


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 		_SCREAMING_SNAKE_CASE    :


     def __init__(     self       ,				A_       ,				A_=13       ,				A_=32       ,				A_=1       ,				A_=3       ,				A_=16       ,				A_=[1, 2, 1]       ,				A_=[2, 2, 4]       ,				A_=2       ,				A_=2.0       ,				A_=True       ,				A_=0.0       ,				A_=0.0       ,				A_=0.1       ,				A_="gelu"       ,				A_=False       ,				A_=True       ,				A_=0.0_2       ,				A_=1e-5       ,				A_=True       ,				A_=None       ,				A_=False       ,				A_=2       ,				):
       _UpperCAmelCase		:       Optional[int]       =    parent
       _UpperCAmelCase		:       str       =    batch_size
       _UpperCAmelCase		:       Any       =    image_size
       _UpperCAmelCase		:       Union[str, Any]       =    patch_size
       _UpperCAmelCase		:       Optional[Any]       =    num_channels
       _UpperCAmelCase		:       int       =    embed_dim
       _UpperCAmelCase		:       Optional[int]       =    depths
       _UpperCAmelCase		:       List[Any]       =    num_heads
       _UpperCAmelCase		:       Union[str, Any]       =    window_size
       _UpperCAmelCase		:       List[str]       =    mlp_ratio
       _UpperCAmelCase		:       Any       =    qkv_bias
       _UpperCAmelCase		:       List[Any]       =    hidden_dropout_prob
       _UpperCAmelCase		:       List[Any]       =    attention_probs_dropout_prob
       _UpperCAmelCase		:       Dict       =    drop_path_rate
       _UpperCAmelCase		:       List[Any]       =    hidden_act
       _UpperCAmelCase		:       int       =    use_absolute_embeddings
       _UpperCAmelCase		:       Union[str, Any]       =    patch_norm
       _UpperCAmelCase		:       Optional[int]       =    layer_norm_eps
       _UpperCAmelCase		:       int       =    initializer_range
       _UpperCAmelCase		:       List[str]       =    is_training
       _UpperCAmelCase		:       Tuple       =    scope
       _UpperCAmelCase		:       List[str]       =    use_labels
       _UpperCAmelCase		:       int       =    upscale

       # here we set some attributes to make tests pass
       _UpperCAmelCase		:       List[str]       =    len(A_						)
       _UpperCAmelCase		:       List[Any]       =    embed_dim
       _UpperCAmelCase		:       Optional[int]       =    (image_size // patch_size) ** 2


     def 					__snake_case(     self						):
       _UpperCAmelCase		:       Union[str, Any]       =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

       _UpperCAmelCase		:       int       =    None
       if self.use_labels:
         _UpperCAmelCase		:       Union[str, Any]       =    ids_tensor([self.batch_size]       ,				self.type_sequence_label_size						)

       _UpperCAmelCase		:       Tuple       =    self.get_config()

       return config, pixel_values, labels


     def 					__snake_case(     self						):
       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 					__snake_case(     self       ,				A_       ,				A_       ,				A_						):
       _UpperCAmelCase		:       Dict       =    SwinaSRModel(config=A_						)
       model.to(A_						)
       model.eval()
       _UpperCAmelCase		:       Union[str, Any]       =    model(A_						)

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


     def 					__snake_case(     self       ,				A_       ,				A_       ,				A_						):
       _UpperCAmelCase		:       str       =    SwinaSRForImageSuperResolution(A_						)
       model.to(A_						)
       model.eval()
       _UpperCAmelCase		:       Dict       =    model(A_						)

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




     def 					__snake_case(     self						):
       _UpperCAmelCase		:       str       =    self.prepare_config_and_inputs()
       _UpperCAmelCase,_UpperCAmelCase,_UpperCAmelCase		:       Any       =    config_and_inputs
       _UpperCAmelCase		:       Any       =    {"""pixel_values""": pixel_values}
       return config, inputs_dict


@require_torch
class 		_SCREAMING_SNAKE_CASE    (	A       ,  A       ,  unittest.TestCase    ):
     __SCREAMING_SNAKE_CASE    				=						(SwinaSRModel, SwinaSRForImageSuperResolution) if is_torch_available() else ()
     __SCREAMING_SNAKE_CASE    				=						{'''feature-extraction''': SwinaSRModel} if is_torch_available() else {}

     __SCREAMING_SNAKE_CASE    				=						False
     __SCREAMING_SNAKE_CASE    				=						False
     __SCREAMING_SNAKE_CASE    				=						False
     __SCREAMING_SNAKE_CASE    				=						False
     __SCREAMING_SNAKE_CASE    				=						False


     def 					__snake_case(     self						):
       _UpperCAmelCase		:       Dict       =    SwinaSRModelTester(self						)
       _UpperCAmelCase		:       Tuple       =    ConfigTester(self       ,				config_class=A_       ,				embed_dim=37						)


     def 					__snake_case(     self						):
       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 					__snake_case(     self						):
       _UpperCAmelCase		:       Dict       =    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*A_						)


     def 					__snake_case(     self						):
       _UpperCAmelCase		:       Optional[Any]       =    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 					__snake_case(     self						):
       pass


     @unittest.skip(reason="""Swin2SR does not use inputs_embeds"""						)
     def 					__snake_case(     self						):
       pass


     @unittest.skip(reason="""Swin2SR does not support training yet"""						)
     def 					__snake_case(     self						):
       pass


     @unittest.skip(reason="""Swin2SR does not support training yet"""						)
     def 					__snake_case(     self						):
       pass


     def 					__snake_case(     self						):
       _UpperCAmelCase,_UpperCAmelCase		:       Optional[int]       =    self.model_tester.prepare_config_and_inputs_for_common()

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


     def 					__snake_case(     self						):
       _UpperCAmelCase,_UpperCAmelCase		:       Tuple       =    self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
         _UpperCAmelCase		:       int       =    model_class(A_						)
         _UpperCAmelCase		:       Tuple       =    inspect.signature(model.forward						)
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         _UpperCAmelCase		:       Union[str, Any]       =    [*signature.parameters.keys()]

         _UpperCAmelCase		:       Tuple       =    ["""pixel_values"""]
         self.assertListEqual(arg_names[:1]       ,				A_						)


     @slow
     def 					__snake_case(     self						):
       for model_name in SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _UpperCAmelCase		:       Optional[Any]       =    SwinaSRModel.from_pretrained(A_						)
         self.assertIsNotNone(A_						)


     def 					__snake_case(     self						):
       _UpperCAmelCase,_UpperCAmelCase		:       Any       =    self.model_tester.prepare_config_and_inputs_for_common()

       _UpperCAmelCase		:       Dict       =    _config_zero_init(A_						)
       for model_class in self.all_model_classes:
         _UpperCAmelCase		:       Any       =    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 					__snake_case(     self						):
       _UpperCAmelCase,_UpperCAmelCase		:       List[str]       =    self.model_tester.prepare_config_and_inputs_for_common()
       _UpperCAmelCase		:       Union[str, Any]       =    True

       for model_class in self.all_model_classes:
         _UpperCAmelCase		:       List[str]       =    True
         _UpperCAmelCase		:       Any       =    False
         _UpperCAmelCase		:       str       =    True
         _UpperCAmelCase		:       Any       =    model_class(A_						)
         model.to(A_						)
         model.eval()
         with torch.no_grad():
           _UpperCAmelCase		:       int       =    model(**self._prepare_for_class(A_       ,				A_						)						)
         _UpperCAmelCase		:       Union[str, Any]       =    outputs.attentions
         _UpperCAmelCase		:       Tuple       =    len(self.model_tester.depths						)
         self.assertEqual(len(A_						)       ,				A_						)

         # check that output_attentions also work using config
         del inputs_dict["output_attentions"]
         _UpperCAmelCase		:       List[Any]       =    True
         _UpperCAmelCase		:       Union[str, Any]       =    config.window_size**2
         _UpperCAmelCase		:       Dict       =    model_class(A_						)
         model.to(A_						)
         model.eval()
         with torch.no_grad():
           _UpperCAmelCase		:       Optional[int]       =    model(**self._prepare_for_class(A_       ,				A_						)						)
         _UpperCAmelCase		:       List[str]       =    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		:       Optional[int]       =    len(A_						)

         # Check attention is always last and order is fine
         _UpperCAmelCase		:       Dict       =    True
         _UpperCAmelCase		:       Any       =    True
         _UpperCAmelCase		:       Optional[Any]       =    model_class(A_						)
         model.to(A_						)
         model.eval()
         with torch.no_grad():
           _UpperCAmelCase		:       Any       =    model(**self._prepare_for_class(A_       ,				A_						)						)

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

         _UpperCAmelCase		:       Optional[Any]       =    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 		_SCREAMING_SNAKE_CASE    (	unittest.TestCase    ):


     def 					__snake_case(     self						):
       _UpperCAmelCase		:       Optional[int]       =    SwinaSRImageProcessor()
       _UpperCAmelCase		:       List[str]       =    SwinaSRForImageSuperResolution.from_pretrained("""caidas/swin2SR-classical-sr-x2-64"""						).to(A_						)

       _UpperCAmelCase		:       int       =    Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)
       _UpperCAmelCase		:       Dict       =    processor(images=A_       ,				return_tensors="""pt"""						).to(A_						)

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

       # verify the logits
       _UpperCAmelCase		:       Optional[Any]       =    torch.Size([1, 3, 9_76, 12_96]						)
       self.assertEqual(outputs.reconstruction.shape       ,				A_						)
       _UpperCAmelCase		:       int       =    torch.tensor(
           [[0.5_4_5_8, 0.5_5_4_6, 0.5_6_3_8], [0.5_5_2_6, 0.5_5_6_5, 0.5_6_5_1], [0.5_3_9_6, 0.5_4_2_6, 0.5_6_2_1]]						).to(A_						)
       self.assertTrue(torch.allclose(outputs.reconstruction[0, 0, :3, :3]       ,				A_       ,				atol=1e-4						)						)









import os

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

SCREAMING_SNAKE_CASE__				:    int         =    {'UserAgent': UserAgent().random}
SCREAMING_SNAKE_CASE__				:    Tuple         =    'https://www.mywaifulist.moe/random'




def       a__						(		snake_case__				:       str	,      snake_case__				:       str    ):
  _UpperCAmelCase		:       Any       =    requests.get(snake_case__	,      headers=snake_case__    )
  with open(snake_case__	,      """wb"""    ) as file:
    file.write(image.content    )




def       a__						(		):
  _UpperCAmelCase		:       List[Any]       =    BeautifulSoup(requests.get(snake_case__	,      headers=snake_case__    ).text	,      """html.parser"""    )
  _UpperCAmelCase		:       Optional[int]       =    soup.find("""meta"""	,      attrs={"""property""": """og:title"""}    ).attrs["""content"""]
  _UpperCAmelCase		:       int       =    soup.find("""meta"""	,      attrs={"""property""": """og:image"""}    ).attrs["""content"""]
  _UpperCAmelCase		:       Any       =    soup.find("""p"""	,      id="""description"""    ).get_text()
  _UpperCAmelCase,_UpperCAmelCase		:       Dict       =    os.path.splitext(os.path.basename(snake_case__    )    )
  _UpperCAmelCase		:       Tuple       =    title.strip().replace(""" """	,      """_"""    )
  _UpperCAmelCase		:       Optional[Any]       =    f'''{image_title}{image_extension}'''
  save_image(snake_case__	,      snake_case__    )
  return (title, description, image_title)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE__     ,				SCREAMING_SNAKE_CASE__     ,				SCREAMING_SNAKE_CASE__				:    Optional[int]         =    random_anime_character()
 print(F'{title}\n\n{desc}\n\nImage saved : {image_title}')





