california_housing()
  a     ,			a		:      Optional[Any]       =  data_handling(_A       )
  a     ,			a     ,			a     ,			a		:      Dict       =  train_test_split(
      _A				,    _A				,    test_size=0.25				,    random_state=1       )
  a		:      List[str]       =  xgboost(_A				,    _A				,    _A       )
  # Error printing
  print(f"""Mean Absolute Error : {mean_absolute_error(_A				,    _A       )}"""       )
  print(f"""Mean Square Error  : {mean_squared_error(_A				,    _A       )}"""       )


if __name__ == "__main__":
     import doctest

     doctest.testmod(verbose=True)
     main()