

import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()

class   _a					(			unittest.TestCase				):




      """simple docstring"""

      @property
      def 		__A	(   self							:				List[Any]	):
            torch.manual_seed(0	)
            A_						  =				UNetaDModel(
                block_out_channels=(32, 64)						,			layers_per_block=2						,			sample_size=32						,			in_channels=3						,			out_channels=3						,			down_block_types=("DownBlock2D", "AttnDownBlock2D")						,			up_block_types=("AttnUpBlock2D", "UpBlock2D")						,			)
            return model





      @property
      def 		__A	(   self							:				int	):
            torch.manual_seed(0	)
            A_						  =				VQModel(
                block_out_channels=[32, 64]						,			in_channels=3						,			out_channels=3						,			down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]						,			up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]						,			latent_channels=3						,			)
            return model





      @property
      def 		__A	(   self							:				Optional[Any]	):
            torch.manual_seed(0	)
            A_						  =				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						,			)
            return CLIPTextModel(UpperCAmelCase	)







      def 		__A	(   self							:				Optional[int]	):
            A_						  =				self.dummy_uncond_unet
            A_						  =				DDIMScheduler()
            A_						  =				self.dummy_vq_model

            A_						  =				LDMPipeline(unet=UpperCAmelCase						,			vqvae=UpperCAmelCase						,			scheduler=UpperCAmelCase	)
            ldm.to(UpperCAmelCase	)
            ldm.set_progress_bar_config(disable=UpperCAmelCase	)

            A_						  =				torch.manual_seed(0	)
            A_						  =				ldm(generator=UpperCAmelCase						,			num_inference_steps=2						,			output_type="numpy"	).images

            A_						  =				torch.manual_seed(0	)
            A_						  =				ldm(generator=UpperCAmelCase						,			num_inference_steps=2						,			output_type="numpy"						,			return_dict=UpperCAmelCase	)[0]

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

            assert image.shape == (1, 64, 64, 3)
            A_						  =				np.array([0.8_512, 0.818, 0.6_411, 0.6_808, 0.4_465, 0.5_618, 0.46, 0.6_231, 0.5_172]	)
            A_						  =				1E-2 if torch_device != "mps" else 3E-2

            assert np.abs(image_slice.flatten() - expected_slice	).max() < tolerance
            assert np.abs(image_from_tuple_slice.flatten() - expected_slice	).max() < tolerance




@slow
@require_torch
class   _a					(			unittest.TestCase				):




      """simple docstring"""

      def 		__A	(   self							:				Optional[int]	):
            A_						  =				LDMPipeline.from_pretrained("CompVis/ldm-celebahq-256"	)
            ldm.to(UpperCAmelCase	)
            ldm.set_progress_bar_config(disable=UpperCAmelCase	)

            A_						  =				torch.manual_seed(0	)
            A_						  =				ldm(generator=UpperCAmelCase						,			num_inference_steps=5						,			output_type="numpy"	).images

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

            assert image.shape == (1, 256, 256, 3)
            A_						  =				np.array([0.4_399, 0.44_975, 0.46_825, 0.474, 0.4_359, 0.4_581, 0.45_095, 0.4_341, 0.4_447]	)
            A_						  =				1E-2 if torch_device != "mps" else 3E-2

            assert np.abs(image_slice.flatten() - expected_slice	).max() < tolerance






"""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 						__lowercase	(  snake_case_    :    Optional[int]		)  ->str:



    '''simple docstring'''







    __A       :		Tuple							=	tmp_path / '''file.csv'''
    __A       :		Union[str, Any]							=	textwrap.dedent(
        '''\
        header1,header2
        1,2
        10,20
        '''		)
    with open(snake_case_							,'''w'''		) as f:
        f.write(snake_case_		)
    return str(snake_case_		)







@pytest.fixture
def 						__lowercase	(  snake_case_    :    str		)  ->Optional[Any]:



    '''simple docstring'''







    __A       :		List[str]							=	tmp_path / '''malformed_file.csv'''
    __A       :		Optional[Any]							=	textwrap.dedent(
        '''\
        header1,header2
        1,2
        10,20,
        '''		)
    with open(snake_case_							,'''w'''		) as f:
        f.write(snake_case_		)
    return str(snake_case_		)







@pytest.fixture
def 						__lowercase	(  snake_case_    :    Any							,snake_case_    :    Optional[int]		)  ->Optional[Any]:



    '''simple docstring'''







    __A       :		List[str]							=	tmp_path / '''csv_with_image.csv'''
    __A       :		List[str]							=	textwrap.dedent(
        F"""\
        image
        {image_file}
        """		)
    with open(snake_case_							,'''w'''		) as f:
        f.write(snake_case_		)
    return str(snake_case_		)







@pytest.fixture
def 						__lowercase	(  snake_case_    :    Optional[int]		)  ->Tuple:



    '''simple docstring'''







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







@pytest.fixture
def 						__lowercase	(  snake_case_    :    Dict		)  ->Optional[Any]:



    '''simple docstring'''







    __A       :		Optional[int]							=	tmp_path / '''csv_with_int_list.csv'''
    __A       :		Union[str, Any]							=	textwrap.dedent(
        '''\
        int_list
        1 2 3
        4 5 6
        7 8 9
        '''		)
    with open(snake_case_							,'''w'''		) as f:
        f.write(snake_case_		)
    return str(snake_case_		)







def 						__lowercase	(  snake_case_    :    List[str]							,snake_case_    :    Optional[int]							,snake_case_    :    Optional[Any]		)  ->Dict:



    '''simple docstring'''







    __A       :		str							=	Csv()
    __A       :		List[Any]							=	csv._generate_tables([[csv_file, malformed_csv_file]]		)
    with pytest.raises(snake_case_							,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(snake_case_		) in record.message
        for record in caplog.records		)







@require_pil
def 						__lowercase	(  snake_case_    :    Optional[int]		)  ->Union[str, Any]:



    '''simple docstring'''







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







def 						__lowercase	(  snake_case_    :    Any		)  ->List[Any]:



    '''simple docstring'''







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







def 						__lowercase	(  snake_case_    :    str		)  ->Dict:



    '''simple docstring'''







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




