



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    SCREAMING_SNAKE_CASE			(							__lowerCAmelCase			,      __lowerCAmelCase			,      __lowerCAmelCase			,      unittest.TestCase					):
							__lowerCamelCase	:	Optional[Any]    =AltDiffusionPipeline
							__lowerCamelCase	:	Tuple    =TEXT_TO_IMAGE_PARAMS
							__lowerCamelCase	:	Optional[int]    =TEXT_TO_IMAGE_BATCH_PARAMS
							__lowerCamelCase	:	Dict    =TEXT_TO_IMAGE_IMAGE_PARAMS
							__lowerCamelCase	:	Any    =TEXT_TO_IMAGE_IMAGE_PARAMS





							def 						UpperCamelCase_					(   self		:		int	):

											'''simple docstring'''
											torch.manual_seed(0	)
											__a       =		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      ,				)
											__a       =		DDIMScheduler(
											    beta_start=0.00085      ,				beta_end=0.012      ,				beta_schedule="""scaled_linear"""      ,				clip_sample=lowerCamelCase__      ,				set_alpha_to_one=lowerCamelCase__      ,				)
											torch.manual_seed(0	)
											__a       =		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	)
											__a       =		CLIPTextConfig(
											    bos_token_id=0      ,				eos_token_id=2      ,				hidden_size=32      ,				projection_dim=32      ,				intermediate_size=37      ,				layer_norm_eps=1E-05      ,				num_attention_heads=4      ,				num_hidden_layers=5      ,				pad_token_id=1      ,				vocab_size=5002      ,				)
											__a       =		CLIPTextModel(lowerCamelCase__	)

											__a       =		XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta"""	)
											__a       =		77

											__a       =		{
											    '''unet''': unet,
											    '''scheduler''': scheduler,
											    '''vae''': vae,
											    '''text_encoder''': text_encoder,
											    '''tokenizer''': tokenizer,
											    '''safety_checker''': None,
											    '''feature_extractor''': None,
											}
											return components





							def 						UpperCamelCase_					(   self		:		Tuple      ,				__lowercase		:		Optional[int]      ,				__lowercase		:		Tuple=0	):

											'''simple docstring'''






											if str(lowerCamelCase__	).startswith("""mps"""	):
															__a       =		torch.manual_seed(lowerCamelCase__	)
											else:
															__a       =		torch.Generator(device=lowerCamelCase__	).manual_seed(lowerCamelCase__	)
											__a       =		{
											    '''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 						UpperCamelCase_					(   self		:		int	):

											'''simple docstring'''
											super().test_attention_slicing_forward_pass(expected_max_diff=3E-3	)





							def 						UpperCamelCase_					(   self		:		List[str]	):

											'''simple docstring'''
											super().test_inference_batch_single_identical(expected_max_diff=3E-3	)





							def 						UpperCamelCase_					(   self		:		int	):

											'''simple docstring'''
											__a       =		'''cpu'''  # ensure determinism for the device-dependent torch.Generator

											__a       =		self.get_dummy_components()
											torch.manual_seed(0	)
											__a       =		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      ,				)
											# TODO: remove after fixing the non-deterministic text encoder
											__a       =		RobertaSeriesModelWithTransformation(lowerCamelCase__	)
											__a       =		text_encoder

											__a       =		AltDiffusionPipeline(**lowerCamelCase__	)
											__a       =		alt_pipe.to(lowerCamelCase__	)
											alt_pipe.set_progress_bar_config(disable=lowerCamelCase__	)

											__a       =		self.get_dummy_inputs(lowerCamelCase__	)
											__a       =		'''A photo of an astronaut'''
											__a       =		alt_pipe(**lowerCamelCase__	)
											__a       =		output.images
											__a       =		image[0, -3:, -3:, -1]

											assert image.shape == (1, 64, 64, 3)
											__a       =		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 						UpperCamelCase_					(   self		:		Optional[int]	):

											'''simple docstring'''
											__a       =		'''cpu'''  # ensure determinism for the device-dependent torch.Generator

											__a       =		self.get_dummy_components()
											__a       =		PNDMScheduler(skip_prk_steps=lowerCamelCase__	)
											torch.manual_seed(0	)
											__a       =		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      ,				)
											# TODO: remove after fixing the non-deterministic text encoder
											__a       =		RobertaSeriesModelWithTransformation(lowerCamelCase__	)
											__a       =		text_encoder
											__a       =		AltDiffusionPipeline(**lowerCamelCase__	)
											__a       =		alt_pipe.to(lowerCamelCase__	)
											alt_pipe.set_progress_bar_config(disable=lowerCamelCase__	)

											__a       =		self.get_dummy_inputs(lowerCamelCase__	)
											__a       =		alt_pipe(**lowerCamelCase__	)
											__a       =		output.images
											__a       =		image[0, -3:, -3:, -1]

											assert image.shape == (1, 64, 64, 3)
											__a       =		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    SCREAMING_SNAKE_CASE			(							unittest.TestCase					):





							def 						UpperCamelCase_					(   self		:		Dict	):

											'''simple docstring'''
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()





							def 						UpperCamelCase_					(   self		:		Any	):

											'''simple docstring'''
											__a       =		AltDiffusionPipeline.from_pretrained("""BAAI/AltDiffusion"""      ,				safety_checker=lowerCamelCase__	)
											__a       =		alt_pipe.to(lowerCamelCase__	)
											alt_pipe.set_progress_bar_config(disable=lowerCamelCase__	)

											__a       =		'''A painting of a squirrel eating a burger'''
											__a       =		torch.manual_seed(0	)
											__a       =		alt_pipe([prompt]      ,				generator=lowerCamelCase__      ,				guidance_scale=6.0      ,				num_inference_steps=20      ,				output_type="""np"""	)

											__a       =		output.images

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

											assert image.shape == (1, 512, 512, 3)
											__a       =		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 						UpperCamelCase_					(   self		:		Any	):

											'''simple docstring'''
											__a       =		DDIMScheduler.from_pretrained("""BAAI/AltDiffusion"""      ,				subfolder="""scheduler"""	)

											__a       =		AltDiffusionPipeline.from_pretrained("""BAAI/AltDiffusion"""      ,				scheduler=lowerCamelCase__      ,				safety_checker=lowerCamelCase__	)
											__a       =		alt_pipe.to(lowerCamelCase__	)
											alt_pipe.set_progress_bar_config(disable=lowerCamelCase__	)

											__a       =		'''A painting of a squirrel eating a burger'''
											__a       =		torch.manual_seed(0	)

											__a       =		alt_pipe([prompt]      ,				generator=lowerCamelCase__      ,				num_inference_steps=2      ,				output_type="""numpy"""	)
											__a       =		output.images

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

											assert image.shape == (1, 512, 512, 3)
											__a       =		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






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCamelCase__			= {
    """configuration_nezha""": ["""NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NezhaConfig"""],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__			= [
						    """NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """NezhaForNextSentencePrediction""",
						    """NezhaForMaskedLM""",
						    """NezhaForPreTraining""",
						    """NezhaForMultipleChoice""",
						    """NezhaForQuestionAnswering""",
						    """NezhaForSequenceClassification""",
						    """NezhaForTokenClassification""",
						    """NezhaModel""",
						    """NezhaPreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_nezha import (
												    NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
												    NezhaForMaskedLM,
												    NezhaForMultipleChoice,
												    NezhaForNextSentencePrediction,
												    NezhaForPreTraining,
												    NezhaForQuestionAnswering,
												    NezhaForSequenceClassification,
												    NezhaForTokenClassification,
												    NezhaModel,
												    NezhaPreTrainedModel,
												)


else:
						import sys

						lowerCamelCase__			= _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
