


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 				SCREAMING_SNAKE_CASE__						(       lowercase__		,					unittest.TestCase    ):
      snake_case__       :  Optional[int]       =		KandinskyInpaintPipeline
      snake_case__       :  List[str]       =		['''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__       :  Optional[int]       =		False
      @property
      def  SCREAMING_SNAKE_CASE							( self       : Tuple  )	->    List[str]:
             return 3_2
      @property
      def  SCREAMING_SNAKE_CASE							( self       : int  )	->    List[Any]:
             return 3_2
      @property
      def  SCREAMING_SNAKE_CASE							( self       : List[str]  )	->    Tuple:
             return self.time_input_dim
      @property
      def  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  )	->    Any:
             return self.time_input_dim * 4
      @property
      def  SCREAMING_SNAKE_CASE							( self       : Tuple  )	->    Union[str, Any]:
             return 1_0_0
      @property
      def  SCREAMING_SNAKE_CASE							( self       : Union[str, Any]  )	->    str:
             a_      :	int							=						XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'  )
             return tokenizer
      @property
      def  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  )	->    List[str]:
             torch.manual_seed(0  )
             a_      :	List[Any]							=						MCLIPConfig(
                 numDims=self.cross_attention_dim  ,							transformerDimensions=self.text_embedder_hidden_size  ,							hidden_size=self.text_embedder_hidden_size  ,							intermediate_size=3_7  ,							num_attention_heads=4  ,							num_hidden_layers=5  ,							vocab_size=1_0_0_5  ,							)

             a_      :	Union[str, Any]							=						MultilingualCLIP(SCREAMING_SNAKE_CASE__  )
             a_      :	Dict							=						text_encoder.eval()

             return text_encoder
      @property
      def  SCREAMING_SNAKE_CASE							( self       : str  )	->    str:
             torch.manual_seed(0  )

             a_      :	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,
             }

             a_      :	Optional[int]							=						UNetaDConditionModel(**SCREAMING_SNAKE_CASE__  )
             return model
      @property
      def  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  )	->    str:
             return {
                 "block_out_channels": [3_2, 6_4],
                 "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": 1_2,
                 "out_channels": 3,
                 "up_block_types": [
                     "AttnUpDecoderBlock2D",
                     "UpDecoderBlock2D",
                 ],
                 "vq_embed_dim": 4,
             }
      @property
      def  SCREAMING_SNAKE_CASE							( self       : Tuple  )	->    List[Any]:
             torch.manual_seed(0  )
             a_      :	Optional[int]							=						VQModel(**self.dummy_movq_kwargs  )
             return model
      def  SCREAMING_SNAKE_CASE							( self       : List[Any]  )	->    Tuple:
             a_      :	Tuple							=						self.dummy_text_encoder
             a_      :	Optional[int]							=						self.dummy_tokenizer
             a_      :	List[str]							=						self.dummy_unet
             a_      :	Optional[int]							=						self.dummy_movq

             a_      :	Optional[int]							=						DDIMScheduler(
                 num_train_timesteps=1_0_0_0  ,							beta_schedule='linear'  ,							beta_start=0.00085  ,							beta_end=0.012  ,							clip_sample=SCREAMING_SNAKE_CASE__  ,							set_alpha_to_one=SCREAMING_SNAKE_CASE__  ,							steps_offset=1  ,							prediction_type='epsilon'  ,							thresholding=SCREAMING_SNAKE_CASE__  ,							)

             a_      :	Dict							=						{
                 'text_encoder': text_encoder,
                 'tokenizer': tokenizer,
                 'unet': unet,
                 'scheduler': scheduler,
                 'movq': movq,
             }

             return components
      def  SCREAMING_SNAKE_CASE							( self       : Dict  ,							SCREAMING_SNAKE_CASE__       : Any  ,							SCREAMING_SNAKE_CASE__       : Dict=0  )	->    List[Any]:
             a_      :	Union[str, Any]							=						floats_tensor((1, self.cross_attention_dim)  ,							rng=random.Random(SCREAMING_SNAKE_CASE__  )  ).to(SCREAMING_SNAKE_CASE__  )
             a_      :	List[Any]							=						floats_tensor((1, self.cross_attention_dim)  ,							rng=random.Random(seed + 1  )  ).to(SCREAMING_SNAKE_CASE__  )
             # create init_image
             a_      :	Any							=						floats_tensor((1, 3, 6_4, 6_4)  ,							rng=random.Random(SCREAMING_SNAKE_CASE__  )  ).to(SCREAMING_SNAKE_CASE__  )
             a_      :	Any							=						image.cpu().permute(0  ,							2  ,							3  ,							1  )[0]
             a_      :	Any							=						Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE__  )  ).convert('RGB'  ).resize((2_5_6, 2_5_6)  )
             # create mask
             a_      :	Union[str, Any]							=						np.ones((6_4, 6_4)  ,							dtype=np.floataa  )
             a_      :	List[Any]							=						0

             if str(SCREAMING_SNAKE_CASE__  ).startswith('mps'  ):
                    a_      :	Optional[Any]							=						torch.manual_seed(SCREAMING_SNAKE_CASE__  )
             else:
                    a_      :	int							=						torch.Generator(device=SCREAMING_SNAKE_CASE__  ).manual_seed(SCREAMING_SNAKE_CASE__  )
             a_      :	int							=						{
                 'prompt': 'horse',
                 'image': init_image,
                 'mask_image': mask,
                 'image_embeds': image_embeds,
                 'negative_image_embeds': negative_image_embeds,
                 'generator': generator,
                 'height': 6_4,
                 'width': 6_4,
                 'num_inference_steps': 2,
                 'guidance_scale': 4.0,
                 'output_type': 'np',
             }
             return inputs
      def  SCREAMING_SNAKE_CASE							( self       : Union[str, Any]  )	->    Tuple:
             a_      :	Optional[Any]							=						'cpu'

             a_      :	List[Any]							=						self.get_dummy_components()

             a_      :	List[str]							=						self.pipeline_class(**SCREAMING_SNAKE_CASE__  )
             a_      :	Optional[int]							=						pipe.to(SCREAMING_SNAKE_CASE__  )

             pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__  )

             a_      :	str							=						pipe(**self.get_dummy_inputs(SCREAMING_SNAKE_CASE__  )  )
             a_      :	Dict							=						output.images

             a_      :	Union[str, Any]							=						pipe(
                 **self.get_dummy_inputs(SCREAMING_SNAKE_CASE__  )  ,							return_dict=SCREAMING_SNAKE_CASE__  ,							)[0]

             a_      :	Dict							=						image[0, -3:, -3:, -1]
             a_      :	Optional[int]							=						image_from_tuple[0, -3:, -3:, -1]

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

             assert image.shape == (1, 6_4, 6_4, 3)

             a_      :	Union[str, Any]							=						np.array(
                 [0.8326919, 0.73790467, 0.20918581, 0.9309612, 0.5511791, 0.43713328, 0.5513321, 0.49922934, 0.59497786]  )

             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  SCREAMING_SNAKE_CASE							( self       : Any  )	->    Any:
             super().test_inference_batch_single_identical(expected_max_diff=3E-3  )
@slow
@require_torch_gpu
class 				SCREAMING_SNAKE_CASE__						(       unittest.TestCase    ):
      def  SCREAMING_SNAKE_CASE							( self       : int  )	->    int:
             # clean up the VRAM after each test
             super().tearDown()
             gc.collect()
             torch.cuda.empty_cache()

      def  SCREAMING_SNAKE_CASE							( self       : Dict  )	->    Union[str, Any]:
             a_      :	Dict							=						load_numpy(
                 'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
                 '/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'  )

             a_      :	Any							=						load_image(
                 'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'  )
             a_      :	Union[str, Any]							=						np.ones((7_6_8, 7_6_8)  ,							dtype=np.floataa  )
             a_      :	Any							=						0

             a_      :	List[str]							=						'a hat'

             a_      :	Dict							=						KandinskyPriorPipeline.from_pretrained(
                 'kandinsky-community/kandinsky-2-1-prior'  ,							torch_dtype=torch.floataa  )
             pipe_prior.to(SCREAMING_SNAKE_CASE__  )

             a_      :	List[str]							=						KandinskyInpaintPipeline.from_pretrained(
                 'kandinsky-community/kandinsky-2-1-inpaint'  ,							torch_dtype=torch.floataa  )
             a_      :	Union[str, Any]							=						pipeline.to(SCREAMING_SNAKE_CASE__  )
             pipeline.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__  )

             a_      :	Tuple							=						torch.Generator(device='cpu'  ).manual_seed(0  )
             a_							,	a_      :	List[Any]							=						pipe_prior(
                 SCREAMING_SNAKE_CASE__  ,							generator=SCREAMING_SNAKE_CASE__  ,							num_inference_steps=5  ,							negative_prompt=''  ,							).to_tuple()

             a_      :	List[str]							=						pipeline(
                 SCREAMING_SNAKE_CASE__  ,							image=SCREAMING_SNAKE_CASE__  ,							mask_image=SCREAMING_SNAKE_CASE__  ,							image_embeds=SCREAMING_SNAKE_CASE__  ,							negative_image_embeds=SCREAMING_SNAKE_CASE__  ,							generator=SCREAMING_SNAKE_CASE__  ,							num_inference_steps=1_0_0  ,							height=7_6_8  ,							width=7_6_8  ,							output_type='np'  ,							)

             a_      :	Optional[int]							=						output.images[0]

             assert image.shape == (7_6_8, 7_6_8, 3)

             assert_mean_pixel_difference(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )






from typing import List, Optional

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


UpperCAmelCase_   :				Dict						      = logging.get_logger(__name__)

UpperCAmelCase_   :				int						      = {
    'huggingface/autoformer-tourism-monthly': 'https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json',
}
class 				SCREAMING_SNAKE_CASE__						(       lowercase__    ):
      snake_case__       :  Union[str, Any]       =		'''autoformer'''
      snake_case__       :  Any       =		{
          '''hidden_size''': '''d_model''',
          '''num_attention_heads''': '''encoder_attention_heads''',
          '''num_hidden_layers''': '''encoder_layers''',
      }
      def __init__( self       : Optional[int]  ,							SCREAMING_SNAKE_CASE__       : Optional[int] = None  ,							SCREAMING_SNAKE_CASE__       : Optional[int] = None  ,							SCREAMING_SNAKE_CASE__       : str = "student_t"  ,							SCREAMING_SNAKE_CASE__       : str = "nll"  ,							SCREAMING_SNAKE_CASE__       : int = 1  ,							SCREAMING_SNAKE_CASE__       : List[int] = [1, 2, 3, 4, 5, 6, 7]  ,							SCREAMING_SNAKE_CASE__       : bool = True  ,							SCREAMING_SNAKE_CASE__       : int = 0  ,							SCREAMING_SNAKE_CASE__       : int = 0  ,							SCREAMING_SNAKE_CASE__       : int = 0  ,							SCREAMING_SNAKE_CASE__       : int = 0  ,							SCREAMING_SNAKE_CASE__       : Optional[List[int]] = None  ,							SCREAMING_SNAKE_CASE__       : Optional[List[int]] = None  ,							SCREAMING_SNAKE_CASE__       : int = 6_4  ,							SCREAMING_SNAKE_CASE__       : int = 2  ,							SCREAMING_SNAKE_CASE__       : int = 2  ,							SCREAMING_SNAKE_CASE__       : int = 2  ,							SCREAMING_SNAKE_CASE__       : int = 2  ,							SCREAMING_SNAKE_CASE__       : int = 3_2  ,							SCREAMING_SNAKE_CASE__       : int = 3_2  ,							SCREAMING_SNAKE_CASE__       : str = "gelu"  ,							SCREAMING_SNAKE_CASE__       : float = 0.1  ,							SCREAMING_SNAKE_CASE__       : float = 0.1  ,							SCREAMING_SNAKE_CASE__       : float = 0.1  ,							SCREAMING_SNAKE_CASE__       : float = 0.1  ,							SCREAMING_SNAKE_CASE__       : float = 0.1  ,							SCREAMING_SNAKE_CASE__       : int = 1_0_0  ,							SCREAMING_SNAKE_CASE__       : float = 0.02  ,							SCREAMING_SNAKE_CASE__       : bool = True  ,							SCREAMING_SNAKE_CASE__       : List[str]=True  ,							SCREAMING_SNAKE_CASE__       : int = 1_0  ,							SCREAMING_SNAKE_CASE__       : int = 2_5  ,							SCREAMING_SNAKE_CASE__       : int = 3  ,							**SCREAMING_SNAKE_CASE__       : int  ,							)	->    Union[str, Any]:
             # time series specific configuration
             a_      :	Optional[int]							=						prediction_length
             a_      :	Tuple							=						context_length if context_length is not None else prediction_length
             a_      :	Tuple							=						distribution_output
             a_      :	int							=						loss
             a_      :	Tuple							=						input_size
             a_      :	int							=						num_time_features
             a_      :	Dict							=						lags_sequence
             a_      :	List[str]							=						scaling
             a_      :	Tuple							=						num_dynamic_real_features
             a_      :	Dict							=						num_static_real_features
             a_      :	Dict							=						num_static_categorical_features
             if cardinality is not None and num_static_categorical_features > 0:
                    if len(SCREAMING_SNAKE_CASE__  ) != num_static_categorical_features:
                           raise ValueError(
                               'The cardinality should be a list of the same length as `num_static_categorical_features`'  )
                    a_      :	Any							=						cardinality
             else:
                    a_      :	Tuple							=						[0]
             if embedding_dimension is not None and num_static_categorical_features > 0:
                    if len(SCREAMING_SNAKE_CASE__  ) != num_static_categorical_features:
                           raise ValueError(
                               'The embedding dimension should be a list of the same length as `num_static_categorical_features`'  )
                    a_      :	Tuple							=						embedding_dimension
             else:
                    a_      :	Optional[Any]							=						[min(5_0  ,							(cat + 1) // 2  ) for cat in self.cardinality]
             a_      :	Optional[Any]							=						num_parallel_samples

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

             a_      :	List[Any]							=						dropout
             a_      :	Tuple							=						attention_dropout
             a_      :	Union[str, Any]							=						activation_dropout
             a_      :	List[Any]							=						encoder_layerdrop
             a_      :	List[str]							=						decoder_layerdrop

             a_      :	Optional[Any]							=						activation_function
             a_      :	Union[str, Any]							=						init_std

             a_      :	List[str]							=						use_cache

             # Autoformer
             a_      :	Tuple							=						label_length
             a_      :	List[Any]							=						moving_average
             a_      :	Optional[Any]							=						autocorrelation_factor

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

      @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
             )


