





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


_UpperCAmelCase										=					False



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

       def lowercase			(		self:			Dict		)							->			List[Any]:




              """simple docstring"""


              super().tearDown()
              gc.collect()
              torch.cuda.empty_cache()

       def lowercase			(		self:			int		)							->			Optional[Any]:




              """simple docstring"""


              UpperCamelCase_ 							=	VersatileDiffusionDualGuidedPipeline.from_pretrained("shi-labs/versatile-diffusion"		)
              # remove text_unet
              pipe.remove_unused_weights()
              pipe.to(_SCREAMING_SNAKE_CASE		)
              pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_ 							=	load_image(
                  "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"		)

              UpperCamelCase_ 							=	torch.manual_seed(0		)
              UpperCamelCase_ 							=	pipe(
                  prompt="first prompt"       , image=_SCREAMING_SNAKE_CASE       , text_to_image_strength=0.75       , generator=_SCREAMING_SNAKE_CASE       , guidance_scale=7.5       , num_inference_steps=2       , output_type="numpy"       , ).images

              with tempfile.TemporaryDirectory() as tmpdirname:
                     pipe.save_pretrained(_SCREAMING_SNAKE_CASE		)
                     UpperCamelCase_ 							=	VersatileDiffusionDualGuidedPipeline.from_pretrained(_SCREAMING_SNAKE_CASE		)

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

              UpperCamelCase_ 							=	generator.manual_seed(0		)
              UpperCamelCase_ 							=	pipe(
                  prompt="first prompt"       , image=_SCREAMING_SNAKE_CASE       , text_to_image_strength=0.75       , generator=_SCREAMING_SNAKE_CASE       , 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 lowercase			(		self:			Tuple		)							->			List[str]:




              """simple docstring"""


              UpperCamelCase_ 							=	VersatileDiffusionDualGuidedPipeline.from_pretrained("shi-labs/versatile-diffusion"		)
              pipe.remove_unused_weights()
              pipe.to(_SCREAMING_SNAKE_CASE		)
              pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_ 							=	"cyberpunk 2077"
              UpperCamelCase_ 							=	load_image(
                  "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"		)
              UpperCamelCase_ 							=	torch.manual_seed(0		)
              UpperCamelCase_ 							=	pipe(
                  prompt=_SCREAMING_SNAKE_CASE       , image=_SCREAMING_SNAKE_CASE       , text_to_image_strength=0.75       , generator=_SCREAMING_SNAKE_CASE       , guidance_scale=7.5       , num_inference_steps=50       , output_type="numpy"       , ).images

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

              assert image.shape == (1, 512, 512, 3)
              UpperCamelCase_ 							=	np.array([0.07_87, 0.08_49, 0.08_26, 0.08_12, 0.08_07, 0.07_95, 0.08_18, 0.07_98, 0.07_79]		)

              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_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase_	 ={
    """configuration_camembert""": ["""CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CamembertConfig""", """CamembertOnnxConfig"""],
}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase_	 =["""CamembertTokenizer"""]

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase_	 =["""CamembertTokenizerFast"""]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase_	 =[
						    """CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """CamembertForCausalLM""",
						    """CamembertForMaskedLM""",
						    """CamembertForMultipleChoice""",
						    """CamembertForQuestionAnswering""",
						    """CamembertForSequenceClassification""",
						    """CamembertForTokenClassification""",
						    """CamembertModel""",
						    """CamembertPreTrainedModel""",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase_	 =[
						    """TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """TFCamembertForCausalLM""",
						    """TFCamembertForMaskedLM""",
						    """TFCamembertForMultipleChoice""",
						    """TFCamembertForQuestionAnswering""",
						    """TFCamembertForSequenceClassification""",
						    """TFCamembertForTokenClassification""",
						    """TFCamembertModel""",
						    """TFCamembertPreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_camembert import CamembertTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_camembert_fast import CamembertTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_camembert import (
												    CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    CamembertForCausalLM,
												    CamembertForMaskedLM,
												    CamembertForMultipleChoice,
												    CamembertForQuestionAnswering,
												    CamembertForSequenceClassification,
												    CamembertForTokenClassification,
												    CamembertModel,
												    CamembertPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_camembert import (
												    TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFCamembertForCausalLM,
												    TFCamembertForMaskedLM,
												    TFCamembertForMultipleChoice,
												    TFCamembertForQuestionAnswering,
												    TFCamembertForSequenceClassification,
												    TFCamembertForTokenClassification,
												    TFCamembertModel,
												    TFCamembertPreTrainedModel,
												)

else:
						import sys

						UpperCamelCase_	 =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



