

import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
       import onnxruntime as ort



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

  @property
  def 						lowerCAmelCase__     (					self):
      return (
          "CUDAExecutionProvider",
          {
              "gpu_mem_limit": "15000000000",  # 15GB
              "arena_extend_strategy": "kSameAsRequested",
          },
      )

  @property
  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						ort.SessionOptions()
      SCREAMING_SNAKE_CASE_									=						False
      return options


  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/in_paint/overture-creations-5sI6fQgYIuo.png')
      SCREAMING_SNAKE_CASE_									=						load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/in_paint/overture-creations-5sI6fQgYIuo_mask.png')
      SCREAMING_SNAKE_CASE_									=						load_numpy(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy')

      # using the PNDM scheduler by default
      SCREAMING_SNAKE_CASE_									=						OnnxStableDiffusionInpaintPipelineLegacy.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)

      SCREAMING_SNAKE_CASE_									=						'A red cat sitting on a park bench'

      SCREAMING_SNAKE_CASE_									=						np.random.RandomState(0)
      SCREAMING_SNAKE_CASE_									=						pipe(
          prompt=_A						,		image=_A						,		mask_image=_A						,		strength=0.7_5						,		guidance_scale=7.5						,		num_inference_steps=15						,		generator=_A						,		output_type='np'						,		)

      SCREAMING_SNAKE_CASE_									=						output.images[0]

      assert image.shape == (512, 512, 3)
      assert np.abs(expected_image - image).max() < 1E-2





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


UpperCamelCase__  :      List[Any]			=    logging.get_logger(__name__)

UpperCamelCase__  :      List[str]			=    {
    "microsoft/biogpt": "https://huggingface.co/microsoft/biogpt/resolve/main/config.json",
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}



class       __snake_case  (    lowerCAmelCase__       ):
  __lowerCAmelCase     :	Any						 =  'biogpt'

  def __init__(					self						,		_A=42384						,		_A=1024						,		_A=24						,		_A=16						,		_A=4096						,		_A="gelu"						,		_A=0.1						,		_A=0.1						,		_A=1024						,		_A=0.0_2						,		_A=1E-12						,		_A=True						,		_A=True						,		_A=0.0						,		_A=0.0						,		_A=1						,		_A=0						,		_A=2						,		**_A						,		):
      SCREAMING_SNAKE_CASE_									=						vocab_size
      SCREAMING_SNAKE_CASE_									=						max_position_embeddings
      SCREAMING_SNAKE_CASE_									=						hidden_size
      SCREAMING_SNAKE_CASE_									=						num_hidden_layers
      SCREAMING_SNAKE_CASE_									=						num_attention_heads
      SCREAMING_SNAKE_CASE_									=						intermediate_size
      SCREAMING_SNAKE_CASE_									=						hidden_act
      SCREAMING_SNAKE_CASE_									=						hidden_dropout_prob
      SCREAMING_SNAKE_CASE_									=						attention_probs_dropout_prob
      SCREAMING_SNAKE_CASE_									=						initializer_range
      SCREAMING_SNAKE_CASE_									=						layer_norm_eps
      SCREAMING_SNAKE_CASE_									=						scale_embedding
      SCREAMING_SNAKE_CASE_									=						use_cache
      SCREAMING_SNAKE_CASE_									=						layerdrop
      SCREAMING_SNAKE_CASE_									=						activation_dropout
      super().__init__(pad_token_id=_A						,		bos_token_id=_A						,		eos_token_id=_A						,		**_A)


