



import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaInpaintPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, 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  __snake_case      (   SCREAMING_SNAKE_CASE			,unittest.TestCase):







							'''simple docstring'''






							UpperCamelCase__				:      Optional[int]         =					KandinskyVaaInpaintPipeline
							UpperCamelCase__				:      List[str]         =					["""image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
							UpperCamelCase__				:      Any         =					[
							    """image_embeds""",
							    """negative_image_embeds""",
							    """image""",
							    """mask_image""",
							]
							UpperCamelCase__				:      Optional[int]         =					[
							    """generator""",
							    """height""",
							    """width""",
							    """latents""",
							    """guidance_scale""",
							    """num_inference_steps""",
							    """return_dict""",
							    """guidance_scale""",
							    """num_images_per_prompt""",
							    """output_type""",
							    """return_dict""",
							]
							UpperCamelCase__				:      Union[str, Any]         =					False


							@property
							def _a      (							self		):
											return 32


							@property
							def _a      (							self		):
											return 32


							@property
							def _a      (							self		):
											return self.time_input_dim


							@property
							def _a      (							self		):
											return self.time_input_dim * 4


							@property
							def _a      (							self		):
											return 100


							@property
							def _a      (							self		):
											torch.manual_seed(0		)

											a__				      =		{
											    """in_channels""": 9,
											    # Out channels is double in channels because predicts mean and variance
											    """out_channels""": 8,
											    """addition_embed_type""": """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""": """image_proj""",
											    """cross_attention_dim""": self.cross_attention_dim,
											    """attention_head_dim""": 4,
											    """resnet_time_scale_shift""": """scale_shift""",
											    """class_embed_type""": None,
											}

											a__				      =		UNetaDConditionModel(**a_		)
											return model


							@property
							def _a      (							self		):
											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 _a      (							self		):
											torch.manual_seed(0		)
											a__				      =		VQModel(**self.dummy_movq_kwargs		)
											return model


							def _a      (							self		):
											a__				      =		self.dummy_unet
											a__				      =		self.dummy_movq

											a__				      =		DDIMScheduler(
											    num_train_timesteps=1_000  ,  beta_schedule="""linear"""  ,  beta_start=0.00_085  ,  beta_end=0.012  ,  clip_sample=a_  ,  set_alpha_to_one=a_  ,  steps_offset=1  ,  prediction_type="""epsilon"""  ,  thresholding=a_  ,  )

											a__				      =		{
											    """unet""": unet,
											    """scheduler""": scheduler,
											    """movq""": movq,
											}

											return components


							def _a      (							self  ,  a_  ,  a_=0		):
											a__				      =		floats_tensor((1, self.text_embedder_hidden_size)  ,  rng=random.Random(a_		)		).to(a_		)
											a__				      =		floats_tensor((1, self.text_embedder_hidden_size)  ,  rng=random.Random(seed + 1		)		).to(
											    a_		)
											# create init_image
											a__				      =		floats_tensor((1, 3, 64, 64)  ,  rng=random.Random(a_		)		).to(a_		)
											a__				      =		image.cpu().permute(0  ,  2  ,  3  ,  1		)[0]
											a__				      =		Image.fromarray(np.uinta(a_		)		).convert("""RGB"""		).resize((256, 256)		)
											# create mask
											a__				      =		np.ones((64, 64)  ,  dtype=np.floataa		)
											a__				      =		0

											if str(a_		).startswith("""mps"""		):
															a__				      =		torch.manual_seed(a_		)
											else:
															a__				      =		torch.Generator(device=a_		).manual_seed(a_		)
											a__				      =		{
											    """image""": init_image,
											    """mask_image""": mask,
											    """image_embeds""": image_embeds,
											    """negative_image_embeds""": negative_image_embeds,
											    """generator""": generator,
											    """height""": 64,
											    """width""": 64,
											    """num_inference_steps""": 2,
											    """guidance_scale""": 4.0,
											    """output_type""": """np""",
											}
											return inputs


							def _a      (							self		):
											a__				      =		"""cpu"""

											a__				      =		self.get_dummy_components()

											a__				      =		self.pipeline_class(**a_		)
											a__				      =		pipe.to(a_		)

											pipe.set_progress_bar_config(disable=a_		)

											a__				      =		pipe(**self.get_dummy_inputs(a_		)		)
											a__				      =		output.images

											a__				      =		pipe(
											    **self.get_dummy_inputs(a_		)  ,  return_dict=a_  ,  )[0]

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

											print(F'''image.shape {image.shape}'''		)

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

											a__				      =		np.array(
											    [0.50_775_903, 0.49_527_195, 0.48_824_543, 0.50_192_237, 0.48_644_906, 0.49_373_814, 0.4_780_598, 0.47_234_827, 0.48_327_848]		)

											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()}'''







							def _a      (							self		):
											super().test_inference_batch_single_identical(expected_max_diff=3E-3		)




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







							'''simple docstring'''






							def _a      (							self		):
											# clean up the VRAM after each test
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()







							def _a      (							self		):
											a__				      =		load_numpy(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
											    """/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy"""		)

											a__				      =		load_image(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""		)
											a__				      =		np.ones((768, 768)  ,  dtype=np.floataa		)
											a__				      =		0

											a__				      =		"""a hat"""

											a__				      =		KandinskyVaaPriorPipeline.from_pretrained(
											    """kandinsky-community/kandinsky-2-2-prior"""  ,  torch_dtype=torch.floataa		)
											pipe_prior.to(a_		)

											a__				      =		KandinskyVaaInpaintPipeline.from_pretrained(
											    """kandinsky-community/kandinsky-2-2-decoder-inpaint"""  ,  torch_dtype=torch.floataa		)
											a__				      =		pipeline.to(a_		)
											pipeline.set_progress_bar_config(disable=a_		)

											a__				      =		torch.Generator(device="""cpu"""		).manual_seed(0		)
											a__      ,			a__				      =		pipe_prior(
											    a_  ,  generator=a_  ,  num_inference_steps=5  ,  negative_prompt=""""""  ,  ).to_tuple()

											a__				      =		pipeline(
											    image=a_  ,  mask_image=a_  ,  image_embeds=a_  ,  negative_image_embeds=a_  ,  generator=a_  ,  num_inference_steps=100  ,  height=768  ,  width=768  ,  output_type="""np"""  ,  )

											a__				      =		output.images[0]

											assert image.shape == (768, 768, 3)

											assert_mean_pixel_difference(a_  ,  a_		)






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase							 =					{
    """configuration_xlm_roberta""": [
        """XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """XLMRobertaConfig""",
        """XLMRobertaOnnxConfig""",
    ],
}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase							 =					["""XLMRobertaTokenizer"""]

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase							 =					["""XLMRobertaTokenizerFast"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase							 =					[
	    """XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """XLMRobertaForCausalLM""",
	    """XLMRobertaForMaskedLM""",
	    """XLMRobertaForMultipleChoice""",
	    """XLMRobertaForQuestionAnswering""",
	    """XLMRobertaForSequenceClassification""",
	    """XLMRobertaForTokenClassification""",
	    """XLMRobertaModel""",
	    """XLMRobertaPreTrainedModel""",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase							 =					[
	    """TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFXLMRobertaForCausalLM""",
	    """TFXLMRobertaForMaskedLM""",
	    """TFXLMRobertaForMultipleChoice""",
	    """TFXLMRobertaForQuestionAnswering""",
	    """TFXLMRobertaForSequenceClassification""",
	    """TFXLMRobertaForTokenClassification""",
	    """TFXLMRobertaModel""",
	    """TFXLMRobertaPreTrainedModel""",
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase							 =					[
	    """FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """FlaxXLMRobertaForMaskedLM""",
	    """FlaxXLMRobertaForCausalLM""",
	    """FlaxXLMRobertaForMultipleChoice""",
	    """FlaxXLMRobertaForQuestionAnswering""",
	    """FlaxXLMRobertaForSequenceClassification""",
	    """FlaxXLMRobertaForTokenClassification""",
	    """FlaxXLMRobertaModel""",
	    """FlaxXLMRobertaPreTrainedModel""",
	]

if TYPE_CHECKING:
	from .configuration_xlm_roberta import (
	    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    XLMRobertaConfig,
	    XLMRobertaOnnxConfig,
	)

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_xlm_roberta import XLMRobertaTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_xlm_roberta_fast import XLMRobertaTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_xlm_roberta import (
		    XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
		    XLMRobertaForCausalLM,
		    XLMRobertaForMaskedLM,
		    XLMRobertaForMultipleChoice,
		    XLMRobertaForQuestionAnswering,
		    XLMRobertaForSequenceClassification,
		    XLMRobertaForTokenClassification,
		    XLMRobertaModel,
		    XLMRobertaPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_xlm_roberta import (
		    TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFXLMRobertaForCausalLM,
		    TFXLMRobertaForMaskedLM,
		    TFXLMRobertaForMultipleChoice,
		    TFXLMRobertaForQuestionAnswering,
		    TFXLMRobertaForSequenceClassification,
		    TFXLMRobertaForTokenClassification,
		    TFXLMRobertaModel,
		    TFXLMRobertaPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_xlm_roberta import (
		    FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
		    FlaxXLMRobertaForCausalLM,
		    FlaxXLMRobertaForMaskedLM,
		    FlaxXLMRobertaForMultipleChoice,
		    FlaxXLMRobertaForQuestionAnswering,
		    FlaxXLMRobertaForSequenceClassification,
		    FlaxXLMRobertaForTokenClassification,
		    FlaxXLMRobertaModel,
		    FlaxXLMRobertaPreTrainedModel,
		)

else:
	import sys

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

