



'''simple docstring'''





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


lowercase_	 =						logging.get_logger(__name__)

lowercase_	 =						{
    "google/switch-base-8": "https://huggingface.co/google/switch-base-8/blob/main/config.json",
}






class __A    (						A		):



 '''simple docstring'''







 __lowerCamelCase					: int    = 'switch_transformers'
 __lowerCamelCase					: Optional[int]    = ['past_key_values']
 __lowerCamelCase					: List[Any]    = {'hidden_size': 'd_model', 'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}



 def __init__(self				,     A=32_128				,     A=768				,     A=64				,     A=2_048				,     A=64				,     A=12				,     A=3				,     A=12				,     A=3				,     A=12				,     A=8				,     A=False				,     A=0.01				,     A="float32"				,     A=False				,     A=32				,     A=128				,     A=0.1				,     A=1E-6				,     A=0.001				,     A=0.001				,     A=1.0				,     A="relu"				,     A=True				,     A=False				,     A=True				,     A=0				,     A=1				,     **A				,     )					->  Tuple:


      """simple docstring"""



      _a											=						vocab_size
      _a											=						d_model
      _a											=						d_kv
      _a											=						d_ff

      _a											=						num_sparse_encoder_layers

      _a											=						num_layers
      _a											=						(
          num_decoder_layers if num_decoder_layers is not None else self.num_layers
      )  # default = symmetry
      _a											=						num_sparse_decoder_layers

      # This tells us, each how many encoder layer we'll have to set a sparse layer.
      if self.num_sparse_encoder_layers > 0:
           _a											=						self.num_layers // self.num_sparse_encoder_layers
      else:
           _a											=						self.num_layers  # HACK: this will create 0 sparse layers

      # This tells us, each how many encoder layer we'll have to set a sparse layer.
      if self.num_sparse_decoder_layers > 0:
           _a											=						self.num_decoder_layers // self.num_sparse_decoder_layers
      else:
           _a											=						self.num_decoder_layers  # HACK: this will create 0 sparse layers

      _a											=						num_heads
      _a											=						num_experts
      _a											=						expert_capacity
      _a											=						router_bias
      _a											=						router_jitter_noise
      if router_dtype not in ["float32", "float16", "bfloat16"]:
           raise ValueError(f'''`router_dtype` must be one of \'float32\', \'float16\' or \'bfloat16\', got {router_dtype}''' )
      _a											=						router_dtype

      _a											=						router_ignore_padding_tokens
      _a											=						relative_attention_num_buckets
      _a											=						relative_attention_max_distance

      _a											=						dropout_rate
      _a											=						layer_norm_epsilon
      _a											=						initializer_factor
      _a											=						feed_forward_proj
      _a											=						use_cache
      _a											=						add_router_probs

      _a											=						router_z_loss_coef
      _a											=						router_aux_loss_coef

      _a											=						self.feed_forward_proj.split('''-''' )
      _a											=						act_info[-1]
      _a											=						act_info[0] == '''gated'''

      if len(A ) > 1 and act_info[0] != "gated" or len(A ) > 2:
           raise ValueError(
               f'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
               '''Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '''
               '''\'gated-gelu\' or \'relu\'''' )

      # for backwards compatibility
      if feed_forward_proj == "gated-gelu":
           _a											=						'''gelu_new'''

      super().__init__(
          pad_token_id=A				,     eos_token_id=A				,     is_encoder_decoder=A				,     **A				,     )








'''simple docstring'''





import random
import unittest

import numpy as np

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionImgaImgPipeline,
    PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
  import onnxruntime as ort






class __A    (						A     ,     unittest.TestCase		):



 '''simple docstring'''







 __lowerCamelCase					: Dict    = 'hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'



 def       a__						(self				,     A=0 )					->  str:


      """simple docstring"""



      _a											=						floats_tensor((1, 3, 128, 128)				,     rng=random.Random(A ) )
      _a											=						np.random.RandomState(A )
      _a											=						{
          '''prompt''': '''A painting of a squirrel eating a burger''',
          '''image''': image,
          '''generator''': generator,
          '''num_inference_steps''': 3,
          '''strength''': 0.75,
          '''guidance_scale''': 7.5,
          '''output_type''': '''numpy''',
      }
      return inputs



 def       a__						(self )					->  str:


      """simple docstring"""



      _a											=						OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint				,     provider='''CPUExecutionProvider''' )
      pipe.set_progress_bar_config(disable=A )

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

      assert image.shape == (1, 128, 128, 3)
      _a											=						np.array([0.69643, 0.58484, 0.50314, 0.58760, 0.55368, 0.59643, 0.51529, 0.41217, 0.49087] )
      assert np.abs(image_slice - expected_slice ).max() < 1E-1



 def       a__						(self )					->  Tuple:


      """simple docstring"""



      _a											=						OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint				,     provider='''CPUExecutionProvider''' )
      _a											=						PNDMScheduler.from_config(pipe.scheduler.config				,     skip_prk_steps=A )
      pipe.set_progress_bar_config(disable=A )

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

      assert image.shape == (1, 128, 128, 3)
      _a											=						np.array([0.61737, 0.54642, 0.53183, 0.54465, 0.52742, 0.60525, 0.49969, 0.40655, 0.48154] )

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



 def       a__						(self )					->  int:


      """simple docstring"""



      _a											=						OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint				,     provider='''CPUExecutionProvider''' )
      _a											=						LMSDiscreteScheduler.from_config(pipe.scheduler.config )
      pipe.set_progress_bar_config(disable=A )

      # warmup pass to apply optimizations
      _a											=						pipe(**self.get_dummy_inputs() )

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

      assert image.shape == (1, 128, 128, 3)
      _a											=						np.array([0.52761, 0.59977, 0.49033, 0.49619, 0.54282, 0.50311, 0.47600, 0.40918, 0.45203] )

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



 def       a__						(self )					->  Dict:


      """simple docstring"""



      _a											=						OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint				,     provider='''CPUExecutionProvider''' )
      _a											=						EulerDiscreteScheduler.from_config(pipe.scheduler.config )
      pipe.set_progress_bar_config(disable=A )

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

      assert image.shape == (1, 128, 128, 3)
      _a											=						np.array([0.52911, 0.60004, 0.49229, 0.49805, 0.54502, 0.50680, 0.47777, 0.41028, 0.45304] )

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



 def       a__						(self )					->  Tuple:


      """simple docstring"""



      _a											=						OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint				,     provider='''CPUExecutionProvider''' )
      _a											=						EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config )
      pipe.set_progress_bar_config(disable=A )

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

      assert image.shape == (1, 128, 128, 3)
      _a											=						np.array([0.52911, 0.60004, 0.49229, 0.49805, 0.54502, 0.50680, 0.47777, 0.41028, 0.45304] )

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



 def       a__						(self )					->  List[str]:


      """simple docstring"""



      _a											=						OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint				,     provider='''CPUExecutionProvider''' )
      _a											=						DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
      pipe.set_progress_bar_config(disable=A )

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

      assert image.shape == (1, 128, 128, 3)
      _a											=						np.array([0.65331, 0.58277, 0.48204, 0.56059, 0.53665, 0.56235, 0.50969, 0.40009, 0.46552] )

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






@nightly
@require_onnxruntime
@require_torch_gpu
class __A    (						unittest.TestCase		):



 '''simple docstring'''







 @property
 def       a__						(self )					->  Tuple:


      """simple docstring"""



      return (
          "CUDAExecutionProvider",
          {
              "gpu_mem_limit": "15000000000",  # 15GB
              "arena_extend_strategy": "kSameAsRequested",
          },
      )



 @property
 def       a__						(self )					->  Any:


      """simple docstring"""



      _a											=						ort.SessionOptions()
      _a											=						False
      return options



 def       a__						(self )					->  str:


      """simple docstring"""



      _a											=						load_image(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
          '''/img2img/sketch-mountains-input.jpg''' )
      _a											=						init_image.resize((768, 512) )
      # using the PNDM scheduler by default
      _a											=						OnnxStableDiffusionImgaImgPipeline.from_pretrained(
          '''CompVis/stable-diffusion-v1-4'''				,     revision='''onnx'''				,     safety_checker=A				,     feature_extractor=A				,     provider=self.gpu_provider				,     sess_options=self.gpu_options				,     )
      pipe.set_progress_bar_config(disable=A )

      _a											=						'''A fantasy landscape, trending on artstation'''

      _a											=						np.random.RandomState(0 )
      _a											=						pipe(
          prompt=A				,     image=A				,     strength=0.75				,     guidance_scale=7.5				,     num_inference_steps=10				,     generator=A				,     output_type='''np'''				,     )
      _a											=						output.images
      _a											=						images[0, 255:258, 383:386, -1]

      assert images.shape == (1, 512, 768, 3)
      _a											=						np.array([0.4909, 0.5059, 0.5372, 0.4623, 0.4876, 0.5049, 0.4820, 0.4956, 0.5019] )
      # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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



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


      """simple docstring"""



      _a											=						load_image(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
          '''/img2img/sketch-mountains-input.jpg''' )
      _a											=						init_image.resize((768, 512) )
      _a											=						LMSDiscreteScheduler.from_pretrained(
          '''runwayml/stable-diffusion-v1-5'''				,     subfolder='''scheduler'''				,     revision='''onnx''' )
      _a											=						OnnxStableDiffusionImgaImgPipeline.from_pretrained(
          '''runwayml/stable-diffusion-v1-5'''				,     revision='''onnx'''				,     scheduler=A				,     safety_checker=A				,     feature_extractor=A				,     provider=self.gpu_provider				,     sess_options=self.gpu_options				,     )
      pipe.set_progress_bar_config(disable=A )

      _a											=						'''A fantasy landscape, trending on artstation'''

      _a											=						np.random.RandomState(0 )
      _a											=						pipe(
          prompt=A				,     image=A				,     strength=0.75				,     guidance_scale=7.5				,     num_inference_steps=20				,     generator=A				,     output_type='''np'''				,     )
      _a											=						output.images
      _a											=						images[0, 255:258, 383:386, -1]

      assert images.shape == (1, 512, 768, 3)
      _a											=						np.array([0.8043, 0.926, 0.9581, 0.8119, 0.8954, 0.913, 0.7209, 0.7463, 0.7431] )
      # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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



