
import inspect
import unittest

from transformers import ResNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
							import jax
							import jax.numpy as jnp

							from transformers.models.resnet.modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel

if is_vision_available():
							from PIL import Image

							from transformers import AutoImageProcessor







class 							lowercase						(   unittest.TestCase						):




							def __init__(							self  ,A__  ,A__=3  ,A__=3_2  ,A__=3  ,A__=1_0  ,A__=[1_0, 2_0, 3_0, 4_0]  ,A__=[1, 1, 2, 1]  ,A__=True  ,A__=True  ,A__="relu"  ,A__=3  ,A__=None  ,):
												lowercase		      =		parent
												lowercase		      =		batch_size
												lowercase		      =		image_size
												lowercase		      =		num_channels
												lowercase		      =		embeddings_size
												lowercase		      =		hidden_sizes
												lowercase		      =		depths
												lowercase		      =		is_training
												lowercase		      =		use_labels
												lowercase		      =		hidden_act
												lowercase		      =		num_labels
												lowercase		      =		scope
												lowercase		      =		len(_SCREAMING_SNAKE_CASE)




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

												lowercase		      =		self.get_config()

												return config, pixel_values




							def 					A__ (							self):
												return ResNetConfig(
												    num_channels=self.num_channels  ,embeddings_size=self.embeddings_size  ,hidden_sizes=self.hidden_sizes  ,depths=self.depths  ,hidden_act=self.hidden_act  ,num_labels=self.num_labels  ,image_size=self.image_size  ,)




							def 					A__ (							self  ,A__  ,A__):
												lowercase		      =		FlaxResNetModel(config=_SCREAMING_SNAKE_CASE)
												lowercase		      =		model(_SCREAMING_SNAKE_CASE)

												# Output shape (b, c, h, w)
												self.parent.assertEqual(
												    result.last_hidden_state.shape  ,(self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)  ,)




							def 					A__ (							self  ,A__  ,A__):
												lowercase		      =		self.num_labels
												lowercase		      =		FlaxResNetForImageClassification(config=_SCREAMING_SNAKE_CASE)
												lowercase		      =		model(_SCREAMING_SNAKE_CASE)
												self.parent.assertEqual(result.logits.shape  ,(self.batch_size, self.num_labels))





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







@require_flax
class 							lowercase						(   _a			,    unittest.TestCase						):
							lowercase_			:			Dict			   =(FlaxResNetModel, FlaxResNetForImageClassification) if is_flax_available() else ()

							lowercase_			:			Dict			   =False
							lowercase_			:			Optional[Any]			   =False
							lowercase_			:			Any			   =False




							def 					A__ (							self):
												lowercase		      =		FlaxResNetModelTester(self)
												lowercase		      =		ConfigTester(self  ,config_class=_SCREAMING_SNAKE_CASE  ,has_text_modality=_SCREAMING_SNAKE_CASE)




							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




							def 					A__ (							self):
												lowercase		      =		self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE)




							def 					A__ (							self):
												lowercase		      =		self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE)




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




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




							def 					A__ (							self):
												lowercase		      =		self.model_tester.prepare_config_and_inputs_for_common()

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

																	lowercase		      =		["""pixel_values"""]
																	self.assertListEqual(arg_names[:1]  ,_SCREAMING_SNAKE_CASE)




							def 					A__ (							self):






												def check_hidden_states_output(A__  ,A__  ,A__):
																	lowercase		      =		model_class(_SCREAMING_SNAKE_CASE)

																	lowercase		      =		model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE))

																	lowercase		      =		outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

																	lowercase		      =		self.model_tester.num_stages
																	self.assertEqual(len(_SCREAMING_SNAKE_CASE)  ,expected_num_stages + 1)




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





							def 					A__ (							self):
												lowercase		      =		self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	with self.subTest(model_class.__name__):
																						lowercase		      =		self._prepare_for_class(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE)
																						lowercase		      =		model_class(_SCREAMING_SNAKE_CASE)

																						@jax.jit
																						def model_jitted(A__  ,**A__):
																											return model(pixel_values=_SCREAMING_SNAKE_CASE  ,**_SCREAMING_SNAKE_CASE)

																						with self.subTest('''JIT Enabled'''):
																											lowercase		      =		model_jitted(**_SCREAMING_SNAKE_CASE).to_tuple()

																						with self.subTest('''JIT Disabled'''):
																											with jax.disable_jit():
																																lowercase		      =		model_jitted(**_SCREAMING_SNAKE_CASE).to_tuple()

																						self.assertEqual(len(_SCREAMING_SNAKE_CASE)  ,len(_SCREAMING_SNAKE_CASE))
																						for jitted_output, output in zip(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE):
																											self.assertEqual(jitted_output.shape  ,output.shape)



def 			UpperCamelCase       (				):
					'''simple docstring'''
					lowercase		      =		Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
					return image







@require_flax
class 							lowercase						(   unittest.TestCase						):




							@cached_property
							def 					A__ (							self):
												return AutoImageProcessor.from_pretrained('''microsoft/resnet-50''') if is_vision_available() else None





							@slow
							def 					A__ (							self):
												lowercase		      =		FlaxResNetForImageClassification.from_pretrained('''microsoft/resnet-50''')

												lowercase		      =		self.default_image_processor
												lowercase		      =		prepare_img()
												lowercase		      =		image_processor(images=_SCREAMING_SNAKE_CASE  ,return_tensors='''np''')

												lowercase		      =		model(**_SCREAMING_SNAKE_CASE)

												# verify the logits
												lowercase		      =		(1, 1_0_0_0)
												self.assertEqual(outputs.logits.shape  ,_SCREAMING_SNAKE_CASE)

												lowercase		      =		jnp.array([-11.1069, -9.7877, -8.3777])

												self.assertTrue(jnp.allclose(outputs.logits[0, :3]  ,_SCREAMING_SNAKE_CASE  ,atol=1E-4))






'''simple docstring'''


__SCREAMING_SNAKE_CASE						=      [chr(i) for i in range(32, 126)]
__SCREAMING_SNAKE_CASE						=      list(range(len(alphabets)))
__SCREAMING_SNAKE_CASE						=      list(range(len(alphabets)))
__SCREAMING_SNAKE_CASE						=      list(range(len(alphabets)))
__SCREAMING_SNAKE_CASE						=      list(reversed(range(len(alphabets))))
__SCREAMING_SNAKE_CASE						=      []
__SCREAMING_SNAKE_CASE						=      __SCREAMING_SNAKE_CASE						=      __SCREAMING_SNAKE_CASE						=      0
def 					__magic_name__(       ):
						global gear_one_pos
						global gear_two_pos
						global gear_three_pos
						UpperCamelCase_     :	List[Any]					     =     gear_one[0]
						gear_one.append(UpperCAmelCase__	)
						del gear_one[0]
						gear_one_pos += 1
						if gear_one_pos % int(len(UpperCAmelCase__	)	) == 0:
												UpperCamelCase_     :	List[str]					     =     gear_two[0]
												gear_two.append(UpperCAmelCase__	)
												del gear_two[0]
												gear_two_pos += 1
												if gear_two_pos % int(len(UpperCAmelCase__	)	) == 0:
																		UpperCamelCase_     :	List[Any]					     =     gear_three[0]
																		gear_three.append(UpperCAmelCase__	)
																		del gear_three[0]
																		gear_three_pos += 1
def 					__magic_name__(       UpperCAmelCase__ :       int	):
						UpperCamelCase_     :	Any					     =     alphabets.index(UpperCAmelCase__	)
						UpperCamelCase_     :	List[str]					     =     gear_one[target]
						UpperCamelCase_     :	str					     =     gear_two[target]
						UpperCamelCase_     :	int					     =     gear_three[target]
						UpperCamelCase_     :	Union[str, Any]					     =     reflector[target]
						UpperCamelCase_     :	Dict					     =     gear_three.index(UpperCAmelCase__	)
						UpperCamelCase_     :	Optional[int]					     =     gear_two.index(UpperCAmelCase__	)
						UpperCamelCase_     :	Optional[int]					     =     gear_one.index(UpperCAmelCase__	)
						code.append(alphabets[target]	)
						rotator()


if __name__ == "__main__":
	__SCREAMING_SNAKE_CASE						=      list(input("Type your message:\n"))
	while True:
		try:
			__SCREAMING_SNAKE_CASE						=      int(input("Please set token:(must be only digits)\n"))
			break
		except Exception as error:
			print(error)
	for _ in range(token):
		rotator()
	for j in decode:
		engine(j)
	print("\n" + "".join(code))
	print(
	    f"\nYour Token is {token} please write it down.\nIf you want to decode "
	    "this message again you should input same digits as token!"
	)




