





'''simple docstring'''







import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionDualGuidedPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


snake_case_  :							Dict            =	False


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

							def      UpperCAmelCase__						(				self				:  Dict )       ->							int:







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

							def      UpperCAmelCase__						(				self				:  Tuple )       ->							List[Any]:







													"""simple docstring"""
													UpperCAmelCase_      :   Union[str, Any]      =	VersatileDiffusionDualGuidedPipeline.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_      :   str      =	load_image(
													    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg''' )

													UpperCAmelCase_      :   List[str]      =	torch.manual_seed(0 )
													UpperCAmelCase_      :   Dict      =	pipe(
													    prompt='''first prompt'''							,  image=__magic_name__							,  text_to_image_strength=0.7_5							,  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_      :   List[str]      =	VersatileDiffusionDualGuidedPipeline.from_pretrained(__magic_name__ )

													pipe.to(__magic_name__ )
													pipe.set_progress_bar_config(disable=__magic_name__ )

													UpperCAmelCase_      :   str      =	generator.manual_seed(0 )
													UpperCAmelCase_      :   str      =	pipe(
													    prompt='''first prompt'''							,  image=__magic_name__							,  text_to_image_strength=0.7_5							,  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				:  Optional[int] )       ->							List[Any]:







													"""simple docstring"""
													UpperCAmelCase_      :   Any      =	VersatileDiffusionDualGuidedPipeline.from_pretrained('''shi-labs/versatile-diffusion''' )
													pipe.remove_unused_weights()
													pipe.to(__magic_name__ )
													pipe.set_progress_bar_config(disable=__magic_name__ )

													UpperCAmelCase_      :   Optional[int]      =	'''cyberpunk 2077'''
													UpperCAmelCase_      :   List[Any]      =	load_image(
													    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg''' )
													UpperCAmelCase_      :   List[str]      =	torch.manual_seed(0 )
													UpperCAmelCase_      :   str      =	pipe(
													    prompt=__magic_name__							,  image=__magic_name__							,  text_to_image_strength=0.7_5							,  generator=__magic_name__							,  guidance_scale=7.5							,  num_inference_steps=50							,  output_type='''numpy'''							,  ).images

													UpperCAmelCase_      :   Any      =	image[0, 2_53:2_56, 2_53:2_56, -1]

													assert image.shape == (1, 5_12, 5_12, 3)
													UpperCAmelCase_      :   Optional[Any]      =	np.array([0.0_7_8_7, 0.0_8_4_9, 0.0_8_2_6, 0.0_8_1_2, 0.0_8_0_7, 0.0_7_9_5, 0.0_8_1_8, 0.0_7_9_8, 0.0_7_7_9] )

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











'''simple docstring'''






from typing import TYPE_CHECKING

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


lowercase						:   int      						=							{
    '''configuration_xlm_prophetnet''': ['''XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLMProphetNetConfig'''],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase						:   Any      						=							['''XLMProphetNetTokenizer''']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase						:   Tuple      						=							[
          '''XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''XLMProphetNetDecoder''',
          '''XLMProphetNetEncoder''',
          '''XLMProphetNetForCausalLM''',
          '''XLMProphetNetForConditionalGeneration''',
          '''XLMProphetNetModel''',
          '''XLMProphetNetPreTrainedModel''',
      ]


if TYPE_CHECKING:
      from .configuration_xlm_prophetnet import XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMProphetNetConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_xlm_prophetnet import XLMProphetNetTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_xlm_prophetnet import (
                XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                XLMProphetNetDecoder,
                XLMProphetNetEncoder,
                XLMProphetNetForCausalLM,
                XLMProphetNetForConditionalGeneration,
                XLMProphetNetModel,
                XLMProphetNetPreTrainedModel,
            )

else:
      import sys

      lowercase						:   Optional[Any]      						=							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




