

def  lowerCAmelCase(			SCREAMING_SNAKE_CASE_ = "mytext.txt" ,	SCREAMING_SNAKE_CASE_ = False )->	str:



    """simple docstring"""






    import socket

    UpperCamelCase_      =	1_2_3_1_2  # Reserve a port for your service.
    UpperCamelCase_      =	socket.socket()  # Create a socket object
    UpperCamelCase_      =	socket.gethostname()  # Get local machine name
    sock.bind((host, port) )  # Bind to the port
    sock.listen(5 )  # Now wait for client connection.

    print("Server listening...." )

    while True:
        UpperCamelCase_     ,					UpperCamelCase_      =	sock.accept()  # Establish connection with client.
        print(f"Got connection from {addr}" )
        UpperCamelCase_      =	conn.recv(1_0_2_4 )
        print(f"Server received: {data = }" )

        with open(lowercase__ ,	"rb" ) as in_file:
            UpperCamelCase_      =	in_file.read(1_0_2_4 )
            while data:
                conn.send(lowercase__ )
                print(f"Sent {data!r}" )
                UpperCamelCase_      =	in_file.read(1_0_2_4 )

        print("Done sending" )
        conn.close()
        if testing:  # Allow the test to complete
            break

    sock.shutdown(1 )
    sock.close()


if __name__ == "__main__":
 send_file()








import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class 	__magic_name__				(       snake_case ,			unittest.TestCase							):
     UpperCamelCase_  :Any								=			KandinskyPipeline
     UpperCamelCase_  :str								=			[
         """prompt""",
         """image_embeds""",
         """negative_image_embeds""",
     ]
     UpperCamelCase_  :Union[str, Any]								=			["""prompt""", """negative_prompt""", """image_embeds""", """negative_image_embeds"""]
     UpperCamelCase_  :int								=			[
         """generator""",
         """height""",
         """width""",
         """latents""",
         """guidance_scale""",
         """negative_prompt""",
         """num_inference_steps""",
         """return_dict""",
         """guidance_scale""",
         """num_images_per_prompt""",
         """output_type""",
         """return_dict""",
     ]
     UpperCamelCase_  :Optional[Any]								=			False






     @property
     def   UpperCAmelCase_							(							self  )->    Union[str, Any]:
         return 32






     @property
     def   UpperCAmelCase_							(							self  )->    Optional[Any]:
         return 32






     @property
     def   UpperCAmelCase_							(							self  )->    str:
         return self.time_input_dim






     @property
     def   UpperCAmelCase_							(							self  )->    Union[str, Any]:
         return self.time_input_dim * 4






     @property
     def   UpperCAmelCase_							(							self  )->    List[str]:
         return 100






     @property
     def   UpperCAmelCase_							(							self  )->    List[Any]:
         UpperCamelCase_      =	XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"  )
         return tokenizer






     @property
     def   UpperCAmelCase_							(							self  )->    int:
         torch.manual_seed(0  )
         UpperCamelCase_      =	MCLIPConfig(
             numDims=self.cross_attention_dim		,	transformerDimensions=self.text_embedder_hidden_size		,	hidden_size=self.text_embedder_hidden_size		,	intermediate_size=37		,	num_attention_heads=4		,	num_hidden_layers=5		,	vocab_size=1_005		,	)

         UpperCamelCase_      =	MultilingualCLIP(_lowercase  )
         UpperCamelCase_      =	text_encoder.eval()

         return text_encoder






     @property
     def   UpperCAmelCase_							(							self  )->    List[str]:
         torch.manual_seed(0  )

         UpperCamelCase_      =	{
             "in_channels": 4,
             # Out channels is double in channels because predicts mean and variance
             "out_channels": 8,
             "addition_embed_type": "text_image",
             "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
             "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
             "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
             "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
             "layers_per_block": 1,
             "encoder_hid_dim": self.text_embedder_hidden_size,
             "encoder_hid_dim_type": "text_image_proj",
             "cross_attention_dim": self.cross_attention_dim,
             "attention_head_dim": 4,
             "resnet_time_scale_shift": "scale_shift",
             "class_embed_type": None,
         }

         UpperCamelCase_      =	UNetaDConditionModel(**_lowercase  )
         return model






     @property
     def   UpperCAmelCase_							(							self  )->    Dict:
         return {
             "block_out_channels": [32, 64],
             "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
             "in_channels": 3,
             "latent_channels": 4,
             "layers_per_block": 1,
             "norm_num_groups": 8,
             "norm_type": "spatial",
             "num_vq_embeddings": 12,
             "out_channels": 3,
             "up_block_types": [
                 "AttnUpDecoderBlock2D",
                 "UpDecoderBlock2D",
             ],
             "vq_embed_dim": 4,
         }






     @property
     def   UpperCAmelCase_							(							self  )->    Tuple:
         torch.manual_seed(0  )
         UpperCamelCase_      =	VQModel(**self.dummy_movq_kwargs  )
         return model






     def   UpperCAmelCase_							(							self  )->    Dict:
         UpperCamelCase_      =	self.dummy_text_encoder
         UpperCamelCase_      =	self.dummy_tokenizer
         UpperCamelCase_      =	self.dummy_unet
         UpperCamelCase_      =	self.dummy_movq

         UpperCamelCase_      =	DDIMScheduler(
             num_train_timesteps=1_000		,	beta_schedule="linear"		,	beta_start=0.00_085		,	beta_end=0.012		,	clip_sample=_lowercase		,	set_alpha_to_one=_lowercase		,	steps_offset=1		,	prediction_type="epsilon"		,	thresholding=_lowercase		,	)

         UpperCamelCase_      =	{
             "text_encoder": text_encoder,
             "tokenizer": tokenizer,
             "unet": unet,
             "scheduler": scheduler,
             "movq": movq,
         }
         return components






     def   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase=0  )->    Union[str, Any]:
         UpperCamelCase_      =	floats_tensor((1, self.cross_attention_dim)		,	rng=random.Random(_lowercase  )  ).to(_lowercase  )
         UpperCamelCase_      =	floats_tensor((1, self.cross_attention_dim)		,	rng=random.Random(seed + 1  )  ).to(_lowercase  )
         if str(_lowercase  ).startswith("mps"  ):
             UpperCamelCase_      =	torch.manual_seed(_lowercase  )
         else:
             UpperCamelCase_      =	torch.Generator(device=_lowercase  ).manual_seed(_lowercase  )
         UpperCamelCase_      =	{
             "prompt": "horse",
             "image_embeds": image_embeds,
             "negative_image_embeds": negative_image_embeds,
             "generator": generator,
             "height": 64,
             "width": 64,
             "guidance_scale": 4.0,
             "num_inference_steps": 2,
             "output_type": "np",
         }
         return inputs






     def   UpperCAmelCase_							(							self  )->    Tuple:
         UpperCamelCase_      =	"cpu"

         UpperCamelCase_      =	self.get_dummy_components()

         UpperCamelCase_      =	self.pipeline_class(**_lowercase  )
         UpperCamelCase_      =	pipe.to(_lowercase  )

         pipe.set_progress_bar_config(disable=_lowercase  )

         UpperCamelCase_      =	pipe(**self.get_dummy_inputs(_lowercase  )  )
         UpperCamelCase_      =	output.images

         UpperCamelCase_      =	pipe(
             **self.get_dummy_inputs(_lowercase  )		,	return_dict=_lowercase		,	)[0]

         UpperCamelCase_      =	image[0, -3:, -3:, -1]
         UpperCamelCase_      =	image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 64, 64, 3)

         UpperCamelCase_      =	np.array(
             [0.328_663, 1.0, 0.23_216_873, 1.0, 0.92_717_564, 0.4_639_046, 0.96_894_777, 0.31_713_378, 0.6_293_953]  )

         assert (
             np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-2
         ), F" expected_slice {expected_slice}, but got {image_slice.flatten()}"
         assert (
             np.abs(image_from_tuple_slice.flatten() - expected_slice  ).max() < 1e-2
         ), F" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"






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






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






     def   UpperCAmelCase_							(							self  )->    str:
         UpperCamelCase_      =	load_numpy(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/kandinsky/kandinsky_text2img_cat_fp16.npy"  )

         UpperCamelCase_      =	KandinskyPriorPipeline.from_pretrained(
             "kandinsky-community/kandinsky-2-1-prior"		,	torch_dtype=torch.floataa  )
         pipe_prior.to(_lowercase  )

         UpperCamelCase_      =	KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1"		,	torch_dtype=torch.floataa  )
         UpperCamelCase_      =	pipeline.to(_lowercase  )
         pipeline.set_progress_bar_config(disable=_lowercase  )

         UpperCamelCase_      =	"red cat, 4k photo"

         UpperCamelCase_      =	torch.Generator(device="cuda"  ).manual_seed(0  )
         UpperCamelCase_     ,					UpperCamelCase_      =	pipe_prior(
             _lowercase		,	generator=_lowercase		,	num_inference_steps=5		,	negative_prompt=""		,	).to_tuple()

         UpperCamelCase_      =	torch.Generator(device="cuda"  ).manual_seed(0  )
         UpperCamelCase_      =	pipeline(
             _lowercase		,	image_embeds=_lowercase		,	negative_image_embeds=_lowercase		,	generator=_lowercase		,	num_inference_steps=100		,	output_type="np"		,	)

         UpperCamelCase_      =	output.images[0]

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

         assert_mean_pixel_difference(_lowercase		,	_lowercase  )
