




'''simple docstring'''





import os
from pathlib import Path
from unittest.mock import patch

import pytest
import zstandard as zstd

from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
    OfflineModeIsEnabled,
    cached_path,
    fsspec_get,
    fsspec_head,
    ftp_get,
    ftp_head,
    get_from_cache,
    http_get,
    http_head,
)


_SCREAMING_SNAKE_CASE							:   Dict      		= "\\n    Text data.\n    Second line of data."

_SCREAMING_SNAKE_CASE							:   Union[str, Any]      		= "file"


@pytest.fixture(scope="session"     )
def      UpperCamelCase_(	snake_case							:       Any     ):

							'''simple docstring'''






							snake_case_						=      tmp_path_factory.mktemp("data"     ) / (FILE_PATH + ".zstd")
							snake_case_						=      bytes(snake_case							,				"utf-8"     )
							with zstd.open(snake_case							,				"wb"     ) as f:
														f.write(snake_case     )
							return path


@pytest.fixture
def      UpperCamelCase_(	snake_case							:       Dict     ):

							'''simple docstring'''






							with open(os.path.join(tmpfs.local_root_dir							,				snake_case     )							,				"w"     ) as f:
														f.write(snake_case     )
							return FILE_PATH


@pytest.mark.parametrize("compression_format"							,				["gzip", "xz", "zstd"]     )
def      UpperCamelCase_(	snake_case							:       Tuple							,				snake_case							:       Optional[int]							,				snake_case							:       int							,				snake_case							:       Optional[int]							,				snake_case							:       int							,				snake_case							:       Tuple     ):

							'''simple docstring'''






							snake_case_						=      {"gzip": gz_file, "xz": xz_file, "zstd": zstd_path}
							snake_case_						=      input_paths[compression_format]
							snake_case_						=      tmp_path / "cache"
							snake_case_						=      DownloadConfig(cache_dir=snake_case							,				extract_compressed_file=snake_case     )
							snake_case_						=      cached_path(snake_case							,				download_config=snake_case     )
							with open(snake_case     ) as f:
														snake_case_						=      f.read()
							with open(snake_case     ) as f:
														snake_case_						=      f.read()
							assert extracted_file_content == expected_file_content


@pytest.mark.parametrize("default_extracted"							,				[True, False]     )
@pytest.mark.parametrize("default_cache_dir"							,				[True, False]     )
def      UpperCamelCase_(	snake_case							:       Optional[Any]							,				snake_case							:       Dict							,				snake_case							:       Tuple							,				snake_case							:       List[Any]							,				snake_case							:       Tuple     ):

							'''simple docstring'''






							snake_case_						=      "custom_cache"
							snake_case_						=      "custom_extracted_dir"
							snake_case_						=      tmp_path / "custom_extracted_path"
							if default_extracted:
														snake_case_						=      ("downloads" if default_cache_dir else custom_cache_dir, "extracted")
							else:
														monkeypatch.setattr("datasets.config.EXTRACTED_DATASETS_DIR"							,				snake_case     )
														monkeypatch.setattr("datasets.config.EXTRACTED_DATASETS_PATH"							,				str(snake_case     )     )
														snake_case_						=      custom_extracted_path.parts[-2:] if default_cache_dir else (custom_cache_dir, custom_extracted_dir)

							snake_case_						=      xz_file
							snake_case_						=      (
							    DownloadConfig(extract_compressed_file=snake_case     )
							    if default_cache_dir
							    else DownloadConfig(cache_dir=tmp_path / custom_cache_dir							,				extract_compressed_file=snake_case     )
							)
							snake_case_						=      cached_path(snake_case							,				download_config=snake_case     )
							assert Path(snake_case     ).parent.parts[-2:] == expected


def      UpperCamelCase_(	snake_case							:       Optional[Any]     ):

							'''simple docstring'''






							snake_case_						=      str(Path(snake_case     ).resolve()     )
							assert cached_path(snake_case     ) == text_file
							# relative path
							snake_case_						=      str(Path(snake_case     ).resolve().relative_to(Path(os.getcwd()     )     )     )
							assert cached_path(snake_case     ) == text_file


def      UpperCamelCase_(	snake_case							:       Tuple     ):

							'''simple docstring'''






							snake_case_						=      str(tmp_path.resolve() / "__missing_file__.txt"     )
							with pytest.raises(snake_case     ):
														cached_path(snake_case     )
							# relative path
							snake_case_						=      "./__missing_file__.txt"
							with pytest.raises(snake_case     ):
														cached_path(snake_case     )


def      UpperCamelCase_(	snake_case							:       Union[str, Any]     ):

							'''simple docstring'''






							snake_case_						=      get_from_cache(f'tmp://{tmpfs_file}'     )
							with open(snake_case     ) as f:
														snake_case_						=      f.read()
							assert output_file_content == FILE_CONTENT


@patch("datasets.config.HF_DATASETS_OFFLINE"							,				snake_case     )
def      UpperCamelCase_(	):

							'''simple docstring'''






							with pytest.raises(snake_case     ):
														cached_path("https://huggingface.co"     )


@patch("datasets.config.HF_DATASETS_OFFLINE"							,				snake_case     )
def      UpperCamelCase_(	snake_case							:       Union[str, Any]     ):

							'''simple docstring'''






							snake_case_						=      tmp_path_factory.mktemp("data"     ) / "file.html"
							with pytest.raises(snake_case     ):
														http_get("https://huggingface.co"							,				temp_file=snake_case     )
							with pytest.raises(snake_case     ):
														http_head("https://huggingface.co"     )


@patch("datasets.config.HF_DATASETS_OFFLINE"							,				snake_case     )
def      UpperCamelCase_(	snake_case							:       Any     ):

							'''simple docstring'''






							snake_case_						=      tmp_path_factory.mktemp("data"     ) / "file.html"
							with pytest.raises(snake_case     ):
														ftp_get("ftp://huggingface.co"							,				temp_file=snake_case     )
							with pytest.raises(snake_case     ):
														ftp_head("ftp://huggingface.co"     )




@patch("datasets.config.HF_DATASETS_OFFLINE"							,				snake_case     )
def      UpperCamelCase_(	snake_case							:       List[str]     ):

							'''simple docstring'''






							snake_case_						=      tmp_path_factory.mktemp("data"     ) / "file.html"
							with pytest.raises(snake_case     ):
														fsspec_get("s3://huggingface.co"							,				temp_file=snake_case     )
							with pytest.raises(snake_case     ):
														fsspec_head("s3://huggingface.co"     )





'''simple docstring'''





import gc
import random
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
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	):



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



    @property
    def 					_lowercase						(self    :    str ):
         UpperCAmelCase_           =   1
         UpperCAmelCase_           =   3
         UpperCAmelCase_           =   (32, 32)

         UpperCAmelCase_           =   floats_tensor((batch_size, num_channels) + sizes   ,			rng=random.Random(0 ) ).to(__a )
         return image



    @property
    def 					_lowercase						(self    :    int ):
         torch.manual_seed(0 )
         UpperCAmelCase_           =   UNetaDConditionModel(
             block_out_channels=(32, 32, 64)   ,			layers_per_block=2   ,			sample_size=32   ,			in_channels=7   ,			out_channels=4   ,			down_block_types=("DownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D")   ,			up_block_types=("CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "UpBlock2D")   ,			cross_attention_dim=32   ,			attention_head_dim=8   ,			use_linear_projection=__a   ,			only_cross_attention=(True, True, False)   ,			num_class_embeds=100   ,			)
         return model



    @property
    def 					_lowercase						(self    :    Any ):
         torch.manual_seed(0 )
         UpperCAmelCase_           =   AutoencoderKL(
             block_out_channels=[32, 32, 64]   ,			in_channels=3   ,			out_channels=3   ,			down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D", "DownEncoderBlock2D"]   ,			up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"]   ,			latent_channels=4   ,			)
         return model



    @property
    def 					_lowercase						(self    :    Optional[Any] ):
         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   ,			)
         return CLIPTextModel(__a )



    def 					_lowercase						(self    :    Any ):
         UpperCAmelCase_           =   "cpu"  # ensure determinism for the device-dependent torch.Generator
         UpperCAmelCase_           =   self.dummy_cond_unet_upscale
         UpperCAmelCase_           =   DDPMScheduler()
         UpperCAmelCase_           =   DDIMScheduler(prediction_type="v_prediction" )
         UpperCAmelCase_           =   self.dummy_vae
         UpperCAmelCase_           =   self.dummy_text_encoder
         UpperCAmelCase_           =   CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )

         UpperCAmelCase_           =   self.dummy_image.cpu().permute(0   ,			2   ,			3   ,			1 )[0]
         UpperCAmelCase_           =   Image.fromarray(np.uinta(__a ) ).convert("RGB" ).resize((64, 64) )

         # make sure here that pndm scheduler skips prk
         UpperCAmelCase_           =   StableDiffusionUpscalePipeline(
             unet=__a   ,			low_res_scheduler=__a   ,			scheduler=__a   ,			vae=__a   ,			text_encoder=__a   ,			tokenizer=__a   ,			max_noise_level=350   ,			)
         UpperCAmelCase_           =   sd_pipe.to(__a )
         sd_pipe.set_progress_bar_config(disable=__a )

         UpperCAmelCase_           =   "A painting of a squirrel eating a burger"
         UpperCAmelCase_           =   torch.Generator(device=__a ).manual_seed(0 )
         UpperCAmelCase_           =   sd_pipe(
             [prompt]   ,			image=__a   ,			generator=__a   ,			guidance_scale=6.0   ,			noise_level=20   ,			num_inference_steps=2   ,			output_type="np"   ,			)

         UpperCAmelCase_           =   output.images

         UpperCAmelCase_           =   torch.Generator(device=__a ).manual_seed(0 )
         UpperCAmelCase_           =   sd_pipe(
             [prompt]   ,			image=__a   ,			generator=__a   ,			guidance_scale=6.0   ,			noise_level=20   ,			num_inference_steps=2   ,			output_type="np"   ,			return_dict=__a   ,			)[0]

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

         UpperCAmelCase_           =   low_res_image.size[0] * 4
         assert image.shape == (1, expected_height_width, expected_height_width, 3)
         UpperCAmelCase_           =   np.array([0.31_13, 0.39_10, 0.42_72, 0.48_59, 0.50_61, 0.46_52, 0.53_62, 0.57_15, 0.56_61] )

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



    def 					_lowercase						(self    :    Optional[int] ):
         UpperCAmelCase_           =   "cpu"  # ensure determinism for the device-dependent torch.Generator
         UpperCAmelCase_           =   self.dummy_cond_unet_upscale
         UpperCAmelCase_           =   DDPMScheduler()
         UpperCAmelCase_           =   DDIMScheduler(prediction_type="v_prediction" )
         UpperCAmelCase_           =   self.dummy_vae
         UpperCAmelCase_           =   self.dummy_text_encoder
         UpperCAmelCase_           =   CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )

         UpperCAmelCase_           =   self.dummy_image.cpu().permute(0   ,			2   ,			3   ,			1 )[0]
         UpperCAmelCase_           =   Image.fromarray(np.uinta(__a ) ).convert("RGB" ).resize((64, 64) )

         # make sure here that pndm scheduler skips prk
         UpperCAmelCase_           =   StableDiffusionUpscalePipeline(
             unet=__a   ,			low_res_scheduler=__a   ,			scheduler=__a   ,			vae=__a   ,			text_encoder=__a   ,			tokenizer=__a   ,			max_noise_level=350   ,			)
         UpperCAmelCase_           =   sd_pipe.to(__a )
         sd_pipe.set_progress_bar_config(disable=__a )

         UpperCAmelCase_           =   "A painting of a squirrel eating a burger"
         UpperCAmelCase_           =   sd_pipe(
             2 * [prompt]   ,			image=2 * [low_res_image]   ,			guidance_scale=6.0   ,			noise_level=20   ,			num_inference_steps=2   ,			output_type="np"   ,			)
         UpperCAmelCase_           =   output.images
         assert image.shape[0] == 2

         UpperCAmelCase_           =   torch.Generator(device=__a ).manual_seed(0 )
         UpperCAmelCase_           =   sd_pipe(
             [prompt]   ,			image=__a   ,			generator=__a   ,			num_images_per_prompt=2   ,			guidance_scale=6.0   ,			noise_level=20   ,			num_inference_steps=2   ,			output_type="np"   ,			)
         UpperCAmelCase_           =   output.images
         assert image.shape[0] == 2






    @unittest.skipIf(torch_device != "cuda"   ,			"This test requires a GPU" )
    def 					_lowercase						(self    :    str ):
         UpperCAmelCase_           =   self.dummy_cond_unet_upscale
         UpperCAmelCase_           =   DDPMScheduler()
         UpperCAmelCase_           =   DDIMScheduler(prediction_type="v_prediction" )
         UpperCAmelCase_           =   self.dummy_vae
         UpperCAmelCase_           =   self.dummy_text_encoder
         UpperCAmelCase_           =   CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )

         UpperCAmelCase_           =   self.dummy_image.cpu().permute(0   ,			2   ,			3   ,			1 )[0]
         UpperCAmelCase_           =   Image.fromarray(np.uinta(__a ) ).convert("RGB" ).resize((64, 64) )

         # put models in fp16, except vae as it overflows in fp16
         UpperCAmelCase_           =   unet.half()
         UpperCAmelCase_           =   text_encoder.half()

         # make sure here that pndm scheduler skips prk
         UpperCAmelCase_           =   StableDiffusionUpscalePipeline(
             unet=__a   ,			low_res_scheduler=__a   ,			scheduler=__a   ,			vae=__a   ,			text_encoder=__a   ,			tokenizer=__a   ,			max_noise_level=350   ,			)
         UpperCAmelCase_           =   sd_pipe.to(__a )
         sd_pipe.set_progress_bar_config(disable=__a )

         UpperCAmelCase_           =   "A painting of a squirrel eating a burger"
         UpperCAmelCase_           =   torch.manual_seed(0 )
         UpperCAmelCase_           =   sd_pipe(
             [prompt]   ,			image=__a   ,			generator=__a   ,			num_inference_steps=2   ,			output_type="np"   ,			).images

         UpperCAmelCase_           =   low_res_image.size[0] * 4
         assert image.shape == (1, expected_height_width, expected_height_width, 3)




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



    def 					_lowercase						(self    :    List[str] ):
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()



    def 					_lowercase						(self    :    List[Any] ):
         UpperCAmelCase_           =   load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/sd2-upscale/low_res_cat.png" )
         UpperCAmelCase_           =   load_numpy(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"
             "/upsampled_cat.npy" )

         UpperCAmelCase_           =   "stabilityai/stable-diffusion-x4-upscaler"
         UpperCAmelCase_           =   StableDiffusionUpscalePipeline.from_pretrained(__a )
         pipe.to(__a )
         pipe.set_progress_bar_config(disable=__a )
         pipe.enable_attention_slicing()

         UpperCAmelCase_           =   "a cat sitting on a park bench"

         UpperCAmelCase_           =   torch.manual_seed(0 )
         UpperCAmelCase_           =   pipe(
             prompt=__a   ,			image=__a   ,			generator=__a   ,			output_type="np"   ,			)
         UpperCAmelCase_           =   output.images[0]

         assert image.shape == (512, 512, 3)
         assert np.abs(expected_image - image ).max() < 1E-3



    def 					_lowercase						(self    :    Tuple ):
         UpperCAmelCase_           =   load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/sd2-upscale/low_res_cat.png" )
         UpperCAmelCase_           =   load_numpy(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"
             "/upsampled_cat_fp16.npy" )

         UpperCAmelCase_           =   "stabilityai/stable-diffusion-x4-upscaler"
         UpperCAmelCase_           =   StableDiffusionUpscalePipeline.from_pretrained(
             __a   ,			torch_dtype=torch.floataa   ,			)
         pipe.to(__a )
         pipe.set_progress_bar_config(disable=__a )
         pipe.enable_attention_slicing()

         UpperCAmelCase_           =   "a cat sitting on a park bench"

         UpperCAmelCase_           =   torch.manual_seed(0 )
         UpperCAmelCase_           =   pipe(
             prompt=__a   ,			image=__a   ,			generator=__a   ,			output_type="np"   ,			)
         UpperCAmelCase_           =   output.images[0]

         assert image.shape == (512, 512, 3)
         assert np.abs(expected_image - image ).max() < 5E-1






    def 					_lowercase						(self    :    List[Any] ):
         torch.cuda.empty_cache()
         torch.cuda.reset_max_memory_allocated()
         torch.cuda.reset_peak_memory_stats()

         UpperCAmelCase_           =   load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/sd2-upscale/low_res_cat.png" )

         UpperCAmelCase_           =   "stabilityai/stable-diffusion-x4-upscaler"
         UpperCAmelCase_           =   StableDiffusionUpscalePipeline.from_pretrained(
             __a   ,			torch_dtype=torch.floataa   ,			)
         pipe.to(__a )
         pipe.set_progress_bar_config(disable=__a )
         pipe.enable_attention_slicing(1 )
         pipe.enable_sequential_cpu_offload()

         UpperCAmelCase_           =   "a cat sitting on a park bench"

         UpperCAmelCase_           =   torch.manual_seed(0 )
         UpperCAmelCase_           =   pipe(
             prompt=__a   ,			image=__a   ,			generator=__a   ,			num_inference_steps=5   ,			output_type="np"   ,			)

         UpperCAmelCase_           =   torch.cuda.max_memory_allocated()
         # make sure that less than 2.9 GB is allocated
         assert mem_bytes < 2.9 * 10**9




