



"""simple docstring"""





import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()

@slow
@require_torch_gpu
class 			lowercase__			(      unittest.TestCase    ):
							'''simple docstring'''







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






							def        lowerCamelCase_   (  self						)			->							List[Any]:
									_UpperCAmelCase							 =		StableDiffusionKDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4'						)
									_UpperCAmelCase							 =		sd_pipe.to(snake_case						)
									sd_pipe.set_progress_bar_config(disable=snake_case						)

									sd_pipe.set_scheduler('sample_euler'						)

									_UpperCAmelCase							 =		'A painting of a squirrel eating a burger'
									_UpperCAmelCase							 =		torch.manual_seed(0						)
									_UpperCAmelCase							 =		sd_pipe([prompt]	,       generator=snake_case	,       guidance_scale=9.0	,       num_inference_steps=20	,       output_type='np'						)

									_UpperCAmelCase							 =		output.images

									_UpperCAmelCase							 =		image[0, -3:, -3:, -1]

									assert image.shape == (1, 512, 512, 3)
									_UpperCAmelCase							 =		np.array([0.0447, 0.0492, 0.0468, 0.0408, 0.0383, 0.0408, 0.0354, 0.0380, 0.0339]						)

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






							def        lowerCamelCase_   (  self						)			->							int:
									_UpperCAmelCase							 =		StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'						)
									_UpperCAmelCase							 =		sd_pipe.to(snake_case						)
									sd_pipe.set_progress_bar_config(disable=snake_case						)

									sd_pipe.set_scheduler('sample_euler'						)

									_UpperCAmelCase							 =		'A painting of a squirrel eating a burger'
									_UpperCAmelCase							 =		torch.manual_seed(0						)
									_UpperCAmelCase							 =		sd_pipe([prompt]	,       generator=snake_case	,       guidance_scale=9.0	,       num_inference_steps=20	,       output_type='np'						)

									_UpperCAmelCase							 =		output.images

									_UpperCAmelCase							 =		image[0, -3:, -3:, -1]

									assert image.shape == (1, 512, 512, 3)
									_UpperCAmelCase							 =		np.array([0.1237, 0.1320, 0.1438, 0.1359, 0.1390, 0.1132, 0.1277, 0.1175, 0.1112]						)

									assert np.abs(image_slice.flatten() - expected_slice						).max() < 5E-1






							def        lowerCamelCase_   (  self						)			->							str:
									_UpperCAmelCase							 =		StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'						)
									_UpperCAmelCase							 =		sd_pipe.to(snake_case						)
									sd_pipe.set_progress_bar_config(disable=snake_case						)

									sd_pipe.set_scheduler('sample_dpmpp_2m'						)

									_UpperCAmelCase							 =		'A painting of a squirrel eating a burger'
									_UpperCAmelCase							 =		torch.manual_seed(0						)
									_UpperCAmelCase							 =		sd_pipe(
									    [prompt]	,       generator=snake_case	,       guidance_scale=7.5	,       num_inference_steps=15	,       output_type='np'	,       use_karras_sigmas=snake_case	,       )

									_UpperCAmelCase							 =		output.images

									_UpperCAmelCase							 =		image[0, -3:, -3:, -1]

									assert image.shape == (1, 512, 512, 3)
									_UpperCAmelCase							 =		np.array(
									    [0.11381689, 0.12112921, 0.1389457, 0.12549606, 0.1244964, 0.10831517, 0.11562866, 0.10867816, 0.10499048]						)

									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_roberta''': ['''ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RobertaConfig''', '''RobertaOnnxConfig'''],
    '''tokenization_roberta''': ['''RobertaTokenizer'''],
}

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

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase  							=					[
					    '''ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''RobertaForCausalLM''',
					    '''RobertaForMaskedLM''',
					    '''RobertaForMultipleChoice''',
					    '''RobertaForQuestionAnswering''',
					    '''RobertaForSequenceClassification''',
					    '''RobertaForTokenClassification''',
					    '''RobertaModel''',
					    '''RobertaPreTrainedModel''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase  							=					[
					    '''TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFRobertaForCausalLM''',
					    '''TFRobertaForMaskedLM''',
					    '''TFRobertaForMultipleChoice''',
					    '''TFRobertaForQuestionAnswering''',
					    '''TFRobertaForSequenceClassification''',
					    '''TFRobertaForTokenClassification''',
					    '''TFRobertaMainLayer''',
					    '''TFRobertaModel''',
					    '''TFRobertaPreTrainedModel''',
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase  							=					[
					    '''FlaxRobertaForCausalLM''',
					    '''FlaxRobertaForMaskedLM''',
					    '''FlaxRobertaForMultipleChoice''',
					    '''FlaxRobertaForQuestionAnswering''',
					    '''FlaxRobertaForSequenceClassification''',
					    '''FlaxRobertaForTokenClassification''',
					    '''FlaxRobertaModel''',
					    '''FlaxRobertaPreTrainedModel''',
					]


if TYPE_CHECKING:
					from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
					from .tokenization_roberta import RobertaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_roberta_fast import RobertaTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_roberta import (
										    ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
										    RobertaForCausalLM,
										    RobertaForMaskedLM,
										    RobertaForMultipleChoice,
										    RobertaForQuestionAnswering,
										    RobertaForSequenceClassification,
										    RobertaForTokenClassification,
										    RobertaModel,
										    RobertaPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_roberta import (
										    TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFRobertaForCausalLM,
										    TFRobertaForMaskedLM,
										    TFRobertaForMultipleChoice,
										    TFRobertaForQuestionAnswering,
										    TFRobertaForSequenceClassification,
										    TFRobertaForTokenClassification,
										    TFRobertaMainLayer,
										    TFRobertaModel,
										    TFRobertaPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_roberta import (
										    FlaxRobertaForCausalLM,
										    FlaxRobertaForMaskedLM,
										    FlaxRobertaForMultipleChoice,
										    FlaxRobertaForQuestionAnswering,
										    FlaxRobertaForSequenceClassification,
										    FlaxRobertaForTokenClassification,
										    FlaxRobertaModel,
										    FlaxRobertaPreTrainedModel,
										)

else:
					import sys

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


