






import gc
import unittest

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

from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
class      UpperCamelCase (				snake_case__			,       snake_case__			,       snake_case__			,       unittest.TestCase ):







		"""simple docstring"""



		snake_case    =   AltDiffusionPipeline
		snake_case    =   TEXT_TO_IMAGE_PARAMS
		snake_case    =   TEXT_TO_IMAGE_BATCH_PARAMS
		snake_case    =   TEXT_TO_IMAGE_IMAGE_PARAMS
		snake_case    =   TEXT_TO_IMAGE_IMAGE_PARAMS
		def       A(  self		:       Any		)    ->   Union[str, Any]:


						'''simple docstring'''
						torch.manual_seed(0		)
						A	 =      UNetaDConditionModel(
						    block_out_channels=(3_2, 6_4)						,layers_per_block=2						,sample_size=3_2						,in_channels=4						,out_channels=4						,down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')						,up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')						,cross_attention_dim=3_2						,)
						A	 =      DDIMScheduler(
						    beta_start=0.00085						,beta_end=0.012						,beta_schedule='scaled_linear'						,clip_sample=_SCREAMING_SNAKE_CASE						,set_alpha_to_one=_SCREAMING_SNAKE_CASE						,)
						torch.manual_seed(0		)
						A	 =      AutoencoderKL(
						    block_out_channels=[3_2, 6_4]						,in_channels=3						,out_channels=3						,down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']						,up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']						,latent_channels=4						,)

						# TODO: address the non-deterministic text encoder (fails for save-load tests)
						# torch.manual_seed(0)
						# text_encoder_config = RobertaSeriesConfig(
						#     hidden_size=32,
						#     project_dim=32,
						#     intermediate_size=37,
						#     layer_norm_eps=1e-05,
						#     num_attention_heads=4,
						#     num_hidden_layers=5,
						#     vocab_size=5002,
						# )
						# text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)

						torch.manual_seed(0		)
						A	 =      CLIPTextConfig(
						    bos_token_id=0						,eos_token_id=2						,hidden_size=3_2						,projection_dim=3_2						,intermediate_size=3_7						,layer_norm_eps=1E-05						,num_attention_heads=4						,num_hidden_layers=5						,pad_token_id=1						,vocab_size=5_0_0_2						,)
						A	 =      CLIPTextModel(_SCREAMING_SNAKE_CASE		)

						A	 =      XLMRobertaTokenizer.from_pretrained('hf-internal-testing/tiny-xlm-roberta'		)
						A	 =      7_7

						A	 =      {
						    'unet': unet,
						    'scheduler': scheduler,
						    'vae': vae,
						    'text_encoder': text_encoder,
						    'tokenizer': tokenizer,
						    'safety_checker': None,
						    'feature_extractor': None,
						}
						return components
		def       A(  self		:       Dict						,_SCREAMING_SNAKE_CASE		:       str						,_SCREAMING_SNAKE_CASE		:       Union[str, Any]=0		)    ->   Optional[int]:


						'''simple docstring'''
						if str(_SCREAMING_SNAKE_CASE		).startswith('mps'		):
										A	 =      torch.manual_seed(_SCREAMING_SNAKE_CASE		)
						else:
										A	 =      torch.Generator(device=_SCREAMING_SNAKE_CASE		).manual_seed(_SCREAMING_SNAKE_CASE		)
						A	 =      {
						    'prompt': 'A painting of a squirrel eating a burger',
						    'generator': generator,
						    'num_inference_steps': 2,
						    'guidance_scale': 6.0,
						    'output_type': 'numpy',
						}
						return inputs
		def       A(  self		:       Optional[int]		)    ->   Tuple:


						'''simple docstring'''
						super().test_attention_slicing_forward_pass(expected_max_diff=3E-3		)
		def       A(  self		:       Any		)    ->   Dict:


						'''simple docstring'''
						super().test_inference_batch_single_identical(expected_max_diff=3E-3		)
		def       A(  self		:       List[str]		)    ->   int:


						'''simple docstring'''
						A	 =      'cpu'  # ensure determinism for the device-dependent torch.Generator

						A	 =      self.get_dummy_components()
						torch.manual_seed(0		)
						A	 =      RobertaSeriesConfig(
						    hidden_size=3_2						,project_dim=3_2						,intermediate_size=3_7						,layer_norm_eps=1E-05						,num_attention_heads=4						,num_hidden_layers=5						,vocab_size=5_0_0_2						,)
						# TODO: remove after fixing the non-deterministic text encoder
						A	 =      RobertaSeriesModelWithTransformation(_SCREAMING_SNAKE_CASE		)
						A	 =      text_encoder

						A	 =      AltDiffusionPipeline(**_SCREAMING_SNAKE_CASE		)
						A	 =      alt_pipe.to(_SCREAMING_SNAKE_CASE		)
						alt_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

						A	 =      self.get_dummy_inputs(_SCREAMING_SNAKE_CASE		)
						A	 =      'A photo of an astronaut'
						A	 =      alt_pipe(**_SCREAMING_SNAKE_CASE		)
						A	 =      output.images
						A	 =      image[0, -3:, -3:, -1]

						assert image.shape == (1, 6_4, 6_4, 3)
						A	 =      np.array(
						    [0.5748162, 0.60447145, 0.48821217, 0.50100636, 0.5431185, 0.45763683, 0.49657696, 0.48132733, 0.47573093]		)

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







		def       A(  self		:       Any		)    ->   Union[str, Any]:


						'''simple docstring'''
						A	 =      'cpu'  # ensure determinism for the device-dependent torch.Generator

						A	 =      self.get_dummy_components()
						A	 =      PNDMScheduler(skip_prk_steps=_SCREAMING_SNAKE_CASE		)
						torch.manual_seed(0		)
						A	 =      RobertaSeriesConfig(
						    hidden_size=3_2						,project_dim=3_2						,intermediate_size=3_7						,layer_norm_eps=1E-05						,num_attention_heads=4						,num_hidden_layers=5						,vocab_size=5_0_0_2						,)
						# TODO: remove after fixing the non-deterministic text encoder
						A	 =      RobertaSeriesModelWithTransformation(_SCREAMING_SNAKE_CASE		)
						A	 =      text_encoder
						A	 =      AltDiffusionPipeline(**_SCREAMING_SNAKE_CASE		)
						A	 =      alt_pipe.to(_SCREAMING_SNAKE_CASE		)
						alt_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

						A	 =      self.get_dummy_inputs(_SCREAMING_SNAKE_CASE		)
						A	 =      alt_pipe(**_SCREAMING_SNAKE_CASE		)
						A	 =      output.images
						A	 =      image[0, -3:, -3:, -1]

						assert image.shape == (1, 6_4, 6_4, 3)
						A	 =      np.array(
						    [0.51605093, 0.5707241, 0.47365507, 0.50578886, 0.5633877, 0.4642503, 0.5182081, 0.48763484, 0.49084237]		)

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


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







		"""simple docstring"""



		def       A(  self		:       Tuple		)    ->   Dict:


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


						'''simple docstring'''
						# make sure here that pndm scheduler skips prk
						A	 =      AltDiffusionPipeline.from_pretrained('BAAI/AltDiffusion'						,safety_checker=_SCREAMING_SNAKE_CASE		)
						A	 =      alt_pipe.to(_SCREAMING_SNAKE_CASE		)
						alt_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

						A	 =      'A painting of a squirrel eating a burger'
						A	 =      torch.manual_seed(0		)
						A	 =      alt_pipe([prompt]						,generator=_SCREAMING_SNAKE_CASE						,guidance_scale=6.0						,num_inference_steps=2_0						,output_type='np'		)

						A	 =      output.images

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

						assert image.shape == (1, 5_1_2, 5_1_2, 3)
						A	 =      np.array([0.1010, 0.0800, 0.0794, 0.0885, 0.0843, 0.0762, 0.0769, 0.0729, 0.0586]		)

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







		def       A(  self		:       List[Any]		)    ->   List[str]:


						'''simple docstring'''
						A	 =      DDIMScheduler.from_pretrained('BAAI/AltDiffusion'						,subfolder='scheduler'		)

						A	 =      AltDiffusionPipeline.from_pretrained('BAAI/AltDiffusion'						,scheduler=_SCREAMING_SNAKE_CASE						,safety_checker=_SCREAMING_SNAKE_CASE		)
						A	 =      alt_pipe.to(_SCREAMING_SNAKE_CASE		)
						alt_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

						A	 =      'A painting of a squirrel eating a burger'
						A	 =      torch.manual_seed(0		)

						A	 =      alt_pipe([prompt]						,generator=_SCREAMING_SNAKE_CASE						,num_inference_steps=2						,output_type='numpy'		)
						A	 =      output.images

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

						assert image.shape == (1, 5_1_2, 5_1_2, 3)
						A	 =      np.array([0.4019, 0.4052, 0.3810, 0.4119, 0.3916, 0.3982, 0.4651, 0.4195, 0.5323]		)

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













import os


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
lowerCAmelCase_           = '.'


if __name__ == "__main__":
					lowerCAmelCase_           = os.path.join(REPO_PATH, 'utils/documentation_tests.txt')
					lowerCAmelCase_           = []
					lowerCAmelCase_           = []
					with open(doctest_file_path) as fp:
										for line in fp:
															lowerCAmelCase_           = line.strip()
															lowerCAmelCase_           = os.path.join(REPO_PATH, line)
															if not (os.path.isfile(path) or os.path.isdir(path)):
																				non_existent_paths.append(line)
															all_paths.append(path)
					if len(non_existent_paths) > 0:
										lowerCAmelCase_           = '\n'.join(non_existent_paths)
										raise ValueError(f'''`utils/documentation_tests.txt` contains non-existent paths:\n{non_existent_paths}''')
					if all_paths != sorted(all_paths):
										raise ValueError('Files in `utils/documentation_tests.txt` are not in alphabetical order.')





