


import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, slow, torch_device

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


class 		_SCREAMING_SNAKE_CASE    (	A       ,  A       ,  unittest.TestCase    ):
     __SCREAMING_SNAKE_CASE    				=						UNetaDModel
     __SCREAMING_SNAKE_CASE    				=						'''sample'''


     @property
     def 					__snake_case(     self						):
       _UpperCAmelCase		:       int       =    4
       _UpperCAmelCase		:       Optional[Any]       =    14
       _UpperCAmelCase		:       Tuple       =    16

       _UpperCAmelCase		:       Tuple       =    floats_tensor((batch_size, num_features, seq_len)						).to(A_						)
       _UpperCAmelCase		:       List[Any]       =    torch.tensor([10] * batch_size						).to(A_						)

       return {"sample": noise, "timestep": time_step}


     @property
     def 					__snake_case(     self						):
       return (4, 14, 16)


     @property
     def 					__snake_case(     self						):
       return (4, 14, 16)


     def 					__snake_case(     self						):
       pass


     def 					__snake_case(     self						):
       pass


     @unittest.skipIf(torch_device == """mps"""       ,				"""mish op not supported in MPS"""						)
     def 					__snake_case(     self						):
       super().test_determinism()


     @unittest.skipIf(torch_device == """mps"""       ,				"""mish op not supported in MPS"""						)
     def 					__snake_case(     self						):
       super().test_outputs_equivalence()


     @unittest.skipIf(torch_device == """mps"""       ,				"""mish op not supported in MPS"""						)
     def 					__snake_case(     self						):
       super().test_from_save_pretrained()


     @unittest.skipIf(torch_device == """mps"""       ,				"""mish op not supported in MPS"""						)
     def 					__snake_case(     self						):
       super().test_from_save_pretrained_variant()


     @unittest.skipIf(torch_device == """mps"""       ,				"""mish op not supported in MPS"""						)
     def 					__snake_case(     self						):
       super().test_model_from_pretrained()


     @unittest.skipIf(torch_device == """mps"""       ,				"""mish op not supported in MPS"""						)
     def 					__snake_case(     self						):
       super().test_output()


     def 					__snake_case(     self						):
       _UpperCAmelCase		:       List[Any]       =    {
           """block_out_channels""": (32, 64, 1_28, 2_56),
           """in_channels""": 14,
           """out_channels""": 14,
           """time_embedding_type""": """positional""",
           """use_timestep_embedding""": True,
           """flip_sin_to_cos""": False,
           """freq_shift""": 1.0,
           """out_block_type""": """OutConv1DBlock""",
           """mid_block_type""": """MidResTemporalBlock1D""",
           """down_block_types""": ("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D"""),
           """up_block_types""": ("""UpResnetBlock1D""", """UpResnetBlock1D""", """UpResnetBlock1D"""),
           """act_fn""": """mish""",
       }
       _UpperCAmelCase		:       str       =    self.dummy_input
       return init_dict, inputs_dict


     @unittest.skipIf(torch_device == """mps"""       ,				"""mish op not supported in MPS"""						)
     def 					__snake_case(     self						):
       _UpperCAmelCase,_UpperCAmelCase		:       List[str]       =    UNetaDModel.from_pretrained(
           """bglick13/hopper-medium-v2-value-function-hor32"""       ,				output_loading_info=A_       ,				subfolder="""unet"""						)
       self.assertIsNotNone(A_						)
       self.assertEqual(len(loading_info["""missing_keys"""]						)       ,				0						)

       model.to(A_						)
       _UpperCAmelCase		:       Optional[int]       =    model(**self.dummy_input						)

       assert image is not None, "Make sure output is not None"


     @unittest.skipIf(torch_device == """mps"""       ,				"""mish op not supported in MPS"""						)
     def 					__snake_case(     self						):
       _UpperCAmelCase		:       Any       =    UNetaDModel.from_pretrained("""bglick13/hopper-medium-v2-value-function-hor32"""       ,				subfolder="""unet"""						)
       torch.manual_seed(0						)
       if torch.cuda.is_available():
         torch.cuda.manual_seed_all(0						)

       _UpperCAmelCase		:       Any       =    model.config.in_channels
       _UpperCAmelCase		:       List[str]       =    16
       _UpperCAmelCase		:       Optional[Any]       =    torch.randn((1, seq_len, num_features)						).permute(
           0       ,				2       ,				1						)  # match original, we can update values and remove
       _UpperCAmelCase		:       Dict       =    torch.full((num_features,)       ,				0						)

       with torch.no_grad():
         _UpperCAmelCase		:       Optional[Any]       =    model(A_       ,				A_						).sample.permute(0       ,				2       ,				1						)

       _UpperCAmelCase		:       Optional[int]       =    output[0, -3:, -3:].flatten()
       # fmt: off
       _UpperCAmelCase		:       Optional[int]       =    torch.tensor([-2.1_3_7_1_7_2, 1.1_4_2_6_0_1_6, 0.3_6_8_8_6_8_7, -0.7_6_6_9_2_2, 0.7_3_0_3_1_4_6, 0.1_1_0_3_8_8_6_4, -0.4_7_6_0_6_3_3, 0.1_3_2_7_0_1_7_2, 0.0_2_5_9_1_3_4_8]						)
       # fmt: on
       self.assertTrue(torch.allclose(A_       ,				A_       ,				rtol=1e-3						)						)


     def 					__snake_case(     self						):
       # Not implemented yet for this UNet
       pass




     @slow
     def 					__snake_case(     self						):
       _UpperCAmelCase		:       Any       =    """harmonai/maestro-150k"""
       _UpperCAmelCase		:       int       =    UNetaDModel.from_pretrained(A_       ,				subfolder="""unet"""						)
       model.to(A_						)

       _UpperCAmelCase		:       Dict       =    6_55_36
       _UpperCAmelCase		:       Union[str, Any]       =    torch.sin(torch.arange(A_						)[None, None, :].repeat(1       ,				2       ,				1						)						).to(A_						)
       _UpperCAmelCase		:       int       =    torch.tensor([1]						).to(A_						)

       with torch.no_grad():
         _UpperCAmelCase		:       Optional[int]       =    model(A_       ,				A_						).sample

       _UpperCAmelCase		:       str       =    output.abs().sum()
       _UpperCAmelCase		:       Optional[Any]       =    output.abs().max()

       assert (output_sum - 2_2_4.0_8_9_6).abs() < 0.5
       assert (output_max - 0.0_6_0_7).abs() < 4e-4


class 		_SCREAMING_SNAKE_CASE    (	A       ,  A       ,  unittest.TestCase    ):
     __SCREAMING_SNAKE_CASE    				=						UNetaDModel
     __SCREAMING_SNAKE_CASE    				=						'''sample'''


     @property
     def 					__snake_case(     self						):
       _UpperCAmelCase		:       Dict       =    4
       _UpperCAmelCase		:       str       =    14
       _UpperCAmelCase		:       Optional[Any]       =    16

       _UpperCAmelCase		:       List[Any]       =    floats_tensor((batch_size, num_features, seq_len)						).to(A_						)
       _UpperCAmelCase		:       Dict       =    torch.tensor([10] * batch_size						).to(A_						)

       return {"sample": noise, "timestep": time_step}


     @property
     def 					__snake_case(     self						):
       return (4, 14, 16)


     @property
     def 					__snake_case(     self						):
       return (4, 14, 1)


     @unittest.skipIf(torch_device == """mps"""       ,				"""mish op not supported in MPS"""						)
     def 					__snake_case(     self						):
       super().test_determinism()


     @unittest.skipIf(torch_device == """mps"""       ,				"""mish op not supported in MPS"""						)
     def 					__snake_case(     self						):
       super().test_outputs_equivalence()


     @unittest.skipIf(torch_device == """mps"""       ,				"""mish op not supported in MPS"""						)
     def 					__snake_case(     self						):
       super().test_from_save_pretrained()


     @unittest.skipIf(torch_device == """mps"""       ,				"""mish op not supported in MPS"""						)
     def 					__snake_case(     self						):
       super().test_from_save_pretrained_variant()


     @unittest.skipIf(torch_device == """mps"""       ,				"""mish op not supported in MPS"""						)
     def 					__snake_case(     self						):
       super().test_model_from_pretrained()


     @unittest.skipIf(torch_device == """mps"""       ,				"""mish op not supported in MPS"""						)
     def 					__snake_case(     self						):
       # UNetRL is a value-function is different output shape
       _UpperCAmelCase,_UpperCAmelCase		:       Dict       =    self.prepare_init_args_and_inputs_for_common()
       _UpperCAmelCase		:       List[Any]       =    self.model_class(**A_						)
       model.to(A_						)
       model.eval()

       with torch.no_grad():
         _UpperCAmelCase		:       Tuple       =    model(**A_						)

         if isinstance(A_       ,				A_						):
           _UpperCAmelCase		:       str       =    output.sample

       self.assertIsNotNone(A_						)
       _UpperCAmelCase		:       str       =    torch.Size((inputs_dict["""sample"""].shape[0], 1)						)
       self.assertEqual(output.shape       ,				A_       ,				"""Input and output shapes do not match"""						)


     def 					__snake_case(     self						):
       pass


     def 					__snake_case(     self						):
       pass


     def 					__snake_case(     self						):
       _UpperCAmelCase		:       Optional[int]       =    {
           """in_channels""": 14,
           """out_channels""": 14,
           """down_block_types""": ["""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D"""],
           """up_block_types""": [],
           """out_block_type""": """ValueFunction""",
           """mid_block_type""": """ValueFunctionMidBlock1D""",
           """block_out_channels""": [32, 64, 1_28, 2_56],
           """layers_per_block""": 1,
           """downsample_each_block""": True,
           """use_timestep_embedding""": True,
           """freq_shift""": 1.0,
           """flip_sin_to_cos""": False,
           """time_embedding_type""": """positional""",
           """act_fn""": """mish""",
       }
       _UpperCAmelCase		:       int       =    self.dummy_input
       return init_dict, inputs_dict


     @unittest.skipIf(torch_device == """mps"""       ,				"""mish op not supported in MPS"""						)
     def 					__snake_case(     self						):
       _UpperCAmelCase,_UpperCAmelCase		:       Any       =    UNetaDModel.from_pretrained(
           """bglick13/hopper-medium-v2-value-function-hor32"""       ,				output_loading_info=A_       ,				subfolder="""value_function"""						)
       self.assertIsNotNone(A_						)
       self.assertEqual(len(vf_loading_info["""missing_keys"""]						)       ,				0						)

       value_function.to(A_						)
       _UpperCAmelCase		:       Optional[int]       =    value_function(**self.dummy_input						)

       assert image is not None, "Make sure output is not None"


     @unittest.skipIf(torch_device == """mps"""       ,				"""mish op not supported in MPS"""						)
     def 					__snake_case(     self						):
       _UpperCAmelCase,_UpperCAmelCase		:       Optional[Any]       =    UNetaDModel.from_pretrained(
           """bglick13/hopper-medium-v2-value-function-hor32"""       ,				output_loading_info=A_       ,				subfolder="""value_function"""						)
       torch.manual_seed(0						)
       if torch.cuda.is_available():
         torch.cuda.manual_seed_all(0						)

       _UpperCAmelCase		:       Optional[int]       =    value_function.config.in_channels
       _UpperCAmelCase		:       int       =    14
       _UpperCAmelCase		:       Optional[int]       =    torch.randn((1, seq_len, num_features)						).permute(
           0       ,				2       ,				1						)  # match original, we can update values and remove
       _UpperCAmelCase		:       Dict       =    torch.full((num_features,)       ,				0						)

       with torch.no_grad():
         _UpperCAmelCase		:       Optional[Any]       =    value_function(A_       ,				A_						).sample

       # fmt: off
       _UpperCAmelCase		:       List[Any]       =    torch.tensor([1_6_5.2_5] * seq_len						)
       # fmt: on
       self.assertTrue(torch.allclose(A_       ,				A_       ,				rtol=1e-3						)						)




     def 					__snake_case(     self						):
       # Not implemented yet for this UNet
       pass









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}')





