import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModel,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLIPImgaImgPipeline, UNetaDConditionModel
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    floats_tensor,
    load_image,
    load_numpy,
    require_torch_gpu,
    skip_mps,
    slow,
    torch_device,
)

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


enable_full_determinism()
class  A_			(							UpperCAmelCase__      ,							UpperCAmelCase__      ,							UpperCAmelCase__      ,							unittest.TestCase			):
  _SCREAMING_SNAKE_CASE     					=      StableUnCLIPImgaImgPipeline
  _SCREAMING_SNAKE_CASE     					=      TEXT_GUIDED_IMAGE_VARIATION_PARAMS
  _SCREAMING_SNAKE_CASE     					=      TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
  _SCREAMING_SNAKE_CASE     					=      frozenset(
      []			)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
  _SCREAMING_SNAKE_CASE     					=      frozenset([]			)

  def 		_UpperCAmelCase (		self			:   str							):
   __a							=	32
   __a							=	embedder_hidden_size

   # image encoding components

   __a							=	CLIPImageProcessor(crop_size=32  ,				size=32							)

   torch.manual_seed(0							)
   __a							=	CLIPVisionModelWithProjection(
       CLIPVisionConfig(
           hidden_size=_UpperCAmelCase  ,				projection_dim=_UpperCAmelCase  ,				num_hidden_layers=5  ,				num_attention_heads=4  ,				image_size=32  ,				intermediate_size=37  ,				patch_size=1  ,				)							)

   # regular denoising components

   torch.manual_seed(0							)
   __a							=	StableUnCLIPImageNormalizer(embedding_dim=_UpperCAmelCase							)
   __a							=	DDPMScheduler(beta_schedule="squaredcos_cap_v2"							)

   torch.manual_seed(0							)
   __a							=	CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"							)

   torch.manual_seed(0							)
   __a							=	CLIPTextModel(
       CLIPTextConfig(
           bos_token_id=0  ,				eos_token_id=2  ,				hidden_size=_UpperCAmelCase  ,				projection_dim=32  ,				intermediate_size=37  ,				layer_norm_eps=1E-05  ,				num_attention_heads=4  ,				num_hidden_layers=5  ,				pad_token_id=1  ,				vocab_size=10_00  ,				)							)

   torch.manual_seed(0							)
   __a							=	UNetaDConditionModel(
       sample_size=32  ,				in_channels=4  ,				out_channels=4  ,				down_block_types=("CrossAttnDownBlock2D", "DownBlock2D")  ,				up_block_types=("UpBlock2D", "CrossAttnUpBlock2D")  ,				block_out_channels=(32, 64)  ,				attention_head_dim=(2, 4)  ,				class_embed_type="projection"  ,				projection_class_embeddings_input_dim=embedder_projection_dim * 2  ,				cross_attention_dim=_UpperCAmelCase  ,				layers_per_block=1  ,				upcast_attention=_UpperCAmelCase  ,				use_linear_projection=_UpperCAmelCase  ,				)

   torch.manual_seed(0							)
   __a							=	DDIMScheduler(
       beta_schedule="scaled_linear"  ,				beta_start=0.0_00_85  ,				beta_end=0.0_12  ,				prediction_type="v_prediction"  ,				set_alpha_to_one=_UpperCAmelCase  ,				steps_offset=1  ,				)

   torch.manual_seed(0							)
   __a							=	AutoencoderKL()

   __a							=	{
       # image encoding components
       "feature_extractor": feature_extractor,
       "image_encoder": image_encoder.eval(),
       # image noising components
       "image_normalizer": image_normalizer.eval(),
       "image_noising_scheduler": image_noising_scheduler,
       # regular denoising components
       "tokenizer": tokenizer,
       "text_encoder": text_encoder.eval(),
       "unet": unet.eval(),
       "scheduler": scheduler,
       "vae": vae.eval(),
   }

   return components

  def 		_UpperCAmelCase (		self			:   Dict  ,				__SCREAMING_SNAKE_CASE			:   Tuple  ,				__SCREAMING_SNAKE_CASE			:   Tuple=0  ,				__SCREAMING_SNAKE_CASE			:   Tuple=True							):
   if str(_UpperCAmelCase							).startswith("mps"							):
    __a							=	torch.manual_seed(_UpperCAmelCase							)
   else:
    __a							=	torch.Generator(device=_UpperCAmelCase							).manual_seed(_UpperCAmelCase							)

   __a							=	floats_tensor((1, 3, 32, 32)  ,				rng=random.Random(_UpperCAmelCase							)							).to(_UpperCAmelCase							)

   if pil_image:
    __a							=	input_image * 0.5 + 0.5
    __a							=	input_image.clamp(0  ,				1							)
    __a							=	input_image.cpu().permute(0  ,				2  ,				3  ,				1							).float().numpy()
    __a							=	DiffusionPipeline.numpy_to_pil(_UpperCAmelCase							)[0]

   return {
       "prompt": "An anime racoon running a marathon",
       "image": input_image,
       "generator": generator,
       "num_inference_steps": 2,
       "output_type": "np",
   }

  @skip_mps
  def 		_UpperCAmelCase (		self			:   Union[str, Any]							):
   __a							=	"cpu"  # ensure determinism for the device-dependent torch.Generator
   __a							=	self.get_dummy_components()
   __a							=	StableUnCLIPImgaImgPipeline(**_UpperCAmelCase							)
   __a							=	sd_pipe.to(_UpperCAmelCase							)
   sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase							)

   __a							=	self.get_dummy_inputs(_UpperCAmelCase							)
   inputs.update({"image_embeds": None}							)
   __a							=	sd_pipe(**_UpperCAmelCase							).images
   __a							=	image[0, -3:, -3:, -1]

   assert image.shape == (1, 32, 32, 3)
   __a							=	np.array([0.38_72, 0.72_24, 0.56_01, 0.47_41, 0.68_72, 0.58_14, 0.46_36, 0.38_67, 0.50_78]							)

   assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-3

  def 		_UpperCAmelCase (		self			:   Optional[Any]							):
   __a							=	torch_device in ["cpu", "mps"]

   self._test_attention_slicing_forward_pass(test_max_difference=_UpperCAmelCase							)

  def 		_UpperCAmelCase (		self			:   str							):
   __a							=	torch_device in ["cpu", "mps"]

   self._test_inference_batch_single_identical(test_max_difference=_UpperCAmelCase							)

  @unittest.skipIf(
      torch_device != "cuda" or not is_xformers_available()  ,				reason="XFormers attention is only available with CUDA and `xformers` installed"  ,				)
  def 		_UpperCAmelCase (		self			:   List[Any]							):
   self._test_xformers_attention_forwardGenerator_pass(test_max_difference=_UpperCAmelCase							)
@slow
@require_torch_gpu
class  A_			(							unittest.TestCase			):

  def 		_UpperCAmelCase (		self			:   List[Any]							):
   # clean up the VRAM after each test
   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()

  def 		_UpperCAmelCase (		self			:   Union[str, Any]							):
   __a							=	load_image(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png"							)

   __a							=	load_numpy(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_img2img_anime_turtle_fp16.npy"							)

   __a							=	StableUnCLIPImgaImgPipeline.from_pretrained(
       "fusing/stable-unclip-2-1-l-img2img"  ,				torch_dtype=torch.floataa							)
   pipe.to(_UpperCAmelCase							)
   pipe.set_progress_bar_config(disable=_UpperCAmelCase							)
   # stable unclip will oom when integration tests are run on a V100,
   # so turn on memory savings
   pipe.enable_attention_slicing()
   pipe.enable_sequential_cpu_offload()

   __a							=	torch.Generator(device="cpu"							).manual_seed(0							)
   __a							=	pipe(_UpperCAmelCase  ,				"anime turle"  ,				generator=_UpperCAmelCase  ,				output_type="np"							)

   __a							=	output.images[0]

   assert image.shape == (7_68, 7_68, 3)

   assert_mean_pixel_difference(_UpperCAmelCase  ,				_UpperCAmelCase							)

  def 		_UpperCAmelCase (		self			:   Union[str, Any]							):
   __a							=	load_image(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png"							)

   __a							=	load_numpy(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_h_img2img_anime_turtle_fp16.npy"							)

   __a							=	StableUnCLIPImgaImgPipeline.from_pretrained(
       "fusing/stable-unclip-2-1-h-img2img"  ,				torch_dtype=torch.floataa							)
   pipe.to(_UpperCAmelCase							)
   pipe.set_progress_bar_config(disable=_UpperCAmelCase							)
   # stable unclip will oom when integration tests are run on a V100,
   # so turn on memory savings
   pipe.enable_attention_slicing()
   pipe.enable_sequential_cpu_offload()

   __a							=	torch.Generator(device="cpu"							).manual_seed(0							)
   __a							=	pipe(_UpperCAmelCase  ,				"anime turle"  ,				generator=_UpperCAmelCase  ,				output_type="np"							)

   __a							=	output.images[0]

   assert image.shape == (7_68, 7_68, 3)

   assert_mean_pixel_difference(_UpperCAmelCase  ,				_UpperCAmelCase							)

  def 		_UpperCAmelCase (		self			:   Tuple							):
   __a							=	load_image(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png"							)

   torch.cuda.empty_cache()
   torch.cuda.reset_max_memory_allocated()
   torch.cuda.reset_peak_memory_stats()

   __a							=	StableUnCLIPImgaImgPipeline.from_pretrained(
       "fusing/stable-unclip-2-1-h-img2img"  ,				torch_dtype=torch.floataa							)
   __a							=	pipe.to(_UpperCAmelCase							)
   pipe.set_progress_bar_config(disable=_UpperCAmelCase							)
   pipe.enable_attention_slicing()
   pipe.enable_sequential_cpu_offload()

   __a							=	pipe(
       _UpperCAmelCase  ,				"anime turtle"  ,				num_inference_steps=2  ,				output_type="np"  ,				)

   __a							=	torch.cuda.max_memory_allocated()
   # make sure that less than 7 GB is allocated
   assert mem_bytes < 7 * 10**9










import re
from pathlib import Path
from unittest import TestCase

import pytest


@pytest.mark.integration
class     _a							(	UpperCAmelCase__     ):






  """simple docstring"""





  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			Dict:

     with open(_UpperCAmelCase		,		encoding='utf-8'			) as input_file:
        UpperCamelCase_						=  re.compile(R'(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)'			)
        UpperCamelCase_						=  input_file.read()
        UpperCamelCase_						=  regexp.search(_UpperCAmelCase			)

     return match




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			Dict:

     with open(_UpperCAmelCase		,		encoding='utf-8'			) as input_file:
        UpperCamelCase_						=  re.compile(R'#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()'		,		re.DOTALL			)
        UpperCamelCase_						=  input_file.read()
        # use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
        UpperCamelCase_						=  regexp.finditer(_UpperCAmelCase			)

     UpperCamelCase_						=  [match for match in matches if match is not None and match.group(1			) is not None]
     return matches[0] if matches else None




  def  _UpperCAmelCase  (  self			)      ->			List[str]:
     UpperCamelCase_						=  Path('./datasets'			)
     UpperCamelCase_						=  list(dataset_paths.absolute().glob('**/*.py'			)			)

     for dataset in dataset_files:
        if self._no_encoding_on_file_open(str(_UpperCAmelCase			)			):
           raise AssertionError(f"""open(...) must use utf-8 encoding in {dataset}"""			)




  def  _UpperCAmelCase  (  self			)      ->			Optional[int]:
     UpperCamelCase_						=  Path('./datasets'			)
     UpperCamelCase_						=  list(dataset_paths.absolute().glob('**/*.py'			)			)

     for dataset in dataset_files:
        if self._no_print_statements(str(_UpperCAmelCase			)			):
           raise AssertionError(f"""print statement found in {dataset}. Use datasets.logger/logging instead."""			)





