

'''simple docstring'''






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 						_A	(       A__ ):






 """simple docstring"""



 __lowercase				    =					tmp_path / '''file.csv'''
 __lowercase				    =					textwrap.dedent(
     '''\
        header1,header2
        1,2
        10,20
        ''' )
 with open(A__	,							'''w''' ) as f:
  f.write(A__ )
 return str(A__ )


@pytest.fixture
def 						_A	(       A__ ):






 """simple docstring"""



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


@pytest.fixture
def 						_A	(       A__	,							A__ ):






 """simple docstring"""



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


@pytest.fixture
def 						_A	(       A__ ):






 """simple docstring"""



 __lowercase				    =					tmp_path / '''csv_with_label.csv'''
 __lowercase				    =					textwrap.dedent(
     '''\
        label
        good
        bad
        good
        ''' )
 with open(A__	,							'''w''' ) as f:
  f.write(A__ )
 return str(A__ )


@pytest.fixture
def 						_A	(       A__ ):






 """simple docstring"""



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


def 						_A	(       A__	,							A__	,							A__ ):






 """simple docstring"""



 __lowercase				    =					Csv()
 __lowercase				    =					csv._generate_tables([[csv_file, malformed_csv_file]] )
 with pytest.raises(A__	,							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(A__ ) in record.message
     for record in caplog.records )


@require_pil
def 						_A	(       A__ ):






 """simple docstring"""



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


def 						_A	(       A__ ):






 """simple docstring"""



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






def 						_A	(       A__ ):






 """simple docstring"""



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








import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


UpperCamelCase__           =			False

class 							__SCREAMING_SNAKE_CASE       (  unittest.TestCase							):
							pass






@nightly
@require_torch_gpu
class 							__SCREAMING_SNAKE_CASE       (  unittest.TestCase							):






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






							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      VersatileDiffusionTextToImagePipeline.from_pretrained("""shi-labs/versatile-diffusion"""	)
														# remove text_unet
														pipe.remove_unused_weights()
														pipe.to(__lowerCAmelCase	)
														pipe.set_progress_bar_config(disable=__lowerCAmelCase	)

														UpperCamelCase__					  =      """A painting of a squirrel eating a burger """
														UpperCamelCase__					  =      torch.manual_seed(0	)
														UpperCamelCase__					  =      pipe(
														    prompt=__lowerCAmelCase	,			generator=__lowerCAmelCase	,			guidance_scale=7.5	,			num_inference_steps=2	,			output_type="""numpy"""	).images

														with tempfile.TemporaryDirectory() as tmpdirname:
																					pipe.save_pretrained(__lowerCAmelCase	)
																					UpperCamelCase__					  =      VersatileDiffusionTextToImagePipeline.from_pretrained(__lowerCAmelCase	)
														pipe.to(__lowerCAmelCase	)
														pipe.set_progress_bar_config(disable=__lowerCAmelCase	)

														UpperCamelCase__					  =      generator.manual_seed(0	)
														UpperCamelCase__					  =      pipe(
														    prompt=__lowerCAmelCase	,			generator=__lowerCAmelCase	,			guidance_scale=7.5	,			num_inference_steps=2	,			output_type="""numpy"""	).images

														assert np.abs(image - new_image	).sum() < 1E-5, "Models don't have the same forward pass"







							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      VersatileDiffusionTextToImagePipeline.from_pretrained(
														    """shi-labs/versatile-diffusion"""	,			torch_dtype=torch.floataa	)
														pipe.to(__lowerCAmelCase	)
														pipe.set_progress_bar_config(disable=__lowerCAmelCase	)

														UpperCamelCase__					  =      """A painting of a squirrel eating a burger """
														UpperCamelCase__					  =      torch.manual_seed(0	)
														UpperCamelCase__					  =      pipe(
														    prompt=__lowerCAmelCase	,			generator=__lowerCAmelCase	,			guidance_scale=7.5	,			num_inference_steps=50	,			output_type="""numpy"""	).images

														UpperCamelCase__					  =      image[0, 253:256, 253:256, -1]

														assert image.shape == (1, 512, 512, 3)
														UpperCamelCase__					  =      np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778]	)

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




