




import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionAttendAndExcitePipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_numpy, skip_mps, slow
from diffusers.utils.testing_utils import require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


snake_case_ : str    		=       False







@skip_mps
class __snake_case			(			a ,  a ,  a ,  unittest.TestCase		):
   UpperCAmelCase__  :    str          =  StableDiffusionAttendAndExcitePipeline
   UpperCAmelCase__  :    List[str]          =  False
   UpperCAmelCase__  :    List[Any]          =  TEXT_TO_IMAGE_PARAMS
   UpperCAmelCase__  :    Any          =  TEXT_TO_IMAGE_BATCH_PARAMS.union({'''token_indices'''}		)
   UpperCAmelCase__  :    Optional[Any]          =  TEXT_TO_IMAGE_IMAGE_PARAMS
   UpperCAmelCase__  :    Any          =  TEXT_TO_IMAGE_IMAGE_PARAMS







   @classmethod
   def lowerCamelCase						(      cls		:						str):

        """simple docstring"""


        super().setUpClass()
        torch.use_deterministic_algorithms(_snake_case)







   @classmethod
   def lowerCamelCase						(      cls		:						List[Any]):

        """simple docstring"""


        super().tearDownClass()
        torch.use_deterministic_algorithms(_snake_case)







   def lowerCamelCase						(      self		:						List[str]):

        """simple docstring"""


        torch.manual_seed(0)
        UpperCAmelCase_          =							UNetaDConditionModel(
            block_out_channels=(32, 64)					,						layers_per_block=1					,						sample_size=32					,						in_channels=4					,						out_channels=4					,						down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')					,						up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')					,						cross_attention_dim=32					,						attention_head_dim=(2, 4)					,						use_linear_projection=_snake_case					,						)
        UpperCAmelCase_          =							DDIMScheduler(
            beta_start=0.0_0_0_8_5					,						beta_end=0.0_1_2					,						beta_schedule='''scaled_linear'''					,						clip_sample=_snake_case					,						set_alpha_to_one=_snake_case					,						)
        torch.manual_seed(0)
        UpperCAmelCase_          =							AutoencoderKL(
            block_out_channels=[32, 64]					,						in_channels=3					,						out_channels=3					,						down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']					,						up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']					,						latent_channels=4					,						sample_size=128					,						)
        torch.manual_seed(0)
        UpperCAmelCase_          =							CLIPTextConfig(
            bos_token_id=0					,						eos_token_id=2					,						hidden_size=32					,						intermediate_size=37					,						layer_norm_eps=1e-05					,						num_attention_heads=4					,						num_hidden_layers=5					,						pad_token_id=1					,						vocab_size=1000					,						hidden_act='''gelu'''					,						projection_dim=512					,						)
        UpperCAmelCase_          =							CLIPTextModel(_snake_case)
        UpperCAmelCase_          =							CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

        UpperCAmelCase_          =							{
            '''unet''': unet,
            '''scheduler''': scheduler,
            '''vae''': vae,
            '''text_encoder''': text_encoder,
            '''tokenizer''': tokenizer,
            '''safety_checker''': None,
            '''feature_extractor''': None,
        }

        return components







   def lowerCamelCase						(      self		:						List[str]					,						_snake_case		:						Optional[int]					,						_snake_case		:						List[Any]=0):

        """simple docstring"""


        if str(_snake_case).startswith('''mps'''):
             UpperCAmelCase_          =							torch.manual_seed(_snake_case)
        else:
             UpperCAmelCase_          =							torch.Generator(device=_snake_case).manual_seed(_snake_case)
        UpperCAmelCase_          =							UpperCAmelCase_          =							{
            '''prompt''': '''a cat and a frog''',
            '''token_indices''': [2, 5],
            '''generator''': generator,
            '''num_inference_steps''': 1,
            '''guidance_scale''': 6.0,
            '''output_type''': '''numpy''',
            '''max_iter_to_alter''': 2,
            '''thresholds''': {0: 0.7},
        }
        return inputs







   def lowerCamelCase						(      self		:						str):

        """simple docstring"""


        UpperCAmelCase_          =							'''cpu'''

        UpperCAmelCase_          =							self.get_dummy_components()
        UpperCAmelCase_          =							self.pipeline_class(**_snake_case)
        pipe.to(_snake_case)
        pipe.set_progress_bar_config(disable=_snake_case)

        UpperCAmelCase_          =							self.get_dummy_inputs(_snake_case)
        UpperCAmelCase_          =							pipe(**_snake_case).images
        UpperCAmelCase_          =							image[0, -3:, -3:, -1]

        self.assertEqual(image.shape					,						(1, 64, 64, 3))
        UpperCAmelCase_          =							np.array(
            [0.6_3_9_0_5_3_6_4, 0.6_2_8_9_7_3_0_7, 0.4_8_5_9_9_0_1_7, 0.5_1_3_3_6_2_4, 0.5_5_5_0_0_4_8, 0.4_5_7_6_9_5_1_6, 0.5_0_3_2_6_9_7_3, 0.5_0_2_3_1_3_9, 0.4_5_3_8_4_4_9_6])
        UpperCAmelCase_          =							np.abs(image_slice.flatten() - expected_slice).max()
        self.assertLessEqual(_snake_case					,						1e-3)







   def lowerCamelCase						(      self		:						Tuple):

        """simple docstring"""


        super().test_cpu_offload_forward_pass(expected_max_diff=5e-4)







   def lowerCamelCase						(      self		:						Dict):

        """simple docstring"""


        self._test_inference_batch_consistent(batch_sizes=[1, 2])







   def lowerCamelCase						(      self		:						Optional[Any]):

        """simple docstring"""


        self._test_inference_batch_single_identical(batch_size=2					,						expected_max_diff=7e-4)







   def lowerCamelCase						(      self		:						int):

        """simple docstring"""


        super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3)







   def lowerCamelCase						(      self		:						List[str]):

        """simple docstring"""


        super().test_pt_np_pil_outputs_equivalent(expected_max_diff=5e-4)







   def lowerCamelCase						(      self		:						int):

        """simple docstring"""


        super().test_save_load_local(expected_max_difference=5e-4)







   def lowerCamelCase						(      self		:						int):

        """simple docstring"""


        super().test_save_load_optional_components(expected_max_difference=4e-4)







@require_torch_gpu
@slow
class __snake_case			(			unittest.TestCase		):







   @classmethod
   def lowerCamelCase						(      cls		:						Union[str, Any]):

        """simple docstring"""


        super().setUpClass()
        torch.use_deterministic_algorithms(_snake_case)







   @classmethod
   def lowerCamelCase						(      cls		:						Dict):

        """simple docstring"""


        super().tearDownClass()
        torch.use_deterministic_algorithms(_snake_case)







   def lowerCamelCase						(      self		:						Dict):

        """simple docstring"""


        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()







   def lowerCamelCase						(      self		:						List[Any]):

        """simple docstring"""


        UpperCAmelCase_          =							torch.manual_seed(51)

        UpperCAmelCase_          =							StableDiffusionAttendAndExcitePipeline.from_pretrained(
            '''CompVis/stable-diffusion-v1-4'''					,						safety_checker=_snake_case					,						torch_dtype=torch.floataa)
        pipe.to('''cuda''')

        UpperCAmelCase_          =							'''a painting of an elephant with glasses'''
        UpperCAmelCase_          =							[5, 7]

        UpperCAmelCase_          =							pipe(
            prompt=_snake_case					,						token_indices=_snake_case					,						guidance_scale=7.5					,						generator=_snake_case					,						num_inference_steps=5					,						max_iter_to_alter=5					,						output_type='''numpy'''					,						).images[0]

        UpperCAmelCase_          =							load_numpy(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/attend-and-excite/elephant_glasses.npy''')
        assert np.abs((expected_image - image).max()) < 5e-1












from datetime import datetime

import requests


def    A						(__A   :    str       )   ->      bytes:







     """simple docstring"""


     UpperCAmelCase_          =							'''https://downloadgram.net/wp-json/wppress/video-downloader/video?url='''
     UpperCAmelCase_          =							requests.get(base_url + url       ).json()[0]['''urls'''][0]['''src''']
     return requests.get(__A       ).content


if __name__ == "__main__":
    snake_case_ : Optional[Any]    		=       input("Enter Video/IGTV url: ").strip()
    snake_case_ : Any    		=       f"{datetime.now():%Y-%m-%d_%H:%M:%S}.mp4"
    with open(file_name, "wb") as fp:
        fp.write(download_video(url))
    print(f"Done. Video saved to disk as {file_name}.")






