


'''simple docstring'''







import copy
import inspect
import unittest

from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor


if is_torch_available():
 import torch

 from transformers import TimmBackbone, TimmBackboneConfig

from ...test_pipeline_mixin import PipelineTesterMixin


class  lowerCAmelCase_						:





  """simple docstring"""



  def __init__(					self     :							str				,							SCREAMING_SNAKE_CASE__     :							List[str]				,							SCREAMING_SNAKE_CASE__     :							Optional[int]=None				,							SCREAMING_SNAKE_CASE__     :							Any=None				,							SCREAMING_SNAKE_CASE__     :							List[str]=None				,							SCREAMING_SNAKE_CASE__     :							Tuple="resnet50"				,							SCREAMING_SNAKE_CASE__     :							Dict=3				,							SCREAMING_SNAKE_CASE__     :							Optional[Any]=3_2				,							SCREAMING_SNAKE_CASE__     :							Optional[Any]=3				,							SCREAMING_SNAKE_CASE__     :							List[Any]=True				,							SCREAMING_SNAKE_CASE__     :							Dict=True				,							):





       '''simple docstring'''

       __a       =   parent
       __a       =   out_indices if out_indices is not None else [4]
       __a       =   stage_names
       __a       =   out_features
       __a       =   backbone
       __a       =   batch_size
       __a       =   image_size
       __a       =   num_channels
       __a       =   use_pretrained_backbone
       __a       =   is_training

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





       '''simple docstring'''

       __a       =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)
       __a       =   self.get_config()

       return config, pixel_values

  def __a						(					self     :							Dict						):





       '''simple docstring'''

       return TimmBackboneConfig(
           image_size=self.image_size				,							num_channels=self.num_channels				,							out_features=self.out_features				,							out_indices=self.out_indices				,							stage_names=self.stage_names				,							use_pretrained_backbone=self.use_pretrained_backbone				,							backbone=self.backbone				,							)

  def __a						(					self     :							List[str]				,							SCREAMING_SNAKE_CASE__     :							Tuple				,							SCREAMING_SNAKE_CASE__     :							str						):





       '''simple docstring'''

       __a       =   TimmBackbone(config=lowercase__						)
       model.to(lowercase__						)
       model.eval()
       with torch.no_grad():
            __a       =   model(lowercase__						)
       self.parent.assertEqual(
           result.feature_map[-1].shape				,							(self.batch_size, model.channels[-1], 1_4, 1_4)				,							)


  def __a						(					self     :							Optional[Any]						):





       '''simple docstring'''

       __a       =   self.prepare_config_and_inputs()
       __a       =   config_and_inputs
       __a       =   {"""pixel_values""": pixel_values}
       return config, inputs_dict


@require_torch
@require_timm
class  lowerCAmelCase_						(       _UpperCAmelCase		,    _UpperCAmelCase		,    _UpperCAmelCase		,    unittest.TestCase      ):





  """simple docstring"""



  a_       :Union[str, Any]												=(TimmBackbone,) if is_torch_available() else ()
  a_       :Union[str, Any]												={"""feature-extraction""": TimmBackbone} if is_torch_available() else {}
  a_       :Optional[Any]												=False
  a_       :Dict												=False
  a_       :List[Any]												=False
  a_       :int												=False

  def __a						(					self     :							Any						):





       '''simple docstring'''

       __a       =   TimmBackboneModelTester(self						)
       __a       =   ConfigTester(self				,							config_class=lowercase__				,							has_text_modality=lowercase__						)

  def __a						(					self     :							List[Any]						):





       '''simple docstring'''

       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 __a						(					self     :							Optional[Any]						):





       '''simple docstring'''

       __a       =   """resnet18"""
       __a       =   """microsoft/resnet-18"""

       __a       =   AutoBackbone.from_pretrained(lowercase__				,							use_timm_backbone=lowercase__						)
       __a       =   AutoBackbone.from_pretrained(lowercase__						)

       self.assertEqual(len(timm_model.out_features						)				,							len(transformers_model.out_features						)						)
       self.assertEqual(len(timm_model.stage_names						)				,							len(transformers_model.stage_names						)						)
       self.assertEqual(timm_model.channels				,							transformers_model.channels						)
       # Out indices are set to the last layer by default. For timm models, we don't know
       # the number of layers in advance, so we set it to (-1,), whereas for transformers
       # models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
       self.assertEqual(timm_model.out_indices				,							(-1,)						)
       self.assertEqual(transformers_model.out_indices				,							[len(timm_model.stage_names						) - 1]						)

       __a       =   AutoBackbone.from_pretrained(lowercase__				,							use_timm_backbone=lowercase__				,							out_indices=[1, 2, 3]						)
       __a       =   AutoBackbone.from_pretrained(lowercase__				,							out_indices=[1, 2, 3]						)

       self.assertEqual(timm_model.out_indices				,							transformers_model.out_indices						)
       self.assertEqual(len(timm_model.out_features						)				,							len(transformers_model.out_features						)						)
       self.assertEqual(timm_model.channels				,							transformers_model.channels						)

  @unittest.skip("""TimmBackbone doesn't support feed forward chunking"""						)
  def __a						(					self     :							Tuple						):





       '''simple docstring'''

       pass

  @unittest.skip("""TimmBackbone doesn't have num_hidden_layers attribute"""						)
  def __a						(					self     :							Any						):





       '''simple docstring'''

       pass

  @unittest.skip("""TimmBackbone initialization is managed on the timm side"""						)
  def __a						(					self     :							Optional[Any]						):





       '''simple docstring'''

       pass

  @unittest.skip("""TimmBackbone models doesn't have inputs_embeds"""						)
  def __a						(					self     :							Any						):





       '''simple docstring'''

       pass

  @unittest.skip("""TimmBackbone models doesn't have inputs_embeds"""						)
  def __a						(					self     :							List[Any]						):





       '''simple docstring'''

       pass

  @unittest.skip("""TimmBackbone model cannot be created without specifying a backbone checkpoint"""						)
  def __a						(					self     :							str						):





       '''simple docstring'''

       pass

  @unittest.skip("""Only checkpoints on timm can be loaded into TimmBackbone"""						)
  def __a						(					self     :							Any						):





       '''simple docstring'''

       pass

  @unittest.skip("""model weights aren't tied in TimmBackbone."""						)
  def __a						(					self     :							Dict						):





       '''simple docstring'''

       pass

  @unittest.skip("""model weights aren't tied in TimmBackbone."""						)
  def __a						(					self     :							Optional[Any]						):





       '''simple docstring'''

       pass

  @unittest.skip("""Only checkpoints on timm can be loaded into TimmBackbone"""						)
  def __a						(					self     :							Dict						):





       '''simple docstring'''

       pass

  @unittest.skip("""Only checkpoints on timm can be loaded into TimmBackbone"""						)
  def __a						(					self     :							List[Any]						):





       '''simple docstring'''

       pass

  @unittest.skip("""TimmBackbone doesn't have hidden size info in its configuration."""						)
  def __a						(					self     :							List[str]						):





       '''simple docstring'''

       pass

  @unittest.skip("""TimmBackbone doesn't support output_attentions."""						)
  def __a						(					self     :							int						):





       '''simple docstring'''

       pass

  @unittest.skip("""Safetensors is not supported by timm."""						)
  def __a						(					self     :							Optional[int]						):





       '''simple docstring'''

       pass

  @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""						)
  def __a						(					self     :							Tuple						):





       '''simple docstring'''

       pass

  def __a						(					self     :							Any						):





       '''simple docstring'''

       __a       =   self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            __a       =   model_class(lowercase__						)
            __a       =   inspect.signature(model.forward						)
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            __a       =   [*signature.parameters.keys()]

            __a       =   ["""pixel_values"""]
            self.assertListEqual(arg_names[:1]				,							lowercase__						)

  def __a						(					self     :							Tuple						):





       '''simple docstring'''

       __a       =   self.model_tester.prepare_config_and_inputs_for_common()
       __a       =   True
       __a       =   self.has_attentions

       # no need to test all models as different heads yield the same functionality
       __a       =   self.all_model_classes[0]
       __a       =   model_class(lowercase__						)
       model.to(lowercase__						)

       __a       =   self._prepare_for_class(lowercase__				,							lowercase__						)
       __a       =   model(**lowercase__						)
       __a       =   outputs[0][-1]

       # Encoder-/Decoder-only models
       __a       =   outputs.hidden_states[0]
       hidden_states.retain_grad()

       if self.has_attentions:
            __a       =   outputs.attentions[0]
            attentions.retain_grad()

       output.flatten()[0].backward(retain_graph=lowercase__						)

       self.assertIsNotNone(hidden_states.grad						)

       if self.has_attentions:
            self.assertIsNotNone(attentions.grad						)


  def __a						(					self     :							Optional[Any]						):





       '''simple docstring'''

       __a       =   self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            __a       =   model_class(lowercase__						)
            model.to(lowercase__						)
            model.eval()
            __a       =   model(**lowercase__						)

            self.assertEqual(len(result.feature_maps						)				,							len(config.out_indices						)						)
            self.assertEqual(len(model.channels						)				,							len(config.out_indices						)						)

            # Check output of last stage is taken if out_features=None, out_indices=None
            __a       =   copy.deepcopy(lowercase__						)
            __a       =   None
            __a       =   model_class(lowercase__						)
            model.to(lowercase__						)
            model.eval()
            __a       =   model(**lowercase__						)

            self.assertEqual(len(result.feature_maps						)				,							1						)
            self.assertEqual(len(model.channels						)				,							1						)

            # Check backbone can be initialized with fresh weights
            __a       =   copy.deepcopy(lowercase__						)
            __a       =   False
            __a       =   model_class(lowercase__						)
            model.to(lowercase__						)
            model.eval()
            __a       =   model(**lowercase__						)










'''simple docstring'''







from math import pi, sqrt




def        __lowercase						(      __SCREAMING_SNAKE_CASE		)				->					float:
     """simple docstring"""




     if num <= 0:
          raise ValueError("""math domain error"""		)
     if num > 171.5:
          raise OverflowError("""math range error"""		)
     elif num - int(__SCREAMING_SNAKE_CASE		) not in (0, 0.5):
          raise NotImplementedError("""num must be an integer or a half-integer"""		)
     elif num == 0.5:
          return sqrt(__SCREAMING_SNAKE_CASE		)
     else:
          return 1.0 if num == 1 else (num - 1) * gamma(num - 1		)




def        __lowercase						(      )				->					None:
     """simple docstring"""




     assert gamma(0.5		) == sqrt(__SCREAMING_SNAKE_CASE		)
     assert gamma(1		) == 1.0
     assert gamma(2		) == 1.0


if __name__ == "__main__":
 from doctest import testmod

 testmod()
 SCREAMING_SNAKE_CASE_      =			1.0
 while num:
  SCREAMING_SNAKE_CASE_      =			float(input('Gamma of: '))
  print(f"""gamma({num}) = {gamma(num)}""")
  print('\nEnter 0 to exit...')






