


import inspect
import unittest

from transformers import 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_MAPPING, GLPNConfig, GLPNForDepthEstimation, GLPNModel
     from transformers.models.glpn.modeling_glpn import GLPN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import GLPNImageProcessor







class 	_snake_case						(   _snake_case							):




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:Optional[int]											= self.config_class(**self.inputs_dict					)
       self.parent.assertTrue(hasattr(_lowerCamelCase      ,						'''hidden_sizes'''					)					)
       self.parent.assertTrue(hasattr(_lowerCamelCase      ,						'''num_attention_heads'''					)					)
       self.parent.assertTrue(hasattr(_lowerCamelCase      ,						'''num_encoder_blocks'''					)					)







class 	_snake_case						:




 def __init__(	self      ,						_lowerCamelCase      ,						_lowerCamelCase=13      ,						_lowerCamelCase=64      ,						_lowerCamelCase=3      ,						_lowerCamelCase=4      ,						_lowerCamelCase=[2, 2, 2, 2]      ,						_lowerCamelCase=[8, 4, 2, 1]      ,						_lowerCamelCase=[16, 32, 64, 128]      ,						_lowerCamelCase=[1, 4, 8, 16]      ,						_lowerCamelCase=[1, 2, 4, 8]      ,						_lowerCamelCase=True      ,						_lowerCamelCase=True      ,						_lowerCamelCase="gelu"      ,						_lowerCamelCase=0.1      ,						_lowerCamelCase=0.1      ,						_lowerCamelCase=0.02      ,						_lowerCamelCase=16      ,						_lowerCamelCase=3      ,						_lowerCamelCase=None      ,						):
       a		:int											= parent
       a		:List[Any]											= batch_size
       a		:str											= image_size
       a		:Dict											= num_channels
       a		:Any											= num_encoder_blocks
       a		:Union[str, Any]											= sr_ratios
       a		:Dict											= depths
       a		:Union[str, Any]											= hidden_sizes
       a		:str											= downsampling_rates
       a		:Optional[Any]											= num_attention_heads
       a		:Optional[int]											= is_training
       a		:Union[str, Any]											= use_labels
       a		:Optional[int]											= hidden_act
       a		:Union[str, Any]											= hidden_dropout_prob
       a		:str											= attention_probs_dropout_prob
       a		:Union[str, Any]											= initializer_range
       a		:Optional[int]											= decoder_hidden_size
       a		:Optional[int]											= num_labels
       a		:List[Any]											= scope




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:Optional[int]											= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

       a		:Optional[int]											= None
       if self.use_labels:
             a		:Tuple											= ids_tensor([self.batch_size, self.image_size, self.image_size]      ,						self.num_labels					)

       a		:int											= self.get_config()
       return config, pixel_values, labels




 def  SCREAMING_SNAKE_CASE__       (	self					):
       return GLPNConfig(
           image_size=self.image_size      ,						num_channels=self.num_channels      ,						num_encoder_blocks=self.num_encoder_blocks      ,						depths=self.depths      ,						hidden_sizes=self.hidden_sizes      ,						num_attention_heads=self.num_attention_heads      ,						hidden_act=self.hidden_act      ,						hidden_dropout_prob=self.hidden_dropout_prob      ,						attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,						initializer_range=self.initializer_range      ,						decoder_hidden_size=self.decoder_hidden_size      ,						)




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase					):
       a		:Optional[Any]											= GLPNModel(config=_lowerCamelCase					)
       model.to(_lowerCamelCase					)
       model.eval()
       a		:Optional[int]											= model(_lowerCamelCase					)
       a		:Dict											= self.image_size // (self.downsampling_rates[-1] * 2)
       self.parent.assertEqual(
           result.last_hidden_state.shape      ,						(self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)					)




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase					):
       a		:str											= self.num_labels
       a		:Union[str, Any]											= GLPNForDepthEstimation(_lowerCamelCase					)
       model.to(_lowerCamelCase					)
       model.eval()
       a		:Union[str, Any]											= model(_lowerCamelCase					)
       self.parent.assertEqual(result.predicted_depth.shape      ,						(self.batch_size, self.image_size, self.image_size)					)
       a		:Tuple											= model(_lowerCamelCase      ,						labels=_lowerCamelCase					)
       self.parent.assertEqual(result.predicted_depth.shape      ,						(self.batch_size, self.image_size, self.image_size)					)




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:Optional[Any]											= self.prepare_config_and_inputs()
       a							,       a							,       a		:List[str]											= config_and_inputs
       a		:Union[str, Any]											= {'''pixel_values''': pixel_values}
       return config, inputs_dict







@require_torch
class 	_snake_case						(   _snake_case		,					_snake_case		,					unittest.TestCase							):
 SCREAMING_SNAKE_CASE__   = (GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
 SCREAMING_SNAKE_CASE__   = (
     {'depth-estimation': GLPNForDepthEstimation, 'feature-extraction': GLPNModel} if is_torch_available() else {}
 )

 SCREAMING_SNAKE_CASE__   = False
 SCREAMING_SNAKE_CASE__   = False
 SCREAMING_SNAKE_CASE__   = False




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:Union[str, Any]											= GLPNModelTester(self					)
       a		:Tuple											= GLPNConfigTester(self      ,						config_class=_lowerCamelCase					)




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




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:Dict											= self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_lowerCamelCase					)




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:Optional[Any]											= self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_depth_estimation(*_lowerCamelCase					)




 @unittest.skip('''GLPN does not use inputs_embeds'''					)
 def  SCREAMING_SNAKE_CASE__       (	self					):
       pass




 @unittest.skip('''GLPN does not have get_input_embeddings method and get_output_embeddings methods'''					)
 def  SCREAMING_SNAKE_CASE__       (	self					):
       pass




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a							,       a		:Optional[int]											= self.model_tester.prepare_config_and_inputs_for_common()

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

             a		:str											= ['''pixel_values''']
             self.assertListEqual(arg_names[:1]      ,						_lowerCamelCase					)




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a							,       a		:Any											= self.model_tester.prepare_config_and_inputs_for_common()
       a		:Optional[Any]											= True

       for model_class in self.all_model_classes:
             a		:Optional[int]											= True
             a		:Tuple											= False
             a		:int											= True
             a		:List[str]											= model_class(_lowerCamelCase					)
             model.to(_lowerCamelCase					)
             model.eval()
             with torch.no_grad():
                   a		:str											= model(**self._prepare_for_class(_lowerCamelCase      ,						_lowerCamelCase					)					)
             a		:Tuple											= outputs.attentions

             a		:Tuple											= sum(self.model_tester.depths					)
             self.assertEqual(len(_lowerCamelCase					)      ,						_lowerCamelCase					)

             # check that output_attentions also work using config
             del inputs_dict["output_attentions"]
             a		:List[Any]											= True
             a		:Union[str, Any]											= model_class(_lowerCamelCase					)
             model.to(_lowerCamelCase					)
             model.eval()
             with torch.no_grad():
                   a		:Any											= model(**self._prepare_for_class(_lowerCamelCase      ,						_lowerCamelCase					)					)
             a		:Dict											= outputs.attentions

             self.assertEqual(len(_lowerCamelCase					)      ,						_lowerCamelCase					)

             # verify the first attentions (first block, first layer)
             a		:Any											= (self.model_tester.image_size // 4) ** 2
             a		:List[Any]											= (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
             self.assertListEqual(
                 list(attentions[0].shape[-3:]					)      ,						[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]      ,						)

             # verify the last attentions (last block, last layer)
             a		:List[str]											= (self.model_tester.image_size // 32) ** 2
             a		:int											= (self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
             self.assertListEqual(
                 list(attentions[-1].shape[-3:]					)      ,						[self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len]      ,						)
             a		:List[str]											= len(_lowerCamelCase					)

             # Check attention is always last and order is fine
             a		:str											= True
             a		:int											= True
             a		:str											= model_class(_lowerCamelCase					)
             model.to(_lowerCamelCase					)
             model.eval()
             with torch.no_grad():
                   a		:List[Any]											= model(**self._prepare_for_class(_lowerCamelCase      ,						_lowerCamelCase					)					)

             self.assertEqual(out_len + 1      ,						len(_lowerCamelCase					)					)

             a		:Tuple											= outputs.attentions

             self.assertEqual(len(_lowerCamelCase					)      ,						_lowerCamelCase					)
             # verify the first attentions (first block, first layer)
             a		:List[Any]											= (self.model_tester.image_size // 4) ** 2
             a		:str											= (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
             self.assertListEqual(
                 list(self_attentions[0].shape[-3:]					)      ,						[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]      ,						)




 def  SCREAMING_SNAKE_CASE__       (	self					):
       def check_hidden_states_output(_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase					):
             a		:Dict											= model_class(_lowerCamelCase					)
             model.to(_lowerCamelCase					)
             model.eval()

             with torch.no_grad():
                   a		:Optional[int]											= model(**self._prepare_for_class(_lowerCamelCase      ,						_lowerCamelCase					)					)

             a		:int											= outputs.hidden_states

             a		:Union[str, Any]											= self.model_tester.num_encoder_blocks
             self.assertEqual(len(_lowerCamelCase					)      ,						_lowerCamelCase					)

             # verify the first hidden states (first block)
             self.assertListEqual(
                 list(hidden_states[0].shape[-3:]					)      ,						[
                     self.model_tester.hidden_sizes[0],
                     self.model_tester.image_size // 4,
                     self.model_tester.image_size // 4,
                 ]      ,						)

       a							,       a		:int											= self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             a		:str											= True
             check_hidden_states_output(_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase					)

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

             check_hidden_states_output(_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase					)




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

       a							,       a		:Tuple											= self.model_tester.prepare_config_and_inputs_for_common()
       a		:Union[str, Any]											= True

       for model_class in self.all_model_classes:
             if model_class in get_values(_lowerCamelCase					):
                   continue
             # TODO: remove the following 3 lines once we have a MODEL_FOR_DEPTH_ESTIMATION_MAPPING
             # this can then be incorporated into _prepare_for_class in test_modeling_common.py
             if model_class.__name__ == "GLPNForDepthEstimation":
                   a							,       a							,       a							,       a		:Dict											= inputs_dict['''pixel_values'''].shape
                   a		:str											= torch.zeros(
                       [self.model_tester.batch_size, height, width]      ,						device=_lowerCamelCase					).long()
             a		:str											= model_class(_lowerCamelCase					)
             model.to(_lowerCamelCase					)
             model.train()
             a		:Tuple											= self._prepare_for_class(_lowerCamelCase      ,						_lowerCamelCase      ,						return_labels=_lowerCamelCase					)
             a		:Tuple											= model(**_lowerCamelCase					).loss
             loss.backward()




 @slow
 def  SCREAMING_SNAKE_CASE__       (	self					):
       for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             a		:List[Any]											= GLPNModel.from_pretrained(_lowerCamelCase					)
             self.assertIsNotNone(_lowerCamelCase					)







def        __lowerCamelCase		(				):




      """simple docstring"""





      a		:Optional[int]											= Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
      return image







@require_torch
@require_vision
@slow
class 	_snake_case						(   unittest.TestCase							):




 @slow
 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:str											= GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]					)
       a		:List[str]											= GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]					).to(_lowerCamelCase					)

       a		:str											= prepare_img()
       a		:Dict											= image_processor(images=_lowerCamelCase      ,						return_tensors='''pt'''					).to(_lowerCamelCase					)

       # forward pass
       with torch.no_grad():
             a		:Tuple											= model(**_lowerCamelCase					)

       # verify the predicted depth
       a		:Any											= torch.Size([1, 480, 640]					)
       self.assertEqual(outputs.predicted_depth.shape      ,						_lowerCamelCase					)

       a		:int											= torch.tensor(
           [[3.4291, 2.7865, 2.5151], [3.2841, 2.7021, 2.3502], [3.1147, 2.4625, 2.2481]]					).to(_lowerCamelCase					)

       self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3]      ,						_lowerCamelCase      ,						atol=1e-4					)					)









import os
import sys

from . import rsa_key_generator as rkg

snake_case       :					int        =  1_28
snake_case       :					Any        =  2_56
def        __lowerCamelCase		(				UpperCAmelCase_		:	str				,						UpperCAmelCase_		:	int = DEFAULT_BLOCK_SIZE		):




      """simple docstring"""





      a		:Union[str, Any]											= message.encode('''ascii'''		)

      a		:List[Any]											= []
      for block_start in range(0				,						len(UpperCAmelCase_		)				,						UpperCAmelCase_		):
            a		:List[str]											= 0
            for i in range(UpperCAmelCase_				,						min(block_start + block_size				,						len(UpperCAmelCase_		)		)		):
                  block_int += message_bytes[i] * (BYTE_SIZE ** (i % block_size))
            block_ints.append(UpperCAmelCase_		)
      return block_ints
def        __lowerCamelCase		(				UpperCAmelCase_		:	list[int]				,						UpperCAmelCase_		:	int				,						UpperCAmelCase_		:	int = DEFAULT_BLOCK_SIZE		):




      """simple docstring"""





      a		:list[str]											= []
      for block_int in block_ints:
            a		:list[str]											= []
            for i in range(block_size - 1				,						-1				,						-1		):
                  if len(UpperCAmelCase_		) + i < message_length:
                        a		:List[str]											= block_int // (BYTE_SIZE**i)
                        a		:List[str]											= block_int % (BYTE_SIZE**i)
                        block_message.insert(0				,						chr(UpperCAmelCase_		)		)
            message.extend(UpperCAmelCase_		)
      return "".join(UpperCAmelCase_		)
def        __lowerCamelCase		(				UpperCAmelCase_		:	str				,						UpperCAmelCase_		:	tuple[int, int]				,						UpperCAmelCase_		:	int = DEFAULT_BLOCK_SIZE		):




      """simple docstring"""





      a		:Tuple											= []
      a							,       a		:Any											= key

      for block in get_blocks_from_text(UpperCAmelCase_				,						UpperCAmelCase_		):
            encrypted_blocks.append(pow(UpperCAmelCase_				,						UpperCAmelCase_				,						UpperCAmelCase_		)		)
      return encrypted_blocks
def        __lowerCamelCase		(				UpperCAmelCase_		:	list[int]				,						UpperCAmelCase_		:	int				,						UpperCAmelCase_		:	tuple[int, int]				,						UpperCAmelCase_		:	int = DEFAULT_BLOCK_SIZE				,						):




      """simple docstring"""





      a		:Optional[Any]											= []
      a							,       a		:int											= key
      for block in encrypted_blocks:
            decrypted_blocks.append(pow(UpperCAmelCase_				,						UpperCAmelCase_				,						UpperCAmelCase_		)		)
      return get_text_from_blocks(UpperCAmelCase_				,						UpperCAmelCase_				,						UpperCAmelCase_		)
def        __lowerCamelCase		(				UpperCAmelCase_		:	str		):




      """simple docstring"""





      with open(UpperCAmelCase_		) as fo:
            a		:Dict											= fo.read()
      a							,       a							,       a		:Tuple											= content.split(''','''		)
      return (int(UpperCAmelCase_		), int(UpperCAmelCase_		), int(UpperCAmelCase_		))
def        __lowerCamelCase		(				UpperCAmelCase_		:	str				,						UpperCAmelCase_		:	str				,						UpperCAmelCase_		:	str				,						UpperCAmelCase_		:	int = DEFAULT_BLOCK_SIZE				,						):




      """simple docstring"""





      a							,       a							,       a		:List[str]											= read_key_file(UpperCAmelCase_		)
      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				,						UpperCAmelCase_		)		)

      a		:int											= [str(UpperCAmelCase_		) for i in encrypt_message(UpperCAmelCase_				,						(n, e)				,						UpperCAmelCase_		)]

      a		:str											= ''','''.join(UpperCAmelCase_		)
      a		:Optional[Any]											= F'''{len(UpperCAmelCase_		)}_{block_size}_{encrypted_content}'''
      with open(UpperCAmelCase_				,						'''w'''		) as fo:
            fo.write(UpperCAmelCase_		)
      return encrypted_content
def        __lowerCamelCase		(				UpperCAmelCase_		:	str				,						UpperCAmelCase_		:	str		):




      """simple docstring"""





      a							,       a							,       a		:List[Any]											= read_key_file(UpperCAmelCase_		)
      with open(UpperCAmelCase_		) as fo:
            a		:int											= fo.read()
      a							,       a							,       a		:List[str]											= content.split('''_'''		)
      a		:List[str]											= int(UpperCAmelCase_		)
      a		:List[str]											= int(UpperCAmelCase_		)

      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				,						UpperCAmelCase_		)		)

      a		:Dict											= []
      for block in encrypted_message.split(''','''		):
            encrypted_blocks.append(int(UpperCAmelCase_		)		)

      return decrypt_message(UpperCAmelCase_				,						UpperCAmelCase_				,						(n, d)				,						UpperCAmelCase_		)
def        __lowerCamelCase		(				):




      """simple docstring"""





      a		:Union[str, Any]											= '''encrypted_file.txt'''
      a		:List[Any]											= input(R'''Encrypt\Decrypt [e\d]: '''		)

      if response.lower().startswith('''e'''		):
            a		:int											= '''encrypt'''
      elif response.lower().startswith('''d'''		):
            a		:Optional[Any]											= '''decrypt'''

      if mode == "encrypt":
            if not os.path.exists('''rsa_pubkey.txt'''		):
                  rkg.make_key_files('''rsa'''				,						1024		)

            a		:List[str]											= input('''\nEnter message: '''		)
            a		:str											= '''rsa_pubkey.txt'''
            print(F'''Encrypting and writing to {filename}...'''		)
            a		:Optional[int]											= encrypt_and_write_to_file(UpperCAmelCase_				,						UpperCAmelCase_				,						UpperCAmelCase_		)

            print('''\nEncrypted text:'''		)
            print(UpperCAmelCase_		)

      elif mode == "decrypt":
            a		:str											= '''rsa_privkey.txt'''
            print(F'''Reading from {filename} and decrypting...'''		)
            a		:Optional[int]											= read_from_file_and_decrypt(UpperCAmelCase_				,						UpperCAmelCase_		)
            print('''writing decryption to rsa_decryption.txt...'''		)
            with open('''rsa_decryption.txt'''				,						'''w'''		) as dec:
                  dec.write(UpperCAmelCase_		)

            print('''\nDecryption:'''		)
            print(UpperCAmelCase_		)


if __name__ == "__main__":
     main()





