
'''simple docstring'''





import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLPipeline,
    UNetaDConditionModel,
)
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 PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class        UpperCAmelCase_       (						A				,							A				,							unittest.TestCase							):

 '''simple docstring'''
 lowercase_				:						int				=       StableDiffusionXLPipeline
 lowercase_				:						List[str]				=       TEXT_TO_IMAGE_PARAMS
 lowercase_				:						List[str]				=       TEXT_TO_IMAGE_BATCH_PARAMS
 lowercase_				:						int				=       TEXT_TO_IMAGE_IMAGE_PARAMS
 lowercase_				:						Any				=       TEXT_TO_IMAGE_IMAGE_PARAMS







 def        UpperCamelCase     (				self		:     Union[str, Any]   ):



   '''simple docstring'''




   torch.manual_seed(0   )
   UpperCAmelCase__			:				Union[str, Any]												=       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") ,							attention_head_dim=(2, 4) ,							use_linear_projection=snake_case__ ,							addition_embed_type="text_time" ,							addition_time_embed_dim=8 ,							transformer_layers_per_block=(1, 2) ,							projection_class_embeddings_input_dim=80 ,							cross_attention_dim=64 ,							)
   UpperCAmelCase__			:				str												=       EulerDiscreteScheduler(
       beta_start=0.00085 ,							beta_end=0.012 ,							steps_offset=1 ,							beta_schedule="scaled_linear" ,							timestep_spacing="leading" ,							)
   torch.manual_seed(0   )
   UpperCAmelCase__			:				Tuple												=       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 ,							sample_size=1_28 ,							)
   torch.manual_seed(0   )
   UpperCAmelCase__			:				List[Any]												=       CLIPTextConfig(
       bos_token_id=0 ,							eos_token_id=2 ,							hidden_size=32 ,							intermediate_size=37 ,							layer_norm_eps=1e-05 ,							num_attention_heads=4 ,							num_hidden_layers=5 ,							pad_token_id=1 ,							vocab_size=10_00 ,							hidden_act="gelu" ,							projection_dim=32 ,							)
   UpperCAmelCase__			:				List[Any]												=       CLIPTextModel(snake_case__   )
   UpperCAmelCase__			:				List[str]												=       CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" ,							local_files_only=snake_case__   )

   UpperCAmelCase__			:				Union[str, Any]												=       CLIPTextModelWithProjection(snake_case__   )
   UpperCAmelCase__			:				Dict												=       CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" ,							local_files_only=snake_case__   )

   UpperCAmelCase__			:				Tuple												=       {
       "unet": unet,
       "scheduler": scheduler,
       "vae": vae,
       "text_encoder": text_encoder,
       "tokenizer": tokenizer,
       "text_encoder_2": text_encoder_a,
       "tokenizer_2": tokenizer_a,
       # "safety_checker": None,
       # "feature_extractor": None,
   }
   return components







 def        UpperCamelCase     (				self		:     List[str] ,							snake_case__		:     List[Any] ,							snake_case__		:     List[Any]=0   ):



   '''simple docstring'''




   if str(snake_case__   ).startswith("mps"   ):
     UpperCAmelCase__			:				Tuple												=       torch.manual_seed(snake_case__   )
   else:
     UpperCAmelCase__			:				List[Any]												=       torch.Generator(device=snake_case__   ).manual_seed(snake_case__   )
   UpperCAmelCase__			:				Tuple												=       {
       "prompt": "A painting of a squirrel eating a burger",
       "generator": generator,
       "num_inference_steps": 2,
       "guidance_scale": 5.0,
       "output_type": "numpy",
   }
   return inputs







 def        UpperCamelCase     (				self		:     Optional[Any]   ):



   '''simple docstring'''




   UpperCAmelCase__			:				Optional[int]												=       "cpu"  # ensure determinism for the device-dependent torch.Generator
   UpperCAmelCase__			:				List[Any]												=       self.get_dummy_components()
   UpperCAmelCase__			:				Union[str, Any]												=       StableDiffusionXLPipeline(**snake_case__   )
   UpperCAmelCase__			:				List[Any]												=       sd_pipe.to(snake_case__   )
   sd_pipe.set_progress_bar_config(disable=snake_case__   )

   UpperCAmelCase__			:				Dict												=       self.get_dummy_inputs(snake_case__   )
   UpperCAmelCase__			:				str												=       sd_pipe(**snake_case__   ).images
   UpperCAmelCase__			:				Optional[Any]												=       image[0, -3:, -3:, -1]

   assert image.shape == (1, 64, 64, 3)
   UpperCAmelCase__			:				int												=       np.array([0.5873, 0.6128, 0.4797, 0.5122, 0.5674, 0.4639, 0.5227, 0.5149, 0.4747]   )

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







 def        UpperCamelCase     (				self		:     Optional[int]   ):



   '''simple docstring'''




   UpperCAmelCase__			:				Optional[int]												=       self.get_dummy_components()
   UpperCAmelCase__			:				Optional[Any]												=       StableDiffusionXLPipeline(**snake_case__   )
   UpperCAmelCase__			:				Dict												=       sd_pipe.to(snake_case__   )
   UpperCAmelCase__			:				int												=       sd_pipe.to(snake_case__   )
   sd_pipe.set_progress_bar_config(disable=snake_case__   )

   # forward without prompt embeds
   UpperCAmelCase__			:				List[str]												=       self.get_dummy_inputs(snake_case__   )
   UpperCAmelCase__			:				List[Any]												=       3 * ["this is a negative prompt"]
   UpperCAmelCase__			:				Any												=       negative_prompt
   UpperCAmelCase__			:				Optional[Any]												=       3 * [inputs["prompt"]]

   UpperCAmelCase__			:				Dict												=       sd_pipe(**snake_case__   )
   UpperCAmelCase__			:				Optional[int]												=       output.images[0, -3:, -3:, -1]

   # forward with prompt embeds
   UpperCAmelCase__			:				str												=       self.get_dummy_inputs(snake_case__   )
   UpperCAmelCase__			:				Optional[int]												=       3 * ["this is a negative prompt"]
   UpperCAmelCase__			:				Any												=       3 * [inputs.pop("prompt"   )]

   (
       (
       UpperCAmelCase__
   )					, (
       UpperCAmelCase__
   )					, (
       UpperCAmelCase__
   )					, (
       UpperCAmelCase__
   )					, 
   )			:				Optional[int]												=       sd_pipe.encode_prompt(snake_case__ ,							negative_prompt=snake_case__   )

   UpperCAmelCase__			:				int												=       sd_pipe(
       **snake_case__ ,							prompt_embeds=snake_case__ ,							negative_prompt_embeds=snake_case__ ,							pooled_prompt_embeds=snake_case__ ,							negative_pooled_prompt_embeds=snake_case__ ,							)
   UpperCAmelCase__			:				List[str]												=       output.images[0, -3:, -3:, -1]

   # make sure that it's equal
   assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()   ).max() < 1e-4







 def        UpperCamelCase     (				self		:     Dict   ):



   '''simple docstring'''




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







 def        UpperCamelCase     (				self		:     Tuple   ):



   '''simple docstring'''




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






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

 '''simple docstring'''







 def        UpperCamelCase     (				self		:     List[Any]   ):



   '''simple docstring'''




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







 def        UpperCamelCase     (				self		:     int ,							snake_case__		:     str ,							snake_case__		:     str="cpu" ,							snake_case__		:     Optional[Any]=torch.floataa ,							snake_case__		:     List[str]=0   ):



   '''simple docstring'''




   UpperCAmelCase__			:				str												=       torch.Generator(device=snake_case__   ).manual_seed(snake_case__   )
   UpperCAmelCase__			:				str												=       np.random.RandomState(snake_case__   ).standard_normal((1, 4, 64, 64)   )
   UpperCAmelCase__			:				Tuple												=       torch.from_numpy(snake_case__   ).to(device=snake_case__ ,							dtype=snake_case__   )
   UpperCAmelCase__			:				Union[str, Any]												=       {
       "prompt": "a photograph of an astronaut riding a horse",
       "latents": latents,
       "generator": generator,
       "num_inference_steps": 3,
       "guidance_scale": 7.5,
       "output_type": "numpy",
   }
   return inputs







 def        UpperCamelCase     (				self		:     Dict   ):



   '''simple docstring'''




   UpperCAmelCase__			:				Tuple												=       DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-base"   )
   pipe.to(snake_case__   )
   pipe.set_progress_bar_config(disable=snake_case__   )

   UpperCAmelCase__			:				Tuple												=       self.get_inputs(snake_case__   )
   UpperCAmelCase__			:				Union[str, Any]												=       pipe(**snake_case__   ).images
   UpperCAmelCase__			:				Union[str, Any]												=       image[0, -3:, -3:, -1].flatten()

   assert image.shape == (1, 5_12, 5_12, 3)
   UpperCAmelCase__			:				Optional[Any]												=       np.array([0.49493, 0.47896, 0.40798, 0.54214, 0.53212, 0.48202, 0.47656, 0.46329, 0.48506]   )
   assert np.abs(image_slice - expected_slice   ).max() < 7e-3





'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


SCREAMING_SNAKE_CASE    =					{
    """configuration_donut_swin""": ["""DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DonutSwinConfig"""],
    """processing_donut""": ["""DonutProcessor"""],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE    =					[
          """DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """DonutSwinModel""",
          """DonutSwinPreTrainedModel""",
      ]

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE    =					["""DonutFeatureExtractor"""]
      SCREAMING_SNAKE_CASE    =					["""DonutImageProcessor"""]


if TYPE_CHECKING:
      from .configuration_donut_swin import DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, DonutSwinConfig
      from .processing_donut import DonutProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_donut_swin import (
                DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
                DonutSwinModel,
                DonutSwinPreTrainedModel,
            )

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_donut import DonutFeatureExtractor
            from .image_processing_donut import DonutImageProcessor

else:
      import sys

      SCREAMING_SNAKE_CASE    =					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



