





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   A      (_lowerCAmelCase       ,					unittest.TestCase   ):
			'''simple docstring'''




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






			@property
			def        a_	(      self       :   str       )     ->     int:







					"""simple docstring"""






					return 32






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







					"""simple docstring"""






					return 32






			@property
			def        a_	(      self       :   str       )     ->     Dict:







					"""simple docstring"""






					return self.time_input_dim






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







					"""simple docstring"""






					return self.time_input_dim * 4






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







					"""simple docstring"""






					return 1_00






			@property
			def        a_	(      self       :   Dict       )     ->     List[Any]:







					"""simple docstring"""






					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(**_lowercase       )
					return model






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







					"""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        a_	(      self       :   List[str]       )     ->     Dict:







					"""simple docstring"""






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






			def        a_	(      self       :   Optional[Any]       )     ->     List[Any]:







					"""simple docstring"""






					A__  				=       self.dummy_unet
					A__  				=       self.dummy_movq

					A__  				=       DDIMScheduler(
					    num_train_timesteps=10_00							,     beta_schedule="""linear"""							,     beta_start=0.0_0_0_8_5							,     beta_end=0.0_1_2							,     clip_sample=_lowercase							,     set_alpha_to_one=_lowercase							,     steps_offset=1							,     prediction_type="""epsilon"""							,     thresholding=_lowercase							,     )

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

					return components






			def        a_	(      self       :   Union[str, Any]							,     __lowerCAmelCase       :   Optional[Any]							,     __lowerCAmelCase       :   Optional[Any]=0       )     ->     Tuple:







					"""simple docstring"""






					A__  				=       floats_tensor((1, self.text_embedder_hidden_size)							,     rng=random.Random(_lowercase       )       ).to(_lowercase       )
					A__  				=       floats_tensor((1, self.text_embedder_hidden_size)							,     rng=random.Random(seed + 1       )       ).to(
					    _lowercase       )
					# create init_image
					A__  				=       floats_tensor((1, 3, 64, 64)							,     rng=random.Random(_lowercase       )       ).to(_lowercase       )
					A__  				=       image.cpu().permute(0							,     2							,     3							,     1       )[0]
					A__  				=       Image.fromarray(np.uinta(_lowercase       )       ).convert("""RGB"""       ).resize((2_56, 2_56)       )
					# create mask
					A__  				=       np.ones((64, 64)							,     dtype=np.floataa       )
					A__  				=       0

					if str(_lowercase       ).startswith("""mps"""       ):
							A__  				=       torch.manual_seed(_lowercase       )
					else:
							A__  				=       torch.Generator(device=_lowercase       ).manual_seed(_lowercase       )
					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       :   int       )     ->     Union[str, 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.images

					A__  				=       pipe(
					    **self.get_dummy_inputs(_lowercase       )							,     return_dict=_lowercase							,     )[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(
					    [0.5_0_7_7_5_9_0_3, 0.4_9_5_2_7_1_9_5, 0.4_8_8_2_4_5_4_3, 0.5_0_1_9_2_2_3_7, 0.4_8_6_4_4_9_0_6, 0.4_9_3_7_3_8_1_4, 0.4_7_8_0_5_9_8, 0.4_7_2_3_4_8_2_7, 0.4_8_3_2_7_8_4_8]       )

					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       :   Union[str, Any]       )     ->     str:







					"""simple docstring"""






					super().test_inference_batch_single_identical(expected_max_diff=3e-3       )


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




			def        a_	(      self       :   Optional[Any]       )     ->     Union[str, Any]:







					"""simple docstring"""






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







			def        a_	(      self       :   Any       )     ->     int:







					"""simple docstring"""






					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((7_68, 7_68)							,     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(_lowercase       )

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

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

					A__  				=       pipeline(
					    image=_lowercase							,     mask_image=_lowercase							,     image_embeds=_lowercase							,     negative_image_embeds=_lowercase							,     generator=_lowercase							,     num_inference_steps=1_00							,     height=7_68							,     width=7_68							,     output_type="""np"""							,     )

					A__  				=       output.images[0]

					assert image.shape == (7_68, 7_68, 3)

					assert_mean_pixel_difference(_lowercase							,     _lowercase       )










from typing import List, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


A   : Optional[Any]			=				logging.get_logger(__name__)

A   : List[str]			=				{
    '''huggingface/time-series-transformer-tourism-monthly''': (
        '''https://huggingface.co/huggingface/time-series-transformer-tourism-monthly/resolve/main/config.json'''
    ),
    # See all TimeSeriesTransformer models at https://huggingface.co/models?filter=time_series_transformer
}


class   A      (SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''




			__lowerCamelCase     :	Any          =   '''time_series_transformer'''
			__lowerCamelCase     :	Dict          =   {
			    '''hidden_size''': '''d_model''',
			    '''num_attention_heads''': '''encoder_attention_heads''',
			    '''num_hidden_layers''': '''encoder_layers''',
			}






			def __init__(      self       :   Union[str, Any]							,     __lowerCAmelCase       :   Optional[int] = None							,     __lowerCAmelCase       :   Optional[int] = None							,     __lowerCAmelCase       :   str = "student_t"							,     __lowerCAmelCase       :   str = "nll"							,     __lowerCAmelCase       :   int = 1							,     __lowerCAmelCase       :   List[int] = [1, 2, 3, 4, 5, 6, 7]							,     __lowerCAmelCase       :   Optional[Union[str, bool]] = "mean"							,     __lowerCAmelCase       :   int = 0							,     __lowerCAmelCase       :   int = 0							,     __lowerCAmelCase       :   int = 0							,     __lowerCAmelCase       :   int = 0							,     __lowerCAmelCase       :   Optional[List[int]] = None							,     __lowerCAmelCase       :   Optional[List[int]] = None							,     __lowerCAmelCase       :   int = 32							,     __lowerCAmelCase       :   int = 32							,     __lowerCAmelCase       :   int = 2							,     __lowerCAmelCase       :   int = 2							,     __lowerCAmelCase       :   int = 2							,     __lowerCAmelCase       :   int = 2							,     __lowerCAmelCase       :   bool = True							,     __lowerCAmelCase       :   str = "gelu"							,     __lowerCAmelCase       :   int = 64							,     __lowerCAmelCase       :   float = 0.1							,     __lowerCAmelCase       :   float = 0.1							,     __lowerCAmelCase       :   float = 0.1							,     __lowerCAmelCase       :   float = 0.1							,     __lowerCAmelCase       :   float = 0.1							,     __lowerCAmelCase       :   int = 1_00							,     __lowerCAmelCase       :   float = 0.0_2							,     __lowerCAmelCase       :   Optional[Any]=True							,     **__lowerCAmelCase       :   Union[str, Any]							,     )     ->     Optional[int]:







					"""simple docstring"""






					A__  				=       prediction_length
					A__  				=       context_length or prediction_length
					A__  				=       distribution_output
					A__  				=       loss
					A__  				=       input_size
					A__  				=       num_time_features
					A__  				=       lags_sequence
					A__  				=       scaling
					A__  				=       num_dynamic_real_features
					A__  				=       num_static_real_features
					A__  				=       num_static_categorical_features
					if cardinality and num_static_categorical_features > 0:
							if len(__lowerCAmelCase       ) != num_static_categorical_features:
									raise ValueError(
									    """The cardinality should be a list of the same length as `num_static_categorical_features`"""       )
							A__  				=       cardinality
					else:
							A__  				=       [0]
					if embedding_dimension and num_static_categorical_features > 0:
							if len(__lowerCAmelCase       ) != num_static_categorical_features:
									raise ValueError(
									    """The embedding dimension should be a list of the same length as `num_static_categorical_features`"""       )
							A__  				=       embedding_dimension
					else:
							A__  				=       [min(50							,     (cat + 1) // 2       ) for cat in self.cardinality]
					A__  				=       num_parallel_samples

					# Transformer architecture configuration
					A__  				=       input_size * len(__lowerCAmelCase       ) + self._number_of_features
					A__  				=       d_model
					A__  				=       encoder_attention_heads
					A__  				=       decoder_attention_heads
					A__  				=       encoder_ffn_dim
					A__  				=       decoder_ffn_dim
					A__  				=       encoder_layers
					A__  				=       decoder_layers

					A__  				=       dropout
					A__  				=       attention_dropout
					A__  				=       activation_dropout
					A__  				=       encoder_layerdrop
					A__  				=       decoder_layerdrop

					A__  				=       activation_function
					A__  				=       init_std

					A__  				=       use_cache

					super().__init__(is_encoder_decoder=__lowerCAmelCase							,     **__lowerCAmelCase       )







			@property
			def        a_	(      self       :   List[Any]       )     ->     int:







					"""simple docstring"""






					return (
					    sum(self.embedding_dimension       )
					    + self.num_dynamic_real_features
					    + self.num_time_features
					    + self.num_static_real_features
					    + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
					)



