code: 
  
  
  
  '''simple docstring'''
  
  
  
  import collections
  import inspect
  import unittest
  
  from transformers import NatConfig
  from transformers.testing_utils import require_natten, 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 NatBackbone, NatForImageClassification, NatModel
   from transformers.models.nat.modeling_nat import NAT_PRETRAINED_MODEL_ARCHIVE_LIST
  
  if is_vision_available():
   from PIL import Image
  
   from transformers import AutoImageProcessor
  class        UpperCAmelCase__  :
  
  
  
   def __init__(  self		,	lowercase		,	lowercase=1_3		,	lowercase=6_4		,	lowercase=4		,	lowercase=3		,	lowercase=1_6		,	lowercase=[1, 2, 1]		,	lowercase=[2, 4, 8]		,	lowercase=3		,	lowercase=2.0		,	lowercase=True		,	lowercase=0.0		,	lowercase=0.0		,	lowercase=0.1		,	lowercase="gelu"		,	lowercase=True		,	lowercase=0.02		,	lowercase=1E-5		,	lowercase=True		,	lowercase=None		,	lowercase=True		,	lowercase=1_0		,	lowercase=["stage1", "stage2"]		,	lowercase=[1, 2]		,	)  ->							str:
    __UpperCamelCase				      =  parent
    __UpperCamelCase				      =  batch_size
    __UpperCamelCase				      =  image_size
    __UpperCamelCase				      =  patch_size
    __UpperCamelCase				      =  num_channels
    __UpperCamelCase				      =  embed_dim
    __UpperCamelCase				      =  depths
    __UpperCamelCase				      =  num_heads
    __UpperCamelCase				      =  kernel_size
    __UpperCamelCase				      =  mlp_ratio
    __UpperCamelCase				      =  qkv_bias
    __UpperCamelCase				      =  hidden_dropout_prob
    __UpperCamelCase				      =  attention_probs_dropout_prob
    __UpperCamelCase				      =  drop_path_rate
    __UpperCamelCase				      =  hidden_act
    __UpperCamelCase				      =  patch_norm
    __UpperCamelCase				      =  layer_norm_eps
    __UpperCamelCase				      =  initializer_range
    __UpperCamelCase				      =  is_training
    __UpperCamelCase				      =  scope
    __UpperCamelCase				      =  use_labels
    __UpperCamelCase				      =  num_labels
    __UpperCamelCase				      =  out_features
    __UpperCamelCase				      =  out_indices
  
  
  
   def    __lowerCamelCase				(  self			)  ->							Optional[Any]:
    __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.num_labels			)
  
    __UpperCamelCase				      =  self.get_config()
  
    return config, pixel_values, labels
  
  
  
   def    __lowerCamelCase				(  self			)  ->							List[Any]:
    return NatConfig(
        num_labels=self.num_labels		,	image_size=self.image_size		,	patch_size=self.patch_size		,	num_channels=self.num_channels		,	embed_dim=self.embed_dim		,	depths=self.depths		,	num_heads=self.num_heads		,	kernel_size=self.kernel_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		,	patch_norm=self.patch_norm		,	layer_norm_eps=self.layer_norm_eps		,	initializer_range=self.initializer_range		,	out_features=self.out_features		,	out_indices=self.out_indices		,	)
  
  
  
   def    __lowerCamelCase				(  self		,	lowercase		,	lowercase		,	lowercase			)  ->							List[str]:
    __UpperCamelCase				      =  NatModel(config=lowercase			)
    model.to(lowercase			)
    model.eval()
    __UpperCamelCase				      =  model(lowercase			)
  
    __UpperCamelCase				      =  __UpperCamelCase				      =  (config.image_size // config.patch_size) // (2 ** (len(config.depths			) - 1))
    __UpperCamelCase				      =  int(config.embed_dim * 2 ** (len(config.depths			) - 1)			)
  
    self.parent.assertEqual(
        result.last_hidden_state.shape		,	(self.batch_size, expected_height, expected_width, expected_dim)			)
  
  
  
   def    __lowerCamelCase				(  self		,	lowercase		,	lowercase		,	lowercase			)  ->							List[str]:
    __UpperCamelCase				      =  NatForImageClassification(lowercase			)
    model.to(lowercase			)
    model.eval()
    __UpperCamelCase				      =  model(lowercase		,	labels=lowercase			)
    self.parent.assertEqual(result.logits.shape		,	(self.batch_size, self.num_labels)			)
  
    # test greyscale images
    __UpperCamelCase				      =  1
    __UpperCamelCase				      =  NatForImageClassification(lowercase			)
    model.to(lowercase			)
    model.eval()
  
    __UpperCamelCase				      =  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
    __UpperCamelCase				      =  model(lowercase			)
    self.parent.assertEqual(result.logits.shape		,	(self.batch_size, self.num_labels)			)
  
  
  
   def    __lowerCamelCase				(  self		,	lowercase		,	lowercase		,	lowercase			)  ->							Tuple:
    __UpperCamelCase				      =  NatBackbone(config=lowercase			)
    model.to(lowercase			)
    model.eval()
    __UpperCamelCase				      =  model(lowercase			)
  
    # verify hidden states
    self.parent.assertEqual(len(result.feature_maps			)		,	len(config.out_features			)			)
    self.parent.assertListEqual(list(result.feature_maps[0].shape			)		,	[self.batch_size, model.channels[0], 1_6, 1_6]			)
  
    # verify channels
    self.parent.assertEqual(len(model.channels			)		,	len(config.out_features			)			)
  
    # verify backbone works with out_features=None
    __UpperCamelCase				      =  None
    __UpperCamelCase				      =  NatBackbone(config=lowercase			)
    model.to(lowercase			)
    model.eval()
    __UpperCamelCase				      =  model(lowercase			)
  
    # verify feature maps
    self.parent.assertEqual(len(result.feature_maps			)		,	1			)
    self.parent.assertListEqual(list(result.feature_maps[0].shape			)		,	[self.batch_size, model.channels[-1], 4, 4]			)
  
    # verify channels
    self.parent.assertEqual(len(model.channels			)		,	1			)
  
  
  
  
   def    __lowerCamelCase				(  self			)  ->							str:
    __UpperCamelCase				      =  self.prepare_config_and_inputs()
    __UpperCamelCase      ,		__UpperCamelCase      ,		__UpperCamelCase				      =  config_and_inputs
    __UpperCamelCase				      =  {"""pixel_values""": pixel_values}
    return config, inputs_dict
  
  
  @require_natten
  @require_torch
  class        UpperCAmelCase__  (    UpperCAmelCase_							,			UpperCAmelCase_							,			unittest.TestCase):
   __SCREAMING_SNAKE_CASE					      =				(
       (
           NatModel,
           NatForImageClassification,
           NatBackbone,
       )
       if is_torch_available()
       else ()
   )
   __SCREAMING_SNAKE_CASE					      =				(
       {'''feature-extraction''': NatModel, '''image-classification''': NatForImageClassification}
       if is_torch_available()
       else {}
   )
   __SCREAMING_SNAKE_CASE					      =				False
  
   __SCREAMING_SNAKE_CASE					      =				False
   __SCREAMING_SNAKE_CASE					      =				False
   __SCREAMING_SNAKE_CASE					      =				False
   __SCREAMING_SNAKE_CASE					      =				False
  
  
  
   def    __lowerCamelCase				(  self			)  ->							Dict:
    __UpperCamelCase				      =  NatModelTester(self			)
    __UpperCamelCase				      =  ConfigTester(self		,	config_class=lowercase		,	embed_dim=3_7			)
  
  
  
   def    __lowerCamelCase				(  self			)  ->							Tuple:
    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    __lowerCamelCase				(  self			)  ->							Tuple:
    return
  
  
  
   def    __lowerCamelCase				(  self			)  ->							Tuple:
    __UpperCamelCase				      =  self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*lowercase			)
  
  
  
   def    __lowerCamelCase				(  self			)  ->							str:
    __UpperCamelCase				      =  self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_image_classification(*lowercase			)
  
  
  
   def    __lowerCamelCase				(  self			)  ->							List[str]:
    __UpperCamelCase				      =  self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_backbone(*lowercase			)
  
  
  
   @unittest.skip(reason="""Nat does not use inputs_embeds"""			)
   def    __lowerCamelCase				(  self			)  ->							Tuple:
    pass
  
  
  
   @unittest.skip(reason="""Nat does not use feedforward chunking"""			)
   def    __lowerCamelCase				(  self			)  ->							str:
    pass
  
  
  
   def    __lowerCamelCase				(  self			)  ->							Any:
    __UpperCamelCase      ,		__UpperCamelCase				      =  self.model_tester.prepare_config_and_inputs_for_common()
  
    for model_class in self.all_model_classes:
     __UpperCamelCase				      =  model_class(lowercase			)
     self.assertIsInstance(model.get_input_embeddings()		,	(nn.Module)			)
     __UpperCamelCase				      =  model.get_output_embeddings()
     self.assertTrue(x is None or isinstance(lowercase		,	nn.Linear			)			)
  
  
  
   def    __lowerCamelCase				(  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(lowercase			)
     __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]		,	lowercase			)
  
  
  
   def    __lowerCamelCase				(  self			)  ->							Tuple:
    self.skipTest("""Nat's attention operation is handled entirely by NATTEN."""			)
  
  
  
   def    __lowerCamelCase				(  self		,	lowercase		,	lowercase		,	lowercase		,	lowercase			)  ->							Dict:
    __UpperCamelCase				      =  model_class(lowercase			)
    model.to(lowercase			)
    model.eval()
  
    with torch.no_grad():
     __UpperCamelCase				      =  model(**self._prepare_for_class(lowercase		,	lowercase			)			)
  
    __UpperCamelCase				      =  outputs.hidden_states
  
    __UpperCamelCase				      =  getattr(
        self.model_tester		,	"""expected_num_hidden_layers"""		,	len(self.model_tester.depths			) + 1			)
    self.assertEqual(len(lowercase			)		,	lowercase			)
  
    # Nat has a different seq_length
    __UpperCamelCase				      =  (
        config.patch_size
        if isinstance(config.patch_size		,	collections.abc.Iterable			)
        else (config.patch_size, config.patch_size)
    )
  
    __UpperCamelCase				      =  image_size[0] // patch_size[0]
    __UpperCamelCase				      =  image_size[1] // patch_size[1]
  
    self.assertListEqual(
        list(hidden_states[0].shape[-3:]			)		,	[height, width, self.model_tester.embed_dim]		,	)
  
    if model_class.__name__ != "NatBackbone":
     __UpperCamelCase				      =  outputs.reshaped_hidden_states
     self.assertEqual(len(lowercase			)		,	lowercase			)
  
     __UpperCamelCase      ,		__UpperCamelCase      ,		__UpperCamelCase      ,		__UpperCamelCase				      =  reshaped_hidden_states[0].shape
     __UpperCamelCase				      =  (
         reshaped_hidden_states[0].view(lowercase		,	lowercase		,	lowercase		,	lowercase			).permute(0		,	2		,	3		,	1			)
     )
     self.assertListEqual(
         list(reshaped_hidden_states.shape[-3:]			)		,	[height, width, self.model_tester.embed_dim]		,	)
  
  
  
   def    __lowerCamelCase				(  self			)  ->							str:
    __UpperCamelCase      ,		__UpperCamelCase				      =  self.model_tester.prepare_config_and_inputs_for_common()
  
    __UpperCamelCase				      =  (
        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:
     __UpperCamelCase				      =  True
     self.check_hidden_states_output(lowercase		,	lowercase		,	lowercase		,	lowercase			)
  
     # check that output_hidden_states also work using config
     del inputs_dict["output_hidden_states"]
     __UpperCamelCase				      =  True
  
     self.check_hidden_states_output(lowercase		,	lowercase		,	lowercase		,	lowercase			)
  
  
  
   @slow
   def    __lowerCamelCase				(  self			)  ->							Union[str, Any]:
    for model_name in NAT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     __UpperCamelCase				      =  NatModel.from_pretrained(lowercase			)
     self.assertIsNotNone(lowercase			)
  
  
  
  
   def    __lowerCamelCase				(  self			)  ->							Optional[Any]:
    __UpperCamelCase      ,		__UpperCamelCase				      =  self.model_tester.prepare_config_and_inputs_for_common()
  
    __UpperCamelCase				      =  _config_zero_init(lowercase			)
    for model_class in self.all_model_classes:
     __UpperCamelCase				      =  model_class(config=lowercase			)
     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_natten
  @require_vision
  @require_torch
  class        UpperCAmelCase__  (    unittest.TestCase):
  
  
  
   @cached_property
   def    __lowerCamelCase				(  self			)  ->							Tuple:
    return AutoImageProcessor.from_pretrained("""shi-labs/nat-mini-in1k-224"""			) if is_vision_available() else None
  
  
  
  
   @slow
   def    __lowerCamelCase				(  self			)  ->							Optional[int]:
    __UpperCamelCase				      =  NatForImageClassification.from_pretrained("""shi-labs/nat-mini-in1k-224"""			).to(lowercase			)
    __UpperCamelCase				      =  self.default_image_processor
  
    __UpperCamelCase				      =  Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""			)
    __UpperCamelCase				      =  image_processor(images=lowercase		,	return_tensors="""pt"""			).to(lowercase			)
  
    # forward pass
    with torch.no_grad():
     __UpperCamelCase				      =  model(**lowercase			)
  
    # verify the logits
    __UpperCamelCase				      =  torch.Size((1, 1_0_0_0)			)
    self.assertEqual(outputs.logits.shape		,	lowercase			)
    __UpperCamelCase				      =  torch.tensor([0.3_805, -0.8_676, -0.3_912]			).to(lowercase			)
    self.assertTrue(torch.allclose(outputs.logits[0, :3]		,	lowercase		,	atol=1E-4			)			)
  
  
  @require_torch
  @require_natten
  class        UpperCAmelCase__  (    unittest.TestCase							,			UpperCAmelCase_):
   __SCREAMING_SNAKE_CASE					      =				(NatBackbone,) if is_torch_available() else ()
   __SCREAMING_SNAKE_CASE					      =				NatConfig
  
  
  
   def    __lowerCamelCase				(  self			)  ->							int:
    __UpperCamelCase				      =  NatModelTester(self			)
  
  
  
  
  
  
style_context: 
  
  
  
  '''simple docstring'''
  
  
  
  import textwrap
  
  import pyarrow as pa
  import pytest
  
  from datasets import Features, Image
  from datasets.packaged_modules.text.text import Text
  
  from ..utils import require_pil
  
  
  
  
  
  
  
  @pytest.fixture
  def _lowercase   (  __A       ):
  
  
  
   '''simple docstring'''
  
  
  
   __UpperCamelCase				      =  tmp_path / """text.txt"""
   __UpperCamelCase				      =  textwrap.dedent(
       """\
          Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
          Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
          Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
          Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
  
          Second paragraph:
          Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
          Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
          Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
          Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
          """       )
   with open(__A							,"""w"""							,encoding="""utf-8"""       ) as f:
    f.write(__A       )
   return str(__A       )
  
  
  
  
  
  
  
  @pytest.fixture
  def _lowercase   (  __A							,__A       ):
  
  
  
   '''simple docstring'''
  
  
  
   __UpperCamelCase				      =  tmp_path / """text_with_image.txt"""
   with open(__A							,"""w"""							,encoding="""utf-8"""       ) as f:
    f.write(__A       )
   return str(__A       )
  
  
  
  
  
  
  
  @pytest.mark.parametrize("""keep_linebreaks"""							,[True, False]       )
  def _lowercase   (  __A							,__A       ):
  
  
  
   '''simple docstring'''
  
  
  
   with open(__A							,encoding="""utf-8"""       ) as f:
    __UpperCamelCase				      =  f.read().splitlines(keepends=__A       )
   __UpperCamelCase				      =  Text(keep_linebreaks=__A							,encoding="""utf-8"""       )
   __UpperCamelCase				      =  text._generate_tables([[text_file]]       )
   __UpperCamelCase				      =  pa.concat_tables([table for _, table in generator]       ).to_pydict()["""text"""]
   assert generated_content == expected_content
  
  
  
  
  
  
  
  @require_pil
  def _lowercase   (  __A       ):
  
  
  
   '''simple docstring'''
  
  
  
   with open(__A							,encoding="""utf-8"""       ) as f:
    __UpperCamelCase				      =  f.read().splitlines()[0]
   __UpperCamelCase				      =  Text(encoding="""utf-8"""							,features=Features({"""image""": Image()}       )       )
   __UpperCamelCase				      =  text._generate_tables([[text_file_with_image]]       )
   __UpperCamelCase				      =  pa.concat_tables([table for _, table in generator]       )
   assert pa_table.schema.field("""image"""       ).type == Image()()
   __UpperCamelCase				      =  pa_table.to_pydict()["""image"""]
   assert generated_content == [{"path": image_file, "bytes": None}]
  
  
  
  
  
  
  
  @pytest.mark.parametrize("""sample_by"""							,["""line""", """paragraph""", """document"""]       )
  def _lowercase   (  __A							,__A       ):
  
  
  
   '''simple docstring'''
  
  
  
   with open(__A							,encoding="""utf-8"""       ) as f:
    __UpperCamelCase				      =  f.read()
   if sample_by == "line":
    __UpperCamelCase				      =  expected_content.splitlines()
   elif sample_by == "paragraph":
    __UpperCamelCase				      =  expected_content.split("""\n\n"""       )
   elif sample_by == "document":
    __UpperCamelCase				      =  [expected_content]
   __UpperCamelCase				      =  Text(sample_by=__A							,encoding="""utf-8"""							,chunksize=100       )
   __UpperCamelCase				      =  text._generate_tables([[text_file]]       )
   __UpperCamelCase				      =  pa.concat_tables([table for _, table in generator]       ).to_pydict()["""text"""]
   assert generated_content == expected_content
  
  
  
  
  
  