





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      lowerCamelCase(						__snake_case			, __snake_case			, __snake_case			, unittest.TestCase	):
     '''simple docstring'''


     __magic_name__   =			AltDiffusionPipeline
     __magic_name__   =			TEXT_TO_IMAGE_PARAMS
     __magic_name__   =			TEXT_TO_IMAGE_BATCH_PARAMS
     __magic_name__   =			TEXT_TO_IMAGE_IMAGE_PARAMS
     __magic_name__   =			TEXT_TO_IMAGE_IMAGE_PARAMS




     def        lowerCAmelCase__		(    self							):
         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'				,     clip_sample=snake_case_				,     set_alpha_to_one=snake_case_				,     )
         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				,     )

         # 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=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				,     )
         _A     =							CLIPTextModel(snake_case_							)

         _A     =							XLMRobertaTokenizer.from_pretrained('hf-internal-testing/tiny-xlm-roberta'							)
         _A     =							77

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




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_=0							):
         if str(snake_case_							).startswith('mps'							):
             _A     =							torch.manual_seed(snake_case_							)
         else:
             _A     =							torch.Generator(device=snake_case_							).manual_seed(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        lowerCAmelCase__		(    self							):
         super().test_attention_slicing_forward_pass(expected_max_diff=3E-3							)




     def        lowerCAmelCase__		(    self							):
         super().test_inference_batch_single_identical(expected_max_diff=3E-3							)




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

         _A     =							self.get_dummy_components()
         torch.manual_seed(0							)
         _A     =							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
         _A     =							RobertaSeriesModelWithTransformation(snake_case_							)
         _A     =							text_encoder

         _A     =							AltDiffusionPipeline(**snake_case_							)
         _A     =							alt_pipe.to(snake_case_							)
         alt_pipe.set_progress_bar_config(disable=snake_case_							)

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

         assert image.shape == (1, 64, 64, 3)
         _A     =							np.array(
             [0.574_8162, 0.6044_7145, 0.4882_1217, 0.5010_0636, 0.543_1185, 0.4576_3683, 0.4965_7696, 0.4813_2733, 0.4757_3093]							)

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




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

         _A     =							self.get_dummy_components()
         _A     =							PNDMScheduler(skip_prk_steps=snake_case_							)
         torch.manual_seed(0							)
         _A     =							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
         _A     =							RobertaSeriesModelWithTransformation(snake_case_							)
         _A     =							text_encoder
         _A     =							AltDiffusionPipeline(**snake_case_							)
         _A     =							alt_pipe.to(snake_case_							)
         alt_pipe.set_progress_bar_config(disable=snake_case_							)

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

         assert image.shape == (1, 64, 64, 3)
         _A     =							np.array(
             [0.5160_5093, 0.570_7241, 0.4736_5507, 0.5057_8886, 0.563_3877, 0.464_2503, 0.518_2081, 0.4876_3484, 0.4908_4237]							)

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







@slow
@require_torch_gpu
class      lowerCamelCase(						unittest.TestCase	):
     '''simple docstring'''


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




     def        lowerCAmelCase__		(    self							):
         # make sure here that pndm scheduler skips prk
         _A     =							AltDiffusionPipeline.from_pretrained('BAAI/AltDiffusion'				,     safety_checker=snake_case_							)
         _A     =							alt_pipe.to(snake_case_							)
         alt_pipe.set_progress_bar_config(disable=snake_case_							)

         _A     =							'A painting of a squirrel eating a burger'
         _A     =							torch.manual_seed(0							)
         _A     =							alt_pipe([prompt]				,     generator=snake_case_				,     guidance_scale=6.0				,     num_inference_steps=20				,     output_type='np'							)

         _A     =							output.images

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

         assert image.shape == (1, 512, 512, 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        lowerCAmelCase__		(    self							):
         _A     =							DDIMScheduler.from_pretrained('BAAI/AltDiffusion'				,     subfolder='scheduler'							)

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

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

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

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

         assert image.shape == (1, 512, 512, 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









"""simple docstring"""






import os





def 				A_       ( )					->  Any:
			with open(os.path.dirname(snake_case__		) + '''/p022_names.txt'''		) as file:
						_UpperCamelCase					:Optional[Any]					     =	str(file.readlines()[0]		)
						_UpperCamelCase					:Dict					     =	names.replace('''"'''       ,					''''''		).split(''','''		)

			names.sort()

			_UpperCamelCase					:str					     =	0
			_UpperCamelCase					:Union[str, Any]					     =	0

			for i, name in enumerate(snake_case__		):
						for letter in name:
									name_score += ord(snake_case__		) - 64

						total_score += (i + 1) * name_score
						_UpperCamelCase					:List[Any]					     =	0
			return total_score


if __name__ == "__main__":
					print(solution())

