'''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


__lowercase				   =  False






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


			'''simple docstring'''






			def 							a_	(       self):




					"""simple docstring"""


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



			def 							a_	(       self):




					"""simple docstring"""


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

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

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

					with tempfile.TemporaryDirectory() as tmpdirname:
							pipe.save_pretrained(__lowerCAmelCase)
							lowerCAmelCase        =  VersatileDiffusionDualGuidedPipeline.from_pretrained(__lowerCAmelCase)

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

					lowerCAmelCase        =  generator.manual_seed(0)
					lowerCAmelCase        =  pipe(
					    prompt="""first prompt"""	,   image=__lowerCAmelCase	,   text_to_image_strength=0.75	,   generator=__lowerCAmelCase	,   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 							a_	(       self):




					"""simple docstring"""


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

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

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

					assert image.shape == (1, 512, 512, 3)
					lowerCAmelCase        =  np.array([0.0787, 0.0849, 0.0826, 0.0812, 0.0807, 0.0795, 0.0818, 0.0798, 0.0779])

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





'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowercase				   =  {
    '''configuration_bart''': ['''BART_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BartConfig''', '''BartOnnxConfig'''],
    '''tokenization_bart''': ['''BartTokenizer'''],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowercase				   =  ['''BartTokenizerFast''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowercase				   =  [
						    '''BART_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''BartForCausalLM''',
						    '''BartForConditionalGeneration''',
						    '''BartForQuestionAnswering''',
						    '''BartForSequenceClassification''',
						    '''BartModel''',
						    '''BartPretrainedModel''',
						    '''PretrainedBartModel''',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowercase				   =  [
						    '''TFBartForConditionalGeneration''',
						    '''TFBartForSequenceClassification''',
						    '''TFBartModel''',
						    '''TFBartPretrainedModel''',
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowercase				   =  [
						    '''FlaxBartDecoderPreTrainedModel''',
						    '''FlaxBartForCausalLM''',
						    '''FlaxBartForConditionalGeneration''',
						    '''FlaxBartForQuestionAnswering''',
						    '''FlaxBartForSequenceClassification''',
						    '''FlaxBartModel''',
						    '''FlaxBartPreTrainedModel''',
						]

if TYPE_CHECKING:
						from .configuration_bart import BART_PRETRAINED_CONFIG_ARCHIVE_MAP, BartConfig, BartOnnxConfig
						from .tokenization_bart import BartTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_bart_fast import BartTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_bart import (
												    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
												    BartForCausalLM,
												    BartForConditionalGeneration,
												    BartForQuestionAnswering,
												    BartForSequenceClassification,
												    BartModel,
												    BartPretrainedModel,
												    PretrainedBartModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_bart import (
												    TFBartForConditionalGeneration,
												    TFBartForSequenceClassification,
												    TFBartModel,
												    TFBartPretrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_bart import (
												    FlaxBartDecoderPreTrainedModel,
												    FlaxBartForCausalLM,
												    FlaxBartForConditionalGeneration,
												    FlaxBartForQuestionAnswering,
												    FlaxBartForSequenceClassification,
												    FlaxBartModel,
												    FlaxBartPreTrainedModel,
												)

else:
						import sys

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




