


'''simple docstring'''

import inspect
import unittest

from transformers import SwinaSRConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import 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 SwinaSRForImageSuperResolution, SwinaSRModel
       from transformers.models.swinasr.modeling_swinasr import SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
       from PIL import Image

       from transformers import SwinaSRImageProcessor







class 			UpperCAmelCase			:

 def __init__(			self			:						List[str],       a_			:						Optional[Any],       a_			:						Optional[int]=13,       a_			:						Any=32,       a_			:						List[Any]=1,       a_			:						List[str]=3,       a_			:						Dict=16,       a_			:						int=[1, 2, 1],       a_			:						Tuple=[2, 2, 4],       a_			:						Optional[Any]=2,       a_			:						str=2.0,       a_			:						List[Any]=True,       a_			:						str=0.0,       a_			:						Optional[Any]=0.0,       a_			:						Optional[int]=0.1,       a_			:						Tuple="gelu",       a_			:						List[str]=False,       a_			:						Union[str, Any]=True,       a_			:						Union[str, Any]=0.02,       a_			:						Tuple=1e-5,       a_			:						Union[str, Any]=True,       a_			:						List[Any]=None,       a_			:						List[Any]=False,       a_			:						Optional[Any]=2,       ):





    """simple docstring"""



    UpperCamelCase__			    =    parent
    UpperCamelCase__			    =    batch_size
    UpperCamelCase__			    =    image_size
    UpperCamelCase__			    =    patch_size
    UpperCamelCase__			    =    num_channels
    UpperCamelCase__			    =    embed_dim
    UpperCamelCase__			    =    depths
    UpperCamelCase__			    =    num_heads
    UpperCamelCase__			    =    window_size
    UpperCamelCase__			    =    mlp_ratio
    UpperCamelCase__			    =    qkv_bias
    UpperCamelCase__			    =    hidden_dropout_prob
    UpperCamelCase__			    =    attention_probs_dropout_prob
    UpperCamelCase__			    =    drop_path_rate
    UpperCamelCase__			    =    hidden_act
    UpperCamelCase__			    =    use_absolute_embeddings
    UpperCamelCase__			    =    patch_norm
    UpperCamelCase__			    =    layer_norm_eps
    UpperCamelCase__			    =    initializer_range
    UpperCamelCase__			    =    is_training
    UpperCamelCase__			    =    scope
    UpperCamelCase__			    =    use_labels
    UpperCamelCase__			    =    upscale

    # here we set some attributes to make tests pass
    UpperCamelCase__			    =    len(a_       )
    UpperCamelCase__			    =    embed_dim
    UpperCamelCase__			    =    (image_size // patch_size) ** 2

 def 		lowercase_    (			self			:						List[Any]       ):





    """simple docstring"""



    UpperCamelCase__			    =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

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

    UpperCamelCase__			    =    self.get_config()

    return config, pixel_values, labels

 def 		lowercase_    (			self			:						List[str]       ):





    """simple docstring"""



    return SwinaSRConfig(
        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,       upscale=self.upscale,       )

 def 		lowercase_    (			self			:						Tuple,       a_			:						Dict,       a_			:						Optional[Any],       a_			:						int       ):





    """simple docstring"""



    UpperCamelCase__			    =    SwinaSRModel(config=a_       )
    model.to(a_       )
    model.eval()
    UpperCamelCase__			    =    model(a_       )

    self.parent.assertEqual(
        result.last_hidden_state.shape,       (self.batch_size, self.embed_dim, self.image_size, self.image_size)       )

 def 		lowercase_    (			self			:						Union[str, Any],       a_			:						Union[str, Any],       a_			:						Union[str, Any],       a_			:						str       ):





    """simple docstring"""



    UpperCamelCase__			    =    SwinaSRForImageSuperResolution(a_       )
    model.to(a_       )
    model.eval()
    UpperCamelCase__			    =    model(a_       )

    UpperCamelCase__			    =    self.image_size * self.upscale
    self.parent.assertEqual(
        result.reconstruction.shape,       (self.batch_size, self.num_channels, expected_image_size, expected_image_size)       )

 def 		lowercase_    (			self			:						Optional[Any]       ):





    """simple docstring"""



    UpperCamelCase__			    =    self.prepare_config_and_inputs()
    UpperCamelCase__    ,		UpperCamelCase__    ,		UpperCamelCase__			    =    config_and_inputs
    UpperCamelCase__			    =    {"pixel_values": pixel_values}
    return config, inputs_dict







@require_torch
class 			UpperCAmelCase			(							SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__		,      unittest.TestCase):
 _lowerCamelCase			:       int            =       (SwinaSRModel, SwinaSRForImageSuperResolution) if is_torch_available() else ()
 _lowerCamelCase			:       str            =       {'feature-extraction': SwinaSRModel} if is_torch_available() else {}

 _lowerCamelCase			:       Tuple            =       False
 _lowerCamelCase			:       str            =       False
 _lowerCamelCase			:       int            =       False
 _lowerCamelCase			:       List[str]            =       False
 _lowerCamelCase			:       Tuple            =       False

 def 		lowercase_    (			self			:						List[str]       ):





    """simple docstring"""



    UpperCamelCase__			    =    SwinaSRModelTester(self       )
    UpperCamelCase__			    =    ConfigTester(self,       config_class=a_,       embed_dim=37       )

 def 		lowercase_    (			self			:						List[str]       ):





    """simple docstring"""



    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 		lowercase_    (			self			:						List[Any]       ):





    """simple docstring"""



    UpperCamelCase__			    =    self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*a_       )

 def 		lowercase_    (			self			:						int       ):





    """simple docstring"""



    UpperCamelCase__			    =    self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_image_super_resolution(*a_       )

 @unittest.skip(reason="Got `CUDA error: misaligned address` with PyTorch 2.0.0."       )
 def 		lowercase_    (			self			:						List[Any]       ):





    """simple docstring"""



    pass

 @unittest.skip(reason="Swin2SR does not use inputs_embeds"       )
 def 		lowercase_    (			self			:						List[str]       ):





    """simple docstring"""



    pass

 @unittest.skip(reason="Swin2SR does not support training yet"       )
 def 		lowercase_    (			self			:						Optional[int]       ):





    """simple docstring"""



    pass

 @unittest.skip(reason="Swin2SR does not support training yet"       )
 def 		lowercase_    (			self			:						Optional[int]       ):





    """simple docstring"""



    pass

 def 		lowercase_    (			self			:						Optional[Any]       ):





    """simple docstring"""



    UpperCamelCase__    ,		UpperCamelCase__			    =    self.model_tester.prepare_config_and_inputs_for_common()

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

 def 		lowercase_    (			self			:						int       ):





    """simple docstring"""



    UpperCamelCase__    ,		UpperCamelCase__			    =    self.model_tester.prepare_config_and_inputs_for_common()

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

       UpperCamelCase__			    =    ["pixel_values"]
       self.assertListEqual(arg_names[:1],       a_       )

 @slow
 def 		lowercase_    (			self			:						Dict       ):





    """simple docstring"""



    for model_name in SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       UpperCamelCase__			    =    SwinaSRModel.from_pretrained(a_       )
       self.assertIsNotNone(a_       )

 def 		lowercase_    (			self			:						List[str]       ):





    """simple docstring"""



    UpperCamelCase__    ,		UpperCamelCase__			    =    self.model_tester.prepare_config_and_inputs_for_common()

    UpperCamelCase__			    =    _config_zero_init(a_       )
    for model_class in self.all_model_classes:
       UpperCamelCase__			    =    model_class(config=a_       )
       for name, param in model.named_parameters():
          if "logit_scale" 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 		lowercase_    (			self			:						Any       ):





    """simple docstring"""



    UpperCamelCase__    ,		UpperCamelCase__			    =    self.model_tester.prepare_config_and_inputs_for_common()
    UpperCamelCase__			    =    True

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

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

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

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

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

       UpperCamelCase__			    =    outputs.attentions

       self.assertEqual(len(a_       ),       a_       )

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







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

 def 		lowercase_    (			self			:						Optional[int]       ):





    """simple docstring"""



    UpperCamelCase__			    =    SwinaSRImageProcessor()
    UpperCamelCase__			    =    SwinaSRForImageSuperResolution.from_pretrained("caidas/swin2SR-classical-sr-x2-64"       ).to(a_       )

    UpperCamelCase__			    =    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
    UpperCamelCase__			    =    processor(images=a_,       return_tensors="pt"       ).to(a_       )

    # forward pass
    with torch.no_grad():
       UpperCamelCase__			    =    model(**a_       )

    # verify the logits
    UpperCamelCase__			    =    torch.Size([1, 3, 976, 1296]       )
    self.assertEqual(outputs.reconstruction.shape,       a_       )
    UpperCamelCase__			    =    torch.tensor(
        [[0.5_458, 0.5_546, 0.5_638], [0.5_526, 0.5_565, 0.5_651], [0.5_396, 0.5_426, 0.5_621]]       ).to(a_       )
    self.assertTrue(torch.allclose(outputs.reconstruction[0, 0, :3, :3],       a_,       atol=1e-4       )       )

from __future__ import annotations

import random
import string







class 							lowercase						:




							def __init__(							self  ,A__ = None):
												lowercase		      =		passcode or self.__passcode_creator()
												lowercase		      =		self.__make_key_list()
												lowercase		      =		self.__make_shift_key()




							def __str__(							self):
												return "".join(self.__passcode)




							def 					A__ (							self  ,A__):
												for i in range(1  ,len(A__)  ,2):
																	iterlist[i] *= -1
												return iterlist




							def 					A__ (							self):
												lowercase		      =		string.ascii_letters + string.digits
												lowercase		      =		[random.choice(A__) for _ in range(random.randint(1_0  ,2_0))]
												return password




							def 					A__ (							self):
												lowercase		      =		(
												    string.ascii_letters + string.digits + string.punctuation + ''' \t\n'''
												)

												lowercase		      =		[]

												# creates points known as breakpoints to break the key_list_options at those
												# points and pivot each substring
												lowercase		      =		sorted(set(self.__passcode))
												lowercase		      =		[]

												# algorithm for creating a new shuffled list, keys_l, out of key_list_options
												for i in key_list_options:
																	temp_list.extend(A__)

																	# checking breakpoints at which to pivot temporary sublist and add it into
																	# keys_l
																	if i in breakpoints or i == key_list_options[-1]:
																						keys_l.extend(temp_list[::-1])
																						temp_list.clear()

        # returning a shuffled keys_l to prevent brute force guessing of shift key
												return keys_l




							def 					A__ (							self):
												lowercase		      =		sum(self.__neg_pos([ord(A__) for x in self.__passcode]))
												return num if num > 0 else len(self.__passcode)




							def 					A__ (							self  ,A__):
												lowercase		      =		''''''

												# decoding shift like Caesar cipher algorithm implementing negative shift or
												# reverse shift or left shift
												for i in encoded_message:
																	lowercase		      =		self.__key_list.index(A__)
																	decoded_message += self.__key_list[
																	    (position - self.__shift_key) % -len(self.__key_list)
																	]

												return decoded_message





							def 					A__ (							self  ,A__):
												lowercase		      =		''''''

												# encoding shift like Caesar cipher algorithm implementing positive shift or
												# forward shift or right shift
												for i in plaintext:
																	lowercase		      =		self.__key_list.index(A__)
																	encoded_message += self.__key_list[
																	    (position + self.__shift_key) % len(self.__key_list)
																	]

												return encoded_message



def 			UpperCamelCase       (				lowerCAmelCase__ = "Hello, this is a modified Caesar cipher"						):
					'''simple docstring'''
					lowercase		      =		ShuffledShiftCipher()
					return cipa.decrypt(cipa.encrypt(lowerCAmelCase__						)						)


if __name__ == "__main__":
							import doctest

							doctest.testmod()




