





from typing import TYPE_CHECKING

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


UpperCAmelCase_  							=				{
    'configuration_roc_bert': ['ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoCBertConfig'],
    'tokenization_roc_bert': ['RoCBertTokenizer'],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  pass

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_  							=				[
      'ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'RoCBertForCausalLM',
      'RoCBertForMaskedLM',
      'RoCBertForMultipleChoice',
      'RoCBertForPreTraining',
      'RoCBertForQuestionAnswering',
      'RoCBertForSequenceClassification',
      'RoCBertForTokenClassification',
      'RoCBertLayer',
      'RoCBertModel',
      'RoCBertPreTrainedModel',
      'load_tf_weights_in_roc_bert',
  ]

if TYPE_CHECKING:
  from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
  from .tokenization_roc_bert import RoCBertTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    raise OptionalDependencyNotAvailable()

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_roc_bert import (
        ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        RoCBertForCausalLM,
        RoCBertForMaskedLM,
        RoCBertForMultipleChoice,
        RoCBertForPreTraining,
        RoCBertForQuestionAnswering,
        RoCBertForSequenceClassification,
        RoCBertForTokenClassification,
        RoCBertLayer,
        RoCBertModel,
        RoCBertPreTrainedModel,
        load_tf_weights_in_roc_bert,
    )


else:
  import sys

  UpperCAmelCase_  							=				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)








import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


UpperCAmelCase_  							=				False






class 					lowercase__ (			unittest.TestCase       ):

   '''simple docstring'''


   pass






@nightly
@require_torch_gpu
class 					lowercase__ (			unittest.TestCase       ):

   '''simple docstring'''


   def    UpperCamelCase__						(					self							)				->   Optional[Any]:






       """simple docstring"""
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()




   def    UpperCamelCase__						(					self							)				->   List[Any]:






       """simple docstring"""
       UpperCamelCase__							:       Dict      =   VersatileDiffusionTextToImagePipeline.from_pretrained('''shi-labs/versatile-diffusion'''							)
       # remove text_unet
       pipe.remove_unused_weights()
       pipe.to(__magic_name__							)
       pipe.set_progress_bar_config(disable=__magic_name__							)

       UpperCamelCase__							:       int      =   '''A painting of a squirrel eating a burger '''
       UpperCamelCase__							:       str      =   torch.manual_seed(0							)
       UpperCamelCase__							:       int      =   pipe(
           prompt=__magic_name__,	generator=__magic_name__,	guidance_scale=7.5,	num_inference_steps=2,	output_type='''numpy'''							).images

       with tempfile.TemporaryDirectory() as tmpdirname:
           pipe.save_pretrained(__magic_name__							)
           UpperCamelCase__							:       Union[str, Any]      =   VersatileDiffusionTextToImagePipeline.from_pretrained(__magic_name__							)
       pipe.to(__magic_name__							)
       pipe.set_progress_bar_config(disable=__magic_name__							)

       UpperCamelCase__							:       Optional[Any]      =   generator.manual_seed(0							)
       UpperCamelCase__							:       str      =   pipe(
           prompt=__magic_name__,	generator=__magic_name__,	guidance_scale=7.5,	num_inference_steps=2,	output_type='''numpy'''							).images

       assert np.abs(image - new_image							).sum() < 1E-5, "Models don't have the same forward pass"




   def    UpperCamelCase__						(					self							)				->   Any:






       """simple docstring"""
       UpperCamelCase__							:       str      =   VersatileDiffusionTextToImagePipeline.from_pretrained(
           '''shi-labs/versatile-diffusion''',	torch_dtype=torch.floataa							)
       pipe.to(__magic_name__							)
       pipe.set_progress_bar_config(disable=__magic_name__							)

       UpperCamelCase__							:       Optional[int]      =   '''A painting of a squirrel eating a burger '''
       UpperCamelCase__							:       str      =   torch.manual_seed(0							)
       UpperCamelCase__							:       Optional[int]      =   pipe(
           prompt=__magic_name__,	generator=__magic_name__,	guidance_scale=7.5,	num_inference_steps=50,	output_type='''numpy'''							).images

       UpperCamelCase__							:       int      =   image[0, 253:256, 253:256, -1]

       assert image.shape == (1, 512, 512, 3)
       UpperCamelCase__							:       int      =   np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778]							)

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

