






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__	(					A_:						str		)	->				Optional[int]:







       """simple docstring"""






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







@pytest.fixture
def       lowercase__	(					A_:						Union[str, Any]		)	->				Dict:







       """simple docstring"""






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







@pytest.fixture
def       lowercase__	(					A_:						Union[str, Any]  ,      A_:						List[Any]		)	->				List[Any]:







       """simple docstring"""






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







@pytest.fixture
def       lowercase__	(					A_:						Optional[int]		)	->				List[str]:







       """simple docstring"""






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







@pytest.fixture
def       lowercase__	(					A_:						List[str]		)	->				Union[str, Any]:







       """simple docstring"""






       __UpperCAmelCase     				=tmp_path / """csv_with_int_list.csv"""
       __UpperCAmelCase     				=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       lowercase__	(					A_:						Dict  ,      A_:						Optional[Any]  ,      A_:						Union[str, Any]		)	->				Dict:







       """simple docstring"""






       __UpperCAmelCase     				=Csv()
       __UpperCAmelCase     				=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       lowercase__	(					A_:						Tuple		)	->				Union[str, Any]:







       """simple docstring"""






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







def       lowercase__	(					A_:						List[str]		)	->				Any:







       """simple docstring"""






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







def       lowercase__	(					A_:						List[str]		)	->				Optional[Any]:







       """simple docstring"""






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








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

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

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, 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, skip_mps

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class        lowerCAmelCase_  (     UpperCAmelCase      ,							UpperCAmelCase      ,							unittest.TestCase	):
       __UpperCAmelCase					=CycleDiffusionPipeline
       __UpperCAmelCase					=TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
           'negative_prompt',
           'height',
           'width',
           'negative_prompt_embeds',
       }
       __UpperCAmelCase					=PipelineTesterMixin.required_optional_params - {'latents'}
       __UpperCAmelCase					=TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'source_prompt'}	)
       __UpperCAmelCase					=IMAGE_TO_IMAGE_IMAGE_PARAMS
       __UpperCAmelCase					=IMAGE_TO_IMAGE_IMAGE_PARAMS






       def   UpperCamelCase						( self   )->      Dict:
            torch.manual_seed(0   )
            _A				=							UNetaDConditionModel(
                block_out_channels=(32, 64)			,     layers_per_block=2			,     sample_size=32			,     in_channels=4			,     out_channels=4			,     down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')			,     up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')			,     cross_attention_dim=32			,     )
            _A				=							DDIMScheduler(
                beta_start=0.0_0085			,     beta_end=0.012			,     beta_schedule='scaled_linear'			,     num_train_timesteps=1000			,     clip_sample=_UpperCamelCase			,     set_alpha_to_one=_UpperCamelCase			,     )
            torch.manual_seed(0   )
            _A				=							AutoencoderKL(
                block_out_channels=[32, 64]			,     in_channels=3			,     out_channels=3			,     down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']			,     up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']			,     latent_channels=4			,     )
            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			,     )
            _A				=							CLIPTextModel(_UpperCamelCase   )
            _A				=							CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'   )

            _A				=							{
                'unet': unet,
                'scheduler': scheduler,
                'vae': vae,
                'text_encoder': text_encoder,
                'tokenizer': tokenizer,
                'safety_checker': None,
                'feature_extractor': None,
            }
            return components






       def   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase=0   )->      List[Any]:
            _A				=							floats_tensor((1, 3, 32, 32)			,     rng=random.Random(_UpperCamelCase   )   ).to(_UpperCamelCase   )
            _A				=							image / 2 + 0.5
            if str(_UpperCamelCase   ).startswith('mps'   ):
                 _A				=							torch.manual_seed(_UpperCamelCase   )
            else:
                 _A				=							torch.Generator(device=_UpperCamelCase   ).manual_seed(_UpperCamelCase   )
            _A				=							{
                'prompt': 'An astronaut riding an elephant',
                'source_prompt': 'An astronaut riding a horse',
                'image': image,
                'generator': generator,
                'num_inference_steps': 2,
                'eta': 0.1,
                'strength': 0.8,
                'guidance_scale': 3,
                'source_guidance_scale': 1,
                'output_type': 'numpy',
            }
            return inputs






       def   UpperCamelCase						( self   )->      Any:
            _A				=							'cpu'  # ensure determinism for the device-dependent torch.Generator

            _A				=							self.get_dummy_components()
            _A				=							CycleDiffusionPipeline(**_UpperCamelCase   )
            _A				=							pipe.to(_UpperCamelCase   )
            pipe.set_progress_bar_config(disable=_UpperCamelCase   )

            _A				=							self.get_dummy_inputs(_UpperCamelCase   )
            _A				=							pipe(**_UpperCamelCase   )
            _A				=							output.images

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

            assert images.shape == (1, 32, 32, 3)
            _A				=							np.array([0.4459, 0.4943, 0.4544, 0.6643, 0.5474, 0.4327, 0.5701, 0.5959, 0.5179]   )

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






       @unittest.skipIf(torch_device != 'cuda'			,     'This test requires a GPU'   )
       def   UpperCamelCase						( self   )->      List[Any]:
            _A				=							self.get_dummy_components()
            for name, module in components.items():
                 if hasattr(_UpperCamelCase			,     'half'   ):
                      _A				=							module.half()
            _A				=							CycleDiffusionPipeline(**_UpperCamelCase   )
            _A				=							pipe.to(_UpperCamelCase   )
            pipe.set_progress_bar_config(disable=_UpperCamelCase   )

            _A				=							self.get_dummy_inputs(_UpperCamelCase   )
            _A				=							pipe(**_UpperCamelCase   )
            _A				=							output.images

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

            assert images.shape == (1, 32, 32, 3)
            _A				=							np.array([0.3506, 0.4543, 0.446, 0.4575, 0.5195, 0.4155, 0.5273, 0.518, 0.4116]   )

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






       @skip_mps
       def   UpperCamelCase						( self   )->      Any:
            return super().test_save_load_local()






       @unittest.skip('non-deterministic pipeline'   )
       def   UpperCamelCase						( self   )->      List[Any]:
            return super().test_inference_batch_single_identical()






       @skip_mps
       def   UpperCamelCase						( self   )->      int:
            return super().test_dict_tuple_outputs_equivalent()






       @skip_mps
       def   UpperCamelCase						( self   )->      Tuple:
            return super().test_save_load_optional_components()






       @skip_mps
       def   UpperCamelCase						( self   )->      Optional[Any]:
            return super().test_attention_slicing_forward_pass()






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






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






       def   UpperCamelCase						( self   )->      int:
            _A				=							load_image(
                'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
                '/cycle-diffusion/black_colored_car.png'   )
            _A				=							load_numpy(
                'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy'   )
            _A				=							init_image.resize((512, 512)   )

            _A				=							'CompVis/stable-diffusion-v1-4'
            _A				=							DDIMScheduler.from_pretrained(_UpperCamelCase			,     subfolder='scheduler'   )
            _A				=							CycleDiffusionPipeline.from_pretrained(
                _UpperCamelCase			,     scheduler=_UpperCamelCase			,     safety_checker=_UpperCamelCase			,     torch_dtype=torch.floataa			,     revision='fp16'   )

            pipe.to(_UpperCamelCase   )
            pipe.set_progress_bar_config(disable=_UpperCamelCase   )
            pipe.enable_attention_slicing()

            _A				=							'A black colored car'
            _A				=							'A blue colored car'

            _A				=							torch.manual_seed(0   )
            _A				=							pipe(
                prompt=_UpperCamelCase			,     source_prompt=_UpperCamelCase			,     image=_UpperCamelCase			,     num_inference_steps=100			,     eta=0.1			,     strength=0.85			,     guidance_scale=3			,     source_guidance_scale=1			,     generator=_UpperCamelCase			,     output_type='np'			,     )
            _A				=							output.images

            # the values aren't exactly equal, but the images look the same visually
            assert np.abs(image - expected_image   ).max() < 5e-1






       def   UpperCamelCase						( self   )->      Union[str, Any]:
            _A				=							load_image(
                'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
                '/cycle-diffusion/black_colored_car.png'   )
            _A				=							load_numpy(
                'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy'   )
            _A				=							init_image.resize((512, 512)   )

            _A				=							'CompVis/stable-diffusion-v1-4'
            _A				=							DDIMScheduler.from_pretrained(_UpperCamelCase			,     subfolder='scheduler'   )
            _A				=							CycleDiffusionPipeline.from_pretrained(_UpperCamelCase			,     scheduler=_UpperCamelCase			,     safety_checker=_UpperCamelCase   )

            pipe.to(_UpperCamelCase   )
            pipe.set_progress_bar_config(disable=_UpperCamelCase   )
            pipe.enable_attention_slicing()

            _A				=							'A black colored car'
            _A				=							'A blue colored car'

            _A				=							torch.manual_seed(0   )
            _A				=							pipe(
                prompt=_UpperCamelCase			,     source_prompt=_UpperCamelCase			,     image=_UpperCamelCase			,     num_inference_steps=100			,     eta=0.1			,     strength=0.85			,     guidance_scale=3			,     source_guidance_scale=1			,     generator=_UpperCamelCase			,     output_type='np'			,     )
            _A				=							output.images

            assert np.abs(image - expected_image   ).max() < 2e-2





