


'''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  __magic_name__							(		lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase ,unittest.TestCase    ):
       UpperCAmelCase										=AltDiffusionPipeline
       UpperCAmelCase										=TEXT_TO_IMAGE_PARAMS
       UpperCAmelCase										=TEXT_TO_IMAGE_BATCH_PARAMS
       UpperCAmelCase										=TEXT_TO_IMAGE_IMAGE_PARAMS
       UpperCAmelCase										=TEXT_TO_IMAGE_IMAGE_PARAMS






       def       lowerCAmelCase		(      self)						->  Optional[int]:




          '''simple docstring'''
          torch.manual_seed(0)
          _UpperCAmelCase       :       Union[str, Any]							   =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  ,							)
          _UpperCAmelCase       :       Dict							   =DDIMScheduler(
              beta_start=0.0_00_85  ,							beta_end=0.0_12  ,							beta_schedule='scaled_linear'  ,							clip_sample=snake_case  ,							set_alpha_to_one=snake_case  ,							)
          torch.manual_seed(0)
          _UpperCAmelCase       :       List[Any]							   =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)
          _UpperCAmelCase       :       Optional[int]							   =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-0_5  ,							num_attention_heads=4  ,							num_hidden_layers=5  ,							pad_token_id=1  ,							vocab_size=5_0_0_2  ,							)
          _UpperCAmelCase       :       Dict							   =CLIPTextModel(snake_case)

          _UpperCAmelCase       :       Dict							   =XLMRobertaTokenizer.from_pretrained('hf-internal-testing/tiny-xlm-roberta')
          _UpperCAmelCase       :       Union[str, Any]							   =7_7

          _UpperCAmelCase       :       int							   ={
              '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)						->  Tuple:




          '''simple docstring'''
          if str(snake_case).startswith('mps'):
             _UpperCAmelCase       :       List[Any]							   =torch.manual_seed(snake_case)
          else:
             _UpperCAmelCase       :       Optional[int]							   =torch.Generator(device=snake_case).manual_seed(snake_case)
          _UpperCAmelCase       :       str							   ={
              '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)						->  Optional[Any]:




          '''simple docstring'''
          super().test_attention_slicing_forward_pass(expected_max_diff=3E-3)






       def       lowerCAmelCase		(      self)						->  int:




          '''simple docstring'''
          super().test_inference_batch_single_identical(expected_max_diff=3E-3)






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




          '''simple docstring'''
          _UpperCAmelCase       :       List[str]							   ='cpu'  # ensure determinism for the device-dependent torch.Generator

          _UpperCAmelCase       :       List[str]							   =self.get_dummy_components()
          torch.manual_seed(0)
          _UpperCAmelCase       :       Tuple							   =RobertaSeriesConfig(
              hidden_size=3_2  ,							project_dim=3_2  ,							intermediate_size=3_7  ,							layer_norm_eps=1E-0_5  ,							num_attention_heads=4  ,							num_hidden_layers=5  ,							vocab_size=5_0_0_2  ,							)
          # TODO: remove after fixing the non-deterministic text encoder
          _UpperCAmelCase       :       List[str]							   =RobertaSeriesModelWithTransformation(snake_case)
          _UpperCAmelCase       :       List[Any]							   =text_encoder

          _UpperCAmelCase       :       Optional[Any]							   =AltDiffusionPipeline(**snake_case)
          _UpperCAmelCase       :       Optional[Any]							   =alt_pipe.to(snake_case)
          alt_pipe.set_progress_bar_config(disable=snake_case)

          _UpperCAmelCase       :       List[str]							   =self.get_dummy_inputs(snake_case)
          _UpperCAmelCase       :       Union[str, Any]							   ='A photo of an astronaut'
          _UpperCAmelCase       :       Tuple							   =alt_pipe(**snake_case)
          _UpperCAmelCase       :       str							   =output.images
          _UpperCAmelCase       :       int							   =image[0, -3:, -3:, -1]

          assert image.shape == (1, 6_4, 6_4, 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       lowerCAmelCase		(      self)						->  Optional[int]:




          '''simple docstring'''
          _UpperCAmelCase       :       List[Any]							   ='cpu'  # ensure determinism for the device-dependent torch.Generator

          _UpperCAmelCase       :       Any							   =self.get_dummy_components()
          _UpperCAmelCase       :       Any							   =PNDMScheduler(skip_prk_steps=snake_case)
          torch.manual_seed(0)
          _UpperCAmelCase       :       List[Any]							   =RobertaSeriesConfig(
              hidden_size=3_2  ,							project_dim=3_2  ,							intermediate_size=3_7  ,							layer_norm_eps=1E-0_5  ,							num_attention_heads=4  ,							num_hidden_layers=5  ,							vocab_size=5_0_0_2  ,							)
          # TODO: remove after fixing the non-deterministic text encoder
          _UpperCAmelCase       :       Optional[int]							   =RobertaSeriesModelWithTransformation(snake_case)
          _UpperCAmelCase       :       Any							   =text_encoder
          _UpperCAmelCase       :       Tuple							   =AltDiffusionPipeline(**snake_case)
          _UpperCAmelCase       :       List[str]							   =alt_pipe.to(snake_case)
          alt_pipe.set_progress_bar_config(disable=snake_case)

          _UpperCAmelCase       :       Tuple							   =self.get_dummy_inputs(snake_case)
          _UpperCAmelCase       :       int							   =alt_pipe(**snake_case)
          _UpperCAmelCase       :       Dict							   =output.images
          _UpperCAmelCase       :       str							   =image[0, -3:, -3:, -1]

          assert image.shape == (1, 6_4, 6_4, 3)
          _UpperCAmelCase       :       Optional[int]							   =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  __magic_name__							(		unittest.TestCase    ):






       def       lowerCAmelCase		(      self)						->  int:




          '''simple docstring'''
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()






       def       lowerCAmelCase		(      self)						->  int:




          '''simple docstring'''
          # make sure here that pndm scheduler skips prk
          _UpperCAmelCase       :       List[Any]							   =AltDiffusionPipeline.from_pretrained('BAAI/AltDiffusion'  ,							safety_checker=snake_case)
          _UpperCAmelCase       :       Union[str, Any]							   =alt_pipe.to(snake_case)
          alt_pipe.set_progress_bar_config(disable=snake_case)

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

          _UpperCAmelCase       :       int							   =output.images

          _UpperCAmelCase       :       Tuple							   =image[0, -3:, -3:, -1]

          assert image.shape == (1, 5_1_2, 5_1_2, 3)
          _UpperCAmelCase       :       Union[str, Any]							   =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       lowerCAmelCase		(      self)						->  Optional[Any]:




          '''simple docstring'''
          _UpperCAmelCase       :       List[Any]							   =DDIMScheduler.from_pretrained('BAAI/AltDiffusion'  ,							subfolder='scheduler')

          _UpperCAmelCase       :       Dict							   =AltDiffusionPipeline.from_pretrained('BAAI/AltDiffusion'  ,							scheduler=snake_case  ,							safety_checker=snake_case)
          _UpperCAmelCase       :       int							   =alt_pipe.to(snake_case)
          alt_pipe.set_progress_bar_config(disable=snake_case)

          _UpperCAmelCase       :       str							   ='A painting of a squirrel eating a burger'
          _UpperCAmelCase       :       int							   =torch.manual_seed(0)

          _UpperCAmelCase       :       Tuple							   =alt_pipe([prompt]  ,							generator=snake_case  ,							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, 5_1_2, 5_1_2, 3)
          _UpperCAmelCase       :       Optional[int]							   =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





'''simple docstring'''






from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase							=logging.get_logger(__name__)

lowercase							={
    'vinvino02/glpn-kitti': 'https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json',
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}






class  __magic_name__							(		lowerCAmelCase    ):
       UpperCAmelCase										="glpn"






       def __init__(      self  ,							snake_case=3  ,							snake_case=4  ,							snake_case=[2, 2, 2, 2]  ,							snake_case=[8, 4, 2, 1]  ,							snake_case=[3_2, 6_4, 1_6_0, 2_5_6]  ,							snake_case=[7, 3, 3, 3]  ,							snake_case=[4, 2, 2, 2]  ,							snake_case=[1, 2, 5, 8]  ,							snake_case=[4, 4, 4, 4]  ,							snake_case="gelu"  ,							snake_case=0.0  ,							snake_case=0.0  ,							snake_case=0.02  ,							snake_case=0.1  ,							snake_case=1E-6  ,							snake_case=6_4  ,							snake_case=1_0  ,							snake_case=-1  ,							**snake_case  ,							)						->  Tuple:




          '''simple docstring'''
          super().__init__(**snake_case)

          _UpperCAmelCase       :       Any							   =num_channels
          _UpperCAmelCase       :       List[str]							   =num_encoder_blocks
          _UpperCAmelCase       :       Optional[Any]							   =depths
          _UpperCAmelCase       :       str							   =sr_ratios
          _UpperCAmelCase       :       Dict							   =hidden_sizes
          _UpperCAmelCase       :       List[str]							   =patch_sizes
          _UpperCAmelCase       :       Any							   =strides
          _UpperCAmelCase       :       List[str]							   =mlp_ratios
          _UpperCAmelCase       :       Dict							   =num_attention_heads
          _UpperCAmelCase       :       List[str]							   =hidden_act
          _UpperCAmelCase       :       int							   =hidden_dropout_prob
          _UpperCAmelCase       :       List[Any]							   =attention_probs_dropout_prob
          _UpperCAmelCase       :       Union[str, Any]							   =initializer_range
          _UpperCAmelCase       :       Tuple							   =drop_path_rate
          _UpperCAmelCase       :       str							   =layer_norm_eps
          _UpperCAmelCase       :       Optional[int]							   =decoder_hidden_size
          _UpperCAmelCase       :       List[str]							   =max_depth
          _UpperCAmelCase       :       Dict							   =head_in_index

