



import os
import textwrap

import pyarrow as pa
import pytest

from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv

from ..utils import require_pil






@pytest.fixture
def      lowerCAmelCase__		(   lowerCamelCase_		:		Optional[Any]):





				'''simple docstring'''

				lowerCAmelCase__					:    Optional[int]												=					tmp_path / '''file.csv'''
				lowerCAmelCase__					:    List[str]												=					textwrap.dedent(
				    '''\
        header1,header2
        1,2
        10,20
        ''')
				with open(lowerCamelCase_	,'''w''') as f:
								f.write(lowerCamelCase_)
				return str(lowerCamelCase_)






@pytest.fixture
def      lowerCAmelCase__		(   lowerCamelCase_		:		Union[str, Any]):





				'''simple docstring'''

				lowerCAmelCase__					:    str												=					tmp_path / '''malformed_file.csv'''
				lowerCAmelCase__					:    Tuple												=					textwrap.dedent(
				    '''\
        header1,header2
        1,2
        10,20,
        ''')
				with open(lowerCamelCase_	,'''w''') as f:
								f.write(lowerCamelCase_)
				return str(lowerCamelCase_)






@pytest.fixture
def      lowerCAmelCase__		(   lowerCamelCase_		:		Union[str, Any]	,lowerCamelCase_		:		Tuple):





				'''simple docstring'''

				lowerCAmelCase__					:    str												=					tmp_path / '''csv_with_image.csv'''
				lowerCAmelCase__					:    str												=					textwrap.dedent(
				    f"""\
        image
        {image_file}
        """)
				with open(lowerCamelCase_	,'''w''') as f:
								f.write(lowerCamelCase_)
				return str(lowerCamelCase_)






@pytest.fixture
def      lowerCAmelCase__		(   lowerCamelCase_		:		Optional[Any]):





				'''simple docstring'''

				lowerCAmelCase__					:    List[str]												=					tmp_path / '''csv_with_label.csv'''
				lowerCAmelCase__					:    Dict												=					textwrap.dedent(
				    '''\
        label
        good
        bad
        good
        ''')
				with open(lowerCamelCase_	,'''w''') as f:
								f.write(lowerCamelCase_)
				return str(lowerCamelCase_)






@pytest.fixture
def      lowerCAmelCase__		(   lowerCamelCase_		:		int):





				'''simple docstring'''

				lowerCAmelCase__					:    Any												=					tmp_path / '''csv_with_int_list.csv'''
				lowerCAmelCase__					:    Tuple												=					textwrap.dedent(
				    '''\
        int_list
        1 2 3
        4 5 6
        7 8 9
        ''')
				with open(lowerCamelCase_	,'''w''') as f:
								f.write(lowerCamelCase_)
				return str(lowerCamelCase_)






def      lowerCAmelCase__		(   lowerCamelCase_		:		Any	,lowerCamelCase_		:		List[Any]	,lowerCamelCase_		:		Optional[Any]):





				'''simple docstring'''

				lowerCAmelCase__					:    int												=					Csv()
				lowerCAmelCase__					:    Optional[int]												=					csv._generate_tables([[csv_file, malformed_csv_file]])
				with pytest.raises(lowerCamelCase_	,match='''Error tokenizing data'''):
								for _ in generator:
												pass
				assert any(
				    record.levelname == '''ERROR'''
				    and '''Failed to read file''' in record.message
				    and os.path.basename(lowerCamelCase_) in record.message
				    for record in caplog.records)






@require_pil
def      lowerCAmelCase__		(   lowerCamelCase_		:		Union[str, Any]):





				'''simple docstring'''

				with open(lowerCamelCase_	,encoding='''utf-8''') as f:
								lowerCAmelCase__					:    Any												=					f.read().splitlines()[1]
				lowerCAmelCase__					:    List[str]												=					Csv(encoding='''utf-8'''	,features=Features({'''image''': Image()}))
				lowerCAmelCase__					:    Optional[int]												=					csv._generate_tables([[csv_file_with_image]])
				lowerCAmelCase__					:    Optional[int]												=					pa.concat_tables([table for _, table in generator])
				assert pa_table.schema.field('''image''').type == Image()()
				lowerCAmelCase__					:    Optional[Any]												=					pa_table.to_pydict()['''image''']
				assert generated_content == [{"path": image_file, "bytes": None}]






def      lowerCAmelCase__		(   lowerCamelCase_		:		int):





				'''simple docstring'''

				with open(lowerCamelCase_	,encoding='''utf-8''') as f:
								lowerCAmelCase__					:    List[Any]												=					f.read().splitlines()[1:]
				lowerCAmelCase__					:    List[Any]												=					Csv(encoding='''utf-8'''	,features=Features({'''label''': ClassLabel(names=['''good''', '''bad'''])}))
				lowerCAmelCase__					:    Optional[int]												=					csv._generate_tables([[csv_file_with_label]])
				lowerCAmelCase__					:    List[Any]												=					pa.concat_tables([table for _, table in generator])
				assert pa_table.schema.field('''label''').type == ClassLabel(names=['''good''', '''bad'''])()
				lowerCAmelCase__					:    Optional[int]												=					pa_table.to_pydict()['''label''']
				assert generated_content == [ClassLabel(names=['''good''', '''bad''']).straint(lowerCamelCase_) for label in labels]






def      lowerCAmelCase__		(   lowerCamelCase_		:		Dict):





				'''simple docstring'''

				lowerCAmelCase__					:    Any												=					Csv(encoding='''utf-8'''	,sep=''','''	,converters={'''int_list''': lambda lowerCamelCase_: [int(lowerCamelCase_) for i in x.split()]})
				lowerCAmelCase__					:    str												=					csv._generate_tables([[csv_file_with_int_list]])
				lowerCAmelCase__					:    Dict												=					pa.concat_tables([table for _, table in generator])
				assert pa.types.is_list(pa_table.schema.field('''int_list''').type)
				lowerCAmelCase__					:    str												=					pa_table.to_pydict()['''int_list''']
				assert generated_content == [[1, 2, 3], [4, 5, 6], [7, 8, 9]]






import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin



@skip_mps
class 	lowerCamelCase__      (							lowerCamelCase__				,    lowerCamelCase__				,    unittest.TestCase):




		'''simple docstring'''



		snake_case_     =IFInpaintingSuperResolutionPipeline
		snake_case_     =TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""width""", """height"""}
		snake_case_     =TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({"""original_image"""})
		snake_case_     =PipelineTesterMixin.required_optional_params - {"""latents"""}



		def 		lowerCAmelCase__				(self			)					->      Union[str, Any]:

						"""simple docstring"""






						return self._get_superresolution_dummy_components()



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase=0			)					->      Dict:

						"""simple docstring"""






						if str(__lowerCamelCase			).startswith('''mps'''			):
										lowerCAmelCase__					:    Union[str, Any]												=					torch.manual_seed(__lowerCamelCase			)
						else:
										lowerCAmelCase__					:    int												=					torch.Generator(device=__lowerCamelCase			).manual_seed(__lowerCamelCase			)

						lowerCAmelCase__					:    str												=					floats_tensor((1, 3, 16, 16)		,rng=random.Random(__lowerCamelCase			)			).to(__lowerCamelCase			)
						lowerCAmelCase__					:    Optional[Any]												=					floats_tensor((1, 3, 32, 32)		,rng=random.Random(__lowerCamelCase			)			).to(__lowerCamelCase			)
						lowerCAmelCase__					:    Union[str, Any]												=					floats_tensor((1, 3, 32, 32)		,rng=random.Random(__lowerCamelCase			)			).to(__lowerCamelCase			)

						lowerCAmelCase__					:    Dict												=					{
						    '''prompt''': '''A painting of a squirrel eating a burger''',
						    '''image''': image,
						    '''original_image''': original_image,
						    '''mask_image''': mask_image,
						    '''generator''': generator,
						    '''num_inference_steps''': 2,
						    '''output_type''': '''numpy''',
						}

						return inputs



		@unittest.skipIf(
		    torch_device != '''cuda''' or not is_xformers_available()		,reason='''XFormers attention is only available with CUDA and `xformers` installed'''		,)
		def 		lowerCAmelCase__				(self			)					->      int:

						"""simple docstring"""






						self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3			)



		def 		lowerCAmelCase__				(self			)					->      int:

						"""simple docstring"""






						self._test_save_load_optional_components()



		@unittest.skipIf(torch_device != '''cuda'''		,reason='''float16 requires CUDA'''			)
		def 		lowerCAmelCase__				(self			)					->      str:

						"""simple docstring"""






						super().test_save_load_floataa(expected_max_diff=1e-1			)



		def 		lowerCAmelCase__				(self			)					->      Any:

						"""simple docstring"""






						self._test_attention_slicing_forward_pass(expected_max_diff=1e-2			)



		def 		lowerCAmelCase__				(self			)					->      Optional[Any]:

						"""simple docstring"""






						self._test_save_load_local()



		def 		lowerCAmelCase__				(self			)					->      Any:

						"""simple docstring"""






						self._test_inference_batch_single_identical(
						    expected_max_diff=1e-2		,)

