import inspect
import unittest

from transformers import DataaVecVisionConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, 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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch
					from torch import nn

					from transformers import (
					    MODEL_MAPPING,
					    DataaVecVisionForImageClassification,
					    DataaVecVisionForSemanticSegmentation,
					    DataaVecVisionModel,
					)
					from transformers.models.dataavec.modeling_dataavec_vision import DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import BeitImageProcessor


class _a       :

	'''simple docstring'''



	def __init__(       self							,			__UpperCAmelCase							,			__UpperCAmelCase=100							,			__UpperCAmelCase=13							,			__UpperCAmelCase=30							,			__UpperCAmelCase=2							,			__UpperCAmelCase=3							,			__UpperCAmelCase=True							,			__UpperCAmelCase=True							,			__UpperCAmelCase=32							,			__UpperCAmelCase=4							,			__UpperCAmelCase=4							,			__UpperCAmelCase=37							,			__UpperCAmelCase="gelu"							,			__UpperCAmelCase=0.1							,			__UpperCAmelCase=0.1							,			__UpperCAmelCase=10							,			__UpperCAmelCase=0.02							,			__UpperCAmelCase=3							,			__UpperCAmelCase=None							,			__UpperCAmelCase=[0, 1, 2, 3]							,			):
			__A							:   int	  =				parent
			__A							:   Optional[Any]	  =				100
			__A							:   List[str]	  =				batch_size
			__A							:   Union[str, Any]	  =				image_size
			__A							:   str	  =				patch_size
			__A							:   str	  =				num_channels
			__A							:   str	  =				is_training
			__A							:   str	  =				use_labels
			__A							:   Any	  =				hidden_size
			__A							:   Tuple	  =				num_hidden_layers
			__A							:   Any	  =				num_attention_heads
			__A							:   List[str]	  =				intermediate_size
			__A							:   Any	  =				hidden_act
			__A							:   List[str]	  =				hidden_dropout_prob
			__A							:   Any	  =				attention_probs_dropout_prob
			__A							:   Tuple	  =				type_sequence_label_size
			__A							:   Optional[Any]	  =				initializer_range
			__A							:   Tuple	  =				scope
			__A							:   Dict	  =				out_indices
			__A							:   str	  =				num_labels

			# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
			__A							:   int	  =				(image_size // patch_size) ** 2
			__A							:   Dict	  =				num_patches + 1

	def 					__UpperCAmelCase(       self   ):
			__A							:   Dict	  =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

			__A							:   Tuple	  =				None
			__A							:   List[str]	  =				None
			if self.use_labels:
					__A							:   Optional[int]	  =				ids_tensor([self.batch_size]							,			self.type_sequence_label_size   )
					__A							:   Optional[Any]	  =				ids_tensor([self.batch_size, self.image_size, self.image_size]							,			self.num_labels   )

			__A							:   List[Any]	  =				self.get_config()

			return config, pixel_values, labels, pixel_labels

	def 					__UpperCAmelCase(       self   ):
			return DataaVecVisionConfig(
			    vocab_size=self.vocab_size							,			image_size=self.image_size							,			patch_size=self.patch_size							,			num_channels=self.num_channels							,			hidden_size=self.hidden_size							,			num_hidden_layers=self.num_hidden_layers							,			num_attention_heads=self.num_attention_heads							,			intermediate_size=self.intermediate_size							,			hidden_act=self.hidden_act							,			hidden_dropout_prob=self.hidden_dropout_prob							,			attention_probs_dropout_prob=self.attention_probs_dropout_prob							,			is_decoder=__UpperCAmelCase							,			initializer_range=self.initializer_range							,			out_indices=self.out_indices							,			)

	def 					__UpperCAmelCase(       self							,			__UpperCAmelCase							,			__UpperCAmelCase							,			__UpperCAmelCase							,			__UpperCAmelCase   ):
			__A							:   Dict	  =				DataaVecVisionModel(config=__UpperCAmelCase   )
			model.to(__UpperCAmelCase   )
			model.eval()
			__A							:   Any	  =				model(__UpperCAmelCase   )
			# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
			__A							:   Tuple	  =				(self.image_size // self.patch_size) ** 2
			self.parent.assertEqual(result.last_hidden_state.shape							,			(self.batch_size, num_patches + 1, self.hidden_size)   )

	def 					__UpperCAmelCase(       self							,			__UpperCAmelCase							,			__UpperCAmelCase							,			__UpperCAmelCase							,			__UpperCAmelCase   ):
			__A							:   List[str]	  =				self.type_sequence_label_size
			__A							:   int	  =				DataaVecVisionForImageClassification(__UpperCAmelCase   )
			model.to(__UpperCAmelCase   )
			model.eval()
			__A							:   int	  =				model(__UpperCAmelCase							,			labels=__UpperCAmelCase   )
			self.parent.assertEqual(result.logits.shape							,			(self.batch_size, self.type_sequence_label_size)   )

	def 					__UpperCAmelCase(       self							,			__UpperCAmelCase							,			__UpperCAmelCase							,			__UpperCAmelCase							,			__UpperCAmelCase   ):
			__A							:   Union[str, Any]	  =				self.num_labels
			__A							:   Any	  =				DataaVecVisionForSemanticSegmentation(__UpperCAmelCase   )
			model.to(__UpperCAmelCase   )
			model.eval()
			__A							:   List[Any]	  =				model(__UpperCAmelCase   )
			self.parent.assertEqual(
			    result.logits.shape							,			(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)   )
			__A							:   Any	  =				model(__UpperCAmelCase							,			labels=__UpperCAmelCase   )
			self.parent.assertEqual(
			    result.logits.shape							,			(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)   )







	def 					__UpperCAmelCase(       self   ):
			__A							:   Any	  =				self.prepare_config_and_inputs()
			__A  ,       __A  ,       __A  ,       __A							:   Optional[int]	  =				config_and_inputs
			__A							:   str	  =				{"pixel_values": pixel_values}
			return config, inputs_dict


@require_torch
class _a       (	lowerCAmelCase__   ,							lowerCAmelCase__   ,							unittest.TestCase ):

	'''simple docstring'''



	lowerCamelCase_   :      Optional[int]       =	(
	    (DataaVecVisionModel, DataaVecVisionForImageClassification, DataaVecVisionForSemanticSegmentation)
	    if is_torch_available()
	    else ()
	)
	lowerCamelCase_   :      Dict       =	(
	    {
	        """feature-extraction""": DataaVecVisionModel,
	        """image-classification""": DataaVecVisionForImageClassification,
	        """image-segmentation""": DataaVecVisionForSemanticSegmentation,
	    }
	    if is_torch_available()
	    else {}
	)

	lowerCamelCase_   :      Dict       =	False
	lowerCamelCase_   :      Tuple       =	False
	lowerCamelCase_   :      str       =	False

	def 					__UpperCAmelCase(       self   ):
			__A							:   Dict	  =				DataaVecVisionModelTester(self   )
			__A							:   Tuple	  =				ConfigTester(
			    self							,			config_class=__UpperCAmelCase							,			has_text_modality=__UpperCAmelCase							,			hidden_size=37   )

	def 					__UpperCAmelCase(       self   ):
			self.config_tester.run_common_tests()

	def 					__UpperCAmelCase(       self   ):
			# Data2VecVision does not use inputs_embeds
			pass

	@require_torch_multi_gpu
	@unittest.skip(
	    reason="Data2VecVision has some layers using `add_module` which doesn't work well with `nn.DataParallel`"   )
	def 					__UpperCAmelCase(       self   ):
			pass

	def 					__UpperCAmelCase(       self   ):
			__A  ,       __A							:   Union[str, Any]	  =				self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
					__A							:   Optional[Any]	  =				model_class(__UpperCAmelCase   )
					self.assertIsInstance(model.get_input_embeddings()							,			(nn.Module)   )
					__A							:   Tuple	  =				model.get_output_embeddings()
					self.assertTrue(x is None or isinstance(__UpperCAmelCase							,			nn.Linear   )   )

	def 					__UpperCAmelCase(       self   ):
			__A  ,       __A							:   Dict	  =				self.model_tester.prepare_config_and_inputs_for_common()

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

					__A							:   str	  =				["pixel_values"]
					self.assertListEqual(arg_names[:1]							,			__UpperCAmelCase   )

	def 					__UpperCAmelCase(       self   ):
			__A							:   str	  =				self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*__UpperCAmelCase   )

	def 					__UpperCAmelCase(       self   ):
			__A							:   Optional[Any]	  =				self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_image_segmentation(*__UpperCAmelCase   )

	def 					__UpperCAmelCase(       self   ):
			if not self.model_tester.is_training:
					return

			__A  ,       __A							:   Union[str, Any]	  =				self.model_tester.prepare_config_and_inputs_for_common()
			__A							:   Optional[int]	  =				True

			for model_class in self.all_model_classes:
					if model_class in [*get_values(__UpperCAmelCase   )]:
							continue

					__A							:   Any	  =				model_class(__UpperCAmelCase   )
					model.to(__UpperCAmelCase   )
					model.train()
					__A							:   Optional[int]	  =				self._prepare_for_class(__UpperCAmelCase							,			__UpperCAmelCase							,			return_labels=__UpperCAmelCase   )
					__A							:   List[str]	  =				model(**__UpperCAmelCase   ).loss
					loss.backward()

	def 					__UpperCAmelCase(       self   ):
			__A  ,       __A							:   Union[str, Any]	  =				self.model_tester.prepare_config_and_inputs_for_common()
			if not self.model_tester.is_training:
					return

			__A							:   Union[str, Any]	  =				False
			__A							:   List[Any]	  =				True

			for model_class in self.all_model_classes:
					if model_class in [*get_values(__UpperCAmelCase   )] or not model_class.supports_gradient_checkpointing:
							continue
					# TODO: remove the following 3 lines once we have a MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING
					# this can then be incorporated into _prepare_for_class in test_modeling_common.py
					elif model_class.__name__ == "Data2VecVisionForSemanticSegmentation":
							__A  ,       __A  ,       __A  ,       __A							:   List[str]	  =				inputs_dict["pixel_values"].shape
							__A							:   List[str]	  =				torch.zeros(
							    [self.model_tester.batch_size, height, width]							,			device=__UpperCAmelCase   ).long()
					__A							:   Any	  =				model_class(__UpperCAmelCase   )
					model.gradient_checkpointing_enable()
					model.to(__UpperCAmelCase   )
					model.train()
					__A							:   Any	  =				self._prepare_for_class(__UpperCAmelCase							,			__UpperCAmelCase							,			return_labels=__UpperCAmelCase   )
					__A							:   Any	  =				model(**__UpperCAmelCase   ).loss
					loss.backward()

	def 					__UpperCAmelCase(       self   ):
			__A  ,       __A							:   Tuple	  =				self.model_tester.prepare_config_and_inputs_for_common()

			__A							:   Optional[Any]	  =				_config_zero_init(__UpperCAmelCase   )
			for model_class in self.all_model_classes:
					__A							:   Optional[int]	  =				model_class(config=__UpperCAmelCase   )
					for name, param in model.named_parameters():
							# we skip lambda parameters as these require special initial values
							# determined by config.layer_scale_init_value
							if "lambda" in name:
									continue
							if param.requires_grad:
									self.assertIn(
									    ((param.data.mean() * 1e9).round() / 1e9).item()							,			[0.0, 1.0]							,			msg=F"Parameter {name} of model {model_class} seems not properly initialized"							,			)

	def 					__UpperCAmelCase(       self							,			__UpperCAmelCase							,			__UpperCAmelCase							,			__UpperCAmelCase							,			__UpperCAmelCase=2e-4							,			__UpperCAmelCase="outputs"							,			__UpperCAmelCase=None   ):
			# We override with a slightly higher tol value, as semseg models tend to diverge a bit more
			super().check_pt_tf_outputs(__UpperCAmelCase							,			__UpperCAmelCase							,			__UpperCAmelCase							,			__UpperCAmelCase							,			__UpperCAmelCase							,			__UpperCAmelCase   )

	def 					__UpperCAmelCase(       self   ):
			__A							:   int	  =				self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase   )







	@slow
	def 					__UpperCAmelCase(       self   ):
			for model_name in DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					__A							:   List[Any]	  =				DataaVecVisionModel.from_pretrained(__UpperCAmelCase   )
					self.assertIsNotNone(__UpperCAmelCase   )



def    lowerCamelCase_   (  )							->	Optional[int]:
		__A							:   Optional[int]	  =				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
		return image





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

	'''simple docstring'''



	@cached_property
	def 					__UpperCAmelCase(       self   ):
			return (
			    BeitImageProcessor.from_pretrained("facebook/data2vec-vision-base-ft1k"   ) if is_vision_available() else None
			)







	@slow
	def 					__UpperCAmelCase(       self   ):
			__A							:   List[Any]	  =				DataaVecVisionForImageClassification.from_pretrained("facebook/data2vec-vision-base-ft1k"   ).to(
			    __UpperCAmelCase   )

			__A							:   List[Any]	  =				self.default_image_processor
			__A							:   List[Any]	  =				prepare_img()
			__A							:   Tuple	  =				image_processor(images=__UpperCAmelCase							,			return_tensors="pt"   ).to(__UpperCAmelCase   )

			# forward pass
			with torch.no_grad():
					__A							:   Any	  =				model(**__UpperCAmelCase   )
			__A							:   List[str]	  =				outputs.logits

			# verify the logits
			__A							:   Optional[int]	  =				torch.Size((1, 1_000)   )
			self.assertEqual(logits.shape							,			__UpperCAmelCase   )

			__A							:   Union[str, Any]	  =				torch.tensor([0.32_77, -0.13_95, 0.09_11]   ).to(__UpperCAmelCase   )

			self.assertTrue(torch.allclose(logits[0, :3]							,			__UpperCAmelCase							,			atol=1e-4   )   )

			__A							:   Dict	  =				[model.config.labelaid[i] for i in ["remote control, remote", "tabby, tabby cat"]]
			self.assertEqual(logits[0].topk(2   ).indices.cpu().tolist()							,			__UpperCAmelCase   )






import os
import sys

from . import rsa_key_generator as rkg

UpperCamelCase      			=     128
UpperCamelCase      			=     256



def    lowerCamelCase_   (  _lowercase ,     _lowercase = DEFAULT_BLOCK_SIZE    )							->	list[int]:
		__A							:   List[str]	  =				message.encode("ascii"    )

		__A							:   str	  =				[]
		for block_start in range(0 ,     len(_lowercase    ) ,     _lowercase    ):
				__A							:   str	  =				0
				for i in range(_lowercase ,     min(block_start + block_size ,     len(_lowercase    )    )    ):
						block_int += message_bytes[i] * (BYTE_SIZE ** (i % block_size))
				block_ints.append(_lowercase    )
		return block_ints



def    lowerCamelCase_   (  _lowercase ,     _lowercase ,     _lowercase = DEFAULT_BLOCK_SIZE    )							->	str:
		__A							:   list[str]	  =				[]
		for block_int in block_ints:
				__A							:   list[str]	  =				[]
				for i in range(block_size - 1 ,     -1 ,     -1    ):
						if len(_lowercase    ) + i < message_length:
								__A							:   Optional[Any]	  =				block_int // (BYTE_SIZE**i)
								__A							:   Union[str, Any]	  =				block_int % (BYTE_SIZE**i)
								block_message.insert(0 ,     chr(_lowercase    )    )
				message.extend(_lowercase    )
		return "".join(_lowercase    )



def    lowerCamelCase_   (  _lowercase ,     _lowercase ,     _lowercase = DEFAULT_BLOCK_SIZE    )							->	list[int]:
		__A							:   List[str]	  =				[]
		__A  ,       __A							:   List[Any]	  =				key

		for block in get_blocks_from_text(_lowercase ,     _lowercase    ):
				encrypted_blocks.append(pow(_lowercase ,     _lowercase ,     _lowercase    )    )
		return encrypted_blocks



def    lowerCamelCase_   (  _lowercase ,     _lowercase ,     _lowercase ,     _lowercase = DEFAULT_BLOCK_SIZE ,     )							->	str:
		__A							:   Optional[Any]	  =				[]
		__A  ,       __A							:   Dict	  =				key
		for block in encrypted_blocks:
				decrypted_blocks.append(pow(_lowercase ,     _lowercase ,     _lowercase    )    )
		return get_text_from_blocks(_lowercase ,     _lowercase ,     _lowercase    )



def    lowerCamelCase_   (  _lowercase    )							->	tuple[int, int, int]:
		with open(_lowercase    ) as fo:
				__A							:   Optional[int]	  =				fo.read()
		__A  ,       __A  ,       __A							:   Optional[int]	  =				content.split(","    )
		return (int(_lowercase    ), int(_lowercase    ), int(_lowercase    ))



def    lowerCamelCase_   (  _lowercase ,     _lowercase ,     _lowercase ,     _lowercase = DEFAULT_BLOCK_SIZE ,     )							->	str:
		__A  ,       __A  ,       __A							:   Optional[int]	  =				read_key_file(_lowercase    )
		if key_size < block_size * 8:
				sys.exit(
				    "ERROR: Block size is {} bits and key size is {} bits. The RSA cipher "
				    "requires the block size to be equal to or greater than the key size. "
				    "Either decrease the block size or use different keys.".format(
				        block_size * 8 ,     _lowercase    )    )

		__A							:   Optional[int]	  =				[str(_lowercase    ) for i in encrypt_message(_lowercase ,     (n, e) ,     _lowercase    )]

		__A							:   int	  =				",".join(_lowercase    )
		__A							:   List[Any]	  =				F"{len(_lowercase    )}_{block_size}_{encrypted_content}"
		with open(_lowercase ,     "w"    ) as fo:
				fo.write(_lowercase    )
		return encrypted_content



def    lowerCamelCase_   (  _lowercase ,     _lowercase    )							->	str:
		__A  ,       __A  ,       __A							:   List[str]	  =				read_key_file(_lowercase    )
		with open(_lowercase    ) as fo:
				__A							:   Optional[Any]	  =				fo.read()
		__A  ,       __A  ,       __A							:   int	  =				content.split("_"    )
		__A							:   Tuple	  =				int(_lowercase    )
		__A							:   Any	  =				int(_lowercase    )

		if key_size < block_size * 8:
				sys.exit(
				    "ERROR: Block size is {} bits and key size is {} bits. The RSA cipher "
				    "requires the block size to be equal to or greater than the key size. "
				    "Did you specify the correct key file and encrypted file?".format(
				        block_size * 8 ,     _lowercase    )    )

		__A							:   Dict	  =				[]
		for block in encrypted_message.split(","    ):
				encrypted_blocks.append(int(_lowercase    )    )

		return decrypt_message(_lowercase ,     _lowercase ,     (n, d) ,     _lowercase    )



def    lowerCamelCase_   (  )							->	None:
		__A							:   int	  =				"encrypted_file.txt"
		__A							:   int	  =				input(r"Encrypt\Decrypt [e\d]: "    )

		if response.lower().startswith("e"    ):
				__A							:   str	  =				"encrypt"
		elif response.lower().startswith("d"    ):
				__A							:   str	  =				"decrypt"

		if mode == "encrypt":
				if not os.path.exists("rsa_pubkey.txt"    ):
						rkg.make_key_files("rsa" ,     1_024    )

				__A							:   Any	  =				input("\nEnter message: "    )
				__A							:   Any	  =				"rsa_pubkey.txt"
				print(F"Encrypting and writing to {filename}..."    )
				__A							:   List[str]	  =				encrypt_and_write_to_file(_lowercase ,     _lowercase ,     _lowercase    )

				print("\nEncrypted text:"    )
				print(_lowercase    )

		elif mode == "decrypt":
				__A							:   Dict	  =				"rsa_privkey.txt"
				print(F"Reading from {filename} and decrypting..."    )
				__A							:   Dict	  =				read_from_file_and_decrypt(_lowercase ,     _lowercase    )
				print("writing decryption to rsa_decryption.txt..."    )
				with open("rsa_decryption.txt" ,     "w"    ) as dec:
						dec.write(_lowercase    )

				print("\nDecryption:"    )
				print(_lowercase    )


if __name__ == "__main__":
					main()





