import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, 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_		(			__lowerCAmelCase     ,    unittest.TestCase		):

   '''simple docstring'''





   __lowerCAmelCase											=							KandinskyPipeline
   __lowerCAmelCase											=							[
       """prompt""",
       """image_embeds""",
       """negative_image_embeds""",
   ]
   __lowerCAmelCase											=							["""prompt""", """negative_prompt""", """image_embeds""", """negative_image_embeds"""]
   __lowerCAmelCase											=							[
       """generator""",
       """height""",
       """width""",
       """latents""",
       """guidance_scale""",
       """negative_prompt""",
       """num_inference_steps""",
       """return_dict""",
       """guidance_scale""",
       """num_images_per_prompt""",
       """output_type""",
       """return_dict""",
   ]
   __lowerCAmelCase											=							False







   @property
   def 	lowerCamelCase_  (    self	:	Optional[int]							):





    """simple docstring"""


    return 32







   @property
   def 	lowerCamelCase_  (    self	:	Optional[int]							):





    """simple docstring"""


    return 32







   @property
   def 	lowerCamelCase_  (    self	:	Any							):





    """simple docstring"""


    return self.time_input_dim







   @property
   def 	lowerCamelCase_  (    self	:	Dict							):





    """simple docstring"""


    return self.time_input_dim * 4







   @property
   def 	lowerCamelCase_  (    self	:	int							):





    """simple docstring"""


    return 100







   @property
   def 	lowerCamelCase_  (    self	:	Optional[int]							):





    """simple docstring"""


    UpperCamelCase      =			XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""							)
    return tokenizer







   @property
   def 	lowerCamelCase_  (    self	:	Dict							):





    """simple docstring"""


    torch.manual_seed(0							)
    UpperCamelCase      =			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=1005      ,      )

    UpperCamelCase      =			MultilingualCLIP(lowerCamelCase_							)
    UpperCamelCase      =			text_encoder.eval()

    return text_encoder







   @property
   def 	lowerCamelCase_  (    self	:	int							):





    """simple docstring"""


    torch.manual_seed(0							)

    UpperCamelCase      =			{
        """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,
    }

    UpperCamelCase      =			UNetaDConditionModel(**lowerCamelCase_							)
    return model







   @property
   def 	lowerCamelCase_  (    self	:	List[str]							):





    """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 	lowerCamelCase_  (    self	:	Any							):





    """simple docstring"""


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







   def 	lowerCamelCase_  (    self	:	Optional[int]							):





    """simple docstring"""


    UpperCamelCase      =			self.dummy_text_encoder
    UpperCamelCase      =			self.dummy_tokenizer
    UpperCamelCase      =			self.dummy_unet
    UpperCamelCase      =			self.dummy_movq

    UpperCamelCase      =			DDIMScheduler(
        num_train_timesteps=1000      ,      beta_schedule="""linear"""      ,      beta_start=0.0_0_0_8_5      ,      beta_end=0.0_1_2      ,      clip_sample=lowerCamelCase_      ,      set_alpha_to_one=lowerCamelCase_      ,      steps_offset=1      ,      prediction_type="""epsilon"""      ,      thresholding=lowerCamelCase_      ,      )

    UpperCamelCase      =			{
        """text_encoder""": text_encoder,
        """tokenizer""": tokenizer,
        """unet""": unet,
        """scheduler""": scheduler,
        """movq""": movq,
    }
    return components







   def 	lowerCamelCase_  (    self	:	Union[str, Any]      ,      lowerCamelCase_	:	Optional[int]      ,      lowerCamelCase_	:	str=0							):





    """simple docstring"""


    UpperCamelCase      =			floats_tensor((1, self.cross_attention_dim)      ,      rng=random.Random(lowerCamelCase_							)							).to(lowerCamelCase_							)
    UpperCamelCase      =			floats_tensor((1, self.cross_attention_dim)      ,      rng=random.Random(seed + 1							)							).to(lowerCamelCase_							)
    if str(lowerCamelCase_							).startswith("""mps"""							):
     UpperCamelCase      =			torch.manual_seed(lowerCamelCase_							)
    else:
     UpperCamelCase      =			torch.Generator(device=lowerCamelCase_							).manual_seed(lowerCamelCase_							)
    UpperCamelCase      =			{
        """prompt""": """horse""",
        """image_embeds""": image_embeds,
        """negative_image_embeds""": negative_image_embeds,
        """generator""": generator,
        """height""": 64,
        """width""": 64,
        """guidance_scale""": 4.0,
        """num_inference_steps""": 2,
        """output_type""": """np""",
    }
    return inputs







   def 	lowerCamelCase_  (    self	:	Optional[int]							):





    """simple docstring"""


    UpperCamelCase      =			"""cpu"""

    UpperCamelCase      =			self.get_dummy_components()

    UpperCamelCase      =			self.pipeline_class(**lowerCamelCase_							)
    UpperCamelCase      =			pipe.to(lowerCamelCase_							)

    pipe.set_progress_bar_config(disable=lowerCamelCase_							)

    UpperCamelCase      =			pipe(**self.get_dummy_inputs(lowerCamelCase_							)							)
    UpperCamelCase      =			output.images

    UpperCamelCase      =			pipe(
        **self.get_dummy_inputs(lowerCamelCase_							)      ,      return_dict=lowerCamelCase_      ,      )[0]

    UpperCamelCase      =			image[0, -3:, -3:, -1]
    UpperCamelCase      =			image_from_tuple[0, -3:, -3:, -1]

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

    UpperCamelCase      =			np.array(
        [0.3_2_8_6_6_3, 1.0, 0.2_3_2_1_6_8_7_3, 1.0, 0.9_2_7_1_7_5_6_4, 0.4_6_3_9_0_4_6, 0.9_6_8_9_4_7_7_7, 0.3_1_7_1_3_3_7_8, 0.6_2_9_3_9_5_3]							)

    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 SCREAMING_SNAKE_CASE_		(			unittest.TestCase		):

   '''simple docstring'''





   def 	lowerCamelCase_  (    self	:	Tuple							):





    """simple docstring"""


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







   def 	lowerCamelCase_  (    self	:	Dict							):





    """simple docstring"""


    UpperCamelCase      =			load_numpy(
        """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
        """/kandinsky/kandinsky_text2img_cat_fp16.npy"""							)

    UpperCamelCase      =			KandinskyPriorPipeline.from_pretrained(
        """kandinsky-community/kandinsky-2-1-prior"""      ,      torch_dtype=torch.floataa							)
    pipe_prior.to(lowerCamelCase_							)

    UpperCamelCase      =			KandinskyPipeline.from_pretrained("""kandinsky-community/kandinsky-2-1"""      ,      torch_dtype=torch.floataa							)
    UpperCamelCase      =			pipeline.to(lowerCamelCase_							)
    pipeline.set_progress_bar_config(disable=lowerCamelCase_							)

    UpperCamelCase      =			"""red cat, 4k photo"""

    UpperCamelCase      =			torch.Generator(device="""cuda"""							).manual_seed(0							)
    UpperCamelCase     ,    UpperCamelCase      =			pipe_prior(
        lowerCamelCase_      ,      generator=lowerCamelCase_      ,      num_inference_steps=5      ,      negative_prompt=""""""      ,      ).to_tuple()

    UpperCamelCase      =			torch.Generator(device="""cuda"""							).manual_seed(0							)
    UpperCamelCase      =			pipeline(
        lowerCamelCase_      ,      image_embeds=lowerCamelCase_      ,      negative_image_embeds=lowerCamelCase_      ,      generator=lowerCamelCase_      ,      num_inference_steps=100      ,      output_type="""np"""      ,      )

    UpperCamelCase      =			output.images[0]

    assert image.shape == (512, 512, 3)

    assert_mean_pixel_difference(lowerCamelCase_      ,      lowerCamelCase_							)

import numpy as np





def  lowercase(  UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_					)		->  List[str]:







 '''simple docstring'''
 UpperCamelCase      =			n_draws * class_counts / class_counts.sum()
 # floored means we don't overshoot n_samples, but probably undershoot
 UpperCamelCase      =			np.floor(UpperCamelCase_					)
 # we add samples according to how much "left over" probability
 # they had, until we arrive at n_samples
 UpperCamelCase      =			int(n_draws - floored.sum()					)
 if need_to_add > 0:
  UpperCamelCase      =			continuous - floored
  UpperCamelCase      =			np.sort(np.unique(UpperCamelCase_					)					)[::-1]
  # add according to remainder, but break ties
  # randomly to avoid biases
  for value in values:
   ((UpperCamelCase)     ,    )      =			np.where(remainder == value					)
   # if we need_to_add less than what's in inds
   # we draw randomly from them.
   # if we need to add more, we add them all and
   # go to the next value
   UpperCamelCase      =			min(len(UpperCamelCase_					)       ,				UpperCamelCase_					)
   UpperCamelCase      =			rng.choice(UpperCamelCase_       ,				size=UpperCamelCase_       ,				replace=UpperCamelCase_					)
   floored[inds] += 1
   need_to_add -= add_now
   if need_to_add == 0:
    break
 return floored.astype(np.intaa					)





def  lowercase(  UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_=10					)		->  Dict:







 '''simple docstring'''
 UpperCamelCase     ,    UpperCamelCase      =			np.unique(UpperCamelCase_       ,				return_inverse=UpperCamelCase_					)
 UpperCamelCase      =			classes.shape[0]
 UpperCamelCase      =			np.bincount(UpperCamelCase_					)
 if np.min(UpperCamelCase_					) < 2:
  raise ValueError("""Minimum class count error"""					)
 if n_train < n_classes:
  raise ValueError(
      """The train_size = %d should be greater or """ """equal to the number of classes = %d""" % (n_train, n_classes)					)
 if n_test < n_classes:
  raise ValueError(
      """The test_size = %d should be greater or """ """equal to the number of classes = %d""" % (n_test, n_classes)					)
 UpperCamelCase      =			np.split(np.argsort(UpperCamelCase_       ,				kind="""mergesort"""					)       ,				np.cumsum(UpperCamelCase_					)[:-1]					)
 for _ in range(UpperCamelCase_					):
  UpperCamelCase      =			approximate_mode(UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_					)
  UpperCamelCase      =			class_counts - n_i
  UpperCamelCase      =			approximate_mode(UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_					)

  UpperCamelCase      =			[]
  UpperCamelCase      =			[]

  for i in range(UpperCamelCase_					):
   UpperCamelCase      =			rng.permutation(class_counts[i]					)
   UpperCamelCase      =			class_indices[i].take(UpperCamelCase_       ,				mode="""clip"""					)
   train.extend(perm_indices_class_i[: n_i[i]]					)
   test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]					)
  UpperCamelCase      =			rng.permutation(UpperCamelCase_					)
  UpperCamelCase      =			rng.permutation(UpperCamelCase_					)

  yield train, test
