






'''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 							__SCREAMING_SNAKE_CASE       (							__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      unittest.TestCase     ):
      __a  					=AltDiffusionPipeline
      __a  					=TEXT_TO_IMAGE_PARAMS
      __a  					=TEXT_TO_IMAGE_BATCH_PARAMS
      __a  					=TEXT_TO_IMAGE_IMAGE_PARAMS
      __a  					=TEXT_TO_IMAGE_IMAGE_PARAMS




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



         '''simple docstring'''
         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=0.00085 ,   beta_end=0.012 ,   beta_schedule='scaled_linear' ,   clip_sample=_a ,   set_alpha_to_one=_a ,   )
         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(_a      )

         __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  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase=0      )							->int:



         '''simple docstring'''




         if str(_a      ).startswith('mps'      ):
            __a				     =    torch.manual_seed(_a      )
         else:
            __a				     =    torch.Generator(device=_a      ).manual_seed(_a      )
         __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  __UpperCamelCase							(  self      )							->Dict:



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




      def  __UpperCamelCase							(  self      )							->int:



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




      def  __UpperCamelCase							(  self      )							->Dict:



         '''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=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(_a      )
         __a				     =    text_encoder

         __a				     =    AltDiffusionPipeline(**_a      )
         __a				     =    alt_pipe.to(_a      )
         alt_pipe.set_progress_bar_config(disable=_a      )

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

         assert image.shape == (1, 64, 64, 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  __UpperCamelCase							(  self      )							->int:



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

         __a				     =    self.get_dummy_components()
         __a				     =    PNDMScheduler(skip_prk_steps=_a      )
         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(_a      )
         __a				     =    text_encoder
         __a				     =    AltDiffusionPipeline(**_a      )
         __a				     =    alt_pipe.to(_a      )
         alt_pipe.set_progress_bar_config(disable=_a      )

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

         assert image.shape == (1, 64, 64, 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 							__SCREAMING_SNAKE_CASE       (							unittest.TestCase     ):




      def  __UpperCamelCase							(  self      )							->Optional[Any]:



         '''simple docstring'''
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()




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



         '''simple docstring'''
         __a				     =    AltDiffusionPipeline.from_pretrained('BAAI/AltDiffusion' ,   safety_checker=_a      )
         __a				     =    alt_pipe.to(_a      )
         alt_pipe.set_progress_bar_config(disable=_a      )

         __a				     =    """A painting of a squirrel eating a burger"""
         __a				     =    torch.manual_seed(0      )
         __a				     =    alt_pipe([prompt] ,   generator=_a ,   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  __UpperCamelCase							(  self      )							->int:



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

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

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

         __a				     =    alt_pipe([prompt] ,   generator=_a ,   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'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCamelCase      :       Tuple      			=      {
    """configuration_blip_2""": [
        """BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """Blip2Config""",
        """Blip2QFormerConfig""",
        """Blip2VisionConfig""",
    ],
    """processing_blip_2""": ["""Blip2Processor"""],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __UpperCamelCase      :       Optional[int]      			=      [
     """BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """Blip2Model""",
     """Blip2QFormerModel""",
     """Blip2PreTrainedModel""",
     """Blip2ForConditionalGeneration""",
     """Blip2VisionModel""",
 ]

if TYPE_CHECKING:
 from .configuration_blip_a import (
     BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
     BlipaConfig,
     BlipaQFormerConfig,
     BlipaVisionConfig,
 )
 from .processing_blip_a import BlipaProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_blip_a import (
      BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST,
      BlipaForConditionalGeneration,
      BlipaModel,
      BlipaPreTrainedModel,
      BlipaQFormerModel,
      BlipaVisionModel,
  )

else:
 import sys

 __UpperCamelCase      :       Dict      			=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)