




'''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    _UpperCamelCase      (				SCREAMING_SNAKE_CASE__			:      Tuple       ):
 '''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(SCREAMING_SNAKE_CASE__       ,						"""w"""       ,						encoding="""utf-8"""       ) as f:
  f.write(SCREAMING_SNAKE_CASE__       )
 return str(SCREAMING_SNAKE_CASE__       )

@pytest.fixture
def    _UpperCamelCase      (				SCREAMING_SNAKE_CASE__			:      Tuple       ,						SCREAMING_SNAKE_CASE__			:      Union[str, Any]       ):
 '''simple docstring'''





 UpperCAmelCase__										=    tmp_path / """text_with_image.txt"""
 with open(SCREAMING_SNAKE_CASE__       ,						"""w"""       ,						encoding="""utf-8"""       ) as f:
  f.write(SCREAMING_SNAKE_CASE__       )
 return str(SCREAMING_SNAKE_CASE__       )

@pytest.mark.parametrize("""keep_linebreaks"""       ,						[True, False]       )
def    _UpperCamelCase      (				SCREAMING_SNAKE_CASE__			:      Tuple       ,						SCREAMING_SNAKE_CASE__			:      str       ):
 '''simple docstring'''





 with open(SCREAMING_SNAKE_CASE__       ,						encoding="""utf-8"""       ) as f:
  UpperCAmelCase__										=    f.read().splitlines(keepends=SCREAMING_SNAKE_CASE__       )
 UpperCAmelCase__										=    Text(keep_linebreaks=SCREAMING_SNAKE_CASE__       ,						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    _UpperCamelCase      (				SCREAMING_SNAKE_CASE__			:      Optional[int]       ):
 '''simple docstring'''





 with open(SCREAMING_SNAKE_CASE__       ,						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    _UpperCamelCase      (				SCREAMING_SNAKE_CASE__			:      str       ,						SCREAMING_SNAKE_CASE__			:      Union[str, Any]       ):
 '''simple docstring'''





 with open(SCREAMING_SNAKE_CASE__       ,						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=SCREAMING_SNAKE_CASE__       ,						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






'''simple docstring'''




import collections
import inspect
import unittest

from transformers import DinatConfig
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 DinatBackbone, DinatForImageClassification, DinatModel
    from transformers.models.dinat.modeling_dinat import DINAT_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor

class 						lowerCAmelCase_   :







  '''simple docstring'''







  def __init__(						self    :	Union[str, Any]						,		_UpperCAmelCase    :	List[Any]						,		_UpperCAmelCase    :	int=13						,		_UpperCAmelCase    :	List[str]=64						,		_UpperCAmelCase    :	List[str]=4						,		_UpperCAmelCase    :	int=3						,		_UpperCAmelCase    :	Tuple=16						,		_UpperCAmelCase    :	Optional[Any]=[1, 2, 1]						,		_UpperCAmelCase    :	List[Any]=[2, 4, 8]						,		_UpperCAmelCase    :	str=3						,		_UpperCAmelCase    :	Tuple=[[3], [1, 2], [1]]						,		_UpperCAmelCase    :	Union[str, Any]=2.0						,		_UpperCAmelCase    :	List[str]=True						,		_UpperCAmelCase    :	Optional[int]=0.0						,		_UpperCAmelCase    :	Optional[Any]=0.0						,		_UpperCAmelCase    :	List[str]=0.1						,		_UpperCAmelCase    :	str="gelu"						,		_UpperCAmelCase    :	List[str]=True						,		_UpperCAmelCase    :	Tuple=0.02						,		_UpperCAmelCase    :	Optional[Any]=1E-5						,		_UpperCAmelCase    :	Any=True						,		_UpperCAmelCase    :	List[str]=None						,		_UpperCAmelCase    :	Any=True						,		_UpperCAmelCase    :	List[str]=10						,		_UpperCAmelCase    :	Any=["stage1", "stage2"]						,		_UpperCAmelCase    :	Union[str, Any]=[1, 2]						,		):






   """simple docstring"""






   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__										=    dilations
   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 							SCREAMING_SNAKE_CASE__			(						self    :	Optional[int] ):






   """simple docstring"""






   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 							SCREAMING_SNAKE_CASE__			(						self    :	Any ):






   """simple docstring"""






   return DinatConfig(
       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						,		dilations=self.dilations						,		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 							SCREAMING_SNAKE_CASE__			(						self    :	int						,		_UpperCAmelCase    :	Optional[Any]						,		_UpperCAmelCase    :	int						,		_UpperCAmelCase    :	Tuple ):






   """simple docstring"""






   UpperCAmelCase__										=    DinatModel(config=_UpperCAmelCase )
   model.to(_UpperCAmelCase )
   model.eval()
   UpperCAmelCase__										=    model(_UpperCAmelCase )

   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 							SCREAMING_SNAKE_CASE__			(						self    :	Optional[int]						,		_UpperCAmelCase    :	str						,		_UpperCAmelCase    :	int						,		_UpperCAmelCase    :	str ):






   """simple docstring"""






   UpperCAmelCase__										=    DinatForImageClassification(_UpperCAmelCase )
   model.to(_UpperCAmelCase )
   model.eval()
   UpperCAmelCase__										=    model(_UpperCAmelCase						,		labels=_UpperCAmelCase )
   self.parent.assertEqual(result.logits.shape						,		(self.batch_size, self.num_labels) )

   # test greyscale images
   UpperCAmelCase__										=    1
   UpperCAmelCase__										=    DinatForImageClassification(_UpperCAmelCase )
   model.to(_UpperCAmelCase )
   model.eval()

   UpperCAmelCase__										=    floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
   UpperCAmelCase__										=    model(_UpperCAmelCase )
   self.parent.assertEqual(result.logits.shape						,		(self.batch_size, self.num_labels) )


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Any						,		_UpperCAmelCase    :	List[Any]						,		_UpperCAmelCase    :	List[str]						,		_UpperCAmelCase    :	Any ):






   """simple docstring"""






   UpperCAmelCase__										=    DinatBackbone(config=_UpperCAmelCase )
   model.to(_UpperCAmelCase )
   model.eval()
   UpperCAmelCase__										=    model(_UpperCAmelCase )

   # 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], 16, 16] )

   # verify channels
   self.parent.assertEqual(len(model.channels )						,		len(config.out_features ) )

   # verify backbone works with out_features=None
   UpperCAmelCase__										=    None
   UpperCAmelCase__										=    DinatBackbone(config=_UpperCAmelCase )
   model.to(_UpperCAmelCase )
   model.eval()
   UpperCAmelCase__										=    model(_UpperCAmelCase )

   # 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 							SCREAMING_SNAKE_CASE__			(						self    :	Tuple ):






   """simple docstring"""






   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 						lowerCAmelCase_   ( lowerCamelCase_			,  lowerCamelCase_			,  unittest.TestCase   ):







  '''simple docstring'''







  lowerCAmelCase_				:					List[str]				     =						(
      (
          DinatModel,
          DinatForImageClassification,
          DinatBackbone,
      )
      if is_torch_available()
      else ()
  )
  lowerCAmelCase_				:					List[str]				     =						(
      {"""feature-extraction""": DinatModel, """image-classification""": DinatForImageClassification}
      if is_torch_available()
      else {}
  )
  lowerCAmelCase_				:					Union[str, Any]				     =						False

  lowerCAmelCase_				:					Union[str, Any]				     =						False
  lowerCAmelCase_				:					List[str]				     =						False
  lowerCAmelCase_				:					Any				     =						False
  lowerCAmelCase_				:					Any				     =						False


  def 							SCREAMING_SNAKE_CASE__			(						self    :	List[Any] ):






   """simple docstring"""






   UpperCAmelCase__										=    DinatModelTester(self )
   UpperCAmelCase__										=    ConfigTester(self						,		config_class=_UpperCAmelCase						,		embed_dim=37 )


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Optional[Any] ):






   """simple docstring"""






   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 							SCREAMING_SNAKE_CASE__			(						self    :	int ):






   """simple docstring"""






   return


  def 							SCREAMING_SNAKE_CASE__			(						self    :	List[str] ):






   """simple docstring"""






   UpperCAmelCase__										=    self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*_UpperCAmelCase )


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Union[str, Any] ):






   """simple docstring"""






   UpperCAmelCase__										=    self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase )


  def 							SCREAMING_SNAKE_CASE__			(						self    :	str ):






   """simple docstring"""






   UpperCAmelCase__										=    self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_backbone(*_UpperCAmelCase )


  @unittest.skip(reason="""Dinat does not use inputs_embeds""" )
  def 							SCREAMING_SNAKE_CASE__			(						self    :	Dict ):






   """simple docstring"""






   pass


  @unittest.skip(reason="""Dinat does not use feedforward chunking""" )
  def 							SCREAMING_SNAKE_CASE__			(						self    :	List[str] ):






   """simple docstring"""






   pass


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Optional[int] ):






   """simple docstring"""






   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 							SCREAMING_SNAKE_CASE__			(						self    :	Optional[Any] ):






   """simple docstring"""






   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 							SCREAMING_SNAKE_CASE__			(						self    :	Any ):






   """simple docstring"""






   self.skipTest("""Dinat's attention operation is handled entirely by NATTEN.""" )


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Any						,		_UpperCAmelCase    :	Optional[Any]						,		_UpperCAmelCase    :	List[str]						,		_UpperCAmelCase    :	Optional[Any]						,		_UpperCAmelCase    :	Dict ):






   """simple docstring"""






   UpperCAmelCase__										=    model_class(_UpperCAmelCase )
   model.to(_UpperCAmelCase )
   model.eval()

   with torch.no_grad():
    UpperCAmelCase__										=    model(**self._prepare_for_class(_UpperCAmelCase						,		_UpperCAmelCase ) )

   UpperCAmelCase__										=    outputs.hidden_states

   UpperCAmelCase__										=    getattr(
       self.model_tester						,		"""expected_num_hidden_layers"""						,		len(self.model_tester.depths ) + 1 )
   self.assertEqual(len(_UpperCAmelCase )						,		_UpperCAmelCase )

   # Dinat 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__ != "DinatBackbone":
    UpperCAmelCase__										=    outputs.reshaped_hidden_states
    self.assertEqual(len(_UpperCAmelCase )						,		_UpperCAmelCase )

    UpperCAmelCase__							,			UpperCAmelCase__							,			UpperCAmelCase__							,			UpperCAmelCase__										=    reshaped_hidden_states[0].shape
    UpperCAmelCase__										=    (
        reshaped_hidden_states[0].view(_UpperCAmelCase						,		_UpperCAmelCase						,		_UpperCAmelCase						,		_UpperCAmelCase ).permute(0						,		2						,		3						,		1 )
    )
    self.assertListEqual(
        list(reshaped_hidden_states.shape[-3:] )						,		[height, width, self.model_tester.embed_dim]						,		)


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Optional[int] ):






   """simple docstring"""






   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(_UpperCAmelCase						,		_UpperCAmelCase						,		_UpperCAmelCase						,		_UpperCAmelCase )

    # check that output_hidden_states also work using config
    del inputs_dict["output_hidden_states"]
    UpperCAmelCase__										=    True

    self.check_hidden_states_output(_UpperCAmelCase						,		_UpperCAmelCase						,		_UpperCAmelCase						,		_UpperCAmelCase )


  @slow
  def 							SCREAMING_SNAKE_CASE__			(						self    :	Optional[Any] ):






   """simple docstring"""






   for model_name in DINAT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
    UpperCAmelCase__										=    DinatModel.from_pretrained(_UpperCAmelCase )
    self.assertIsNotNone(_UpperCAmelCase )



  def 							SCREAMING_SNAKE_CASE__			(						self    :	Dict ):






   """simple docstring"""






   UpperCAmelCase__							,			UpperCAmelCase__										=    self.model_tester.prepare_config_and_inputs_for_common()

   UpperCAmelCase__										=    _config_zero_init(_UpperCAmelCase )
   for model_class in self.all_model_classes:
    UpperCAmelCase__										=    model_class(config=_UpperCAmelCase )
    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 						lowerCAmelCase_   ( unittest.TestCase   ):







  '''simple docstring'''







  @cached_property
  def 							SCREAMING_SNAKE_CASE__			(						self    :	Any ):






   """simple docstring"""






   return AutoImageProcessor.from_pretrained("""shi-labs/dinat-mini-in1k-224""" ) if is_vision_available() else None



  @slow
  def 							SCREAMING_SNAKE_CASE__			(						self    :	Optional[int] ):






   """simple docstring"""






   UpperCAmelCase__										=    DinatForImageClassification.from_pretrained("""shi-labs/dinat-mini-in1k-224""" ).to(_UpperCAmelCase )
   UpperCAmelCase__										=    self.default_image_processor

   UpperCAmelCase__										=    Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
   UpperCAmelCase__										=    image_processor(images=_UpperCAmelCase						,		return_tensors="""pt""" ).to(_UpperCAmelCase )

   # forward pass
   with torch.no_grad():
    UpperCAmelCase__										=    model(**_UpperCAmelCase )

   # verify the logits
   UpperCAmelCase__										=    torch.Size((1, 10_00) )
   self.assertEqual(outputs.logits.shape						,		_UpperCAmelCase )
   UpperCAmelCase__										=    torch.tensor([-0.1545, -0.7667, 0.4642] ).to(_UpperCAmelCase )
   self.assertTrue(torch.allclose(outputs.logits[0, :3]						,		_UpperCAmelCase						,		atol=1E-4 ) )

@require_torch
@require_natten
class 						lowerCAmelCase_   ( unittest.TestCase			,  lowerCamelCase_   ):







  '''simple docstring'''







  lowerCAmelCase_				:					Any				     =						(DinatBackbone,) if is_torch_available() else ()
  lowerCAmelCase_				:					Optional[Any]				     =						DinatConfig


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Optional[int] ):






   """simple docstring"""






   UpperCAmelCase__										=    DinatModelTester(self )
