


import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()



class 							A					( UpperCAmelCase__				,						unittest.TestCase							):
					'''simple docstring'''






					A__    =				KandinskyPipeline
					A__    =				[
					    '''prompt''',
					    '''image_embeds''',
					    '''negative_image_embeds''',
					]
					A__    =				['''prompt''', '''negative_prompt''', '''image_embeds''', '''negative_image_embeds''']
					A__    =				[
					    '''generator''',
					    '''height''',
					    '''width''',
					    '''latents''',
					    '''guidance_scale''',
					    '''negative_prompt''',
					    '''num_inference_steps''',
					    '''return_dict''',
					    '''guidance_scale''',
					    '''num_images_per_prompt''',
					    '''output_type''',
					    '''return_dict''',
					]
					A__    =				False

					@property
					def 						lowerCamelCase__  (self					:		List[str]	)		->     List[Any]:




							"""simple docstring"""


							return 32

					@property
					def 						lowerCamelCase__  (self					:		List[str]	)		->     List[str]:




							"""simple docstring"""


							return 32

					@property
					def 						lowerCamelCase__  (self					:		str	)		->     Optional[Any]:




							"""simple docstring"""


							return self.time_input_dim

					@property
					def 						lowerCamelCase__  (self					:		Any	)		->     Tuple:




							"""simple docstring"""


							return self.time_input_dim * 4

					@property
					def 						lowerCamelCase__  (self					:		Any	)		->     List[str]:




							"""simple docstring"""


							return 100

					@property
					def 						lowerCamelCase__  (self					:		Optional[int]	)		->     Union[str, Any]:




							"""simple docstring"""


							lowercase__      =	XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""	)
							return tokenizer

					@property
					def 						lowerCamelCase__  (self					:		Any	)		->     Tuple:




							"""simple docstring"""


							torch.manual_seed(0	)
							lowercase__      =	MCLIPConfig(
							    numDims=self.cross_attention_dim   ,      transformerDimensions=self.text_embedder_hidden_size   ,      hidden_size=self.text_embedder_hidden_size   ,      intermediate_size=37   ,      num_attention_heads=4   ,      num_hidden_layers=5   ,      vocab_size=1005   ,      )

							lowercase__      =	MultilingualCLIP(_UpperCAmelCase	)
							lowercase__      =	text_encoder.eval()

							return text_encoder

					@property
					def 						lowerCamelCase__  (self					:		int	)		->     Union[str, Any]:




							"""simple docstring"""


							torch.manual_seed(0	)

							lowercase__      =	{
							    """in_channels""": 4,
							    # Out channels is double in channels because predicts mean and variance
							    """out_channels""": 8,
							    """addition_embed_type""": """text_image""",
							    """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
							    """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
							    """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
							    """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
							    """layers_per_block""": 1,
							    """encoder_hid_dim""": self.text_embedder_hidden_size,
							    """encoder_hid_dim_type""": """text_image_proj""",
							    """cross_attention_dim""": self.cross_attention_dim,
							    """attention_head_dim""": 4,
							    """resnet_time_scale_shift""": """scale_shift""",
							    """class_embed_type""": None,
							}

							lowercase__      =	UNetaDConditionModel(**_UpperCAmelCase	)
							return model

					@property
					def 						lowerCamelCase__  (self					:		str	)		->     List[str]:




							"""simple docstring"""


							return {
							    "block_out_channels": [32, 64],
							    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
							    "in_channels": 3,
							    "latent_channels": 4,
							    "layers_per_block": 1,
							    "norm_num_groups": 8,
							    "norm_type": "spatial",
							    "num_vq_embeddings": 12,
							    "out_channels": 3,
							    "up_block_types": [
							        "AttnUpDecoderBlock2D",
							        "UpDecoderBlock2D",
							    ],
							    "vq_embed_dim": 4,
							}

					@property
					def 						lowerCamelCase__  (self					:		int	)		->     Tuple:




							"""simple docstring"""


							torch.manual_seed(0	)
							lowercase__      =	VQModel(**self.dummy_movq_kwargs	)
							return model

					def 						lowerCamelCase__  (self					:		Tuple	)		->     int:




							"""simple docstring"""


							lowercase__      =	self.dummy_text_encoder
							lowercase__      =	self.dummy_tokenizer
							lowercase__      =	self.dummy_unet
							lowercase__      =	self.dummy_movq

							lowercase__      =	DDIMScheduler(
							    num_train_timesteps=1000   ,      beta_schedule="""linear"""   ,      beta_start=0.00_085   ,      beta_end=0.012   ,      clip_sample=_UpperCAmelCase   ,      set_alpha_to_one=_UpperCAmelCase   ,      steps_offset=1   ,      prediction_type="""epsilon"""   ,      thresholding=_UpperCAmelCase   ,      )

							lowercase__      =	{
							    """text_encoder""": text_encoder,
							    """tokenizer""": tokenizer,
							    """unet""": unet,
							    """scheduler""": scheduler,
							    """movq""": movq,
							}
							return components

					def 						lowerCamelCase__  (self					:		List[Any]   ,      _UpperCAmelCase					:		Dict   ,      _UpperCAmelCase					:		Dict=0	)		->     Tuple:




							"""simple docstring"""


							lowercase__      =	floats_tensor((1, self.cross_attention_dim)   ,      rng=random.Random(_UpperCAmelCase	)	).to(_UpperCAmelCase	)
							lowercase__      =	floats_tensor((1, self.cross_attention_dim)   ,      rng=random.Random(seed + 1	)	).to(_UpperCAmelCase	)
							if str(_UpperCAmelCase	).startswith("""mps"""	):
									lowercase__      =	torch.manual_seed(_UpperCAmelCase	)
							else:
									lowercase__      =	torch.Generator(device=_UpperCAmelCase	).manual_seed(_UpperCAmelCase	)
							lowercase__      =	{
							    """prompt""": """horse""",
							    """image_embeds""": image_embeds,
							    """negative_image_embeds""": negative_image_embeds,
							    """generator""": generator,
							    """height""": 64,
							    """width""": 64,
							    """guidance_scale""": 4.0,
							    """num_inference_steps""": 2,
							    """output_type""": """np""",
							}
							return inputs


					def 						lowerCamelCase__  (self					:		Union[str, Any]	)		->     Tuple:




							"""simple docstring"""


							lowercase__      =	"""cpu"""

							lowercase__      =	self.get_dummy_components()

							lowercase__      =	self.pipeline_class(**_UpperCAmelCase	)
							lowercase__      =	pipe.to(_UpperCAmelCase	)

							pipe.set_progress_bar_config(disable=_UpperCAmelCase	)

							lowercase__      =	pipe(**self.get_dummy_inputs(_UpperCAmelCase	)	)
							lowercase__      =	output.images

							lowercase__      =	pipe(
							    **self.get_dummy_inputs(_UpperCAmelCase	)   ,      return_dict=_UpperCAmelCase   ,      )[0]

							lowercase__      =	image[0, -3:, -3:, -1]
							lowercase__      =	image_from_tuple[0, -3:, -3:, -1]

							assert image.shape == (1, 64, 64, 3)

							lowercase__      =	np.array(
							    [0.328_663, 1.0, 0.23_216_873, 1.0, 0.92_717_564, 0.4_639_046, 0.96_894_777, 0.31_713_378, 0.6_293_953]	)

							assert (
							    np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2
							), f''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
							assert (
							    np.abs(image_from_tuple_slice.flatten() - expected_slice	).max() < 1E-2
							), f''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''






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






					def 						lowerCamelCase__  (self					:		Dict	)		->     Any:




							"""simple docstring"""


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


					def 						lowerCamelCase__  (self					:		List[str]	)		->     Optional[int]:




							"""simple docstring"""


							lowercase__      =	load_numpy(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
							    """/kandinsky/kandinsky_text2img_cat_fp16.npy"""	)

							lowercase__      =	KandinskyPriorPipeline.from_pretrained(
							    """kandinsky-community/kandinsky-2-1-prior"""   ,      torch_dtype=torch.floataa	)
							pipe_prior.to(_UpperCAmelCase	)

							lowercase__      =	KandinskyPipeline.from_pretrained("""kandinsky-community/kandinsky-2-1"""   ,      torch_dtype=torch.floataa	)
							lowercase__      =	pipeline.to(_UpperCAmelCase	)
							pipeline.set_progress_bar_config(disable=_UpperCAmelCase	)

							lowercase__      =	"""red cat, 4k photo"""

							lowercase__      =	torch.Generator(device="""cuda"""	).manual_seed(0	)
							lowercase__    ,			lowercase__      =	pipe_prior(
							    _UpperCAmelCase   ,      generator=_UpperCAmelCase   ,      num_inference_steps=5   ,      negative_prompt=""""""   ,      ).to_tuple()

							lowercase__      =	torch.Generator(device="""cuda"""	).manual_seed(0	)
							lowercase__      =	pipeline(
							    _UpperCAmelCase   ,      image_embeds=_UpperCAmelCase   ,      negative_image_embeds=_UpperCAmelCase   ,      generator=_UpperCAmelCase   ,      num_inference_steps=100   ,      output_type="""np"""   ,      )

							lowercase__      =	output.images[0]

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

							assert_mean_pixel_difference(_UpperCAmelCase   ,      _UpperCAmelCase	)





from typing import TYPE_CHECKING

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


A :	str												=     {
    'configuration_nystromformer': ['NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'NystromformerConfig'],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A :	Optional[int]												=     [
	    'NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'NystromformerForMaskedLM',
	    'NystromformerForMultipleChoice',
	    'NystromformerForQuestionAnswering',
	    'NystromformerForSequenceClassification',
	    'NystromformerForTokenClassification',
	    'NystromformerLayer',
	    'NystromformerModel',
	    'NystromformerPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_nystromformer import NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, NystromformerConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_nystromformer import (
		    NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    NystromformerForMaskedLM,
		    NystromformerForMultipleChoice,
		    NystromformerForQuestionAnswering,
		    NystromformerForSequenceClassification,
		    NystromformerForTokenClassification,
		    NystromformerLayer,
		    NystromformerModel,
		    NystromformerPreTrainedModel,
		)


else:
	import sys

	A :	Optional[int]												=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

