

import inspect
import unittest

from transformers import EfficientNetConfig
from transformers.testing_utils import is_pipeline_test, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import EfficientNetForImageClassification, EfficientNetModel
							from transformers.models.efficientnet.modeling_efficientnet import EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

							from transformers import AutoImageProcessor







class 						lowercase     :


		"""simple docstring"""




		def __init__(					self						,							__snake_case						,							__snake_case=13						,							__snake_case=32						,							__snake_case=3						,							__snake_case=[3, 3, 5]						,							__snake_case=[32, 16, 24]						,							__snake_case=[16, 24, 40]						,							__snake_case=[1, 1, 2]						,							__snake_case=[1, 1, 2]						,							__snake_case=[1, 6, 6]						,							__snake_case=True						,							__snake_case=True						,							__snake_case=37						,							__snake_case="gelu"						,							__snake_case=10						,							):
			_UpperCamelCase :		str								=    parent
			_UpperCamelCase :		List[Any]								=    batch_size
			_UpperCamelCase :		List[str]								=    image_size
			_UpperCamelCase :		Union[str, Any]								=    num_channels
			_UpperCamelCase :		int								=    kernel_sizes
			_UpperCamelCase :		Optional[Any]								=    in_channels
			_UpperCamelCase :		Dict								=    out_channels
			_UpperCamelCase :		Optional[int]								=    strides
			_UpperCamelCase :		str								=    num_block_repeats
			_UpperCamelCase :		List[str]								=    expand_ratios
			_UpperCamelCase :		Optional[int]								=    is_training
			_UpperCamelCase :		Union[str, Any]								=    hidden_act
			_UpperCamelCase :		Tuple								=    num_labels
			_UpperCamelCase :		Optional[Any]								=    use_labels


		def 		A__       (					self):
			_UpperCamelCase :		str								=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

			_UpperCamelCase :		List[Any]								=    self.get_config()
			return config, pixel_values, labels


		def 		A__       (					self):
			return EfficientNetConfig(
			    num_channels=self.num_channels						,							kernel_sizes=self.kernel_sizes						,							in_channels=self.in_channels						,							out_channels=self.out_channels						,							strides=self.strides						,							num_block_repeats=self.num_block_repeats						,							expand_ratios=self.expand_ratios						,							hidden_act=self.hidden_act						,							num_labels=self.num_labels						,							)


		def 		A__       (					self						,							__snake_case						,							__snake_case						,							__snake_case):
			_UpperCamelCase :		str								=    EfficientNetModel(config=__snake_case)
			model.to(__snake_case)
			model.eval()
			_UpperCamelCase :		List[str]								=    model(__snake_case)
			# expected last hidden states: B, C, H // 4, W // 4
			self.parent.assertEqual(
			    result.last_hidden_state.shape						,							(self.batch_size, config.hidden_dim, self.image_size // 4, self.image_size // 4)						,							)


		def 		A__       (					self						,							__snake_case						,							__snake_case						,							__snake_case):
			_UpperCamelCase :		Any								=    EfficientNetForImageClassification(__snake_case)
			model.to(__snake_case)
			model.eval()
			_UpperCamelCase :		List[str]								=    model(__snake_case						,							labels=__snake_case)
			self.parent.assertEqual(result.logits.shape						,							(self.batch_size, self.num_labels))






		def 		A__       (					self):
			_UpperCamelCase :		Any								=    self.prepare_config_and_inputs()
			_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase :		Any								=    config_and_inputs
			_UpperCamelCase :		Optional[int]								=    {'pixel_values': pixel_values}
			return config, inputs_dict







@require_torch
class 						lowercase     (					_lowercase     ,				_lowercase     ,				unittest.TestCase				):


		"""simple docstring"""




		a__       							=	(EfficientNetModel, EfficientNetForImageClassification) if is_torch_available() else ()
		a__       							=	(
		    {"feature-extraction": EfficientNetModel, "image-classification": EfficientNetForImageClassification}
		    if is_torch_available()
		    else {}
		)

		a__       							=	False
		a__       							=	False
		a__       							=	False
		a__       							=	False
		a__       							=	False


		def 		A__       (					self):
			_UpperCamelCase :		Union[str, Any]								=    EfficientNetModelTester(self)
			_UpperCamelCase :		int								=    ConfigTester(
			    self						,							config_class=__snake_case						,							has_text_modality=__snake_case						,							hidden_size=37)


		def 		A__       (					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 		A__       (					self):
			return


		@unittest.skip(reason='EfficientNet does not use inputs_embeds')
		def 		A__       (					self):
			pass


		@unittest.skip(reason='EfficientNet does not support input and output embeddings')
		def 		A__       (					self):
			pass


		@unittest.skip(reason='EfficientNet does not use feedforward chunking')
		def 		A__       (					self):
			pass


		def 		A__       (					self):
			_UpperCamelCase						,		_UpperCamelCase :		Tuple								=    self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
				_UpperCamelCase :		List[Any]								=    model_class(__snake_case)
				_UpperCamelCase :		Union[str, Any]								=    inspect.signature(model.forward)
				# signature.parameters is an OrderedDict => so arg_names order is deterministic
				_UpperCamelCase :		Union[str, Any]								=    [*signature.parameters.keys()]

				_UpperCamelCase :		Dict								=    ['pixel_values']
				self.assertListEqual(arg_names[:1]						,							__snake_case)


		def 		A__       (					self):
			_UpperCamelCase :		int								=    self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*__snake_case)


		def 		A__       (					self):
			def check_hidden_states_output(__snake_case						,							__snake_case						,							__snake_case):
				_UpperCamelCase :		str								=    model_class(__snake_case)
				model.to(__snake_case)
				model.eval()

				with torch.no_grad():
					_UpperCamelCase :		List[Any]								=    model(**self._prepare_for_class(__snake_case						,							__snake_case))

				_UpperCamelCase :		Tuple								=    outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
				_UpperCamelCase :		Optional[Any]								=    sum(config.num_block_repeats) * 4
				self.assertEqual(len(__snake_case)						,							__snake_case)

				# EfficientNet's feature maps are of shape (batch_size, num_channels, height, width)
				self.assertListEqual(
				    list(hidden_states[0].shape[-2:])						,							[self.model_tester.image_size // 2, self.model_tester.image_size // 2]						,							)

			_UpperCamelCase						,		_UpperCamelCase :		Optional[Any]								=    self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
				_UpperCamelCase :		Tuple								=    True
				check_hidden_states_output(__snake_case						,							__snake_case						,							__snake_case)

				# check that output_hidden_states also work using config
				del inputs_dict["output_hidden_states"]
				_UpperCamelCase :		Optional[int]								=    True

				check_hidden_states_output(__snake_case						,							__snake_case						,							__snake_case)


		def 		A__       (					self):
			_UpperCamelCase :		Tuple								=    self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_image_classification(*__snake_case)


		@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.')
		def 		A__       (					self):
			pass


		@slow
		def 		A__       (					self):
			for model_name in EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
				_UpperCamelCase :		Dict								=    EfficientNetModel.from_pretrained(__snake_case)
				self.assertIsNotNone(__snake_case)






		@is_pipeline_test
		@require_vision
		@slow
		def 		A__       (					self):
			super().test_pipeline_image_classification()






def        lowerCamelCase_  (				)    ->					Dict:




	'''simple docstring'''




	_UpperCamelCase :		List[str]								=    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
	return image







@require_torch
@require_vision
class 						lowercase     (					unittest.TestCase				):


		"""simple docstring"""




		@cached_property
		def 		A__       (					self):
			return AutoImageProcessor.from_pretrained('google/efficientnet-b7') if is_vision_available() else None






		@slow
		def 		A__       (					self):
			_UpperCamelCase :		List[Any]								=    EfficientNetForImageClassification.from_pretrained('google/efficientnet-b7').to(__snake_case)

			_UpperCamelCase :		Optional[Any]								=    self.default_image_processor
			_UpperCamelCase :		Union[str, Any]								=    prepare_img()
			_UpperCamelCase :		str								=    image_processor(images=__snake_case						,							return_tensors='pt').to(__snake_case)

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

			# verify the logits
			_UpperCamelCase :		Any								=    torch.Size((1, 10_00))
			self.assertEqual(outputs.logits.shape						,							__snake_case)

			_UpperCamelCase :		Optional[int]								=    torch.tensor([0.0_0_0_1, 0.0_0_0_2, 0.0_0_0_2]).to(__snake_case)
			self.assertTrue(torch.allclose(outputs.logits[0, :3]						,							__snake_case						,							atol=1e-4))





"""simple docstring"""
import random
import sys

from . import cryptomath_module as cryptomath

lowercase__   			=      (
    r' !"#$%&\'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`'
    r'abcdefghijklmnopqrstuvwxyz{|}~'
)







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )      ->None:
  if mode == "encrypt":
    if key_a == 1:
      sys.exit(
          'The affine cipher becomes weak when key '
          'A is set to 1. Choose different key'  )
    if key_b == 0:
      sys.exit(
          'The affine cipher becomes weak when key '
          'B is set to 0. Choose different key'  )
  if key_a < 0 or key_b < 0 or key_b > len(_SCREAMING_SNAKE_CASE  ) - 1:
    sys.exit(
        'Key A must be greater than 0 and key B must '
        F'be between 0 and {len(_SCREAMING_SNAKE_CASE  ) - 1}.'  )
  if cryptomath.gcd(_SCREAMING_SNAKE_CASE						,	len(_SCREAMING_SNAKE_CASE  )  ) != 1:
    sys.exit(
        F'Key A {key_a} and the symbol set size {len(_SCREAMING_SNAKE_CASE  )} '
        'are not relatively prime. Choose a different key.'  )







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )      ->str:
  a__				,       a__:	Optional[Any]     				=      divmod(_SCREAMING_SNAKE_CASE						,	len(_SCREAMING_SNAKE_CASE  )  )
  check_keys(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	'encrypt'  )
  a__:	Dict     				=      ''
  for symbol in message:
    if symbol in SYMBOLS:
      a__:	int     				=      SYMBOLS.find(_SCREAMING_SNAKE_CASE  )
      cipher_text += SYMBOLS[(sym_index * key_a + key_b) % len(_SCREAMING_SNAKE_CASE  )]
    else:
      cipher_text += symbol
  return cipher_text







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )      ->str:
  a__				,       a__:	List[str]     				=      divmod(_SCREAMING_SNAKE_CASE						,	len(_SCREAMING_SNAKE_CASE  )  )
  check_keys(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	'decrypt'  )
  a__:	List[Any]     				=      ''
  a__:	str     				=      cryptomath.find_mod_inverse(_SCREAMING_SNAKE_CASE						,	len(_SCREAMING_SNAKE_CASE  )  )
  for symbol in message:
    if symbol in SYMBOLS:
      a__:	List[str]     				=      SYMBOLS.find(_SCREAMING_SNAKE_CASE  )
      plain_text += SYMBOLS[
          (sym_index - key_b) * mod_inverse_of_key_a % len(_SCREAMING_SNAKE_CASE  )
      ]
    else:
      plain_text += symbol
  return plain_text







def 		__a  (      )      ->int:
  while True:
    a__:	int     				=      random.randint(2						,	len(_SCREAMING_SNAKE_CASE  )  )
    a__:	List[Any]     				=      random.randint(2						,	len(_SCREAMING_SNAKE_CASE  )  )
    if cryptomath.gcd(_SCREAMING_SNAKE_CASE						,	len(_SCREAMING_SNAKE_CASE  )  ) == 1 and key_b % len(_SCREAMING_SNAKE_CASE  ) != 0:
      return key_b * len(_SCREAMING_SNAKE_CASE  ) + key_b







def 		__a  (      )      ->None:
  a__:	Union[str, Any]     				=      input('Enter message: '  ).strip()
  a__:	List[Any]     				=      int(input('Enter key [2000 - 9000]: '  ).strip()  )
  a__:	List[Any]     				=      input('Encrypt/Decrypt [E/D]: '  ).strip().lower()

  if mode.startswith('e'  ):
    a__:	int     				=      'encrypt'
    a__:	List[str]     				=      encrypt_message(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )
  elif mode.startswith('d'  ):
    a__:	Dict     				=      'decrypt'
    a__:	Any     				=      decrypt_message(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )
  print(F'\n{mode.title()}ed text: \n{translated}'  )


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 # main()


