




"""simple docstring"""

import inspect
import unittest
from math import floor

from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
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 CvtForImageClassification, CvtModel
       from transformers.models.cvt.modeling_cvt import CVT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from PIL import Image

       from transformers import AutoImageProcessor





class     lowercase_	(					__lowerCAmelCase							):



 '''simple docstring'''

 def       lowerCAmelCase_							(			self	:	Optional[int]		):
       _A        =       self.config_class(**self.inputs_dict		)
       self.parent.assertTrue(hasattr(_UpperCAmelCase   ,							'embed_dim'		)		)
       self.parent.assertTrue(hasattr(_UpperCAmelCase   ,							'num_heads'		)		)





class     lowercase_	:



 '''simple docstring'''

 def __init__(			self	:	List[Any]   ,							_UpperCAmelCase	:	Optional[Any]   ,							_UpperCAmelCase	:	Optional[Any]=13   ,							_UpperCAmelCase	:	Optional[Any]=64   ,							_UpperCAmelCase	:	Any=3   ,							_UpperCAmelCase	:	Optional[int]=[16, 48, 96]   ,							_UpperCAmelCase	:	Any=[1, 3, 6]   ,							_UpperCAmelCase	:	Optional[int]=[1, 2, 10]   ,							_UpperCAmelCase	:	Optional[int]=[7, 3, 3]   ,							_UpperCAmelCase	:	Optional[Any]=[4, 2, 2]   ,							_UpperCAmelCase	:	int=[2, 1, 1]   ,							_UpperCAmelCase	:	Optional[Any]=[2, 2, 2]   ,							_UpperCAmelCase	:	Union[str, Any]=[False, False, True]   ,							_UpperCAmelCase	:	Union[str, Any]=[0.0, 0.0, 0.0]   ,							_UpperCAmelCase	:	Optional[Any]=0.02   ,							_UpperCAmelCase	:	Dict=1E-1_2   ,							_UpperCAmelCase	:	List[Any]=True   ,							_UpperCAmelCase	:	List[str]=True   ,							_UpperCAmelCase	:	Dict=2   ,							):
       _A        =       parent
       _A        =       batch_size
       _A        =       image_size
       _A        =       patch_sizes
       _A        =       patch_stride
       _A        =       patch_padding
       _A        =       is_training
       _A        =       use_labels
       _A        =       num_labels
       _A        =       num_channels
       _A        =       embed_dim
       _A        =       num_heads
       _A        =       stride_kv
       _A        =       depth
       _A        =       cls_token
       _A        =       attention_drop_rate
       _A        =       initializer_range
       _A        =       layer_norm_eps

 def       lowerCAmelCase_							(			self	:	List[Any]		):
       _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.num_labels		)

       _A        =       self.get_config()
       return config, pixel_values, labels

 def       lowerCAmelCase_							(			self	:	Dict		):
       return CvtConfig(
           image_size=self.image_size   ,							num_labels=self.num_labels   ,							num_channels=self.num_channels   ,							embed_dim=self.embed_dim   ,							num_heads=self.num_heads   ,							patch_sizes=self.patch_sizes   ,							patch_padding=self.patch_padding   ,							patch_stride=self.patch_stride   ,							stride_kv=self.stride_kv   ,							depth=self.depth   ,							cls_token=self.cls_token   ,							attention_drop_rate=self.attention_drop_rate   ,							initializer_range=self.initializer_range   ,							)

 def       lowerCAmelCase_							(			self	:	Optional[Any]   ,							_UpperCAmelCase	:	Optional[Any]   ,							_UpperCAmelCase	:	Optional[Any]   ,							_UpperCAmelCase	:	Any		):
       _A        =       CvtModel(config=_UpperCAmelCase		)
       model.to(_UpperCAmelCase		)
       model.eval()
       _A        =       model(_UpperCAmelCase		)
       _A        =       (self.image_size, self.image_size)
       _A       ,   _A        =       image_size[0], image_size[1]
       for i in range(len(self.depth		)		):
             _A        =       floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1		)
             _A        =       floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1		)
       self.parent.assertEqual(result.last_hidden_state.shape   ,							(self.batch_size, self.embed_dim[-1], height, width)		)

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



 def       lowerCAmelCase_							(			self	:	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   :					List[str]				  =     (CvtModel, CvtForImageClassification) if is_torch_available() else ()
 UpperCAmelCase   :					Tuple				  =     (
     {'''feature-extraction''': CvtModel, '''image-classification''': CvtForImageClassification}
     if is_torch_available()
     else {}
 )

 UpperCAmelCase   :					Tuple				  =     False
 UpperCAmelCase   :					Optional[int]				  =     False
 UpperCAmelCase   :					Optional[int]				  =     False
 UpperCAmelCase   :					Optional[int]				  =     False
 UpperCAmelCase   :					str				  =     False

 def       lowerCAmelCase_							(			self	:	int		):
       _A        =       CvtModelTester(self		)
       _A        =       ConfigTester(self   ,							config_class=_UpperCAmelCase   ,							has_text_modality=_UpperCAmelCase   ,							hidden_size=37		)

 def       lowerCAmelCase_							(			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       lowerCAmelCase_							(			self	:	Tuple		):
       return

 @unittest.skip(reason='Cvt does not output attentions'		)
 def       lowerCAmelCase_							(			self	:	List[Any]		):
       pass

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

 @unittest.skip(reason='Cvt does not support input and output embeddings'		)
 def       lowerCAmelCase_							(			self	:	List[Any]		):
       pass

 def       lowerCAmelCase_							(			self	:	str		):
       _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	:	int		):
       _A        =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	Union[str, Any]		):
       def check_hidden_states_output(_UpperCAmelCase	:	Optional[int]   ,							_UpperCAmelCase	:	Optional[int]   ,							_UpperCAmelCase	:	Any		):
             _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        =       len(self.model_tester.depth		)
             self.assertEqual(len(_UpperCAmelCase		)   ,							_UpperCAmelCase		)

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

       _A       ,   _A        =       self.model_tester.prepare_config_and_inputs_for_common()

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

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

             check_hidden_states_output(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase		)

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

 @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'		)
 def       lowerCAmelCase_							(			self	:	str		):
       pass



 @slow
 def       lowerCAmelCase_							(			self	:	Optional[int]		):
       for model_name in CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             _A        =       CvtModel.from_pretrained(_UpperCAmelCase		)
             self.assertIsNotNone(_UpperCAmelCase		)






def 						_snake_case					( )   -> int:






      '''simple docstring'''


      _A        =       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
      return image







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



 '''simple docstring'''

 @cached_property
 def       lowerCAmelCase_							(			self	:	Optional[Any]		):
       return AutoImageProcessor.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]		)



 @slow
 def       lowerCAmelCase_							(			self	:	Optional[int]		):
       _A        =       CvtForImageClassification.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]		).to(_UpperCAmelCase		)

       _A        =       self.default_image_processor
       _A        =       prepare_img()
       _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([0.9285, 0.9015, -0.3150]		).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()

