




"""simple docstring"""

import collections
import inspect
import unittest

from transformers import SwinvaConfig
from transformers.testing_utils import 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, _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 SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
       from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
       from PIL import Image

       from transformers import AutoImageProcessor





class     lowercase_	:



 '''simple docstring'''

 def __init__(			self	:	Union[str, Any]   ,							_UpperCAmelCase	:	List[Any]   ,							_UpperCAmelCase	:	Dict=13   ,							_UpperCAmelCase	:	List[str]=32   ,							_UpperCAmelCase	:	Dict=2   ,							_UpperCAmelCase	:	str=3   ,							_UpperCAmelCase	:	Tuple=16   ,							_UpperCAmelCase	:	Dict=[1, 2, 1]   ,							_UpperCAmelCase	:	Dict=[2, 2, 4]   ,							_UpperCAmelCase	:	Optional[Any]=2   ,							_UpperCAmelCase	:	Optional[Any]=2.0   ,							_UpperCAmelCase	:	int=True   ,							_UpperCAmelCase	:	Optional[Any]=0.0   ,							_UpperCAmelCase	:	str=0.0   ,							_UpperCAmelCase	:	Dict=0.1   ,							_UpperCAmelCase	:	Any="gelu"   ,							_UpperCAmelCase	:	Dict=False   ,							_UpperCAmelCase	:	str=True   ,							_UpperCAmelCase	:	Union[str, Any]=0.02   ,							_UpperCAmelCase	:	Union[str, Any]=1E-5   ,							_UpperCAmelCase	:	Optional[Any]=True   ,							_UpperCAmelCase	:	Any=None   ,							_UpperCAmelCase	:	str=True   ,							_UpperCAmelCase	:	Union[str, Any]=10   ,							_UpperCAmelCase	:	Optional[Any]=8   ,							):
       _A        =       parent
       _A        =       batch_size
       _A        =       image_size
       _A        =       patch_size
       _A        =       num_channels
       _A        =       embed_dim
       _A        =       depths
       _A        =       num_heads
       _A        =       window_size
       _A        =       mlp_ratio
       _A        =       qkv_bias
       _A        =       hidden_dropout_prob
       _A        =       attention_probs_dropout_prob
       _A        =       drop_path_rate
       _A        =       hidden_act
       _A        =       use_absolute_embeddings
       _A        =       patch_norm
       _A        =       layer_norm_eps
       _A        =       initializer_range
       _A        =       is_training
       _A        =       scope
       _A        =       use_labels
       _A        =       type_sequence_label_size
       _A        =       encoder_stride

 def       lowerCAmelCase_							(			self	:	int		):
       _A        =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

       _A        =       None
       if self.use_labels:
             _A        =       ids_tensor([self.batch_size]   ,							self.type_sequence_label_size		)

       _A        =       self.get_config()

       return config, pixel_values, labels

 def       lowerCAmelCase_							(			self	:	List[Any]		):
       return SwinvaConfig(
           image_size=self.image_size   ,							patch_size=self.patch_size   ,							num_channels=self.num_channels   ,							embed_dim=self.embed_dim   ,							depths=self.depths   ,							num_heads=self.num_heads   ,							window_size=self.window_size   ,							mlp_ratio=self.mlp_ratio   ,							qkv_bias=self.qkv_bias   ,							hidden_dropout_prob=self.hidden_dropout_prob   ,							attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,							drop_path_rate=self.drop_path_rate   ,							hidden_act=self.hidden_act   ,							use_absolute_embeddings=self.use_absolute_embeddings   ,							path_norm=self.patch_norm   ,							layer_norm_eps=self.layer_norm_eps   ,							initializer_range=self.initializer_range   ,							encoder_stride=self.encoder_stride   ,							)

 def       lowerCAmelCase_							(			self	:	Tuple   ,							_UpperCAmelCase	:	Optional[Any]   ,							_UpperCAmelCase	:	str   ,							_UpperCAmelCase	:	Any		):
       _A        =       SwinvaModel(config=_UpperCAmelCase		)
       model.to(_UpperCAmelCase		)
       model.eval()
       _A        =       model(_UpperCAmelCase		)

       _A        =       ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths		) - 1))
       _A        =       int(config.embed_dim * 2 ** (len(config.depths		) - 1)		)

       self.parent.assertEqual(result.last_hidden_state.shape   ,							(self.batch_size, expected_seq_len, expected_dim)		)

 def       lowerCAmelCase_							(			self	:	List[Any]   ,							_UpperCAmelCase	:	Union[str, Any]   ,							_UpperCAmelCase	:	Any   ,							_UpperCAmelCase	:	Tuple		):
       _A        =       SwinvaForMaskedImageModeling(config=_UpperCAmelCase		)
       model.to(_UpperCAmelCase		)
       model.eval()
       _A        =       model(_UpperCAmelCase		)
       self.parent.assertEqual(
           result.logits.shape   ,							(self.batch_size, self.num_channels, self.image_size, self.image_size)		)

       # test greyscale images
       _A        =       1
       _A        =       SwinvaForMaskedImageModeling(_UpperCAmelCase		)
       model.to(_UpperCAmelCase		)
       model.eval()

       _A        =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
       _A        =       model(_UpperCAmelCase		)
       self.parent.assertEqual(result.logits.shape   ,							(self.batch_size, 1, self.image_size, self.image_size)		)

 def       lowerCAmelCase_							(			self	:	List[Any]   ,							_UpperCAmelCase	:	Any   ,							_UpperCAmelCase	:	str   ,							_UpperCAmelCase	:	Optional[int]		):
       _A        =       self.type_sequence_label_size
       _A        =       SwinvaForImageClassification(_UpperCAmelCase		)
       model.to(_UpperCAmelCase		)
       model.eval()
       _A        =       model(_UpperCAmelCase   ,							labels=_UpperCAmelCase		)
       self.parent.assertEqual(result.logits.shape   ,							(self.batch_size, self.type_sequence_label_size)		)



 def       lowerCAmelCase_							(			self	:	Union[str, Any]		):
       _A        =       self.prepare_config_and_inputs()
       _A       ,   _A       ,   _A        =       config_and_inputs
       _A        =       {'pixel_values': pixel_values}
       return config, inputs_dict





@require_torch
class     lowercase_	(					__lowerCAmelCase      ,     __lowerCAmelCase      ,     unittest.TestCase							):



 '''simple docstring'''
 UpperCAmelCase   :					Tuple				  =     (
     (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
 )
 UpperCAmelCase   :					Dict				  =     (
     {'''feature-extraction''': SwinvaModel, '''image-classification''': SwinvaForImageClassification}
     if is_torch_available()
     else {}
 )

 UpperCAmelCase   :					List[str]				  =     False
 UpperCAmelCase   :					List[str]				  =     False
 UpperCAmelCase   :					Any				  =     False
 UpperCAmelCase   :					Tuple				  =     False

 def       lowerCAmelCase_							(			self	:	str		):
       _A        =       SwinvaModelTester(self		)
       _A        =       ConfigTester(self   ,							config_class=_UpperCAmelCase   ,							embed_dim=37		)

 def       lowerCAmelCase_							(			self	:	Any		):
       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       lowerCAmelCase_							(			self	:	List[Any]		):
       _A        =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_UpperCAmelCase		)

 @unittest.skip(reason='Got `CUDA error: misaligned address` with PyTorch 2.0.0.'		)
 def       lowerCAmelCase_							(			self	:	int		):
       pass

 @unittest.skip(reason='Swinv2 does not use inputs_embeds'		)
 def       lowerCAmelCase_							(			self	:	Optional[Any]		):
       pass

 def       lowerCAmelCase_							(			self	:	Tuple		):
       _A       ,   _A        =       self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             _A        =       model_class(_UpperCAmelCase		)
             self.assertIsInstance(model.get_input_embeddings()   ,							(nn.Module)		)
             _A        =       model.get_output_embeddings()
             self.assertTrue(x is None or isinstance(_UpperCAmelCase   ,							nn.Linear		)		)

 def       lowerCAmelCase_							(			self	:	Dict		):
       _A       ,   _A        =       self.model_tester.prepare_config_and_inputs_for_common()

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

             _A        =       ['pixel_values']
             self.assertListEqual(arg_names[:1]   ,							_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	List[Any]		):
       _A       ,   _A        =       self.model_tester.prepare_config_and_inputs_for_common()
       _A        =       True

       for model_class in self.all_model_classes:
             _A        =       True
             _A        =       False
             _A        =       True
             _A        =       model_class(_UpperCAmelCase		)
             model.to(_UpperCAmelCase		)
             model.eval()
             with torch.no_grad():
                   _A        =       model(**self._prepare_for_class(_UpperCAmelCase   ,							_UpperCAmelCase		)		)
             _A        =       outputs.attentions
             _A        =       len(self.model_tester.depths		)
             self.assertEqual(len(_UpperCAmelCase		)   ,							_UpperCAmelCase		)

             # check that output_attentions also work using config
             del inputs_dict["output_attentions"]
             _A        =       True
             _A        =       config.window_size**2
             _A        =       model_class(_UpperCAmelCase		)
             model.to(_UpperCAmelCase		)
             model.eval()
             with torch.no_grad():
                   _A        =       model(**self._prepare_for_class(_UpperCAmelCase   ,							_UpperCAmelCase		)		)
             _A        =       outputs.attentions
             self.assertEqual(len(_UpperCAmelCase		)   ,							_UpperCAmelCase		)

             self.assertListEqual(
                 list(attentions[0].shape[-3:]		)   ,							[self.model_tester.num_heads[0], window_size_squared, window_size_squared]   ,							)
             _A        =       len(_UpperCAmelCase		)

             # Check attention is always last and order is fine
             _A        =       True
             _A        =       True
             _A        =       model_class(_UpperCAmelCase		)
             model.to(_UpperCAmelCase		)
             model.eval()
             with torch.no_grad():
                   _A        =       model(**self._prepare_for_class(_UpperCAmelCase   ,							_UpperCAmelCase		)		)

             if hasattr(self.model_tester   ,							'num_hidden_states_types'		):
                   _A        =       self.model_tester.num_hidden_states_types
             else:
                   # also another +1 for reshaped_hidden_states
                   _A        =       2
             self.assertEqual(out_len + added_hidden_states   ,							len(_UpperCAmelCase		)		)

             _A        =       outputs.attentions

             self.assertEqual(len(_UpperCAmelCase		)   ,							_UpperCAmelCase		)

             self.assertListEqual(
                 list(self_attentions[0].shape[-3:]		)   ,							[self.model_tester.num_heads[0], window_size_squared, window_size_squared]   ,							)

 def       lowerCAmelCase_							(			self	:	Optional[Any]   ,							_UpperCAmelCase	:	Dict   ,							_UpperCAmelCase	:	List[str]   ,							_UpperCAmelCase	:	Tuple   ,							_UpperCAmelCase	:	List[str]		):
       _A        =       model_class(_UpperCAmelCase		)
       model.to(_UpperCAmelCase		)
       model.eval()

       with torch.no_grad():
             _A        =       model(**self._prepare_for_class(_UpperCAmelCase   ,							_UpperCAmelCase		)		)

       _A        =       outputs.hidden_states

       _A        =       getattr(
           self.model_tester   ,							'expected_num_hidden_layers'   ,							len(self.model_tester.depths		) + 1		)
       self.assertEqual(len(_UpperCAmelCase		)   ,							_UpperCAmelCase		)

       # Swinv2 has a different seq_length
       _A        =       (
           config.patch_size
           if isinstance(config.patch_size   ,							collections.abc.Iterable		)
           else (config.patch_size, config.patch_size)
       )

       _A        =       (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

       self.assertListEqual(
           list(hidden_states[0].shape[-2:]		)   ,							[num_patches, self.model_tester.embed_dim]   ,							)

       _A        =       outputs.reshaped_hidden_states
       self.assertEqual(len(_UpperCAmelCase		)   ,							_UpperCAmelCase		)

       _A       ,   _A       ,   _A       ,   _A        =       reshaped_hidden_states[0].shape
       _A        =       (
           reshaped_hidden_states[0].view(_UpperCAmelCase   ,							_UpperCAmelCase   ,							height * width		).permute(0   ,							2   ,							1		)
       )
       self.assertListEqual(
           list(reshaped_hidden_states.shape[-2:]		)   ,							[num_patches, self.model_tester.embed_dim]   ,							)

 def       lowerCAmelCase_							(			self	:	Optional[Any]		):
       _A       ,   _A        =       self.model_tester.prepare_config_and_inputs_for_common()

       _A        =       (
           self.model_tester.image_size
           if isinstance(self.model_tester.image_size   ,							collections.abc.Iterable		)
           else (self.model_tester.image_size, self.model_tester.image_size)
       )

       for model_class in self.all_model_classes:
             _A        =       True
             self.check_hidden_states_output(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase		)

             # check that output_hidden_states also work using config
             del inputs_dict["output_hidden_states"]
             _A        =       True

             self.check_hidden_states_output(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	Optional[int]		):
       _A       ,   _A        =       self.model_tester.prepare_config_and_inputs_for_common()
       _A        =       3

       _A        =       (
           self.model_tester.image_size
           if isinstance(self.model_tester.image_size   ,							collections.abc.Iterable		)
           else (self.model_tester.image_size, self.model_tester.image_size)
       )
       _A        =       (
           config.patch_size
           if isinstance(config.patch_size   ,							collections.abc.Iterable		)
           else (config.patch_size, config.patch_size)
       )

       _A        =       image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
       _A        =       image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

       for model_class in self.all_model_classes:
             _A        =       True
             self.check_hidden_states_output(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							(padded_height, padded_width)		)

             # check that output_hidden_states also work using config
             del inputs_dict["output_hidden_states"]
             _A        =       True
             self.check_hidden_states_output(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							(padded_height, padded_width)		)

 def       lowerCAmelCase_							(			self	:	List[Any]		):
       _A        =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_image_modeling(*_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	Optional[Any]		):
       _A        =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase		)

 @slow
 def       lowerCAmelCase_							(			self	:	str		):
       for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             _A        =       SwinvaModel.from_pretrained(_UpperCAmelCase		)
             self.assertIsNotNone(_UpperCAmelCase		)



 def       lowerCAmelCase_							(			self	:	List[str]		):
       _A       ,   _A        =       self.model_tester.prepare_config_and_inputs_for_common()

       _A        =       _config_zero_init(_UpperCAmelCase		)
       for model_class in self.all_model_classes:
             _A        =       model_class(config=_UpperCAmelCase		)
             for name, param in model.named_parameters():
                   if "embeddings" not in name and "logit_scale" not in name and 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'''   ,							)





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



 '''simple docstring'''

 @cached_property
 def       lowerCAmelCase_							(			self	:	Dict		):
       return (
           AutoImageProcessor.from_pretrained('microsoft/swinv2-tiny-patch4-window8-256'		)
           if is_vision_available()
           else None
       )



 @slow
 def       lowerCAmelCase_							(			self	:	Optional[Any]		):
       _A        =       SwinvaForImageClassification.from_pretrained('microsoft/swinv2-tiny-patch4-window8-256'		).to(
           _UpperCAmelCase		)
       _A        =       self.default_image_processor

       _A        =       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'		)
       _A        =       image_processor(images=_UpperCAmelCase   ,							return_tensors='pt'		).to(_UpperCAmelCase		)

       # forward pass
       with torch.no_grad():
             _A        =       model(**_UpperCAmelCase		)

       # verify the logits
       _A        =       torch.Size((1, 1_000)		)
       self.assertEqual(outputs.logits.shape   ,							_UpperCAmelCase		)
       _A        =       torch.tensor([-0.3947, -0.4306, 0.0026]		).to(_UpperCAmelCase		)
       self.assertTrue(torch.allclose(outputs.logits[0, :3]   ,							_UpperCAmelCase   ,							atol=1E-4		)		)







"""simple docstring"""

import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation






def 						_snake_case					( _snake_case							: int = 8							)   -> str:






      '''simple docstring'''


      _A        =       ascii_letters + digits + punctuation
      return "".join(secrets.choice(_snake_case							) for _ in range(_snake_case							)							)






def 						_snake_case					( _snake_case							: str    ,  _snake_case							: int							)   -> str:






      '''simple docstring'''


      i -= len(_snake_case							)
      _A        =       i // 3
      _A        =       i % 3
      # chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
      #     random_number(digits, i / 3) + random_characters(punctuation, i / 3)
      _A        =       (
          chars_incl
          + random(_snake_case    ,  quotient + remainder							)
          + random(_snake_case    ,  _snake_case							)
          + random(_snake_case    ,  _snake_case							)
      )
      _A        =       list(_snake_case							)
      shuffle(_snake_case							)
      return "".join(_snake_case							)

      # random is a generalised function for letters, characters and numbers






def 						_snake_case					( _snake_case							: str    ,  _snake_case							: int							)   -> str:






      '''simple docstring'''


      return "".join(secrets.choice(_snake_case							) for _ in range(_snake_case							)							)






def 						_snake_case					( _snake_case							: Dict    ,  _snake_case							: Optional[int]							)   -> int:






      '''simple docstring'''


      pass  # Put your code here...






def 						_snake_case					( _snake_case							: Any    ,  _snake_case							: str							)   -> Dict:






      '''simple docstring'''


      pass  # Put your code here...






def 						_snake_case					( _snake_case							: Union[str, Any]    ,  _snake_case							: int							)   -> int:






      '''simple docstring'''


      pass  # Put your code here...






def 						_snake_case					( _snake_case							: str    ,  _snake_case							: int = 8							)   -> bool:






      '''simple docstring'''


      if len(_snake_case							) < min_length:
            # Your Password must be at least 8 characters long
            return False

      _A        =       any(char in ascii_uppercase for char in password							)
      _A        =       any(char in ascii_lowercase for char in password							)
      _A        =       any(char in digits for char in password							)
      _A        =       any(char in punctuation for char in password							)

      return upper and lower and num and spec_char
      # Passwords should contain UPPERCASE, lowerase
      # numbers, and special characters






def 						_snake_case					( )   -> Optional[Any]:






      '''simple docstring'''


      _A        =       int(input('Please indicate the max length of your password: '							).strip()							)
      _A        =       input(
          'Please indicate the characters that must be in your password: '							).strip()
      print('Password generated:'    ,  password_generator(_snake_case							)							)
      print(
          'Alternative Password generated:'    ,  alternative_password_generator(_snake_case    ,  _snake_case							)    ,  )
      print('[If you are thinking of using this passsword, You better save it.]'							)


if __name__ == "__main__":
       main()

