'''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 				_lowercase     (				__UpperCAmelCase  ,							__UpperCAmelCase  ,							unittest.TestCase			):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       List[str]      	=					CycleDiffusionPipeline
 _SCREAMING_SNAKE_CASE     :       str      	=					TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
     '''negative_prompt''',
     '''height''',
     '''width''',
     '''negative_prompt_embeds''',
 }
 _SCREAMING_SNAKE_CASE     :       Any      	=					PipelineTesterMixin.required_optional_params - {'''latents'''}
 _SCREAMING_SNAKE_CASE     :       Tuple      	=					TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""source_prompt"""}			)
 _SCREAMING_SNAKE_CASE     :       Union[str, Any]      	=					IMAGE_TO_IMAGE_IMAGE_PARAMS
 _SCREAMING_SNAKE_CASE     :       Any      	=					IMAGE_TO_IMAGE_IMAGE_PARAMS
 def a					(      self   :	Tuple							)  ->    List[Any]:
  torch.manual_seed(0							)
  __lowerCAmelCase											=						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      ,       )
  __lowerCAmelCase											=						DDIMScheduler(
      beta_start=0.0_0_0_8_5      ,       beta_end=0.0_1_2      ,       beta_schedule="""scaled_linear"""      ,       num_train_timesteps=10_00      ,       clip_sample=__SCREAMING_SNAKE_CASE      ,       set_alpha_to_one=__SCREAMING_SNAKE_CASE      ,       )
  torch.manual_seed(0							)
  __lowerCAmelCase											=						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							)
  __lowerCAmelCase											=						CLIPTextConfig(
      bos_token_id=0      ,       eos_token_id=2      ,       hidden_size=32      ,       intermediate_size=37      ,       layer_norm_eps=1e-0_5      ,       num_attention_heads=4      ,       num_hidden_layers=5      ,       pad_token_id=1      ,       vocab_size=10_00      ,       )
  __lowerCAmelCase											=						CLIPTextModel(__SCREAMING_SNAKE_CASE							)
  __lowerCAmelCase											=						CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""							)

  __lowerCAmelCase											=						{
      """unet""": unet,
      """scheduler""": scheduler,
      """vae""": vae,
      """text_encoder""": text_encoder,
      """tokenizer""": tokenizer,
      """safety_checker""": None,
      """feature_extractor""": None,
  }
  return components
 def a					(      self   :	Optional[int]      ,       SCREAMING_SNAKE_CASE__   :	int      ,       SCREAMING_SNAKE_CASE__   :	Dict=0							)  ->    List[str]:
  __lowerCAmelCase											=						floats_tensor((1, 3, 32, 32)      ,       rng=random.Random(__SCREAMING_SNAKE_CASE							)							).to(__SCREAMING_SNAKE_CASE							)
  __lowerCAmelCase											=						image / 2 + 0.5
  if str(__SCREAMING_SNAKE_CASE							).startswith("""mps"""							):
   __lowerCAmelCase											=						torch.manual_seed(__SCREAMING_SNAKE_CASE							)
  else:
   __lowerCAmelCase											=						torch.Generator(device=__SCREAMING_SNAKE_CASE							).manual_seed(__SCREAMING_SNAKE_CASE							)
  __lowerCAmelCase											=						{
      """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 a					(      self   :	Dict							)  ->    Optional[int]:
  __lowerCAmelCase											=						"""cpu"""  # ensure determinism for the device-dependent torch.Generator

  __lowerCAmelCase											=						self.get_dummy_components()
  __lowerCAmelCase											=						CycleDiffusionPipeline(**__SCREAMING_SNAKE_CASE							)
  __lowerCAmelCase											=						pipe.to(__SCREAMING_SNAKE_CASE							)
  pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE							)

  __lowerCAmelCase											=						self.get_dummy_inputs(__SCREAMING_SNAKE_CASE							)
  __lowerCAmelCase											=						pipe(**__SCREAMING_SNAKE_CASE							)
  __lowerCAmelCase											=						output.images

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

  assert images.shape == (1, 32, 32, 3)
  __lowerCAmelCase											=						np.array([0.4_4_5_9, 0.4_9_4_3, 0.4_5_4_4, 0.6_6_4_3, 0.5_4_7_4, 0.4_3_2_7, 0.5_7_0_1, 0.5_9_5_9, 0.5_1_7_9]							)

  assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2
 @unittest.skipIf(torch_device != """cuda"""      ,       """This test requires a GPU"""							)
 def a					(      self   :	List[Any]							)  ->    List[Any]:
  __lowerCAmelCase											=						self.get_dummy_components()
  for name, module in components.items():
   if hasattr(__SCREAMING_SNAKE_CASE      ,       """half"""							):
    __lowerCAmelCase											=						module.half()
  __lowerCAmelCase											=						CycleDiffusionPipeline(**__SCREAMING_SNAKE_CASE							)
  __lowerCAmelCase											=						pipe.to(__SCREAMING_SNAKE_CASE							)
  pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE							)

  __lowerCAmelCase											=						self.get_dummy_inputs(__SCREAMING_SNAKE_CASE							)
  __lowerCAmelCase											=						pipe(**__SCREAMING_SNAKE_CASE							)
  __lowerCAmelCase											=						output.images

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

  assert images.shape == (1, 32, 32, 3)
  __lowerCAmelCase											=						np.array([0.3_5_0_6, 0.4_5_4_3, 0.4_4_6, 0.4_5_7_5, 0.5_1_9_5, 0.4_1_5_5, 0.5_2_7_3, 0.5_1_8, 0.4_1_1_6]							)

  assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2
 @skip_mps
 def a					(      self   :	Union[str, Any]							)  ->    Optional[int]:
  return super().test_save_load_local()
 @unittest.skip("""non-deterministic pipeline"""							)
 def a					(      self   :	Optional[Any]							)  ->    Union[str, Any]:
  return super().test_inference_batch_single_identical()
 @skip_mps
 def a					(      self   :	Tuple							)  ->    Union[str, Any]:
  return super().test_dict_tuple_outputs_equivalent()
 @skip_mps
 def a					(      self   :	List[Any]							)  ->    int:
  return super().test_save_load_optional_components()





 @skip_mps
 def a					(      self   :	int							)  ->    str:
  return super().test_attention_slicing_forward_pass()







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


 '''simple docstring'''






 def a					(      self   :	List[str]							)  ->    List[Any]:
  super().tearDown()
  gc.collect()
  torch.cuda.empty_cache()
 def a					(      self   :	List[Any]							)  ->    Tuple:
  __lowerCAmelCase											=						load_image(
      """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
      """/cycle-diffusion/black_colored_car.png"""							)
  __lowerCAmelCase											=						load_numpy(
      """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy"""							)
  __lowerCAmelCase											=						init_image.resize((5_12, 5_12)							)

  __lowerCAmelCase											=						"""CompVis/stable-diffusion-v1-4"""
  __lowerCAmelCase											=						DDIMScheduler.from_pretrained(__SCREAMING_SNAKE_CASE      ,       subfolder="""scheduler"""							)
  __lowerCAmelCase											=						CycleDiffusionPipeline.from_pretrained(
      __SCREAMING_SNAKE_CASE      ,       scheduler=__SCREAMING_SNAKE_CASE      ,       safety_checker=__SCREAMING_SNAKE_CASE      ,       torch_dtype=torch.floataa      ,       revision="""fp16"""							)

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

  __lowerCAmelCase											=						"""A black colored car"""
  __lowerCAmelCase											=						"""A blue colored car"""

  __lowerCAmelCase											=						torch.manual_seed(0							)
  __lowerCAmelCase											=						pipe(
      prompt=__SCREAMING_SNAKE_CASE      ,       source_prompt=__SCREAMING_SNAKE_CASE      ,       image=__SCREAMING_SNAKE_CASE      ,       num_inference_steps=1_00      ,       eta=0.1      ,       strength=0.8_5      ,       guidance_scale=3      ,       source_guidance_scale=1      ,       generator=__SCREAMING_SNAKE_CASE      ,       output_type="""np"""      ,       )
  __lowerCAmelCase											=						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 a					(      self   :	Union[str, Any]							)  ->    int:
  __lowerCAmelCase											=						load_image(
      """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
      """/cycle-diffusion/black_colored_car.png"""							)
  __lowerCAmelCase											=						load_numpy(
      """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy"""							)
  __lowerCAmelCase											=						init_image.resize((5_12, 5_12)							)

  __lowerCAmelCase											=						"""CompVis/stable-diffusion-v1-4"""
  __lowerCAmelCase											=						DDIMScheduler.from_pretrained(__SCREAMING_SNAKE_CASE      ,       subfolder="""scheduler"""							)
  __lowerCAmelCase											=						CycleDiffusionPipeline.from_pretrained(__SCREAMING_SNAKE_CASE      ,       scheduler=__SCREAMING_SNAKE_CASE      ,       safety_checker=__SCREAMING_SNAKE_CASE							)

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

  __lowerCAmelCase											=						"""A black colored car"""
  __lowerCAmelCase											=						"""A blue colored car"""

  __lowerCAmelCase											=						torch.manual_seed(0							)
  __lowerCAmelCase											=						pipe(
      prompt=__SCREAMING_SNAKE_CASE      ,       source_prompt=__SCREAMING_SNAKE_CASE      ,       image=__SCREAMING_SNAKE_CASE      ,       num_inference_steps=1_00      ,       eta=0.1      ,       strength=0.8_5      ,       guidance_scale=3      ,       source_guidance_scale=1      ,       generator=__SCREAMING_SNAKE_CASE      ,       output_type="""np"""      ,       )
  __lowerCAmelCase											=						output.images

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











import os
from pathlib import Path
from unittest.mock import patch

import pytest
import zstandard as zstd

from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
    OfflineModeIsEnabled,
    cached_path,
    fsspec_get,
    fsspec_head,
    ftp_get,
    ftp_head,
    get_from_cache,
    http_get,
    http_head,
)


__snake_case						:List[str]      		=     '''\
    Text data.
    Second line of data.'''

__snake_case						:Optional[Any]      		=     '''file'''





@pytest.fixture(scope='''session'''				)
def      __snake_case	(			_UpperCAmelCase				):
    __a						       =				tmp_path_factory.mktemp('''data'''				) / (FILE_PATH + '''.zstd''')
    __a						       =				bytes(_UpperCAmelCase   , '''utf-8'''				)
    with zstd.open(_UpperCAmelCase   , '''wb'''				) as f:
        f.write(_UpperCAmelCase				)
    return path





@pytest.fixture
def      __snake_case	(			_UpperCAmelCase				):
    with open(os.path.join(tmpfs.local_root_dir   , _UpperCAmelCase				)   , '''w'''				) as f:
        f.write(_UpperCAmelCase				)
    return FILE_PATH





@pytest.mark.parametrize('''compression_format'''   , ['''gzip''', '''xz''', '''zstd''']				)
def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase				):
    __a						       =				{'''gzip''': gz_file, '''xz''': xz_file, '''zstd''': zstd_path}
    __a						       =				input_paths[compression_format]
    __a						       =				tmp_path / '''cache'''
    __a						       =				DownloadConfig(cache_dir=_UpperCAmelCase   , extract_compressed_file=_UpperCAmelCase				)
    __a						       =				cached_path(_UpperCAmelCase   , download_config=_UpperCAmelCase				)
    with open(_UpperCAmelCase				) as f:
        __a						       =				f.read()
    with open(_UpperCAmelCase				) as f:
        __a						       =				f.read()
    assert extracted_file_content == expected_file_content





@pytest.mark.parametrize('''default_extracted'''   , [True, False]				)
@pytest.mark.parametrize('''default_cache_dir'''   , [True, False]				)
def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase				):
    __a						       =				'''custom_cache'''
    __a						       =				'''custom_extracted_dir'''
    __a						       =				tmp_path / '''custom_extracted_path'''
    if default_extracted:
        __a						       =				('''downloads''' if default_cache_dir else custom_cache_dir, '''extracted''')
    else:
        monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_DIR'''   , _UpperCAmelCase				)
        monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_PATH'''   , str(_UpperCAmelCase				)				)
        __a						       =				custom_extracted_path.parts[-2:] if default_cache_dir else (custom_cache_dir, custom_extracted_dir)

    __a						       =				xz_file
    __a						       =				(
        DownloadConfig(extract_compressed_file=_UpperCAmelCase				)
        if default_cache_dir
        else DownloadConfig(cache_dir=tmp_path / custom_cache_dir   , extract_compressed_file=_UpperCAmelCase				)
    )
    __a						       =				cached_path(_UpperCAmelCase   , download_config=_UpperCAmelCase				)
    assert Path(_UpperCAmelCase				).parent.parts[-2:] == expected





def      __snake_case	(			_UpperCAmelCase				):
    # absolute path
    __a						       =				str(Path(_UpperCAmelCase				).resolve()				)
    assert cached_path(_UpperCAmelCase				) == text_file
    # relative path
    __a						       =				str(Path(_UpperCAmelCase				).resolve().relative_to(Path(os.getcwd()				)				)				)
    assert cached_path(_UpperCAmelCase				) == text_file





def      __snake_case	(			_UpperCAmelCase				):
    # absolute path
    __a						       =				str(tmp_path.resolve() / '''__missing_file__.txt'''				)
    with pytest.raises(_UpperCAmelCase				):
        cached_path(_UpperCAmelCase				)
    # relative path
    __a						       =				'''./__missing_file__.txt'''
    with pytest.raises(_UpperCAmelCase				):
        cached_path(_UpperCAmelCase				)





def      __snake_case	(			_UpperCAmelCase				):
    __a						       =				get_from_cache(f'tmp://{tmpfs_file}'				)
    with open(_UpperCAmelCase				) as f:
        __a						       =				f.read()
    assert output_file_content == FILE_CONTENT





@patch('''datasets.config.HF_DATASETS_OFFLINE'''   , _UpperCAmelCase				)
def      __snake_case	(			):
    with pytest.raises(_UpperCAmelCase				):
        cached_path('''https://huggingface.co'''				)





@patch('''datasets.config.HF_DATASETS_OFFLINE'''   , _UpperCAmelCase				)
def      __snake_case	(			_UpperCAmelCase				):
    __a						       =				tmp_path_factory.mktemp('''data'''				) / '''file.html'''
    with pytest.raises(_UpperCAmelCase				):
        http_get('''https://huggingface.co'''   , temp_file=_UpperCAmelCase				)
    with pytest.raises(_UpperCAmelCase				):
        http_head('''https://huggingface.co'''				)





@patch('''datasets.config.HF_DATASETS_OFFLINE'''   , _UpperCAmelCase				)
def      __snake_case	(			_UpperCAmelCase				):
    __a						       =				tmp_path_factory.mktemp('''data'''				) / '''file.html'''
    with pytest.raises(_UpperCAmelCase				):
        ftp_get('''ftp://huggingface.co'''   , temp_file=_UpperCAmelCase				)
    with pytest.raises(_UpperCAmelCase				):
        ftp_head('''ftp://huggingface.co'''				)





@patch('''datasets.config.HF_DATASETS_OFFLINE'''   , _UpperCAmelCase				)
def      __snake_case	(			_UpperCAmelCase				):
    __a						       =				tmp_path_factory.mktemp('''data'''				) / '''file.html'''
    with pytest.raises(_UpperCAmelCase				):
        fsspec_get('''s3://huggingface.co'''   , temp_file=_UpperCAmelCase				)
    with pytest.raises(_UpperCAmelCase				):
        fsspec_head('''s3://huggingface.co'''				)





