





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 					A__   (       __snake_case       ,   __snake_case       ,   __snake_case       ,   unittest.TestCase			):
      _UpperCAmelCase	:Union[str, Any]						    =   AltDiffusionPipeline
      _UpperCAmelCase	:Any						    =   TEXT_TO_IMAGE_PARAMS
      _UpperCAmelCase	:Tuple						    =   TEXT_TO_IMAGE_BATCH_PARAMS
      _UpperCAmelCase	:Any						    =   TEXT_TO_IMAGE_IMAGE_PARAMS
      _UpperCAmelCase	:Optional[Any]						    =   TEXT_TO_IMAGE_IMAGE_PARAMS





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       torch.manual_seed(0   )
       UpperCamelCase	:					str       	=   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       ,							)
       UpperCamelCase	:					int       	=   DDIMScheduler(
           beta_start=0.0_00_85       ,							beta_end=0.0_12       ,							beta_schedule="scaled_linear"       ,							clip_sample=A_       ,							set_alpha_to_one=A_       ,							)
       torch.manual_seed(0   )
       UpperCamelCase	:					List[str]       	=   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       ,							)

       # 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   )
       UpperCamelCase	:					int       	=   CLIPTextConfig(
           bos_token_id=0       ,							eos_token_id=2       ,							hidden_size=32       ,							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=5002       ,							)
       UpperCamelCase	:					Dict       	=   CLIPTextModel(A_   )

       UpperCamelCase	:					Optional[int]       	=   XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta"   )
       UpperCamelCase	:					Dict       	=   77

       UpperCamelCase	:					List[Any]       	=   {
           "unet": unet,
           "scheduler": scheduler,
           "vae": vae,
           "text_encoder": text_encoder,
           "tokenizer": tokenizer,
           "safety_checker": None,
           "feature_extractor": None,
       }
       return components





      def        __UpperCamelCase( self       ,							A_       ,							A_=0   ):
       '''simple docstring'''




       if str(A_   ).startswith("mps"   ):
        UpperCamelCase	:					Union[str, Any]       	=   torch.manual_seed(A_   )
       else:
        UpperCamelCase	:					List[Any]       	=   torch.Generator(device=A_   ).manual_seed(A_   )
       UpperCamelCase	:					Any       	=   {
           "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        __UpperCamelCase( self   ):
       '''simple docstring'''




       super().test_attention_slicing_forward_pass(expected_max_diff=3e-3   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       super().test_inference_batch_single_identical(expected_max_diff=3e-3   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					List[str]       	=   "cpu"  # ensure determinism for the device-dependent torch.Generator

       UpperCamelCase	:					Tuple       	=   self.get_dummy_components()
       torch.manual_seed(0   )
       UpperCamelCase	:					Optional[int]       	=   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       ,							)
       # TODO: remove after fixing the non-deterministic text encoder
       UpperCamelCase	:					List[Any]       	=   RobertaSeriesModelWithTransformation(A_   )
       UpperCamelCase	:					List[Any]       	=   text_encoder

       UpperCamelCase	:					str       	=   AltDiffusionPipeline(**A_   )
       UpperCamelCase	:					Optional[Any]       	=   alt_pipe.to(A_   )
       alt_pipe.set_progress_bar_config(disable=A_   )

       UpperCamelCase	:					List[str]       	=   self.get_dummy_inputs(A_   )
       UpperCamelCase	:					Tuple       	=   "A photo of an astronaut"
       UpperCamelCase	:					Dict       	=   alt_pipe(**A_   )
       UpperCamelCase	:					int       	=   output.images
       UpperCamelCase	:					Optional[int]       	=   image[0, -3:, -3:, -1]

       assert image.shape == (1, 64, 64, 3)
       UpperCamelCase	:					int       	=   np.array(
           [0.5_74_81_62, 0.60_44_71_45, 0.48_82_12_17, 0.50_10_06_36, 0.5_43_11_85, 0.45_76_36_83, 0.49_65_76_96, 0.48_13_27_33, 0.47_57_30_93]   )

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





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Any       	=   "cpu"  # ensure determinism for the device-dependent torch.Generator

       UpperCamelCase	:					int       	=   self.get_dummy_components()
       UpperCamelCase	:					Optional[Any]       	=   PNDMScheduler(skip_prk_steps=A_   )
       torch.manual_seed(0   )
       UpperCamelCase	:					str       	=   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       ,							)
       # TODO: remove after fixing the non-deterministic text encoder
       UpperCamelCase	:					Optional[int]       	=   RobertaSeriesModelWithTransformation(A_   )
       UpperCamelCase	:					List[Any]       	=   text_encoder
       UpperCamelCase	:					Dict       	=   AltDiffusionPipeline(**A_   )
       UpperCamelCase	:					Optional[Any]       	=   alt_pipe.to(A_   )
       alt_pipe.set_progress_bar_config(disable=A_   )

       UpperCamelCase	:					Any       	=   self.get_dummy_inputs(A_   )
       UpperCamelCase	:					Optional[int]       	=   alt_pipe(**A_   )
       UpperCamelCase	:					Dict       	=   output.images
       UpperCamelCase	:					Dict       	=   image[0, -3:, -3:, -1]

       assert image.shape == (1, 64, 64, 3)
       UpperCamelCase	:					Optional[Any]       	=   np.array(
           [0.51_60_50_93, 0.5_70_72_41, 0.47_36_55_07, 0.50_57_88_86, 0.5_63_38_77, 0.4_64_25_03, 0.5_18_20_81, 0.48_76_34_84, 0.49_08_42_37]   )

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



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





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					str       	=   AltDiffusionPipeline.from_pretrained("BAAI/AltDiffusion"       ,							safety_checker=A_   )
       UpperCamelCase	:					Union[str, Any]       	=   alt_pipe.to(A_   )
       alt_pipe.set_progress_bar_config(disable=A_   )

       UpperCamelCase	:					Optional[Any]       	=   "A painting of a squirrel eating a burger"
       UpperCamelCase	:					Union[str, Any]       	=   torch.manual_seed(0   )
       UpperCamelCase	:					Optional[Any]       	=   alt_pipe([prompt]       ,							generator=A_       ,							guidance_scale=6.0       ,							num_inference_steps=20       ,							output_type="np"   )

       UpperCamelCase	:					Optional[Any]       	=   output.images

       UpperCamelCase	:					List[str]       	=   image[0, -3:, -3:, -1]

       assert image.shape == (1, 512, 512, 3)
       UpperCamelCase	:					List[str]       	=   np.array([0.10_10, 0.08_00, 0.07_94, 0.08_85, 0.08_43, 0.07_62, 0.07_69, 0.07_29, 0.05_86]   )

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





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Dict       	=   DDIMScheduler.from_pretrained("BAAI/AltDiffusion"       ,							subfolder="scheduler"   )

       UpperCamelCase	:					Optional[Any]       	=   AltDiffusionPipeline.from_pretrained("BAAI/AltDiffusion"       ,							scheduler=A_       ,							safety_checker=A_   )
       UpperCamelCase	:					Optional[int]       	=   alt_pipe.to(A_   )
       alt_pipe.set_progress_bar_config(disable=A_   )

       UpperCamelCase	:					int       	=   "A painting of a squirrel eating a burger"
       UpperCamelCase	:					int       	=   torch.manual_seed(0   )

       UpperCamelCase	:					Dict       	=   alt_pipe([prompt]       ,							generator=A_       ,							num_inference_steps=2       ,							output_type="numpy"   )
       UpperCamelCase	:					Union[str, Any]       	=   output.images

       UpperCamelCase	:					List[Any]       	=   image[0, -3:, -3:, -1]

       assert image.shape == (1, 512, 512, 3)
       UpperCamelCase	:					List[str]       	=   np.array([0.40_19, 0.40_52, 0.38_10, 0.41_19, 0.39_16, 0.39_82, 0.46_51, 0.41_95, 0.53_23]   )

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







def 		A_				(     _lowerCAmelCase     )						-> str:
 UpperCamelCase	:					Optional[int]       	=   int(_lowerCAmelCase     )
 if decimal in (0, 1):  # Exit cases for the recursion
  return str(_lowerCAmelCase     )
 UpperCamelCase	,    UpperCamelCase	:					Dict       	=   divmod(_lowerCAmelCase							,   2     )
 return binary_recursive(_lowerCAmelCase     ) + str(_lowerCAmelCase     )



def 		A_				(     _lowerCAmelCase     )						-> str:
 UpperCamelCase	:					Tuple       	=   str(_lowerCAmelCase     ).strip()
 if not number:
  raise ValueError("No input value was provided"     )
 UpperCamelCase	:					Optional[int]       	=   "-" if number.startswith("-"     ) else ""
 UpperCamelCase	:					Any       	=   number.lstrip("-"     )
 if not number.isnumeric():
  raise ValueError("Input value is not an integer"     )
 return F"""{negative}0b{binary_recursive(int(_lowerCAmelCase     )     )}"""


if __name__ == "__main__":
    from doctest import testmod

    testmod()
