





import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test


sys.path.append(str(Path(__file__).parent.parent / 'utils'))

from test_module.custom_image_processing import CustomImageProcessor  # noqa E402


_A :   str  			=  get_tests_dir('fixtures')


class        __SCREAMING_SNAKE_CASE      (			unittest.TestCase ):







       def 						__lowerCamelCase   (				self					:     List[str]      )       ->List[Any]:
          # A mock response for an HTTP head request to emulate server down
          lowerCamelCase__       :						Tuple        =							mock.Mock()
          lowerCamelCase__       :						Tuple        =							5_0_0
          lowerCamelCase__       :						List[str]        =							{}
          lowerCamelCase__       :						Tuple        =							HTTPError
          lowerCamelCase__       :						Any        =							{}

          # Download this model to make sure it's in the cache.
          lowerCamelCase__       :						Optional[Any]        =							ViTImageProcessor.from_pretrained('''hf-internal-testing/tiny-random-vit'''      )
          # Under the mock environment we get a 500 error when trying to reach the model.
          with mock.patch('''requests.Session.request'''		, return_value=A      ) as mock_head:
             lowerCamelCase__       :						Optional[Any]        =							ViTImageProcessor.from_pretrained('''hf-internal-testing/tiny-random-vit'''      )
             # This check we did call the fake head request
             mock_head.assert_called()







       def 						__lowerCamelCase   (				self					:     Union[str, Any]      )       ->List[Any]:
          # This test is for deprecated behavior and can be removed in v5
          lowerCamelCase__       :						str        =							ViTImageProcessor.from_pretrained(
              '''https://huggingface.co/hf-internal-testing/tiny-random-vit/resolve/main/preprocessor_config.json'''      )







       def 						__lowerCamelCase   (				self					:     Optional[Any]      )       ->Optional[int]:
          with self.assertRaises(A      ):
             # config is in subfolder, the following should not work without specifying the subfolder
             lowerCamelCase__       :						List[str]        =							AutoImageProcessor.from_pretrained('''hf-internal-testing/stable-diffusion-all-variants'''      )

          lowerCamelCase__       :						Any        =							AutoImageProcessor.from_pretrained(
              '''hf-internal-testing/stable-diffusion-all-variants'''		, subfolder='''feature_extractor'''      )

          self.assertIsNotNone(A      )


@is_staging_test
class        __SCREAMING_SNAKE_CASE      (			unittest.TestCase ):







       @classmethod
       def 						__lowerCamelCase   (				cls					:     int      )       ->Optional[int]:
          lowerCamelCase__       :						List[Any]        =							TOKEN
          HfFolder.save_token(A      )







       @classmethod
       def 						__lowerCamelCase   (				cls					:     List[Any]      )       ->Optional[Any]:
          try:
             delete_repo(token=cls._token		, repo_id='''test-image-processor'''      )
          except HTTPError:
             pass

          try:
             delete_repo(token=cls._token		, repo_id='''valid_org/test-image-processor-org'''      )
          except HTTPError:
             pass

          try:
             delete_repo(token=cls._token		, repo_id='''test-dynamic-image-processor'''      )
          except HTTPError:
             pass







       def 						__lowerCamelCase   (				self					:     Dict      )       ->Optional[Any]:
          lowerCamelCase__       :						List[str]        =							ViTImageProcessor.from_pretrained(A      )
          image_processor.push_to_hub('''test-image-processor'''		, use_auth_token=self._token      )

          lowerCamelCase__       :						int        =							ViTImageProcessor.from_pretrained(F"{USER}/test-image-processor"      )
          for k, v in image_processor.__dict__.items():
             self.assertEqual(A		, getattr(A		, A      )      )

          # Reset repo
          delete_repo(token=self._token		, repo_id='''test-image-processor'''      )

          # Push to hub via save_pretrained
          with tempfile.TemporaryDirectory() as tmp_dir:
             image_processor.save_pretrained(
                 A		, repo_id='''test-image-processor'''		, push_to_hub=A		, use_auth_token=self._token      )

          lowerCamelCase__       :						Dict        =							ViTImageProcessor.from_pretrained(F"{USER}/test-image-processor"      )
          for k, v in image_processor.__dict__.items():
             self.assertEqual(A		, getattr(A		, A      )      )







       def 						__lowerCamelCase   (				self					:     Tuple      )       ->Union[str, Any]:
          lowerCamelCase__       :						Dict        =							ViTImageProcessor.from_pretrained(A      )
          image_processor.push_to_hub('''valid_org/test-image-processor'''		, use_auth_token=self._token      )

          lowerCamelCase__       :						Any        =							ViTImageProcessor.from_pretrained('''valid_org/test-image-processor'''      )
          for k, v in image_processor.__dict__.items():
             self.assertEqual(A		, getattr(A		, A      )      )

          # Reset repo
          delete_repo(token=self._token		, repo_id='''valid_org/test-image-processor'''      )

          # Push to hub via save_pretrained
          with tempfile.TemporaryDirectory() as tmp_dir:
             image_processor.save_pretrained(
                 A		, repo_id='''valid_org/test-image-processor-org'''		, push_to_hub=A		, use_auth_token=self._token      )

          lowerCamelCase__       :						Dict        =							ViTImageProcessor.from_pretrained('''valid_org/test-image-processor-org'''      )
          for k, v in image_processor.__dict__.items():
             self.assertEqual(A		, getattr(A		, A      )      )







       def 						__lowerCamelCase   (				self					:     List[str]      )       ->Optional[int]:
          CustomImageProcessor.register_for_auto_class()
          lowerCamelCase__       :						List[Any]        =							CustomImageProcessor.from_pretrained(A      )

          image_processor.push_to_hub('''test-dynamic-image-processor'''		, use_auth_token=self._token      )

          # This has added the proper auto_map field to the config
          self.assertDictEqual(
              image_processor.auto_map		, {'''AutoImageProcessor''': '''custom_image_processing.CustomImageProcessor'''}		, )

          lowerCamelCase__       :						List[Any]        =							AutoImageProcessor.from_pretrained(
              F"{USER}/test-dynamic-image-processor"		, trust_remote_code=A      )
          # Can't make an isinstance check because the new_image_processor is from the CustomImageProcessor class of a dynamic module
          self.assertEqual(new_image_processor.__class__.__name__		, '''CustomImageProcessor'''      )










import math
import random



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase = False					)    ->    float:







   """simple docstring"""
   if deriv:
      return value * (1 - value)
   return 1 / (1 + math.exp(-value					))


# Initial Value
_A :   Tuple  			=  0.02



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase					)    ->    float:







   """simple docstring"""
   lowerCamelCase__       :						List[Any]        =							float(2 * (random.randint(1      ,  100					)) - 1					)

   for _ in range(UpperCAmelCase					):
      # Forward propagation
      lowerCamelCase__       :						Optional[int]        =							sigmoid_function(INITIAL_VALUE * weight					)
      # How much did we miss?
      lowerCamelCase__       :						int        =							(expected / 100) - layer_a
      # Error delta
      lowerCamelCase__       :						Union[str, Any]        =							layer_1_error * sigmoid_function(UpperCAmelCase      ,  UpperCAmelCase					)
      # Update weight
      weight += INITIAL_VALUE * layer_1_delta

   return layer_a * 100


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       _A :   Union[str, Any]  			=  int(input('Expected value: '))
       _A :   int  			=  int(input('Number of propagations: '))
       print(forward_propagation(expected, number_propagations))



