import inspect
import unittest
import warnings
from math import ceil, floor

from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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 (
	    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
	    MODEL_MAPPING,
	    LevitForImageClassification,
	    LevitForImageClassificationWithTeacher,
	    LevitModel,
	)
	from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import LevitImageProcessor

class        UpperCamelCase						(				__a				):





						def        A_							(self						)				->				Any:
												UpperCamelCase_		:							Dict  =		self.config_class(**self.inputs_dict						)
												self.parent.assertTrue(hasattr(__UpperCamelCase			,     """hidden_sizes"""						)						)
												self.parent.assertTrue(hasattr(__UpperCamelCase			,     """num_attention_heads"""						)						)


class        UpperCamelCase						:





						def __init__(self			,     __UpperCamelCase			,     __UpperCamelCase=13			,     __UpperCamelCase=64			,     __UpperCamelCase=3			,     __UpperCamelCase=3			,     __UpperCamelCase=2			,     __UpperCamelCase=1			,     __UpperCamelCase=16			,     __UpperCamelCase=[128, 256, 384]			,     __UpperCamelCase=[4, 6, 8]			,     __UpperCamelCase=[2, 3, 4]			,     __UpperCamelCase=[16, 16, 16]			,     __UpperCamelCase=0			,     __UpperCamelCase=[2, 2, 2]			,     __UpperCamelCase=[2, 2, 2]			,     __UpperCamelCase=0.02			,     __UpperCamelCase=True			,     __UpperCamelCase=True			,     __UpperCamelCase=2			,     )				->				Optional[int]:
												UpperCamelCase_		:							Tuple  =		parent
												UpperCamelCase_		:							Optional[Any]  =		batch_size
												UpperCamelCase_		:							Dict  =		image_size
												UpperCamelCase_		:							Dict  =		num_channels
												UpperCamelCase_		:							Optional[Any]  =		kernel_size
												UpperCamelCase_		:							int  =		stride
												UpperCamelCase_		:							str  =		padding
												UpperCamelCase_		:							Tuple  =		hidden_sizes
												UpperCamelCase_		:							int  =		num_attention_heads
												UpperCamelCase_		:							List[str]  =		depths
												UpperCamelCase_		:							Dict  =		key_dim
												UpperCamelCase_		:							Any  =		drop_path_rate
												UpperCamelCase_		:							List[Any]  =		patch_size
												UpperCamelCase_		:							Any  =		attention_ratio
												UpperCamelCase_		:							Optional[Any]  =		mlp_ratio
												UpperCamelCase_		:							Optional[int]  =		initializer_range
												UpperCamelCase_		:							Optional[Any]  =		[
												    ["""Subsample""", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
												    ["""Subsample""", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
												]
												UpperCamelCase_		:							Tuple  =		is_training
												UpperCamelCase_		:							Any  =		use_labels
												UpperCamelCase_		:							Dict  =		num_labels
												UpperCamelCase_		:							List[str]  =		initializer_range





						def        A_							(self						)				->				Dict:
												UpperCamelCase_		:							Optional[Any]  =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

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

												UpperCamelCase_		:							Any  =		self.get_config()
												return config, pixel_values, labels





						def        A_							(self						)				->				Optional[int]:
												return LevitConfig(
												    image_size=self.image_size			,     num_channels=self.num_channels			,     kernel_size=self.kernel_size			,     stride=self.stride			,     padding=self.padding			,     patch_size=self.patch_size			,     hidden_sizes=self.hidden_sizes			,     num_attention_heads=self.num_attention_heads			,     depths=self.depths			,     key_dim=self.key_dim			,     drop_path_rate=self.drop_path_rate			,     mlp_ratio=self.mlp_ratio			,     attention_ratio=self.attention_ratio			,     initializer_range=self.initializer_range			,     down_ops=self.down_ops			,     )





						def        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase						)				->				List[Any]:
												UpperCamelCase_		:							int  =		LevitModel(config=__UpperCamelCase						)
												model.to(__UpperCamelCase						)
												model.eval()
												UpperCamelCase_		:							List[Any]  =		model(__UpperCamelCase						)
												UpperCamelCase_		:							int  =		(self.image_size, self.image_size)
												UpperCamelCase_,UpperCamelCase_		:							Optional[int]  =		image_size[0], image_size[1]
												for _ in range(4						):
																		UpperCamelCase_		:							Union[str, Any]  =		floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1						)
																		UpperCamelCase_		:							List[Any]  =		floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1						)
												self.parent.assertEqual(
												    result.last_hidden_state.shape			,     (self.batch_size, ceil(height / 4						) * ceil(width / 4						), self.hidden_sizes[-1])			,     )





						def        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase						)				->				Any:
												UpperCamelCase_		:							List[str]  =		self.num_labels
												UpperCamelCase_		:							Any  =		LevitForImageClassification(__UpperCamelCase						)
												model.to(__UpperCamelCase						)
												model.eval()
												UpperCamelCase_		:							int  =		model(__UpperCamelCase			,     labels=__UpperCamelCase						)
												self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.num_labels)						)





						def        A_							(self						)				->				str:
												UpperCamelCase_		:							Tuple  =		self.prepare_config_and_inputs()
												UpperCamelCase_,UpperCamelCase_,UpperCamelCase_		:							Any  =		config_and_inputs
												UpperCamelCase_		:							Tuple  =		{"""pixel_values""": pixel_values}
												return config, inputs_dict


@require_torch
class        UpperCamelCase						(				__a	,	__a	,	unittest.TestCase				):
						a__		:Any      =			(
						    (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
						    if is_torch_available()
						    else ()
						)
						a__		:str      =			(
						    {
						        '''feature-extraction''': LevitModel,
						        '''image-classification''': (LevitForImageClassification, LevitForImageClassificationWithTeacher),
						    }
						    if is_torch_available()
						    else {}
						)

						a__		:Optional[Any]      =			False
						a__		:Optional[int]      =			False
						a__		:Tuple      =			False
						a__		:List[str]      =			False
						a__		:Dict      =			False





						def        A_							(self						)				->				List[Any]:
												UpperCamelCase_		:							int  =		LevitModelTester(self						)
												UpperCamelCase_		:							Union[str, Any]  =		ConfigTester(self			,     config_class=__UpperCamelCase			,     has_text_modality=__UpperCamelCase			,     hidden_size=37						)





						def        A_							(self						)				->				Dict:
												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						)				->				Optional[Any]:
												return





						@unittest.skip(reason="""Levit does not use inputs_embeds"""						)
						def        A_							(self						)				->				int:
												pass





						@unittest.skip(reason="""Levit does not support input and output embeddings"""						)
						def        A_							(self						)				->				Any:
												pass





						@unittest.skip(reason="""Levit does not output attentions"""						)
						def        A_							(self						)				->				List[str]:
												pass





						def        A_							(self						)				->				Union[str, Any]:
												UpperCamelCase_,UpperCamelCase_		:							Any  =		self.model_tester.prepare_config_and_inputs_for_common()

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

																		UpperCamelCase_		:							Tuple  =		["""pixel_values"""]
																		self.assertListEqual(arg_names[:1]			,     __UpperCamelCase						)





						def        A_							(self						)				->				Optional[Any]:
												def check_hidden_states_output(__UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase						):
																		UpperCamelCase_		:							List[str]  =		model_class(__UpperCamelCase						)
																		model.to(__UpperCamelCase						)
																		model.eval()

																		with torch.no_grad():
																								UpperCamelCase_		:							Union[str, Any]  =		model(**self._prepare_for_class(__UpperCamelCase			,     __UpperCamelCase						)						)

																		UpperCamelCase_		:							Optional[Any]  =		outputs.hidden_states

																		UpperCamelCase_		:							Optional[int]  =		len(self.model_tester.depths						) + 1
																		self.assertEqual(len(__UpperCamelCase						)			,     __UpperCamelCase						)

																		UpperCamelCase_		:							Tuple  =		(self.model_tester.image_size, self.model_tester.image_size)
																		UpperCamelCase_,UpperCamelCase_		:							Optional[int]  =		image_size[0], image_size[1]
																		for _ in range(4						):
																								UpperCamelCase_		:							Dict  =		floor(
																								    (
																								        (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
																								        / self.model_tester.stride
																								    )
																								    + 1						)
																								UpperCamelCase_		:							List[str]  =		floor(
																								    (
																								        (width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
																								        / self.model_tester.stride
																								    )
																								    + 1						)
																		# verify the first hidden states (first block)
																		self.assertListEqual(
																		    list(hidden_states[0].shape[-2:]						)			,     [
																		        height * width,
																		        self.model_tester.hidden_sizes[0],
																		    ]			,     )

												UpperCamelCase_,UpperCamelCase_		:							Optional[int]  =		self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																		UpperCamelCase_		:							Union[str, Any]  =		True
																		check_hidden_states_output(__UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase						)

																		# check that output_hidden_states also work using config
																		del inputs_dict["output_hidden_states"]
																		UpperCamelCase_		:							List[str]  =		True

																		check_hidden_states_output(__UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase						)





						@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""						)
						def        A_							(self						)				->				Dict:
												pass





						def        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase=False						)				->				Tuple:
												UpperCamelCase_		:							List[str]  =		super()._prepare_for_class(__UpperCamelCase			,     __UpperCamelCase			,     return_labels=__UpperCamelCase						)

												if return_labels:
																		if model_class.__name__ == "LevitForImageClassificationWithTeacher":
																								del inputs_dict["labels"]

												return inputs_dict





						def        A_							(self						)				->				Tuple:
												UpperCamelCase_		:							Tuple  =		self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*__UpperCamelCase						)





						def        A_							(self						)				->				List[Any]:
												UpperCamelCase_		:							int  =		self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase						)





						def        A_							(self						)				->				Optional[Any]:
												if not self.model_tester.is_training:
																		return

												UpperCamelCase_,UpperCamelCase_		:							List[str]  =		self.model_tester.prepare_config_and_inputs_for_common()
												UpperCamelCase_		:							List[Any]  =		True

												for model_class in self.all_model_classes:
																		# LevitForImageClassificationWithTeacher supports inference-only
																		if (
																		    model_class in get_values(__UpperCamelCase						)
																		    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
																		):
																								continue
																		UpperCamelCase_		:							int  =		model_class(__UpperCamelCase						)
																		model.to(__UpperCamelCase						)
																		model.train()
																		UpperCamelCase_		:							List[str]  =		self._prepare_for_class(__UpperCamelCase			,     __UpperCamelCase			,     return_labels=__UpperCamelCase						)
																		UpperCamelCase_		:							int  =		model(**__UpperCamelCase						).loss
																		loss.backward()





						def        A_							(self						)				->				Union[str, Any]:
												UpperCamelCase_,UpperCamelCase_		:							Optional[int]  =		self.model_tester.prepare_config_and_inputs_for_common()
												if not self.model_tester.is_training:
																		return

												UpperCamelCase_		:							Tuple  =		False
												UpperCamelCase_		:							str  =		True

												for model_class in self.all_model_classes:
																		if model_class in get_values(__UpperCamelCase						) or not model_class.supports_gradient_checkpointing:
																								continue
																		# LevitForImageClassificationWithTeacher supports inference-only
																		if model_class.__name__ == "LevitForImageClassificationWithTeacher":
																								continue
																		UpperCamelCase_		:							str  =		model_class(__UpperCamelCase						)
																		model.gradient_checkpointing_enable()
																		model.to(__UpperCamelCase						)
																		model.train()
																		UpperCamelCase_		:							Optional[Any]  =		self._prepare_for_class(__UpperCamelCase			,     __UpperCamelCase			,     return_labels=__UpperCamelCase						)
																		UpperCamelCase_		:							Optional[Any]  =		model(**__UpperCamelCase						).loss
																		loss.backward()





						def        A_							(self						)				->				Union[str, Any]:
												UpperCamelCase_,UpperCamelCase_		:							Optional[Any]  =		self.model_tester.prepare_config_and_inputs_for_common()

												UpperCamelCase_		:							Any  =		[
												    {"""title""": """multi_label_classification""", """num_labels""": 2, """dtype""": torch.float},
												    {"""title""": """single_label_classification""", """num_labels""": 1, """dtype""": torch.long},
												    {"""title""": """regression""", """num_labels""": 1, """dtype""": torch.float},
												]

												for model_class in self.all_model_classes:
																		if (
																		    model_class
																		    not in [
																		        *get_values(__UpperCamelCase						),
																		    ]
																		    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
																		):
																								continue

																		for problem_type in problem_types:
																								with self.subTest(msg=f'''Testing {model_class} with {problem_type["title"]}'''						):
																														UpperCamelCase_		:							Any  =		problem_type["""title"""]
																														UpperCamelCase_		:							Dict  =		problem_type["""num_labels"""]

																														UpperCamelCase_		:							Dict  =		model_class(__UpperCamelCase						)
																														model.to(__UpperCamelCase						)
																														model.train()

																														UpperCamelCase_		:							Optional[int]  =		self._prepare_for_class(__UpperCamelCase			,     __UpperCamelCase			,     return_labels=__UpperCamelCase						)

																														if problem_type["num_labels"] > 1:
																																				UpperCamelCase_		:							Union[str, Any]  =		inputs["""labels"""].unsqueeze(1						).repeat(1			,     problem_type["""num_labels"""]						)

																														UpperCamelCase_		:							Tuple  =		inputs["""labels"""].to(problem_type["""dtype"""]						)

																														# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
																														# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
																														# they have the same size." which is a symptom something in wrong for the regression problem.
																														# See https://github.com/huggingface/transformers/issues/11780
																														with warnings.catch_warnings(record=__UpperCamelCase						) as warning_list:
																																				UpperCamelCase_		:							str  =		model(**__UpperCamelCase						).loss
																														for w in warning_list:
																																				if "Using a target size that is different to the input size" in str(w.message						):
																																										raise ValueError(
																																										    f'''Something is going wrong in the regression problem: intercepted {w.message}'''						)

																														loss.backward()





						@slow
						def        A_							(self						)				->				Dict:
												for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		UpperCamelCase_		:							Any  =		LevitModel.from_pretrained(__UpperCamelCase						)
																		self.assertIsNotNone(__UpperCamelCase						)






def     lowerCAmelCase_			(   ):
						UpperCamelCase_		:							str  =		Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""			)
						return image







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





						@cached_property
						def        A_							(self						)				->				Any:
												return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)





						@slow
						def        A_							(self						)				->				str:
												UpperCamelCase_		:							Tuple  =		LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]						).to(
												    __UpperCamelCase						)

												UpperCamelCase_		:							Optional[Any]  =		self.default_image_processor
												UpperCamelCase_		:							List[str]  =		prepare_img()
												UpperCamelCase_		:							Optional[int]  =		image_processor(images=__UpperCamelCase			,     return_tensors="""pt"""						).to(__UpperCamelCase						)

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

												# verify the logits
												UpperCamelCase_		:							List[str]  =		torch.Size((1, 1_000)						)
												self.assertEqual(outputs.logits.shape			,     __UpperCamelCase						)

												UpperCamelCase_		:							Any  =		torch.tensor([1.0_448, -0.3_745, -1.8_317]						).to(__UpperCamelCase						)

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







import os
import random
import sys

from . import cryptomath_module as cryptoMath  # noqa: N812
from . import rabin_miller as rabinMiller  # noqa: N812






def     lowerCAmelCase_			(   ):
						print("""Making key files..."""			)
						make_key_files("""rsa"""		, 1024			)
						print("""Key files generation successful."""			)






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				int			):
						print("""Generating prime p..."""			)
						UpperCamelCase_		:							Tuple  =		rabinMiller.generate_large_prime(_SCREAMING_SNAKE_CASE			)
						print("""Generating prime q..."""			)
						UpperCamelCase_		:							List[Any]  =		rabinMiller.generate_large_prime(_SCREAMING_SNAKE_CASE			)
						UpperCamelCase_		:							List[str]  =		p * q

						print("""Generating e that is relatively prime to (p - 1) * (q - 1)..."""			)
						while True:
												UpperCamelCase_		:							Union[str, Any]  =		random.randrange(2 ** (key_size - 1)		, 2 ** (key_size)			)
												if cryptoMath.gcd(_SCREAMING_SNAKE_CASE		, (p - 1) * (q - 1)			) == 1:
																		break

						print("""Calculating d that is mod inverse of e..."""			)
						UpperCamelCase_		:							Tuple  =		cryptoMath.find_mod_inverse(_SCREAMING_SNAKE_CASE		, (p - 1) * (q - 1)			)

						UpperCamelCase_		:							Tuple  =		(n, e)
						UpperCamelCase_		:							int  =		(n, d)
						return (public_key, private_key)






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				str		, _SCREAMING_SNAKE_CASE     :				int			):
						if os.path.exists(f'''{name}_pubkey.txt'''			) or os.path.exists(f'''{name}_privkey.txt'''			):
												print("""\nWARNING:"""			)
												print(
												    f'''"{name}_pubkey.txt" or "{name}_privkey.txt" already exists. \n'''
												    """Use a different name or delete these files and re-run this program."""			)
												sys.exit()

						UpperCamelCase_,UpperCamelCase_		:							str  =		generate_key(_SCREAMING_SNAKE_CASE			)
						print(f'''\nWriting public key to file {name}_pubkey.txt...'''			)
						with open(f'''{name}_pubkey.txt'''		, """w"""			) as out_file:
												out_file.write(f'''{key_size},{public_key[0]},{public_key[1]}'''			)

						print(f'''Writing private key to file {name}_privkey.txt...'''			)
						with open(f'''{name}_privkey.txt'''		, """w"""			) as out_file:
												out_file.write(f'''{key_size},{private_key[0]},{private_key[1]}'''			)


if __name__ == "__main__":
	main()






