
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 		UpperCamelCase__  (	__lowercase   ,unittest.TestCase   ):
   _SCREAMING_SNAKE_CASE				:							Optional[int]     	=				KandinskyInpaintPipeline
   _SCREAMING_SNAKE_CASE				:							Optional[int]     	=				["prompt", "image_embeds", "negative_image_embeds", "image", "mask_image"]
   _SCREAMING_SNAKE_CASE				:							Union[str, Any]     	=				[
       "prompt",
       "negative_prompt",
       "image_embeds",
       "negative_image_embeds",
       "image",
       "mask_image",
   ]
   _SCREAMING_SNAKE_CASE				:							Optional[int]     	=				[
       "generator",
       "height",
       "width",
       "latents",
       "guidance_scale",
       "negative_prompt",
       "num_inference_steps",
       "return_dict",
       "guidance_scale",
       "num_images_per_prompt",
       "output_type",
       "return_dict",
   ]
   _SCREAMING_SNAKE_CASE				:							Any     	=				False
   @property
   def 				lowerCAmelCase      (self			: List[Any]       ):
    return 3_2
   @property
   def 				lowerCAmelCase      (self			: List[Any]       ):
    return 3_2
   @property
   def 				lowerCAmelCase      (self			: Tuple       ):
    return self.time_input_dim
   @property
   def 				lowerCAmelCase      (self			: Dict       ):
    return self.time_input_dim * 4
   @property
   def 				lowerCAmelCase      (self			: List[Any]       ):
    return 1_0_0
   @property
   def 				lowerCAmelCase      (self			: Tuple       ):
    __a			:  Tuple		    =  XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''       )
    return tokenizer
   @property
   def 				lowerCAmelCase      (self			: int       ):
    torch.manual_seed(0       )
    __a			:  Union[str, 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			:  Optional[Any]		    =  MultilingualCLIP(snake_case_       )
    __a			:  str		    =  text_encoder.eval()

    return text_encoder
   @property
   def 				lowerCAmelCase      (self			: str       ):
    torch.manual_seed(0       )

    __a			:  Union[str, 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			:  List[str]		    =  UNetaDConditionModel(**snake_case_       )
    return model
   @property
   def 				lowerCAmelCase      (self			: Optional[Any]       ):
    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 				lowerCAmelCase      (self			: List[str]       ):
    torch.manual_seed(0       )
    __a			:  Union[str, Any]		    =  VQModel(**self.dummy_movq_kwargs       )
    return model
   def 				lowerCAmelCase      (self			: Union[str, Any]       ):
    __a			:  int		    =  self.dummy_text_encoder
    __a			:  Dict		    =  self.dummy_tokenizer
    __a			:  Dict		    =  self.dummy_unet
    __a			:  Optional[int]		    =  self.dummy_movq

    __a			:  str		    =  DDIMScheduler(
        num_train_timesteps=1_0_0_0 ,    beta_schedule='''linear''' ,    beta_start=0.0_0085 ,    beta_end=0.012 ,    clip_sample=snake_case_ ,    set_alpha_to_one=snake_case_ ,    steps_offset=1 ,    prediction_type='''epsilon''' ,    thresholding=snake_case_ ,    )

    __a			:  Optional[int]		    =  {
        '''text_encoder''': text_encoder,
        '''tokenizer''': tokenizer,
        '''unet''': unet,
        '''scheduler''': scheduler,
        '''movq''': movq,
    }

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

    if str(snake_case_       ).startswith('''mps'''       ):
     __a			:  int		    =  torch.manual_seed(snake_case_       )
    else:
     __a			:  Optional[int]		    =  torch.Generator(device=snake_case_       ).manual_seed(snake_case_       )
    __a			:  List[Any]		    =  {
        '''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 				lowerCAmelCase      (self			: Dict       ):
    __a			:  Optional[int]		    =  '''cpu'''

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

    __a			:  Tuple		    =  self.pipeline_class(**snake_case_       )
    __a			:  Any		    =  pipe.to(snake_case_       )

    pipe.set_progress_bar_config(disable=snake_case_       )

    __a			:  Optional[Any]		    =  pipe(**self.get_dummy_inputs(snake_case_       )       )
    __a			:  Union[str, Any]		    =  output.images

    __a			:  Dict		    =  pipe(
        **self.get_dummy_inputs(snake_case_       ) ,    return_dict=snake_case_ ,    )[0]

    __a			:  List[str]		    =  image[0, -3:, -3:, -1]
    __a			:  Tuple		    =  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(
        [0.832_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 				lowerCAmelCase      (self			: List[Any]       ):
    super().test_inference_batch_single_identical(expected_max_diff=3E-3       )





@slow
@require_torch_gpu
class 		UpperCamelCase__  (	unittest.TestCase   ):
   def 				lowerCAmelCase      (self			: Optional[Any]       ):
    # clean up the VRAM after each test
    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()







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

    __a			:  Optional[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			:  Union[str, Any]		    =  0

    __a			:  Tuple		    =  '''a hat'''

    __a			:  int		    =  KandinskyPriorPipeline.from_pretrained(
        '''kandinsky-community/kandinsky-2-1-prior''' ,    torch_dtype=torch.floataa       )
    pipe_prior.to(snake_case_       )

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

    __a			:  Optional[Any]		    =  torch.Generator(device='''cpu'''       ).manual_seed(0       )
    __a    ,	__a			:  Union[str, Any]		    =  pipe_prior(
        snake_case_ ,    generator=snake_case_ ,    num_inference_steps=5 ,    negative_prompt='''''' ,    ).to_tuple()

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

    __a			:  int		    =  output.images[0]

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

    assert_mean_pixel_difference(snake_case_ ,    snake_case_       )


def        __UpperCamelCase					(    lowerCAmelCase__							:    str  ,       lowerCAmelCase__							:    str  ):
 __a			:  Any		    =  len(lowerCAmelCase__  )
 __a			:  Union[str, Any]		    =  []
 for i in range(len(lowerCAmelCase__  ) - pat_len + 1  ):
  __a			:  List[Any]		    =  True
  for j in range(lowerCAmelCase__  ):
   if s[i + j] != pattern[j]:
    __a			:  Union[str, Any]		    =  False
    break
  if match_found:
   position.append(lowerCAmelCase__  )
 return position


if __name__ == "__main__":
      assert naive_pattern_search('ABCDEFG', 'DE') == [3]
      print(naive_pattern_search('ABAAABCDBBABCDDEBCABC', 'ABC'))
