






import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
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        lowerCAmelCase_				(      lowercase						,      unittest.TestCase	):







				"""simple docstring"""




				_snake_case				:   List[str]        =			KandinskyInpaintPipeline
				_snake_case				:   int        =			["""prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
				_snake_case				:   Any        =			[
				    """prompt""",
				    """negative_prompt""",
				    """image_embeds""",
				    """negative_image_embeds""",
				    """image""",
				    """mask_image""",
				]
				_snake_case				:   List[Any]        =			[
				    """generator""",
				    """height""",
				    """width""",
				    """latents""",
				    """guidance_scale""",
				    """negative_prompt""",
				    """num_inference_steps""",
				    """return_dict""",
				    """guidance_scale""",
				    """num_images_per_prompt""",
				    """output_type""",
				    """return_dict""",
				]
				_snake_case				:   Tuple        =			False







				@property
				def 				__a   ( self     :Optional[int]     ):
					return 32







				@property
				def 				__a   ( self     :Any     ):
					return 32







				@property
				def 				__a   ( self     :str     ):
					return self.time_input_dim







				@property
				def 				__a   ( self     :List[Any]     ):
					return self.time_input_dim * 4







				@property
				def 				__a   ( self     :Dict     ):
					return 1_00







				@property
				def 				__a   ( self     :List[str]     ):
					UpperCamelCase__ :Any						=      XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""     )
					return tokenizer







				@property
				def 				__a   ( self     :Dict     ):
					torch.manual_seed(0     )
					UpperCamelCase__ :Union[str, Any]						=      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=10_05  ,     )

					UpperCamelCase__ :Optional[Any]						=      MultilingualCLIP(lowerCamelCase__     )
					UpperCamelCase__ :Optional[Any]						=      text_encoder.eval()

					return text_encoder







				@property
				def 				__a   ( self     :Dict     ):
					torch.manual_seed(0     )

					UpperCamelCase__ :Optional[Any]						=      {
					    """in_channels""": 9,
					    # 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,
					}

					UpperCamelCase__ :Optional[Any]						=      UNetaDConditionModel(**lowerCamelCase__     )
					return model







				@property
				def 				__a   ( self     :List[Any]     ):
					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     :str     ):
					torch.manual_seed(0     )
					UpperCamelCase__ :Tuple						=      VQModel(**self.dummy_movq_kwargs     )
					return model







				def 				__a   ( self     :List[str]     ):
					UpperCamelCase__ :Optional[Any]						=      self.dummy_text_encoder
					UpperCamelCase__ :str						=      self.dummy_tokenizer
					UpperCamelCase__ :List[str]						=      self.dummy_unet
					UpperCamelCase__ :Any						=      self.dummy_movq

					UpperCamelCase__ :Optional[int]						=      DDIMScheduler(
					    num_train_timesteps=10_00  ,     beta_schedule="""linear"""  ,     beta_start=0.0_0085  ,     beta_end=0.012  ,     clip_sample=lowerCamelCase__  ,     set_alpha_to_one=lowerCamelCase__  ,     steps_offset=1  ,     prediction_type="""epsilon"""  ,     thresholding=lowerCamelCase__  ,     )

					UpperCamelCase__ :Optional[int]						=      {
					    """text_encoder""": text_encoder,
					    """tokenizer""": tokenizer,
					    """unet""": unet,
					    """scheduler""": scheduler,
					    """movq""": movq,
					}

					return components







				def 				__a   ( self     :Union[str, Any]  ,     lowerCamelCase__     :Optional[Any]  ,     lowerCamelCase__     :int=0     ):
					UpperCamelCase__ :Union[str, Any]						=      floats_tensor((1, self.cross_attention_dim)  ,     rng=random.Random(lowerCamelCase__     )     ).to(lowerCamelCase__     )
					UpperCamelCase__ :int						=      floats_tensor((1, self.cross_attention_dim)  ,     rng=random.Random(seed + 1     )     ).to(lowerCamelCase__     )
					# create init_image
					UpperCamelCase__ :Optional[int]						=      floats_tensor((1, 3, 64, 64)  ,     rng=random.Random(lowerCamelCase__     )     ).to(lowerCamelCase__     )
					UpperCamelCase__ :str						=      image.cpu().permute(0  ,     2  ,     3  ,     1     )[0]
					UpperCamelCase__ :Any						=      Image.fromarray(np.uinta(lowerCamelCase__     )     ).convert("""RGB"""     ).resize((2_56, 2_56)     )
					# create mask
					UpperCamelCase__ :Any						=      np.ones((64, 64)  ,     dtype=np.floataa     )
					UpperCamelCase__ :Optional[int]						=      0

					if str(lowerCamelCase__     ).startswith("""mps"""     ):
						UpperCamelCase__ :List[str]						=      torch.manual_seed(lowerCamelCase__     )
					else:
						UpperCamelCase__ :str						=      torch.Generator(device=lowerCamelCase__     ).manual_seed(lowerCamelCase__     )
					UpperCamelCase__ :List[str]						=      {
					    """prompt""": """horse""",
					    """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     :str     ):
					UpperCamelCase__ :Dict						=      """cpu"""

					UpperCamelCase__ :Any						=      self.get_dummy_components()

					UpperCamelCase__ :List[Any]						=      self.pipeline_class(**lowerCamelCase__     )
					UpperCamelCase__ :Union[str, Any]						=      pipe.to(lowerCamelCase__     )

					pipe.set_progress_bar_config(disable=lowerCamelCase__     )

					UpperCamelCase__ :List[Any]						=      pipe(**self.get_dummy_inputs(lowerCamelCase__     )     )
					UpperCamelCase__ :List[str]						=      output.images

					UpperCamelCase__ :str						=      pipe(
					    **self.get_dummy_inputs(lowerCamelCase__     )  ,     return_dict=lowerCamelCase__  ,     )[0]

					UpperCamelCase__ :str						=      image[0, -3:, -3:, -1]
					UpperCamelCase__ :Union[str, Any]						=      image_from_tuple[0, -3:, -3:, -1]

					print(f"""image.shape {image.shape}"""     )

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

					UpperCamelCase__ :Optional[int]						=      np.array(
					    [0.832_6919, 0.7379_0467, 0.2091_8581, 0.930_9612, 0.551_1791, 0.4371_3328, 0.551_3321, 0.4992_2934, 0.5949_7786]     )

					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     :Optional[Any]     ):
					super().test_inference_batch_single_identical(expected_max_diff=3e-3     )

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







				"""simple docstring"""




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







				def 				__a   ( self     :Union[str, Any]     ):
					UpperCamelCase__ :Union[str, Any]						=      load_numpy(
					    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
					    """/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy"""     )

					UpperCamelCase__ :List[str]						=      load_image(
					    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""     )
					UpperCamelCase__ :List[Any]						=      np.ones((7_68, 7_68)  ,     dtype=np.floataa     )
					UpperCamelCase__ :Dict						=      0

					UpperCamelCase__ :Optional[int]						=      """a hat"""

					UpperCamelCase__ :Dict						=      KandinskyPriorPipeline.from_pretrained(
					    """kandinsky-community/kandinsky-2-1-prior"""  ,     torch_dtype=torch.floataa     )
					pipe_prior.to(lowerCamelCase__     )

					UpperCamelCase__ :Union[str, Any]						=      KandinskyInpaintPipeline.from_pretrained(
					    """kandinsky-community/kandinsky-2-1-inpaint"""  ,     torch_dtype=torch.floataa     )
					UpperCamelCase__ :Optional[Any]						=      pipeline.to(lowerCamelCase__     )
					pipeline.set_progress_bar_config(disable=lowerCamelCase__     )

					UpperCamelCase__ :Optional[Any]						=      torch.Generator(device="""cpu"""     ).manual_seed(0     )
					UpperCamelCase__				,		UpperCamelCase__ :str						=      pipe_prior(
					    lowerCamelCase__  ,     generator=lowerCamelCase__  ,     num_inference_steps=5  ,     negative_prompt=""""""  ,     ).to_tuple()

					UpperCamelCase__ :int						=      pipeline(
					    lowerCamelCase__  ,     image=lowerCamelCase__  ,     mask_image=lowerCamelCase__  ,     image_embeds=lowerCamelCase__  ,     negative_image_embeds=lowerCamelCase__  ,     generator=lowerCamelCase__  ,     num_inference_steps=1_00  ,     height=7_68  ,     width=7_68  ,     output_type="""np"""  ,     )

					UpperCamelCase__ :List[str]						=      output.images[0]

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

					assert_mean_pixel_difference(lowerCamelCase__  ,     lowerCamelCase__     )







"""simple docstring"""
from typing import List, Optional, Union

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


lowerCamelCase_							      = logging.get_logger(__name__)

lowerCamelCase_							      = {
    '''huggingface/informer-tourism-monthly''': (
        '''https://huggingface.co/huggingface/informer-tourism-monthly/resolve/main/config.json'''
    ),
    # See all Informer models at https://huggingface.co/models?filter=informer
}







class      UpperCamelCase_			(__A	):
	__magic_name__       =							'''informer'''
	__magic_name__       =							{
	    '''hidden_size''': '''d_model''',
	    '''num_attention_heads''': '''encoder_attention_heads''',
	    '''num_hidden_layers''': '''encoder_layers''',
	}





	def __init__( self   :					Optional[Any]      ,      lowerCAmelCase_   :					Optional[int] = None      ,      lowerCAmelCase_   :					Optional[int] = None      ,      lowerCAmelCase_   :					str = "student_t"      ,      lowerCAmelCase_   :					str = "nll"      ,      lowerCAmelCase_   :					int = 1      ,      lowerCAmelCase_   :					List[int] = None      ,      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 = 64      ,      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_   :					float = 0.0_5      ,      lowerCAmelCase_   :					float = 0.1      ,      lowerCAmelCase_   :					float = 0.1      ,      lowerCAmelCase_   :					float = 0.1      ,      lowerCAmelCase_   :					float = 0.1      ,      lowerCAmelCase_   :					int = 100      ,      lowerCAmelCase_   :					float = 0.0_2      ,      lowerCAmelCase_   :					List[Any]=True      ,      lowerCAmelCase_   :					str = "prob"      ,      lowerCAmelCase_   :					int = 5      ,      lowerCAmelCase_   :					bool = True      ,      **lowerCAmelCase_   :					Tuple      ,      )		->     Tuple:
				# time series specific configuration
				UpperCAmelCase_	:					str     	=   prediction_length
				UpperCAmelCase_	:					Tuple     	=   context_length or prediction_length
				UpperCAmelCase_	:					Any     	=   distribution_output
				UpperCAmelCase_	:					Union[str, Any]     	=   loss
				UpperCAmelCase_	:					Any     	=   input_size
				UpperCAmelCase_	:					int     	=   num_time_features
				UpperCAmelCase_	:					Union[str, Any]     	=   lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
				UpperCAmelCase_	:					List[Any]     	=   scaling
				UpperCAmelCase_	:					List[str]     	=   num_dynamic_real_features
				UpperCAmelCase_	:					int     	=   num_static_real_features
				UpperCAmelCase_	:					Any     	=   num_static_categorical_features

				# set cardinality
				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`"		)
							UpperCAmelCase_	:					Optional[Any]     	=   cardinality
				else:
							UpperCAmelCase_	:					Optional[int]     	=   [0]

				# set embedding_dimension
				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`"		)
							UpperCAmelCase_	:					Any     	=   embedding_dimension
				else:
							UpperCAmelCase_	:					Optional[Any]     	=   [min(50      ,      (cat + 1) // 2		) for cat in self.cardinality]

				UpperCAmelCase_	:					Dict     	=   num_parallel_samples

				# Transformer architecture configuration
				UpperCAmelCase_	:					Optional[Any]     	=   input_size * len(self.lags_sequence		) + self._number_of_features
				UpperCAmelCase_	:					List[Any]     	=   d_model
				UpperCAmelCase_	:					List[str]     	=   encoder_attention_heads
				UpperCAmelCase_	:					List[str]     	=   decoder_attention_heads
				UpperCAmelCase_	:					Union[str, Any]     	=   encoder_ffn_dim
				UpperCAmelCase_	:					Union[str, Any]     	=   decoder_ffn_dim
				UpperCAmelCase_	:					Tuple     	=   encoder_layers
				UpperCAmelCase_	:					List[Any]     	=   decoder_layers

				UpperCAmelCase_	:					List[str]     	=   dropout
				UpperCAmelCase_	:					Optional[Any]     	=   attention_dropout
				UpperCAmelCase_	:					Any     	=   activation_dropout
				UpperCAmelCase_	:					Union[str, Any]     	=   encoder_layerdrop
				UpperCAmelCase_	:					Optional[int]     	=   decoder_layerdrop

				UpperCAmelCase_	:					Union[str, Any]     	=   activation_function
				UpperCAmelCase_	:					int     	=   init_std

				UpperCAmelCase_	:					Optional[Any]     	=   use_cache

				# Informer
				UpperCAmelCase_	:					int     	=   attention_type
				UpperCAmelCase_	:					List[str]     	=   sampling_factor
				UpperCAmelCase_	:					Union[str, Any]     	=   distil

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





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



