






from unittest.mock import Mock, patch

from file_transfer.send_file import send_file

@patch("socket.socket"						)
@patch("builtins.open"						)
def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__						)->    List[str]:



			"""simple docstring"""



			snake_case_				:					List[str]   =       Mock()
			snake_case_				:					Any   =       conn, Mock()
			snake_case_				:					int   =       iter([1, None]						)
			snake_case_				:					str   =       lambda __magic_name__						: next(lowercase__						)

			# ===== invoke =====
			send_file(filename="mytext.txt"     ,testing=lowercase__						)

			# ===== ensurance =====
			sock.assert_called_once()
			sock.return_value.bind.assert_called_once()
			sock.return_value.listen.assert_called_once()
			sock.return_value.accept.assert_called_once()
			conn.recv.assert_called_once()

			file.return_value.__enter__.assert_called_once()
			file.return_value.__enter__.return_value.read.assert_called()

			conn.send.assert_called_once()
			conn.close.assert_called_once()
			sock.return_value.shutdown.assert_called_once()
			sock.return_value.close.assert_called_once()












'''simple docstring'''




import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizer

from diffusers import (
    AltDiffusionImgaImgPipeline,
    AutoencoderKL,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()







class    A_	(unittest.TestCase							):
							"""simple docstring"""




							def 							_A	(		self			:Any				)				->  str:


										'''simple docstring'''




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

							@property
							def 							_A	(		self			:List[Any]				)				->  List[str]:


										'''simple docstring'''




										snake_case_				:					Any   =       1
										snake_case_				:					Dict   =       3
										snake_case_				:					Union[str, Any]   =       (32, 32)

										snake_case_				:					Optional[int]   =       floats_tensor((batch_size, num_channels) + sizes       ,							rng=random.Random(0				)				).to(lowerCAmelCase__				)
										return image

							@property
							def 							_A	(		self			:Optional[int]				)				->  Any:


										'''simple docstring'''




										torch.manual_seed(0				)
										snake_case_				:					List[str]   =       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       ,							)
										return model

							@property
							def 							_A	(		self			:Dict				)				->  Any:


										'''simple docstring'''




										torch.manual_seed(0				)
										snake_case_				:					Optional[Any]   =       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       ,							)
										return model

							@property
							def 							_A	(		self			:Dict				)				->  Optional[int]:


										'''simple docstring'''




										torch.manual_seed(0				)
										snake_case_				:					str   =       RobertaSeriesConfig(
										    hidden_size=32       ,							project_dim=32       ,							intermediate_size=37       ,							layer_norm_eps=1E-0_5       ,							num_attention_heads=4       ,							num_hidden_layers=5       ,							pad_token_id=1       ,							vocab_size=5_006       ,							)
										return RobertaSeriesModelWithTransformation(lowerCAmelCase__				)

							@property
							def 							_A	(		self			:Any				)				->  str:


										'''simple docstring'''




										def extract(*lowerCAmelCase__			:Any       ,							**lowerCAmelCase__			:List[str]				):







													class    A_	:
																				"""simple docstring"""




																				def __init__(		self			:Optional[int]				)				->  List[str]:


																							'''simple docstring'''




																							snake_case_				:					str   =       torch.ones([0]				)






																				def 							_A	(		self			:int       ,							lowerCAmelCase__			:List[Any]				)				->  Tuple:


																							'''simple docstring'''




																							self.pixel_values.to(lowerCAmelCase__				)
																							return self
													return Out()

										return extract

							def 							_A	(		self			:int				)				->  Dict:


										'''simple docstring'''




										snake_case_				:					str   =       "cpu"  # ensure determinism for the device-dependent torch.Generator
										snake_case_				:					str   =       self.dummy_cond_unet
										snake_case_				:					Optional[int]   =       PNDMScheduler(skip_prk_steps=lowerCAmelCase__				)
										snake_case_				:					Dict   =       self.dummy_vae
										snake_case_				:					Dict   =       self.dummy_text_encoder
										snake_case_				:					Optional[int]   =       XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta"				)
										snake_case_				:					str   =       77

										snake_case_				:					Any   =       self.dummy_image.to(lowerCAmelCase__				)
										snake_case_				:					Tuple   =       init_image / 2 + 0.5

										# make sure here that pndm scheduler skips prk
										snake_case_				:					Optional[Any]   =       AltDiffusionImgaImgPipeline(
										    unet=lowerCAmelCase__       ,							scheduler=lowerCAmelCase__       ,							vae=lowerCAmelCase__       ,							text_encoder=lowerCAmelCase__       ,							tokenizer=lowerCAmelCase__       ,							safety_checker=lowerCAmelCase__       ,							feature_extractor=self.dummy_extractor       ,							)
										snake_case_				:					Union[str, Any]   =       VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor       ,							do_normalize=lowerCAmelCase__				)
										snake_case_				:					Optional[Any]   =       alt_pipe.to(lowerCAmelCase__				)
										alt_pipe.set_progress_bar_config(disable=lowerCAmelCase__				)

										snake_case_				:					Dict   =       "A painting of a squirrel eating a burger"
										snake_case_				:					List[str]   =       torch.Generator(device=lowerCAmelCase__				).manual_seed(0				)
										snake_case_				:					Dict   =       alt_pipe(
										    [prompt]       ,							generator=lowerCAmelCase__       ,							guidance_scale=6.0       ,							num_inference_steps=2       ,							output_type="np"       ,							image=lowerCAmelCase__       ,							)

										snake_case_				:					Any   =       output.images

										snake_case_				:					List[str]   =       torch.Generator(device=lowerCAmelCase__				).manual_seed(0				)
										snake_case_				:					Optional[Any]   =       alt_pipe(
										    [prompt]       ,							generator=lowerCAmelCase__       ,							guidance_scale=6.0       ,							num_inference_steps=2       ,							output_type="np"       ,							image=lowerCAmelCase__       ,							return_dict=lowerCAmelCase__       ,							)[0]

										snake_case_				:					Tuple   =       image[0, -3:, -3:, -1]
										snake_case_				:					Dict   =       image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 32, 32, 3)
										snake_case_				:					int   =       np.array([0.4_4_2_7, 0.3_7_3_1, 0.4_2_4_9, 0.4_9_4_1, 0.4_5_4_6, 0.4_1_4_8, 0.4_1_9_3, 0.4_6_6_6, 0.4_4_9_9]				)

										assert np.abs(image_slice.flatten() - expected_slice				).max() < 5E-3
										assert np.abs(image_from_tuple_slice.flatten() - expected_slice				).max() < 5E-3

							@unittest.skipIf(torch_device != "cuda"       ,							"This test requires a GPU"				)
							def 							_A	(		self			:int				)				->  List[str]:


										'''simple docstring'''




										snake_case_				:					Union[str, Any]   =       self.dummy_cond_unet
										snake_case_				:					Union[str, Any]   =       PNDMScheduler(skip_prk_steps=lowerCAmelCase__				)
										snake_case_				:					int   =       self.dummy_vae
										snake_case_				:					List[Any]   =       self.dummy_text_encoder
										snake_case_				:					int   =       XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta"				)
										snake_case_				:					int   =       77

										snake_case_				:					Dict   =       self.dummy_image.to(lowerCAmelCase__				)

										# put models in fp16
										snake_case_				:					Optional[Any]   =       unet.half()
										snake_case_				:					Tuple   =       vae.half()
										snake_case_				:					List[str]   =       bert.half()

										# make sure here that pndm scheduler skips prk
										snake_case_				:					Optional[int]   =       AltDiffusionImgaImgPipeline(
										    unet=lowerCAmelCase__       ,							scheduler=lowerCAmelCase__       ,							vae=lowerCAmelCase__       ,							text_encoder=lowerCAmelCase__       ,							tokenizer=lowerCAmelCase__       ,							safety_checker=lowerCAmelCase__       ,							feature_extractor=self.dummy_extractor       ,							)
										snake_case_				:					List[str]   =       VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor       ,							do_normalize=lowerCAmelCase__				)
										snake_case_				:					Optional[Any]   =       alt_pipe.to(lowerCAmelCase__				)
										alt_pipe.set_progress_bar_config(disable=lowerCAmelCase__				)

										snake_case_				:					List[Any]   =       "A painting of a squirrel eating a burger"
										snake_case_				:					str   =       torch.manual_seed(0				)
										snake_case_				:					Any   =       alt_pipe(
										    [prompt]       ,							generator=lowerCAmelCase__       ,							num_inference_steps=2       ,							output_type="np"       ,							image=lowerCAmelCase__       ,							).images

										assert image.shape == (1, 32, 32, 3)






							@unittest.skipIf(torch_device != "cuda"       ,							"This test requires a GPU"				)
							def 							_A	(		self			:Optional[int]				)				->  Any:


										'''simple docstring'''




										snake_case_				:					Union[str, Any]   =       load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
										    "/img2img/sketch-mountains-input.jpg"				)
										# resize to resolution that is divisible by 8 but not 16 or 32
										snake_case_				:					str   =       init_image.resize((760, 504)				)

										snake_case_				:					Optional[Any]   =       "BAAI/AltDiffusion"
										snake_case_				:					int   =       AltDiffusionImgaImgPipeline.from_pretrained(
										    lowerCAmelCase__       ,							safety_checker=lowerCAmelCase__       ,							)
										pipe.to(lowerCAmelCase__				)
										pipe.set_progress_bar_config(disable=lowerCAmelCase__				)
										pipe.enable_attention_slicing()

										snake_case_				:					Tuple   =       "A fantasy landscape, trending on artstation"

										snake_case_				:					int   =       torch.manual_seed(0				)
										snake_case_				:					List[str]   =       pipe(
										    prompt=lowerCAmelCase__       ,							image=lowerCAmelCase__       ,							strength=0.7_5       ,							guidance_scale=7.5       ,							generator=lowerCAmelCase__       ,							output_type="np"       ,							)
										snake_case_				:					str   =       output.images[0]

										snake_case_				:					List[Any]   =       image[255:258, 383:386, -1]

										assert image.shape == (504, 760, 3)
										snake_case_				:					Tuple   =       np.array([0.9_3_5_8, 0.9_3_9_7, 0.9_5_9_9, 0.9_9_0_1, 1.0_0_0_0, 1.0_0_0_0, 0.9_8_8_2, 1.0_0_0_0, 1.0_0_0_0]				)

										assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2







@slow
@require_torch_gpu
class    A_	(unittest.TestCase							):
							"""simple docstring"""




							def 							_A	(		self			:Optional[Any]				)				->  Optional[int]:


										'''simple docstring'''




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






							def 							_A	(		self			:str				)				->  Any:


										'''simple docstring'''




										snake_case_				:					Optional[Any]   =       load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
										    "/img2img/sketch-mountains-input.jpg"				)
										snake_case_				:					List[Any]   =       init_image.resize((768, 512)				)
										snake_case_				:					Tuple   =       load_numpy(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy"				)

										snake_case_				:					Any   =       "BAAI/AltDiffusion"
										snake_case_				:					List[str]   =       AltDiffusionImgaImgPipeline.from_pretrained(
										    lowerCAmelCase__       ,							safety_checker=lowerCAmelCase__       ,							)
										pipe.to(lowerCAmelCase__				)
										pipe.set_progress_bar_config(disable=lowerCAmelCase__				)
										pipe.enable_attention_slicing()

										snake_case_				:					Tuple   =       "A fantasy landscape, trending on artstation"

										snake_case_				:					Tuple   =       torch.manual_seed(0				)
										snake_case_				:					List[Any]   =       pipe(
										    prompt=lowerCAmelCase__       ,							image=lowerCAmelCase__       ,							strength=0.7_5       ,							guidance_scale=7.5       ,							generator=lowerCAmelCase__       ,							output_type="np"       ,							)
										snake_case_				:					Optional[int]   =       output.images[0]

										assert image.shape == (512, 768, 3)
										# img2img is flaky across GPUs even in fp32, so using MAE here
										assert np.abs(expected_image - image				).max() < 1E-2




