


'''simple docstring'''






import gc
import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




class 	a       (       _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			unittest.TestCase    ):
 _lowerCAmelCase	   =		StableDiffusionXLImgaImgPipeline
 _lowerCAmelCase	   =		TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width"""}
 _lowerCAmelCase	   =		PipelineTesterMixin.required_optional_params - {"""latents"""}
 _lowerCAmelCase	   =		TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
 _lowerCAmelCase	   =		IMAGE_TO_IMAGE_IMAGE_PARAMS
 _lowerCAmelCase	   =		IMAGE_TO_IMAGE_IMAGE_PARAMS
 def 							__UpperCAmelCase						(	self  )   ->							Tuple:
      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') ,			attention_head_dim=(2, 4) ,			use_linear_projection=__magic_name__ ,			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 ,			)
      _a      =							EulerDiscreteScheduler(
          beta_start=0.0_0_0_8_5 ,			beta_end=0.0_1_2 ,			steps_offset=1 ,			beta_schedule='scaled_linear' ,			timestep_spacing='leading' ,			)
      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 ,			sample_size=1_28 ,			)
      torch.manual_seed(0  )
      _a      =							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 ,			)
      _a      =							CLIPTextModel(__magic_name__  )
      _a      =							CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' ,			local_files_only=__magic_name__  )

      _a      =							CLIPTextModelWithProjection(__magic_name__  )
      _a      =							CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' ,			local_files_only=__magic_name__  )

      _a      =							{
          '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 ,			__magic_name__ ,			__magic_name__=0  )   ->							Union[str, Any]:
      _a      =							floats_tensor((1, 3, 32, 32) ,			rng=random.Random(__magic_name__  )  ).to(__magic_name__  )
      _a      =							image / 2 + 0.5
      if str(__magic_name__  ).startswith('mps'  ):
           _a      =							torch.manual_seed(__magic_name__  )
      else:
           _a      =							torch.Generator(device=__magic_name__  ).manual_seed(__magic_name__  )
      _a      =							{
          'prompt': 'A painting of a squirrel eating a burger',
          'image': image,
          'generator': generator,
          'num_inference_steps': 2,
          'guidance_scale': 5.0,
          'output_type': 'numpy',
          'strength': 0.7_5,
      }
      return inputs
 def 							__UpperCAmelCase						(	self  )   ->							Optional[int]:
      _a      =							'cpu'  # ensure determinism for the device-dependent torch.Generator
      _a      =							self.get_dummy_components()
      _a      =							StableDiffusionXLImgaImgPipeline(**__magic_name__  )
      _a      =							sd_pipe.to(__magic_name__  )
      sd_pipe.set_progress_bar_config(disable=__magic_name__  )

      _a      =							self.get_dummy_inputs(__magic_name__  )
      _a      =							sd_pipe(**__magic_name__  ).images
      _a      =							image[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)

      _a      =							np.array([0.4_6_5_6, 0.4_8_4_0, 0.4_4_3_9, 0.6_6_9_8, 0.5_5_7_4, 0.4_5_2_4, 0.5_7_9_9, 0.5_9_4_3, 0.5_1_6_5]  )

      assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-2
 def 							__UpperCAmelCase						(	self  )   ->							int:
      super().test_attention_slicing_forward_pass(expected_max_diff=3e-3  )
 def 							__UpperCAmelCase						(	self  )   ->							List[str]:
      super().test_inference_batch_single_identical(expected_max_diff=3e-3  )
 def 							__UpperCAmelCase						(	self  )   ->							List[str]:
      pass




 def 							__UpperCAmelCase						(	self  )   ->							str:
      _a      =							self.get_dummy_components()
      _a      =							StableDiffusionXLImgaImgPipeline(**__magic_name__  )
      _a      =							sd_pipe.to(__magic_name__  )
      _a      =							sd_pipe.to(__magic_name__  )
      sd_pipe.set_progress_bar_config(disable=__magic_name__  )

      # forward without prompt embeds
      _a      =							self.get_dummy_inputs(__magic_name__  )
      _a      =							3 * ['this is a negative prompt']
      _a      =							negative_prompt
      _a      =							3 * [inputs['prompt']]

      _a      =							sd_pipe(**__magic_name__  )
      _a      =							output.images[0, -3:, -3:, -1]

      # forward with prompt embeds
      _a      =							self.get_dummy_inputs(__magic_name__  )
      _a      =							3 * ['this is a negative prompt']
      _a      =							3 * [inputs.pop('prompt'  )]

      (
          (
          _a
      ) ,				(
          _a
      ) ,				(
          _a
      ) ,				(
          _a
      ) ,				
      )      =							sd_pipe.encode_prompt(__magic_name__ ,			negative_prompt=__magic_name__  )

      _a      =							sd_pipe(
          **__magic_name__ ,			prompt_embeds=__magic_name__ ,			negative_prompt_embeds=__magic_name__ ,			pooled_prompt_embeds=__magic_name__ ,			negative_pooled_prompt_embeds=__magic_name__ ,			)
      _a      =							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





@slow
@require_torch_gpu
class 	a       (       unittest.TestCase    ):
 def 							__UpperCAmelCase						(	self  )   ->							Optional[Any]:
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__="cpu" ,			__magic_name__=torch.floataa ,			__magic_name__=0  )   ->							int:
      _a      =							torch.Generator(device=__magic_name__  ).manual_seed(__magic_name__  )
      _a      =							np.random.RandomState(__magic_name__  ).standard_normal((1, 4, 64, 64)  )
      _a      =							torch.from_numpy(__magic_name__  ).to(device=__magic_name__ ,			dtype=__magic_name__  )
      _a      =							{
          '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  )   ->							List[Any]:
      _a      =							DiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-base'  )
      pipe.to(__magic_name__  )
      pipe.set_progress_bar_config(disable=__magic_name__  )

      _a      =							self.get_inputs(__magic_name__  )
      _a      =							pipe(**__magic_name__  ).images
      _a      =							image[0, -3:, -3:, -1].flatten()

      assert image.shape == (1, 5_12, 5_12, 3)
      _a      =							np.array([0.4_9_4_9_3, 0.4_7_8_9_6, 0.4_0_7_9_8, 0.5_4_2_1_4, 0.5_3_2_1_2, 0.4_8_2_0_2, 0.4_7_6_5_6, 0.4_6_3_2_9, 0.4_8_5_0_6]  )
      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


a_   :      str				    =      {
    "configuration_nllb_moe": [
        "NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "NllbMoeConfig",
    ]
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :      Tuple				    =      [
     "NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST",
     "NllbMoeForConditionalGeneration",
     "NllbMoeModel",
     "NllbMoePreTrainedModel",
     "NllbMoeTop2Router",
     "NllbMoeSparseMLP",
 ]


if TYPE_CHECKING:
 from .configuration_nllb_moe import (
     NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
     NllbMoeConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_nllb_moe import (
      NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
      NllbMoeForConditionalGeneration,
      NllbMoeModel,
      NllbMoePreTrainedModel,
      NllbMoeSparseMLP,
      NllbMoeTopaRouter,
  )


else:
 import sys

 a_   :      str				    =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

