


'''simple docstring'''




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 			_lowercase (	_UpperCamelCase						,					_UpperCamelCase						,					_UpperCamelCase						,					unittest.TestCase							):
   _SCREAMING_SNAKE_CASE  :  Any          =	AltDiffusionPipeline
   _SCREAMING_SNAKE_CASE  :  Dict          =	TEXT_TO_IMAGE_PARAMS
   _SCREAMING_SNAKE_CASE  :  List[str]          =	TEXT_TO_IMAGE_BATCH_PARAMS
   _SCREAMING_SNAKE_CASE  :  str          =	TEXT_TO_IMAGE_IMAGE_PARAMS
   _SCREAMING_SNAKE_CASE  :  Optional[Any]          =	TEXT_TO_IMAGE_IMAGE_PARAMS
   def a  (						self       :      Dict      ) ->							List[str]:
        torch.manual_seed(0      )
        __snake_case           =						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       ,   )
        __snake_case           =						DDIMScheduler(
            beta_start=0.0_0_0_8_5       ,   beta_end=0.0_1_2       ,   beta_schedule='scaled_linear'       ,   clip_sample=UpperCamelCase__       ,   set_alpha_to_one=UpperCamelCase__       ,   )
        torch.manual_seed(0      )
        __snake_case           =						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      )
        __snake_case           =						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       ,   )
        __snake_case           =						CLIPTextModel(UpperCamelCase__      )

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

        __snake_case           =						{
            'unet': unet,
            'scheduler': scheduler,
            'vae': vae,
            'text_encoder': text_encoder,
            'tokenizer': tokenizer,
            'safety_checker': None,
            'feature_extractor': None,
        }
        return components
   def a  (						self       :      Union[str, Any]       ,   SCREAMING_SNAKE_CASE_       :      str       ,   SCREAMING_SNAKE_CASE_       :      str=0      ) ->							Tuple:
        if str(UpperCamelCase__      ).startswith('mps'      ):
             __snake_case           =						torch.manual_seed(UpperCamelCase__      )
        else:
             __snake_case           =						torch.Generator(device=UpperCamelCase__      ).manual_seed(UpperCamelCase__      )
        __snake_case           =						{
            '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       :      str      ) ->							Dict:
        super().test_attention_slicing_forward_pass(expected_max_diff=3e-3      )
   def a  (						self       :      List[Any]      ) ->							Any:
        super().test_inference_batch_single_identical(expected_max_diff=3e-3      )
   def a  (						self       :      Any      ) ->							Union[str, Any]:
        __snake_case           =						'cpu'  # ensure determinism for the device-dependent torch.Generator

        __snake_case           =						self.get_dummy_components()
        torch.manual_seed(0      )
        __snake_case           =						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
        __snake_case           =						RobertaSeriesModelWithTransformation(UpperCamelCase__      )
        __snake_case           =						text_encoder

        __snake_case           =						AltDiffusionPipeline(**UpperCamelCase__      )
        __snake_case           =						alt_pipe.to(UpperCamelCase__      )
        alt_pipe.set_progress_bar_config(disable=UpperCamelCase__      )

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

        assert image.shape == (1, 64, 64, 3)
        __snake_case           =						np.array(
            [0.5_7_4_8_1_6_2, 0.6_0_4_4_7_1_4_5, 0.4_8_8_2_1_2_1_7, 0.5_0_1_0_0_6_3_6, 0.5_4_3_1_1_8_5, 0.4_5_7_6_3_6_8_3, 0.4_9_6_5_7_6_9_6, 0.4_8_1_3_2_7_3_3, 0.4_7_5_7_3_0_9_3]      )

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





   def a  (						self       :      List[Any]      ) ->							Optional[Any]:
        __snake_case           =						'cpu'  # ensure determinism for the device-dependent torch.Generator

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

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

        assert image.shape == (1, 64, 64, 3)
        __snake_case           =						np.array(
            [0.5_1_6_0_5_0_9_3, 0.5_7_0_7_2_4_1, 0.4_7_3_6_5_5_0_7, 0.5_0_5_7_8_8_8_6, 0.5_6_3_3_8_7_7, 0.4_6_4_2_5_0_3, 0.5_1_8_2_0_8_1, 0.4_8_7_6_3_4_8_4, 0.4_9_0_8_4_2_3_7]      )

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




@slow
@require_torch_gpu
class 			_lowercase (	unittest.TestCase							):
   def a  (						self       :      str      ) ->							Optional[Any]:
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()
   def a  (						self       :      List[str]      ) ->							str:
        # make sure here that pndm scheduler skips prk
        __snake_case           =						AltDiffusionPipeline.from_pretrained('BAAI/AltDiffusion'       ,   safety_checker=UpperCamelCase__      )
        __snake_case           =						alt_pipe.to(UpperCamelCase__      )
        alt_pipe.set_progress_bar_config(disable=UpperCamelCase__      )

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

        __snake_case           =						output.images

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

        assert image.shape == (1, 512, 512, 3)
        __snake_case           =						np.array([0.1_0_1_0, 0.0_8_0_0, 0.0_7_9_4, 0.0_8_8_5, 0.0_8_4_3, 0.0_7_6_2, 0.0_7_6_9, 0.0_7_2_9, 0.0_5_8_6]      )

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





   def a  (						self       :      Dict      ) ->							Dict:
        __snake_case           =						DDIMScheduler.from_pretrained('BAAI/AltDiffusion'       ,   subfolder='scheduler'      )

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

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

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

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

        assert image.shape == (1, 512, 512, 3)
        __snake_case           =						np.array([0.4_0_1_9, 0.4_0_5_2, 0.3_8_1_0, 0.4_1_1_9, 0.3_9_1_6, 0.3_9_8_2, 0.4_6_5_1, 0.4_1_9_5, 0.5_3_2_3]      )

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





'''simple docstring'''






_UpperCAmelCase    :     Any         =       '''0.21.0'''

from .accelerator import Accelerator
from .big_modeling import (
    cpu_offload,
    cpu_offload_with_hook,
    disk_offload,
    dispatch_model,
    init_empty_weights,
    init_on_device,
    load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    FullyShardedDataParallelPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    find_executable_batch_size,
    infer_auto_device_map,
    is_rich_available,
    load_checkpoint_in_model,
    synchronize_rng_states,
)


if is_rich_available():
   from .utils import rich



