


"""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       ):







				def     lowerCamelCase_    (   self      ):

					'''simple docstring'''






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







				def     lowerCamelCase_    (   self      ):

					'''simple docstring'''






					lowerCAmelCase_			       = VersatileDiffusionDualGuidedPipeline.from_pretrained('''shi-labs/versatile-diffusion'''      )
					# remove text_unet
					pipe.remove_unused_weights()
					pipe.to(UpperCAmelCase      )
					pipe.set_progress_bar_config(disable=UpperCAmelCase      )

					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=UpperCAmelCase			,  text_to_image_strength=0.7_5			,  generator=UpperCAmelCase			,  guidance_scale=7.5			,  num_inference_steps=2			,  output_type='''numpy'''			,  ).images

					with tempfile.TemporaryDirectory() as tmpdirname:
						pipe.save_pretrained(UpperCAmelCase      )
						lowerCAmelCase_			       = VersatileDiffusionDualGuidedPipeline.from_pretrained(UpperCAmelCase      )

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

					lowerCAmelCase_			       = generator.manual_seed(0      )
					lowerCAmelCase_			       = pipe(
					    prompt='''first prompt'''			,  image=UpperCAmelCase			,  text_to_image_strength=0.7_5			,  generator=UpperCAmelCase			,  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     lowerCamelCase_    (   self      ):

					'''simple docstring'''






					lowerCAmelCase_			       = VersatileDiffusionDualGuidedPipeline.from_pretrained('''shi-labs/versatile-diffusion'''      )
					pipe.remove_unused_weights()
					pipe.to(UpperCAmelCase      )
					pipe.set_progress_bar_config(disable=UpperCAmelCase      )

					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=UpperCAmelCase			,  image=UpperCAmelCase			,  text_to_image_strength=0.7_5			,  generator=UpperCAmelCase			,  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([0.0_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_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase_							 =       {'configuration_pegasus': ['PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PegasusConfig']}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase_							 =       ['PegasusTokenizer']

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

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase_							 =       [
		    'PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'PegasusForCausalLM',
		    'PegasusForConditionalGeneration',
		    'PegasusModel',
		    'PegasusPreTrainedModel',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase_							 =       [
		    'TFPegasusForConditionalGeneration',
		    'TFPegasusModel',
		    'TFPegasusPreTrainedModel',
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase_							 =       [
		    'FlaxPegasusForConditionalGeneration',
		    'FlaxPegasusModel',
		    'FlaxPegasusPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_pegasus import PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusConfig

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_pegasus import PegasusTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_pegasus_fast import PegasusTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_pegasus import (
				    PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
				    PegasusForCausalLM,
				    PegasusForConditionalGeneration,
				    PegasusModel,
				    PegasusPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_pegasus import TFPegasusForConditionalGeneration, TFPegasusModel, TFPegasusPreTrainedModel

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_pegasus import (
				    FlaxPegasusForConditionalGeneration,
				    FlaxPegasusModel,
				    FlaxPegasusPreTrainedModel,
				)

else:
		import sys

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