




'''simple docstring'''




from typing import Any

import numpy as np

class 						lowerCAmelCase_   :







  '''simple docstring'''







  def __init__(						self    :	Any						,		_UpperCAmelCase    :	np.ndarray						,		_UpperCAmelCase    :	int ):






   """simple docstring"""






   if np.any(tableau[:, -1]						,		where=tableau[:, -1] < 0 ):
    raise ValueError("""RHS must be > 0""" )

   UpperCAmelCase__										=    tableau
   UpperCAmelCase__							,			UpperCAmelCase__										=    tableau.shape

   # Number of decision variables x1, x2, x3...
   UpperCAmelCase__										=    n_vars

   # Number of artificial variables to be minimised
   UpperCAmelCase__										=    len(np.where(tableau[self.n_vars : -1] == -1 )[0] )

   # 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
   UpperCAmelCase__										=    (self.n_art_vars > 0) + 1

   # Number of slack variables added to make inequalities into equalities
   UpperCAmelCase__										=    self.n_rows - self.n_stages

   # Objectives for each stage
   UpperCAmelCase__										=    ["""max"""]

   # In two stage simplex, first minimise then maximise
   if self.n_art_vars:
    self.objectives.append("""min""" )

   UpperCAmelCase__										=    [""""""]

   # Index of current pivot row and column
   UpperCAmelCase__										=    None
   UpperCAmelCase__										=    None

   # Does objective row only contain (non)-negative values?
   UpperCAmelCase__										=    False


  @staticmethod
  def 							SCREAMING_SNAKE_CASE__			(						*_UpperCAmelCase    :	int ):






   """simple docstring"""






   if len(_UpperCAmelCase ) != 3:
    raise ValueError("""Must provide n_vars, n_slack, and n_art_vars""" )

   if not all(x >= 0 and isinstance(_UpperCAmelCase						,		_UpperCAmelCase ) for x in args ):
    raise ValueError("""All arguments must be non-negative integers""" )

   # decision | slack | artificial
   UpperCAmelCase__										=    ["""x""", """s""", """a"""]
   UpperCAmelCase__										=    []
   for i in range(3 ):
    for j in range(args[i] ):
     titles.append(string_starts[i] + str(j + 1 ) )
   titles.append("""RHS""" )
   return titles


  def 							SCREAMING_SNAKE_CASE__			(						self    :	str						,		_UpperCAmelCase    :	np.ndarray ):






   """simple docstring"""






   UpperCAmelCase__										=    self.objectives[-1]

   # Find entries of highest magnitude in objective rows
   UpperCAmelCase__										=    (objective == """min""") - (objective == """max""")
   UpperCAmelCase__										=    np.argmax(sign * tableau[0, : self.n_vars] )

   # Choice is only valid if below 0 for maximise, and above for minimise
   if sign * self.tableau[0, col_idx] <= 0:
    UpperCAmelCase__										=    True
    return 0, 0

   # Pivot row is chosen as having the lowest quotient when elements of
   # the pivot column divide the right-hand side

   # Slice excluding the objective rows
   UpperCAmelCase__										=    slice(self.n_stages						,		self.n_rows )

   # RHS
   UpperCAmelCase__										=    tableau[s, -1]

   # Elements of pivot column within slice
   UpperCAmelCase__										=    tableau[s, col_idx]

   # Array filled with nans
   UpperCAmelCase__										=    np.full(self.n_rows - self.n_stages						,		np.nan )

   # If element in pivot column is greater than zeron_stages, return
   # quotient or nan otherwise
   UpperCAmelCase__										=    np.divide(_UpperCAmelCase						,		_UpperCAmelCase						,		out=_UpperCAmelCase						,		where=divisor > 0 )

   # Arg of minimum quotient excluding the nan values. n_stages is added
   # to compensate for earlier exclusion of objective columns
   UpperCAmelCase__										=    np.nanargmin(_UpperCAmelCase ) + self.n_stages
   return row_idx, col_idx


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Tuple						,		_UpperCAmelCase    :	np.ndarray						,		_UpperCAmelCase    :	int						,		_UpperCAmelCase    :	int ):






   """simple docstring"""






   UpperCAmelCase__										=    tableau[row_idx].copy()

   UpperCAmelCase__										=    piv_row[col_idx]

   # Entry becomes 1
   piv_row *= 1 / piv_val

   # Variable in pivot column becomes basic, ie the only non-zero entry
   for idx, coeff in enumerate(tableau[:, col_idx] ):
    tableau[idx] += -coeff * piv_row
   UpperCAmelCase__										=    piv_row
   return tableau


  def 							SCREAMING_SNAKE_CASE__			(						self    :	List[Any]						,		_UpperCAmelCase    :	np.ndarray ):






   """simple docstring"""






   self.objectives.pop()

   if not self.objectives:
    return tableau

   # Slice containing ids for artificial columns
   UpperCAmelCase__										=    slice(-self.n_art_vars - 1						,		-1 )

   # Delete the artificial variable columns
   UpperCAmelCase__										=    np.delete(_UpperCAmelCase						,		_UpperCAmelCase						,		axis=1 )

   # Delete the objective row of the first stage
   UpperCAmelCase__										=    np.delete(_UpperCAmelCase						,		0						,		axis=0 )

   UpperCAmelCase__										=    1
   self.n_rows -= 1
   UpperCAmelCase__										=    0
   UpperCAmelCase__										=    False
   return tableau


  def 							SCREAMING_SNAKE_CASE__			(						self    :	str ):






   """simple docstring"""






   for _ in range(1_00 ):
    # Completion of each stage removes an objective. If both stages
    # are complete, then no objectives are left
    if not self.objectives:
     UpperCAmelCase__										=    self.generate_col_titles(
         self.n_vars						,		self.n_slack						,		self.n_art_vars )

     # Find the values of each variable at optimal solution
     return self.interpret_tableau(self.tableau						,		self.col_titles )

    UpperCAmelCase__							,			UpperCAmelCase__										=    self.find_pivot(self.tableau )

    # If there are no more negative values in objective row
    if self.stop_iter:
     # Delete artificial variable columns and rows. Update attributes
     UpperCAmelCase__										=    self.change_stage(self.tableau )
    else:
     UpperCAmelCase__										=    self.pivot(self.tableau						,		_UpperCAmelCase						,		_UpperCAmelCase )
   return {}



  def 							SCREAMING_SNAKE_CASE__			(						self    :	Union[str, Any]						,		_UpperCAmelCase    :	np.ndarray						,		_UpperCAmelCase    :	list[str] ):






   """simple docstring"""






   UpperCAmelCase__										=    {"""P""": abs(tableau[0, -1] )}

   for i in range(self.n_vars ):
    # Gives ids of nonzero entries in the ith column
    UpperCAmelCase__										=    np.nonzero(tableau[:, i] )
    UpperCAmelCase__										=    len(nonzero[0] )

    # First entry in the nonzero ids
    UpperCAmelCase__										=    nonzero[0][0]
    UpperCAmelCase__										=    tableau[nonzero_rowidx, i]

    # If there is only one nonzero value in column, which is one
    if n_nonzero == nonzero_val == 1:
     UpperCAmelCase__										=    tableau[nonzero_rowidx, -1]
     UpperCAmelCase__										=    rhs_val

        # Check for basic variables
   for title in col_titles:
    # Don't add RHS or slack variables to output dict
    if title[0] not in "R-s-a":
     output_dict.setdefault(_UpperCAmelCase						,		0 )
   return output_dict


if __name__ == "__main__":
    import doctest

    doctest.testmod()






'''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 )
