
'''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  UpperCAmelCase_					:





 def __init__(     self			,							lowercase_			,							lowercase_=13			,							lowercase_=64			,							lowercase_=4			,							lowercase_=3			,							lowercase_=16			,							lowercase_=[1, 2, 1]			,							lowercase_=[2, 4, 8]			,							lowercase_=3			,							lowercase_=[[3], [1, 2], [1]]			,							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_=10			,							lowercase_=["stage1", "stage2"]			,							lowercase_=[1, 2]			,							):
     snake_case_			:     str        =    parent
     snake_case_			:     int        =    batch_size
     snake_case_			:     int        =    image_size
     snake_case_			:     Union[str, Any]        =    patch_size
     snake_case_			:     int        =    num_channels
     snake_case_			:     Optional[int]        =    embed_dim
     snake_case_			:     Optional[int]        =    depths
     snake_case_			:     str        =    num_heads
     snake_case_			:     List[str]        =    kernel_size
     snake_case_			:     Optional[Any]        =    dilations
     snake_case_			:     Optional[int]        =    mlp_ratio
     snake_case_			:     Optional[int]        =    qkv_bias
     snake_case_			:     List[Any]        =    hidden_dropout_prob
     snake_case_			:     Union[str, Any]        =    attention_probs_dropout_prob
     snake_case_			:     Optional[Any]        =    drop_path_rate
     snake_case_			:     Optional[int]        =    hidden_act
     snake_case_			:     Tuple        =    patch_norm
     snake_case_			:     List[str]        =    layer_norm_eps
     snake_case_			:     int        =    initializer_range
     snake_case_			:     List[str]        =    is_training
     snake_case_			:     List[Any]        =    scope
     snake_case_			:     Optional[Any]        =    use_labels
     snake_case_			:     List[Any]        =    num_labels
     snake_case_			:     str        =    out_features
     snake_case_			:     Tuple        =    out_indices





 def 							snake_case__		(     self):
     snake_case_			:     str        =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

     snake_case_			:     Optional[Any]        =    self.get_config()

     return config, pixel_values, labels





 def 							snake_case__		(     self):
     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 							snake_case__		(     self			,							lowercase_			,							lowercase_			,							lowercase_):
     snake_case_			:     Optional[Any]        =    DinatModel(config=lowercase_)
     model.to(lowercase_)
     model.eval()
     snake_case_			:     List[Any]        =    model(lowercase_)

     snake_case_			:     List[str]        =    (config.image_size // config.patch_size) // (2 ** (len(config.depths) - 1))
     snake_case_			:     List[str]        =    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 							snake_case__		(     self			,							lowercase_			,							lowercase_			,							lowercase_):
     snake_case_			:     str        =    DinatForImageClassification(lowercase_)
     model.to(lowercase_)
     model.eval()
     snake_case_			:     Tuple        =    model(lowercase_			,							labels=lowercase_)
     self.parent.assertEqual(result.logits.shape			,							(self.batch_size, self.num_labels))

     # test greyscale images
     snake_case_			:     Tuple        =    1
     snake_case_			:     List[Any]        =    DinatForImageClassification(lowercase_)
     model.to(lowercase_)
     model.eval()

     snake_case_			:     List[Any]        =    floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
     snake_case_			:     Dict        =    model(lowercase_)
     self.parent.assertEqual(result.logits.shape			,							(self.batch_size, self.num_labels))





 def 							snake_case__		(     self			,							lowercase_			,							lowercase_			,							lowercase_):
     snake_case_			:     Union[str, Any]        =    DinatBackbone(config=lowercase_)
     model.to(lowercase_)
     model.eval()
     snake_case_			:     List[Any]        =    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], 16, 16])

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

     # verify backbone works with out_features=None
     snake_case_			:     Dict        =    None
     snake_case_			:     List[str]        =    DinatBackbone(config=lowercase_)
     model.to(lowercase_)
     model.eval()
     snake_case_			:     str        =    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 							snake_case__		(     self):
     snake_case_			:     Union[str, Any]        =    self.prepare_config_and_inputs()
     snake_case_      ,			snake_case_      ,			snake_case_			:     Optional[Any]        =    config_and_inputs
     snake_case_			:     Dict        =    {"pixel_values": pixel_values}
     return config, inputs_dict






@require_natten
@require_torch
class  UpperCAmelCase_					(						snake_case__     ,    snake_case__     ,    unittest.TestCase       ):
 UpperCAmelCase_		     =    (
     (
         DinatModel,
         DinatForImageClassification,
         DinatBackbone,
     )
     if is_torch_available()
     else ()
 )
 UpperCAmelCase_		     =    (
     {"""feature-extraction""": DinatModel, """image-classification""": DinatForImageClassification}
     if is_torch_available()
     else {}
 )
 UpperCAmelCase_		     =    False

 UpperCAmelCase_		     =    False
 UpperCAmelCase_		     =    False
 UpperCAmelCase_		     =    False
 UpperCAmelCase_		     =    False





 def 							snake_case__		(     self):
     snake_case_			:     Optional[Any]        =    DinatModelTester(self)
     snake_case_			:     Dict        =    ConfigTester(self			,							config_class=lowercase_			,							embed_dim=37)





 def 							snake_case__		(     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 							snake_case__		(     self):
     return





 def 							snake_case__		(     self):
     snake_case_			:     Optional[int]        =    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*lowercase_)





 def 							snake_case__		(     self):
     snake_case_			:     Union[str, Any]        =    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*lowercase_)





 def 							snake_case__		(     self):
     snake_case_			:     List[Any]        =    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_backbone(*lowercase_)





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





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





 def 							snake_case__		(     self):
     snake_case_      ,			snake_case_			:     Dict        =    self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         snake_case_			:     Optional[Any]        =    model_class(lowercase_)
         self.assertIsInstance(model.get_input_embeddings()			,							(nn.Module))
         snake_case_			:     Tuple        =    model.get_output_embeddings()
         self.assertTrue(x is None or isinstance(lowercase_			,							nn.Linear))





 def 							snake_case__		(     self):
     snake_case_      ,			snake_case_			:     int        =    self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         snake_case_			:     Optional[Any]        =    model_class(lowercase_)
         snake_case_			:     Optional[int]        =    inspect.signature(model.forward)
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         snake_case_			:     str        =    [*signature.parameters.keys()]

         snake_case_			:     Union[str, Any]        =    ["pixel_values"]
         self.assertListEqual(arg_names[:1]			,							lowercase_)





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





 def 							snake_case__		(     self			,							lowercase_			,							lowercase_			,							lowercase_			,							lowercase_):
     snake_case_			:     Union[str, Any]        =    model_class(lowercase_)
     model.to(lowercase_)
     model.eval()

     with torch.no_grad():
         snake_case_			:     Dict        =    model(**self._prepare_for_class(lowercase_			,							lowercase_))

     snake_case_			:     int        =    outputs.hidden_states

     snake_case_			:     Optional[int]        =    getattr(
         self.model_tester			,							"expected_num_hidden_layers"			,							len(self.model_tester.depths) + 1)
     self.assertEqual(len(lowercase_)			,							lowercase_)

     # Dinat has a different seq_length
     snake_case_			:     Any        =    (
         config.patch_size
         if isinstance(config.patch_size			,							collections.abc.Iterable)
         else (config.patch_size, config.patch_size)
     )

     snake_case_			:     List[Any]        =    image_size[0] // patch_size[0]
     snake_case_			:     Optional[int]        =    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":
         snake_case_			:     int        =    outputs.reshaped_hidden_states
         self.assertEqual(len(lowercase_)			,							lowercase_)

         snake_case_      ,			snake_case_      ,			snake_case_      ,			snake_case_			:     Union[str, Any]        =    reshaped_hidden_states[0].shape
         snake_case_			:     str        =    (
             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 							snake_case__		(     self):
     snake_case_      ,			snake_case_			:     List[Any]        =    self.model_tester.prepare_config_and_inputs_for_common()

     snake_case_			:     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:
         snake_case_			:     Tuple        =    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"]
         snake_case_			:     Optional[int]        =    True

         self.check_hidden_states_output(lowercase_			,							lowercase_			,							lowercase_			,							lowercase_)





 @slow
 def 							snake_case__		(     self):
     for model_name in DINAT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         snake_case_			:     Tuple        =    DinatModel.from_pretrained(lowercase_)
         self.assertIsNotNone(lowercase_)





 def 							snake_case__		(     self):
     snake_case_      ,			snake_case_			:     Any        =    self.model_tester.prepare_config_and_inputs_for_common()

     snake_case_			:     List[str]        =    _config_zero_init(lowercase_)
     for model_class in self.all_model_classes:
         snake_case_			:     Optional[int]        =    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 							snake_case__		(     self):
     return AutoImageProcessor.from_pretrained("shi-labs/dinat-mini-in1k-224") if is_vision_available() else None





 @slow
 def 							snake_case__		(     self):
     snake_case_			:     str        =    DinatForImageClassification.from_pretrained("shi-labs/dinat-mini-in1k-224").to(lowercase_)
     snake_case_			:     Tuple        =    self.default_image_processor

     snake_case_			:     int        =    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
     snake_case_			:     Union[str, Any]        =    image_processor(images=lowercase_			,							return_tensors="pt").to(lowercase_)

     # forward pass
     with torch.no_grad():
         snake_case_			:     Union[str, Any]        =    model(**lowercase_)

     # verify the logits
     snake_case_			:     Any        =    torch.Size((1, 10_00))
     self.assertEqual(outputs.logits.shape			,							lowercase_)
     snake_case_			:     Union[str, Any]        =    torch.tensor([-0.1_545, -0.7_667, 0.4_642]).to(lowercase_)
     self.assertTrue(torch.allclose(outputs.logits[0, :3]			,							lowercase_			,							atol=1E-4))






@require_torch
@require_natten
class  UpperCAmelCase_					(						unittest.TestCase     ,    snake_case__       ):
 UpperCAmelCase_		     =    (DinatBackbone,) if is_torch_available() else ()
 UpperCAmelCase_		     =    DinatConfig





 def 							snake_case__		(     self):
     snake_case_			:     List[str]        =    DinatModelTester(self)






'''simple docstring'''
def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    if number != int(__SCREAMING_SNAKE_CASE							):
        raise ValueError("factorial() only accepts integral values"							)
    if number < 0:
        raise ValueError("factorial() not defined for negative values"							)
    snake_case_			:     int        =    1
    for i in range(1,		number + 1							):
        value *= i
    return value

def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    if not isinstance(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							):
        raise ValueError("factorial() only accepts integral values"							)
    if n < 0:
        raise ValueError("factorial() not defined for negative values"							)
    return 1 if n == 0 or n == 1 else n * factorial(n - 1							)


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  a_           =    int(input("Enter a positive integer: ").strip() or 0)
  print(f"""factorial{n} is {factorial(n)}""")




