



import unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class    SCREAMING_SNAKE_CASE			(							lowerCamelCase__			,      unittest.TestCase					):
							__lowerCamelCase	:	Optional[int]    =KandinskyVaaPriorPipeline
							__lowerCamelCase	:	Union[str, Any]    =['prompt']
							__lowerCamelCase	:	Optional[Any]    =['prompt', 'negative_prompt']
							__lowerCamelCase	:	Optional[int]    =[
							    'num_images_per_prompt',
							    'generator',
							    'num_inference_steps',
							    'latents',
							    'negative_prompt',
							    'guidance_scale',
							    'output_type',
							    'return_dict',
							]
							__lowerCamelCase	:	Any    =False





							@property
							def 						UpperCamelCase_					(   self		:		int	):

											'''simple docstring'''
											return 32





							@property
							def 						UpperCamelCase_					(   self		:		Optional[Any]	):

											'''simple docstring'''
											return 32





							@property
							def 						UpperCamelCase_					(   self		:		Union[str, Any]	):

											'''simple docstring'''
											return self.time_input_dim





							@property
							def 						UpperCamelCase_					(   self		:		Union[str, Any]	):

											'''simple docstring'''
											return self.time_input_dim * 4





							@property
							def 						UpperCamelCase_					(   self		:		List[Any]	):

											'''simple docstring'''
											return 100





							@property
							def 						UpperCamelCase_					(   self		:		str	):

											'''simple docstring'''
											__a       =		CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""	)
											return tokenizer





							@property
							def 						UpperCamelCase_					(   self		:		int	):

											'''simple docstring'''
											torch.manual_seed(0	)
											__a       =		CLIPTextConfig(
											    bos_token_id=0      ,				eos_token_id=2      ,				hidden_size=self.text_embedder_hidden_size      ,				projection_dim=self.text_embedder_hidden_size      ,				intermediate_size=37      ,				layer_norm_eps=1E-05      ,				num_attention_heads=4      ,				num_hidden_layers=5      ,				pad_token_id=1      ,				vocab_size=1000      ,				)
											return CLIPTextModelWithProjection(__lowercase	)





							@property
							def 						UpperCamelCase_					(   self		:		List[Any]	):

											'''simple docstring'''
											torch.manual_seed(0	)

											__a       =		{
											    """num_attention_heads""": 2,
											    """attention_head_dim""": 12,
											    """embedding_dim""": self.text_embedder_hidden_size,
											    """num_layers""": 1,
											}

											__a       =		PriorTransformer(**__lowercase	)
											# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
											__a       =		nn.Parameter(torch.ones(model.clip_std.shape	)	)
											return model





							@property
							def 						UpperCamelCase_					(   self		:		Optional[Any]	):

											'''simple docstring'''
											torch.manual_seed(0	)
											__a       =		CLIPVisionConfig(
											    hidden_size=self.text_embedder_hidden_size      ,				image_size=224      ,				projection_dim=self.text_embedder_hidden_size      ,				intermediate_size=37      ,				num_attention_heads=4      ,				num_channels=3      ,				num_hidden_layers=5      ,				patch_size=14      ,				)

											__a       =		CLIPVisionModelWithProjection(__lowercase	)
											return model





							@property
							def 						UpperCamelCase_					(   self		:		str	):

											'''simple docstring'''
											__a       =		CLIPImageProcessor(
											    crop_size=224      ,				do_center_crop=__lowercase      ,				do_normalize=__lowercase      ,				do_resize=__lowercase      ,				image_mean=[0.48145466, 0.4578275, 0.40821073]      ,				image_std=[0.26862954, 0.26130258, 0.27577711]      ,				resample=3      ,				size=224      ,				)

											return image_processor





							def 						UpperCamelCase_					(   self		:		Optional[Any]	):

											'''simple docstring'''
											__a       =		self.dummy_prior
											__a       =		self.dummy_image_encoder
											__a       =		self.dummy_text_encoder
											__a       =		self.dummy_tokenizer
											__a       =		self.dummy_image_processor

											__a       =		UnCLIPScheduler(
											    variance_type="""fixed_small_log"""      ,				prediction_type="""sample"""      ,				num_train_timesteps=1000      ,				clip_sample=__lowercase      ,				clip_sample_range=10.0      ,				)

											__a       =		{
											    """prior""": prior,
											    """image_encoder""": image_encoder,
											    """text_encoder""": text_encoder,
											    """tokenizer""": tokenizer,
											    """scheduler""": scheduler,
											    """image_processor""": image_processor,
											}

											return components





							def 						UpperCamelCase_					(   self		:		Dict      ,				__lowercase		:		int      ,				__lowercase		:		List[str]=0	):

											'''simple docstring'''
											if str(__lowercase	).startswith("""mps"""	):
															__a       =		torch.manual_seed(__lowercase	)
											else:
															__a       =		torch.Generator(device=__lowercase	).manual_seed(__lowercase	)
											__a       =		{
											    """prompt""": """horse""",
											    """generator""": generator,
											    """guidance_scale""": 4.0,
											    """num_inference_steps""": 2,
											    """output_type""": """np""",
											}
											return inputs





							def 						UpperCamelCase_					(   self		:		Any	):

											'''simple docstring'''
											__a       =		"""cpu"""

											__a       =		self.get_dummy_components()

											__a       =		self.pipeline_class(**__lowercase	)
											__a       =		pipe.to(__lowercase	)

											pipe.set_progress_bar_config(disable=__lowercase	)

											__a       =		pipe(**self.get_dummy_inputs(__lowercase	)	)
											__a       =		output.image_embeds

											__a       =		pipe(
											    **self.get_dummy_inputs(__lowercase	)      ,				return_dict=__lowercase      ,				)[0]

											__a       =		image[0, -10:]
											__a       =		image_from_tuple[0, -10:]

											assert image.shape == (1, 32)

											__a       =		np.array(
											    [-0.0532, 1.7120, 0.3656, -1.0852, -0.8946, -1.1756, 0.4348, 0.2482, 0.5146, -0.1156]	)

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





							@skip_mps
							def 						UpperCamelCase_					(   self		:		int	):

											'''simple docstring'''
											__a       =		torch_device == """cpu"""
											__a       =		True
											__a       =		False

											self._test_inference_batch_single_identical(
											    test_max_difference=__lowercase      ,				relax_max_difference=__lowercase      ,				test_mean_pixel_difference=__lowercase      ,				)







							@skip_mps
							def 						UpperCamelCase_					(   self		:		Dict	):

											'''simple docstring'''
											__a       =		torch_device == """cpu"""
											__a       =		False

											self._test_attention_slicing_forward_pass(
											    test_max_difference=__lowercase      ,				test_mean_pixel_difference=__lowercase      ,				)






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__)
