


"""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


_lowerCamelCase  =	False


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





				"""simple docstring"""







				def 	lowerCAmelCase__		(    self  ):
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()



				def 	lowerCAmelCase__		(    self  ):
								SCREAMING_SNAKE_CASE__  				=					VersatileDiffusionDualGuidedPipeline.from_pretrained("shi-labs/versatile-diffusion"  )
								# remove text_unet
								pipe.remove_unused_weights()
								pipe.to(UpperCAmelCase__  )
								pipe.set_progress_bar_config(disable=UpperCAmelCase__  )

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

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

								with tempfile.TemporaryDirectory() as tmpdirname:
												pipe.save_pretrained(UpperCAmelCase__  )
												SCREAMING_SNAKE_CASE__  				=					VersatileDiffusionDualGuidedPipeline.from_pretrained(UpperCAmelCase__  )

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

								SCREAMING_SNAKE_CASE__  				=					generator.manual_seed(0  )
								SCREAMING_SNAKE_CASE__  				=					pipe(
								    prompt="first prompt"							,		image=UpperCAmelCase__							,		text_to_image_strength=0.75							,		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  ):
								SCREAMING_SNAKE_CASE__  				=					VersatileDiffusionDualGuidedPipeline.from_pretrained("shi-labs/versatile-diffusion"  )
								pipe.remove_unused_weights()
								pipe.to(UpperCAmelCase__  )
								pipe.set_progress_bar_config(disable=UpperCAmelCase__  )

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

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

								assert image.shape == (1, 512, 512, 3)
								SCREAMING_SNAKE_CASE__  				=					np.array([0.0_787, 0.0_849, 0.0_826, 0.0_812, 0.0_807, 0.0_795, 0.0_818, 0.0_798, 0.0_779]  )

								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_tokenizers_available,
    is_torch_available,
)


_lowerCamelCase  =	{
    'configuration_open_llama': ['OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OpenLlamaConfig'],
}

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

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

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase  =	[
	    'OpenLlamaForCausalLM',
	    'OpenLlamaModel',
	    'OpenLlamaPreTrainedModel',
	    'OpenLlamaForSequenceClassification',
	]


if TYPE_CHECKING:
	from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from transformers import LlamaTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from transformers import LlamaTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_open_llama import (
		    OpenLlamaForCausalLM,
		    OpenLlamaForSequenceClassification,
		    OpenLlamaModel,
		    OpenLlamaPreTrainedModel,
		)


else:
	import sys

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