




"""simple docstring"""




import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases



def        a__					(							snake_case__			,					snake_case__							)		->   Union[str, Any]:
			assert isinstance(__UpperCamelCase			,					__UpperCamelCase							)
			assert dataset.num_rows == 4
			assert dataset.num_columns == 1
			assert dataset.column_names == ["text"]
			for feature, expected_dtype in expected_features.items():
						assert dataset.features[feature].dtype == expected_dtype



@pytest.mark.parametrize("""keep_in_memory"""			,					[False, True]							)
def        a__					(							snake_case__			,					snake_case__			,					snake_case__							)		->   Optional[Any]:
			lowerCamelCase      =   tmp_path / """cache"""
			lowerCamelCase      =   {"""text""": """string"""}
			with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
						lowerCamelCase      =   TextDatasetReader(__UpperCamelCase			,					cache_dir=__UpperCamelCase			,					keep_in_memory=__UpperCamelCase							).read()
			_check_text_dataset(__UpperCamelCase			,					__UpperCamelCase							)



@pytest.mark.parametrize(
    """features"""			,					[
        None,
        {"""text""": """string"""},
        {"""text""": """int32"""},
        {"""text""": """float32"""},
    ]			,					)
def        a__					(							snake_case__			,					snake_case__			,					snake_case__							)		->   Optional[int]:
			lowerCamelCase      =   tmp_path / """cache"""
			lowerCamelCase      =   {"""text""": """string"""}
			lowerCamelCase      =   features.copy() if features else default_expected_features
			lowerCamelCase      =   (
			    Features({feature: Value(__UpperCamelCase							) for feature, dtype in features.items()}							) if features is not None else None
			)
			lowerCamelCase      =   TextDatasetReader(__UpperCamelCase			,					features=__UpperCamelCase			,					cache_dir=__UpperCamelCase							).read()
			_check_text_dataset(__UpperCamelCase			,					__UpperCamelCase							)



@pytest.mark.parametrize("""split"""			,					[None, NamedSplit("""train"""							), """train""", """test"""]							)
def        a__					(							snake_case__			,					snake_case__			,					snake_case__							)		->   str:
			lowerCamelCase      =   tmp_path / """cache"""
			lowerCamelCase      =   {"""text""": """string"""}
			lowerCamelCase      =   TextDatasetReader(__UpperCamelCase			,					cache_dir=__UpperCamelCase			,					split=__UpperCamelCase							).read()
			_check_text_dataset(__UpperCamelCase			,					__UpperCamelCase							)
			assert dataset.split == split if split else "train"



@pytest.mark.parametrize("""path_type"""			,					[str, list]							)
def        a__					(							snake_case__			,					snake_case__			,					snake_case__							)		->   List[str]:
			if issubclass(__UpperCamelCase			,					__UpperCamelCase							):
						lowerCamelCase      =   text_path
			elif issubclass(__UpperCamelCase			,					__UpperCamelCase							):
						lowerCamelCase      =   [text_path]
			lowerCamelCase      =   tmp_path / """cache"""
			lowerCamelCase      =   {"""text""": """string"""}
			lowerCamelCase      =   TextDatasetReader(__UpperCamelCase			,					cache_dir=__UpperCamelCase							).read()
			_check_text_dataset(__UpperCamelCase			,					__UpperCamelCase							)



def        a__					(							snake_case__			,					snake_case__			,					snake_case__=("train",)							)		->   int:
			assert isinstance(__UpperCamelCase			,					__UpperCamelCase							)
			for split in splits:
						lowerCamelCase      =   dataset_dict[split]
						assert dataset.num_rows == 4
						assert dataset.num_columns == 1
						assert dataset.column_names == ["text"]
						for feature, expected_dtype in expected_features.items():
									assert dataset.features[feature].dtype == expected_dtype



@pytest.mark.parametrize("""keep_in_memory"""			,					[False, True]							)
def        a__					(							snake_case__			,					snake_case__			,					snake_case__							)		->   List[str]:
			lowerCamelCase      =   tmp_path / """cache"""
			lowerCamelCase      =   {"""text""": """string"""}
			with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
						lowerCamelCase      =   TextDatasetReader({"""train""": text_path}			,					cache_dir=__UpperCamelCase			,					keep_in_memory=__UpperCamelCase							).read()
			_check_text_datasetdict(__UpperCamelCase			,					__UpperCamelCase							)



@pytest.mark.parametrize(
    """features"""			,					[
        None,
        {"""text""": """string"""},
        {"""text""": """int32"""},
        {"""text""": """float32"""},
    ]			,					)
def        a__					(							snake_case__			,					snake_case__			,					snake_case__							)		->   Any:
			lowerCamelCase      =   tmp_path / """cache"""
			# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
			lowerCamelCase      =   {"""text""": """string"""}
			lowerCamelCase      =   features.copy() if features else default_expected_features
			lowerCamelCase      =   (
			    Features({feature: Value(__UpperCamelCase							) for feature, dtype in features.items()}							) if features is not None else None
			)
			lowerCamelCase      =   TextDatasetReader({"""train""": text_path}			,					features=__UpperCamelCase			,					cache_dir=__UpperCamelCase							).read()
			_check_text_datasetdict(__UpperCamelCase			,					__UpperCamelCase							)



@pytest.mark.parametrize("""split"""			,					[None, NamedSplit("""train"""							), """train""", """test"""]							)
def        a__					(							snake_case__			,					snake_case__			,					snake_case__							)		->   str:
			if split:
						lowerCamelCase      =   {split: text_path}
			else:
						lowerCamelCase      =   """train"""
						lowerCamelCase      =   {"""train""": text_path, """test""": text_path}
			lowerCamelCase      =   tmp_path / """cache"""
			lowerCamelCase      =   {"""text""": """string"""}
			lowerCamelCase      =   TextDatasetReader(__UpperCamelCase			,					cache_dir=__UpperCamelCase							).read()
			_check_text_datasetdict(__UpperCamelCase			,					__UpperCamelCase			,					splits=list(path.keys()							)							)
			assert all(dataset[split].split == split for split in path.keys()							)







'''simple docstring'''
import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()






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






       def   UpperCamelCase						( self   )->      Optional[int]:
            # clean up the VRAM after each test
            super().tearDown()
            gc.collect()
            torch.cuda.empty_cache()






       def   UpperCamelCase						( self   )->      Union[str, Any]:
            _A				=							StableDiffusionKDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4'   )
            _A				=							sd_pipe.to(_UpperCamelCase   )
            sd_pipe.set_progress_bar_config(disable=_UpperCamelCase   )

            sd_pipe.set_scheduler('sample_euler'   )

            _A				=							'A painting of a squirrel eating a burger'
            _A				=							torch.manual_seed(0   )
            _A				=							sd_pipe([prompt]			,     generator=_UpperCamelCase			,     guidance_scale=9.0			,     num_inference_steps=20			,     output_type='np'   )

            _A				=							output.images

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

            assert image.shape == (1, 512, 512, 3)
            _A				=							np.array([0.0447, 0.0492, 0.0468, 0.0408, 0.0383, 0.0408, 0.0354, 0.0380, 0.0339]   )

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






       def   UpperCamelCase						( self   )->      Optional[Any]:
            _A				=							StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'   )
            _A				=							sd_pipe.to(_UpperCamelCase   )
            sd_pipe.set_progress_bar_config(disable=_UpperCamelCase   )

            sd_pipe.set_scheduler('sample_euler'   )

            _A				=							'A painting of a squirrel eating a burger'
            _A				=							torch.manual_seed(0   )
            _A				=							sd_pipe([prompt]			,     generator=_UpperCamelCase			,     guidance_scale=9.0			,     num_inference_steps=20			,     output_type='np'   )

            _A				=							output.images

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

            assert image.shape == (1, 512, 512, 3)
            _A				=							np.array([0.1237, 0.1320, 0.1438, 0.1359, 0.1390, 0.1132, 0.1277, 0.1175, 0.1112]   )

            assert np.abs(image_slice.flatten() - expected_slice   ).max() < 5e-1






       def   UpperCamelCase						( self   )->      Optional[int]:
            _A				=							StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'   )
            _A				=							sd_pipe.to(_UpperCamelCase   )
            sd_pipe.set_progress_bar_config(disable=_UpperCamelCase   )

            sd_pipe.set_scheduler('sample_dpmpp_2m'   )

            _A				=							'A painting of a squirrel eating a burger'
            _A				=							torch.manual_seed(0   )
            _A				=							sd_pipe(
                [prompt]			,     generator=_UpperCamelCase			,     guidance_scale=7.5			,     num_inference_steps=15			,     output_type='np'			,     use_karras_sigmas=_UpperCamelCase			,     )

            _A				=							output.images

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

            assert image.shape == (1, 512, 512, 3)
            _A				=							np.array(
                [0.1138_1689, 0.1211_2921, 0.138_9457, 0.1254_9606, 0.124_4964, 0.1083_1517, 0.1156_2866, 0.1086_7816, 0.1049_9048]   )

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





