


'''simple docstring'''



import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionAttendAndExcitePipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_numpy, skip_mps, slow
from diffusers.utils.testing_utils import 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


__lowercase	=     False





@skip_mps
class 							__UpperCamelCase    (		lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				unittest.TestCase ):
	A_           =   StableDiffusionAttendAndExcitePipeline
	A_           =   False
	A_           =   TEXT_TO_IMAGE_PARAMS
	A_           =   TEXT_TO_IMAGE_BATCH_PARAMS.union({"token_indices"} )
	A_           =   TEXT_TO_IMAGE_IMAGE_PARAMS
	A_           =   TEXT_TO_IMAGE_IMAGE_PARAMS




	@classmethod
	def 				__UpperCAmelCase							(       cls							):

						'''simple docstring'''





						super().setUpClass()
						torch.use_deterministic_algorithms(__a							)




	@classmethod
	def 				__UpperCAmelCase							(       cls							):

						'''simple docstring'''





						super().tearDownClass()
						torch.use_deterministic_algorithms(__a							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						torch.manual_seed(0							)
						__a      :	Tuple    =  UNetaDConditionModel(
						    block_out_channels=(32, 64)						,    layers_per_block=1						,    sample_size=32						,    in_channels=4						,    out_channels=4						,    down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')						,    up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')						,    cross_attention_dim=32						,    attention_head_dim=(2, 4)						,    use_linear_projection=__a						,    )
						__a      :	List[Any]    =  DDIMScheduler(
						    beta_start=0.00085						,    beta_end=0.012						,    beta_schedule='scaled_linear'						,    clip_sample=__a						,    set_alpha_to_one=__a						,    )
						torch.manual_seed(0							)
						__a      :	List[Any]    =  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						,    sample_size=128						,    )
						torch.manual_seed(0							)
						__a      :	Optional[Any]    =  CLIPTextConfig(
						    bos_token_id=0						,    eos_token_id=2						,    hidden_size=32						,    intermediate_size=37						,    layer_norm_eps=1E-0_5						,    num_attention_heads=4						,    num_hidden_layers=5						,    pad_token_id=1						,    vocab_size=1000						,    hidden_act='gelu'						,    projection_dim=512						,    )
						__a      :	List[Any]    =  CLIPTextModel(__a							)
						__a      :	Optional[Any]    =  CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'							)

						__a      :	List[str]    =  {
						    'unet': unet,
						    'scheduler': scheduler,
						    'vae': vae,
						    'text_encoder': text_encoder,
						    'tokenizer': tokenizer,
						    'safety_checker': None,
						    'feature_extractor': None,
						}

						return components




	def 				__UpperCAmelCase							(       self						,    __a						,    __a=0							):

						'''simple docstring'''





						if str(__a							).startswith('mps'							):
											__a      :	List[Any]    =  torch.manual_seed(__a							)
						else:
											__a      :	Tuple    =  torch.Generator(device=__a							).manual_seed(__a							)
						__a      :	str    =  {
						    'prompt': 'a cat and a frog',
						    'token_indices': [2, 5],
						    'generator': generator,
						    'num_inference_steps': 1,
						    'guidance_scale': 6.0,
						    'output_type': 'numpy',
						    'max_iter_to_alter': 2,
						    'thresholds': {0: 0.7},
						}
						return inputs




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Any    =  'cpu'

						__a      :	List[Any]    =  self.get_dummy_components()
						__a      :	List[Any]    =  self.pipeline_class(**__a							)
						pipe.to(__a							)
						pipe.set_progress_bar_config(disable=__a							)

						__a      :	str    =  self.get_dummy_inputs(__a							)
						__a      :	str    =  pipe(**__a							).images
						__a      :	Dict    =  image[0, -3:, -3:, -1]

						self.assertEqual(image.shape						,    (1, 64, 64, 3)							)
						__a      :	int    =  np.array(
						    [0.63905364, 0.62897307, 0.48599017, 0.5133624, 0.5550048, 0.45769516, 0.50326973, 0.5023139, 0.45384496]							)
						__a      :	Optional[int]    =  np.abs(image_slice.flatten() - expected_slice							).max()
						self.assertLessEqual(__a						,    1E-3							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						super().test_cpu_offload_forward_pass(expected_max_diff=5E-4							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						self._test_inference_batch_consistent(batch_sizes=[1, 2]							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						self._test_inference_batch_single_identical(batch_size=2						,    expected_max_diff=7E-4							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						super().test_pt_np_pil_outputs_equivalent(expected_max_diff=5E-4							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						super().test_save_load_local(expected_max_difference=5E-4							)





	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						super().test_save_load_optional_components(expected_max_difference=4E-4							)





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




	@classmethod
	def 				__UpperCAmelCase							(       cls							):

						'''simple docstring'''





						super().setUpClass()
						torch.use_deterministic_algorithms(__a							)




	@classmethod
	def 				__UpperCAmelCase							(       cls							):

						'''simple docstring'''





						super().tearDownClass()
						torch.use_deterministic_algorithms(__a							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





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





	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Any    =  torch.manual_seed(51							)

						__a      :	List[str]    =  StableDiffusionAttendAndExcitePipeline.from_pretrained(
						    'CompVis/stable-diffusion-v1-4'						,    safety_checker=__a						,    torch_dtype=torch.floataa							)
						pipe.to('cuda'							)

						__a      :	Any    =  'a painting of an elephant with glasses'
						__a      :	str    =  [5, 7]

						__a      :	Any    =  pipe(
						    prompt=__a						,    token_indices=__a						,    guidance_scale=7.5						,    generator=__a						,    num_inference_steps=5						,    max_iter_to_alter=5						,    output_type='numpy'						,    ).images[0]

						__a      :	Tuple    =  load_numpy(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/attend-and-excite/elephant_glasses.npy'							)
						assert np.abs((expected_image - image).max()							) < 5E-1







'''simple docstring'''



from typing import TYPE_CHECKING

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


__lowercase     :						Union[str, Any]	=     {
    'configuration_roc_bert': ['ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoCBertConfig'],
    'tokenization_roc_bert': ['RoCBertTokenizer'],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	pass

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowercase     :						List[str]	=     [
	    'ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'RoCBertForCausalLM',
	    'RoCBertForMaskedLM',
	    'RoCBertForMultipleChoice',
	    'RoCBertForPreTraining',
	    'RoCBertForQuestionAnswering',
	    'RoCBertForSequenceClassification',
	    'RoCBertForTokenClassification',
	    'RoCBertLayer',
	    'RoCBertModel',
	    'RoCBertPreTrainedModel',
	    'load_tf_weights_in_roc_bert',
	]

if TYPE_CHECKING:
	from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
	from .tokenization_roc_bert import RoCBertTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		raise OptionalDependencyNotAvailable()

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_roc_bert import (
		    ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    RoCBertForCausalLM,
		    RoCBertForMaskedLM,
		    RoCBertForMultipleChoice,
		    RoCBertForPreTraining,
		    RoCBertForQuestionAnswering,
		    RoCBertForSequenceClassification,
		    RoCBertForTokenClassification,
		    RoCBertLayer,
		    RoCBertModel,
		    RoCBertPreTrainedModel,
		    load_tf_weights_in_roc_bert,
		)


else:
	import sys

	__lowercase     :						Any	=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



