





"""simple docstring"""





import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionSAGPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, 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 PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class 		lowercase					(					_UpperCAmelCase     ,  _UpperCAmelCase     ,  unittest.TestCase					):
      _SCREAMING_SNAKE_CASE					=      StableDiffusionSAGPipeline
      _SCREAMING_SNAKE_CASE					=      TEXT_TO_IMAGE_PARAMS
      _SCREAMING_SNAKE_CASE					=      TEXT_TO_IMAGE_BATCH_PARAMS
      _SCREAMING_SNAKE_CASE					=      TEXT_TO_IMAGE_IMAGE_PARAMS
      _SCREAMING_SNAKE_CASE					=      TEXT_TO_IMAGE_IMAGE_PARAMS
      _SCREAMING_SNAKE_CASE					=      False





      def     _snake_case			(       self )		->     Union[str, Any]:
            torch.manual_seed(0 )
            lowerCAmelCase  						=       UNetaDConditionModel(
                block_out_channels=(32, 64)      ,						layers_per_block=2      ,						sample_size=32      ,						in_channels=4      ,						out_channels=4      ,						down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")      ,						up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")      ,						cross_attention_dim=32      ,						)
            lowerCAmelCase  						=       DDIMScheduler(
                beta_start=0.00_085      ,						beta_end=0.012      ,						beta_schedule="""scaled_linear"""      ,						clip_sample=lowercase      ,						set_alpha_to_one=lowercase      ,						)
            torch.manual_seed(0 )
            lowerCAmelCase  						=       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      ,						)
            torch.manual_seed(0 )
            lowerCAmelCase  						=       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=1_000      ,						)
            lowerCAmelCase  						=       CLIPTextModel(lowercase )
            lowerCAmelCase  						=       CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )

            lowerCAmelCase  						=       {
                """unet""": unet,
                """scheduler""": scheduler,
                """vae""": vae,
                """text_encoder""": text_encoder,
                """tokenizer""": tokenizer,
                """safety_checker""": None,
                """feature_extractor""": None,
            }
            return components





      def     _snake_case			(       self      ,						lowercase      ,						lowercase=0 )		->     Union[str, Any]:
            if str(lowercase ).startswith("""mps""" ):
                  lowerCAmelCase  						=       torch.manual_seed(lowercase )
            else:
                  lowerCAmelCase  						=       torch.Generator(device=lowercase ).manual_seed(lowercase )
            lowerCAmelCase  						=       {
                """prompt""": """.""",
                """generator""": generator,
                """num_inference_steps""": 2,
                """guidance_scale""": 1.0,
                """sag_scale""": 1.0,
                """output_type""": """numpy""",
            }
            return inputs





      def     _snake_case			(       self )		->     str:
            super().test_inference_batch_single_identical(expected_max_diff=3e-3 )







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





      def     _snake_case			(       self )		->     Tuple:
            # clean up the VRAM after each test
            super().tearDown()
            gc.collect()
            torch.cuda.empty_cache()





      def     _snake_case			(       self )		->     Optional[Any]:
            lowerCAmelCase  						=       StableDiffusionSAGPipeline.from_pretrained("""CompVis/stable-diffusion-v1-4""" )
            lowerCAmelCase  						=       sag_pipe.to(lowercase )
            sag_pipe.set_progress_bar_config(disable=lowercase )

            lowerCAmelCase  						=       """."""
            lowerCAmelCase  						=       torch.manual_seed(0 )
            lowerCAmelCase  						=       sag_pipe(
                [prompt]      ,						generator=lowercase      ,						guidance_scale=7.5      ,						sag_scale=1.0      ,						num_inference_steps=20      ,						output_type="""np""" )

            lowerCAmelCase  						=       output.images

            lowerCAmelCase  						=       image[0, -3:, -3:, -1]

            assert image.shape == (1, 512, 512, 3)
            lowerCAmelCase  						=       np.array([0.1_568, 0.1_738, 0.1_695, 0.1_693, 0.1_507, 0.1_705, 0.1_547, 0.1_751, 0.1_949] )

            assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-2





      def     _snake_case			(       self )		->     str:
            lowerCAmelCase  						=       StableDiffusionSAGPipeline.from_pretrained("""stabilityai/stable-diffusion-2-1-base""" )
            lowerCAmelCase  						=       sag_pipe.to(lowercase )
            sag_pipe.set_progress_bar_config(disable=lowercase )

            lowerCAmelCase  						=       """."""
            lowerCAmelCase  						=       torch.manual_seed(0 )
            lowerCAmelCase  						=       sag_pipe(
                [prompt]      ,						generator=lowercase      ,						guidance_scale=7.5      ,						sag_scale=1.0      ,						num_inference_steps=20      ,						output_type="""np""" )

            lowerCAmelCase  						=       output.images

            lowerCAmelCase  						=       image[0, -3:, -3:, -1]

            assert image.shape == (1, 512, 512, 3)
            lowerCAmelCase  						=       np.array([0.3_459, 0.2_876, 0.2_537, 0.3_002, 0.2_671, 0.2_160, 0.3_026, 0.2_262, 0.2_371] )

            assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-2





      def     _snake_case			(       self )		->     Tuple:
            lowerCAmelCase  						=       StableDiffusionSAGPipeline.from_pretrained("""stabilityai/stable-diffusion-2-1-base""" )
            lowerCAmelCase  						=       sag_pipe.to(lowercase )
            sag_pipe.set_progress_bar_config(disable=lowercase )

            lowerCAmelCase  						=       """."""
            lowerCAmelCase  						=       torch.manual_seed(0 )
            lowerCAmelCase  						=       sag_pipe(
                [prompt]      ,						width=768      ,						height=512      ,						generator=lowercase      ,						guidance_scale=7.5      ,						sag_scale=1.0      ,						num_inference_steps=20      ,						output_type="""np"""      ,						)

            lowerCAmelCase  						=       output.images

            assert image.shape == (1, 512, 768, 3)













"""simple docstring"""





SCREAMING_SNAKE_CASE__					=   {
    "A": ".-", "B": "-...", "C": "-.-.", "D": "-..", "E": ".", "F": "..-.", "G": "--.",
    "H": "....", "I": "..", "J": ".---", "K": "-.-", "L": ".-..", "M": "--", "N": "-.",
    "O": "---", "P": ".--.", "Q": "--.-", "R": ".-.", "S": "...", "T": "-", "U": "..-",
    "V": "...-", "W": ".--", "X": "-..-", "Y": "-.--", "Z": "--..", "1": ".----",
    "2": "..---", "3": "...--", "4": "....-", "5": ".....", "6": "-....", "7": "--...",
    "8": "---..", "9": "----.", "0": "-----", "&": ".-...", "@": ".--.-.",
    ":": "---...", ",": "--..--", ".": ".-.-.-", "'": ".----.", "\"": ".-..-.",
    "?": "..--..", "/": "-..-.", "=": "-...-", "+": ".-.-.", "-": "-....-",
    "(": "-.--.", ")": "-.--.-", "!": "-.-.--", " ": "/"
}  # Exclamation mark is not in ITU-R recommendation
# fmt: on
SCREAMING_SNAKE_CASE__					=   {value: key for key, value in MORSE_CODE_DICT.items()}


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						str			):







      '''simple docstring'''




      return " ".join(MORSE_CODE_DICT[char] for char in message.upper()			)


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						str			):







      '''simple docstring'''




      return "".join(REVERSE_DICT[char] for char in message.split()			)


def 			UpperCAmelCase__							(  ):







      '''simple docstring'''




      lowerCAmelCase  						=       """Morse code here!"""
      print(SCREAMING_SNAKE_CASE			)
      lowerCAmelCase  						=       encrypt(SCREAMING_SNAKE_CASE			)
      print(SCREAMING_SNAKE_CASE			)
      lowerCAmelCase  						=       decrypt(SCREAMING_SNAKE_CASE			)
      print(SCREAMING_SNAKE_CASE			)


if __name__ == "__main__":
       main()






