




import gc
import random
import unittest

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

from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, 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       (							lowerCAmelCase      , unittest.TestCase):
       _a 					=						KandinskyImgaImgPipeline
       _a 					=						['''prompt''', '''image_embeds''', '''negative_image_embeds''', '''image''']
       _a 					=						[
           '''prompt''',
           '''negative_prompt''',
           '''image_embeds''',
           '''negative_image_embeds''',
           '''image''',
       ]
       _a 					=						[
           '''generator''',
           '''height''',
           '''width''',
           '''strength''',
           '''guidance_scale''',
           '''negative_prompt''',
           '''num_inference_steps''',
           '''return_dict''',
           '''guidance_scale''',
           '''num_images_per_prompt''',
           '''output_type''',
           '''return_dict''',
       ]
       _a 					=						False






       @property
       def 					SCREAMING_SNAKE_CASE    (   self:					Dict				):
         return 32






       @property
       def 					SCREAMING_SNAKE_CASE    (   self:					Dict				):
         return 32






       @property
       def 					SCREAMING_SNAKE_CASE    (   self:					Any				):
         return self.time_input_dim






       @property
       def 					SCREAMING_SNAKE_CASE    (   self:					Union[str, Any]				):
         return self.time_input_dim * 4






       @property
       def 					SCREAMING_SNAKE_CASE    (   self:					Tuple				):
         return 1_00






       @property
       def 					SCREAMING_SNAKE_CASE    (   self:					Optional[Any]				):
         lowercase    :Optional[int]      	=						XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"				)
         return tokenizer






       @property
       def 					SCREAMING_SNAKE_CASE    (   self:					Any				):
         torch.manual_seed(0				)
         lowercase    :Optional[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       ,					)

         lowercase    :Any      	=						MultilingualCLIP(_SCREAMING_SNAKE_CASE				)
         lowercase    :Tuple      	=						text_encoder.eval()

         return text_encoder






       @property
       def 					SCREAMING_SNAKE_CASE    (   self:					List[Any]				):
         torch.manual_seed(0				)

         lowercase    :Optional[int]      	=						{
             "in_channels": 4,
             # 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,
         }

         lowercase    :Tuple      	=						UNetaDConditionModel(**_SCREAMING_SNAKE_CASE				)
         return model






       @property
       def 					SCREAMING_SNAKE_CASE    (   self:					int				):
         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 					SCREAMING_SNAKE_CASE    (   self:					Optional[Any]				):
         torch.manual_seed(0				)
         lowercase    :Dict      	=						VQModel(**self.dummy_movq_kwargs				)
         return model






       def 					SCREAMING_SNAKE_CASE    (   self:					Optional[Any]				):
         lowercase    :Optional[int]      	=						self.dummy_text_encoder
         lowercase    :Optional[int]      	=						self.dummy_tokenizer
         lowercase    :List[Any]      	=						self.dummy_unet
         lowercase    :Any      	=						self.dummy_movq

         lowercase    :int      	=						{
             "num_train_timesteps": 10_00,
             "beta_schedule": "linear",
             "beta_start": 0.0_00_85,
             "beta_end": 0.0_12,
             "clip_sample": False,
             "set_alpha_to_one": False,
             "steps_offset": 0,
             "prediction_type": "epsilon",
             "thresholding": False,
         }

         lowercase    :Optional[Any]      	=						DDIMScheduler(**_SCREAMING_SNAKE_CASE				)

         lowercase    :Tuple      	=						{
             "text_encoder": text_encoder,
             "tokenizer": tokenizer,
             "unet": unet,
             "scheduler": scheduler,
             "movq": movq,
         }

         return components






       def 					SCREAMING_SNAKE_CASE    (   self:					Tuple       ,					_lowerCAmelCase:					int       ,					_lowerCAmelCase:					Optional[Any]=0				):
         lowercase    :str      	=						floats_tensor((1, self.cross_attention_dim)       ,					rng=random.Random(_SCREAMING_SNAKE_CASE				)				).to(_SCREAMING_SNAKE_CASE				)
         lowercase    :Dict      	=						floats_tensor((1, self.cross_attention_dim)       ,					rng=random.Random(seed + 1				)				).to(_SCREAMING_SNAKE_CASE				)
         # create init_image
         lowercase    :List[str]      	=						floats_tensor((1, 3, 64, 64)       ,					rng=random.Random(_SCREAMING_SNAKE_CASE				)				).to(_SCREAMING_SNAKE_CASE				)
         lowercase    :Dict      	=						image.cpu().permute(0       ,					2       ,					3       ,					1				)[0]
         lowercase    :Union[str, Any]      	=						Image.fromarray(np.uinta(_SCREAMING_SNAKE_CASE				)				).convert("RGB"				).resize((2_56, 2_56)				)

         if str(_SCREAMING_SNAKE_CASE				).startswith("mps"				):
           lowercase    :Optional[int]      	=						torch.manual_seed(_SCREAMING_SNAKE_CASE				)
         else:
           lowercase    :str      	=						torch.Generator(device=_SCREAMING_SNAKE_CASE				).manual_seed(_SCREAMING_SNAKE_CASE				)
         lowercase    :Any      	=						{
             "prompt": "horse",
             "image": init_image,
             "image_embeds": image_embeds,
             "negative_image_embeds": negative_image_embeds,
             "generator": generator,
             "height": 64,
             "width": 64,
             "num_inference_steps": 10,
             "guidance_scale": 7.0,
             "strength": 0.2,
             "output_type": "np",
         }
         return inputs






       def 					SCREAMING_SNAKE_CASE    (   self:					Any				):
         lowercase    :List[Any]      	=						"cpu"

         lowercase    :int      	=						self.get_dummy_components()

         lowercase    :Any      	=						self.pipeline_class(**_SCREAMING_SNAKE_CASE				)
         lowercase    :List[str]      	=						pipe.to(_SCREAMING_SNAKE_CASE				)

         pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE				)

         lowercase    :Union[str, Any]      	=						pipe(**self.get_dummy_inputs(_SCREAMING_SNAKE_CASE				)				)
         lowercase    :Union[str, Any]      	=						output.images

         lowercase    :Dict      	=						pipe(
             **self.get_dummy_inputs(_SCREAMING_SNAKE_CASE				)       ,					return_dict=_SCREAMING_SNAKE_CASE       ,					)[0]

         lowercase    :Any      	=						image[0, -3:, -3:, -1]
         lowercase    :Any      	=						image_from_tuple[0, -3:, -3:, -1]

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

         lowercase    :List[str]      	=						np.array(
             [0.61_47_49_43, 0.6_07_35_39, 0.43_30_85_44, 0.5_92_82_69, 0.47_49_35_95, 0.46_75_59_73, 0.4_61_38_38, 0.45_36_87_97, 0.50_11_92_33]				)
         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()}"







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






       def 					SCREAMING_SNAKE_CASE    (   self:					Dict				):
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()






       def 					SCREAMING_SNAKE_CASE    (   self:					Union[str, Any]				):
         lowercase    :List[str]      	=						load_numpy(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/kandinsky/kandinsky_img2img_frog.npy"				)

         lowercase    :Union[str, Any]      	=						load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"				)
         lowercase    :Tuple      	=						"A red cartoon frog, 4k"

         lowercase    :Optional[int]      	=						KandinskyPriorPipeline.from_pretrained(
             "kandinsky-community/kandinsky-2-1-prior"       ,					torch_dtype=torch.floataa				)
         pipe_prior.to(_SCREAMING_SNAKE_CASE				)

         lowercase    :Optional[int]      	=						KandinskyImgaImgPipeline.from_pretrained(
             "kandinsky-community/kandinsky-2-1"       ,					torch_dtype=torch.floataa				)
         lowercase    :List[Any]      	=						pipeline.to(_SCREAMING_SNAKE_CASE				)

         pipeline.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE				)

         lowercase    :str      	=						torch.Generator(device="cpu"				).manual_seed(0				)
         lowercase	,    lowercase    :Union[str, Any]      	=						pipe_prior(
             _SCREAMING_SNAKE_CASE       ,					generator=_SCREAMING_SNAKE_CASE       ,					num_inference_steps=5       ,					negative_prompt=""       ,					).to_tuple()

         lowercase    :Dict      	=						pipeline(
             _SCREAMING_SNAKE_CASE       ,					image=_SCREAMING_SNAKE_CASE       ,					image_embeds=_SCREAMING_SNAKE_CASE       ,					negative_image_embeds=_SCREAMING_SNAKE_CASE       ,					generator=_SCREAMING_SNAKE_CASE       ,					num_inference_steps=1_00       ,					height=7_68       ,					width=7_68       ,					strength=0.2       ,					output_type="np"       ,					)

         lowercase    :int      	=						output.images[0]

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

         assert_mean_pixel_difference(_SCREAMING_SNAKE_CASE       ,					_SCREAMING_SNAKE_CASE				)









def       UpperCAmelCase__  (     lowerCamelCase = "The quick brown fox jumps over the lazy dog",			):
  lowercase    :Dict      	=						set()

  # Replace all the whitespace in our sentence
  lowercase    :Optional[int]      	=						input_str.replace(" ",			""   )
  for alpha in input_str:
    if "a" <= alpha.lower() <= "z":
      frequency.add(alpha.lower()   )
  return len(lowerCamelCase   ) == 26



def       UpperCAmelCase__  (     lowerCamelCase = "The quick brown fox jumps over the lazy dog",			):
  lowercase    :Optional[Any]      	=						[False] * 26
  for char in input_str:
    if char.islower():
      lowercase    :Union[str, Any]      	=						True
    elif char.isupper():
      lowercase    :int      	=						True
  return all(lowerCamelCase   )



def       UpperCAmelCase__  (     lowerCamelCase = "The quick brown fox jumps over the lazy dog",			):
  return len({char for char in input_str.lower() if char.isalpha()}   ) == 26



def       UpperCAmelCase__  (     ):
  from timeit import timeit

  lowercase    :Optional[Any]      	=						"from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest"
  print(timeit("is_pangram()",			setup=lowerCamelCase   )   )
  print(timeit("is_pangram_faster()",			setup=lowerCamelCase   )   )
  print(timeit("is_pangram_fastest()",			setup=lowerCamelCase   )   )
  # 5.348480500048026, 2.6477354579837993, 1.8470395830227062
  # 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     benchmark()



