


"""simple docstring"""






import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer

from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()





class      lowerCamelCase__   (     A     ,    A     ,    A     ,    unittest.TestCase				):







					"""simple docstring"""


					__a	       =							AltDiffusionPipeline
					__a	       =							TEXT_TO_IMAGE_PARAMS
					__a	       =							TEXT_TO_IMAGE_BATCH_PARAMS
					__a	       =							TEXT_TO_IMAGE_IMAGE_PARAMS
					__a	       =							TEXT_TO_IMAGE_IMAGE_PARAMS






					def lowerCamelCase__							(       self    : int  ):
												'''simple docstring'''






												torch.manual_seed(0  )
												__UpperCAmelCase   :						Optional[Any]      							=					UNetaDConditionModel(
												    block_out_channels=(32, 64) ,       layers_per_block=2 ,       sample_size=32 ,       in_channels=4 ,       out_channels=4 ,       down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") ,       up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") ,       cross_attention_dim=32 ,       )
												__UpperCAmelCase   :						Any      							=					DDIMScheduler(
												    beta_start=0.00085 ,       beta_end=0.012 ,       beta_schedule="""scaled_linear""" ,       clip_sample=UpperCamelCase ,       set_alpha_to_one=UpperCamelCase ,       )
												torch.manual_seed(0  )
												__UpperCAmelCase   :						int      							=					AutoencoderKL(
												    block_out_channels=[32, 64] ,       in_channels=3 ,       out_channels=3 ,       down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] ,       up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] ,       latent_channels=4 ,       )

												# TODO: address the non-deterministic text encoder (fails for save-load tests)
												# torch.manual_seed(0)
												# text_encoder_config = RobertaSeriesConfig(
												#     hidden_size=32,
												#     project_dim=32,
												#     intermediate_size=37,
												#     layer_norm_eps=1e-05,
												#     num_attention_heads=4,
												#     num_hidden_layers=5,
												#     vocab_size=5002,
												# )
												# text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)

												torch.manual_seed(0  )
												__UpperCAmelCase   :						Optional[int]      							=					CLIPTextConfig(
												    bos_token_id=0 ,       eos_token_id=2 ,       hidden_size=32 ,       projection_dim=32 ,       intermediate_size=37 ,       layer_norm_eps=1e-0_5 ,       num_attention_heads=4 ,       num_hidden_layers=5 ,       pad_token_id=1 ,       vocab_size=5_002 ,       )
												__UpperCAmelCase   :						int      							=					CLIPTextModel(UpperCamelCase  )

												__UpperCAmelCase   :						Optional[Any]      							=					XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta"""  )
												__UpperCAmelCase   :						Union[str, Any]      							=					77

												__UpperCAmelCase   :						Optional[int]      							=					{
												    """unet""": unet,
												    """scheduler""": scheduler,
												    """vae""": vae,
												    """text_encoder""": text_encoder,
												    """tokenizer""": tokenizer,
												    """safety_checker""": None,
												    """feature_extractor""": None,
												}
												return components






					def lowerCamelCase__							(       self    : Union[str, Any] ,       UpperCamelCase    : Tuple ,       UpperCamelCase    : Tuple=0  ):
												'''simple docstring'''






												if str(UpperCamelCase  ).startswith("""mps"""  ):
																			__UpperCAmelCase   :						Optional[Any]      							=					torch.manual_seed(UpperCamelCase  )
												else:
																			__UpperCAmelCase   :						str      							=					torch.Generator(device=UpperCamelCase  ).manual_seed(UpperCamelCase  )
												__UpperCAmelCase   :						Union[str, Any]      							=					{
												    """prompt""": """A painting of a squirrel eating a burger""",
												    """generator""": generator,
												    """num_inference_steps""": 2,
												    """guidance_scale""": 6.0,
												    """output_type""": """numpy""",
												}
												return inputs






					def lowerCamelCase__							(       self    : str  ):
												'''simple docstring'''






												super().test_attention_slicing_forward_pass(expected_max_diff=3e-3  )






					def lowerCamelCase__							(       self    : Optional[Any]  ):
												'''simple docstring'''






												super().test_inference_batch_single_identical(expected_max_diff=3e-3  )






					def lowerCamelCase__							(       self    : List[Any]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Union[str, Any]      							=					"""cpu"""  # ensure determinism for the device-dependent torch.Generator

												__UpperCAmelCase   :						List[Any]      							=					self.get_dummy_components()
												torch.manual_seed(0  )
												__UpperCAmelCase   :						Dict      							=					RobertaSeriesConfig(
												    hidden_size=32 ,       project_dim=32 ,       intermediate_size=37 ,       layer_norm_eps=1e-0_5 ,       num_attention_heads=4 ,       num_hidden_layers=5 ,       vocab_size=5_002 ,       )
												# TODO: remove after fixing the non-deterministic text encoder
												__UpperCAmelCase   :						Optional[Any]      							=					RobertaSeriesModelWithTransformation(UpperCamelCase  )
												__UpperCAmelCase   :						Optional[int]      							=					text_encoder

												__UpperCAmelCase   :						Optional[Any]      							=					AltDiffusionPipeline(**UpperCamelCase  )
												__UpperCAmelCase   :						Tuple      							=					alt_pipe.to(UpperCamelCase  )
												alt_pipe.set_progress_bar_config(disable=UpperCamelCase  )

												__UpperCAmelCase   :						Any      							=					self.get_dummy_inputs(UpperCamelCase  )
												__UpperCAmelCase   :						int      							=					"""A photo of an astronaut"""
												__UpperCAmelCase   :						Any      							=					alt_pipe(**UpperCamelCase  )
												__UpperCAmelCase   :						Dict      							=					output.images
												__UpperCAmelCase   :						List[Any]      							=					image[0, -3:, -3:, -1]

												assert image.shape == (1, 64, 64, 3)
												__UpperCAmelCase   :						List[Any]      							=					np.array(
												    [0.5748162, 0.60447145, 0.48821217, 0.50100636, 0.5431185, 0.45763683, 0.49657696, 0.48132733, 0.47573093]  )

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






					def lowerCamelCase__							(       self    : List[str]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Optional[Any]      							=					"""cpu"""  # ensure determinism for the device-dependent torch.Generator

												__UpperCAmelCase   :						str      							=					self.get_dummy_components()
												__UpperCAmelCase   :						Any      							=					PNDMScheduler(skip_prk_steps=UpperCamelCase  )
												torch.manual_seed(0  )
												__UpperCAmelCase   :						Dict      							=					RobertaSeriesConfig(
												    hidden_size=32 ,       project_dim=32 ,       intermediate_size=37 ,       layer_norm_eps=1e-0_5 ,       num_attention_heads=4 ,       num_hidden_layers=5 ,       vocab_size=5_002 ,       )
												# TODO: remove after fixing the non-deterministic text encoder
												__UpperCAmelCase   :						Tuple      							=					RobertaSeriesModelWithTransformation(UpperCamelCase  )
												__UpperCAmelCase   :						Optional[int]      							=					text_encoder
												__UpperCAmelCase   :						List[Any]      							=					AltDiffusionPipeline(**UpperCamelCase  )
												__UpperCAmelCase   :						str      							=					alt_pipe.to(UpperCamelCase  )
												alt_pipe.set_progress_bar_config(disable=UpperCamelCase  )

												__UpperCAmelCase   :						Tuple      							=					self.get_dummy_inputs(UpperCamelCase  )
												__UpperCAmelCase   :						List[str]      							=					alt_pipe(**UpperCamelCase  )
												__UpperCAmelCase   :						Dict      							=					output.images
												__UpperCAmelCase   :						Optional[Any]      							=					image[0, -3:, -3:, -1]

												assert image.shape == (1, 64, 64, 3)
												__UpperCAmelCase   :						Any      							=					np.array(
												    [0.51605093, 0.5707241, 0.47365507, 0.50578886, 0.5633877, 0.4642503, 0.5182081, 0.48763484, 0.49084237]  )

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






@slow
@require_torch_gpu
class      lowerCamelCase__   (     unittest.TestCase				):







					"""simple docstring"""


					def lowerCamelCase__							(       self    : Any  ):
												'''simple docstring'''






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






					def lowerCamelCase__							(       self    : str  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Any      							=					AltDiffusionPipeline.from_pretrained("""BAAI/AltDiffusion""" ,       safety_checker=UpperCamelCase  )
												__UpperCAmelCase   :						Dict      							=					alt_pipe.to(UpperCamelCase  )
												alt_pipe.set_progress_bar_config(disable=UpperCamelCase  )

												__UpperCAmelCase   :						Optional[Any]      							=					"""A painting of a squirrel eating a burger"""
												__UpperCAmelCase   :						Optional[Any]      							=					torch.manual_seed(0  )
												__UpperCAmelCase   :						Any      							=					alt_pipe([prompt] ,       generator=UpperCamelCase ,       guidance_scale=6.0 ,       num_inference_steps=20 ,       output_type="""np"""  )

												__UpperCAmelCase   :						List[Any]      							=					output.images

												__UpperCAmelCase   :						Union[str, Any]      							=					image[0, -3:, -3:, -1]

												assert image.shape == (1, 512, 512, 3)
												__UpperCAmelCase   :						Optional[int]      							=					np.array([0.1010, 0.0800, 0.0794, 0.0885, 0.0843, 0.0762, 0.0769, 0.0729, 0.0586]  )

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






					def lowerCamelCase__							(       self    : Union[str, Any]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						List[Any]      							=					DDIMScheduler.from_pretrained("""BAAI/AltDiffusion""" ,       subfolder="""scheduler"""  )

												__UpperCAmelCase   :						Union[str, Any]      							=					AltDiffusionPipeline.from_pretrained("""BAAI/AltDiffusion""" ,       scheduler=UpperCamelCase ,       safety_checker=UpperCamelCase  )
												__UpperCAmelCase   :						Optional[Any]      							=					alt_pipe.to(UpperCamelCase  )
												alt_pipe.set_progress_bar_config(disable=UpperCamelCase  )

												__UpperCAmelCase   :						int      							=					"""A painting of a squirrel eating a burger"""
												__UpperCAmelCase   :						Optional[Any]      							=					torch.manual_seed(0  )

												__UpperCAmelCase   :						List[str]      							=					alt_pipe([prompt] ,       generator=UpperCamelCase ,       num_inference_steps=2 ,       output_type="""numpy"""  )
												__UpperCAmelCase   :						Union[str, Any]      							=					output.images

												__UpperCAmelCase   :						str      							=					image[0, -3:, -3:, -1]

												assert image.shape == (1, 512, 512, 3)
												__UpperCAmelCase   :						Optional[Any]      							=					np.array([0.4019, 0.4052, 0.3810, 0.4119, 0.3916, 0.3982, 0.4651, 0.4195, 0.5323]  )

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




"""simple docstring"""






# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCAmelCase    :     Union[str, Any]   						=		{'configuration_mra': ['MRA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MraConfig']}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase    :     int   						=		[
							    'MRA_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'MraForMaskedLM',
							    'MraForMultipleChoice',
							    'MraForQuestionAnswering',
							    'MraForSequenceClassification',
							    'MraForTokenClassification',
							    'MraLayer',
							    'MraModel',
							    'MraPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mra import (
														    MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MraForMaskedLM,
														    MraForMultipleChoice,
														    MraForQuestionAnswering,
														    MraForSequenceClassification,
														    MraForTokenClassification,
														    MraLayer,
														    MraModel,
														    MraPreTrainedModel,
														)
else:
							import sys

							UpperCAmelCase    :     Tuple   						=		_LazyModule(__name__, globals()['__file__'], _import_structure)
