"""simple docstring"""



import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    PNDMScheduler,
    StableDiffusionLDMaDPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import nightly, 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


enable_full_determinism()






class  snake_case__      (       unittest.TestCase       ):
 _snake_case       :					Tuple							  =						StableDiffusionLDMaDPipeline
 _snake_case       :					Optional[int]							  =						TEXT_TO_IMAGE_PARAMS
 _snake_case       :					str							  =						TEXT_TO_IMAGE_BATCH_PARAMS
 _snake_case       :					Dict							  =						TEXT_TO_IMAGE_IMAGE_PARAMS


 def 						a__   ( self						):
        torch.manual_seed(0						)
        __a       =    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    ,		)
        __a       =    DDIMScheduler(
            beta_start=0.0_0085    ,		beta_end=0.012    ,		beta_schedule="scaled_linear"    ,		clip_sample=__a    ,		set_alpha_to_one=__a    ,		)
        torch.manual_seed(0						)
        __a       =    AutoencoderKL(
            block_out_channels=[32, 64]    ,		in_channels=6    ,		out_channels=6    ,		down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]    ,		up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]    ,		latent_channels=4    ,		)
        torch.manual_seed(0						)
        __a       =    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    ,		)
        __a       =    CLIPTextModel(__a						)
        __a       =    CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"						)

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


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase=0						):



        if str(__a						).startswith("mps"						):
               __a       =    torch.manual_seed(__a						)
        else:
               __a       =    torch.Generator(device=__a						).manual_seed(__a						)
        __a       =    {
            "prompt": "A painting of a squirrel eating a burger",
            "generator": generator,
            "num_inference_steps": 2,
            "guidance_scale": 6.0,
            "output_type": "numpy",
        }
        return inputs


 def 						a__   ( self						):
        __a       =    "cpu"  # ensure determinism for the device-dependent torch.Generator

        __a       =    self.get_dummy_components()
        __a       =    StableDiffusionLDMaDPipeline(**__a						)
        __a       =    ldmad_pipe.to(__a						)
        ldmad_pipe.set_progress_bar_config(disable=__a						)

        __a       =    self.get_dummy_inputs(__a						)
        __a       =    ldmad_pipe(**__a						)
        __a ,   __a       =    output.rgb, output.depth

        __a       =    rgb[0, -3:, -3:, -1]
        __a       =    depth[0, -3:, -1]

        assert rgb.shape == (1, 64, 64, 3)
        assert depth.shape == (1, 64, 64)

        __a       =    np.array(
            [0.3733_8176, 0.7_0247, 0.7420_3193, 0.5164_3604, 0.5825_6793, 0.6093_2136, 0.418_1095, 0.4835_5877, 0.4653_5262]						)
        __a       =    np.array([103.4_6727, 85.81_2004, 87.84_9236]						)

        assert np.abs(image_slice_rgb.flatten() - expected_slice_rgb						).max() < 1E-2
        assert np.abs(image_slice_depth.flatten() - expected_slice_depth						).max() < 1E-2


 def 						a__   ( self						):
        __a       =    self.get_dummy_components()
        __a       =    StableDiffusionLDMaDPipeline(**__a						)
        __a       =    ldmad_pipe.to(__a						)
        ldmad_pipe.set_progress_bar_config(disable=__a						)

        __a       =    self.get_dummy_inputs(__a						)
        __a       =    3 * [inputs["prompt"]]

        # forward
        __a       =    ldmad_pipe(**__a						)
        __a ,   __a       =    output.rgb, output.depth
        __a       =    rgb_slice_a[0, -3:, -3:, -1]
        __a       =    depth_slice_a[0, -3:, -1]

        __a       =    self.get_dummy_inputs(__a						)
        __a       =    3 * [inputs.pop("prompt"						)]

        __a       =    ldmad_pipe.tokenizer(
            __a    ,		padding="max_length"    ,		max_length=ldmad_pipe.tokenizer.model_max_length    ,		truncation=__a    ,		return_tensors="pt"    ,		)
        __a       =    text_inputs["input_ids"].to(__a						)

        __a       =    ldmad_pipe.text_encoder(__a						)[0]

        __a       =    prompt_embeds

        # forward
        __a       =    ldmad_pipe(**__a						)
        __a ,   __a       =    output.rgb, output.depth
        __a       =    rgb_slice_a[0, -3:, -3:, -1]
        __a       =    depth_slice_a[0, -3:, -1]

        assert np.abs(rgb_slice_a.flatten() - rgb_slice_a.flatten()						).max() < 1E-4
        assert np.abs(depth_slice_a.flatten() - depth_slice_a.flatten()						).max() < 1E-4






 def 						a__   ( self						):
        __a       =    "cpu"  # ensure determinism for the device-dependent torch.Generator
        __a       =    self.get_dummy_components()
        __a       =    PNDMScheduler(skip_prk_steps=__a						)
        __a       =    StableDiffusionLDMaDPipeline(**__a						)
        __a       =    ldmad_pipe.to(__a						)
        ldmad_pipe.set_progress_bar_config(disable=__a						)

        __a       =    self.get_dummy_inputs(__a						)
        __a       =    "french fries"
        __a       =    ldmad_pipe(**__a    ,		negative_prompt=__a						)

        __a ,   __a       =    output.rgb, output.depth
        __a       =    rgb[0, -3:, -3:, -1]
        __a       =    depth[0, -3:, -1]

        assert rgb.shape == (1, 64, 64, 3)
        assert depth.shape == (1, 64, 64)

        __a       =    np.array(
            [0.3_7044, 0.7181_1503, 0.722_3251, 0.4860_3675, 0.563_8391, 0.636_4948, 0.4283_3704, 0.490_1315, 0.4792_6217]						)
        __a       =    np.array([107.8_4738, 84.6_2802, 89.96_2135]						)
        assert np.abs(rgb_slice.flatten() - expected_slice_rgb						).max() < 1E-2
        assert np.abs(depth_slice.flatten() - expected_slice_depth						).max() < 1E-2






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


 def 						a__   ( self						):
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase="cpu"    ,		lowerCamelCase=torch.floataa    ,		lowerCamelCase=0						):
        __a       =    torch.Generator(device=__a						).manual_seed(__a						)
        __a       =    np.random.RandomState(__a						).standard_normal((1, 4, 64, 64)						)
        __a       =    torch.from_numpy(__a						).to(device=__a    ,		dtype=__a						)
        __a       =    {
            "prompt": "a photograph of an astronaut riding a horse",
            "latents": latents,
            "generator": generator,
            "num_inference_steps": 3,
            "guidance_scale": 7.5,
            "output_type": "numpy",
        }
        return inputs






 def 						a__   ( self						):
        __a       =    StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d"						)
        __a       =    ldmad_pipe.to(__a						)
        ldmad_pipe.set_progress_bar_config(disable=__a						)

        __a       =    self.get_inputs(__a						)
        __a       =    ldmad_pipe(**__a						)
        __a ,   __a       =    output.rgb, output.depth
        __a       =    rgb[0, -3:, -3:, -1].flatten()
        __a       =    rgb[0, -3:, -1].flatten()

        assert rgb.shape == (1, 512, 512, 3)
        assert depth.shape == (1, 512, 512)

        __a       =    np.array(
            [0.5380_5465, 0.5670_7305, 0.548_6515, 0.5701_2236, 0.581_4511, 0.5625_3487, 0.5484_3014, 0.5509_2263, 0.645_9706]						)
        __a       =    np.array(
            [0.926_3781, 0.667_8672, 0.548_6515, 0.9220_2145, 0.6783_1135, 0.5625_3487, 0.924_1694, 0.755_1478, 0.645_9706]						)
        assert np.abs(rgb_slice - expected_slice_rgb						).max() < 3E-3
        assert np.abs(depth_slice - expected_slice_depth						).max() < 3E-3






@nightly
@require_torch_gpu
class  snake_case__      (       unittest.TestCase       ):


 def 						a__   ( self						):
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase="cpu"    ,		lowerCamelCase=torch.floataa    ,		lowerCamelCase=0						):
        __a       =    torch.Generator(device=__a						).manual_seed(__a						)
        __a       =    np.random.RandomState(__a						).standard_normal((1, 4, 64, 64)						)
        __a       =    torch.from_numpy(__a						).to(device=__a    ,		dtype=__a						)
        __a       =    {
            "prompt": "a photograph of an astronaut riding a horse",
            "latents": latents,
            "generator": generator,
            "num_inference_steps": 50,
            "guidance_scale": 7.5,
            "output_type": "numpy",
        }
        return inputs


 def 						a__   ( self						):
        __a       =    StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d"						).to(__a						)
        ldmad_pipe.set_progress_bar_config(disable=__a						)

        __a       =    self.get_inputs(__a						)
        __a       =    ldmad_pipe(**__a						)
        __a ,   __a       =    output.rgb, output.depth

        __a       =    0.49_5586
        __a       =    0.3379_5515
        __a       =    112.4_8518
        __a       =    98.48_9746
        assert np.abs(expected_rgb_mean - rgb.mean()						) < 1E-3
        assert np.abs(expected_rgb_std - rgb.std()						) < 1E-3
        assert np.abs(expected_depth_mean - depth.mean()						) < 1E-3
        assert np.abs(expected_depth_std - depth.std()						) < 1E-3






 def 						a__   ( self						):
        __a       =    StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d-4c"						).to(__a						)
        ldmad_pipe.set_progress_bar_config(disable=__a						)

        __a       =    self.get_inputs(__a						)
        __a       =    ldmad_pipe(**__a						)
        __a ,   __a       =    output.rgb, output.depth

        __a       =    0.419_4127
        __a       =    0.3537_5586
        __a       =    0.563_8502
        __a       =    0.3468_6103

        assert rgb.shape == (1, 512, 512, 3)
        assert depth.shape == (1, 512, 512, 1)
        assert np.abs(expected_rgb_mean - rgb.mean()						) < 1E-3
        assert np.abs(expected_rgb_std - rgb.std()						) < 1E-3
        assert np.abs(expected_depth_mean - depth.mean()						) < 1E-3
        assert np.abs(expected_depth_std - depth.std()						) < 1E-3





import socket

def      __lowercase(						):


      """simple docstring"""



      lowerCamelCase             =		socket.socket(socket.AF_INET , socket.SOCK_STREAM			)
      lowerCamelCase             =		socket.gethostname()
      lowerCamelCase             =		12312

      sock.connect((host, port)			)
      sock.send(B"Hello server!"			)

      with open("Received_file" , "wb"			) as out_file:
            print("File opened"			)
            print("Receiving data..."			)
            while True:
                  lowerCamelCase             =		sock.recv(1024			)
                  if not data:
                        break
                  out_file.write(UpperCAmelCase__			)

      print("Successfully received the file"			)
      sock.close()
      print("Connection closed"			)


if __name__ == "__main__":
     main()