

import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


UpperCamelCase__  :      List[Any]			=    False



class       __snake_case  (    unittest.TestCase       ):
  pass






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

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						VersatileDiffusionImageVariationPipeline.from_pretrained('shi-labs/versatile-diffusion')
      pipe.to(_A)
      pipe.set_progress_bar_config(disable=_A)

      SCREAMING_SNAKE_CASE_									=						load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg')
      SCREAMING_SNAKE_CASE_									=						torch.manual_seed(0)
      SCREAMING_SNAKE_CASE_									=						pipe(
          image=_A						,		generator=_A						,		guidance_scale=7.5						,		num_inference_steps=50						,		output_type='numpy'						,		).images

      SCREAMING_SNAKE_CASE_									=						image[0, 253:256, 253:256, -1]

      assert image.shape == (1, 512, 512, 3)
      SCREAMING_SNAKE_CASE_									=						np.array([0.0_4_4_1, 0.0_4_6_9, 0.0_5_0_7, 0.0_5_7_5, 0.0_6_3_2, 0.0_6_5_0, 0.0_8_6_5, 0.0_9_0_9, 0.0_9_4_5])

      assert np.abs(image_slice.flatten() - expected_slice).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)


